1. A smart city traffic control system based on an AI Internet of things technology is characterized by comprising an information acquisition terminal, an intelligent base station, a vehicle-mounted information terminal and a cloud server; wherein the content of the first and second substances,

the information acquisition terminal is arranged on the road side and used for acquiring traffic information of the road and transmitting the acquired traffic information to the intelligent base station;

the vehicle-mounted information terminal is used for acquiring vehicle state information and transmitting the acquired vehicle state information to the intelligent base station;

the intelligent base station comprises an edge calculation module and a communication module; wherein the content of the first and second substances,

the communication module is used for establishing communication connection with the information acquisition terminal and the vehicle-mounted information terminal;

the edge calculation module is used for analyzing and processing the received traffic information and the vehicle state information to obtain a road traffic analysis result, sending a corresponding prompt message to the vehicle-mounted information terminal according to the road traffic analysis result, and sending a corresponding regulation and control instruction to the traffic guidance equipment according to the road traffic analysis result; transmitting the traffic analysis result to a cloud server;

the cloud server is used for updating the urban traffic condition in real time according to the received traffic analysis result;

and the vehicle-mounted information terminal is also used for receiving and displaying the prompt message sent by the intelligent base station.

2. The intelligent city traffic control system based on the AI internet of things technology as claimed in claim 1, wherein the information acquisition terminal comprises a sensor arranged at the roadside, wherein the sensor comprises a traffic flow sensor, a vehicle speed sensor and a video sensor; the traffic information of the road is collected through a sensor, wherein the traffic information comprises traffic flow data, speed data and road video image data of the road.

3. The AI IOT technology based intelligent city traffic control system of claim 2, wherein the vehicle-mounted information terminal obtains vehicle status information, wherein the vehicle status information comprises vehicle positioning information, driving path planning information, vehicle operation status information and vehicle abnormal status information, wherein the vehicle operation status information comprises vehicle speed, and the vehicle abnormal status information comprises vehicle failure status and traffic accident status.

4. The AI IOT technology based intelligent city traffic control system of claim 3, wherein the intelligent base station and a plurality of sensors and a plurality of vehicle information terminals arranged at the roadside within its coverage area together form a wireless communication network, the sensors and the vehicle information terminals are respectively used as sub-nodes in the wireless communication network, and each sub-node in the wireless communication network transmits data to the intelligent base station by a single-hop or multi-hop data transmission manner.

5. The AI IOT technology based intelligent city traffic control system of claim 4, wherein the edge calculation module comprises a traffic analysis unit;

the road condition analysis unit is used for analyzing the road condition of the road according to the traffic information of the road and the received vehicle state information based on an artificial intelligence algorithm, and acquiring a road condition analysis result, wherein the analysis includes current road condition analysis and road condition prediction; sending corresponding prompt messages to the vehicle-mounted information terminals within the coverage range of the base station according to the road condition analysis result; meanwhile, sending a control instruction corresponding to the road condition analysis result to corresponding traffic guidance equipment according to the road condition analysis result; and uploading the road condition analysis result to the cloud server.

6. The AI IOT technology based intelligent city traffic control system of claim 5, wherein the edge calculation module further comprises an anomaly detection unit;

the system comprises an abnormality detection unit, a cloud server and a traffic information acquisition unit, wherein the abnormality detection unit is used for carrying out vehicle violation detection according to road video image data uploaded by an information acquisition terminal, recording violation information when the violation is detected to occur, and transmitting the violation information to the cloud server;

the cloud server further comprises a management module, and the management module is used for managing the received violation information for the third-party terminal to look up and call.

7. The AI IOT technology based intelligent city traffic control system of claim 5, wherein the cloud server comprises a map module and an access module;

the map module is used for displaying the urban road map and updating the road condition analysis result transmitted by the intelligent base station into the corresponding road of the urban road map for display;

the access module is used for the third party terminal to access the cloud server and obtain real-time urban road map information.

