Intelligent security management method based on artificial intelligence

Abstract

The invention discloses an intelligent security management method based on artificial intelligence, and relates to the technical field of security management. The intelligent security and protection management method based on artificial intelligence comprises the following steps: S1, traffic jam assessment; s2, traffic signal fault assessment; s3, traffic management evaluation; and S4, emergency security and protection feedback. According to the invention, whether traffic signal fault assessment is carried out is judged through the traffic jam index obtained through the traffic data, then whether traffic signal management is carried out is judged based on the traffic signal fault index obtained through the traffic signal data, and whether an emergency security strategy is carried out is judged based on the traffic management effective index obtained through the management data. And finally, whether feedback is performed or not is judged based on the traffic management effective index obtained after the emergency security strategy is implemented, so that the effect of improving the security management efficiency of road traffic is achieved, and the problem that the state change of traffic signals is not fully considered in the security management of the road traffic in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent security management, and particularly to an intelligent security management method based on artificial intelligence. Background Art

[0002] Traditional security management systems mainly rely on manual monitoring and static prevention measures, and it has been difficult to meet the requirements of the current complex and changeable traffic environment. Once a traffic signal fails or is abnormal, it often causes traffic participants to be unable to accurately judge the traffic conditions, not only reducing the road traffic efficiency, but also seriously threatening driving safety. To address this challenge, the existing technology has begun to introduce artificial intelligence into the field of intelligent security management, and through advanced technologies such as computer vision, it realizes real-time monitoring and intelligent early warning of traffic signals, so as to timely discover and handle faults or abnormal situations, and thus effectively prevent traffic chaos and safety accidents.

[0003] In the existing technology, the images captured by a monitoring camera are processed and analyzed in real time, and then image processing algorithms are used to analyze and identify the images in real time to determine whether there are abnormalities in traffic facilities, and finally relevant departments are prompted to repair or replace them to improve traffic safety.

[0004] For example, a traffic facility management method and terminal device disclosed in the invention patent announcement with the publication number of CN108986448B includes: first, obtaining first traffic facility information sent by a collection terminal, where the first traffic facility information includes the captured image and position information of the traffic facility; then, according to the position information of the traffic facility, narrowing the recognition range of the traffic facility to a first recognition range; determining first status information of the traffic facility according to the first recognition range and the captured image; and finally, determining a maintenance task of the traffic facility according to the first status information.

[0005] For example, a traffic information management big data analysis system disclosed in the invention patent announcement with the publication number of CN106971587B includes: a facility fault diagnosis unit is provided for a traffic facility, and the fault diagnosis unit is used to detect the fault of the traffic facility and output corresponding facility fault information; a traffic facility fault terminal, connected to the traffic facility and used to receive the facility fault information, and outputting a device repair request information according to the facility fault information; an image acquisition device; a traffic passage fault terminal, connected to the image acquisition device and used to receive the passage fault information, and outputting a traffic processing request information according to the passage fault information; a processing terminal, connected to the traffic facility fault terminal and the traffic passage fault terminal; the processing terminal pushes device repair instructions and traffic processing instructions to the terminals where device repair personnel and traffic management personnel are located respectively.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] Existing security management systems mainly focus on static security prevention measures such as video surveillance and intrusion detection, but pay less attention to the dynamic and changing security element of traffic signal facilities. As a result, it is difficult for security management systems to detect and handle traffic signal failures in a timely manner, missing the best opportunity to prevent chaos and traffic accidents, thus affecting traffic efficiency and driving safety. There is a problem that the state changes of traffic signals are not fully considered in the security management of road traffic. Summary of the Invention

[0008] By providing an intelligent security management method based on artificial intelligence in an embodiment of the present application, the problem that the state changes of traffic signals are not fully considered in the security management of road traffic in the prior art is solved, and the improvement of the security management efficiency of road traffic is realized.

[0009] An embodiment of the present application provides an intelligent security management method based on artificial intelligence, including the following steps: S1, performing a traffic congestion assessment on the basis of the obtained traffic data to obtain a traffic congestion index, and determining whether to perform a traffic signal failure assessment based on the traffic congestion index, where the traffic congestion index is used to quantitatively evaluate the congestion degree of road traffic; S2, if a traffic signal failure assessment is to be performed, performing a traffic signal failure assessment on the basis of the obtained traffic signal data to obtain a traffic signal failure index, and determining whether to perform traffic signal management based on the traffic signal failure index, where the traffic signal failure index is used to quantitatively evaluate the probability of the existence of a traffic signal failure; S3, if traffic signal management is to be performed, performing a traffic management assessment on the basis of the traffic congestion index and management data after traffic signal management to obtain a traffic management effectiveness index, and determining whether to implement an emergency security strategy based on the traffic management effectiveness index, where the traffic management effectiveness index is used to quantitatively evaluate the effectiveness of traffic signal management; S4, determining whether to perform feedback based on the traffic management effectiveness index obtained after the implementation of the emergency security strategy.

[0010] Furthermore, the traffic data includes non-motor vehicle lane data and motor vehicle lane data; the non-motor vehicle lane data includes the average non-motor vehicle speed and the non-motor vehicle lane congestion length; the motor vehicle lane data includes the average motor vehicle speed and the motor vehicle lane congestion length; the traffic signal data includes signal light deviation data, fault duration, and fault frequency; the management data includes accident reduction rate, average motor vehicle speed increase coefficient, and traffic signal adjustment duration; the accident reduction rate represents the quantitative data of the influence degree of the number of accidents after traffic management and the number of accidents before traffic management on the accident reduction rate; the average motor vehicle speed increase coefficient represents the quantitative data of the influence degree of the average speed after traffic management and the average speed before traffic management on the average motor vehicle speed increase coefficient.

[0011] Further, the signal light deviation data includes green light duration deviation, red light duration deviation, and yellow light duration deviation; the green light duration deviation represents the quantification data of the influence degree of the green light duration and the preset green light duration in the corresponding time period on the green light duration deviation; the red light duration deviation represents the quantification data of the influence degree of the red light duration and the preset red light duration in the corresponding time period on the red light duration deviation; the yellow light duration deviation represents the quantification data of the influence degree of the yellow light duration and the preset yellow light duration in the corresponding time period on the yellow light duration deviation.

