1. A signal lamp control method based on information intelligent analysis is characterized in that the method is applied to a management server, the management server establishes network connection with at least one control terminal to realize data information transmission, and the method comprises the following steps:

sending a preset monitoring instruction to each control terminal so as to obtain pavement monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction;

analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal;

acquiring node basic information corresponding to each road network node in the basic road network map from the monitoring analysis information according to a prestored basic road network map;

acquiring node characteristic information corresponding to each road network node according to pre-stored current environment information and node basic information of each road network node;

analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain the regulation and control proportion information corresponding to each road network node;

and sending the regulation and control proportion information of each road network node to a control terminal corresponding to each road network node, so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding regulation and control proportion information.

2. The signal lamp control method based on intelligent information analysis according to claim 1, wherein the image analysis model includes a contour extraction rule, a feature extraction rule and a classification neural network, and the analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal includes:

extracting image contour information corresponding to each monitoring image from the monitoring images contained in the road surface monitoring information according to the contour extraction rule;

extracting a region contour image corresponding to the image contour information from each monitoring image according to the image contour information;

extracting corresponding contour image feature information from each region contour image according to the feature extraction rule;

classifying the characteristic information of each contour image according to the classification neural network to obtain a classification result corresponding to the characteristic information of each contour image;

and counting the classification result of the profile image characteristic information contained in the monitoring image corresponding to each control terminal to obtain the monitoring analysis information corresponding to each control terminal.

3. The signal lamp control method based on information intelligent analysis according to claim 2, wherein the counting the classification result of the profile image feature information included in the monitoring image corresponding to each control terminal to obtain the monitoring analysis information corresponding to each control terminal includes:

carrying out classification statistics on the profile image characteristic information contained in each control terminal in a preset unit time period according to the classification result to obtain corresponding initial statistical information;

judging whether two continuous monitoring images of each control terminal contain similar individual characteristic information or not;

if two continuous monitoring images of the control terminal contain similar individual characteristic information, the similar individual characteristic information is removed from initial statistical information corresponding to the control terminal to obtain corresponding monitoring analysis information;

and if any two continuous monitoring images of the control terminal do not contain similar individual characteristic information, taking the initial statistical information of the control terminal as corresponding monitoring analysis information.

4. The signal lamp control method based on information intelligent analysis according to claim 1, wherein the obtaining of the node basis information corresponding to each road network node in the basic road network map from the monitoring analysis information according to a pre-stored basic road network map comprises:

acquiring a control terminal corresponding to a road network node adjacent to each road network node according to the incidence relation among the road network nodes in the basic road network map, and using the control terminal as a target control terminal corresponding to each road network node;

and integrating data of which the target direction is the road network node in the monitoring analysis information of the target control terminal of each road network node to obtain the node basic information corresponding to each road network node.

5. The signal lamp control method based on intelligent information analysis according to claim 1, wherein the obtaining of the node characteristic information corresponding to each road network node according to the pre-stored current environment information and the node basic information of each road network node comprises:

acquiring entry data information corresponding to each node entry in the node basic information according to the node entry contained in each road network node;

trend judgment is carried out on the entry data information corresponding to each node entry, and a trend judgment result corresponding to each node entry is obtained;

acquiring node environment information corresponding to each road network node from the current environment information;

and acquiring corresponding node characteristic information from the entry data information, the trend judgment result and the node environment information corresponding to each road network node.

6. The signal lamp control method based on intelligent information analysis according to claim 1, wherein the data analysis model includes a prediction neural network and a regulation and control proportion calculation formula, and the analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain the regulation and control proportion information corresponding to each road network node includes:

respectively inputting the node characteristic information of each road network node into the prediction neural network to obtain the driving prediction time of each road network node;

acquiring traffic flow information of each road network node from node basic information corresponding to the road network node according to the driving prediction time of each road network node;

and calculating the traffic flow information of each road network node according to the regulation and control proportion calculation formula to obtain the regulation and control proportion information corresponding to each road network node.

7. The signal light control method based on intelligent information analysis according to claim 6, wherein the obtaining traffic flow information of each road network node from the node basis information corresponding to the road network node according to the driving forecast time of each road network node comprises:

determining an acquisition time period of a node inlet contained in each road network node according to the regulation and control cycle time and the driving prediction time of each road network node;

and acquiring traffic flow information corresponding to each road network node from the corresponding node basic information according to the acquisition time period of the node inlet of each road network node.

8. A signal lamp control device based on information intelligent analysis is characterized by comprising:

the road surface monitoring information acquisition unit is used for sending a preset monitoring instruction to each control terminal so as to acquire road surface monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction;

the monitoring analysis information acquisition unit is used for analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal;

a node basic information acquisition unit, configured to acquire, from the monitoring analysis information, node basic information corresponding to each road network node in a basic road network map according to a pre-stored basic road network map;

the node characteristic information acquisition unit is used for acquiring node characteristic information corresponding to each road network node according to pre-stored current environment information and the node basic information of each road network node;

the regulation and control proportion information acquisition unit is used for analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain regulation and control proportion information corresponding to each road network node;

and the control proportion information sending unit is used for sending the control proportion information of each road network node to the control terminal corresponding to each road network node so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding control proportion information.

9. Computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for signal light control based on intelligent analysis of information according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the signal light control method based on intelligent analysis of information according to any one of claims 1 to 7.

