Auxiliary law enforcement method and device based on artificial intelligence and storage medium

Abstract

The invention discloses an auxiliary law enforcement method and device based on artificial intelligence and a storage medium, and particularly relates to the technical field of intelligent law enforcement recognition. Comprising the steps of road law enforcement monitoring area division, urban road monitoring data acquisition, urban road utilization rate monitoring analysis, urban road traffic safety monitoring analysis, urban road law enforcement monitoring intelligent identification, urban road law enforcement monitoring intelligent evaluation and urban road condition abnormity processing. The road monitoring data of each monitoring sub-area of the road law enforcement monitoring area of the target city is collected through the intelligent monitoring device, the road utilization rate early warning index and the road traffic safety management index are calculated, and the road safety monitoring evaluation coefficient is obtained through analysis, so that the road safety performance is judged, and the road safety performance is improved. The intelligent level of urban traffic management is improved, and the law enforcement management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent law enforcement identification technology, and more specifically, to an artificial intelligence-based auxiliary law enforcement method, device and storage medium. Background Art

[0002] In the field of modern law enforcement, the application of artificial intelligence technology has demonstrated its powerful functions and potential. Through deep learning intelligent processes, computer systems can autonomously learn, reason, plan and solve problems, realize real-time monitoring and analysis of urban traffic conditions, and identify and classify different traffic violations.

[0003] With the acceleration of urbanization, urban traffic problems have become increasingly prominent. Traffic congestion, frequent accidents and other problems have seriously affected the sustainable development of cities. Real-time monitoring and analysis of road conditions through artificial intelligence technology can effectively prevent and reduce traffic accidents, improve road utilization efficiency, and optimize traffic flow, thereby providing urban residents with a safer and more convenient travel environment.

[0004] However, in actual use, it still has some shortcomings. For example, the monitoring method currently used for road assisted law enforcement has limitations and fails to effectively integrate the data collected by various intelligent monitoring devices and realize the linkage operation between devices. This method leads to low law enforcement efficiency and makes it difficult to implement targeted handling of abnormal situations in specific areas.

[0005] In addition, the existing road safety management of assisted road law enforcement has a low level of intelligence and automation, and lacks accurate assessment of road safety performance, resulting in unnecessary waste of law enforcement resources. We urgently need to take effective measures to improve and optimize it. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an artificial intelligence-based assisted law enforcement method, device and storage medium for solving the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based assisted law enforcement method, comprising:

[0008] Step S01: Road enforcement monitoring area division: used to divide the target city road enforcement monitoring area into various monitoring sub-areas according to an equal area division method, and number each monitoring sub-area of the target city road enforcement monitoring area.

[0009] Step S02: Urban road monitoring data collection: used to collect road monitoring data of each monitoring sub-area in the target urban road law enforcement monitoring area through intelligent monitoring equipment. The urban road monitoring data collection step includes a road usage status data collection unit and a road traffic safety data collection unit. The road monitoring data includes road usage status data and road traffic safety data.

[0010] Step S03: Urban road usage monitoring and analysis: used to calculate the road usage early warning index of each monitoring sub-area in the target urban road law enforcement monitoring area based on the road usage status data of the road usage status data collection unit.

[0011] Step S04: Urban road traffic safety monitoring and analysis: used to calculate the road traffic safety management index of each monitoring sub-area in the target city road law enforcement monitoring area based on the road traffic safety data of the road traffic safety data acquisition unit.

[0012] Step S05: Intelligent identification of urban road law enforcement monitoring: used to calculate the road safety monitoring evaluation coefficient of each monitoring sub-area in the target city road law enforcement monitoring area based on the road usage rate warning index and road traffic safety management index of each monitoring sub-area in the target city road law enforcement monitoring area.

[0013] Step S06: Intelligent evaluation of urban road law enforcement monitoring: used to obtain the road safety monitoring evaluation coefficient of each monitoring sub-area in the target urban road law enforcement monitoring area, compare it with the preset road safety monitoring evaluation coefficient, and process it.

