1. A Bayesian network-based traffic network dynamic prediction method is characterized by comprising the following steps:

collecting traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of a traffic network needing prediction;

performing correlation analysis on the time sequence to determine a training set;

training a Bayesian network structure through the training set to obtain a prediction model;

predicting each road section under different time delays in a prediction range based on the prediction model to obtain prediction results of each road section at different moments; the prediction result is the traffic state quantity of each road section;

according to the prediction result, determining predictable road sections in a traffic network under different time delays to obtain a maximum connected sub-group;

determining the phase change moment of the maximum connected sub-cluster, and predicting the time delay of a traffic network;

and determining a dynamic prediction result of the traffic network based on the predictable time delay of the traffic network.

2. The Bayesian network-based traffic network dynamic prediction method as recited in claim 1, wherein the performing correlation analysis on the time series to determine a training set specifically comprises:

calculating the Pearson correlation coefficients of the prediction target road section and all road sections in the prediction range under different time delays based on the time sequence;

and selecting the time sequence with the Pearson correlation coefficient larger than a set threshold value as a training set.

3. The bayesian network-based dynamic traffic network prediction method according to claim 1, wherein the predicting of each road segment within a prediction range at different time delays based on the prediction model to obtain the prediction results of each road segment at different times specifically comprises:

calculating a prediction joint matrix of each road section under different delays, wherein the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

determining the optimal distribution number of each prediction joint matrix;

and respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model, and calculating to obtain prediction results under different delays.

4. The Bayesian network-based traffic network dynamic prediction method according to claim 3, wherein the determining of the optimal distribution number of each prediction combination matrix specifically comprises:

calculating the information quantity of the Chichi cells with different distribution numbers of the prediction combined matrixes;

and selecting the distribution number with the minimum red pool information amount as the optimal distribution number.

5. A Bayesian network-based traffic network dynamic prediction system is characterized by comprising:

the acquisition module is used for acquiring traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of a traffic network needing prediction;

the training set determining module is used for carrying out correlation analysis on the time sequence and determining a training set;

the training module is used for training the Bayesian network structure through the training set to obtain a prediction model;

the prediction module is used for predicting each road section within a prediction range under different time delays based on the prediction model to obtain the prediction result of each road section at different moments; the prediction result is the traffic state quantity of each road section;

the maximum connected sub-cluster determining module is used for determining predictable road sections in a traffic network under different time delays according to the prediction result to obtain a maximum connected sub-cluster;

the phase change moment determining module is used for determining the phase change moment of the maximum connected sub-cluster, and the phase change moment is a predictable time delay of a traffic network;

and the traffic network dynamic prediction result determining module is used for determining a traffic network dynamic prediction result based on the predictable time delay of the traffic network.

6. The Bayesian network based traffic network dynamic prediction system as recited in claim 5, wherein said training set determination module specifically comprises:

a Pearson correlation coefficient calculation unit for calculating Pearson correlation coefficients of the prediction target link and all links within the prediction range at different time delays based on the time series;

and the selection unit is used for selecting the time sequence with the Pearson correlation coefficient larger than the set threshold value as the training set.

7. The Bayesian network based traffic network dynamic prediction system as recited in claim 5, wherein said prediction module comprises:

the prediction joint matrix calculation unit is used for calculating a prediction joint matrix of each road section under different delays, and the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

an optimal distribution number determining unit, configured to determine an optimal distribution number of each prediction joint matrix;

and the input unit is used for respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model and calculating to obtain prediction results under different delays.

8. The Bayesian network based traffic network dynamic prediction system according to claim 7, wherein the optimal distribution number determining unit specifically comprises:

the red pool information amount calculation operator unit is used for calculating the red pool information amounts of different distribution numbers of the prediction combined matrixes;

and the selecting subunit is used for selecting the distribution number with the minimum red pool information amount as the optimal distribution number.

Background

With the rapid development of economy and the advancement of urbanization, the reserve of motor vehicles in China shows a rapid growth trend. The increasing number of vehicles makes urban traffic congestion increasingly serious. The essence of traffic jam generation is imbalance of traffic capacity demand and supply, in order to improve the current situation of traffic jam, on one hand, the supply of traffic capacity can be increased by strengthening the construction of a basic road, on the other hand, relevant vehicle restriction measures can be adopted to reduce the demand of traffic capacity, and the demand and the supply are balanced by increasing the supply and reducing the demand. Under the limited land space of a large city, the construction of urban basic roads has certain difficulty. Although cities in the world are constantly investing in capital to build urban traffic infrastructures, the speed of urban traffic infrastructure construction is far behind the increase of urban traffic capacity demand from the current results. After many attempts, more and more research institutions and government agencies have placed the desire to manage traffic congestion in the direction of reasonable traffic control methods, and have sought to address traffic congestion by using more reasonable resource allocation methods.