[0012] Further, the specific method for obtaining the traffic congestion index by evaluating traffic congestion based on the acquired traffic data is as follows: Obtain the non-motor vehicle passing coefficient according to the relative relationship between the non-motor vehicle lane data, the non-motor vehicle lane reference data obtained from the preset database, and the traffic weights. The non-motor vehicle passing coefficient is used to evaluate the congestion degree of the non-motor vehicle lane. The non-motor vehicle lane reference data includes the preset maximum speed of the non-motor vehicle lane and the total length of the non-motor vehicle lane; Obtain the motor vehicle passing coefficient according to the relative relationship between the motor vehicle lane data, the motor vehicle lane reference data obtained from the preset database, and the traffic weights. The motor vehicle passing coefficient is used to evaluate the congestion degree of the motor vehicle lane. The motor vehicle lane reference data includes the preset maximum speed of the motor vehicle lane and the total length of the motor vehicle lane; The traffic weights include speed weight and congestion length weight; Process the non-motor vehicle passing coefficient, the motor vehicle passing coefficient, and the congestion weights obtained from the preset database to obtain the traffic congestion index; The congestion weights include non-motor vehicle weight and motor vehicle weight.

[0013] Further, the specific process for determining whether to perform a traffic signal fault assessment based on the traffic congestion index is as follows: Determine whether the traffic congestion index is less than the preset traffic congestion threshold obtained from the preset database: If the traffic congestion index is not less than the preset traffic congestion threshold obtained from the preset database, do not perform a traffic signal fault assessment and continue to monitor to obtain the traffic congestion index; If the traffic congestion index is less than the preset traffic congestion threshold obtained from the preset database, perform a traffic signal fault assessment.

[0014] Further, the specific method for obtaining the traffic signal fault index by evaluating traffic signal faults based on the acquired traffic signal data is as follows: Obtain a fault duration coefficient according to the relative relationship between the fault duration and the total duration of a preset time period retrieved from a preset database. The fault duration coefficient represents the relative deviation between the fault duration and the total duration of the preset time period. Obtain a signal lamp coefficient according to the relative relationship between the signal lamp deviation data and the signal lamp weights retrieved from the preset database. The signal lamp coefficient represents the quantitative data of the influence degree of the signal lamp deviation data on the traffic signal fault index. The signal lamp weights include a green light weight, a red light weight, and a yellow light weight. Process the fault duration coefficient, the signal lamp coefficient, the fault frequency factor retrieved from the preset database, and the signal weights to obtain the traffic signal fault index.

[0015] Further, the specific process for determining whether to perform traffic signal management based on the traffic signal fault index is as follows: Determine whether the traffic signal fault index is less than a preset signal fault threshold retrieved from the preset database. If the traffic signal fault index is not less than the preset signal fault threshold retrieved from the preset database, traffic signal management is not performed. If the traffic signal fault index is less than the preset signal fault threshold retrieved from the preset database, traffic signal management is performed. The traffic signal management includes signal cycle adjustment and fault information push. The fault information push means pushing traffic signal fault information to preset personnel in real time.

[0016] Further, the specific method for obtaining the traffic management effectiveness index is as follows: Obtain a congestion management coefficient according to the relative relationship between the traffic congestion index before traffic signal management and the traffic congestion index after traffic signal management. The congestion management coefficient represents the quantitative data of the combined influence degree of the traffic congestion index before traffic signal management and the traffic congestion index after traffic signal management on the traffic management effectiveness index. Obtain a vehicle speed increase coefficient deviation according to the relative relationship between the average vehicle speed increase coefficient of motor vehicles and a preset vehicle speed increase coefficient retrieved from the preset database. The vehicle speed increase coefficient deviation represents the deviation degree between the average vehicle speed increase coefficient of motor vehicles and the preset vehicle speed increase coefficient. Obtain the traffic management effectiveness index according to the relative relationship between the congestion management coefficient, the vehicle speed increase coefficient deviation, the accident reduction rate, the traffic signal adjustment duration, and the traffic management weights retrieved from the preset database. The traffic management weights include accident management weights, vehicle speed management weights, and signal lamp adjustment weights.

[0017] Further, the specific process of determining whether to implement the emergency security and protection strategy based on the traffic management effectiveness index is as follows: Determine whether the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database. If the traffic management effectiveness index is not less than the preset management effectiveness threshold obtained from the preset database, the emergency security and protection strategy is not implemented. If the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database, the emergency security and protection strategy is implemented.

[0018] Further, the specific process of implementing the emergency security and protection strategy is as follows: Implementing the emergency security and protection strategy means automatically dispatching patrol personnel based on historical traffic data. The patrol personnel dispatching includes the number, route, and frequency of dispatching patrol personnel. Determine whether the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is less than the preset management effectiveness threshold obtained from the preset database. If the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is not less than the preset management effectiveness threshold obtained from the preset database, no feedback is given. If the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is less than the preset management effectiveness threshold obtained from the preset database, feedback is given.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. Determine whether to conduct a traffic signal fault assessment based on the traffic congestion index obtained from traffic data, then determine whether to conduct traffic signal management based on the traffic signal fault index obtained from traffic signal data, and finally determine whether to implement the emergency security and protection strategy based on the traffic management effectiveness index obtained from management data, thereby improving the efficiency of traffic signal management in intelligent security management, enhancing the initiative and predictability of security management, and then realizing the overall coordination and efficient operation of the security management system, effectively solving the problem that the state change of traffic signals is not fully considered in the security management of road traffic in the prior art.

[0021] 2. Obtain the congestion management coefficient from the traffic congestion index before traffic signal management and the traffic congestion index after traffic signal management, then obtain the vehicle speed increase coefficient deviation based on the relative relationship between the average vehicle speed increase coefficient of motor vehicles and the preset vehicle speed increase coefficient, and finally obtain the traffic management effectiveness index based on the relative relationship between the congestion management coefficient, the vehicle speed increase coefficient deviation, the accident reduction rate, the traffic signal adjustment duration, and the traffic management weight, thereby quantitatively evaluating the impact of traffic signal management on driving safety, and then improving the intelligent level of the security management system, enabling it to respond to traffic changes faster.