8. The AI IOT technology based intelligent city traffic control system of claim 5, wherein the vehicle information terminal includes a display unit for displaying the prompt message sent by the intelligent base station.

Background

At present, most of urban intelligent traffic management systems are built on the basis of cloud platforms, road conditions are collected in real time by arranging monitoring nodes on the road sides of roads, road monitoring data are uploaded to the cloud platforms to be processed in a unified mode, and then the cloud platforms control signal indicating equipment of the roads according to actual road traffic conditions so as to improve driving efficiency. However, as the scale of urbanization is continuously enlarged, the number of road detection devices and road indication devices is rapidly increased, massive road monitoring data brings great pressure to data transmission and data processing of a cloud platform, and a centralized intelligent traffic management system using the cloud platform as a core gradually cannot meet the requirements for real-time performance and reliability of smart city traffic control.

Disclosure of Invention

In order to solve the problems, the invention aims to provide a smart city traffic control system based on an AI (Internet of things) technology.

The purpose of the invention is realized by adopting the following technical scheme:

the invention discloses an AI Internet of things technology-based smart city traffic control system, which comprises an information acquisition terminal, an intelligent base station, a vehicle-mounted information terminal and a cloud server, wherein the information acquisition terminal is connected with the intelligent base station; wherein the content of the first and second substances,

the information acquisition terminal is arranged on the road side and used for acquiring traffic information of the road and transmitting the acquired traffic information to the intelligent base station;

the vehicle-mounted information terminal is used for acquiring vehicle state information and transmitting the acquired vehicle state information to the intelligent base station;

the intelligent base station comprises an edge calculation module and a communication module;

the communication module is used for establishing communication connection with the information acquisition terminal and the vehicle-mounted information terminal;

the edge calculation module is used for analyzing and processing the received traffic information and the vehicle state information to obtain a road traffic analysis result, sending a corresponding prompt message to the vehicle-mounted information terminal according to the road traffic analysis result, and sending a corresponding regulation and control instruction to the traffic guidance equipment according to the road traffic analysis result; transmitting the traffic analysis result to a cloud server;

the cloud server is used for updating the urban traffic condition in real time according to the received traffic analysis result;

and the vehicle-mounted information terminal is also used for receiving and displaying the prompt message sent by the intelligent base station.

In one embodiment, the information acquisition terminal comprises a sensor arranged on the road side, wherein the sensor comprises a traffic flow sensor, a vehicle speed sensor and a video sensor; the traffic information of the road is collected through a sensor, wherein the traffic information comprises traffic flow data, speed data and road video image data of the road.

In one implementation, the vehicle-mounted information terminal acquires vehicle state information, wherein the vehicle state information comprises vehicle positioning information, driving path planning information, vehicle running state information and vehicle abnormal state information, the vehicle running state information comprises a vehicle speed, and the vehicle abnormal state information comprises a vehicle fault state and a traffic accident state.

In one embodiment, the intelligent base station, a plurality of sensors arranged on the road side in the coverage area of the intelligent base station and a plurality of vehicle-mounted information terminals jointly form a wireless communication network, the sensors and the vehicle-mounted information terminals are respectively used as child nodes in the wireless communication network, and each child node in the wireless communication network transmits data to the intelligent base station in a single-hop or multi-hop data transmission mode.

In one embodiment, the edge calculation module includes a traffic analysis unit;

the road condition analysis unit is used for analyzing the road condition of the road according to the traffic information of the road and the received vehicle state information based on an artificial intelligence algorithm, and acquiring a road condition analysis result, wherein the analysis includes current road condition analysis and road condition prediction; sending corresponding prompt messages to the vehicle-mounted information terminals within the coverage range of the base station according to the road condition analysis result; meanwhile, sending a control instruction corresponding to the road condition analysis result to corresponding traffic guidance equipment according to the road condition analysis result; and uploading the road condition analysis result to the cloud server.