Background

The signal lamps are generally required to be arranged in intersections constructed in cities, the traffic flow of the intersections is controlled through the control of the signal lamps, the existing signal lamps are controlled through control boxes arranged at the intersections, and the signal lamps are controlled through considering parameters in the configuration signal boxes. However, in the practical application process, the transverse traffic flow and the longitudinal traffic flow at the intersection are changed, the ratio of the transverse traffic flow to the longitudinal traffic flow is not equal to the ratio of the traffic time of the signal lamp, the traffic time of the signal lamp cannot be flexibly adjusted in time by adopting a mode of manually configuring parameters of the signal box, the traffic jam can be caused by the unequal ratio of the traffic time of the signal lamp to the traffic flow, and the traffic efficiency of road traffic is seriously influenced. Therefore, the method in the prior art has the problem that the passing time of the signal lamp cannot be flexibly adjusted in time.

Disclosure of Invention

The embodiment of the invention provides a signal lamp control method, a signal lamp control device, signal lamp control equipment and a signal lamp control medium based on intelligent information analysis, and aims to solve the problem that the passing time of a signal lamp cannot be flexibly adjusted in time in the prior art.

In a first aspect, an embodiment of the present invention provides a signal lamp control method based on intelligent information analysis, including:

sending a preset monitoring instruction to each control terminal so as to obtain pavement monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction;

analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal;

acquiring node basic information corresponding to each road network node in the basic road network map from the monitoring analysis information according to a prestored basic road network map;

acquiring node characteristic information corresponding to each road network node according to pre-stored current environment information and node basic information of each road network node;

analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain the regulation and control proportion information corresponding to each road network node;

and sending the regulation and control proportion information of each road network node to a control terminal corresponding to each road network node, so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding regulation and control proportion information.

In a second aspect, an embodiment of the present invention provides a signal lamp control device based on intelligent information analysis, including:

the road surface monitoring information acquisition unit is used for sending a preset monitoring instruction to each control terminal so as to acquire road surface monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction;

the monitoring analysis information acquisition unit is used for analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal;

a node basic information acquisition unit, configured to acquire, from the monitoring analysis information, node basic information corresponding to each road network node in a basic road network map according to a pre-stored basic road network map;

the node characteristic information acquisition unit is used for acquiring node characteristic information corresponding to each road network node according to pre-stored current environment information and the node basic information of each road network node;

the regulation and control proportion information acquisition unit is used for analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain regulation and control proportion information corresponding to each road network node;

and the control proportion information sending unit is used for sending the control proportion information of each road network node to the control terminal corresponding to each road network node so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding control proportion information.

In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the signal light control method based on intelligent information analysis according to the first aspect.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the signal lamp control method based on intelligent information analysis according to the first aspect.

The embodiment of the invention provides a signal lamp control method, a signal lamp control device, signal lamp control equipment and a signal lamp control medium based on intelligent information analysis. The method comprises the steps of obtaining road surface monitoring information of periodic monitoring from a control terminal, analyzing to obtain monitoring analysis information of each control terminal, obtaining node basic information of each road network node from the monitoring analysis information according to a basic road network map, obtaining node characteristic information of each road network node by combining current environment information, analyzing the node characteristic information and the node basic information of each road network node according to a data analysis model to obtain regulation and control proportion information, sending the regulation and control proportion information to the control terminal corresponding to the road network node, and enabling the control terminal to control signal lamps according to the regulation and control proportion information. By the method, the road surface monitoring information obtained by periodic monitoring based on the control terminal and the current environment information are combined and analyzed to obtain the regulation and control proportion information of each road network node, so that the control terminal of each road network node can flexibly adjust the passing time of the signal lamp according to the regulation and control proportion information, and the intelligence and the flexibility of controlling the signal lamp are greatly improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flowchart of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 2 is a schematic view of an application scenario of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 3 is a schematic sub-flow diagram of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 4 is another schematic sub-flow diagram of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 5 is another schematic sub-flow diagram of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 6 is another schematic sub-flow diagram of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 7 is another schematic sub-flow diagram of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 8 is a schematic view of another sub-flow of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention;

fig. 9 is a schematic block diagram of a signal lamp control device based on intelligent information analysis according to an embodiment of the present invention;

FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow chart illustrating a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention; fig. 2 is a schematic view of an application scenario of a signal lamp control method based on intelligent information analysis according to an embodiment of the present invention; the signal lamp control method based on the intelligent information analysis is applied to a management server 10, the signal lamp control method based on the intelligent information analysis is executed through application software installed in the management server 10, the management server 10 is simultaneously connected with a plurality of control terminals 20 and a network to realize the transmission of data information, concretely, a wireless communication connection (based on a 4G network or a 5G network) or a wired communication connection can be established between the control terminals 20 and the management server 10, each control terminal 20 correspondingly controls a plurality of monitoring probes 30 and a plurality of signal lamps 40, the control terminals 20 are terminal devices which are arranged at intersections in a city and control the signal lamps 40 and the monitoring probes 30 of the intersections, the monitoring probes 30 are information collecting devices which are arranged at the intersections in the city to monitor road surfaces in real time, the signal lamps 40 are devices which are arranged at the intersections in the city to display signals, the management server 10 is a server that can obtain the road surface monitoring information from the control terminal 20, perform intelligent analysis to obtain the regulation and control ratio information, and send the regulation and control ratio information, such as a server constructed by an enterprise or a traffic department. As shown in fig. 1, the method includes steps S110 to S160.

And S110, sending a preset monitoring instruction to each control terminal so as to obtain road surface monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction.

