A smart city traffic management system

Abstract

The invention discloses a smart city traffic management system, and relates to the field of urban traffic technology. The invention comprises: a traffic data acquisition module, a traffic data processing module, a fit analysis module, a trunk road constraint processing module, an LED display terminal and a database. The invention obtains a first traffic operation fit and a second traffic operation fit of each trunk road in the city through information aggregation processing, thereby improving the matching of the layout of the traffic sensor devices on the road and the distribution of the network signal with the actual situation. When special circumstances occur, it avoids the inability to provide accurate characteristic data as a basis for judgment. The invention gathers a collection of group labels of the trunk roads in the city, divides the roads in a targeted manner, avoids the subsequent unified implementation standards for the maintenance and deployment of police force on the trunk roads, optimizes the maintenance and management costs of the trunk roads in the city, and improves the rationality of the allocation of police force on the trunk roads in the city.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic technology, and in particular to a smart city traffic management system. Background Art

[0002] With the continuous acceleration of urbanization, the urban population has increased dramatically, and urban traffic problems have become increasingly serious. Through real-time monitoring and analysis of urban traffic data, potential traffic safety hazards can be discovered in a timely manner, and early warning information can be provided to traffic management departments, so that corresponding measures can be taken to prevent traffic accidents, thereby improving urban traffic efficiency, alleviating traffic congestion, reducing the incidence of traffic accidents, and improving the travel experience of urban residents. It is extremely necessary to manage urban traffic.

[0003] Prior art, such as the smart city traffic management system disclosed in the invention application patent with announcement number: CN114724368A, includes: a central server, a node device and a user terminal, wherein the node device includes an intelligent HUD device mounted on a vehicle, wherein the intelligent HUD device is connected to the vehicle system through a vehicle data interface to obtain vehicle data, and the intelligent HUD device is used to realize vehicle collision detection, and is also used to realize free parking space data sharing and free parking space navigation, wherein the central server is used to store evidence chain data and free parking space data, and realize access management of the evidence chain data and free parking space data, and the user terminal is used to bind the node device and request the central server to obtain the evidence chain data, and the invention thus uses the on-board cameras of vehicles traveling on the road to jointly construct a blockchain for traffic accident evidence collection, so that the system can solve the current problems of traffic jams and difficulty in finding parking spaces caused by traffic accidents, and further improve urban traffic efficiency;

[0004] Prior art, such as the Internet-based smart city traffic management system disclosed in the invention application patent with announcement number: CN117994978A, includes a data collection module, a passage management module and a traffic management module, which can detect accidents on the road in real time and issue warnings to drivers of nearby vehicles to pay attention to safety and seek help from emergency departments and traffic police departments. Traffic accidents can be handled in a timely manner, travel efficiency can be improved, data transmission time can be reduced, the real-time nature of traffic information can be greatly improved, the probability of accidents can be reduced, the safety of drivers can be protected, and travel safety can be improved.

[0005] Combined with the above scheme, it can be found that the current limitations of urban traffic management are: there are few existing technologies that analyze the compatibility of traffic sensor devices and network signals with road traffic data, and there is no targeted division of roads. Due to the differences in road traffic data, there are certain differences in the demand for road traffic sensor devices and network signals. At the same time, due to the compatibility of road traffic sensor devices and network signals, there are also differences in the demand for subsequent daily maintenance and police force. Therefore, the analysis of this level in the existing technology, on the one hand, makes it difficult to match the layout of road traffic sensor devices and the distribution of network signals with the actual situation. In special circumstances, it is easy to fail to provide accurate characteristic data, thereby lacking a basis for judgment. On the other hand, if the subsequent maintenance of trunk roads and the deployment of police force are uniformly implemented, the maintenance and management costs of urban trunk roads will be increased, and the rationality of the allocation of police force on urban trunk roads will be limited. Summary of the invention

[0006] The purpose of the present invention is to provide a smart city traffic management system that solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a smart city traffic management system, including a traffic data acquisition module, which is used to obtain traffic information of various main roads in the city based on traffic sensor devices installed in the city in combination with the city traffic safety center.

[0008] The traffic data processing module is used to integrate and process the traffic information of each main road in the city and the traffic signal intensity distribution map of the city management center to obtain the first traffic restriction factor and the second traffic restriction factor of each main road in the city.

