Road emergency analysis method and device

Abstract

The invention discloses a highway emergency analysis method and device. A server side method comprises the following steps: determining a key monitoring area; based on the key monitoring area, establishing an adjacent relationship between each video point location and an adjacent portal frame corresponding to each video point location; obtaining an expressway roadside video of each video point location in a historical time period, obtaining expressway portal frame data of an adjacent portal frame corresponding to each video point location through an adjacent relation, and determining a high-value time period according to the expressway roadside video and the expressway portal frame data; floating car data on the highway section in the key monitoring area are obtained, abnormal clues are generated according to the traffic flow data or the floating car data in the high-value time period, the abnormal clues comprise traffic abnormal clues or speed abnormal clues, and the clues are sent to a client side to judge emergencies. Therefore, by adopting the embodiment of the invention, the recognition capability of emergencies can be improved, and differentiated and targeted monitoring of high-risk areas can be realized.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method and device for analyzing highway emergencies. Background Art

[0002] In the field of intelligent traffic management, especially in highway operation management, a typical application scenario is to deal with traffic interruptions caused by emergencies such as bad weather, geological disasters or traffic accidents. These events not only affect the smoothness of traffic, but may also cause serious safety accidents. Therefore, quickly and accurately identifying and responding to these emergencies is crucial to ensuring road traffic safety and efficiency.

[0003] In related technologies, surveillance cameras deployed along highways capture real-time video streams and use visual recognition technology to monitor traffic conditions and events. Or, combined with meteorological monitoring data, traffic events that may be caused by weather changes are predicted and early warnings are issued. Or, personnel are dispatched to conduct field inspections of highways regularly or on demand to discover and report emergencies. Existing monitoring methods are single and fail to effectively utilize multiple data sources and monitoring technologies, resulting in limited ability to identify emergencies and difficulty in achieving differentiated and targeted monitoring of high-risk areas. Summary of the invention

[0004] The embodiments of the present application provide a method and device for analyzing emergencies on a highway. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing an emergency event on a highway, which is applied to a server, and the method includes:

[0006] Identify key monitoring areas

[0007] Based on the key monitoring area, establish the adjacent relationship between each video point and the adjacent gantry corresponding to each video point;

[0008] Obtain the highway roadside video of each video point within the historical time period, and obtain the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship;

[0009] Determine high-value time periods based on highway roadside videos and highway gantry data;

[0010] Obtain floating vehicle data on expressway sections within key monitoring areas, and generate abnormal clues based on traffic flow data or floating vehicle data during high-value time periods, including abnormal flow clues or abnormal speed clues;

[0011] Send traffic anomaly clues and speed anomaly clues to the client for emergency investigation to determine whether there is an emergency in the abnormal road section.

[0012] In a second aspect, an embodiment of the present application provides a method for analyzing highway emergencies, which is applied to a client and includes:

[0013] Receive abnormal traffic and speed clues sent by the server;

[0014] Determine whether there is a high-speed video with a clarity greater than a preset threshold on the road section indicated by the abnormal traffic flow clues and the abnormal speed clues;

[0015] If it exists, retrieve the high-speed video for display;

[0016] If it does not exist, generate instructions for on-site verification for display.

[0017] In a third aspect, an embodiment of the present application provides a highway emergency analysis device, the device comprising:

[0018] An extraction module is used to determine a key monitoring area, and based on the key monitoring area, establish an adjacent relationship between each video point and an adjacent gantry corresponding to each video point;

[0019] An acquisition module is used to acquire the highway roadside video of each video point within a historical time period, and acquire the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship;

[0020] A determination module, used for determining a high-value time period according to the highway roadside video and the highway gantry data;

[0021] A generation module, used to obtain floating vehicle data on the expressway section in the key monitoring area, and generate abnormal clues according to the vehicle flow data of the high-value time period or the floating vehicle data, wherein the abnormal clues include abnormal flow clues or abnormal speed clues;

[0022] The sending module is used to send traffic anomaly clues and speed anomaly clues to the client for emergency investigation to determine whether there is an emergency in the abnormal road section.

[0023] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0024] In an embodiment of the present application, on the one hand, traffic anomaly clues are generated based on highway roadside videos and highway gantry data. This process not only utilizes video surveillance data, but also combines gantry data to form a three-dimensional monitoring network. This combination enables the system to capture abnormal changes in traffic flow from multiple angles, thereby more accurately identifying emergencies; on the other hand, speed anomaly clues are generated based on floating vehicle data. The floating vehicle data in this process provides real-time speed and location information, which is crucial for identifying speed changes caused by traffic accidents or other events. By combining these data with traffic anomaly clues, the system can more accurately monitor high-risk areas and promptly detect and respond to possible emergencies. This differentiated and targeted monitoring strategy not only improves monitoring efficiency, but also improves the response speed and processing capabilities to emergencies.