And sending a preset monitoring instruction to each control terminal so as to obtain the road surface monitoring information obtained by periodic monitoring from each control terminal according to the monitoring instruction. The management server can send a monitoring instruction to each control terminal, the control terminals periodically monitor the road surface according to the received monitoring instruction, namely periodically acquire monitoring images from the monitoring probes controlled by the control terminals, obtain road surface monitoring information corresponding to each control terminal and feed the road surface monitoring information back to the management server, and the management server can receive the road surface monitoring information obtained by periodically monitoring each control terminal. The road surface monitoring information may be image information or video information. If the monitoring interval time configured in the monitoring instruction is 2 seconds, the control terminal acquires an image or a video from each monitoring probe controlled by the control terminal every 2 seconds and sends the image or the video as road surface monitoring information corresponding to the control terminal to the management server, each monitoring probe can monitor one incoming direction of the intersection, for example, a common intersection usually includes 4 incoming directions, and 4 monitoring probes can be correspondingly configured to monitor 4 incoming directions respectively.

And S120, analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal.

And analyzing the road surface monitoring information according to a preset image analysis model to obtain monitoring analysis information corresponding to each control terminal. After the road surface monitoring information of each control terminal is obtained, the image in the road surface monitoring information can be analyzed to obtain the monitoring analysis information of each control terminal, and the monitoring analysis information is the road condition information obtained by analyzing the road surface monitoring information of the control terminal. The image analysis model comprises a contour extraction rule, a feature extraction rule and a classification neural network. The contour extraction rule is a specific rule for extracting a vehicle contour contained in an image of the road surface monitoring information, the feature extraction rule is a specific rule for extracting features corresponding to the vehicle contour, and the classification neural network is a neural network for classifying the features corresponding to the vehicle contour.

In an embodiment, as shown in fig. 3, step S120 includes sub-steps S121, S122, S123, S124, and S125.

And S121, extracting image contour information corresponding to each monitoring image from the monitoring images contained in the road surface monitoring information according to the contour extraction rule.

The method comprises the steps of obtaining a monitoring image from road surface monitoring information, directly obtaining image information as the monitoring image for analysis if the road surface monitoring information contains the image information, intercepting a frame of image from each section of video as a corresponding monitoring image for analysis if the road surface monitoring information contains video information, wherein the monitoring image is a color image, each pixel point in the color image corresponds to an RGB pixel value, the RGB pixel values are pixel values of pixels in three color channels of R (red), G (green) and B (blue), and the value range of the pixel values is an integer within [0,255 ]. The contour extraction rule includes a division size and an effective area interval. Specifically, the monitoring image in the road monitoring information may be subjected to gray processing to obtain a gray image, and the gray image may be divided according to a division size to obtain a plurality of non-overlapping gray image blocks corresponding to each gray image, for example, the division size may be 5 × 5 or 6 × 6; calculating the gray average value of pixel points contained in each gray image block, taking the gray average value of each gray image block as a preset, and respectively binarizing the pixels contained in each gray image block to obtain a corresponding binary image block. Acquiring a positive value connected domain and a negative value connected domain in a binary image, and judging whether the area (the number of pixels) of pixel points in the connected domains is within an effective area interval, wherein an interval extreme value in the effective area interval is formed by combining a minimum vehicle area value and a maximum vehicle area value, and if the area value of the pixel points in the positive value connected domain or the negative value connected domain is within the effective area interval, judging that the connected domain is the effective connected domain; and if the area of the pixel point in the connected domain exceeds the effective area interval, determining that the pixel information corresponding to the connected domain does not belong to the vehicle, and judging that the connected domain is an invalid connected domain. The image profiles of all the effective connected domains in one monitored image can be obtained to be used as the image profile information of the monitored image, and the image profile information corresponding to each monitored image can be correspondingly obtained by the method.

And S122, extracting a region contour image corresponding to the image contour information from each monitoring image according to the image contour information.

And if one monitoring image corresponds to one piece of image contour information, acquiring a region contour image from the monitoring image corresponding to the image contour information according to the image contour of the effective connected domain contained in the image contour information, namely cutting pixels contained in the monitoring image according to the image contour information, and acquiring the region image information corresponding to the image contour of each effective connected domain from the monitoring image as the region contour image of the monitoring image.

And S123, extracting corresponding profile image feature information from each region profile image according to the feature extraction rules.

Specifically, the feature extraction rule includes a plurality of extraction items, specific individual feature information such as corresponding image color, image size and the like can be respectively extracted from each region image information through the extraction items included in the feature extraction rule, and the individual feature information corresponding to the plurality of region image information included in each region contour image is the contour image feature information corresponding to the region contour image. Specifically, according to the feature extraction rule, the average value of pixels (the average value of pixel values corresponding to pixels in one color channel included in the region image information) and the pixel difference value (the difference value between the maximum pixel value and the minimum pixel value corresponding to pixels in one color channel included in the region image information) corresponding to each of R, G, B three color channels of the region image information included in the region contour image, and the number of region pixels, the lateral size of the region (the maximum value of the number of pixels included in the lateral size of the region), and the longitudinal size of the region (the maximum value of the number of pixels included in the longitudinal size of the region) of each region image information included in the region contour image can be obtained.

And S124, classifying the characteristic information of each contour image according to the classification neural network to obtain a classification result corresponding to the characteristic information of each contour image.