[0014] Step S07: Processing of abnormal road conditions in urban roads: used to receive the numbers of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area transmitted in the urban road law enforcement monitoring intelligent evaluation step, calculate the pavement structure quality indicators of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area based on the pavement thickness and pavement bearing capacity, and judge the road safety performance.

[0015] Preferably, the road enforcement monitoring area is divided into:

[0016] Obtain a target city road law enforcement monitoring area, determine it as the target area, divide the target area into monitoring sub-areas according to an equal area division method, and number the monitoring sub-areas of the target city road law enforcement monitoring area as 1, 2, ..., i, ..., n in sequence.

[0017] Preferably, the urban road monitoring data collection is specifically as follows:

[0018] Road usage data collection unit: used to collect the road area, sidewalk area, vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as kdi 、kf i 、kc i 、kp i , where i = 1, 2, ... n, i represents the number of the i-th monitoring sub-area;

[0019] Road traffic safety data collection unit: used to collect the number of road defects, traffic violation vehicles, and traffic illegal parking in each monitoring sub-area of the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as uq respectively i 、uj i ,ug i .

[0020] Preferably, the urban road usage monitoring and analysis is specifically as follows:

[0021] Step S001: Calculate the traffic flow index based on the vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area:

[0022]

[0023] Among them, BX i Expressed as the traffic flow index of the i-th monitoring sub-area, kc i Expressed as the traffic flow of the ith monitoring sub-area, kp i It is represented as the flow of people in the i-th monitoring sub-area;

[0024] Step S002: Calculate the road area ratio:

[0025]

[0026] Among them, SD i Expressed as the road area ratio of the i-th monitoring sub-area, kd i It is represented as the road area of the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area;

[0027] Step S003: Calculate the sidewalk area ratio:

[0028]

[0029] Among them, SF i Expressed as the sidewalk area ratio of the i-th monitoring sub-area, kf i It is represented as the sidewalk area of the i-th monitoring sub-area;

[0030] Step S004: The calculation formula of the road usage rate warning index is:

[0031]

[0032] Among them, α i Expressed as the road usage warning index of the i-th monitoring sub-area, BX max Expressed as the maximum value of traffic flow index, BX min Expressed as the minimum value of traffic flow index, SD 预 Expressed as the preset road area ratio, SF 预 It is expressed as the preset sidewalk area ratio, and e is expressed as a natural constant.

[0033] Preferably, the urban road traffic safety monitoring and analysis specifically includes:

[0034] Step S001: Calculate the degree of road surface damage based on the number of road surface defects in each monitoring sub-area of the target city's road law enforcement monitoring area:

[0035]

[0036] Among them, DQ i It is expressed as the road pavement defect damage degree of the i-th monitoring sub-area, UQ 预 Indicates the preset road surface defect damage degree, uq i It is represented as the number of road surface defects in the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area;

[0037] Step S002: The calculation formula of the road traffic safety management index is:

[0038]

[0039] Among them, β i Expressed as the road traffic safety management index of the ith monitoring sub-area, DQ max Expressed as the maximum value of road pavement defect damage, DQ min It is expressed as the minimum value of the road surface defect damage degree, uj i Expressed as the number of traffic violation vehicles in the i-th monitoring sub-area, ug i It is represented by the number of traffic violation parking in the ith monitoring sub-area, and n is represented by the number of monitoring sub-areas.

[0040] Preferably, the calculation formula of the road safety monitoring evaluation coefficient is:

[0041]

[0042] Among them, θ i Expressed as the road safety monitoring evaluation coefficient of the i-th monitoring sub-area, α min Expressed as the minimum value of the road usage warning index, α max Expressed as the maximum value of the road usage warning index, βmax Expressed as the maximum value of the road traffic safety management index, β min Expressed as the minimum value of the road traffic safety management index.