[0025] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0027] Figure 1 This is a schematic diagram of a method flow of a highway emergency analysis method provided by an embodiment of the present application;

[0028] Figure 2 is a schematic diagram of a monitoring object and a sensing device provided in an embodiment of the present application;

[0029] Figure 3 It is a schematic diagram of a gantry and a service area existing on an abnormal road section provided by the present application;

[0030] Figure 4 It is a schematic diagram of a process of analyzing a highway emergency provided by the present application;

[0031] Figure 5 It is a structural schematic diagram of a highway emergency analysis device provided by the present application;

[0032] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0034] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0035] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0036] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0037] The following will be combined with the attached Figure 1 -Attached Figure 4 , a method for analyzing highway emergencies provided in an embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on a highway emergency analysis device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0038] See also Figure 1 , which is a flowchart of a method for analyzing highway emergencies provided in the present application embodiment, and is applied to a server. Figure 1 As shown, the method of the embodiment of the present application may include the following steps:

[0039] S101, determining a key monitoring area, and based on the key monitoring area, establishing an adjacent relationship between each video point and an adjacent gantry corresponding to each video point;

[0040] In some embodiments of the present application, the specific process of determining the key monitoring area includes: extracting high-risk facilities and sections in the road network as key monitoring areas through real-time acquired meteorological data, preset facility data and geological disaster data of the road network.

[0041] Among them, the meteorological data of the road network refers to the meteorological information related to the road traffic network, including but not limited to data on meteorological conditions such as temperature, humidity, precipitation, wind speed, visibility, etc. The preset facility data involves the data of various infrastructure on the highway, such as the location and characteristics of bridges, tunnels, toll stations, service areas, etc. The geological disaster data includes information on geological disasters that may affect road traffic, such as landslides, mudslides, ground collapse, etc. These data are usually provided by geological monitoring departments to assess the geological disaster risks in specific areas. High-risk facilities and sections are the results of a comprehensive analysis of meteorological data, preset facility data and geological disaster data, and refer to those sections and facilities that have a higher risk of emergencies due to various reasons (such as bad weather, aging facilities, geological instability, etc.).

[0042] In some embodiments of the present application, the detailed specific method for determining the key monitoring area includes: analyzing the meteorological data of the road network obtained in real time to determine the area with severe weather in the road network as the screening area; superimposing the screening area with the preset facility data and geological disaster data for analysis, extracting high-risk key facilities and disaster risk sections as the key monitoring area, and starting the countdown to continue the step of analyzing the meteorological data of the road network obtained in real time after the preset countdown ends, so as to update the key monitoring area in real time.

[0043] For example, areas with severe weather in the road network are identified as screening areas, including areas with heavy rainfall, heavy snowfall, fog, and typhoons. By superimposing and analyzing the screening area with the preset facility data and geological disaster data, high-risk key facilities and disaster risk sections can be extracted as key monitoring areas. The above calculation is performed according to the duration (such as 1 hour) and the key monitoring areas are updated in real time.

[0044] In other embodiments of the present application, high-risk facilities and sections in the road network are extracted by acquiring real-time meteorological data, preset facility data and geological disaster data of the road network. The specific method as key monitoring areas includes: responding to the user's key monitoring area selection instruction and determining the key monitoring areas.

[0045] For example, in order to meet special application scenarios (such as major event security), it supports manual screening of key facilities and disaster-risk sections on highways to obtain key monitoring areas.

[0046] Among them, for establishing the adjacent relationship between each video point and the adjacent gantry corresponding to each video point based on the key monitoring area, the specific explanation is as follows:

[0047] Among them, video points refer to the locations of surveillance cameras installed on highways. These cameras are used to monitor road traffic conditions in real time, including vehicle flow, speed, accidents and other abnormal events. ETC gantries usually refer to ETC (electronic toll collection system) gantries on highways. They are distributed at different locations on highways and are used for automatic identification and charging of vehicles. Adjacent relationship refers to the spatial topological relationship between detection facility points. Specifically, it refers to the adjacent points that determine the monitoring points have a connected relationship in the road network topological space.