The feature information of each contour image can be classified according to the classification neural network, so that the classification result corresponding to the feature information of each contour image can be obtained according to the specific classification of each individual feature information in the feature information of the contour images by the classification neural network, and before the classification neural network is used, a pre-stored training data set can be adopted for training to obtain the trained classification neural network for use. Specifically, the classification neural network may be a neural network constructed based on artificial intelligence, and the classification neural network may be composed of an input layer, a plurality of intermediate layers, and an output layer, where the input layer and the first intermediate layer, the intermediate layers and other intermediate layers adjacent to each other in front and behind, and the last intermediate layer and the output layer are all associated by an association formula, for example, a certain association formula may be expressed as y ═ p × x + q, and p and q are parameter values in the association formula. The number of input nodes contained in the input layer is equal to the number of extraction items contained in the feature extraction rule, one region outline image contains a plurality of region image information, each region image information corresponds to one individual feature information, each numerical value in the individual feature information corresponding to each region image information in one region outline image corresponds to one input node, the output layer contains a plurality of output nodes, each output node corresponds to one vehicle type classification, and the vehicle type classification can be a common small vehicle, a common medium vehicle, a bus, a police car, an ambulance, a fire truck, a taxi and the like. The individual characteristic information corresponding to each region image information contained in one region contour image is sequentially input into a classification neural network for intelligent analysis, namely, the output analysis information can be obtained from an output layer of the classification neural network, the specific classification corresponding to each region image information can be determined according to the analysis information, and the specific classification of the region image information contained in each region contour image forms the classification result of the region contour image.

And S125, counting the classification results of the profile image feature information contained in the monitoring image corresponding to each control terminal to obtain monitoring analysis information corresponding to each control terminal.

The classification result of the profile image feature information corresponding to one control terminal can be counted to obtain the monitoring analysis information corresponding to each control terminal, and specifically, the traffic flow of each vehicle type corresponding to the control terminal can be counted according to the classification result to obtain the corresponding time-series monitoring analysis information.

In an embodiment, as shown in fig. 4, step S125 includes sub-steps S1251, S1252, S1253 and S1254.

And S1251, carrying out classification statistics on the profile image feature information contained in each control terminal in a preset unit time period according to the classification result to obtain corresponding initial statistical information.

The profile image feature information contained in each control terminal in a preset unit time period can be classified and counted according to the classification result statistics, specifically, one monitoring image corresponding to the control terminal monitors one incoming vehicle direction, one monitoring image contains traffic information of at least one lane, each lane can correspond to one or more driving directions, for example, a lane can only contain one driving direction of ' straight going ' or can also contain two driving directions of ' straight going ' and left turning ', one monitoring image corresponds to the classification result of one profile image feature information, the traffic flow of the intersection corresponding to each control terminal in the unit time period in each driving direction can be counted according to the classification result of the profile image feature information, if one lane only contains one driving direction, the traffic flow on a plurality of lanes in the same driving direction can be comprehensively counted, and if one lane comprises two or more driving directions, carrying out average calculation on the traffic flow on the lane according to the number of the driving directions.

If the preset unit time period is 6 seconds, the classification result of the profile image feature information corresponding to each control terminal in 6 seconds can be obtained, the traffic flow of each vehicle type in each driving direction is counted based on the classification result, a certain lane comprises two driving directions of 'straight driving and left turning', the traffic flow of the lane in 6 seconds is 35, the traffic flow of the lane in the 'straight driving' direction in 6 seconds is 17.5 after averaging, and the traffic flow of the lane in the 'left turning' direction in 6 seconds is 17.5.

S1252, judging whether the two continuous monitoring images of each control terminal contain similar individual feature information.

Specifically, two pieces of individual feature information are respectively obtained from the two continuous monitoring images, a similarity calculation formula is used for calculating the two pieces of individual feature information to obtain corresponding similarity values, whether the similarity values are larger than a similarity threshold value is judged, if the similarity values are larger than the similarity threshold value, the two pieces of individual feature information are judged to be similar, and if the similarity values are not larger than the similarity threshold value, the two pieces of individual feature information are not similar. For example, the similarity threshold may be configured to be 0.85.

For example, the similarity calculation formula can be expressed by formula (1):

wherein f is_iIs the ith numerical value f contained in the first individual characteristic information_iIs the ith numerical value contained in the second individual characteristic information, and N is the total number of the numerical values contained in the individual characteristic information.

S1253, if two continuous monitoring images of the control terminal contain similar individual feature information, removing the similar individual feature information from initial statistical information corresponding to the control terminal to obtain corresponding monitoring analysis information; and S1254, if any two continuous monitoring images of the control terminal do not contain similar individual characteristic information, taking the initial statistical information of the control terminal as corresponding monitoring analysis information.

The same vehicle may be captured in two consecutive monitored images at the same time, and if the two consecutive monitored images of the control terminal contain similar individual characteristic information, it indicates that the similar individual characteristic information is the individual characteristic information of the same vehicle, and the repeated statistics quantity of the similar individual characteristic information needs to be removed from the corresponding initial statistical information, so as to avoid performing repeated statistics on the same individual characteristic information in the initial statistical information. And if the two corresponding individual characteristic information of the same control terminal are similar, subtracting one from the corresponding numerical value in the initial statistical information. After the repeated statistical number in the initial statistical information is removed, the corresponding monitoring analysis information can be obtained, for example, the obtained monitoring analysis information corresponding to a certain control terminal is shown in table 1.