In one embodiment, the edge calculation module further comprises an anomaly detection unit;

the system comprises an abnormality detection unit, a cloud server and a traffic information acquisition unit, wherein the abnormality detection unit is used for carrying out vehicle violation detection according to road video image data uploaded by an information acquisition terminal, recording violation information when the violation is detected to occur, and transmitting the violation information to the cloud server;

the cloud server further comprises a management module, and the management module is used for managing the received violation information for the third-party terminal to look up and call.

In one embodiment, the cloud server comprises a map module and an access module;

the map module is used for displaying the urban road map and updating the road condition analysis result transmitted by the intelligent base station to the corresponding road of the urban road map for display.

The access module is used for the third party terminal to access the cloud server and obtain real-time urban road map information.

In one embodiment, the vehicle-mounted information terminal comprises a display unit, and the display unit is used for displaying the prompt message sent by the intelligent base station.

The invention has the beneficial effects that: the traffic information of the road is collected through an information collecting terminal arranged on the road side of the road, the vehicle state information of the vehicles on the road is obtained through a vehicle-mounted information terminal arranged on the vehicles running on the road, the collected traffic information of the road and the vehicle state information are respectively transmitted to an intelligent base station in real time through the information collecting terminal and the vehicle-mounted information terminal, the intelligent base station carries out local analysis processing based on artificial intelligence based on the received traffic information and the vehicle state information to obtain a road traffic analysis result, on one hand, a prompt message is sent to the vehicles according to the road traffic analysis result so that drivers can know the road conditions and make path adjustment in advance according to the prompt message, on the other hand, a regulation and control instruction is sent to traffic guidance equipment according to the road traffic analysis result to control the traffic guidance equipment to adjust the corresponding traffic evacuation strategy, so as to relieve the road congestion condition in time according to the actual road condition; the intelligent base station based road traffic control system has the advantages that the intelligent base station is used for locally regulating and managing roads in a certain area, so that the traffic control of roads is facilitated, and meanwhile, the pressure of a cloud server on data transmission and data processing is relieved. Meanwhile, the road traffic analysis result is synchronously uploaded to the cloud server, the urban traffic condition in the cloud server is updated in real time, and other requirements of smart urban traffic control are met.

Drawings

The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.

Fig. 1 is a frame structure diagram of a smart city traffic control system based on AI internet of things technology according to the present invention.

Reference numerals:

the system comprises an information acquisition terminal 1, an intelligent base station 2, a vehicle-mounted information terminal 3 and a cloud server 4.

Detailed Description

The invention is further described in connection with the following application scenarios.

Referring to fig. 1, the embodiment of the invention provides a smart city traffic control system based on an AI internet of things technology, which includes an information acquisition terminal 1, an intelligent base station 2, a vehicle-mounted information terminal 3 and a cloud server 4; wherein the content of the first and second substances,

the information acquisition terminal 1 is arranged on the road side and used for acquiring traffic information of the road and transmitting the acquired traffic information to the intelligent base station 2;

the vehicle-mounted information terminal 3 is used for acquiring vehicle state information and transmitting the acquired vehicle state information to the intelligent base station 2;

the intelligent base station 2 comprises an edge calculation module and a communication module;

the communication module is used for establishing communication connection with the information acquisition terminal 1 and the vehicle-mounted information terminal 3;

the edge calculation module is used for analyzing and processing the received traffic information and the vehicle state information to obtain a road traffic analysis result, sending a corresponding prompt message to the vehicle-mounted information terminal 3 according to the road traffic analysis result, and sending a corresponding regulation and control instruction to the traffic guidance equipment according to the road traffic analysis result; and transmitting the traffic analysis result to the cloud server 4;

the cloud server 4 is used for updating the urban traffic condition in real time according to the received traffic analysis result;

the vehicle-mounted information terminal 3 is also used for receiving and displaying the prompt message sent by the intelligent base station 2.