The reasonable allocation of resources needs to know the traffic system more clearly and accurately, and a traffic management department needs to find traffic flow changes which possibly cause traffic jam in time during allocation, identify vulnerable road sections which are easy to cause traffic jam in an urban road network, and guarantee the maximization of regulation and control benefits under the limited traffic regulation and control investment. In view of the above requirements for Traffic control, there is an urgent need for a more Intelligent and efficient Traffic control System, and an Intelligent Traffic System (ITS) is developed at the discretion. The intelligent traffic system was first proposed by IBM in 2008, and it is hoped to establish a real-time, efficient and accurate comprehensive traffic control system in urban road networks and even in a larger range by using advanced information, communication, sensing, control and computer technologies. Once the intelligent transportation system is put forward, it is receiving wide attention from all countries in the world.

The accurate traffic flow prediction is the key point of the intelligent traffic system for sensing the running state of the traffic system, making short-time traffic control measures, giving play to the efficiency of traffic infrastructure and improving the running efficiency of the traffic system. The short-term traffic flow prediction of roads is one of the core parts of the research of intelligent traffic systems. The short-term traffic flow is a complex system of human, vehicle and road interaction, and all parts of the system interact and are mutually coupled, so that high nonlinearity, time variation and uncertainty are presented, and the difficulty of short-term traffic flow prediction is increased.

Disclosure of Invention

Based on the above, the invention aims to provide a traffic network dynamic prediction method and system based on a Bayesian network, which can improve the accuracy of urban traffic network prediction.

In order to achieve the purpose, the invention provides the following scheme:

a traffic road network dynamic prediction method based on a Bayesian network comprises the following steps:

collecting traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of a traffic network needing prediction;

performing correlation analysis on the time sequence to determine a training set;

training a Bayesian network structure through the training set to obtain a prediction model;

predicting each road section under different time delays in a prediction range based on the prediction model to obtain prediction results of each road section at different moments; the prediction result is the traffic state quantity of each road section;

according to the prediction result, determining predictable road sections in a traffic network under different time delays to obtain a maximum connected sub-group;

determining the phase change moment of the maximum connected sub-cluster, and predicting the time delay of a traffic network;

and determining a dynamic prediction result of the traffic network based on the predictable time delay of the traffic network.

Optionally, the performing correlation analysis on the time series to determine a training set specifically includes:

calculating the Pearson correlation coefficients of the prediction target road section and all road sections in the prediction range under different time delays based on the time sequence;

and selecting the time sequence with the Pearson correlation coefficient larger than a set threshold value as a training set.

Optionally, the predicting, based on the prediction model, each road segment within a prediction range under different time delays to obtain a prediction result of each road segment at different times includes:

calculating a prediction joint matrix of each road section under different delays, wherein the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

determining the optimal distribution number of each prediction joint matrix;

and respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model, and calculating to obtain prediction results under different delays.

Optionally, the determining the optimal distribution number of each prediction joint matrix specifically includes:

calculating the information quantity of the Chichi cells with different distribution numbers of the prediction combined matrixes;

and selecting the distribution number with the minimum red pool information amount as the optimal distribution number.

The invention also provides a Bayesian network-based traffic network dynamic prediction system, which comprises the following components:

the acquisition module is used for acquiring traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of a traffic network needing prediction;

the training set determining module is used for carrying out correlation analysis on the time sequence and determining a training set;

the training module is used for training the Bayesian network structure through the training set to obtain a prediction model;

the prediction module is used for predicting each road section within a prediction range under different time delays based on the prediction model to obtain the prediction result of each road section at different moments; the prediction result is the traffic state quantity of each road section;

the maximum connected sub-cluster determining module is used for determining predictable road sections in a traffic network under different time delays according to the prediction result to obtain a maximum connected sub-cluster;

the phase change moment determining module is used for determining the phase change moment of the maximum connected sub-cluster, and the phase change moment is a predictable time delay of a traffic network;

and the traffic network dynamic prediction result determining module is used for determining a traffic network dynamic prediction result based on the predictable time delay of the traffic network.

Optionally, the training set determining module specifically includes:

a Pearson correlation coefficient calculation unit for calculating Pearson correlation coefficients of the prediction target link and all links within the prediction range at different time delays based on the time series;

and the selection unit is used for selecting the time sequence with the Pearson correlation coefficient larger than the set threshold value as the training set.