[0022] 3. The non-motor vehicle passing coefficient is obtained from the relative relationship between non-motor vehicle lane data, non-motor vehicle lane reference data, and traffic weights. Then, the motor vehicle passing coefficient is obtained from the relative relationship between motor vehicle lane data, motor vehicle lane reference data, and traffic weights. Finally, the non-motor vehicle passing coefficient, the motor vehicle passing coefficient, and the congestion weight obtained from the preset database are processed to obtain the traffic congestion index, thereby quantitatively evaluating the congestion degree of road traffic, enabling its security management system to better adapt to the dynamic allocation of urban traffic signals, and providing strong support for the subsequent discovery of traffic signal failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of an intelligent security management method based on artificial intelligence provided by an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of the change of the traffic congestion index with the change of the average non-motor vehicle speed provided by an embodiment of the present application;

[0025] Figure 3 It is a schematic diagram of the change of the traffic congestion index with the change of the non-motor vehicle lane congestion length provided by an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of the change of the traffic congestion index with the change of the average motor vehicle speed provided by an embodiment of the present application;

[0027] Figure 5 It is a schematic diagram of the change of the traffic congestion index with the change of the motor vehicle lane congestion length provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] By providing an intelligent security management method based on artificial intelligence in an embodiment of the present application, the problem that the state change of traffic signals is not fully considered in the security management of road traffic in the prior art is solved. Whether to conduct a traffic signal failure assessment is judged based on the traffic congestion index obtained from traffic data. Then, whether to conduct traffic signal management is judged based on the traffic signal failure index obtained from traffic signal data. Next, whether to implement an emergency security strategy is judged based on the traffic management effectiveness index obtained from management data. Finally, whether to provide feedback is judged based on the traffic management effectiveness index obtained after the implementation of the emergency security strategy, achieving an improvement in the security management efficiency of road traffic.

[0029] The technical solution in the embodiment of the present application is to solve the problem that the state change of traffic signals is not fully considered in the security management of the above-mentioned road traffic. The general idea is as follows:

[0030] Based on the traffic congestion index obtained from traffic data, it is determined whether to conduct a traffic signal fault assessment. Then, based on the traffic signal fault index obtained from traffic signal data, it is determined whether to conduct traffic signal management. Finally, based on the traffic management effectiveness index obtained from management data, it is determined whether to implement an emergency security strategy, achieving the effect of improving the security management efficiency of road traffic.

[0031] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0032] As Figure 1 shown, it is a flowchart of an intelligent security management method based on artificial intelligence provided by an embodiment of the present application. The method includes the following steps: S1, traffic congestion assessment: According to the obtained traffic data, traffic congestion assessment is carried out to obtain a traffic congestion index, and based on the traffic congestion index, it is determined whether to conduct a traffic signal fault assessment. The traffic congestion index is used to quantitatively evaluate the congestion degree of road traffic; S2, traffic signal fault assessment: If a traffic signal fault assessment is to be conducted, then according to the obtained traffic signal data, traffic signal fault assessment is carried out to obtain a traffic signal fault index, and based on the traffic signal fault index, it is determined whether to conduct traffic signal management. The traffic signal fault index is used to quantitatively evaluate the probability of the existence of traffic signal faults; S3, traffic management assessment: If traffic signal management is to be conducted, then according to the traffic congestion index and management data after traffic signal management, traffic management assessment is carried out to obtain a traffic management effectiveness index, and based on the traffic management effectiveness index, it is determined whether to implement an emergency security strategy. The traffic management effectiveness index is used to quantitatively evaluate the effectiveness of traffic signal management; S4, emergency security feedback: Based on the traffic management effectiveness index obtained after the implementation of the emergency security strategy, it is determined whether to conduct feedback.

[0033] The intelligent security management system provided by the solution of the present application mainly serves urban traffic management departments, security monitoring centers, and relevant emergency response teams. The intelligent security management system is usually installed near traffic accident recording systems, traffic signal control systems, security monitoring centers, and key traffic nodes (such as traffic lights, cameras), and connects various sensors, cameras, and management terminals through wired or wireless means to achieve real-time monitoring and management, realizing comprehensive monitoring and management of road traffic, improving traffic efficiency, ensuring driving safety, and providing strong support for the sustainable development of urban traffic.

[0034] It should be noted that traffic data includes non-motor vehicle lane data and motor vehicle lane data; non-motor vehicle lane data includes the average non-motor vehicle speed and the non-motor vehicle lane congestion length; motor vehicle lane data includes the average motor vehicle speed and the motor vehicle lane congestion length; traffic signal data includes signal deviation data, fault duration, and fault frequency; management data includes accident reduction rate, coefficient of increase in average motor vehicle speed, and traffic signal adjustment duration.

[0035] The accident reduction rate represents the quantitative data of the impact degree of the number of accidents after traffic management and the number of accidents before traffic management on the accident reduction rate, which is obtained by performing a difference operation between the absolute value of the difference in the number of accidents before and after traffic management obtained from the traffic accident record system and the number of accidents before traffic management; the coefficient of increase in average motor vehicle speed represents the quantitative data of the impact degree of the average speed after traffic management and the average speed before traffic management on the coefficient of increase in average motor vehicle speed, which is obtained by performing a difference operation between the absolute value of the difference in the average speed before and after traffic management and the average speed before traffic management; the traffic signal adjustment duration is obtained from the operation records of the traffic signal control system.

[0036] When the average motor vehicle speed is less than the preset motor vehicle speed, the traffic condition is determined to be congested; vehicles on the road are identified in real time through background modeling and foreground detection, and then image processing techniques (such as image segmentation, vehicle detection, etc.) are used to identify the start and end positions of the congested area. According to the boundary position of the congested area in the image and the actual geometric layout of the road, and combined with geometric measurement and correction algorithms, the non-motor vehicle lane congestion length and the motor vehicle lane congestion length are calculated; the preset motor vehicle speed is set according to specific road requirements. For example, on the urban main road, it can be set to 30 km / h; the average non-motor vehicle speed and the average motor vehicle speed are measured by the radar speedometer equipped with the camera.

[0037] The signal light deviation data includes the green light duration deviation, the red light duration deviation, and the yellow light duration deviation; the green light duration deviation represents the quantitative data of the influence degree of the green light duration and the preset green light duration in the corresponding time period on the green light duration deviation, and is obtained by performing a ratio operation on the absolute value of the difference between the green light duration and the preset green light duration in the corresponding time period and the preset green light duration in the corresponding time period; the red light duration deviation represents the quantitative data of the influence degree of the red light duration and the preset red light duration in the corresponding time period on the red light duration deviation, and is obtained by performing a ratio operation on the absolute value of the difference between the red light duration and the preset red light duration in the corresponding time period and the preset red light duration in the corresponding time period; the yellow light duration deviation represents the quantitative data of the influence degree of the yellow light duration and the preset yellow light duration in the corresponding time period on the yellow light duration deviation, and is obtained by performing a ratio operation on the absolute value of the difference between the yellow light duration and the preset yellow light duration in the corresponding time period and the preset yellow light duration in the corresponding time period; the fault duration and the fault frequency are obtained from the fault records of the traffic signal control system.

