Traffic potential safety hazard identification method

Abstract

The invention discloses a traffic potential safety hazard identification method, and the method comprises the steps: determining the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; for the town to which each source place belongs or the town to which each destination belongs, determining a first number of traffic accidents occurring in the town in a first sub-time period; if it is determined that a vehicle dimension potential safety hazard identification event is triggered according to the first number, it is determined that a traffic potential safety hazard problem exists in the town; according to accident occurrence place data in the accident data of each traffic accident, clustering each traffic accident according to a preset distance, and for each class cluster, if the accident data in the class cluster is matched with specified data in a road dimension potential safety hazard identification event of an area corresponding to the preset class cluster, determining that the road dimension potential safety hazard identification event in the area corresponding to the class cluster is the road dimension potential safety hazard identification event. And determining that the traffic potential safety hazard exists in the region corresponding to the cluster. And the efficiency and the accuracy of traffic potential safety hazard identification are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent traffic data analysis applications, and in particular to a method for identifying traffic safety hazards. Background Art

[0002] Due to the generally low road infrastructure and technical standards in the region, coupled with insufficient traffic management resources and public safety awareness, road traffic safety issues have become a significant risk point. In this complex traffic environment, identifying and troubleshooting various potential traffic safety hazards is crucial to effectively grasp and solve the key links of traffic safety hazards.

[0003] As the assessment of "reducing the number of accidents and controlling the size of accidents" continues to deepen, it has become a core demand to guide accident prevention based on scientific and accurate hidden danger excavation. At present, the discovery of hidden dangers is still at the manual level, mainly relying on the human sea tactics to check hidden dangers, but the disadvantage is that grassroots managers can only identify surface hidden dangers, and it is difficult to identify key and deep hidden dangers. Therefore, the existing technology has the problem of poor accuracy in identifying traffic safety hidden dangers. Summary of the invention

[0004] The present application provides a method for identifying traffic safety hazards, which is used to solve the problem of poor accuracy in identifying traffic safety hazards in the prior art.

[0005] In a first aspect, the present application provides a method for identifying traffic safety hazards, the method comprising:

[0006] Obtaining accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data and accident location data;

[0007] Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem;

[0008] According to the accident location data in the accident data of each traffic accident, each traffic accident is clustered according to a preset distance to obtain each cluster; wherein each cluster includes at least one of an accident location cluster, an accident section cluster and an accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

[0009] The above technical solution has the following advantages or beneficial effects:

[0010] In this application, firstly, the accident vehicle data and accident site data of each traffic accident in the target area in the historical time period are obtained. If the source and destination of each accident vehicle are determined based on the accident vehicle data in the accident data of each traffic accident; for the town or street to which each source belongs or the town or street to which each destination belongs, the first number of traffic accidents occurring in the town or street in the first sub-time period is determined; if the vehicle dimension safety hazard identification event is determined to be triggered based on the first number, it is determined that the town or street has a traffic safety hazard problem; in addition, based on the accident site data in the accident data of each traffic accident, the traffic accidents are clustered according to a preset distance to obtain each cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem. This application combines the accident vehicle data and the accident site data to identify traffic safety hazards through vehicle dimension safety hazard identification events and road dimension safety hazard identification events. Compared with solutions that rely on human wave tactics to detect hidden dangers, it can automatically and intelligently identify safety hazards in vehicle and road dimensions, reducing labor costs and improving the efficiency and accuracy of identifying traffic safety hazards.

[0011] In an optional implementation, after determining the source and destination of each accident vehicle, the method further includes:

[0012] For the towns and streets to which each source point belongs or the towns and streets to which each destination point belongs, determine the second number of traffic accidents that occurred in the towns and streets within the second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered based on the second number, and there are preset categories of enterprises within the preset area centered on the source point or destination included in the town and street, it is determined that the town and street has a traffic safety hazard problem.

[0013] The above technical solution has the following advantages or beneficial effects:

[0014] In this application, after determining the source and destination of each accident vehicle based on the accident vehicle data in the accident data of each traffic accident, the second number of traffic accidents occurring in the town or street in the second sub-time period is determined for the town or street to which each source belongs or the town or street to which each destination belongs. Then, based on the second number, it is determined whether the enterprise-dimensional safety hazard identification rule is triggered. If so, it is further determined whether there are enterprises of preset categories within the preset area centered on the source or destination included in the town or street. If so, it is determined that the town or street has traffic safety hazard problems. This application combines accident vehicle data to identify traffic safety hazards through enterprise-dimensional safety hazard identification events. The comprehensiveness, efficiency and accuracy of traffic safety hazard identification are further improved.

[0015] In an optional implementation, the accident data further includes data of persons associated with the accident, and the method further includes:

[0016] According to the accident-related personnel data in the accident data of each traffic accident, the town or street to which each accident-related person belongs is determined; for each town or street, if the third number of accident-related personnel corresponding to the town or street is used to determine the triggering personnel dimension safety hazard identification rules, it is determined that the town or street has a traffic safety hazard problem.

[0017] The above technical solution has the following advantages or beneficial effects:

[0018] This application combines the accident-related personnel data in the accident data and identifies traffic safety hazards through personnel-dimensional safety hazard identification events, further improving the comprehensiveness, efficiency and accuracy of traffic safety hazard identification.

[0019] In an optional implementation, if the triggering personnel dimension safety hazard identification rule is determined based on the third number of accident-related persons corresponding to the town or street, determining that the town or street has a traffic safety hazard problem includes:

[0020] Determine a third number of accident-related persons whose ages are younger than a preset first age threshold corresponding to the town or street, and if a triggering person-dimensional safety hazard identification rule is determined based on the third number, it is determined that the town or street has a traffic safety hazard problem; or

[0021] Determine a third number of accident-related persons whose ages are greater than a preset second age threshold corresponding to the town or street, and if a person-dimensional safety hazard identification rule is triggered based on the third number, determine that the town or street has a traffic safety hazard problem;

[0022] Wherein, the preset first age threshold is smaller than the preset second age threshold.

[0023] The above technical solution has the following advantages or beneficial effects:

[0024] In this application, when the personnel dimension safety hazard identification rule is determined to be triggered according to the third number of accident-related personnel whose ages are younger than the preset first age threshold corresponding to the town or street, it is determined that there is a traffic safety hazard problem in the town or street; or when the personnel dimension safety hazard identification rule is determined to be triggered according to the third number of accident-related personnel whose ages are older than the preset second age threshold corresponding to the town or street, it is determined that there is a traffic safety hazard problem in the town or street. The preset first age threshold is, for example, 16 years old, 18 years old, etc.; the preset second age threshold is, for example, 50 years old, 58 years old, etc. This further improves the accuracy of identifying traffic safety hazard problems related to students or the elderly.

[0025] In an optional implementation, the accident data further includes accident behavior type data, and the method further includes:

[0026] According to the accident behavior type data in the accident data of each traffic accident, determine the accidents involving drunk driving; determine the source of the drunk drivers, the source of the vehicles, the source of the drinking, the place where the accident occurred, and the route of the accident; determine whether the towns and streets to which the above-mentioned places belong have traffic safety hazards.