Optionally, the prediction module specifically includes:

the prediction joint matrix calculation unit is used for calculating a prediction joint matrix of each road section under different delays, and the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

an optimal distribution number determining unit, configured to determine an optimal distribution number of each prediction joint matrix;

and the input unit is used for respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model and calculating to obtain prediction results under different delays.

Optionally, the optimal distribution number determining unit specifically includes:

the red pool information amount calculation operator unit is used for calculating the red pool information amounts of different distribution numbers of the prediction combined matrixes;

and the selecting subunit is used for selecting the distribution number with the minimum red pool information amount as the optimal distribution number.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the method screens the parents of the Bayesian network prediction model in the whole range of the road network based on the correlation analysis, can better screen the parents with stronger correlation in the prediction range, and brings the long-distance correlation in the road network into the parent selection range, so that the offspring parent structure of the Bayesian network has better interpretability in time and space. Secondly, the method adopts the algorithm for solving the maximum connected sub-cluster to find the phase change point of the maximum connected sub-cluster of the road network predicted in the process that the time delay of the road network is continuously reduced, excavates the optimal predictable time delay of the road network, and provides the optimal predictable time delay after optimization for the overall prediction of the urban traffic road network. The phase change point for predicting the maximum connected sub-cluster of the road network can effectively solve the problem of selecting the predicted time length in advance for predicting the traffic information in the urban traffic network, so that the urban traffic network can be more accurately grasped for predicting the urban traffic network, and better reference indexes are provided for the urban traffic network prediction.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a flow chart of a Bayesian network-based traffic network dynamic prediction method according to an embodiment of the present invention;

FIG. 2 is a diagram illustrating the variation of the curve for predicting the maximum accuracy of the sub-cluster.

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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.

The invention aims to provide a Bayesian network-based traffic network dynamic prediction method and system, which can improve the accuracy of urban traffic network prediction.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

As shown in fig. 1, a method for dynamically predicting a traffic network based on a bayesian network includes the following steps:

step 101: collecting traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of the traffic network needing prediction.

And collecting traffic information of a traffic network section within a prediction range for a period of time, wherein the traffic information comprises traffic speed, traffic density and traffic flow. The concrete content comprises:

(1) determining the range of the traffic network to be predicted: and counting the longitude and latitude range of the traffic network to be predicted, the number of road sections in the traffic network, the topological structure among the road sections and road information of the road sections, wherein the longitude and latitude range comprises the length of the road sections, the grade of the road and longitude and latitude coordinates of the starting point of the road sections.

(2) Collecting traffic flow information of a traffic network section within a prediction range for a period of time to form a time sequence of traffic state quantity of each section within the prediction range:

and (2) for each road section in the step (1), collecting traffic flow information in a certain time, including traffic flow speed, traffic flow density and traffic flow, and performing data compensation on the road section with the loss. And forming a time sequence with the same length of each traffic information volume of each road section after compensation.

Step 102: and performing correlation analysis on the time sequence to determine a training set. Specifically, the method comprises the following steps: calculating the Pearson correlation coefficients of the prediction target road section and all road sections in the prediction range under different time delays based on the time sequence; and selecting the time sequence with the Pearson correlation coefficient larger than a set threshold value as a training set.

(1) And (3) carrying out correlation analysis: the pearson correlation coefficient is a measure of the degree of correlation between two variables. First, a suitable time delay is selected, and for short-term prediction, 10 time steps can be selected as the time delay. For each time sequence of traffic information of each road section, t is taken₀As the time point to be predicted, t₀Section c of which the time of day needs to be predicted₁Is recorded asThen there is t₀N time certain section c_kIn a time sequence ofAnd calculating the Pearson correlation coefficients of the prediction target road section and all the road sections in the prediction area under different time delays.

(2) Determining a Bayesian network structure of a prediction model: and (3) determining a proper threshold value according to the Pearson correlation coefficient obtained in the step (1) to screen out the parent of the Bayesian network, and determining the structural relationship between the child and the parent of the Bayesian network.

Here, "parent" refers to a time series of some links in the bayesian network model for which the past time period of the road network as input information needs to be screened, and "child" refers to one link in the road network as a prediction target in the bayesian network model. For convenience of calculation, generally, only at most 30 parents are selected for a road section, and the number of the parents of different road sections can be different, but all the parents need to be higher than a given threshold value. The given threshold is determined by the fact that the pearson correlation coefficient calculated for a particular region is, in general, not less than 0.3. And determining the number of parents of each child, the position of the parents and the occurrence time of each parent according to the given limit of the number of the parents and the given limit of a threshold value, and forming the structures of the children and the parents of the Bayesian network of the prediction model.