[0009] The compatibility analysis module is used to obtain the city's traffic sensor device layout map and traffic signal device distribution map from the city management center, and obtain the first traffic operation compatibility and second traffic operation compatibility of each main road in the city through information aggregation processing.

[0010] The main road constraint processing module determines the correction value of the traffic sensor device and the correction value of the signal strength of each main road in the city according to the first traffic operation conformity and the second traffic operation conformity of each main road in the city, generates a group label of the main road in the city, and gathers a collection of the group labels of the main roads in the city.

[0011] The LED display terminal is used to import a collection of urban trunk road group labels into a preset LED display interface for visual display.

[0012] The beneficial effects of the present invention are: (1) The present invention obtains traffic information of various main roads in the city based on the traffic sensor devices installed in the city and the urban traffic safety center in the traffic data acquisition module, laying a foundation for the subsequent analysis of the first traffic restriction factors and the second traffic restriction factors of various main roads in the city.

[0013] (2) In the traffic data processing module, the present invention analyzes the first traffic restriction factor of each main road in the city through the offset of each characteristic traffic data of each main road in the city, and at the same time analyzes the second traffic restriction factor of each main road in the city through the offset of each basic traffic data of each main road in the city, thereby providing data support for the subsequent evaluation of the compatibility of traffic sensor devices on each main road in the city and the evaluation of the compatibility of network signals.

[0014] (3) In the compatibility analysis module of the present invention, the first traffic operation compatibility and the second traffic operation compatibility of each main road in the city are obtained by combining the layout map of the traffic sensor device and the distribution map of the traffic signal device in the city through information aggregation processing, thereby improving the matching of the layout of the traffic sensor device and the distribution of the network signal with the actual situation. In special circumstances, the problem of being unable to provide accurate characteristic data is avoided, thereby ensuring the source of the basis for judgment.

[0015] (4) The present invention generates a group label of urban trunk roads in the trunk road constraint processing module, and aggregates the collection of urban trunk road group labels, so as to divide the roads in a targeted manner, avoid the subsequent unified implementation standards for the maintenance and deployment of police forces on the trunk roads, optimize the maintenance and management costs of urban trunk roads, and improve the rationality of the allocation of police forces on urban trunk roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0018] Figure 2 Schematic diagram of the distribution of identification points involved in an embodiment of the present invention.

[0019] Figure numerals: 1, first orientation original identification point, 2, second orientation original identification point, 3, third orientation original identification point, 4, fourth orientation original identification point signal strength value, 5, first orientation 1st order identification point, 6, second orientation 1st order identification point, 7, third orientation 1st order identification point, 8, fourth orientation 1st order identification point, 9, first orientation g-th order identification point, 10, second orientation g-th order identification point, 11, third orientation g-th order identification point, 12, fourth orientation g-th order identification point, ... are circular symbols. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the present invention provides a smart city traffic management system, including: a traffic data acquisition module, a traffic data processing module, a fit analysis module, a main road constraint processing module, and an LED display terminal.

[0022] The traffic data acquisition module is used to acquire traffic information of various main roads in the city based on traffic sensor devices installed in the city in combination with the city traffic safety center.

[0023] In a specific embodiment of the present invention, the traffic information includes basic traffic data and characteristic traffic data, wherein the basic traffic data includes vehicle flow, passenger flow, vehicle stay time, and vehicle speed; the characteristic traffic data includes construction time, the affected area and number of each type of road obstacle, the number of casualties in the accident, and the amount of property loss in the accident.

[0024] The present invention obtains traffic information of various main roads in the city based on traffic sensor devices installed in the city and the city traffic safety center in the traffic data acquisition module, laying a foundation for the subsequent analysis of the first traffic restriction factor and the second traffic restriction factor of various main roads in the city.

[0025] The traffic data processing module is used to integrate and process the traffic information of each main road in the city in combination with the traffic signal intensity distribution map of the city management center to obtain the first traffic restriction factor and the second traffic restriction factor of each main road in the city.