[0027] The above technical solution has the following advantages or beneficial effects:

[0028] In this application, accidents involving drunk driving are determined based on the accident behavior type data in the accident data of each traffic accident. For accidents involving drunk driving, the source of the drunk driver, the source of the vehicle, the source of drinking, the place where the accident occurred, and the route of the accident are determined respectively. The source of the drunk driver includes the registered residence or permanent residence of the drunk driver; the source of the vehicle refers to the source of the accident vehicle determined based on the accident vehicle data; the source of drinking refers to the address of the place where the drunk driver drank; the place where the accident occurred refers to the address of the place where the traffic accident occurred; the route of the accident refers to the vehicle driving trajectory from the source of drinking to the place where the accident occurred. It is determined that the towns and streets to which the above-mentioned locations belong have traffic safety hazards. Thereby, the identification of traffic safety hazards in the management of drunk driving is realized, and the comprehensiveness and accuracy of the identification of traffic safety hazards is further improved.

[0029] In an optional implementation, determining the source and destination of each accident vehicle includes:

[0030] For each accident vehicle, determine the travel chains in which the accident vehicle passes through the accident site; determine the longest common subsequence of the capture points of any two travel chains based on the identification information of the capture points contained in the travel chains; determine the similarity of the any two travel chains based on the longest common subsequence; based on the similarity between the travel chains, group the travel chains with similarities greater than a preset similarity threshold into one category; determine the category containing the largest number of travel chains as a high-frequency travel chain set; select a high-frequency travel chain from the high-frequency travel chain set, use the starting capture point of the high-frequency travel chain as the source of the accident vehicle, and use the ending capture point of the high-frequency travel chain as the destination of the accident vehicle.

[0031] The above technical solution has the following advantages or beneficial effects:

[0032] In the present application, for each accident vehicle, when determining the source and destination of the accident vehicle, first determine the various travel chains of the accident vehicle passing the accident site; wherein, if the interval between consecutive vehicle passing records is set to a time length, it is considered that a stop occurs, and the two travel chains are separated at this point, so that the various travel chains of the accident vehicle passing the accident site can be obtained. The set time length is, for example, 30 minutes, 40 minutes, etc. Then determine the longest common subsequence of the capture points of any two travel chains; based on the longest common subsequence, determine the similarity of the any two travel chains; based on the similarity between each travel chain, cluster the travel chains with similarity greater than a preset similarity threshold into one category; determine a high-frequency travel chain set; and select a high-frequency travel chain from the high-frequency travel chain set. Finally, determine the source and destination of the accident vehicle based on the high-frequency travel chain, thereby improving the accuracy of determining the source and destination of the accident vehicle.

[0033] In an optional implementation, selecting a high-frequency travel chain from the high-frequency travel chain set includes:

[0034] For each travel chain in the high-frequency travel chain set, determine the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than the preset similarity threshold; and use the number as the associated number corresponding to the travel chain;

[0035] The travel chain with the largest number of corresponding associations is selected as the high-frequency travel chain.

[0036] The above technical solution has the following advantages or beneficial effects:

[0037] In the present application, for each travel chain in the high-frequency travel chain set, the high-frequency travel chain is determined according to the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than a preset similarity threshold, thereby improving the accuracy of determining the high-frequency travel chain.

[0038] In an optional implementation, selecting the travel chain with the largest number of corresponding associations as the high-frequency travel chain includes:

[0039] If there are multiple travel chains with the largest number of corresponding associations, the travel chain with the largest number of corresponding associations is used as a candidate travel chain;

[0040] For each candidate travel chain, determine the travel chain in the high-frequency travel chain set whose similarity with the candidate travel chain is greater than the preset similarity threshold as an associated travel chain; determine the sum of the similarities between the candidate travel chain and each associated travel chain as the associated similarity corresponding to the candidate travel chain;

[0041] The candidate travel chain with the largest corresponding correlation similarity is selected as the high-frequency travel chain.

[0042] The above technical solution has the following advantages or beneficial effects:

[0043] In the present application, multiple candidate travel chains with the largest number of corresponding associations are determined, and then the association similarities corresponding to the candidate travel chains are determined; finally, the candidate travel chain with the largest corresponding association similarity is selected as the high-frequency travel chain, which further improves the accuracy of determining the high-frequency travel chain.

[0044] In an optional implementation, based on the accident location data in the accident data of each traffic accident, the traffic accidents are clustered according to a preset distance, and each cluster obtained includes:

[0045] Clustering the traffic accidents whose accident locations are less than a preset first distance according to the accident location data of the traffic accidents to obtain the accident location cluster;

[0046] According to the accident location data in the accident data of each traffic accident, clustering each traffic accident whose accident location is greater than the preset first distance and less than the preset second distance to obtain a cluster of the road section where the accident occurred;

[0047] According to the accident location data in the accident data of each traffic accident, each traffic accident whose accident location is greater than the preset second distance and less than the preset third distance is clustered to obtain an accident road cluster.

[0048] The above technical solution has the following advantages or beneficial effects:

[0049] In the present application, according to different preset distances, the accident location cluster, accident section cluster and accident road cluster are determined respectively; thereby improving the accuracy of clustering the various traffic accidents according to the preset distance and obtaining each cluster.

[0050] In an optional implementation manner, the data specified in the road dimension safety hazard identification event of the corresponding area includes at least one of the following:

[0051] In the corresponding area, there are accidents with injuries or fatalities, accidents involving vehicles and non-vehicles or accidents involving vehicles and pedestrians, scratches, no physical separation of roads or central separation, and the traffic safety hazard problem is determined to be that vehicles, non-vehicles and pedestrians can be separated but mixed;

[0052] If the number of traffic accidents occurring in the corresponding area under cloudy weather conditions is greater than a preset first number threshold, it is determined that the potential traffic safety hazard is an area prone to ice, water accumulation or fog under severe weather conditions;

[0053] In the corresponding area, there are accidents involving vehicles and people, accidents involving vehicles and people, collisions, victims crossing the road, no physical separation of roads, or separation between vehicles and people, and the traffic safety hazard is determined to be the lack of central separation facilities;

[0054] There is no traffic light control in the corresponding area, and the traffic safety hazard is determined to be that the intersection or road section that has met the conditions for setting up traffic lights has no traffic lights installed;

[0055] In the corresponding area, there are speed measurement points in the first area where rear-end collisions, collisions with moving vehicles, and accident-prone locations are set, and it is determined that the traffic safety hazard is due to improper selection of speed measurement point locations;

[0056] In the corresponding area, there are speeding, running red lights, driving in the wrong lane, and entering restricted areas. It is set that there is no such illegal capture equipment on the road section within the second area. The traffic safety hazard problem is determined to be large traffic flow, many motor vehicle traffic violations, and electronic equipment can be controlled but the settings are missing or insufficient;

[0057] There is no lighting at night in the corresponding area, and the number of traffic accidents that meet the above conditions is greater than a preset second number threshold, and the traffic safety hazard problem is determined to be no lighting or insufficient brightness;

[0058] When a heavy truck is driven in the corresponding area, and the truck is driving in the right turn, left turn, or U-turn state, and the number of traffic accidents that meet the above conditions is greater than the preset third number threshold, it is determined that the traffic safety hazard problem is that there are no safety auxiliary facilities installed in the truck's turning blind spot.

[0059] The above technical solution has the following advantages or beneficial effects:

[0060] In the present application, corresponding traffic safety hazard problems are determined for safety hazard identification events in different road dimensions, thereby further improving the accuracy and comprehensiveness of traffic safety hazard identification.

[0061] In a second aspect, the present application provides a traffic safety hazard identification device, the device comprising:

[0062] An acquisition module, used to acquire accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data and accident location data;

[0063] The first determination module is used to determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; for the town or street to which each source belongs or the town or street to which each destination belongs, determine the first number of traffic accidents that occurred in the town or street within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, it is determined that the town or street has a traffic safety hazard problem;

[0064] The second determination module is used to cluster the traffic accidents according to the accident location data in the accident data of the traffic accidents according to a preset distance to obtain clusters; wherein the clusters include at least one of the accident location cluster, the accident section cluster and the accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

[0065] In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0066] Memory, used to store computer programs;

[0067] The processor is used to implement the method when executing the program stored in the memory.