The above embodiment of the present invention provides a smart city traffic control system based on artificial intelligence and internet of things, wherein an information collecting terminal 1 disposed at the road side of a road is used to collect traffic information of the road, a vehicle-mounted information terminal 3 disposed on a vehicle traveling on the road is used to obtain vehicle state information of the vehicle on the road, the information collecting terminal 1 and the vehicle-mounted information terminal 3 respectively transmit the collected traffic information of the road and the vehicle state information to an intelligent base station 2 in real time, the intelligent base station 2 performs local analysis processing based on artificial intelligence based on the received traffic information and vehicle state information to obtain a road traffic analysis result, on one hand, a prompt message is sent to the vehicle according to the road traffic analysis result, so that a driver can know road conditions according to the prompt message and make path adjustment in advance, on the other hand, a regulation and control instruction is sent to the traffic guidance equipment according to the road traffic analysis result so as to control the traffic guidance equipment (such as a traffic light) to regulate a corresponding traffic dispersion strategy and relieve the road congestion situation according to the actual road condition; the intelligent base station 2 is used for locally regulating and managing roads in a certain area, so that the traffic regulation and control of roads are facilitated, and the pressure of the cloud server 4 on data transmission and data processing is relieved. Meanwhile, the road traffic analysis result is synchronously uploaded to the cloud server 4, the urban traffic condition in the cloud server 4 is updated in real time, and other requirements of intelligent urban traffic control are met.

In one embodiment, the information acquisition terminal 1 comprises a sensor arranged at the roadside, wherein the sensor comprises a traffic flow sensor, a vehicle speed sensor, a video sensor and an environment sensor; the traffic information of the road is collected through a sensor, wherein the traffic information comprises traffic flow data, speed data, road video image data and environment data of the road, and the environment data comprises rainfall, water level and the like.

In one embodiment, the vehicle-mounted information terminal 3 acquires vehicle state information, wherein the vehicle state information includes vehicle positioning information, driving path planning information, vehicle running state information and vehicle abnormal state information, wherein the vehicle running state information includes a vehicle speed, and the vehicle abnormal state information includes a vehicle fault state and a traffic accident state.

In one embodiment, the intelligent base station 2, a plurality of sensors arranged on the road side in the coverage area of the intelligent base station and a plurality of vehicle-mounted information terminals 3 jointly form a wireless communication network, the sensors and the vehicle-mounted information terminals 3 are respectively used as sub-nodes in the wireless communication network, and each sub-node in the wireless communication network transmits data to the intelligent base station 2 in a single-hop or multi-hop data transmission mode.

In order to further reduce the overall energy consumption of the intelligent base station 2 for transmitting data to the intelligent base station 2 by the information acquisition terminal 1 and the vehicle-mounted information terminal, in the above embodiment, a wireless communication network is also provided, which is constructed according to the sensor of the information acquisition terminal 1 and the vehicle-mounted information terminal 3 in the coverage of the intelligent base station 2, so that the sensor and the vehicle-mounted information terminal 3 are used as sub-nodes in the wireless communication network together, and the data is transmitted and forwarded to the intelligent base station 2 in a single-picking or multi-hop data transmission manner, which is helpful for reducing the overall energy consumption of data transmission in the coverage of the intelligent base station 2.

In one embodiment, the intelligent base station 2 sends its own positioning information to the child node in its coverage area, the child node makes a judgment according to its own positioning information and the positioning information of the intelligent base station 2, if the distance between the child node and the intelligent base station 2 is less than the one-hop communication distance of the child node, the child node directly transmits data to the intelligent base station 2 in a one-hop data transmission manner; if the distance between the child node and the intelligent base station 2 exceeds the one-hop communication distance of the child node, the child node selects a next-level node from the neighbor nodes of the child node, transmits the data to the next-level node, and transmits the data to the intelligent base station 2 in a multi-hop forwarding mode.