[0026] In a specific embodiment of the present invention, the integration process obtains the first traffic restriction factor of each trunk road in the city, and the specific analysis method is: according to the characteristic traffic data of each trunk road in the city, combined with the reference characteristic traffic data stored in the database, after numerical fitting processing, the offset of each characteristic traffic data of each trunk road in the city is obtained, and according to the following traffic restriction model,

[0027] It should be noted that the construction time, affected area and emerging number of each road obstacle type, number of casualties in accidents and amount of property loss in the characteristic traffic data of each urban trunk road are compared with the construction time, affected area and emerging number of each road obstacle type, number of casualties in accidents and amount of property loss in accidents in the reference characteristic traffic data in turn, and the construction time offset of each urban trunk road, the affected area offset of each road obstacle type and the emerging number offset, the number of casualties in accidents and the amount of property loss in accidents are obtained, and the offsets of each characteristic traffic data of each urban trunk road are summarized.

[0028] The first traffic restriction factor α of each main road in the city is obtained by processing i , where A is the traffic restriction factor corresponding to the reference characteristic traffic data, β im is the offset of the mth characteristic traffic data of the i-th trunk road in the city, β′ m , β′ m ′ are the left and right endpoints of the allowable offset interval of the mth characteristic traffic data defined in the database, η1 (m→1) ,η1 (m→2) They respectively represent the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of the mth characteristic traffic data defined in the database, i is the number of each main road, i=1,2,...,n, n is an arbitrary integer greater than 2, m is the number of each characteristic traffic data, m=1,2,...,l, l is an arbitrary integer greater than 2.

[0029] In a specific embodiment of the present invention, the integration process obtains the second traffic restriction factor of each trunk road in the city. The specific analysis method is as follows: according to the basic traffic data of each trunk road in the city, combined with the reference basic traffic data stored in the database, after numerical fitting processing, the offset of each basic traffic data of each trunk road in the city is obtained, and according to the following traffic restriction model,

[0030] It should be noted that the vehicle flow, pedestrian flow, vehicle stop time and vehicle speed in the basic traffic data of each main road in the city are compared with the vehicle flow, pedestrian flow, vehicle stop time and vehicle speed of the reference basic traffic data in turn to obtain the vehicle flow offset, pedestrian flow offset, vehicle stop time offset and vehicle speed offset of each main road in the city, and the offset of each basic traffic data of each main road in the city is summarized.

[0031] The second traffic restriction factor ε of each main road in the city is obtained by processing i , where B is the traffic restriction factor corresponding to the basic traffic data, C i is the modified traffic restriction factor of the i-th trunk road in the city, χ ip is the offset of the pth basic traffic data of the ith trunk road in the city, χ′ p , χ′ p ′ are the left and right endpoints of the allowed offset interval of the pth basic traffic data defined in the database, η2 (p→1) ,η2 (p→2) They respectively represent the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of the p-th basic traffic data defined in the database, p is the number of each basic traffic data, p=1,2,...,q, q is any integer greater than 2.

[0032] In a specific embodiment of the present invention, the modified traffic restriction factor of each trunk road in the city is specifically analyzed by referring to Figure 2 As shown, based on the traffic signal intensity distribution map of the city, the center point position of each main road in the city is mapped and located, and the functional area of ​​each main road in the city is obtained by dividing it with a set segmentation radius, and the original identification point of each direction is obtained by positioning in each direction at a set extraction interval, and the identification points of each order in each direction are obtained by positioning in each direction at a set extraction interval of each enhanced order.

[0033] It should be noted that the settings of each increasing order are specifically first order, second order, third order, etc., wherein when the increasing order is first order, the first order extraction interval is set to twice the extraction interval, when the increasing order is second order, the second order extraction interval is set to three times the extraction interval, and when the increasing order is third order, the third order extraction interval is set to four times the extraction interval.

[0034] From the traffic signal intensity distribution map of the city, the signal intensity values ​​of the original identification points of each direction and the identification points of each order in each direction of each main road in the city are counted.

[0035] The signal strength values ​​of the original identification points of each direction and the identification points of each order in each direction of each main road in the city are filled in the matrix in order from left to right and from top to bottom to obtain the signal strength original identification matrix of each main road in the city. Sum signal strength identification matrix of each order

[0036] The above Q 1i , Q 2i , Q 3i , Q 4i They are the signal strength values ​​of the first azimuth source identification point, the second azimuth source identification point, the third azimuth source identification point, and the fourth azimuth source identification point of the i-th main road in the city, Q1′ ih , Q2′ ih , Q3′ ih , Q4′ ih They are the signal strength values ​​of the h-th order identification point at the first azimuth, the h-th order identification point at the second azimuth, the h-th order identification point at the third azimuth, and the h-th order identification point at the fourth azimuth of the ith main road in the city, respectively. h is the number of each order of identification point, h=1,2,...,g, and g is any integer greater than 2.