[0068] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the described method is implemented.

[0069] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises an executable program, and the executable program is executed by a processor to implement the described method. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 A schematic diagram of the first traffic safety hazard identification process provided for this application;

[0072] Figure 2 Schematic diagram of the traffic safety hazard warning prediction model architecture provided for this application;

[0073] Figure 3 A schematic diagram of the vehicle high-frequency trajectory calculation process provided for this application;

[0074] Figure 4 Schematic diagram of the closed-loop management interface for traffic safety hazard identification provided for this application;

[0075] Figure 5 Traffic safety hazard identification and hazard analysis interface diagram provided for this application;

[0076] Figure 6 The interface diagram for identifying and troubleshooting traffic safety hazards provided for this application;

[0077] Figure 7 The interface diagram for identifying and rectifying traffic safety hazards provided for this application;

[0078] Figure 8 The interface diagram for traffic safety hazard identification and hazard effect tracking provided for this application;

[0079] Fig. 9 A schematic diagram of the structure of a traffic safety hazard identification device is provided for this application;

[0080] Fig.10 A schematic diagram of the structure of an electronic device is provided for this application. DETAILED DESCRIPTION

[0081] In order to make the purpose and implementation method of the present application clearer, the exemplary implementation method of the present application will be clearly and completely described below in conjunction with the drawings in the exemplary embodiments of the present application. Obviously, the described exemplary embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0082] It should be noted that the brief description of terms in this application is only for the convenience of understanding the embodiments described below, and is not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their common and usual meanings.

[0083] The terms "first", "second", "third", etc. in the specification and claims of this application and the above drawings are used to distinguish similar or similar objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances.

[0084] The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device comprising a list of components is not necessarily limited to all the components expressly listed but may include other components not expressly listed or inherent to such product or device.

[0085] The term "module" refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0087] For the convenience of explanation, the above description has been made in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or limit the embodiments to the specific forms disclosed above. Based on the above teachings, various modifications and variations can be obtained. The selection and description of the above embodiments are to better explain the principles and practical applications, so that those skilled in the art can better use the embodiments and various different variations of the embodiments suitable for specific use considerations.

[0088] This application involves spatiotemporal analysis and big data processing technology, and belongs to the application field of intelligent traffic data analysis. Breakthroughs in technologies such as vehicle trajectory tracing, vehicle travel chain construction, and accident association rule mining, accurately mine source data, discover the relationships and rules existing in traffic accident data, and accurately identify all-round hidden dangers in five aspects: people, vehicles, roads, enterprises, and management mechanisms. Combined with big data processing technology, the accident data is automatically completed through tracing and analyzing the accident vehicle's trajectory in the past three months. This technology is based on a real-time stream computing framework for big data (strom, MQ, redis, ES), which can achieve the screening of 20 million vehicle records within 1 second, and complete the accident vehicle tracing and accident data completion within 3 seconds.

[0089] The main function of this application is to explore hidden dangers from five aspects, namely people, vehicles, roads, enterprises, and management, based on six-in-one accident and vehicle passing data according to certain rules, and automatically recommend possible hidden dangers to assist police officers in deeply exploring and investigating hidden dangers and conducting more targeted hidden danger management. The basic workflow is as follows: 1. Complete the six-in-one accident information based on vehicle passing data, and use the travel chain algorithm to supplement the source and destination of passenger and freight vehicles related to the accident, as well as the street where the residents of the accident-related personnel belong; 2. Based on the completed accident data, aggregate and process the accident data and extract features from five aspects, namely people, vehicles, roads, enterprises, and management, through time, space and other dimensions, to identify and generate hidden dangers.

[0090] Figure 1 The first traffic safety hazard identification process diagram provided for this application includes the following steps:

[0091] S101: Acquire accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data and accident location data;

[0092] S102: Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem;

[0093] S103: Based on the accident location data in the accident data of each traffic accident, cluster the each traffic accident according to a preset distance to obtain each cluster; wherein, each cluster includes at least one of an accident location cluster, an accident section cluster and an accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

[0094] The traffic safety hazard identification method provided in the present application is applied to electronic devices, which may be PCs, computers, servers and other devices.

[0095] The target area is, for example, an area in a certain urban area; the historical time period is, for example, the historical three months, the historical six months, etc. The accident vehicle data and accident location data of each traffic accident in the historical time period in the target area are obtained. The accident vehicle data is, for example, the license plate number of the accident vehicle. The accident location data is, for example, the geographical location data of the place where the accident occurred.

[0096] According to the accident vehicle data in the accident data of each traffic accident, the source and destination of each accident vehicle are determined. Then determine the town or street to which each source belongs or the town or street to which each destination belongs, and then for each town or street, determine the first number of traffic accidents that occurred in the town or street within the first sub-time period. The first sub-time period is, for example, two months, three months, etc. If the first number of traffic accidents that occurred in the town or street within the first sub-time period is greater than the set number threshold, it is determined that a vehicle-dimensional safety hazard identification event is triggered; or the first number of traffic accidents that occurred in the town or street within the first sub-time period ranks high among the first numbers of traffic accidents that occurred in all towns and streets, such as the top five, then it is determined that a vehicle-dimensional safety hazard identification event is triggered. If the vehicle-dimensional safety hazard identification event is determined to be triggered based on the first number, it is determined that there is a traffic safety hazard problem in the town or street.

[0097] According to the accident location data in the accident data of each traffic accident, each traffic accident is clustered according to the preset distance to obtain each cluster. For example, if the accident location data of accident A and accident B is less than the preset distance, then accident A and accident B are clustered into one category; in addition, if the accident location data of accident B and accident C is less than the preset distance, then accident B and accident C are clustered into one category, and at this time, accident A, accident B and accident C belong to the same cluster. Each cluster is obtained by analogy. Among them, each cluster includes at least one of the accident location cluster, the accident section cluster and the accident road cluster. For each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster in advance, it is determined that the area corresponding to the cluster has a traffic safety hazard problem. It should be noted that if the cluster is a cluster of the road where the accident occurred, the area corresponding to the cluster is the area where the entire road is located. If the cluster is a cluster of the road section where the accident occurred, the area corresponding to the cluster is the area where the road section is located. If the cluster is the accident location cluster, then the area corresponding to the cluster is the area where the location is located.