[0043] Preferably, the intelligent assessment of urban road law enforcement monitoring is specifically as follows:

[0044] Obtain the road safety monitoring assessment coefficient of each monitoring sub-area in the target city's road law enforcement monitoring area, and compare it with the preset road safety monitoring assessment coefficient. If the road safety monitoring assessment coefficient of a monitoring sub-area is greater than the preset road safety monitoring assessment coefficient, it indicates that there is an abnormality in the road safety monitoring in the monitoring sub-area. The road law enforcement personnel should be notified immediately for on-site management, and the number of the monitoring sub-area with the abnormality should be transmitted to the urban road condition abnormality processing step. Otherwise, it indicates that there is no abnormality in the road safety monitoring in the monitoring sub-area.

[0045] Preferably, the abnormal urban road condition processing is specifically as follows:

[0046] Step S001: receiving the numbers of the abnormal monitoring sub-areas in the target city's road law enforcement monitoring area, and renumbering them in descending order as 1, 2, ...j, ...m according to the road safety monitoring evaluation coefficient;

[0047] Step S002: Calculate the pavement structure quality index by obtaining the pavement thickness and pavement bearing capacity of each abnormal monitoring sub-area in the target city road law enforcement monitoring area:

[0048]

[0049] in, Expressed as the pavement structure quality index of the jth abnormal monitoring sub-area, hy j is represented by the pavement thickness of the jth abnormal monitoring sub-area, Δhy is represented by the mean pavement thickness, and hy max Expressed as the maximum value of the road surface thickness, zy j is represented by the pavement bearing capacity of the jth abnormal monitoring sub-area, Δzy is represented by the mean pavement bearing capacity, and zy max Expressed as the maximum value of the pavement bearing capacity;

[0050] Step S003: Obtain the road safety monitoring evaluation coefficient corresponding to the jth abnormal monitoring sub-area and calculate Obtain the pavement structure quality anomaly coefficient of each abnormal monitoring sub-area. If the pavement structure quality anomaly coefficient of a monitoring sub-area is greater than the preset pavement structure quality anomaly coefficient, it indicates that the safety performance of the road pavement structure in the monitoring sub-area is abnormal, and the road law enforcement personnel should be notified to conduct on-site management. Otherwise, it indicates that there is no abnormality in the safety performance of the road pavement structure in the monitoring sub-area.

[0051] Preferably, a storage medium stores computer program instructions thereon, which, when executed by a processor, cause the processor to execute an artificial intelligence-based assisted law enforcement method.

[0052] Preferably, a device comprises: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the artificial intelligence-based assisted law enforcement method.

[0053] Technical effects and advantages of the present invention:

[0054] 1. The present invention provides an artificial intelligence-based auxiliary law enforcement method, device and storage medium, which collects road monitoring data of each monitoring sub-area in the target city's road law enforcement monitoring area through intelligent monitoring equipment, calculates the road usage rate warning index of each monitoring sub-area in the target city's road law enforcement monitoring area based on the road usage status data of the road usage status data collection unit, calculates the road traffic safety management index of each monitoring sub-area in the target city's road law enforcement monitoring area based on the road traffic safety data of the road traffic safety data collection unit, further analyzes to obtain a road safety monitoring evaluation coefficient, and compares it with a preset road safety monitoring evaluation coefficient. If the road safety monitoring evaluation coefficient of a monitoring sub-area is greater than the preset road safety monitoring evaluation coefficient, it indicates that there is a road safety monitoring problem in the monitoring sub-area. In the event of an abnormality, the road law enforcement personnel should be notified immediately to conduct on-site management, and the number of the monitoring sub-area with the abnormality should be transmitted to the urban road condition abnormality processing step. Otherwise, it indicates that there is no abnormality in the road safety monitoring in the monitoring sub-area. The intelligent method is used to collect road usage data, which provides data-driven decision support for calculating the road usage rate warning index. The intelligent method is used to collect road traffic safety data, which provides data-driven decision support for calculating the road traffic safety management index. By analyzing the road safety monitoring evaluation coefficient, the traffic management department can allocate law enforcement resources more reasonably. For example, for areas with high road safety monitoring evaluation coefficients, law enforcement can be strengthened, monitoring equipment can be added, etc., to improve road safety levels and improve law enforcement management efficiency.