[0037] The characteristic scalars of the signal strength source identification matrix of each trunk road in the city and the characteristic scalars of the signal strength identification matrix of each order are obtained, and the characteristic scalar differences of each trunk road in the city are extracted, which are recorded as ΔΦ ij , where j is the number of each characteristic scalar difference, j=1,2,...,k, and k is any integer greater than 2.

[0038] Specifically, the characteristic scalar of the signal strength source identification matrix of each trunk road in the city and the characteristic variable of the signal strength identification matrix of each order are specifically expressed as: det(X i )、det(Y ih ), where det(X i )=Q 1i *Q 4i -Q 2i *Q 3i , det(Y ih )=Q1′ ih *Q4′ ih -Q2′ ih *Q3′ ih .

[0039] It should be noted that the specific method for extracting the characteristic scalar differences of the main roads in the city is to extract the characteristic scalar differences between the signal strength original identification matrix and the first-order identification matrix of the signal strength of the main roads in the city as the first characteristic scalar difference of the main roads in the city, and to extract the characteristic scalar differences between the first-order identification matrix and the second-order identification matrix of the signal strength of the main roads in the city as the second characteristic scalar differences of the main roads in the city according to the characteristic scalars of the first-order identification matrix of the signal strength of the main roads in the city and the characteristic scalars of the second-order identification matrix of the signal strength, and so on to extract the characteristic scalar differences of the main roads in the city.

[0040] The characteristic scalar differences of the main roads in the city are introduced into the modified traffic restriction factor model, and the modified traffic restriction factors of the main roads in the city are output.

[0041] The modified traffic restriction factor model is specifically: Where Φ1 is the allowable characteristic scalar difference of the signal strength identification matrix stored in the database, ΔΦ i(j-1) is the j-1th characteristic scalar difference of the i-th trunk road in the city, and k is the number of characteristic scalar differences.

[0042] In the traffic data processing module, the present invention analyzes the first traffic restriction factor of each main road in the city through the offset of each characteristic traffic data of each main road in the city, and at the same time analyzes the second traffic restriction factor of each main road in the city through the offset of each basic traffic data of each main road in the city, thereby providing data support for the subsequent evaluation of the compatibility of traffic sensor devices on each main road in the city and the evaluation of the compatibility of network signals.

[0043] The compatibility analysis module is used to obtain the city's traffic sensor device layout map and traffic signal device distribution map from the city management center, and obtain the first traffic operation compatibility and the second traffic operation compatibility of each main road in the city through information aggregation processing.

[0044] In a specific embodiment of the present invention, the first traffic operation conformity of each main road in the city is obtained through information aggregation processing, and the specific method is: multiplying the first traffic restriction factor of each main road in the city with the first traffic characteristic value corresponding to the unit first traffic restriction factor stored in the database to obtain the first traffic characteristic value corresponding to each main road in the city.

[0045] The first traffic characteristic value corresponding to each main road in the city is obtained from the traffic sensor device layout map of the city.

[0046] The layout first traffic characteristic values ​​and the first traffic characteristic values ​​corresponding to each main road in the city are imported into the first traffic operation compatibility model, and the first traffic operation compatibility of each main road in the city is output.

[0047] It should be noted that the traffic operation compatibility model is specifically as follows: Where μ i It is expressed as the first traffic operation fit of the i-th trunk road in the city, e is a natural constant, δ i is the first traffic characteristic value of the layout of the i-th main road in the city, δ i ′ is the first traffic characteristic value of the i-th main road in the city.

[0048] In a specific embodiment of the present invention, the second traffic operation conformity of each main road in the city is obtained through information aggregation processing, and its specific method is: multiplying the second traffic restriction factor of each main road in the city with the second traffic characteristic value corresponding to the unit second traffic restriction factor stored in the database to obtain the second traffic characteristic value corresponding to each main road in the city.

[0049] From the traffic signal device distribution map of the city, the second traffic characteristic value corresponding to each main road in the city is obtained.