[0098] In an optional implementation manner, the data specified in the road dimension safety hazard identification event of the corresponding area includes at least one of the following:

[0099] In the corresponding area, there are accidents with injuries or fatalities, accidents involving vehicles and non-vehicles or accidents involving vehicles and pedestrians, scratches, no physical separation of roads or central separation, and the traffic safety hazard problem is determined to be that vehicles, non-vehicles and pedestrians can be separated but mixed;

[0100] If the number of traffic accidents occurring in the corresponding area under cloudy weather conditions is greater than a preset first number threshold, it is determined that the potential traffic safety hazard is an area prone to ice, water accumulation or fog under severe weather conditions;

[0101] In the corresponding area, there are accidents involving vehicles and people, accidents involving vehicles and people, collisions, victims crossing the road, no physical separation of roads, or separation between vehicles and people, and the traffic safety hazard is determined to be the lack of central separation facilities;

[0102] There is no traffic light control in the corresponding area, and the traffic safety hazard is determined to be that the intersection or road section that has met the conditions for setting up traffic lights has no traffic lights installed;

[0103] In the corresponding area, there are speed measurement points in the first area where rear-end collisions, collisions with moving vehicles, and accident-prone locations are set, and it is determined that the traffic safety hazard is due to improper selection of speed measurement point locations;

[0104] In the corresponding area, there are speeding, running red lights, driving in the wrong lane, and entering restricted areas. It is set that there is no such illegal capture equipment on the road section within the second area. The traffic safety hazard problem is determined to be large traffic flow, many motor vehicle traffic violations, and electronic equipment can be controlled but the settings are missing or insufficient;

[0105] There is no lighting at night in the corresponding area, and the number of traffic accidents that meet the above conditions is greater than a preset second number threshold, and the traffic safety hazard problem is determined to be no lighting or insufficient brightness;

[0106] When a heavy truck is driven in the corresponding area, and the truck is driving in the right turn, left turn, or U-turn state, and the number of traffic accidents that meet the above conditions is greater than the preset third number threshold, it is determined that the traffic safety hazard problem is that there are no safety auxiliary facilities installed in the truck's turning blind spot.

[0107] In the present application, corresponding traffic safety hazard problems are determined for safety hazard identification events in different road dimensions, thereby improving the accuracy and comprehensiveness of traffic safety hazard identification.

[0108] In this application, firstly, the accident vehicle data and accident site data of each traffic accident in the target area in the historical time period are obtained. If the source and destination of each accident vehicle are determined based on the accident vehicle data in the accident data of each traffic accident; for the town or street to which each source belongs or the town or street to which each destination belongs, the first number of traffic accidents occurring in the town or street in the first sub-time period is determined; if the vehicle dimension safety hazard identification event is determined to be triggered based on the first number, it is determined that the town or street has a traffic safety hazard problem; in addition, based on the accident site data in the accident data of each traffic accident, the traffic accidents are clustered according to a preset distance to obtain each cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem. This application combines the accident vehicle data and the accident site data to identify traffic safety hazards through vehicle dimension safety hazard identification events and road dimension safety hazard identification events. Compared with solutions that rely on human wave tactics to detect hidden dangers, it can automatically and intelligently identify safety hazards in vehicle and road dimensions, reducing labor costs and improving the efficiency and accuracy of identifying traffic safety hazards.

[0109] The second traffic safety hazard identification process schematic diagram provided in this application includes the following steps:

[0110] S201: Acquire accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data and accident location data;

[0111] S202: Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem;

[0112] S203: for the town or street to which each source belongs or the town or street to which each destination belongs, determine a second number of traffic accidents occurring in the town or street in a second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered according to the second number, and there are enterprises of a preset category within a preset area centered on the source or destination included in the town or street, it is determined that the town or street has a traffic safety hazard problem;

[0113] S204: Based on the accident location data in the accident data of each traffic accident, cluster the each traffic accident according to a preset distance to obtain each cluster; wherein, each cluster includes at least one of an accident location cluster, an accident section cluster and an accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

[0114] In this application, after determining the source and destination of each accident vehicle based on the accident vehicle data in the accident data of each traffic accident, the second number of traffic accidents occurring in the town or street in the second sub-time period is determined for the town or street to which each source belongs or the town or street to which each destination belongs. Then, based on the second number, it is determined whether the enterprise-dimensional safety hazard identification rule is triggered. If so, it is further determined whether there are enterprises of preset categories within the preset area centered on the source or destination included in the town or street. If so, it is determined that the town or street has traffic safety hazard problems. This application combines accident vehicle data to identify traffic safety hazards through enterprise-dimensional safety hazard identification events. The comprehensiveness, efficiency and accuracy of traffic safety hazard identification are further improved.

[0115] The second sub-time period may be the same as or different from the first sub-time period. The triggering of the enterprise-dimensional safety hazard identification rule based on the second quantity is, for example, that the second number of traffic accidents in the town and street in the second sub-time period ranks among the top eight of all towns and streets, or the number of accidents or the human-vehicle index this year ranks among the top ten of all towns and streets, or the number of accidents this month or the human-vehicle index is greater than 2. The human-vehicle index refers to the number of people involved in the accident or the number of accident vehicles. For example, there are preset categories of enterprises within the preset area centered on the source or destination included in the town and street, for example, within the preset area with a radius of 2 kilometers centered on the source or destination included in the town and street; preset categories of enterprises are, for example, freight companies, construction companies, construction site construction companies, etc.

[0116] The third traffic safety hazard identification process diagram provided in this application includes the following steps:

[0117] S301: Acquire accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data, accident location data, and accident-related personnel data;

[0118] S302: Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem;

[0119] S303: for the town or street to which each source belongs or the town or street to which each destination belongs, determine the second number of traffic accidents that occurred in the town or street in the second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered according to the second number, and there are enterprises of a preset category within a preset area centered on the source or destination included in the town or street, it is determined that the town or street has a traffic safety hazard problem;

[0120] S304: Clustering the traffic accidents according to the accident location data in the accident data of the traffic accidents according to a preset distance to obtain clusters; wherein the clusters include at least one of the accident location cluster, the accident section cluster and the accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem;

[0121] S305: Determine the town or street to which each accident-related person belongs based on the accident data of each traffic accident; for each town or street, if the third number of accident-related persons corresponding to the town or street is used to determine the triggering personnel dimension safety hazard identification rules, it is determined that the town or street has a traffic safety hazard problem.

[0122] This application combines the accident-related personnel data in the accident data and identifies traffic safety hazards through personnel-dimensional safety hazard identification events, further improving the comprehensiveness, efficiency and accuracy of traffic safety hazard identification.

[0123] In this application, if the triggering personnel dimension safety hazard identification rule is determined based on the third number of accident-related personnel corresponding to the town or street, determining that the town or street has a traffic safety hazard problem includes:

[0124] Determine a third number of accident-related persons whose ages are younger than a preset first age threshold corresponding to the town or street, and if a triggering person-dimensional safety hazard identification rule is determined based on the third number, it is determined that the town or street has a traffic safety hazard problem; or

[0125] Determine a third number of accident-related persons whose ages are greater than a preset second age threshold corresponding to the town or street, and if a person-dimensional safety hazard identification rule is triggered based on the third number, determine that the town or street has a traffic safety hazard problem;

[0126] Wherein, the preset first age threshold is smaller than the preset second age threshold.

[0127] In this application, when the personnel dimension safety hazard identification rule is determined to be triggered according to the third number of accident-related personnel whose ages are younger than the preset first age threshold corresponding to the town or street, it is determined that there is a traffic safety hazard problem in the town or street; or when the personnel dimension safety hazard identification rule is determined to be triggered according to the third number of accident-related personnel whose ages are older than the preset second age threshold corresponding to the town or street, it is determined that there is a traffic safety hazard problem in the town or street. The preset first age threshold is, for example, 16 years old, 18 years old, etc.; the preset second age threshold is, for example, 50 years old, 58 years old, etc. This further improves the accuracy of identifying traffic safety hazard problems related to students or the elderly.