[0038] In this embodiment, through machine learning and deep learning algorithms in artificial intelligence (such as background modeling and foreground detection), traffic data, traffic signal data, and management data can be obtained, such as average vehicle speed, congestion length, signal light deviation, etc.; through artificial intelligence, the traffic signal management process and the patrol personnel scheduling can be automatically triggered, and artificial intelligence can also continuously adjust the management strategy according to the feedback results to improve the management efficiency.

[0039] The green light duration, the red light duration, and the yellow light duration are obtained through the interface of the traffic signal control system; the preset green light duration, the preset red light duration, and the preset yellow light duration are represented by the mode of the green light duration, the red light duration, and the yellow light duration in the historical corresponding time period. For example, if the mode of the green light duration from 7 pm to 8 pm in a month is 1800s, then the preset green light duration from 7 pm to 8 pm is set to 1800s.

[0040] Through the above steps, the traffic congestion, traffic signal faults, and the effectiveness of management measures are evaluated, so as to adjust the traffic signals in real time, optimize the traffic flow, reduce the occurrence of traffic accidents, improve the road traffic management efficiency in intelligent security management, and further improve the security management efficiency of road traffic.

[0041] Further, the specific method for obtaining the traffic congestion index by evaluating traffic congestion according to the obtained traffic data is as follows: the non-motor vehicle passing coefficient is obtained according to the relative relationship between the non-motor vehicle lane data and the non-motor vehicle lane reference data and the traffic weight obtained from the preset database, that is The non-motor vehicle passing coefficient is used to evaluate the congestion degree of the non-motor vehicle lane. The reference data of the non-motor vehicle lane includes the preset maximum speed of the non-motor vehicle lane and the total length of the non-motor vehicle lane. The motor vehicle passing coefficient is obtained according to the relative relationship between the motor vehicle lane data, the reference data of the motor vehicle lane obtained from the preset database, and the traffic weights, that is The motor vehicle passing coefficient is used to evaluate the congestion degree of the motor vehicle lane. The reference data of the motor vehicle lane includes the preset maximum speed of the motor vehicle lane and the total length of the motor vehicle lane. The traffic weights include the speed weight and the congestion length weight. The speed weight represents the influence degree of the average non-motor vehicle speed and the average motor vehicle speed on the traffic congestion index. The congestion length weight represents the influence degree of the non-motor vehicle lane congestion length and the motor vehicle lane congestion length on the traffic congestion index. The non-motor vehicle passing coefficient, the motor vehicle passing coefficient, and the congestion weight obtained from the preset database are processed to obtain the traffic congestion index. The congestion weight includes the non-motor vehicle weight and the motor vehicle weight. The non-motor vehicle weight represents the influence degree of the non-motor vehicle passing coefficient on the traffic congestion index. The motor vehicle weight represents the influence degree of the motor vehicle passing coefficient on the traffic congestion index.

[0042] It should be added that the specific method for obtaining the traffic congestion index is as follows:

[0043]

[0044] In the formula, t represents the number of the preset time period, t = 1, 2,..., T, and T represents the total number of the preset time periods. YD t represents the traffic congestion index of the t-th preset time period, FCS t represents the average non-motor vehicle speed within the t-th preset time period, FYC t represents the non-motor vehicle lane congestion length within the t-th preset time period, JCS t represents the average motor vehicle speed within the t-th preset time period, JYC t represents the motor vehicle lane congestion length within the t-th preset time period. FCS0 represents the preset maximum speed of the non-motor vehicle lane, FTC0 represents the total length of the non-motor vehicle lane, JCS0 represents the preset maximum speed of the motor vehicle lane, JTC0 represents the total length of the motor vehicle lane, α1 represents the non-motor vehicle weight, α2 represents the motor vehicle weight, Y1 represents the speed weight, and Y2 represents the congestion length weight.

[0045] In this embodiment, the preset maximum speed of the non-motor vehicle lane is set according to specific road regulations. For example, the preset maximum speed of the non-motor vehicle lane is 30 km / h; the total length of the non-motor vehicle lane is set according to specific road sections. For example, on the urban arterial road, the total length of the non-motor vehicle lane is set to 0.5 km; the preset maximum speed of the motor vehicle lane is set according to specific road regulations. For example, on the urban arterial road, the preset maximum speed of the motor vehicle lane is 60 km / h; the total length of the motor vehicle lane is set according to specific road sections. For example, on the urban arterial road, the total length of the motor vehicle lane is set to 0.5 km; the total lengths of the non-motor vehicle lane and the motor vehicle lane refer to the distance between two traffic lights.

[0046] The non-motor vehicle weight and the motor vehicle weight are obtained from a preset database, and their sum is 1. For example, the average non-motor vehicle speed and the congestion length of the non-motor vehicle lane form a mapping set with the preset non-motor vehicle weight. The real-time average non-motor vehicle speed and the congestion length of the non-motor vehicle lane are input into the mapping set to obtain the corresponding non-motor vehicle weight; the average motor vehicle speed and the congestion length of the motor vehicle lane form a mapping set with the preset motor vehicle weight. The real-time average motor vehicle speed and the congestion length of the motor vehicle lane are input into the mapping set to obtain the corresponding motor vehicle weight; the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0047] The speed weight and the congestion length weight are obtained from a preset database, and their sum is 1. For example, the non-motor vehicle speed and the average motor vehicle speed form a mapping set with the preset speed weight number. The real-time non-motor vehicle speed and the average motor vehicle speed are input into the mapping set to obtain the corresponding speed weight; the congestion length of the non-motor vehicle lane and the congestion length of the motor vehicle lane form a mapping set with the preset congestion length weight. The real-time congestion length of the non-motor vehicle lane and the congestion length of the motor vehicle lane are input into the mapping set to obtain the corresponding congestion length weight; the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0048] In this algorithm, the traffic congestion index is obtained by processing multiple independent variables (non-motor vehicle speed, average motor vehicle speed, congestion length of the non-motor vehicle lane, and congestion length of the motor vehicle lane). There are mutual influence relationships among these independent variables; the congestion length of the lane is usually accompanied by a decrease in vehicle speed, and the two complement each other, jointly leading to traffic congestion. When the non-motor vehicle speed and the average motor vehicle speed decrease, the congestion lengths of the non-motor vehicle lane and the motor vehicle lane increase accordingly; when non-motor vehicles and motor vehicles are mixed, the congestion length of non-motor vehicles increases, which will interfere with the driving of motor vehicles, resulting in a decrease in the average motor vehicle speed; the interference includes frictional interference (when non-motor vehicles approach the motor vehicle lane laterally, the driver reduces the vehicle speed) and blocking interference (non-motor vehicles occupy the motor vehicle lane and block the driving of motor vehicles).