[0050] The second traffic characteristic values ​​and the layout second traffic characteristic values ​​corresponding to each trunk road in the city are imported into the traffic compatibility model, and the second traffic operation compatibility of each trunk road in the city is output and recorded as

[0051] In the compatibility analysis module of the present invention, the traffic sensor device layout map and the traffic signal device distribution map of the city are combined, and the first traffic operation compatibility and the second traffic operation compatibility of each main road in the city are obtained through information aggregation processing, thereby improving the matching of the layout of the traffic sensor devices on the road and the distribution of the network signals with the actual situation. In special circumstances, the problem of being unable to provide accurate characteristic data is avoided, thereby ensuring the source of the basis for judgment.

[0052] The trunk road constraint processing module determines the correction value of the traffic sensor device and the correction value of the signal strength of each trunk road in the city according to the first traffic operation conformity and the second traffic operation conformity of each trunk road in the city, generates a group label of the city trunk roads, and gathers a set of the group labels of the city trunk roads.

[0053] It should be noted that the specific method for determining the correction value of the traffic sensor device and the correction value of the signal strength of each main road in the city is: compare the first traffic operation compliance degree of each main road in the city with the preset first traffic operation compliance degree; if the first traffic operation compliance degree of a main road in the city is greater than or equal to the first traffic operation compliance degree, then the correction value of the traffic sensor device of the main road is recorded as 0; otherwise, the first traffic characteristic value of the main road is used as the correction value of the traffic sensor device of the main road, thereby obtaining the correction values ​​of the traffic sensor devices of each main road in the city.

[0054] Similarly, by combining the second traffic operation compliance of each main road in the city and the preset second traffic operation compliance, the correction value of the signal strength of each main road in the city is obtained.

[0055] It should be understood that the preset first traffic operation compliance degree and the second traffic operation compliance degree can be specifically set according to actual conditions. For example, in order to improve the accuracy of the first traffic operation compliance degree and the second traffic operation compliance degree of the main roads, the first traffic operation compliance degree and the second traffic operation compliance degree can be set to 0.9, and no specific restrictions are made here.

[0056] In a specific embodiment of the present invention, the method of generating a group label of urban trunk roads and gathering a collection of group labels of urban trunk roads is as follows: according to the first traffic operation conformity and the second traffic operation conformity of each urban trunk road, if Then the trunk road in the city is recorded as a type of road function group, where ∧ is a logical symbol, indicating that and, μ1, represents the first traffic operation conformity and the second traffic operation conformity of a main road in the city, μ′, They are respectively the preset first traffic operation compliance degree and the second traffic operation compliance degree.

[0057] like Then the main road in the city is recorded as the second-class road functional group, where ∨ is a logical symbol, representing or.

[0058] like The main roads in the city are recorded as three types of road functional groups.

[0059] The functional groups of each type I road are counted to generate a label for the urban trunk road group, the functional groups of each type II road are counted to generate a label for the urban trunk road group, and the functional groups of each type III road are counted to generate a label for the urban trunk road group.

[0060] It should be noted that, among the urban trunk road group one label, urban trunk road group two label, and urban trunk road group three label, the importance of urban trunk road group one label is less than that of urban trunk road group two label, and the importance of urban trunk road group two label is less than that of urban trunk road group three label. That is to say, the layout of the traffic sensor devices of each trunk road in the urban trunk road group one label and the distribution of the signal strength are more reasonable, which can reduce the frequency of daily maintenance and reduce the number of police maintenance personnel. The layout of the traffic sensor devices of each trunk road in the urban trunk road group two label or the distribution of the signal strength are generally reasonable, which can maintain the batches of historical daily maintenance and the number of police maintenance personnel. The layout of the traffic sensor devices of each trunk road in the urban trunk road group three label and the distribution of the signal strength are less reasonable, which can increase the frequency of daily maintenance and increase the number of police maintenance personnel. It provides solid data support for the subsequent daily maintenance and police force allocation of urban trunk roads, which is conducive to reducing the maintenance and management costs of urban trunk roads and ensuring the rationality of police force allocation to urban trunk roads.

[0061] The correction values ​​of the traffic sensor devices of each main road in the second label of the urban main road group are counted to generate a traffic sensor correction set of the second type of label of the urban main road group; the correction values ​​of the signal strength of each main road in the second label of the urban main road group are counted to generate a signal strength correction set of the second type of label of the urban main road group; and according to this method, the traffic sensor correction set and signal strength correction set of the third type of label of the urban main road group are generated.