TABLE 1

And if any two continuous monitoring images of the control terminal do not contain similar individual characteristic information, directly taking the initial statistical information of the control terminal as monitoring analysis information. And acquiring monitoring analysis information of each control terminal in a plurality of continuous statistical unit time periods, so as to obtain the time sequence monitoring analysis information of each control terminal.

S130, acquiring node basic information corresponding to each road network node in the basic road network map from the monitoring analysis information according to a pre-stored basic road network map.

And acquiring node basic information corresponding to each road network node in the basic road network map from the monitoring analysis information according to a pre-stored basic road network map. The basic road network map is a map pre-stored in the management server, such as a traffic network map of the whole city or a traffic network map of a local city. The node basic information corresponding to each road network node in the basic road network map can be obtained from the monitoring analysis information according to the basic road network map, each road network node corresponds to one intersection in the real road, the node basic information is information for recording the basic traffic information of each road network node, and the basic traffic condition of the corresponding intersection can be reflected through the node basic information.

In one embodiment, as shown in fig. 5, step S130 includes sub-steps S131 and S132.

S131, acquiring a control terminal corresponding to a road network node adjacent to each road network node according to the incidence relation among the road network nodes in the basic road network map, and using the control terminal as a target control terminal corresponding to each road network node.

The method comprises the steps that a correlation relation based on vehicle driving exists among road network nodes, if a vehicle which runs straight to the right side of a left road network node can enter another road network node on the right side of the left road network node, a control terminal corresponding to a road network node adjacent to each road network node can be obtained according to the correlation relation among the road network nodes and serves as a target control terminal corresponding to each road network node, and the other road network nodes adjacent to the road network node are upstream road network nodes which can be driven to the road network node by the vehicle.

S132, integrating data of the road network nodes with the target directions in the monitoring analysis information of the target control terminal of each road network node to obtain node basic information corresponding to each road network node.

The monitoring analysis information of the target monitoring terminal of the road network node comprises traffic data of a plurality of driving directions, so that the data of which the target direction is the road network node can be obtained from the monitoring analysis information of the target control terminal and integrated to obtain the node basic information corresponding to the road network node.

For example, if a road network node is a road network node corresponding to an intersection, the road network node includes four node entries, i.e., an upper node entry, a lower node entry, a left node entry, and a right node entry, and the target direction of one node entry on the left side of the road network node includes a straight vehicle traveling from right to left, a left-turning vehicle traveling from up to down, and a right-turning vehicle traveling from down to up in another road network node on the left side of the road network node, that is, the target directions of the three traffic flows are the left node entries of the road network node, the three traffic flows corresponding to the three traffic flows in the monitoring analysis information of one road network node on the left side of the road network node are integrated, and the data of the left node entry of the road network node can be obtained. After the monitoring analysis information of the target monitoring terminal corresponding to the road network node is integrated, data of four node inlets, namely the data of the four node inlets, namely the node basic information of the road network node, can be obtained.

In addition, in the process of integrating the data of the road network nodes, weighting integration can be performed on different vehicle types based on preset weight values, and the integrated node basic information includes weighted integrated data information.

For example, the weight value of a normal car can be set to 1, the weight value of a normal medium-sized vehicle can be set to 1.5, the weight value of a truck can be set to 2, the weight value of a taxi can be set to 4, the weight value of a bus can be set to 8, the weight value of a bus can be set to 10, and the weight values of an ambulance, a police car and a fire truck can be set to 1000. The node basic information after weighted integration is obtained through the weighted integration mode, the rapid right of passage of an ambulance, a police car and a fire truck can be ensured when the signal lamp is regulated and controlled based on the node basic information, the proper priority right of passage of a taxi, a bus and a bus is ensured, and the passage efficiency of road traffic is further optimized.

And S140, acquiring node characteristic information corresponding to each road network node according to the pre-stored current environment information and the node basic information of each road network node.

And acquiring node characteristic information corresponding to each road network node according to the pre-stored current environment information and the node basic information of each road network node. The management server also prestores current environment information, wherein the current environment information comprises environment information such as rainfall (millimeter: mm) and outdoor illumination brightness (lux), and the like, the current environment information can be data information provided by an environment monitoring department at regular time, and the current environment information prestored in the management server can be updated at regular time, for example, updated once every 5 minutes or 10 minutes. Node characteristic information corresponding to each road network node can be obtained based on the current environment information and the node basic information of each road network node, and the node characteristic information is data information for representing the characteristics of the corresponding road network node.

In an embodiment, as shown in fig. 6, step S140 includes sub-steps S141, S142, S143, and S144.

And S141, acquiring entry data information corresponding to each node entry in the node basic information according to the node entry included in each road network node.

The road network node comprises a plurality of node entries, and the entry data information corresponding to each node entry can be obtained through statistics according to the data of the road network node at each node entry in the node basic information. The node basic information is time sequence statistical information based on a statistical unit time period, and corresponding data can be acquired from the node basic information and counted to obtain entry data information.

For example, if the statistical unit time period is 6 seconds, 10 groups of data included in the node basic information of a node entry corresponding to a certain node entry may be counted to obtain traffic information of the node entry per minute, and the traffic information of the node entry per minute is obtained to obtain corresponding entry data information.

For example, the data information of the entry of a certain node in 6 consecutive minutes is shown in table 2.

Time (minutes)	10:28	10:29	10:30	10:31	10:32	10:33
							Node entry A	43.5	33	20	32.5	37	39.5

TABLE 2

And S142, performing trend judgment on the entry data information corresponding to each node entry to obtain a trend judgment result corresponding to each node entry.