[0049] In this algorithm, the traffic congestion index is negatively correlated with the non-motor vehicle speed and the average motor vehicle speed, and is positively correlated with the non-motor vehicle lane congestion length and the motor vehicle lane congestion length.

[0050] Taking the non-motor vehicle weight, motor vehicle weight, speed weight, and congestion length weight as 0.6, 0.4, 0.3, and 0.7 respectively, the preset maximum speed of the non-motor vehicle lane is 10 km / h, the total length of the non-motor vehicle lane is 10 km, the preset maximum speed of the motor vehicle lane is 60 km / h, and the total length of the motor vehicle lane is 20 km as an example, we get Figures 2 - 5 , and the specific analysis is as follows:

[0051] As Figure 2 shown, it is a schematic diagram of the change of the traffic congestion index with the average non-motor vehicle speed provided by the embodiment of the present application. When the non-motor vehicle lane congestion length, the average motor vehicle speed, and the motor vehicle lane congestion length are 0, the traffic congestion index decreases with the increase of the average non-motor vehicle speed.

[0052] As Figure 3 shown, it is a schematic diagram of the change of the traffic congestion index with the non-motor vehicle lane congestion length provided by the embodiment of the present application. When the average non-motor vehicle speed, the average motor vehicle speed, and the motor vehicle lane congestion length are 0, the traffic congestion index increases with the increase of the non-motor vehicle lane congestion length.

[0053] As Figure 4 shown, it is a schematic diagram of the change of the traffic congestion index with the average motor vehicle speed provided by the embodiment of the present application. When the average non-motor vehicle speed, the non-motor vehicle lane congestion length, and the motor vehicle lane congestion length are 0, the traffic congestion index decreases with the increase of the average motor vehicle speed.

[0054] As Figure 5 shown, it is a schematic diagram of the change of the traffic congestion index with the motor vehicle lane congestion length provided by the embodiment of the present application. When the average non-motor vehicle speed, the non-motor vehicle lane congestion length, and the average motor vehicle speed are 0, the traffic congestion index increases with the increase of the motor vehicle lane congestion length.

[0055] Through the above steps, the congestion degree of road traffic is quantitatively evaluated, and thus strong support is provided for the discovery of subsequent traffic signal failures.

[0056] Further, the specific process for determining whether to conduct a traffic signal fault assessment based on the traffic congestion index is as follows: Determine whether the traffic congestion index is less than the preset traffic congestion threshold obtained from the preset database. If the traffic congestion index is not less than the preset traffic congestion threshold obtained from the preset database, do not conduct a traffic signal fault assessment and continue to monitor to obtain the traffic congestion index. If the traffic congestion index is less than the preset traffic congestion threshold obtained from the preset database, conduct a traffic signal fault assessment (i.e., obtain the traffic signal fault index). The traffic signal fault assessment means obtaining the traffic signal fault index through assessment based on the acquired traffic signal data.

[0057] In this embodiment, the preset traffic congestion threshold is represented by the average value of the qualified traffic congestion indices within the historical time period. Through the above steps, the fault condition of the traffic signal can be detected in a timely manner, thereby improving the management efficiency of road traffic in intelligent security management.

[0058] Further, the specific method for obtaining the traffic signal fault index through traffic signal fault assessment based on the acquired traffic signal data is as follows: Obtain the fault duration coefficient based on the relative relationship between the fault duration and the total duration of the preset time period obtained from the preset database (i.e., ), and the fault duration coefficient represents the relative deviation between the fault duration and the total duration of the preset time period. Obtain the signal lamp coefficient based on the relative relationship between the signal lamp deviation data and the signal lamp weight obtained from the preset database (i.e., XHD t ), and the signal lamp coefficient represents the quantitative data of the influence degree of the signal lamp deviation data on the traffic signal fault index. The signal lamp weight includes the green light weight, the red light weight, and the yellow light weight. The green light weight represents the influence degree of the green light duration deviation on the traffic signal fault index, the red light weight represents the influence degree of the red light duration deviation on the traffic signal fault index, and the yellow light weight represents the influence degree of the yellow light duration deviation on the traffic signal fault index. Process the fault duration coefficient, the signal lamp coefficient, the fault frequency factor obtained from the preset database, and the signal weight to obtain the traffic signal fault index. The fault frequency factor represents the influence degree of the fault frequency on the traffic signal fault index, and the signal weight represents the influence degree of the fault duration on the traffic signal fault index.

[0059] The specific method for obtaining the traffic signal fault index is:

[0060]

[0061] In the formula, t represents the number of the preset time period, t = 1, 2,..., T, T represents the total number of preset time periods, n represents the number of the signal cycle, n = 1, 2,..., N, N represents the total number of signal cycles, GZ t represents the traffic signal fault index of the t-th preset time period, YDt represents the traffic congestion index of the t-th preset time period, GZT t represents the duration of the fault in the t-th preset time period, XHD t represents the signal light coefficient of the t-th preset time period, LVT t.n represents the green light duration deviation of the n-th signal cycle within the t-th preset time period, HOT t.n represents the red light duration deviation of the n-th signal cycle within the t-th preset time period, HUT t.n represents the yellow light duration deviation of the n-th signal cycle within the t-th preset time period. ZT0 represents the total duration of the preset time period, YD0 represents the preset traffic congestion threshold, GP represents the fault frequency factor, β1 represents the green light weight, β2 represents the red light weight, β3 represents the yellow light weight, and γ represents the signal weight.

[0062] In this embodiment, the preset time period is set according to specific requirements. For example, the preset time period is set to 1 hour.

[0063] The green light weight, red light weight, and yellow light weight are obtained from a preset database, and the sum of the three is 1. For example, the green light duration deviation forms a mapping set with the preset green light weight, and the real-time green light duration deviation is input into the mapping set to obtain the corresponding green light weight; the red light duration deviation forms a mapping set with the preset red light weight, and the real-time red light duration deviation is input into the mapping set to obtain the corresponding red light weight; the yellow light duration deviation forms a mapping set with the preset yellow light weight, and the real-time yellow light duration deviation is input into the mapping set to obtain the corresponding yellow light weight; the mapping relationship therein can be one-to-one or many-to-one.