[0062] The present invention generates a group label of urban trunk roads in the trunk road constraint processing module, and gathers a collection of urban trunk road group labels, so as to divide the roads in a targeted manner, avoid the subsequent unified implementation standards for the maintenance and deployment of police forces on the trunk roads, optimize the maintenance and management costs of urban trunk roads, and improve the rationality of the allocation of police forces on urban trunk roads.

[0063] The LED display terminal is used to import a collection of urban trunk road group labels into a preset LED display interface for visual display.

[0064] It should be noted that the present invention also includes a database for storing reference characteristic traffic data, the left endpoint value and the right endpoint value of the allowable offset interval of each characteristic traffic data, the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of each characteristic traffic data, reference basic traffic data, the left endpoint value and the right endpoint value of the allowable offset interval of each basic traffic data, the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of each basic traffic data, the first traffic characteristic value corresponding to the unit first traffic restriction factor, the second traffic characteristic value corresponding to the unit second traffic restriction factor, and the allowable characteristic scalar difference of the signal strength identification matrix.

[0065] It should also be noted that the traffic data acquisition module is connected to the traffic data processing module, the traffic data processing module is connected to the fit analysis module, the fit analysis module is connected to the main road constraint processing module, the main road constraint processing module is connected to the LED display terminal, and the database is respectively connected to the traffic data processing module, the fit analysis module and the main road constraint processing module.

[0066] It should be understood that, herein, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0067] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A smart city traffic management system, characterized in that: include: Traffic data acquisition module, used to obtain traffic information of main roads in the city based on traffic sensor devices installed in the city and in conjunction with the city traffic safety center; The traffic information includes basic traffic data and characteristic traffic data, wherein the basic traffic data includes vehicle flow, passenger flow, vehicle dwelling time, and vehicle speed, and the characteristic traffic data includes construction time, affected area and number of each road obstacle type, number of casualties in accidents, and amount of property losses in accidents; The traffic data processing module is used to integrate and process the traffic information of each trunk road in the city and the traffic signal strength distribution map of the city management center to obtain the first traffic restriction factor and the second traffic restriction factor of each trunk road in the city; The integration process obtains the first traffic restriction factor of each trunk road in the city, and the specific analysis method is as follows: According to the characteristic traffic data of each trunk road in the city, combined with the reference characteristic traffic data stored in the database, after numerical fitting processing, the offset of each characteristic traffic data of each trunk road in the city is obtained, and according to the following traffic restriction model, The first traffic restriction factor α of each main road in the city is obtained by processing i , where A is the traffic restriction factor corresponding to the reference characteristic traffic data, β im is the offset of the mth characteristic traffic data of the i-th trunk road in the city, β′ m , β′ m ′ are the left and right endpoints of the allowable offset interval of the mth characteristic traffic data defined in the database, η1 (m→1) ,η1 (m→2) They represent the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of the mth characteristic traffic data defined in the database, i is the number of each trunk road, i=1,2,...,n, n is any integer greater than 2, m is the number of each characteristic traffic data, m=1,2,...,l, l is any integer greater than 2; The integration process obtains the second traffic restriction factor of each trunk road in the city, and the specific analysis method is: According to the basic traffic data of each trunk road in the city, combined with the reference basic traffic data stored in the database, after numerical fitting processing, the offset of each basic traffic data of each trunk road in the city is obtained, and according to the following traffic restriction model, The second traffic restriction factor ε of each main road in the city is obtained by processing i , where B is the traffic restriction factor corresponding to the basic traffic data, C i is the modified traffic restriction factor of the i-th trunk road in the city, χ ip is the offset of the pth basic traffic data of the ith trunk road in the city, χ′ p , χ′ p ′ are the left and right endpoints of the allowed offset interval of the pth basic traffic data defined in the database, η2 (p→1) ,η2 (p→2) They are respectively represented as the traffic restriction factor increase parameter and the traffic restriction factor reduction parameter corresponding to the unit deviation of the p-th basic traffic data defined in the database, p is the number of each basic traffic data, p=1,2,...,q, q is any integer greater than 2; The conformity analysis module is used to obtain the layout map of traffic sensor devices and the distribution map of traffic signal devices in the city from the city management center, and obtain the first traffic operation conformity and the second traffic operation conformity of each trunk road in the city through information aggregation processing; The specific method of obtaining the first traffic operation conformity of each trunk road in the city through information aggregation processing is as follows: Multiplying the first traffic restriction factor of each trunk road in the city by the first traffic characteristic value corresponding to the unit first traffic restriction factor stored in the database to obtain the first traffic characteristic value corresponding to each trunk road in the city; Obtaining first traffic characteristic values ​​corresponding to each main road in the city from a traffic sensor device layout diagram of the city; Importing the layout first traffic characteristic values ​​and the first traffic characteristic values ​​corresponding to each trunk road in the city into the first traffic operation conformity model, and outputting the first traffic operation conformity of each trunk road in the city; The traffic operation compatibility model is specifically as follows: Where μ i It is expressed as the first traffic operation fit of the i-th trunk road in the city, e is a natural constant, δ i is the first traffic characteristic value of the layout of the i-th main road in the city, δ i ′ is the first traffic characteristic value of the i-th trunk road in the city; The specific method of obtaining the second traffic operation conformity of each trunk road in the city through information aggregation processing is as follows: Multiplying the second traffic restriction factor of each trunk road in the city by the second traffic characteristic value corresponding to the unit second traffic restriction factor stored in the database to obtain the second traffic characteristic value corresponding to each trunk road in the city; From the traffic signal device distribution map of the city, obtain the second traffic characteristic value corresponding to each main road in the city; The second traffic characteristic values ​​and the layout second traffic characteristic values ​​corresponding to each trunk road in the city are imported into the traffic compatibility model, and the second traffic operation compatibility of each trunk road in the city is output and recorded as ; The trunk road constraint processing module determines the correction value of the traffic sensor device and the correction value of the signal strength of each trunk road in the city according to the first traffic operation conformity and the second traffic operation conformity of each trunk road in the city, generates a group label of the trunk road in the city, and gathers a set of the group labels of the trunk roads in the city; The specific method of generating the urban trunk road group labels and gathering the urban trunk road group labels is as follows: According to the first traffic operation compatibility and the second traffic operation compatibility of each main road in the city, if Then the trunk road in the city is recorded as a type of road function group, where ∧ is a logical symbol, indicating that and, μ1, represents the first traffic operation conformity and the second traffic operation conformity of a main road in the city, μ′, They are respectively the preset first traffic operation compliance degree and the second traffic operation compliance degree; like Then the trunk road in the city is recorded as the second-class road functional group, where ∨ is a logical symbol, indicating or; like Then the main roads in the city are recorded as three types of road function groups; Count each type I road functional group to generate a label for the urban trunk road group, count each type II road functional group to generate a label for the urban trunk road group, and count each type III road functional group to generate a label for the urban trunk road group; Count the correction values ​​of the traffic sensor devices of each trunk road in the second label of the urban trunk road group to generate the traffic sensor correction set of the second type of label of the urban trunk road group; count the correction values ​​of the signal strength of each trunk road in the second label of the urban trunk road group to generate the signal strength correction set of the second type of label of the urban trunk road group; and according to this method, generate the traffic sensor correction set and signal strength correction set of the third type of label of the urban trunk road group; The LED display terminal is used to import a collection of urban trunk road group labels into a preset LED display interface for visual display.