[0048] In some embodiments of the present application, based on the key detection area, the specific process of establishing the adjacent relationship between the traffic monitoring facility points includes: unifying the key detection area, highway network data, video point data, and ETC gantry data into the same topological space; utilizing the spatial position relationship to match the monitoring area range and detection facility points to the corresponding positions of the highway network; based on the connectivity relationship of the highway network, retrieving the nearest reachable point for each path of the monitoring facility points in the monitoring area, and determining them as their adjacent points.

[0049] For example Figure 2 As shown in the figure, the key monitoring area is the middle section of the road. Figure 2 Gantry 1, Gantry 2, Gantry 3, video point 1 and all road sections in the figure can be regarded as relevant monitoring equipment in key monitoring areas. Among them, the ETC gantry mainly senses the traffic information of the highway; the video equipment uses AI to identify traffic data, encrypts and fills in the blind spots of the ETC gantry points; the highway section serves as a GPS floating vehicle data carrier to monitor the traffic status of vehicles.

[0050] Among them, the adjacent gantries of the video points can be obtained based on the key monitoring areas and adjacent relationships;

[0051] Among them, the adjacent gantry of the video point refers to the ETC gantry closest to the video point in each accessible path in the expressway network.

[0052] In some embodiments of the present application, based on the adjacent relationship between the key monitoring area and the points, the specific process of obtaining the adjacent gantries of the video points includes: searching for ETC gantry points that are topologically adjacent at both ends of the key monitoring area; using the road section range between the adjacent ETC gantry points as the monitoring range; obtaining all ETC gantries, all video points, and highway sections within the monitoring range; starting from the position of each video point, along the road upstream and downstream respectively, to retrieve its adjacent gantries from all ETC gantries; associating each video point with the adjacent gantries retrieved for each video point, and obtaining the adjacent relationship between each video point and the adjacent gantry corresponding to each video point. S102, obtaining the highway roadside video of each video point within the historical time period, and obtaining the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship;

[0053] The historical time period refers to a period of time in the past, which can be a few hours, a few days or longer. Highway roadside video refers to the video data taken by the video surveillance system installed beside the highway. Highway gantry data refers to the data collected by the ETC gantry installed on the highway, including information such as the time, speed and flow of vehicles passing through the gantry.

[0054] S103, determining a high-value time period according to the highway roadside video and the highway gantry data;

[0055] High-value time periods refer to time periods with good lighting conditions and high vehicle recognition accuracy, excluding time periods at night when lighting conditions are poor and the video has a low vehicle recognition rate.

[0056] In some embodiments of the present application, high-value time periods are determined based on highway roadside videos and the highway gantry data: the historical time period is divided into multiple sub-periods; the number of vehicles in each sub-period of the highway roadside video is identified and counted to obtain the video flow of each sub-period; from the highway gantry data, the gantry flow of the adjacent gantry corresponding to each video point in each sub-period is obtained; based on the video flow of each sub-period and the gantry flow of each sub-period, the difference ratio of each cycle is calculated; and the sub-period in which the difference ratio is less than a preset ratio threshold is defined as a high-value time period.

[0057] For example, the difference ratio If flow_pro is greater than a given ratio threshold, the period is considered to be an unavailable period; otherwise, the period is a high-value period.

[0058] Among them, artificial intelligence technology is used to extract vehicle targets from highway roadside videos, and the number of passing vehicles is counted at 5-minute intervals as available traffic data. For example, a pre-trained neural network model can be used to identify vehicles in each sub-period of highway roadside videos. The model can identify target vehicles in the video. The model can be an existing trained model that can identify target objects. The relevant training process is not repeated here.

[0059] S105, obtaining floating vehicle data on the expressway section in the key monitoring area, and generating abnormal clues based on the vehicle flow data or floating vehicle data in the high-value time period, the abnormal clues including abnormal flow clues or abnormal speed clues;

[0060] Among them, traffic flow data during high-value periods includes video traffic and gantry traffic.

[0061] Among them, the high-value time period of video point data is the available time period for extraction, and the available time period of ETC gantry is the whole day. The combination of the two data sources can significantly improve the coverage rate and increase the detection speed. Traffic anomaly characteristics refer to the significant difference between traffic flow and normal traffic flow, such as abnormal traffic flow returning to zero and abnormal traffic flow dropping. Traffic anomaly clues refer to clue information containing information such as time and spatial location extracted based on the traffic anomaly characteristics of traffic monitoring facilities.