Trend judgment is carried out on the entry data information corresponding to the node entry to obtain pairsAnd judging the result according to the trend. For example, trend judgment can be performed on the data information of each node entry in 6 consecutive minutes, the data information of the entry in 6 consecutive minutes is divided into three groups for summation, the first group is the data information corresponding to the time 10:28 and the time 10:29, the second group is the data information corresponding to the time 10:30 and the time 10:31, the third group is the data information corresponding to the time 10:32 and the time 10:33, and three groups of summation data F obtained after summation are subjected to summation₁、F₂、F₃The size of (2) is judged. If F₁>F₂>F₃If so, the trend judgment result is reduction; if F₁≤F₂≤F₃The trend judgment result is rising; if F₁>F₂≤F₃The trend judgment result is that the trend is firstly decreased and then increased; if F₁≤F₂>F₃The trend judgment result is that the trend is increased first and then decreased.

S143, obtaining the node environment information corresponding to each road network node from the current environment information.

The current environment information includes environment information corresponding to a plurality of geographic locations, and since there may be differences in the environment information of each geographic location, node environment information matching the location information of each road network node needs to be acquired from the current environment information according to the location information of each road network node, and then the node environment information corresponds to the location of the corresponding road network node.

And S144, acquiring corresponding node characteristic information from the entry data information, the trend judgment result and the node environment information corresponding to each road network node.

The corresponding node characteristic information can be obtained from the entry data information, the trend judgment result and the node environment information corresponding to the road network node, and specifically, the trend judgment result can be mapped and converted to obtain corresponding conversion numerical values, such as descending and 0.7 mapping, ascending and 0.3 mapping, descending and ascending and 0 mapping, ascending and descending and 1 mapping. And combining the entrance data information, the conversion numerical value and the node environment information of the road network node to obtain corresponding node characteristic information.

S150, analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain the regulation and control proportion information corresponding to each road network node.

And analyzing the node characteristic information and the node basic information of each road network node according to a preset data analysis model to obtain the regulation and control proportion information corresponding to each road network node. The node characteristic information and the node basic information of the road network nodes can be analyzed according to the data analysis model so as to obtain the regulation and control proportion information corresponding to each road network node, and the regulation and control proportion information comprises specific information for regulating and controlling the signal lamp time length proportion in different traffic directions. The data analysis model comprises a prediction neural network and a regulation and control proportion calculation formula.

In one embodiment, as shown in fig. 7, step S150 includes sub-steps S151, S152, and S153.

And S151, respectively inputting the node characteristic information of each road network node into the prediction neural network to obtain the driving prediction time of each road network node.

Specifically, the predictive neural network may be a neural network constructed based on artificial intelligence, and the specific composition of the predictive neural network is similar to that of the above-mentioned classification neural network, except that there is a difference between an input node included in the input layer and an output node included in the output layer. Before the prediction neural network is used, pre-stored historical data traffic data can be adopted for training, and the trained prediction neural network can be used. The output layer of the prediction neural network comprises four output nodes, and the node characteristic information of each road network node is input into the prediction neural network, so that the four node output information can be obtained from the four output nodes to obtain the driving prediction time of the road network node, wherein the four node output information respectively corresponds to the prediction time of four node inlets, and one prediction time is the prediction time of a vehicle, corresponding to one node inlet in the prediction time, in the road network node to drive to an intersection of the road network node.

S152, obtaining traffic flow information of each road network node from the node basic information corresponding to the road network node according to the driving prediction time of each road network node.

The traffic flow information of each road network node can be obtained from the node basic information corresponding to the road network node based on the driving prediction time of the road network node, and the traffic flow information of the road network node comprises the traffic flow information of each node inlet future of the road network node.

In one embodiment, as shown in fig. 8, step S152 includes sub-steps S1521 and S1522.

S1521, determining an obtaining time period of a node entrance included in each road network node according to the regulation and control cycle time and the driving prediction time of each road network node.

The road network nodes comprise regulation and control cycle time, the regulation and control cycle time is time information for periodically regulating and controlling the signal lamps by the road network nodes, if the regulation and control cycle time is 2 minutes, the road network nodes are indicated to periodically control the signal lamps contained in the road network nodes by taking 2 minutes as a cycle, and the acquisition time period of each node entrance contained in the road network nodes can be determined according to the regulation and control cycle time of the road network nodes and the corresponding driving prediction time.

For example, if the regulation cycle time of a road network node is 2 minutes, the current time is 10:31, the previous adjustment time point is 10:30, the next adjustment time point corresponding to the current time is 10:32 minutes, and the predicted time of a certain node entry of the road network node is 3.3 minutes, it can be determined that the acquisition time period of the node entry in 10:32 minutes is 10:28.7-10: 30.7.

S1522, traffic flow information corresponding to each road network node is obtained from corresponding node basic information according to the obtaining time period of the node entrance of each road network node.

And acquiring traffic flow information of each road network node from the node basic information of the corresponding road network node according to the acquisition time period of each node inlet in each road network node, wherein the traffic flow information of the road network node comprises the actual passing traffic flow of each node inlet in the road network node, and the traffic flow information of one node inlet in the road network node can be used as the predicted traffic flow of the intersection running to the road network node.

If the acquisition time period of a certain node entrance in a certain road network node is 10:28.7-10:30.7, acquiring a plurality of data corresponding to the node entrance and positioned in the acquisition time period from the node basic information of the road network node, and accumulating the plurality of data to obtain the traffic flow of the node entrance. By the method, the traffic flow corresponding to each of the plurality of node inlets in the road network node can be obtained, and the traffic flow information of the road network node is obtained.