[0055] 2. The present invention provides an artificial intelligence-based auxiliary law enforcement method, equipment and storage medium, which utilizes the abnormal road condition processing of urban roads, and calculates the road surface structure quality index of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area based on the road surface thickness and road surface bearing capacity by receiving the numbers of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area transmitted by the urban road law enforcement monitoring intelligent evaluation step, and judges the road safety performance, thereby notifying the road law enforcement personnel to conduct on-site management. By combining the road surface structure quality index and the road safety monitoring evaluation coefficient, the specific road area with problems is accurately located, and the road is analyzed to see if there are potential safety hazards. This helps to reduce blind inspections and ineffective investment, improve the intelligence level of urban traffic management, reduce the time cost of manual inspections and data processing, and further improve management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the connection of the method steps of the present invention.

[0057] Figure 2 This is a schematic diagram of the urban road monitoring data collection structure of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0059] See also Figure 1 As shown, the present invention provides an artificial intelligence-based assisted law enforcement method, including road law enforcement monitoring area division, urban road monitoring data collection, urban road usage rate monitoring and analysis, urban road traffic safety monitoring and analysis, urban road law enforcement monitoring intelligent identification, urban road law enforcement monitoring intelligent evaluation, and urban road road abnormal road condition processing.

[0060] The road law enforcement monitoring area division is connected with the urban road monitoring data collection, the urban road monitoring data collection is connected with the urban road usage rate monitoring and analysis, the urban road usage rate monitoring and analysis is connected with the urban road traffic safety monitoring and analysis, the urban road traffic safety monitoring and analysis is connected with the urban road law enforcement monitoring intelligent identification, the urban road law enforcement monitoring intelligent identification is connected with the urban road law enforcement monitoring intelligent assessment, and the urban road law enforcement monitoring intelligent assessment is connected with the urban road road condition abnormality processing.

[0061] The step S01: road enforcement monitoring area division is used to divide the target city road enforcement monitoring area into various monitoring sub-areas according to an equal area division method, and number each monitoring sub-area of the target city road enforcement monitoring area.

[0062] In one possible design, the road enforcement monitoring area is specifically divided as follows:

[0063] Obtain a target city road law enforcement monitoring area, determine it as the target area, divide the target area into monitoring sub-areas according to an equal area division method, and number the monitoring sub-areas of the target city road law enforcement monitoring area as 1, 2, ..., i, ..., n in sequence.

[0064] See also Figure 2 As shown, the step S02: urban road monitoring data collection is used to collect road monitoring data of each monitoring sub-area in the target urban road law enforcement monitoring area through intelligent monitoring equipment. The urban road monitoring data collection step includes a road usage status data collection unit and a road traffic safety data collection unit. The road monitoring data includes road usage status data and road traffic safety data.

[0065] In one possible design, the urban road monitoring data collection is specifically as follows:

[0066] Road usage data collection unit: used to collect the road area, sidewalk area, vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as kd i 、kf i 、kc i 、kp i , where i = 1, 2, ... n, i represents the number of the i-th monitoring sub-area;

[0067] Road traffic safety data collection unit: used to collect the number of road defects, traffic violation vehicles, and traffic illegal parking in each monitoring sub-area of the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as uq respectively i 、uj i ,ug i .

[0068] The step S03: urban road usage monitoring and analysis is used to calculate the road usage early warning index of each monitoring sub-area in the target urban road law enforcement monitoring area based on the road usage status data of the road usage status data collection unit.

[0069] In one possible design, the urban road usage monitoring and analysis is specifically as follows:

[0070] Step S001: Calculate the traffic flow index based on the vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area:

[0071]

[0072] Among them, BX i Expressed as the traffic flow index of the i-th monitoring sub-area, kc i Expressed as the traffic flow of the ith monitoring sub-area, kp i It is represented as the flow of people in the i-th monitoring sub-area;

[0073] Step S002: Calculate the road area ratio:

[0074]

[0075] Among them, SD i Expressed as the road area ratio of the i-th monitoring sub-area, kd i It is represented as the road area of the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area;