[0064] The signal weight is obtained from a preset database. For example, the duration of the fault forms a mapping set with the preset signal weight, and the real-time duration of the fault is input into the mapping set to obtain the corresponding one; the mapping relationship therein can be one-to-one or many-to-one, and the value range in this example is 0-1.

[0065] The fault frequency factor is obtained from a preset database. For example, according to the relationship between the historical fault frequency and traffic signal data (such as signal light deviation data, fault duration), a mapping set of the fault frequency and its corresponding influence factor is constructed, and the real-time fault frequency is input into the mapping set to obtain the corresponding fault frequency factor; the fault frequency represents the number of faults within the expected time period; the expected time period can be set to 1 month.

[0066] In this algorithm, the traffic signal fault index is obtained by processing multiple independent variables (signal light deviation data, fault duration). There is an interaction relationship between these independent variables; the greater the signal light deviation, it may cause the signal light device to work abnormally, which may lead to an increase in the fault duration; the interaction relationships among the green light duration deviation, red light duration deviation, and yellow light duration deviation are interrelated; any increase in the signal light deviation data may cause an increase in the duration deviation of other signal lights to balance traffic flow and road traffic safety; in this algorithm, the traffic signal fault index is positively correlated with the signal light deviation data and the fault duration.

[0067] Through the above steps, the probability of the existence of traffic signal faults is quantitatively evaluated, and thus strong support is provided for road traffic management in subsequent intelligent security management.

[0068] Furthermore, the specific process of judging whether to perform traffic signal management based on the traffic signal fault index is as follows: Judge whether the traffic signal fault index is less than the preset signal fault threshold obtained from the preset database: If the traffic signal fault index is not less than the preset signal fault threshold obtained from the preset database, traffic signal management is not performed; if the traffic signal fault index is less than the preset signal fault threshold obtained from the preset database, traffic signal management is performed; traffic signal management includes signal cycle adjustment and fault information push; signal cycle adjustment means adjusting the signal light to a preset fixed cycle; fault information push means pushing traffic signal fault information to preset personnel in real time.

[0069] In this embodiment, the preset signal fault threshold is represented by the average value of the qualified traffic signal fault indexes within the historical time period; the preset fixed cycle can be set to a green light duration of 30 seconds, a red light duration of 90 seconds, and a yellow light duration of 3 seconds.

[0070] Fault information push: Push fault information to drivers through traffic navigation software (such as Amap, etc.); push fault information to relevant personnel (such as traffic police, maintenance teams, etc.) through the traffic management bureau; the fault information includes the location of the faulty intersection (for example, intersection number or road name) and the specific type of the fault (such as signal light failure, abnormal cycle, etc.).

[0071] Through the above steps, the improvement of road traffic efficiency in intelligent security management is achieved.

[0072] Further, the specific method for obtaining the traffic management effectiveness index is as follows: Obtain the congestion management coefficient (i.e., YG) based on the relative relationship between the traffic congestion index before traffic signal management and the traffic congestion index after traffic signal management. The congestion management coefficient represents the quantitative data of the influence degree of the traffic congestion index before traffic signal management and the traffic congestion index after traffic signal management on the traffic management effectiveness index; Obtain the vehicle speed increase coefficient deviation (i.e., SG r ) based on the relative relationship between the average vehicle speed increase coefficient of motor vehicles and the preset vehicle speed increase coefficient obtained from the preset database. The vehicle speed increase coefficient deviation represents the deviation degree between the average vehicle speed increase coefficient of motor vehicles and the preset vehicle speed increase coefficient; Obtain the traffic management effectiveness index based on the relative relationship between the congestion management coefficient, the vehicle speed increase coefficient deviation, the accident reduction rate, the traffic signal adjustment duration, and the traffic management weights obtained from the preset database; The traffic management weights include the accident management weight, the vehicle speed management weight, and the signal light adjustment weight. The accident management weight represents the influence relationship of the accident reduction rate on the traffic management effectiveness index, the vehicle speed management weight represents the influence relationship of the average vehicle speed increase coefficient of motor vehicles on the traffic management effectiveness index, and the signal light adjustment weight represents the influence relationship of the traffic signal adjustment duration on the traffic management effectiveness index.

[0073] Among them, the specific method for obtaining the traffic management effectiveness index is:

[0074] GL r =YG * [ln(ω1 * SGJ r + ω2 * SG r + 1)+ ω3 * csch(TZS)], TZS ≥ TZS0;

[0075]

[0076] In the formula, t represents the number of the preset time period, t = 1, 2,..., T, T represents the total number of the preset time periods, r represents the number of the preset time period after traffic signal management, r = 1, 2,..., R, R represents the total number of the preset time periods after traffic signal management, GL r represents the traffic management effectiveness index of the r-th preset time period, YG represents the congestion management coefficient, SG r represents the vehicle speed increase coefficient deviation of the r-th preset time period, YD t represents the traffic congestion index of the t-th preset time period, YD t ′ represents the traffic congestion index of the t-th preset time period after traffic signal management, SGJ r represents the accident reduction rate of the r-th preset time period after traffic signal management, STG rIt represents the coefficient of increase in the average vehicle speed during the r-th preset time period after traffic signal management. TZS represents the traffic signal adjustment duration of traffic signal management. YD0 represents the preset traffic congestion threshold. STG0 represents the preset coefficient of increase in vehicle speed. TZS0 represents the preset traffic signal adjustment duration. ω1 represents the accident management weight. ω2 represents the vehicle speed management weight. ω3 represents the signal light adjustment weight.

[0077] In this embodiment, the preset coefficient of increase in vehicle speed is set according to the target vehicle speed. For example, from 7 pm to 8 pm, the preset coefficient of increase in vehicle speed can be set to 0.2. The preset traffic signal adjustment duration is set according to the traffic conditions. For example, if the traffic signal adjustment duration is too short, it may lead to poor signal adjustment effect. Therefore, the preset traffic signal adjustment duration is set to 5 seconds.

[0078] The accident management weight, vehicle speed management weight, and signal light adjustment weight are obtained from a preset database, and the sum of the three is 1. For example, the accident reduction rate and the preset accident management weight form a mapping set. The real-time accident reduction rate is input into the mapping set to obtain the corresponding accident management weight. The coefficient of increase in the average vehicle speed and the preset vehicle speed management weight form a mapping set. The real-time coefficient of increase in the average vehicle speed is input into the mapping set to obtain the corresponding vehicle speed management weight. The traffic signal adjustment duration and the preset signal light adjustment weight form a mapping set. The real-time traffic signal adjustment duration is input into the mapping set to obtain the corresponding one. The mapping relationship therein can be one-to-one or many-to-one.