[0128] The fourth traffic safety hazard identification process schematic diagram provided by this application includes the following steps:

[0129] S401: Acquire accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data, accident location data, accident-related personnel data, and accident behavior type data;

[0130] S402: Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem;

[0131] S403: for the town or street to which each source belongs or the town or street to which each destination belongs, determine a second number of traffic accidents occurring in the town or street in a second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered according to the second number, and there are enterprises of a preset category within a preset area centered on the source or destination included in the town or street, it is determined that the town or street has a traffic safety hazard problem;

[0132] S404: clustering the traffic accidents according to the accident location data in the accident data of the traffic accidents according to a preset distance to obtain clusters; wherein the clusters include at least one of the accident location cluster, the accident section cluster and the accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem;

[0133] S405: Determine the town or street to which each accident-related person belongs according to the accident data of each traffic accident; for each town or street, if a triggering person-dimensional safety hazard identification rule is determined according to the third number of accident-related persons corresponding to the town or street, it is determined that the town or street has a traffic safety hazard problem;

[0134] S406: Determine accidents involving drunk driving based on accident behavior type data in the accident data of each traffic accident; determine the source of the drunk drivers, the source of the vehicles, the source of the drinking, the place where the accident occurred, and the route of the accident; determine if the towns and streets to which the above-mentioned places belong have traffic safety hazards.

[0135] In this application, accidents involving drunk driving are determined based on the accident behavior type data in the accident data of each traffic accident. For accidents involving drunk driving, the source of the drunk driver, the source of the vehicle, the source of drinking, the place where the accident occurred, and the route of the accident are determined respectively. The source of the drunk driver includes the registered residence or permanent residence of the drunk driver; the source of the vehicle refers to the source of the accident vehicle determined based on the accident vehicle data; the source of drinking refers to the address of the place where the drunk driver drank; the place where the accident occurred refers to the address of the place where the traffic accident occurred; the route of the accident refers to the vehicle driving trajectory from the source of drinking to the place where the accident occurred. It is determined that the towns and streets to which the above-mentioned locations belong have traffic safety hazards. Thereby, the identification of traffic safety hazards in the management of drunk driving is realized, and the comprehensiveness and accuracy of the identification of traffic safety hazards is further improved.

[0136] The present application provides a schematic diagram of a process for determining the source and destination of each accident vehicle, including the following steps:

[0137] S501: for each accident vehicle, determining each travel chain in which the accident vehicle passes through the accident site; and determining the longest common subsequence of the capture points of any two travel chains according to the identification information of the capture points included in each travel chain;

[0138] S502: determining the similarity between any two trip chains according to the longest common subsequence; and clustering the trip chains whose similarity is greater than a preset similarity threshold into one category according to the similarity between the trip chains.

[0139] S503: Determine the category containing the largest number of travel chains as a high-frequency travel chain set; select a high-frequency travel chain from the high-frequency travel chain set, use the starting capture point of the high-frequency travel chain as the source of the accident vehicle, and use the ending capture point of the high-frequency travel chain as the destination of the accident vehicle.

[0140] In the present application, for each accident vehicle, when determining the source and destination of the accident vehicle, first determine the various travel chains of the accident vehicle passing the accident site; wherein, if the interval between consecutive vehicle passing records is set to a time length, it is considered that a stop occurs, and the two travel chains are separated at this point, so that the various travel chains of the accident vehicle passing the accident site can be obtained. The set time length is, for example, 30 minutes, 40 minutes, etc. Then determine the longest common subsequence of the capture points of any two travel chains; based on the longest common subsequence, determine the similarity of the any two travel chains; based on the similarity between each travel chain, cluster the travel chains with similarity greater than a preset similarity threshold into one category; determine a high-frequency travel chain set; and select a high-frequency travel chain from the high-frequency travel chain set. Finally, determine the source and destination of the accident vehicle based on the high-frequency travel chain, thereby improving the accuracy of determining the source and destination of the accident vehicle.

[0141] The first process schematic diagram of determining a high-frequency travel chain provided by the present application includes the following steps:

[0142] S601: for each travel chain in the high-frequency travel chain set, determine the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than the preset similarity threshold; and use the number as the associated number corresponding to the travel chain;

[0143] S602: Select the travel chain with the largest number of corresponding associations as the high-frequency travel chain.

[0144] In the present application, for each travel chain in the high-frequency travel chain set, the high-frequency travel chain is determined according to the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than a preset similarity threshold, thereby improving the accuracy of determining the high-frequency travel chain.

[0145] The second process schematic diagram of determining a high-frequency travel chain provided by the present application includes the following steps:

[0146] S701: If there are multiple travel chains with the largest number of corresponding associations, use the travel chain with the largest number of corresponding associations as a candidate travel chain;

[0147] S702: for each candidate travel chain, determining a travel chain in the high-frequency travel chain set whose similarity with the candidate travel chain is greater than a preset similarity threshold as an associated travel chain; determining a sum of the similarities between the candidate travel chain and each associated travel chain as the associated similarity corresponding to the candidate travel chain;

[0148] S703: Select the candidate travel link with the largest corresponding correlation similarity as the high-frequency travel link.

[0149] In the present application, multiple candidate travel chains with the largest number of corresponding associations are determined, and then the association similarities corresponding to the candidate travel chains are determined; finally, the candidate travel chain with the largest corresponding association similarity is selected as the high-frequency travel chain, which further improves the accuracy of determining the high-frequency travel chain.

[0150] In the present application, based on the accident location data in the accident data of each traffic accident, the traffic accidents are clustered according to a preset distance, and each cluster includes:

[0151] Clustering the traffic accidents whose accident locations are less than a preset first distance according to the accident location data of the traffic accidents to obtain the accident location cluster;

[0152] According to the accident location data in the accident data of each traffic accident, clustering each traffic accident whose accident location is greater than the preset first distance and less than the preset second distance to obtain a cluster of the road section where the accident occurred;

[0153] According to the accident location data in the accident data of each traffic accident, each traffic accident whose accident location is greater than the preset second distance and less than the preset third distance is clustered to obtain an accident road cluster.

[0154] In the present application, according to different preset distances, the accident location cluster, accident section cluster and accident road cluster are determined respectively; thereby improving the accuracy of clustering the various traffic accidents according to the preset distance and obtaining each cluster.

[0155] This application completes the accident information based on the passing vehicle data. The specific process is as follows:

[0156] ①According to the license plate number and license plate type of the relevant passenger and freight vehicles in the accident data, obtain the vehicle passing data for the past three months;

[0157] ② Split the travel chain of the hidden danger vehicle passing information: if there is a 30-minute interval (configurable) between two consecutive passing records, it is considered a travel stop, and the accumulation is broken into two travel chains at this point, and all vehicle trajectory information is split into travel chains;

[0158] ③ Define the similarity and distance of travel chains: Based on the maximum common subsequence of any two travel chains, the trajectory similarity is obtained (which can effectively solve the problem of missed shots, and the directionality can identify the direction of vehicle travel), and then the distance between travel chains is obtained;

[0159] The trajectory similarity of any two trajectories is defined as: Similarity = COUNT (longest common subsequence) / MIN (number of points in trajectory 1, number of points in trajectory 2), and the similarity is between 0 and 1;

[0160] The distance between trajectories = 1-trajectory similarity, and the distance is between 0 and 1.

[0161] ④ Use BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) to cluster the travel chains and obtain the travel chain information in each category, including the number of vehicles, high-frequency time period, collection point set, point set longitude and latitude, number of points, etc.;

[0162] ⑤ Obtain high-frequency travel trajectories: Identify the "highest frequency" category based on the number of vehicles in the category, and select a trajectory from the high-frequency set as the high-frequency route;

[0163] ⑥ Take the address of the starting point of the high-frequency line as the source of the vehicle and the address of the ending point as the destination of the vehicle and add them to the accident information.

[0164] This application clusters and analyzes accident information from five aspects: people, vehicles, roads, enterprises, and management, and outputs potential risks;

[0165] (1) Human aspect.