Step 103: and training the Bayesian network structure through the training set to obtain a prediction model.

Step 104: predicting each road section under different time delays in a prediction range based on the prediction model to obtain prediction results of each road section at different moments; and the prediction result is the traffic state quantity of each road section. The method specifically comprises the following steps:

step 1041: calculating a prediction joint matrix of each road section under different delays, wherein the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

step 1042: determining the optimal distribution number of each prediction joint matrix;

step 1043: and respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model, and calculating to obtain prediction results under different delays.

The principle is as follows:

(1) predicting each of the road segments: firstly, writing a time sequence of a parent and a time sequence of a child into a prediction joint matrix according to the parent structure of the children of the Bayesian network obtained in the step 102, wherein for each child needing to be predicted, the parent needs to be arranged according to prediction time delays from early to late because of the need of multiple predictions under different time delays to form ten prediction matrixes, when the joint matrix with the earliest prediction time delay is formed, only the parent and the child with the earliest prediction time delay are written into the joint matrix, and when the joint matrix with the later prediction time delay is formed, the parents with other prediction time delays are sequentially added to form ten prediction joint matrixes with sequentially increased parents; secondly, for each prediction joint matrix, the distribution number of the prediction joint matrix for performing mixed gaussian distribution fitting needs to be determined, and the determination principle of the distribution number is as follows: calculating the red blood pool information Amount (AIC) of parent offspring prediction combined matrixes under different distribution numbers aiming at each prediction combined matrix, wherein the different distribution numbers generally refer to 1-10, the length of a time sequence is used as an observation number, the distribution number is used as a parameter number, the red blood pool information amounts with different distribution numbers are calculated, and the distribution number with the minimum red blood pool information amount is selected as an optimal distribution number; and finally, inputting the obtained prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution fitting model to obtain the fitted offspring parent multi-dimensional joint distribution.

Obtaining the multidimensional joint distribution of the filial generation and the parent generation after fitting, and writing the conditional probability distribution of the filial generation through joint probability distribution and probability distribution of the parent generation according to the Bayes principle:

the conditional probability distribution of the filial generation can be obtained through the formula, the parent information of the filial generation is input as a prior condition, the expectation of the conditional probability is taken as the predicted value of the target filial generation under the condition, and the predicted target value can be obtained through calculation.

(2) Obtaining the prediction results of the road section under different prediction time delays: and aiming at each road section, obtaining prediction matrixes under different delays, respectively inputting each prediction matrix and the optimal distribution number into a Gaussian mixture distribution model, calculating to obtain prediction results under different delays, comparing real results to obtain prediction precision under the prediction time delay, sequencing the prediction precision according to the time delay and drawing a prediction precision curve.

Step 105: and according to the prediction result, determining predictable road sections in the traffic network under different time delays to obtain the maximum connected sub-group.

(1) Determining predictable road sections in the road network under different prediction time delays, and solving the maximum sub-group of the road network under different prediction time delays: according to step 104, obtaining the prediction results of each road section of the whole road network under different time delays, wherein the parent of a part of the road sections appears earlier, the prediction results exist at the earlier moment, the parent of a part of the road sections appears later, and the prediction results exist at the later moment, aiming at the road network under different prediction time delays, the road sections with the prediction results are marked as predictable road sections, the road sections without the prediction results are marked as unpredictable road sections, the unpredictable road sections are deleted from the road network under the time delay, and the maximum connected sub-cluster of the road network under the prediction time delay is found out in the road network with the unpredictable nodes deleted by utilizing the maximum connected sub-cluster solving algorithm.

Here, "deleting" refers to deleting nodes of unpredictable road segments and all connecting edges of unpredictable road segments in the road network topology under the prediction delay, and "the maximum connected sub-cluster" refers to a set of connected nodes remaining to be still reachable after deleting a certain node in the network.

Step 106: and determining the phase change moment of the maximum connected sub-cluster, and predicting the time delay of the traffic network.

Step 107: and determining a dynamic prediction result of the traffic network based on the predictable time delay of the traffic network.