[0079] In this algorithm, the traffic management effectiveness index involves processing multiple independent variables (accident reduction rate, coefficient of increase in the average vehicle speed, and traffic signal adjustment duration). There are mutual influence relationships among these independent variables. The greater the accident reduction rate, it may lead to an increase in vehicle mobility, and then lead to an increase in the coefficient of increase in the average vehicle speed. The greater the coefficient of increase in the average vehicle speed, it may lead to a decrease in the traffic congestion index, thus leading to an increase in the accident reduction rate. The greater the traffic signal adjustment duration, it may lead to instability of traffic flow, increase the probability of accidents, and thus reduce the accident reduction rate. The shorter the traffic signal adjustment duration, it helps to improve traffic mobility, thus increasing the vehicle speed and the coefficient of increase in the average vehicle speed.

[0080] In this algorithm, the traffic management effectiveness index is positively correlated with the accident reduction rate and the coefficient of increase in the average vehicle speed, and negatively correlated with the traffic signal adjustment duration.

[0081] Through the above steps, the effectiveness of traffic signal management is quantitatively evaluated, and further the improvement of the effectiveness of road traffic management in intelligent security management is achieved.

[0082] Further, the specific process for determining whether to implement the emergency security and protection strategy based on the traffic management effectiveness index is as follows: Determine whether the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database. If the traffic management effectiveness index is not less than the preset management effectiveness threshold obtained from the preset database, then the emergency security and protection strategy is not implemented. If the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database, then the emergency security and protection strategy is implemented.

[0083] Specifically, the specific process for implementing the emergency security and protection strategy is as follows: Implementing the emergency security and protection strategy means automatically dispatching patrol personnel based on historical traffic data. The dispatch of patrol personnel includes the number, route, and frequency of dispatching patrol personnel. Determine whether the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is less than the preset management effectiveness threshold obtained from the preset database. If the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is not less than the preset management effectiveness threshold obtained from the preset database, then no feedback is given. If the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is less than the preset management effectiveness threshold obtained from the preset database, then feedback is given.

[0084] In this embodiment, the preset management effectiveness threshold is represented by the average value of the qualified traffic management effectiveness indexes within the historical time period.

[0085] Dispatch of patrol personnel: Calculate the cosine similarity between the real-time traffic data and the historical traffic data to obtain the similarity. Statistically calculate the maximum similarity among all historical traffic data and real-time traffic data. Dispatch patrol personnel according to the emergency security and protection strategy corresponding to the historical traffic data with the maximum similarity. For example, during the morning rush hour (7:30 - 9:00), the traffic flow at a certain intersection is 500 vehicles per hour, and the section with the highest accident occurrence frequency is located north of this intersection. Calculate the similarity by calculating the cosine similarity between the above traffic data and the historical traffic data. Statistically calculate the maximum similarity among all historical traffic data and real-time traffic data. The emergency security and protection strategy corresponding to the historical traffic data with the maximum similarity is to dispatch 4 patrol personnel to patrol north of this intersection every 15 minutes. Then set the emergency security and protection strategy to dispatch 4 patrol personnel to patrol every 15 minutes, and the priority patrol area for dispatching patrol personnel is north of this intersection, and observe whether there are situations such as illegal parking and abnormal vehicle stops.

[0086] Feedback: When the traffic management effectiveness index obtained after implementing the emergency security and protection strategy is less than the preset management effectiveness threshold obtained from the preset database, an upward feedback mechanism is automatically triggered, that is, the traffic data is sent to the receiving end of the superior department through the preset communication interface to request assistance.

[0087] Through the implementation of the emergency security strategy, it can be ensured that resources can be quickly in place in case of emergency to meet the needs of emergency response. Through the clear division of responsibilities and effective communication and coordination mechanism in the emergency security strategy, it can be ensured that all departments and personnel can quickly respond and cooperate in combat to jointly respond to emergencies, thus achieving the intelligent security management of road traffic in case of emergency; through the above steps, the optimization of resource allocation in intelligent security management is realized, and then the improvement of road traffic management efficiency is achieved.

[0088] In summary, the embodiment of the present application determines whether to conduct a traffic signal fault assessment based on the traffic congestion index obtained from traffic data, then determines whether to conduct traffic signal management based on the traffic signal fault index obtained from traffic signal data, and finally determines whether to implement an emergency security strategy based on the traffic management effectiveness index obtained from management data, thereby realizing the improvement of traffic signal management efficiency in intelligent security management, improving the initiative and predictability of security management, and then realizing the overall coordination and efficient operation of the security management system, effectively solving the problem that the state change of traffic signals is not fully considered in the security management of road traffic in the prior art.

[0089] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions in the process Figure 1one or more processes and / or blocks Figure 1 functions specified in one or more blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one Figure 1 one or more processes and / or blocks Figure 1 or more processes and / or one or more blocks.

[0093] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0094] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent security management method based on artificial intelligence, characterized in that: The following steps are involved: S1, performing traffic congestion assessment according to the acquired traffic data to obtain a traffic congestion index, and determining whether to perform a traffic signal failure assessment based on the traffic congestion index, wherein the traffic congestion index is used to quantitatively assess the degree of road traffic congestion; S2, if a traffic signal fault assessment is to be performed, a traffic signal fault assessment is performed based on the acquired traffic signal data to obtain a traffic signal fault index, and whether to perform traffic signal management is determined based on the traffic signal fault index, wherein the traffic signal fault index is used to quantitatively assess the probability of a traffic signal fault existing; S3, if traffic signal management is performed, a traffic management evaluation is performed based on the traffic congestion index and management data after the traffic signal management to obtain a traffic management effectiveness index, and whether to implement an emergency security strategy is determined based on the traffic management effectiveness index, and the traffic management effectiveness index is used to quantitatively evaluate the effectiveness of traffic signal management; S4, judging whether to provide feedback based on the traffic management effectiveness index obtained after the implementation of the emergency security strategy.

2. The intelligent security management method based on artificial intelligence as claimed in claim 1, characterized in that: The traffic data includes non-motorized vehicle lane data and motor vehicle lane data; The non-motorized vehicle lane data include average non-motorized vehicle speed and non-motorized vehicle lane congestion length; The motor vehicle lane data includes average motor vehicle speed and motor vehicle lane congestion length; The traffic signal data includes signal light deviation data, fault duration and fault frequency; The management data include accident reduction rate, average motor vehicle speed improvement factor and traffic signal adjustment duration; The accident reduction rate represents the quantitative data of the influence of the number of accidents after traffic management and the number of accidents before traffic management on the accident reduction rate; The motor vehicle average speed improvement coefficient represents quantitative data on the degree of influence of the average vehicle speed after traffic management and the average vehicle speed before traffic management on the motor vehicle average speed improvement coefficient.