[0166] ① According to the street where the accident-related personnel belong, count the number of people who were injured or died with equal or less responsibility in the accident. If the number of people aged 50 or above shows a clustering feature, output "this town may have a problem of local education in rural elderly gathering areas";

[0167] ② According to the streets where the people involved in the accident belong, count the number of people who were injured or died with equal or less responsibility in the accident. If the number of people aged 18 or below is clustered, output "This town or street may have a problem with school education in the area where students gather."

[0168] (2) Regarding the car.

[0169] ① According to the number of streets where the source of passenger and freight vehicles related to the accident belongs, which have equal or higher accident responsibility, if the town or street ranks in the top 5 among all towns and streets (configurable), then the output is "the town or street propaganda and education issues of the source of passenger and freight vehicles prone to accidents in this town or street".

[0170] (3) Roads.

[0171] ①Acquire accident data within three years, and group the accident data with a straight-line distance of less than 70 meters from the accident site through a point clustering algorithm. If, after grouping, the data within the group meets the following conditions: the number of accident deaths is greater than or equal to 3, or the number of accidents with a seven-day death toll greater than 0 is greater than or equal to 3, or the number of casualties is greater than or equal to 6, then it is considered to be a high-incidence location;

[0172] ②Acquire accident data within three years, and group the accident data with a straight-line distance of less than 170 meters from the accident site under the same road number through the point clustering algorithm. If the data within the group meets the following conditions after grouping: the number of deaths in the accident is greater than or equal to 3, or the number of accidents with a death toll greater than 0 in seven days is greater than or equal to 3, or the number of accidents with casualties is greater than or equal to 6, then it is considered to be a high-incidence road section;

[0173] ③Acquire accident data within three years, and group the accident data of ordinary roads (road types are first-level, second-level, third-level, fourth-level and non-level roads) with the same road number and the straight-line distance from the accident site is less than 700 meters through the point clustering algorithm. If after grouping, the data in the group meets the conditions that the number of deaths in the accident is greater than or equal to 3, or the number of accidents with a death toll greater than 0 in seven days is greater than or equal to 3, or the number of accidents with casualties is greater than or equal to 6, then it is considered to be a high-incidence road;

[0174] ④ If the three types of high-incidence hidden dangers contain the same accident information, they should be merged into the same high-incidence accident data, with the priority merged upwards, location < road section < road. If the high-incidence location and the high-incidence road section contain the same accident, all accidents in the location will be merged into the high-incidence road section;

[0175] ⑤ According to the rules in Table 1 below, analyze the hidden dangers for each group of high-risk accidents and generate corresponding hidden dangers.

[0176]

[0177]

[0178] Table 1

[0179] (4) Enterprise aspect.

[0180] ①According to the accident statistics based on the towns and streets where the passenger and freight vehicles of the accident originated and where they were destined;

[0181] ② If the number of accidents or the vehicle-to-people index (number of people or vehicles) in the last 12 months of a town or street ranks in the top eight among all towns and streets, or the number of accidents or the vehicle-to-people index this year ranks in the top ten among all towns and streets, or the number of accidents or the vehicle-to-people index this month is greater than 2, then execute step ③;

[0182] ③ If there are freight companies within 2 kilometers of the source of passenger and freight accidents in the warning town, then the output is "there are freight source companies with high-risk hidden dangers in this town";

[0183] ④ If there is a construction site within 2 kilometers of the source of the accident passenger and freight traffic in the town or street being warned, then the output will be “there is a freight construction site with high-risk hidden dangers in this town or street”.

[0184] (5) Management aspects.

[0185] ① If there is a fatal accident caused by drunk driving, analyze the source of people, vehicles, violations, accident locations, and illegal routes of the drunk driving accident, and output "there are hidden dangers of inadequate management of drunk driving in this area."

[0186] Figure 2 The schematic diagram of the traffic safety hazard warning prediction model architecture provided for this application is as follows: Figure 2 As shown in the figure, the traffic safety hazard early warning prediction model includes five aspects: people, vehicles, roads, enterprises, and management mechanisms. The human aspect includes: the problem of education in the local areas where the elderly gather in rural areas; the problem of education in the local schools where students gather; and the problem of education in the local towns and streets where the source of passenger and freight accidents is prone to occur. The vehicle aspect includes: the traffic of motor vehicles, non-motor vehicles, and pedestrians can be separated but mixed. The road aspect includes: under severe weather conditions, sections prone to ice, water accumulation, and fog; lack of central isolation facilities; intersections and sections that have reached the conditions for setting traffic lights but no traffic lights are set; improper selection of speed measurement points; large traffic flow, many motor vehicle traffic violations, electronic equipment can be controlled but the settings are missing or insufficient; no lighting or insufficient brightness; no safety auxiliary facilities are set in the blind spots of truck turns. The enterprise aspect includes: there are high-risk hidden dangers in the source of freight; there are high-risk hidden dangers in freight construction sites. The management mechanism aspect includes: there are hidden dangers of inadequate management of drunk driving.

[0187] Figure 3The schematic diagram of the high-frequency trajectory calculation process of the vehicle provided for this application includes: determining the trajectory of the hidden danger vehicle passing by; travel chain division; similar travel chain aggregation; high-frequency route selection. Determining the trajectory of the hidden danger vehicle passing by includes: determining the list of hidden danger vehicles according to the passing data, and determining the trajectory of the hidden danger vehicle passing by according to the list of hidden danger vehicles. Travel chain division includes: if two consecutive passing records are 30 minutes apart, it is considered that the travel stops, and the accumulation is disconnected at this point into two travel chains. Similar travel chain aggregation includes: defining the track penetration similarity to complete the travel chain aggregation (Class C), judging the number of chains in the class (number of vehicles, number of vehicles) and setting the threshold to complete the high-frequency trajectory screening. Among them, determine the longest common subsequence length Lij of the trajectory (Li, Lj); define the trajectory similarity Sij = Lij / min (count (Li), count (Lj)), count refers to the number of capture points of the trajectory. Use BIRCH (comprehensive hierarchical clustering algorithm) to achieve similar travel chain clustering. After clustering, output high-frequency time period, number of vehicles, number of vehicles, point clustering and other information. High-frequency route selection includes selecting a travel chain as a high-frequency route from the high-frequency trajectory set.

[0188] Figure 4 A schematic diagram of the closed-loop management interface for traffic safety hazard identification provided for this application is shown in Figure 4 As shown, it includes zoning settings such as hidden danger list, hidden danger name, hidden danger reporting, hidden danger investigation, hidden danger treatment, and effect tracking.

[0189] Figure 5 The interface diagram for traffic safety hazard identification and hazard analysis provided for this application includes zoning settings such as hazard name, accident information, surrounding accident information, historical investigation and management status (in the past 3 years), dangerous road section information (in the past 3 years), risky road section, and hazard overview; the zones for accident information, surrounding accident information, and historical investigation and management status (in the past 3 years) include accident number, accident type, time of occurrence, location of occurrence, accident jurisdiction, accident township and street, accident road, and cause of accident. The zones for dangerous road section information (in the past 3 years) include accident number, accident type, time of occurrence, location of occurrence, accident jurisdiction, accident township and street, and accident road. The zones for risky road sections include section number, section name, road to which it belongs, time of discovery, hazard type, and accident township and street.

[0190] Figure 6 The interface diagram for traffic safety hazard identification and inspection provided for this application includes business number, inspection number, inspection overview, inspection report, inspection photos, inspection end time, inspection hazard and other zoning settings.

[0191] Figure 7The interface diagram for traffic safety hazard identification and rectification provided for this application includes partition settings such as business number, number of governance times, governance objectives, governance methods, governance start time, estimated governance time, governance completion time, specific information, hazard photos, and photos after governance.