[0062] Specifically, based on the traffic flow data of the high-value time period, the specific process of generating abnormal clues includes: unifying the video flow and gantry flow in the high-value time period as facilities, determining high-value facilities, and the high-value facilities are facilities in the road network where the traffic flow of each traffic monitoring facility within a preset time period is greater than a preset traffic threshold; from all high-value facilities, selecting high-value facilities in key monitoring areas as facilities to be analyzed; and generating traffic anomaly clues based on the gantry data and the facilities to be analyzed.

[0063] For example, when determining high-value facilities, a fixed time is used as the traffic statistics interval to count the traffic of facilities throughout the day across the country. Given a traffic threshold σ, if the traffic of a facility in a certain period reaches σ, then this facility is a high-value facility in that period; otherwise, it is a low-value facility in that period.

[0064] Specifically, the specific process of generating traffic anomaly clues based on the gantry data and the facilities to be analyzed includes: determining the maximum flow and minimum flow, and the time corresponding to the maximum flow and minimum flow of the facilities to be analyzed within a preset period according to the gantry data; judging whether the facilities to be analyzed meet the preset traffic drop conditions according to the maximum flow and minimum flow, and the time corresponding to the maximum flow and minimum flow; treating the facilities to be analyzed that meet the preset traffic drop conditions as traffic anomaly facilities; treating the traffic anomaly facilities and their associated abnormal road sections as traffic anomaly clues; wherein the preset traffic drop conditions are: flow max and flow min is the maximum flow rate and minimum flow rate within the preset period t, t max and t min are the times corresponding to the maximum flow and the minimum flow respectively.

[0065] S106, generating a speed abnormality clue according to the floating vehicle data;

[0066] Among them, floating vehicle data includes floating vehicle tracks. Floating vehicle data is a type of data collected by devices installed on mobile vehicles (such as GPS locators, speed sensors, etc.), which includes information such as the vehicle's location, speed, and driving direction. Speed ​​anomaly clues refer to signs that are significantly different from normal driving speeds found through analyzing floating vehicle data.

[0067] In some embodiments of the present application, the specific process of generating speed anomaly clues based on floating vehicle data includes: determining sudden deceleration and parking vehicles based on the floating vehicle data; generating speed anomaly clues based on sudden deceleration and parking vehicles. The floating vehicle data includes floating vehicle tracks.

[0068] Specifically, according to the floating vehicle data, the specific process of determining the sudden deceleration and parking vehicles includes: calculating the floating vehicle speed according to the floating vehicle trajectory; calculating the deceleration of the floating vehicle based on the floating vehicle speed; obtaining the vehicle identification whose deceleration is greater than a preset threshold, and obtaining the sudden deceleration and parking vehicles.

[0069] For example, given a rapid deceleration threshold of a max_acc , calculate the deceleration a of the floating car 减 , if a is satisfied 减 >a max_acc , then the floating car will decelerate suddenly. Calculate the current speed v of the floating car. If v = 0, then the floating car will stop.

[0070] Specifically, the specific process of generating speed anomaly clues based on sudden deceleration and parking vehicles includes: matching the road sections to which sudden deceleration and parking vehicles belong from the road network as abnormal road sections; counting the number of sudden deceleration and parking vehicles within a preset single statistical period; when the number of vehicles is greater than a preset number threshold, using the matched abnormal road sections and their associated key monitoring areas as speed anomaly clues.

[0071] For example, the number of vehicles n that suddenly decelerate or stop on the abnormal road section within a preset single statistical period is counted, and if n is greater than or equal to a given threshold n abnormal , then the road section is an abnormal section, and the abnormal section and its associated key monitoring objects are clues to speed anomaly.

[0072] S107, sending the traffic anomaly clue and the speed anomaly clue to the client for emergency investigation to determine whether there is an emergency in the abnormal road section.

[0073] For example, clues about traffic anomalies and speed anomalies can be quickly sent to the clients of relevant responsible units and persons in charge in the form of text messages.

[0074] In some embodiments of the present application, the process by which the client determines whether an emergency occurs in an abnormal road section is as follows: receiving traffic anomaly clues and speed anomaly clues sent by the server; determining whether there is a high-speed video with a clarity greater than a preset threshold in the road section indicated by the traffic anomaly clues and speed anomaly clues; if so, retrieving the high-speed video for display; if not, generating on-site verified indication information for display.

[0075] For example, if there is clear and available high-speed video at the abnormal section, the relevant personnel will retrieve the relevant real-time video for video inspection; if there is no available high-speed video at the clue section, the relevant units and responsible persons will conduct on-site verification. The correct clues verified manually will be fed back to the server through manual marking.