3. The intelligent security management method based on artificial intelligence as claimed in claim 2, characterized in that: The signal light deviation data includes green light duration deviation, red light duration deviation and yellow light duration deviation; The green light duration deviation represents quantitative data of the degree of influence of the green light duration and the preset green light duration of the corresponding time period on the green light duration deviation; The red light duration deviation represents quantitative data of the influence of the red light duration and the preset red light duration of the corresponding time period on the red light duration deviation; The yellow light duration deviation represents quantitative data of the degree of influence of the yellow light duration and the preset yellow light duration in the corresponding time period on the yellow light duration deviation.

4. The intelligent security management method based on artificial intelligence as claimed in claim 3, characterized in that: The specific method of evaluating traffic congestion based on the acquired traffic data to obtain the traffic congestion index is as follows: Obtaining a non-motor vehicle traffic coefficient according to the non-motor vehicle lane data, non-motor vehicle lane reference data obtained from a preset database, and a relative relationship between traffic weights, wherein the non-motor vehicle lane traffic coefficient is used to evaluate the congestion degree of the non-motor vehicle lane, and the non-motor vehicle lane reference data includes a preset maximum speed of the non-motor vehicle lane and a total length of the non-motor vehicle lane; Obtaining a motor vehicle traffic coefficient according to the motor vehicle lane data, the motor vehicle lane reference data obtained from a preset database, and the relative relationship between traffic weights, wherein the motor vehicle traffic coefficient is used to evaluate the congestion degree of the motor vehicle lane, and the motor vehicle lane reference data includes a preset maximum speed of the motor vehicle lane and a total length of the motor vehicle lane; The traffic weight includes speed weight and congestion length weight; The non-motor vehicle traffic coefficient, the motor vehicle traffic coefficient and the congestion weight obtained from the preset database are processed to obtain a traffic congestion index; The congestion weight includes a non-motor vehicle weight and a motor vehicle weight.

5. The intelligent security management method based on artificial intelligence as claimed in claim 1, characterized in that: The specific process of determining whether to perform traffic signal failure assessment based on the traffic congestion index is as follows: Determine whether the traffic congestion index is less than a preset traffic congestion threshold obtained from a preset database: If the traffic congestion index is not less than a preset traffic congestion threshold value obtained from a preset database, no traffic signal failure evaluation is performed and monitoring is continued to obtain the traffic congestion index; If the traffic congestion index is less than a preset traffic congestion threshold obtained from a preset database, a traffic signal failure assessment is performed.

6. The intelligent security management method based on artificial intelligence as claimed in claim 2, characterized in that: The specific method of performing traffic signal failure evaluation based on the acquired traffic signal data to obtain a traffic signal failure index is as follows: Obtaining a fault duration coefficient according to a relative relationship between the fault duration and the total duration of the preset time period obtained from the preset database, wherein the fault duration coefficient represents a relative deviation between the fault duration and the total duration of the preset time period; Obtaining a signal light coefficient according to a relative relationship between the signal light deviation data and the signal light weight obtained from a preset database, wherein the signal light coefficient represents quantitative data of the degree of influence of the signal light deviation data on the traffic signal fault index; The signal light weights include green light weight, red light weight and yellow light weight; The fault duration coefficient, the signal light coefficient, the fault frequency factor obtained from the preset database, and the signal weight are processed to obtain the traffic signal fault index.

7. The intelligent security management method based on artificial intelligence as claimed in claim 1, characterized in that: The specific process of determining whether to perform traffic signal management based on the traffic signal failure index is as follows: Determine whether the traffic signal failure index is less than a preset signal failure threshold obtained from a preset database: If the traffic signal failure index is not less than the preset signal failure threshold obtained from the preset database, the traffic signal management is not performed; If the traffic signal failure index is less than a preset signal failure threshold obtained from a preset database, traffic signal management is performed; The traffic signal management includes signal cycle adjustment and fault information push; The fault information push means pushing the traffic signal fault information to a preset person in real time.

8. The intelligent security management method based on artificial intelligence as claimed in claim 2, characterized in that: The specific method for obtaining the traffic management effectiveness index is as follows: A congestion management coefficient is obtained according to the relative relationship between the traffic congestion index before the traffic signal management and the traffic congestion index after the traffic signal management, wherein the congestion management coefficient represents quantitative data of the degree of influence of the traffic congestion index before the traffic signal management and the traffic congestion index after the traffic signal management on the traffic management effectiveness index; Obtaining a speed increase coefficient deviation according to a relative relationship between an average speed increase coefficient of the motor vehicle and a preset speed increase coefficient obtained from a preset database, wherein the speed increase coefficient deviation indicates a degree of deviation between the average speed increase coefficient of the motor vehicle and the preset speed increase coefficient; The traffic management effectiveness index is obtained according to the relative relationship between the congestion management coefficient, the speed increase coefficient deviation, the accident reduction rate, the traffic signal adjustment time and the traffic management weight obtained from the preset database; The traffic management weights include accident management weights, vehicle speed management weights and traffic light adjustment weights.

9. The intelligent security management method based on artificial intelligence as claimed in claim 1, characterized in that: The specific process of judging whether to implement the emergency security strategy based on the traffic management effectiveness index is as follows: Determine whether the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database: If the traffic management effectiveness index is not less than the preset management effectiveness threshold obtained from the preset database, the emergency security strategy will not be implemented; If the traffic management effectiveness index is less than the preset management effectiveness threshold obtained from the preset database, an emergency security strategy is implemented.

10. The intelligent security management method based on artificial intelligence as claimed in claim 9, characterized in that: The specific process of implementing the emergency security strategy is as follows: The implementation of the emergency security strategy means automatically dispatching patrol personnel based on historical traffic data, and the patrol personnel dispatching includes dispatching the number, route and frequency of patrol personnel; Determine whether the traffic management effectiveness index obtained after implementing the emergency security strategy is less than the preset management effectiveness threshold obtained from the preset database: If the traffic management effectiveness index obtained after the implementation of the emergency security strategy is not less than the preset management effectiveness threshold obtained from the preset database, no feedback will be given; If the traffic management effectiveness index obtained after the implementation of the emergency security strategy is less than the preset management effectiveness threshold obtained from the preset database, feedback is given.

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