[0192] Figure 8 The traffic safety hazard identification and hazard effect tracking interface diagram provided for this application includes the partition settings before hazard treatment, during hazard treatment and during monitoring. Each partition includes the accident number, accident type, accident time, accident location, jurisdiction, township street and road.

[0193] Fig. 9 The present application provides a schematic diagram of a traffic safety hazard identification device, the device comprising:

[0194] The acquisition module 151 is used to acquire the accident data of each traffic accident in the target area in the historical time period; wherein the accident data includes the accident vehicle data and the accident location data;

[0195] The first determination module 152 is used to determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; for the town or street to which each source belongs or the town or street to which each destination belongs, determine the first number of traffic accidents that occurred in the town or street within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, it is determined that the town or street has a traffic safety hazard problem;

[0196] The second determination module 153 is used to cluster the traffic accidents according to the accident location data in the accident data of the traffic accidents according to a preset distance to obtain clusters; wherein the clusters include at least one of the accident location cluster, the accident section cluster and the accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

[0197] In an optional embodiment, the first determination module 152 is also used to determine, for each source town or each destination town, a second number of traffic accidents occurring in the town within a second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered based on the second number, and there are enterprises of a preset category within a preset area centered on the source or destination included in the town, it is determined that the town has a traffic safety hazard problem.

[0198] In an optional embodiment, the device further comprises:

[0199] The third determination module 154 is used to determine the town or street to which each accident-related person belongs based on the accident-related person data in the accident data of the traffic accidents; for each town or street, if the third number of accident-related persons corresponding to the town or street is used to determine the triggering personnel dimension safety hazard identification rules, it is determined that the town or street has a traffic safety hazard problem.

[0200] In an optional embodiment, the third determination module 154 is specifically used to determine the third number of accident-related personnel corresponding to the town and street whose ages are younger than a preset first age threshold, and if the triggering personnel dimension safety hazard identification rule is determined based on the third number, it is determined that the town and street has a traffic safety hazard problem; or determine the third number of accident-related personnel corresponding to the town and street whose ages are older than a preset second age threshold, and if the triggering personnel dimension safety hazard identification rule is determined based on the third number, it is determined that the town and street has a traffic safety hazard problem; wherein the preset first age threshold is smaller than the preset second age threshold.

[0201] In an optional embodiment, the device further comprises:

[0202] The fourth determination module 155 is used to determine accidents involving drunk driving based on the accident behavior type data in the accident data of each traffic accident; determine the source of the drunk driving personnel, the source of the vehicles, the source of drinking, the place where the accident occurred, and the route of the accident; and determine whether the towns and streets to which the above-mentioned places belong have traffic safety hazards.

[0203] In an optional embodiment, the first determination module 152 is specifically used to determine, for each accident vehicle, the travel chains in which the accident vehicle passes through the accident site; determine the longest common subsequence of the capture points of any two travel chains based on the identification information of the capture points contained in the travel chains; determine the similarity of the any two travel chains based on the longest common subsequence; based on the similarity between the travel chains, group the travel chains with similarities greater than a preset similarity threshold into one category; determine the category containing the largest number of travel chains as a high-frequency travel chain set; select a high-frequency travel chain from the high-frequency travel chain set, use the starting capture point of the high-frequency travel chain as the source of the accident vehicle, and use the ending capture point of the high-frequency travel chain as the destination of the accident vehicle.

[0204] In an optional embodiment, the first determination module 152 is specifically used to determine, for each travel chain in the high-frequency travel chain set, the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than the preset similarity threshold; use the number as the number of associated links corresponding to the travel chain; and select the travel chain with the largest number of corresponding associated links as the high-frequency travel chain.

[0205] In an optional implementation, the first determination module 152 is specifically configured to, if there are multiple travel chains with the largest number of corresponding associations, use the travel chain with the largest number of corresponding associations as a candidate travel chain; for each candidate travel chain, determine a travel chain in the high-frequency travel chain set whose similarity with the candidate travel chain is greater than a preset similarity threshold as an associated travel chain; determine the sum of the similarities between the candidate travel chain and each associated travel chain as the associated similarity corresponding to the candidate travel chain; and select the candidate travel chain with the largest corresponding association similarity as the high-frequency travel chain.

[0206] In an optional embodiment, the second determination module 153 is specifically used to cluster the traffic accidents whose accident locations are less than a preset first distance according to the accident location data in the accident data of the traffic accidents to obtain accident location clusters; cluster the traffic accidents whose accident locations are greater than the preset first distance and less than a preset second distance according to the accident location data in the accident data of the traffic accidents to obtain accident section clusters; cluster the traffic accidents whose accident locations are greater than the preset second distance and less than a preset third distance according to the accident location data in the accident data of the traffic accidents to obtain accident road clusters.

[0207] In an optional implementation manner, the data specified in the road dimension safety hazard identification event of the corresponding area includes at least one of the following:

[0208] In the corresponding area, there are accidents with injuries or fatalities, accidents involving vehicles and non-vehicles or accidents involving vehicles and pedestrians, scratches, no physical separation of roads or central separation, and the traffic safety hazard problem is determined to be that vehicles, non-vehicles and pedestrians can be separated but mixed;

[0209] If the number of traffic accidents occurring in the corresponding area under cloudy weather conditions is greater than a preset first number threshold, it is determined that the potential traffic safety hazard is an area prone to ice, water accumulation or fog under severe weather conditions;

[0210] In the corresponding area, there are accidents involving vehicles and people, accidents involving vehicles and people, collisions, victims crossing the road, no physical separation of roads, or separation between vehicles and people, and the traffic safety hazard is determined to be the lack of central separation facilities;

[0211] There is no traffic light control in the corresponding area, and the traffic safety hazard is determined to be that the intersection or road section that has met the conditions for setting up traffic lights has no traffic lights installed;

[0212] In the corresponding area, there are speed measurement points in the first area where rear-end collisions, collisions with moving vehicles, and accident-prone locations are set, and it is determined that the traffic safety hazard is due to improper selection of speed measurement point locations;

[0213] In the corresponding area, there are speeding, running red lights, driving in the wrong lane, and entering restricted areas. It is set that there is no such illegal capture equipment on the road section within the second area. The traffic safety hazard problem is determined to be large traffic flow, many motor vehicle traffic violations, and electronic equipment can be controlled but the settings are missing or insufficient;

[0214] There is no lighting at night in the corresponding area, and the number of traffic accidents that meet the above conditions is greater than a preset second number threshold, and the traffic safety hazard problem is determined to be no lighting or insufficient brightness;

[0215] When a heavy truck is driven in the corresponding area, and the truck is driving in the right turn, left turn, or U-turn state, and the number of traffic accidents that meet the above conditions is greater than the preset third number threshold, it is determined that the traffic safety hazard problem is that there are no safety auxiliary facilities installed in the truck's turning blind spot.

[0216] The present application also provides an electronic device, such as Fig.10 As shown, it includes: a processor 161, a communication interface 162, a memory 163 and a communication bus 164, wherein the processor 161, the communication interface 162, and the memory 163 communicate with each other through the communication bus 164;

[0217] The memory 163 stores a computer program, and when the program is executed by the processor 161, the processor 161 executes any one of the above method steps.

[0218] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0219] The communication interface 162 is used for communication between the above electronic device and other devices.

[0220] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0221] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0222] The present application also provides a computer storage readable storage medium, wherein the computer readable storage medium stores a computer program executable by an electronic device, and when the program runs on the electronic device, the electronic device implements any of the above method steps when executing.