[0076] In some embodiments, the server receives the investigation results fed back from the client; when the investigation results indicate that there are emergencies in the clues of abnormal traffic flow and abnormal speed, the upstream gantry, downstream gantry and service area on the target section in the investigation results are determined; the vehicle information entering the upstream gantry within the target time period is counted to obtain the first vehicle set entering the target section Count the vehicles leaving the downstream gantry within the target time period to obtain the second set of vehicles leaving the target section Count the vehicles entering the service area during the target time period and obtain the third vehicle set entering the service area Count the vehicles that leave the service area during the target time period to get the fourth set of vehicles that leave the service area According to the first vehicle set, the second vehicle set, the third vehicle set and the fourth vehicle set, the set of vehicles affected by the emergency is counted. Among them, the affected vehicles are gathered The calculation method is:

[0077] For example Figure 3 As shown, the abnormal section corresponds to the upstream gantry, the downstream gantry and the service area.

[0078] In other embodiments, the server receives the troubleshooting results fed back from the client; when the troubleshooting results indicate that there is an emergency with abnormal traffic clues and abnormal speed clues, the server releases the relevant information of the emergency through a preset method; wherein the preset method is any one of the ETC publishing software, WeChat public account, operation platform and roadside terminal.

[0079] Furthermore, the emergency information and the affected vehicle information can be promptly pushed to the traffic management department and other relevant departments as information data support for emergency rescue.

[0080] For example Figure 4 As shown, Figure 4 This is a schematic diagram of the process of highway emergency analysis provided by this application. First, the key monitoring area can be determined based on the meteorological data, key facility data and disaster risk data on the highway, or it can be determined by manual screening. For the key monitoring area, the highway video data can be identified based on AI traffic, and the clues of traffic anomalies can be found in combination with the highway gantry data. The clues of speed anomalies can be found based on the floating vehicle data. Finally, multi-source data monitoring and verification, disaster-affected vehicle inspection and emergency information release can be carried out based on the clues of traffic anomalies and speed anomalies.

[0081] In an embodiment of the present application, on the one hand, traffic anomaly clues are generated based on highway roadside videos and highway gantry data. This process not only utilizes video surveillance data, but also combines gantry data to form a three-dimensional monitoring network. This combination enables the system to capture abnormal changes in traffic flow from multiple angles, thereby more accurately identifying emergencies; on the other hand, speed anomaly clues are generated based on floating vehicle data. The floating vehicle data in this process provides real-time speed and location information, which is crucial for identifying speed changes caused by traffic accidents or other events. By combining these data with traffic anomaly clues, the system can more accurately monitor high-risk areas and promptly detect and respond to possible emergencies. This differentiated and targeted monitoring strategy not only improves monitoring efficiency, but also improves the response speed and processing capabilities to emergencies.

[0082] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0083] See also Figure 5, which shows a schematic diagram of the structure of a highway emergency analysis device provided by an exemplary embodiment of the present application. The highway emergency analysis device can be implemented as all or part of an electronic device through software, hardware or a combination of both. The device 1 includes an extraction module 10, an acquisition module 20, a determination module 30, a generation module 40, and a sending module 50.

[0084] An extraction module 10 is used to determine a key monitoring area, and based on the key monitoring area, establish an adjacent relationship between each video point and an adjacent gantry corresponding to each video point;

[0085] The acquisition module 20 is used to acquire the highway roadside video of each video point in the historical time period, and acquire the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship;

[0086] A determination module 30, for determining a high-value time period based on the highway roadside video and the highway gantry data;

[0087] A generating module 40 is used to obtain floating vehicle data on the highway section in the key monitoring area, and generate abnormal clues according to the vehicle flow data of the high-value period or the floating vehicle data, wherein the abnormal clues include abnormal flow clues or abnormal speed clues;

[0088] The sending module 50 is used to send the traffic anomaly clues and speed anomaly clues to the client for emergency event investigation to determine whether there is an emergency event in the abnormal road section.