In one embodiment, when the distance between the child node and the intelligent base station 2 exceeds the one-hop communication distance of the child node, the child node selects a next-level node from its neighbor nodes, which specifically includes:

the sub-nodes periodically acquire the node classification of the sub-nodes according to the node type information and the average speed information of the sub-nodes, wherein when the node type is a road side sensor node, or the node type is a vehicle-mounted information terminal 3 node and the average speed information isLess than or equal to the set standard speed v', i.e.If so, the child node divides the current period of the child node into class A nodes; when the node type is the vehicle-mounted information terminal 3 node and the average speed informationGreater than or equal to the set standard speed v', i.e.If so, the child node divides the current period of the child node into B-type nodes; wherein the set standard speed v' epsilon [20,30 ]]km/h；

The method comprises the steps that a child node broadcasts own state information to neighbor nodes (other child nodes) in a one-hop communication range of the child node, and receives the state information of the neighbor nodes in the one-hop communication range of the child node, wherein the state information comprises node classification information f, node type y, one-hop communication distance information r and average speed information of the child nodePositioning information and residual energy information E; the node types comprise a road side sensor node s and a vehicle-mounted informationA terminal 3 node c; the average speed information is obtained by counting according to the average speed of the child nodes in the last time period, wherein when the child nodes are roadside sensor nodes, the average speed information is 0; the node classification information comprises node classification of a current period of the child nodes, and the node classification comprises class A nodes and class B nodes;

the child nodes respectively calculate multi-hop performance factors of the neighbor nodes according to the state information of the neighbor nodes, and select the neighbor node corresponding to the maximum multi-hop performance factor as a next-stage node;

when the child node is an A-type node, the following first multi-hop performance factor calculation function is adopted to obtain the multi-hop performance factor of the neighbor node:

where α (N) represents a multi-hop performance factor of the nth neighbor node, N is 1,2, …, N represents the total number of neighbor nodes, and K represents the total number of neighbor nodes₁(f_nAnd A) represents a classification judgment factor, wherein f_nRepresenting the node classification of the nth neighbor node, f when the node of the nth neighbor node is classified as a class A node_n＝A，K₁(f_nA) is 1; when the node of the nth neighbor node is classified as a B-type node_n＝B，K₁(f_n,A)＝0.01；K₂(y_n,c>s) represents a type judgment factor, wherein y_nThe node type of the nth neighbor node is shown, and when the node type of the nth neighbor node is the 3 node c of the vehicle-mounted information terminal, K₂(y_n,c>s) ═ Y1; when the node type of the nth neighbor node is a roadside sensor node s, K₂(y_n,c>Y2, where Y1 is much larger than Y2, so that the multi-hop performance factor when the node type of the neighboring node is the telematics terminal 3 node c is all larger than the multi-hop performance factor when the node type of the neighboring node is the roadside sensor node s; r is_nRepresents the one-hop communication distance, D, of the nth neighbor node_nDenotes the distance, E, between the nth neighbor node and the intelligent base station 2_nRepresenting the remaining energy percentage of the nth neighbor node,representing the mean of the remaining energy percentages of neighboring nodes,representing the average speed information of the nth neighbor node, and v' representing the set standard speed; omega_D、ω_E、ω_vRespectively represent the set distance, energy and speed weight adjustment factors omega_D+ω_E+ω_v＝1

When the child node is a B-type node, acquiring the multi-hop performance factor of the neighbor node by adopting the following second multi-hop performance factor calculation function:

in the formula, K₂(y_n,s>c) Represents a type judgment factor, wherein y_nRepresenting the node type of the nth neighbor node, and when the node type of the nth neighbor node is a road side sensor node s, K₂(y_n,s>c) Y3; when the node type of the nth neighbor node is the vehicle-mounted information terminal 3 node c, K₂(y_n,s>c) Y4, where Y3 is much larger than Y4, so that the multi-hop performance factor when the node type of the neighbor node is roadside sensor node s is all larger than the multi-hop performance factor when the node type of the neighbor node is vehicle-mounted information terminal 3 node c; d_0nRepresenting the distance of the child node from the nth neighbor node.

In one scenario, Y1 ═ 1, Y2 ═ 0.01, Y3 ═ 1, and Y4 ═ 0.01.