And S153, calculating the traffic flow information of each road network node according to the regulation proportion calculation formula to obtain the regulation proportion information corresponding to each road network node.

The traffic flow information of the road network nodes can be calculated according to a regulation proportion calculation formula to obtain the regulation proportion information of the road network nodes.

For example, for an intersection composed of two roads, the regulation ratio calculation formula can be represented by formula (2):

wherein B is the regulation and control proportion information corresponding to a certain network node, C_ATraffic flow at the entrance of a node for right-to-left travel, C_BTraffic flow at the entrance of a node for left-to-right driving, C_CFor traffic flow at the entrance of nodes travelling up to and down, C_DThe traffic flow at the entrance of the node driving from bottom to top.

And S160, sending the regulation and control proportion information of each road network node to a control terminal corresponding to each road network node, so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding regulation and control proportion information.

And sending the regulation and control proportion information of each road network node to a control terminal corresponding to each road network node, so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding regulation and control proportion information. And after the control terminal acquires the regulation and control proportion information, the corresponding signal lamp can be controlled according to the regulation and control proportion information and the corresponding regulation and control cycle time.

For example, if the regulation ratio is 0.4:0.6 and the regulation cycle time is 2 minutes, the time period for assigning the right and left traffic is 2 × 0.4 to 0.8 minutes, and the time period for the up and down traffic is 2 × 0.6 to 1.2 minutes.

By the method, the signal lamp in the traffic direction with large traffic flow can be ensured to have long traffic time, the traffic time of the signal lamp can be flexibly adjusted according to the real-time traffic state, and the traffic efficiency of the intersection is greatly improved in the practical application process.

The time of left turn for right to left passage, left turn for left to right passage and left and right and straight going simultaneously can be further distributed in the left and right passage, and the time of left turn for up to down passage, left turn for down to up passage and left turn for up and down and straight going simultaneously can be further distributed in the up and down passage.

For example, right-to-left traffic left turn + right-to-left straight (where left-to-right straight cannot pass): left and right sides move straight simultaneously: left-to-right traffic left turn + left-to-right straight (where right-to-left straight cannot pass) is 0.25:0.5: 0.25. The time period for right-to-left passage left turn + right-to-left straight running is 0.8 × 0.25-0.2 minutes, the time period for left-to-right passage left turn is 0.8 × 0.5-0.4 minutes, and the time period for left-to-right passage left turn + left-to-right straight running is also 0.2 minutes. The way of allocating the passage time length of each specific passage direction in the up-and-down passage is the same as the way of allocating the passage time length of each specific passage direction in the left-and-right passage.

In the signal lamp control method based on information intelligent analysis provided by the embodiment of the invention, the periodic monitored road surface monitoring information is obtained from the control terminal and is analyzed to obtain the monitoring analysis information of each control terminal, the node basic information of each road network node is obtained from the monitoring analysis information according to the basic road network map, the node characteristic information of each road network node is obtained by combining the current environment information, the node characteristic information and the node basic information of each road network node are analyzed according to the data analysis model to obtain the regulation and control proportion information, the regulation and control proportion information is sent to the control terminal corresponding to the road network node, and the control terminal can control the signal lamp according to the regulation and control proportion information. By the method, the road surface monitoring information obtained by periodic monitoring based on the control terminal and the current environment information are combined and analyzed to obtain the regulation and control proportion information of each road network node, so that the control terminal of each road network node can flexibly adjust the passing time of the signal lamp according to the regulation and control proportion information, and the intelligence and the flexibility of controlling the signal lamp are greatly improved.

An embodiment of the present invention further provides a signal lamp control device based on intelligent information analysis, where the signal lamp control device based on intelligent information analysis is configured to execute any embodiment of the signal lamp control method based on intelligent information analysis, specifically, please refer to fig. 9, and fig. 9 is a schematic block diagram of the signal lamp control device based on intelligent information analysis according to the embodiment of the present invention.

As shown in fig. 9, the signal lamp control device 100 based on the intelligent information analysis includes a road surface monitoring information obtaining unit 110, a monitoring analysis information obtaining unit 120, a node basic information obtaining unit 130, a node characteristic information obtaining unit 140, a regulation ratio information obtaining unit 150, and a regulation ratio information transmitting unit 160.

The road surface monitoring information obtaining unit 110 is configured to send a preset monitoring instruction to each of the control terminals, so as to obtain road surface monitoring information obtained by periodic monitoring from each of the control terminals according to the monitoring instruction.

The monitoring analysis information obtaining unit 120 is configured to analyze the road surface monitoring information according to a preset image analysis model, so as to obtain monitoring analysis information corresponding to each control terminal.

In an embodiment, the monitoring analysis information obtaining unit 120 includes sub-units: the image contour information acquisition unit is used for extracting image contour information corresponding to each monitoring image from the monitoring images contained in the road surface monitoring information according to the contour extraction rule; the area contour image acquisition unit is used for extracting an area contour image corresponding to the image contour information from each monitoring image according to the image contour information; the contour image feature information acquisition unit is used for extracting corresponding contour image feature information from each region contour image according to the feature extraction rule; the classification result acquisition unit is used for classifying the characteristic information of each contour image according to the classification neural network to obtain a classification result corresponding to the characteristic information of each contour image; and the classification result counting unit is used for counting the classification results of the profile image feature information contained in the monitoring image corresponding to each control terminal to obtain the monitoring analysis information corresponding to each control terminal.