[0089] It should be noted that the highway emergency event analysis device provided in the above embodiment only uses the division of the above functional modules as an example when executing the highway emergency event analysis method. In actual applications, the above functional distribution can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the highway emergency event analysis device provided in the above embodiment and the highway emergency event analysis method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0090] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0091] In an embodiment of the present application, on the one hand, traffic anomaly clues are generated based on highway roadside videos and highway gantry data. This process not only utilizes video surveillance data, but also combines gantry data to form a three-dimensional monitoring network. This combination enables the system to capture abnormal changes in traffic flow from multiple angles, thereby more accurately identifying emergencies; on the other hand, speed anomaly clues are generated based on floating vehicle data. The floating vehicle data in this process provides real-time speed and location information, which is crucial for identifying speed changes caused by traffic accidents or other events. By combining these data with traffic anomaly clues, the system can more accurately monitor high-risk areas and promptly detect and respond to possible emergencies. This differentiated and targeted monitoring strategy not only improves monitoring efficiency, but also improves the response speed and processing capabilities to emergencies.

[0092] The present application also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the highway emergency event analysis method provided by the above-mentioned various method embodiments is implemented.

[0093] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the highway emergency event analysis method of each of the above method embodiments.

[0094] See also Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0095] The communication bus 1002 is used to realize the connection and communication between these components.

[0096] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0097] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0098] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts within the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0099] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 6 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a highway emergency analysis application.

[0100] exist Figure 6In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 1001 can be used to call the highway emergency analysis application stored in the memory 1005, and specifically perform the following operations:

[0101] Determine the key monitoring area, and based on the key monitoring area, establish the adjacent relationship between each video point and the adjacent gantry corresponding to each video point;

[0102] Obtain the highway roadside video of each video point within the historical time period, and obtain the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship;

[0103] Determine high-value time periods based on highway roadside videos and highway gantry data;

[0104] Obtaining floating vehicle data on expressway sections within key monitoring areas, and generating abnormal clues based on vehicle flow data during high-value time periods or the floating vehicle data, wherein the abnormal clues include abnormal flow clues or abnormal clues;

[0105] Send traffic anomaly clues and speed anomaly clues to the client for emergency investigation to determine whether there is an emergency in the abnormal road section.

[0106] In one embodiment, the processor 1001 determines the high-value time period according to the highway roadside video and the highway gantry data, and specifically performs the following operations:

[0107] Divide the historical time period into multiple sub-periods;

[0108] Identify and count the number of vehicles in each sub-period of the highway roadside video to obtain the video traffic in each sub-period;

[0109] From the highway gantry data, obtain the gantry flow of the adjacent gantry corresponding to each video point in each sub-period;

[0110] According to the video flow of each sub-period and the gantry flow of each sub-period, the difference ratio of each cycle is calculated;

[0111] The sub-period in which the difference ratio is less than a preset ratio threshold is regarded as a high-value period.

[0112] In one embodiment, when the processor 1001 generates abnormal clues based on the traffic flow data of the high-value time period, the processor 1001 specifically performs the following operations:

[0113] The video traffic and gantry traffic in the high-value time period are unified as facilities;

[0114] Determine high-value facilities, wherein the high-value facilities are facilities in the road network where the traffic volume of each traffic monitoring facility within a preset time period is greater than a preset traffic volume threshold;

[0115] From all high-value facilities, select high-value facilities in the key monitoring area as facilities to be analyzed;

[0116] Based on the gantry data and the facilities to be analyzed, clues to abnormal traffic are generated.

[0117] In one embodiment, when the processor 1001 generates a flow anomaly clue according to the gantry data and the facility to be analyzed, the processor 1001 specifically performs the following operations:

[0118] Determine the maximum flow rate and the minimum flow rate of the facility to be analyzed within a preset period and the times corresponding to the maximum flow rate and the minimum flow rate according to the gantry data;

[0119] According to the maximum flow rate and the minimum flow rate, and the times corresponding to the maximum flow rate and the minimum flow rate, determining whether the facility to be analyzed meets a preset flow drop condition;

[0120] The facilities to be analyzed that meet the preset flow drop conditions are regarded as facilities with abnormal flow;

[0121] The abnormal traffic facilities and their associated abnormal road sections are used as clues to abnormal traffic; wherein,

[0122] The preset flow rate drop condition is: flow max and flow min is the maximum flow rate and minimum flow rate within the preset period t, t max and t min are the times corresponding to the maximum flow and the minimum flow respectively.

[0123] In one embodiment, when the processor 1001 generates a clue of abnormal speed according to the floating vehicle data, the processor 1001 specifically performs the following operations:

[0124] Determine the sudden deceleration and parking vehicles according to the floating vehicle data;

[0125] Generate a speed abnormality clue based on the sudden deceleration and parking of the vehicle;

[0126] Matching the road section to which the vehicle suddenly decelerates and stops as an abnormal road section from the road network;

[0127] Counting the number of vehicles that suddenly decelerate and stop within a preset single statistical period;

[0128] When the number of vehicles is greater than a preset number threshold, the matched abnormal road section and its associated key monitoring area are used as clues of abnormal speed.