2. A smart city traffic management system according to claim 1, characterized in that: The specific analysis method of the modified traffic restriction factors of the main roads in the city is as follows: Based on the traffic signal intensity distribution map of the city, the center point of each main road in the city is mapped and located from it, and the functional areas of each main road in the city are divided by a set segmentation radius, and the original identification points of each direction are located in each direction at a set extraction interval, and the identification points of each order in each direction are located in each direction at a set extraction interval; From the traffic signal strength distribution map of the city, the signal strength values ​​of the original identification points of each direction and the identification points of each order in each direction of each main road in the city are counted; The signal strength values ​​of the original identification points of each direction and the identification points of each order of each direction of each main road in the city are sequentially filled into the matrix from left to right and from top to bottom to obtain the signal strength original identification matrix and signal strength order identification matrix of each main road in the city; The characteristic scalars of the signal strength source identification matrix of each trunk road in the city and the characteristic scalars of the signal strength identification matrix of each order are obtained, and the characteristic scalar differences of each trunk road in the city are extracted, which are recorded as ΔΦ ij , where j is the number of each characteristic scalar difference, j = 1, 2, ..., k, k is any integer greater than 2; The characteristic scalar differences of the main roads in the city are introduced into the modified traffic restriction factor model, and the modified traffic restriction factors of the main roads in the city are output.

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