[0223] The present application provides a computer program product, wherein the computer program product comprises an executable program, and when the executable program is executed by a processor, the method described above is implemented.

[0224] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0225] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for identifying traffic safety hazards, characterized in that: The method comprises: Obtaining accident data of each traffic accident in a historical time period in a target area; wherein the accident data includes accident vehicle data and accident location data; Determine the source and destination of each accident vehicle according to the accident vehicle data in the accident data of each traffic accident; determine the first number of traffic accidents that occurred in the town or street to which each source belongs or the town or street to which each destination belongs within the first sub-time period; if a vehicle-dimensional safety hazard identification event is triggered according to the first number, determine that the town or street has a traffic safety hazard problem; According to the accident location data in the accident data of each traffic accident, each traffic accident is clustered according to a preset distance to obtain each cluster; wherein each cluster includes at least one of an accident location cluster, an accident section cluster and an accident road cluster; for each cluster, if the accident data in the cluster matches the data specified in the road dimension safety hazard identification event of the area corresponding to the cluster, it is determined that the area corresponding to the cluster has a traffic safety hazard problem.

2. The method according to claim 1, characterized in that After determining the source and destination of each accident vehicle, the method further includes: For the towns and streets to which each source point belongs or the towns and streets to which each destination point belongs, determine the second number of traffic accidents that occurred in the towns and streets within the second sub-time period; if the enterprise-dimensional safety hazard identification rule is triggered based on the second number, and there are preset categories of enterprises within the preset area centered on the source point or destination included in the town and street, it is determined that the town and street has a traffic safety hazard problem.

3. The method according to claim 1, characterized in that The accident data also includes accident-related personnel data, and the method further includes: According to the accident-related personnel data in the accident data of each traffic accident, the town or street to which each accident-related person belongs is determined; for each town or street, if the third number of accident-related personnel corresponding to the town or street is used to determine the triggering personnel dimension safety hazard identification rules, it is determined that the town or street has a traffic safety hazard problem.

4. The method according to claim 3, characterized in that If the triggering personnel dimension safety hazard identification rule is determined based on the third number of accident-related personnel corresponding to the town or street, it is determined that the town or street has a traffic safety hazard problem including: Determine a third number of accident-related persons whose ages are younger than a preset first age threshold corresponding to the town or street, and if a triggering person-dimensional safety hazard identification rule is determined based on the third number, it is determined that the town or street has a traffic safety hazard problem; or Determine a third number of accident-related persons whose ages are greater than a preset second age threshold corresponding to the town or street, and if a person-dimensional safety hazard identification rule is triggered based on the third number, determine that the town or street has a traffic safety hazard problem; Wherein, the preset first age threshold is smaller than the preset second age threshold.

5. The method according to claim 1, characterized in that The accident data also includes accident behavior type data, and the method further includes: According to the accident behavior type data in the accident data of each traffic accident, determine the accidents involving drunk driving; determine the source of the drunk drivers, the source of the vehicles, the source of the drinking, the place where the accident occurred, and the route of the accident; determine whether the towns and streets to which the above-mentioned places belong have traffic safety hazards.

6. The method according to claim 1, characterized in that Determining the source and destination of each accident vehicle includes: For each accident vehicle, determine the travel chains in which the accident vehicle passes through the accident site; determine the longest common subsequence of the capture points of any two travel chains based on the identification information of the capture points contained in the travel chains; determine the similarity of the any two travel chains based on the longest common subsequence; based on the similarity between the travel chains, group the travel chains with similarities greater than a preset similarity threshold into one category; determine the category containing the largest number of travel chains as a high-frequency travel chain set; select a high-frequency travel chain from the high-frequency travel chain set, use the starting capture point of the high-frequency travel chain as the source of the accident vehicle, and use the ending capture point of the high-frequency travel chain as the destination of the accident vehicle.

7. The method according to claim 6, characterized in that Selecting a high-frequency travel chain from the high-frequency travel chain set includes: For each travel chain in the high-frequency travel chain set, determine the number of travel chains in the high-frequency travel chain set whose similarity with the travel chain is greater than the preset similarity threshold; and use the number as the associated number corresponding to the travel chain; The travel chain with the largest number of corresponding associations is selected as the high-frequency travel chain.

8. The method according to claim 7, characterized in that The selecting of the corresponding travel chain with the largest number of associated links as the high-frequency travel chain includes: If there are multiple travel chains with the largest number of corresponding associations, the travel chain with the largest number of corresponding associations is used as a candidate travel chain; For each candidate travel chain, determine the travel chain in the high-frequency travel chain set whose similarity with the candidate travel chain is greater than the preset similarity threshold as an associated travel chain; determine the sum of the similarities between the candidate travel chain and each associated travel chain as the associated similarity corresponding to the candidate travel chain; The candidate travel chain with the largest corresponding correlation similarity is selected as the high-frequency travel chain.

9. The method according to claim 1, characterized in that According to the accident location data in the accident data of each traffic accident, each traffic accident is clustered according to a preset distance, and each cluster obtained includes: Clustering the traffic accidents whose accident locations are less than a preset first distance according to the accident location data of the traffic accidents to obtain the accident location cluster; According to the accident location data in the accident data of each traffic accident, clustering each traffic accident whose accident location is greater than the preset first distance and less than the preset second distance to obtain a cluster of the road section where the accident occurred; According to the accident location data in the accident data of each traffic accident, each traffic accident whose accident location is greater than the preset second distance and less than the preset third distance is clustered to obtain an accident road cluster.

10. The method according to claim 1, characterized in that The data specified in the road dimension safety hazard identification event of the corresponding area includes at least one of the following: In the corresponding area, there are accidents with injuries or fatalities, accidents involving vehicles and non-vehicles or accidents involving vehicles and pedestrians, scratches, no physical separation of roads or central separation, and the traffic safety hazard problem is determined to be that vehicles, non-vehicles and pedestrians can be separated but mixed; If the number of traffic accidents occurring in the corresponding area under cloudy weather conditions is greater than a preset first number threshold, it is determined that the potential traffic safety hazard is an area prone to ice, water accumulation or fog under severe weather conditions; In the corresponding area, there are accidents involving vehicles and people, accidents involving vehicles and people, collisions, victims crossing the road, no physical separation of roads, or separation between vehicles and people, and the traffic safety hazard is determined to be the lack of central separation facilities; There is no traffic light control in the corresponding area, and the traffic safety hazard is determined to be that the intersection or road section that has met the conditions for setting up traffic lights has no traffic lights installed; In the corresponding area, there are speed measurement points in the first area where rear-end collisions, collisions with moving vehicles, and accident-prone locations are set, and it is determined that the traffic safety hazard is due to improper selection of speed measurement point locations; In the corresponding area, there are speeding, running red lights, driving in the wrong lane, and entering restricted areas. It is set that there is no such illegal capture equipment on the road section within the second area. The traffic safety hazard problem is determined to be large traffic flow, many motor vehicle traffic violations, and electronic equipment can be controlled but the settings are missing or insufficient; There is no lighting at night in the corresponding area, and the number of traffic accidents that meet the above conditions is greater than a preset second number threshold, and the traffic safety hazard problem is determined to be no lighting or insufficient brightness; When a heavy truck is driven in the corresponding area, and the truck is driving in the right turn, left turn, or U-turn state, and the number of traffic accidents that meet the above conditions is greater than the preset third number threshold, it is determined that the traffic safety hazard problem is that there are no safety auxiliary facilities installed in the truck's turning blind spot.

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