[0076] Step S003: Calculate the sidewalk area ratio:

[0077]

[0078] Among them, SF i Expressed as the sidewalk area ratio of the i-th monitoring sub-area, kf i It is represented as the sidewalk area of the i-th monitoring sub-area;

[0079] Step S004: The calculation formula of the road usage rate warning index is:

[0080]

[0081] Among them, α i Expressed as the road usage warning index of the i-th monitoring sub-area, BX max Expressed as the maximum value of traffic flow index, BX min Expressed as the minimum value of traffic flow index, SD 预 Expressed as the preset road area ratio, SF 预 It is expressed as the preset sidewalk area ratio, and e is expressed as a natural constant.

[0082] The step S04: urban road traffic safety monitoring and analysis is used to calculate the road traffic safety management index of each monitoring sub-area in the target urban road law enforcement monitoring area based on the road traffic safety data of the road traffic safety data acquisition unit.

[0083] In one possible design, the urban road traffic safety monitoring and analysis is specifically as follows:

[0084] Step S001: Calculate the degree of road surface damage based on the number of road surface defects in each monitoring sub-area of the target city's road law enforcement monitoring area:

[0085]

[0086] Among them, DQ i It is expressed as the road pavement defect damage degree of the i-th monitoring sub-area, UQ 预 Indicates the preset road surface defect damage degree, uq i It is represented as the number of road surface defects in the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area;

[0087] Step S002: The calculation formula of the road traffic safety management index is:

[0088]

[0089] Among them, β i Expressed as the road traffic safety management index of the ith monitoring sub-area, DQ max Expressed as the maximum value of road pavement defect damage, DQ min It is expressed as the minimum value of the road surface defect damage degree, uj i Expressed as the number of traffic violation vehicles in the i-th monitoring sub-area, ug i It is represented by the number of traffic violation parking in the ith monitoring sub-area, and n is represented by the number of monitoring sub-areas.

[0090] The step S05: urban road law enforcement monitoring intelligent identification is used to calculate the road safety monitoring evaluation coefficient of each monitoring sub-area in the target city road law enforcement monitoring area based on the road usage rate warning index and road traffic safety management index of each monitoring sub-area in the target city road law enforcement monitoring area.

[0091] In one possible design, the calculation formula of the road safety monitoring evaluation coefficient is:

[0092]

[0093] Among them, θ i Expressed as the road safety monitoring evaluation coefficient of the i-th monitoring sub-area, α min Expressed as the minimum value of the road usage warning index, α max Expressed as the maximum value of the road usage warning index, β max Expressed as the maximum value of the road traffic safety management index, β minExpressed as the minimum value of the road traffic safety management index.

[0094] The step S06: the urban road law enforcement monitoring intelligent assessment is used to obtain the road safety monitoring assessment coefficient of each monitoring sub-area in the target urban road law enforcement monitoring area, compare it with the preset road safety monitoring assessment coefficient, and process it.

[0095] In one possible design, the intelligent assessment of urban road law enforcement monitoring is specifically as follows:

[0096] Obtain the road safety monitoring assessment coefficient of each monitoring sub-area in the target city's road law enforcement monitoring area, and compare it with the preset road safety monitoring assessment coefficient. If the road safety monitoring assessment coefficient of a monitoring sub-area is greater than the preset road safety monitoring assessment coefficient, it indicates that there is an abnormality in the road safety monitoring in the monitoring sub-area. The road law enforcement personnel should be notified immediately for on-site management, and the number of the monitoring sub-area with the abnormality should be transmitted to the urban road condition abnormality processing step. Otherwise, it indicates that there is no abnormality in the road safety monitoring in the monitoring sub-area.

[0097] The step S07: urban road condition abnormality processing is used to receive the numbers of each abnormal monitoring sub-area of the target urban road law enforcement monitoring area transmitted by the urban road law enforcement monitoring intelligent evaluation step, calculate the pavement structure quality index of each abnormal monitoring sub-area of the target urban road law enforcement monitoring area based on the pavement thickness and pavement bearing capacity, and judge the road safety performance.