The technical problem that the service life of the road side sensor node is shortened when the road side sensor node continuously bears too many multi-hop forwarding tasks of data transmission of the vehicle-mounted information terminal 3 is solved. The above embodiment particularly provides a technical scheme that when a sub-node (including a roadside sensor and a vehicle-mounted information terminal 3 within a communication coverage area of a base station) in a wireless communication network needs to perform data transmission in a multi-hop data transmission mode, the sub-node can adaptively select a next-level (next-hop) node. The sub-nodes firstly judge the node classification of the sub-nodes according to the state information of the sub-nodes, particularly the node type information and the average speed information, wherein all the road side sensor nodes are divided into A-type nodes; in addition, the nodes of the vehicle-mounted information terminal 3 of the vehicle with lower driving speed are also divided into A-type nodes; the nodes of the vehicle-mounted information terminals 3 of other vehicles are divided into B-type nodes; and the child nodes of different classifications calculate the multi-hop performance factors of the neighbor nodes according to the state information of the neighbor nodes, and select the neighbor nodes with high multi-hop performance factors as the next-stage nodes. The method comprises the steps of dividing the multi-hop performance factor into sub-nodes of different classifications correspondingly, and providing different multi-hop performance factor calculation functions. Considering that the degree of convenience of energy supplement of the vehicle-mounted information terminal 3 is far greater than that of the road-side sensor node, the vehicle-mounted information terminal 3 of a vehicle with a low driving speed is responsible for more data forwarding tasks for a road section with a low driving speed (for example, a road section which is in traffic jam), and the energy consumption of data transmission of the road-side sensor node can be effectively reduced while the reliability of data transmission is ensured, so when the road-side sensor node detects that the vehicle-mounted information terminal 3 node with a low moving speed exists in neighbor nodes (for example, the traffic jam occurs), the next-stage node can be adaptively selected from the vehicle-mounted information terminal 3 nodes in priority, the forwarding tasks of multi-hop data transmission are completed through the vehicle-mounted information terminal 3 node, and in the process of calculating the multi-hop performance factor, under the conventional condition of energy consumption and base station distance, the mobile speed is particularly added as a basis for judging the multi-hop performance of the next-level node, and the reliability of selecting the next-level node according to the multi-hop performance factor is improved. Meanwhile, in the vehicle-mounted information terminal 3 node with the higher vehicle speed, when the moving speed of the child node is higher, the reliability of multi-hop data transmission is easily reduced (for example, the transmission distance is too far or the packet loss occurs due to the reduction of data receiving and sending stability caused by too large displacement), so that for such a vehicle-mounted information terminal 3 node, in the process of calculating the multi-hop performance factor, a roadside sensor node with higher stability is preferentially considered as a next-stage node to ensure the stability of multi-hop data transmission. Through the self-adaption of the subnodes, the next-level nodes are selected according to the state information of the neighbor nodes, the data transmission quality and the energy consumption of the wireless communication network of the intelligent base station 2 can be optimized according to the characteristics of the urban road traffic information acquisition terminal 1 and the vehicle-mounted information terminal 3 in the coverage range of the intelligent base station 2, and the intelligent urban traffic management and control system is indirectly optimized.

In one embodiment, the edge calculation module includes a traffic analysis unit;

the road condition analysis unit is used for analyzing the road condition of the road according to the traffic information of the road and the received vehicle state information based on an artificial intelligence algorithm, and acquiring a road condition analysis result, wherein the analysis includes current road condition analysis and road condition prediction; and sends out corresponding prompt messages to the vehicle-mounted information terminal 3 within the coverage range of the base station according to the road condition analysis result; meanwhile, sending a control instruction corresponding to the road condition analysis result to corresponding traffic guidance equipment according to the road condition analysis result; and uploading the road condition analysis result to the cloud server 4.