In one embodiment, the classification result statistic unit includes a sub-unit: the initial statistical information acquisition unit is used for carrying out classification statistics on the profile image characteristic information contained in each control terminal in a preset unit time period according to the classification result to obtain corresponding initial statistical information; the similarity judging unit is used for judging whether the two continuous monitoring images of each control terminal contain similar individual characteristic information or not; the first monitoring analysis information acquisition unit is used for removing similar individual characteristic information from initial statistical information corresponding to the control terminal to obtain corresponding monitoring analysis information if two continuous monitoring images of the control terminal contain the similar individual characteristic information; and the second monitoring analysis information acquisition unit is used for taking the initial statistical information of the control terminal as corresponding monitoring analysis information if any two continuous monitoring images of the control terminal do not contain similar individual characteristic information.

A node basic information obtaining unit 130, configured to obtain, from the monitoring analysis information according to a pre-stored basic road network map, node basic information corresponding to each road network node in the basic road network map.

In an embodiment, the node basic information obtaining unit 130 includes sub-units: a target control terminal obtaining unit, configured to obtain, according to an association relationship between road network nodes in the basic road network map, a control terminal corresponding to a road network node adjacent to each road network node, as a target control terminal corresponding to each road network node; and the data integration unit is used for integrating the data of which the target direction is the road network node in the monitoring analysis information of the target control terminal of each road network node to obtain the node basic information corresponding to each road network node.

The node characteristic information obtaining unit 140 is configured to obtain node characteristic information corresponding to each road network node according to pre-stored current environment information and node basic information of each road network node.

In one embodiment, the node characteristic information obtaining unit 140 includes sub-units: an entry data information obtaining unit, configured to obtain, according to a node entry included in each road network node, entry data information corresponding to each node entry in the node base information; the trend judgment unit is used for performing trend judgment on the entry data information corresponding to each node entry to obtain a trend judgment result corresponding to each node entry; a node environment information obtaining unit, configured to obtain node environment information corresponding to each of the road network nodes from the current environment information; and the characteristic information acquisition unit is used for acquiring corresponding node characteristic information from the entry data information, the trend judgment result and the node environment information corresponding to each road network node.

And a regulation and control proportion information obtaining unit 150, configured to analyze the node characteristic information and the node basic information of each road network node according to a preset data analysis model, to obtain regulation and control proportion information corresponding to each road network node.

In an embodiment, the regulation ratio information obtaining unit 150 includes sub-units: the driving prediction time acquisition unit is used for respectively inputting the node characteristic information of each road network node into the prediction neural network so as to obtain the driving prediction time of each road network node; the traffic flow information acquisition unit is used for acquiring traffic flow information of each road network node from the node basic information corresponding to the road network node according to the driving prediction time of each road network node; and the regulation and control proportion information calculation unit is used for calculating the traffic flow information of each road network node according to the regulation and control proportion calculation formula to obtain the regulation and control proportion information corresponding to each road network node.

In one embodiment, the traffic flow information acquiring unit includes a sub-unit: the acquisition time period determining unit is used for determining an acquisition time period of a node inlet contained in each road network node according to the regulation and control cycle time and the driving prediction time of each road network node; and the acquisition unit is used for acquiring traffic flow information corresponding to each road network node from corresponding node basic information according to the acquisition time period of the node inlet of each road network node.

And a control proportion information sending unit 160, configured to send the control proportion information of each road network node to the control terminal corresponding to each road network node, so that each control terminal controls the signal lamp controlled by the control terminal according to the corresponding control proportion information.

The signal lamp control device based on the intelligent information analysis provided by the embodiment of the invention is applied to the signal lamp control method based on the intelligent information analysis, the periodically monitored road surface monitoring information is obtained from the control terminal and is analyzed to obtain the monitoring analysis information of each control terminal, the node basic information of each road network node is obtained from the monitoring analysis information according to the basic road network map, the node characteristic information of each road network node is obtained by combining the current environment information, the node characteristic information and the node basic information of each road network node are analyzed according to the data analysis model to obtain the regulation and control proportion information, the regulation and control proportion information is sent to the control terminal corresponding to the road network node, and the control terminal can control the signal lamp according to the regulation and control proportion information. By the method, the road surface monitoring information obtained by periodic monitoring based on the control terminal and the current environment information are combined and analyzed to obtain the regulation and control proportion information of each road network node, so that the control terminal of each road network node can flexibly adjust the passing time of the signal lamp according to the regulation and control proportion information, and the intelligence and the flexibility of controlling the signal lamp are greatly improved.

The signal lamp control device based on intelligent information analysis can be implemented in the form of a computer program, and the computer program can be run on a computer device as shown in fig. 10.

Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be a management server for executing a signal lamp control method based on information intelligent analysis to acquire road surface monitoring information and perform intelligent analysis to acquire regulation and control proportion information to transmit.

Referring to fig. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a storage medium 503 and an internal memory 504.

The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a signal light control method based on intelligent information analysis, wherein the storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.

The internal memory 504 provides an environment for running the computer program 5032 in the storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be enabled to execute a signal lamp control method based on intelligent information analysis.

The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

The processor 502 is configured to run the computer program 5032 stored in the memory to implement the corresponding functions in the signal light control method based on the intelligent information analysis.

Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 10 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 10, and are not described herein again.

It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the above-described signal light control method based on intelligent analysis of information.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.