[0098] In one possible design, the abnormal urban road condition processing is specifically as follows:

[0099] Step S001: receiving the numbers of the abnormal monitoring sub-areas in the target city's road law enforcement monitoring area, and renumbering them in descending order as 1, 2, ...j, ...m according to the road safety monitoring evaluation coefficient;

[0100] Step S002: Calculate the pavement structure quality index by obtaining the pavement thickness and pavement bearing capacity of each abnormal monitoring sub-area in the target city road law enforcement monitoring area:

[0101]

[0102] in, Expressed as the pavement structure quality index of the jth abnormal monitoring sub-area, hy j is represented by the pavement thickness of the jth abnormal monitoring sub-area, Δhy is represented by the mean pavement thickness, and hy max Expressed as the maximum value of the road surface thickness, zy j is represented by the pavement bearing capacity of the jth abnormal monitoring sub-area, Δzy is represented by the mean pavement bearing capacity, and zy maxExpressed as the maximum value of the pavement bearing capacity;

[0103] Step S003: Obtain the road safety monitoring evaluation coefficient corresponding to the jth abnormal monitoring sub-area and calculate Obtain the pavement structure quality anomaly coefficient of each abnormal monitoring sub-area. If the pavement structure quality anomaly coefficient of a monitoring sub-area is greater than the preset pavement structure quality anomaly coefficient, it indicates that the safety performance of the road pavement structure in the monitoring sub-area is abnormal, and the road law enforcement personnel should be notified to conduct on-site management. Otherwise, it indicates that there is no abnormality in the safety performance of the road pavement structure in the monitoring sub-area.

[0104] In this embodiment, it should be specifically explained that the calculation formula for the mean road thickness is: The calculation formula for the mean value of the road bearing capacity is: Where m represents the number of abnormal monitoring sub-regions.

[0105] In this embodiment, it should be specifically explained that the present invention collects road monitoring data of each monitoring sub-area in the target city's road law enforcement monitoring area through intelligent monitoring equipment, calculates the road usage rate warning index of each monitoring sub-area in the target city's road law enforcement monitoring area based on the road usage status data of the road usage status data collection unit, calculates the road traffic safety management index of each monitoring sub-area in the target city's road law enforcement monitoring area based on the road traffic safety data of the road traffic safety data collection unit, further analyzes to obtain a road safety monitoring evaluation coefficient, and compares it with a preset road safety monitoring evaluation coefficient. If the road safety monitoring evaluation coefficient of a monitoring sub-area is greater than the preset road safety monitoring evaluation coefficient, it indicates that there is an abnormality in the road safety monitoring in the monitoring sub-area, and an abnormality should be detected immediately. That is, the road law enforcement personnel are notified to conduct on-site management, and the numbers of the monitoring sub-areas with abnormalities are transmitted to the urban road condition abnormality processing steps. Otherwise, it indicates that there are no abnormalities in the road safety monitoring in the monitoring sub-area. The collection of road usage data is realized in an intelligent way, which provides data-driven decision support for the calculation of the road usage rate warning index. The collection of road traffic safety data is realized in an intelligent way, which provides data-driven decision support for the calculation of the road traffic safety management index. By analyzing the road safety monitoring evaluation coefficient, the traffic management department can allocate law enforcement resources more reasonably. For example, for areas with higher road safety monitoring evaluation coefficients, law enforcement can be strengthened, monitoring equipment can be increased, etc., so as to improve the level of road safety and improve the efficiency of law enforcement management.

[0106] The present invention utilizes the abnormal road condition processing of urban roads. By receiving the numbers of each abnormal monitoring sub-area of the target urban road law enforcement monitoring area transmitted by the urban road law enforcement monitoring intelligent evaluation step, the present invention calculates the pavement structure quality index of each abnormal monitoring sub-area of the target urban road law enforcement monitoring area based on the pavement thickness and pavement bearing capacity, judges the road safety performance, and thus notifies the road law enforcement personnel to conduct on-site management. By combining the pavement structure quality index and the road safety monitoring evaluation coefficient, the specific road area with problems is accurately located, and the road is analyzed for potential safety hazards. This helps to reduce blind inspections and ineffective investment, improve the intelligent level of urban traffic management, reduce the time cost of manual inspections and data processing, and further improve management efficiency.