Finding the phase change time of the maximum sub-cluster of the road section, determining the whole predictable time delay of the road network, and obtaining the prediction result of the road network: solving the maximum connected sub-cluster of the road network under different prediction time delays and the prediction time t₀Comparing and calculating the accuracy of the maximum connected cliques at the moment to obtain the prediction accuracy of the maximum connected cliques of the road network under different prediction delays, drawing a prediction curve (shown in figure 2) of the maximum connected cliques of the road network according to the change of the prediction delay, finding a moment point of phase change of the prediction accuracy of the maximum connected cliques of the road network in the curve to be used as a starting point of the phase change of the whole predictable road network, obtaining the predictable delay of the whole road network, and using the predictable delay of the whole road network and the predicted change curve of the maximum connected cliques of the road network as the prediction result of the road network.

The invention also provides a Bayesian network-based traffic network dynamic prediction system, which comprises the following components:

the acquisition module is used for acquiring traffic flow information of a traffic network within a set time in a prediction range to form a time sequence of traffic state quantity of each road section within the prediction range; the prediction range is the range of a traffic network needing prediction;

the training set determining module is used for carrying out correlation analysis on the time sequence and determining a training set;

the training module is used for training the Bayesian network structure through the training set to obtain a prediction model;

the prediction module is used for predicting each road section within a prediction range under different time delays based on the prediction model to obtain the prediction result of each road section at different moments; the prediction result is the traffic state quantity of each road section;

the maximum connected sub-cluster determining module is used for determining predictable road sections in a traffic network under different time delays according to the prediction result to obtain a maximum connected sub-cluster;

the phase change moment determining module is used for determining the phase change moment of the maximum connected sub-cluster, and the phase change moment is a predictable time delay of a traffic network;

and the traffic network dynamic prediction result determining module is used for determining a traffic network dynamic prediction result based on the predictable time delay of the traffic network.

Wherein, the training set determining module specifically comprises:

a Pearson correlation coefficient calculation unit for calculating Pearson correlation coefficients of the prediction target link and all links within the prediction range at different time delays based on the time series;

and the selection unit is used for selecting the time sequence with the Pearson correlation coefficient larger than the set threshold value as the training set.

Wherein the prediction module specifically comprises:

the prediction joint matrix calculation unit is used for calculating a prediction joint matrix of each road section under different delays, and the prediction joint matrix is determined by a parent structure and a child structure of the prediction model; the parent structure is a training set, and the child structure is a prediction result;

an optimal distribution number determining unit, configured to determine an optimal distribution number of each prediction joint matrix;

and the input unit is used for respectively inputting each prediction joint matrix and the optimal distribution number into a Gaussian mixture distribution model and calculating to obtain prediction results under different delays.

Wherein, the optimal distribution number determining unit specifically includes:

the red pool information amount calculation operator unit is used for calculating the red pool information amounts of different distribution numbers of the prediction combined matrixes;

and the selecting subunit is used for selecting the distribution number with the minimum red pool information amount as the optimal distribution number.

The invention is based on a Bayesian network and an algorithm for solving the maximum connected sub-clusters, aims to predict a traffic network by establishing a traffic network prediction model through the Bayesian network, and solves the maximum connected sub-clusters of the road network at the moment on the traffic network with different prediction time delays by applying the algorithm for solving the maximum connected sub-clusters, so as to obtain the predictable time delay of the whole road network, thereby solving the problem of the time delay of the prediction and selection of the urban traffic network, obtaining the optimal advanced prediction time length of the road network, and improving the predictability of the whole urban road network. The invention has the advantages that: firstly, the classic urban road network Bayesian network prediction method selects historical data to mainly select a road section close to a prediction target and historical information of a corresponding road section as input information to train a prediction model, so that the predictable time delay of each road section is consistent, the method does not consider that influence of different road sections on time and space dimensions is possibly different, does not have good interpretability of a physical layer and difference between different road sections, and ignores long distance correlation in a traffic network. The parent of the Bayesian network prediction model is screened in the whole road network range based on the correlation analysis, the parent with stronger correlation can be better screened in the prediction range, the long-distance correlation in the road network is also included in the parent selection range, and the child parent structure of the Bayesian network has better interpretability in time and space. Secondly, the method adopts the algorithm for solving the maximum connected sub-cluster to find the phase change point of the maximum connected sub-cluster of the road network predicted in the process that the time delay of the road network is continuously reduced, excavates the optimal predictable time delay of the road network, and provides the optimal predictable time delay after optimization for the overall prediction of the urban traffic road network. Particularly, the phase change point for predicting the maximum connected sub-cluster of the road network can effectively solve the problem of selecting the predicted time length in advance for predicting the traffic information in the urban traffic network, so that the urban traffic network prediction can be more accurately grasped, and a better reference index is provided for the urban traffic network prediction.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.