[0129] In one embodiment, when the processor 1001 generates a speed abnormality clue according to the sudden deceleration and parking of the vehicle, the processor 1001 specifically performs the following operations:

[0130] From the road network, the road sections to which the vehicles with sudden deceleration or parking belong are matched as abnormal road sections;

[0131] Count the number of vehicles that decelerate suddenly or stop within a preset single statistical period;

[0132] When the number of vehicles is greater than a preset threshold, the matched abnormal road section and its associated key monitoring area are used as clues of speed anomaly.

[0133] In one embodiment, the processor 1001 further performs the following operations:

[0134] Receive troubleshooting results from client feedback;

[0135] When the investigation results indicate that there are emergencies in the clues of abnormal traffic flow and abnormal speed, determine the upstream gantry, downstream gantry and service area on the target section in the investigation results;

[0136] Count the vehicles entering the upstream gantry during the target time period to obtain the first vehicle set entering the target section

[0137] Count the vehicles leaving the downstream gantry within the target time period to obtain the second set of vehicles leaving the target section

[0138] Count the vehicles entering the service area during the target time period and obtain the third vehicle set entering the service area

[0139] Count the vehicles that leave the service area during the target time period to get the fourth set of vehicles that leave the service area

[0140] According to the first vehicle set, the second vehicle set, the third vehicle set and the fourth vehicle set, the set of vehicles affected by the emergency is counted. in,

[0141] Collection of damaged vehicles The calculation method is:

[0142] In one embodiment, the processor 1001 further performs the following operations:

[0143] Receive troubleshooting results from client feedback;

[0144] When the investigation results indicate that there is an emergency in the abnormal flow clues and speed abnormal clues, relevant information of the emergency is released in a preset way;

[0145] The preset method is any one of the ETC publishing software, WeChat public account, operation platform and roadside terminal.

[0146] In an embodiment of the present application, on the one hand, traffic anomaly clues are generated based on highway roadside videos and highway gantry data. This process not only utilizes video surveillance data, but also combines gantry data to form a three-dimensional monitoring network. This combination enables the system to capture abnormal changes in traffic flow from multiple angles, thereby more accurately identifying emergencies; on the other hand, speed anomaly clues are generated based on floating vehicle data. The floating vehicle data in this process provides real-time speed and location information, which is crucial for identifying speed changes caused by traffic accidents or other events. By combining these data with traffic anomaly clues, the system can more accurately monitor high-risk areas and promptly detect and respond to possible emergencies. This differentiated and targeted monitoring strategy not only improves monitoring efficiency, but also improves the response speed and processing capabilities to emergencies.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program for analyzing highway emergencies can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium of the program for analyzing highway emergencies can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0148] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for analyzing highway emergencies, characterized in that: Applied to the server, the method includes: Determine a key monitoring area, and based on the key monitoring area, establish an adjacent relationship between each video point and an adjacent gantry corresponding to each video point; Obtain the highway roadside video of each video point within the historical time period, and obtain the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship; Determine a high-value time period according to the highway roadside video and the highway gantry data; Acquire floating vehicle data on the expressway section within the key monitoring area, and generate abnormal clues based on the vehicle flow data of the high-value time period or the floating vehicle data, wherein the abnormal clues include abnormal flow clues or abnormal speed clues; The traffic anomaly clue and the speed anomaly clue are sent to a client for emergency investigation to determine whether an emergency occurs in the abnormal road section.

2. The method according to claim 1, characterized in that The determining of the high-value time period according to the highway roadside video and the highway gantry data includes: dividing the historical time period into a plurality of sub-periods; Identify and count the number of vehicles in each sub-period of the highway roadside video to obtain the video flow of each sub-period; From the highway gantry data, obtain the gantry flow of the adjacent gantry corresponding to each video point in each sub-period; Calculate the difference ratio of each cycle according to the video flow of each sub-period and the gantry flow of each sub-period; The sub-period in which the difference ratio is less than a preset ratio threshold is regarded as a high-value period.