[0107] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An artificial intelligence-based assisted law enforcement method, characterized in that: include: Step S01: Road enforcement monitoring area division: used to divide the target city road enforcement monitoring area into various monitoring sub-areas according to a division method of equal area, and number each monitoring sub-area of the target city road enforcement monitoring area; Step S02: Urban road monitoring data collection: used to collect road monitoring data of each monitoring sub-area in the target urban road law enforcement monitoring area through intelligent monitoring equipment. The urban road monitoring data collection step includes a road usage data collection unit and a road traffic safety data collection unit. The road monitoring data includes road usage data and road traffic safety data; Step S03: Urban road usage monitoring and analysis: used to calculate the road usage rate warning index of each monitoring sub-area in the target urban road law enforcement monitoring area based on the road usage status data of the road usage status data collection unit; Step S04: Urban road traffic safety monitoring and analysis: used to calculate the road traffic safety management index of each monitoring sub-area in the target city road law enforcement monitoring area based on the road traffic safety data of the road traffic safety data acquisition unit; Step S05: Intelligent identification of urban road law enforcement monitoring: used to calculate the road safety monitoring assessment coefficient of each monitoring sub-area in the target urban road law enforcement monitoring area based on the road usage rate warning index and road traffic safety management index of each monitoring sub-area in the target urban road law enforcement monitoring area; Step S06: Intelligent evaluation of urban road law enforcement monitoring: used to obtain the road safety monitoring evaluation coefficient of each monitoring sub-area in the target urban road law enforcement monitoring area, compare it with the preset road safety monitoring evaluation coefficient, and process it; Step S07: Processing of abnormal road conditions in urban roads: used to receive the numbers of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area transmitted in the urban road law enforcement monitoring intelligent evaluation step, calculate the pavement structure quality indicators of each abnormal monitoring sub-area in the target urban road law enforcement monitoring area based on the pavement thickness and pavement bearing capacity, and judge the road safety performance.

2. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The road enforcement monitoring area is specifically divided into: Obtain a target city road law enforcement monitoring area, determine it as the target area, divide the target area into monitoring sub-areas according to an equal area division method, and number the monitoring sub-areas of the target city road law enforcement monitoring area as 1, 2, ..., i, ..., n in sequence.

3. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The urban road monitoring data collection is specifically as follows: Road usage data collection unit: used to collect the road area, sidewalk area, vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as kd i 、kf i 、kc i 、kp i , where i = 1, 2, ... n, i represents the number of the i-th monitoring sub-area; Road traffic safety data collection unit: used to collect the number of road defects, traffic violation vehicles, and traffic illegal parking in each monitoring sub-area of the target city road law enforcement monitoring area through intelligent monitoring equipment, marked as uq respectively i 、uj i ,ug i .

4. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The urban road usage monitoring and analysis are specifically as follows: Step S001: Calculate the traffic flow index based on the vehicle flow and pedestrian flow of each monitoring sub-area in the target city road law enforcement monitoring area: Among them, BX i Expressed as the traffic flow index of the i-th monitoring sub-area, kc i Expressed as the traffic flow of the ith monitoring sub-area, kp i It is represented as the flow of people in the i-th monitoring sub-area; Step S002: Calculate the road area ratio: Among them, SD i Expressed as the road area ratio of the i-th monitoring sub-area, kd i It is represented as the road area of the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area; Step S003: Calculate the sidewalk area ratio: Among them, SF i Expressed as the sidewalk area ratio of the i-th monitoring sub-area, kf i It is represented as the sidewalk area of the ith monitoring sub-area; Step S004: The calculation formula of the road usage rate warning index is: Among them, α i Expressed as the road usage warning index of the i-th monitoring sub-area, BX max Expressed as the maximum value of traffic flow index, BX min Expressed as the minimum value of traffic flow index, SD 预 Expressed as the preset road area ratio, SF 预 It is expressed as the preset sidewalk area ratio, and e is expressed as a natural constant.

5. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The urban road traffic safety monitoring and analysis are specifically as follows: Step S001: Calculate the degree of road surface damage based on the number of road surface defects in each monitoring sub-area of the target city's road law enforcement monitoring area: Among them, DQ i It is expressed as the road pavement defect damage degree of the i-th monitoring sub-area, UQ 预 Indicates the preset road surface defect damage degree, uq i It is represented as the number of road surface defects in the ith monitoring sub-area, and S is represented as the area of the monitoring sub-area; Step S002: The calculation formula of the road traffic safety management index is: Among them, β i Expressed as the road traffic safety management index of the ith monitoring sub-area, DQ max Expressed as the maximum value of road pavement defect damage, DQ min It is expressed as the minimum value of the road surface defect damage degree, uj i Expressed as the number of traffic violation vehicles in the i-th monitoring sub-area, ug i It is represented by the number of traffic violation parking in the ith monitoring sub-area, and n is represented by the number of monitoring sub-areas.

6. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The calculation formula of the road safety monitoring evaluation coefficient is: Among them, θ i Expressed as the road safety monitoring evaluation coefficient of the i-th monitoring sub-area, α min Expressed as the minimum value of the road usage warning index, α max Expressed as the maximum value of the road usage warning index, β max Expressed as the maximum value of the road traffic safety management index, β min Expressed as the minimum value of the road traffic safety management index.

7. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The intelligent assessment of urban road law enforcement monitoring is specifically as follows: Obtain the road safety monitoring assessment coefficient of each monitoring sub-area in the target city's road law enforcement monitoring area, and compare it with the preset road safety monitoring assessment coefficient. If the road safety monitoring assessment coefficient of a monitoring sub-area is greater than the preset road safety monitoring assessment coefficient, it indicates that there is an abnormality in the road safety monitoring in the monitoring sub-area. The road law enforcement personnel should be notified immediately for on-site management, and the number of the monitoring sub-area with the abnormality should be transmitted to the urban road condition abnormality processing step. Otherwise, it indicates that there is no abnormality in the road safety monitoring in the monitoring sub-area.

8. The artificial intelligence-based assisted law enforcement method according to claim 1, characterized in that: The specific handling of abnormal urban road conditions is as follows: Step S001: Receive the numbers of each abnormal monitoring sub-area in the target city's road law enforcement monitoring area, and renumber them in descending order as 1, 2, ..., j, ..., m according to the road safety monitoring evaluation coefficient; Step S002: Calculate the pavement structure quality index by obtaining the pavement thickness and pavement bearing capacity of each abnormal monitoring sub-area in the target city road law enforcement monitoring area: in, Expressed as the pavement structure quality index of the jth abnormal monitoring sub-area, hy j is represented by the pavement thickness of the jth abnormal monitoring sub-area, Δhy is represented by the mean pavement thickness, and hy max Expressed as the maximum value of the road surface thickness, zy j is represented by the pavement bearing capacity of the jth abnormal monitoring sub-area, Δzy is represented by the mean pavement bearing capacity, and zy max Expressed as the maximum value of the pavement bearing capacity; Step S003: Obtain the road safety monitoring evaluation coefficient corresponding to the jth abnormal monitoring sub-area and calculate Obtain the pavement structure quality anomaly coefficient of each abnormal monitoring sub-area. If the pavement structure quality anomaly coefficient of a monitoring sub-area is greater than the preset pavement structure quality anomaly coefficient, it indicates that the safety performance of the road pavement structure in the monitoring sub-area is abnormal, and the road law enforcement personnel should be notified to conduct on-site management. Otherwise, it indicates that there is no abnormality in the safety performance of the road pavement structure in the monitoring sub-area.

9. A storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the processor is caused to execute an artificial intelligence-based assisted law enforcement method as described in claims 1-8.

10. A device, characterized in that include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements an artificial intelligence-based assisted law enforcement method as described in claims 1-8.

Images