In one scene, a road condition analysis unit analyzes according to road average vehicle speed measurement data transmitted by an information acquisition terminal 1 and in combination with vehicle speed data transmitted by a vehicle-mounted information terminal 3 of a vehicle in the road, comprehensively acquires the average vehicle speed of the road, compares the acquired average vehicle speed with a set vehicle speed threshold value, judges that the road is congested when the average vehicle speed of the road is detected to be smaller than the set vehicle speed threshold value, screens out the vehicle which is about to approach the congested road section in combination with running path planning information transmitted by the vehicle-mounted information terminal 3 according to the acquired congestion analysis result, and transmits a congestion prompt message of the road section to the corresponding vehicle-mounted information terminal 3, so that a driver can acquire the congestion condition of the road section in time through the vehicle-mounted information terminal 3 and make corresponding path adjustment; meanwhile, the road condition analysis unit sends a corresponding control instruction to the traffic guidance equipment corresponding to the congested road section according to the acquired congestion analysis result, for example, traffic light change strategies corresponding to the road section and peripheral road sections are regulated and controlled to relieve the congestion condition of the congested road section, or intelligent display boards of other roads leading to the congested road section are controlled to display congestion information of the road section, so that drivers of nearby roads can be prompted to avoid the congested road section in time, and congestion is prevented from being aggravated. Meanwhile, the traffic information analysis unit further transmits the congestion information of the road segment to the cloud server 4, so that the cloud server 4 can update the congestion information of the road segment in the urban road traffic map, and when a user views the map information (for example, the user accesses the cloud server 4 through third-party map application software of the user terminal to obtain the urban road traffic information), the real-time situation of the road condition can be grasped in time. Wherein above-mentioned analytic processing is accomplished based on intelligent basic station 2's edge calculation module, carry out the real-time analysis of localization through the road conditions analysis unit to the road conditions of road, can accomplish the regulation and control of highway section traffic guidance equipment, the suggestion of near on-vehicle information terminal 3 and the remote update of cloud server 4 information, can follow near, well, the real-time management and control of far away three dimension completion road traffic, help improving the real-time nature level of traffic management and control in the 2 coverage of intelligent basic station, also help improving the reliability level of the whole traffic management and control in wisdom city.

In one scenario, the traffic analysis unit analyzes and predicts the traffic data change trend of the road section based on an AI analysis algorithm or a trained traffic prediction model according to the acquired traffic data of the road, predicts the traffic of the road in advance, prompts the vehicle-mounted information terminals 3 of nearby vehicles according to the traffic prediction result, and regulates and controls corresponding traffic guidance equipment in advance to avoid the occurrence of road congestion.

In one scenario, when a road condition analysis unit receives a vehicle fault or a traffic accident sent by a vehicle-mounted information terminal 3, or detects that the vehicle fault or the traffic accident occurs in a road and the road is blocked according to road video image data transmitted by an information acquisition terminal 1, a corresponding accident detection result is generated, and meanwhile, a prompt message is sent to a nearby vehicle-mounted information terminal 3 according to the accident detection result, so that a corresponding driver can know the occurrence of the accident in advance to correspondingly avoid, and meanwhile, a corresponding accident prompt message is displayed through a traffic guidance device to prompt a passing vehicle to pay attention to avoiding an accident area (such as changing lanes or changing routes in advance), thereby being beneficial to relieving congestion caused by the accident; when the information sent by the vehicle-mounted information terminal 3 is received or the road which is processed by the accident is recovered to be normal is obtained through the road video image, a corresponding road condition recovery result is generated, the road recovery normal information is sent to the corresponding vehicle-mounted information terminal 3 in time, and a control instruction is sent to the corresponding traffic guidance equipment, so that the traffic guidance equipment recovers to be normal and prompt information is displayed.

The above mentioned roads are one-way roads, and in practical cases, two-way roads can be understood as two different roads.

In one embodiment, the edge calculation module further comprises an anomaly detection unit;

the abnormality detection unit is used for detecting vehicle violation according to road video image data uploaded by the information acquisition terminal 1, recording violation information when violation is detected, and transmitting the violation information to the cloud server 4;

the cloud server 4 further includes a management module, and the management module is configured to manage the received violation information, so that the violation information can be referred and called by a third-party terminal.

The intelligent base station 2 receives road video image data acquired by the information acquisition terminal 1, violation detection (such as vehicle violation, illegal lane change, line pressing, recorded vehicle theft detection and the like) is carried out on vehicles in a road through an abnormity detection unit of the edge calculation module, when vehicle violation is detected, the violation information is automatically recorded and uploaded to the cloud server 4 for storage, so that third-party terminals (such as a public security system, a traffic management system and the like) can look up and call the violation information, the linkage of the traffic management and control system and other fields of city management can be realized, and data support is provided for other administrative or management systems of a smart city.

In one embodiment, before vehicle violation detection is performed according to road video image data uploaded by the information acquisition terminal 1, the abnormality detection unit further pre-processes the received road video image data based on an image processing algorithm to improve the quality of the road video image, so as to avoid that the road video image data acquired by the information acquisition terminal 1 is easily interfered by noise or by the environment (for example, influenced by a vehicle strong light) due to a complex road condition, and thus, the accuracy of vehicle violation detection performed according to the road video image data is influenced.

In one embodiment, the preprocessing the received road video image data in the anomaly detection unit specifically includes:

converting the obtained road video image from an RGB color space to a Lab color space, and obtaining a brightness component L, a color component a and a color component b of the road video image;

and performing brightness adjustment processing on the acquired brightness component, wherein the adopted brightness adjustment function is as follows:

wherein L' (x, y) represents a luminance component value of the luminance-adjusted pixel (x, y), L_YIndicates a set reference luminance component value, wherein L_Y∈[50，75]，Expressing the mean value of the brightness components of all the pixel points in the road video image, L (x, y) expressing the value of the brightness components of the pixel points (x, y) in the road video image, L_5x5(x, y) represents the mean of the luminance components of the pixels in the 5x5 neighborhood centered on pixel (x, y), L_bIndicating a set luminance component compensation value, wherein L_b∈[5,10]，Represents a judgment factor, whereinWhen the temperature of the water is higher than the set temperature,when in useWhen the temperature of the water is higher than the set temperature,ω₁、ω₂and ω₃Respectively represent the set weight adjustment factors, where₁∈[0.3,0.6]，ω₂∈[0.3,0.6]，ω₃∈[0.1,0.2]，ω₁+ω₂+ω₃＝1。

According to the brightness component L after brightness adjustment^′And reconstructing the color component a and the color component b to obtain the preprocessed road video image.

In the above embodiment, a technical solution dedicated to the preprocessing of the road video image is provided, which can adapt to adaptive adjustment of the luminance video image under different conditions such as day and night, so that the overall luminance of the road video image tends to a proper level, and at the same time, adaptive smoothing is performed on exposure points in the road video image due to the influence of vehicle highlights, which is helpful for improving the image definition, and luminance compensation is performed on the luminance distortion condition (for example, the luminance distortion condition caused by the leveling processing performed because the overall luminance in the original image exceeds the value range after the overall luminance adjustment) caused by the adaptive luminance enhancement of the image with relatively low overall luminance (for example, the road video image collected when the lights are relatively low at night), so as to improve the preprocessing effect of the road video image, the method is favorable for improving the definition of the road video image, and lays a foundation for further vehicle violation detection of the abnormality detection unit according to the preprocessed road video image.

In one embodiment, the cloud server 4 comprises a map module and an access module;

the map module is used for displaying the urban road map and updating the road condition analysis result transmitted by the intelligent base station 2 to the corresponding road of the urban road map for display.

The access module is used for the third party terminal to access the cloud server 4 and obtain real-time urban road map information.

In one embodiment, the telematics terminal 3 includes a display unit for displaying a prompt message transmitted by the smart base station 2.

It should be noted that, functional units/modules in the embodiments of the present invention may be integrated into one processing unit/module, or each unit/module may exist alone physically, or two or more units/modules are integrated into one unit/module. The integrated units/modules may be implemented in the form of hardware, or may be implemented in the form of software functional units/modules.

From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be analyzed by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.