3. The method according to claim 1, characterized in that The vehicle flow data during the high-value period includes video flow and gantry flow; The generating of abnormal clues according to the traffic flow data of the high-value time period includes: The video traffic and gantry traffic in the high-value time period are unified as facilities; Determine high-value facilities, wherein the high-value facilities are facilities in the road network where the traffic volume of each traffic monitoring facility within a preset time period is greater than a preset traffic volume threshold; From all high-value facilities, select high-value facilities in the key monitoring area as facilities to be analyzed; Based on the gantry data and the facilities to be analyzed, clues to abnormal traffic are generated.

4. The method according to claim 3, characterized in that The generating of flow anomaly clues according to the gantry data and the facility to be analyzed includes: Determine the maximum flow rate and the minimum flow rate of the facility to be analyzed within a preset period and the times corresponding to the maximum flow rate and the minimum flow rate according to the gantry data; According to the maximum flow rate and the minimum flow rate, and the times corresponding to the maximum flow rate and the minimum flow rate, determining whether the facility to be analyzed meets a preset flow drop condition; The facilities to be analyzed that meet the preset flow drop conditions are regarded as facilities with abnormal flow; The abnormal traffic facilities and their associated abnormal road sections are used as clues to abnormal traffic; wherein, The preset flow rate drop condition is: flow max and flow min is the maximum flow rate and minimum flow rate within the preset period t, t max and t min are the times corresponding to the maximum flow and the minimum flow respectively.

5. The method according to claim 1, characterized in that: Generating a clue of abnormal speed according to the floating vehicle data includes: Determine the sudden deceleration and parking vehicles according to the floating vehicle data; Generate a speed abnormality clue based on the sudden deceleration and parking of the vehicle; Matching the road section to which the vehicle suddenly decelerates and stops as an abnormal road section from the road network; Counting the number of vehicles that suddenly decelerate and stop within a preset single statistical period; When the number of vehicles is greater than a preset number threshold, the matched abnormal road section and its associated key monitoring area are used as clues of abnormal speed.

6. The method according to claim 1, characterized in that The key monitoring areas to be determined include: Analyzing meteorological data of the road network obtained in real time to determine areas in the road network where severe weather exists as screening areas; The screening area is overlaid with the preset facility data and the geological disaster data to extract high-risk key facilities and disaster risk sections as key monitoring areas, and a countdown is started. After the preset time countdown ends, the step of analyzing the meteorological data of the road network obtained in real time is continued to update the key monitoring area in real time; or, In response to a user's instruction to select a key monitoring area, a key monitoring area is determined.

7. The method according to any one of claims 1 to 6, characterized in that: After sending the traffic anomaly clue and the speed anomaly clue to the client for emergency event investigation, the method further includes: Receive troubleshooting results from client feedback; When the investigation result indicates that the abnormal flow clue and the abnormal speed clue have an emergency, determine the upstream gantry, downstream gantry and service area on the target section in the investigation result; Count the vehicle information entering the upstream gantry within the target time period to obtain the first vehicle set entering the target section Count the vehicle information leaving the downstream gantry within the target time period to obtain a second vehicle set leaving the target section Count the vehicle information entering the service area within the target time period to obtain a third vehicle set entering the service area Count the vehicles that leave the service area during the target time period to obtain a fourth vehicle set that leaves the service area. According to the first vehicle set, the second vehicle set, the third vehicle set and the fourth vehicle set, the affected vehicle set affected by the emergency is counted in, Collection of damaged vehicles The calculation method is:

8. The method according to any one of claims 1 to 6, characterized in that: After sending the traffic anomaly clue and the speed anomaly clue to the client for emergency event investigation, the method further includes: Receive troubleshooting results from client feedback; When the investigation result indicates that there is an emergency event between the abnormal flow clue and the abnormal speed clue, relevant information of the emergency event is released in a preset manner; wherein, The preset method is any one of the ETC publishing software, WeChat public account, operation platform and roadside terminal.

9. A method for analyzing highway emergencies implemented by the method according to any one of claims 1 to 6, characterized in that: Applied to the client, including: Receiving the traffic anomaly clue and speed anomaly clue sent by the server; Determine whether there is a high-speed video with a clarity greater than a preset threshold on the road section indicated by the abnormal traffic flow clue and the abnormal speed clue; If it exists, retrieve the high-speed video for display; If it does not exist, generate instructions for on-site verification for display.

10. A highway emergency analysis device, characterized in that: The device comprises: An extraction module is used to determine a key monitoring area, and based on the key monitoring area, establish an adjacent relationship between each video point and an adjacent gantry corresponding to each video point; An acquisition module, used to acquire the highway roadside video of each video point within a historical time period, and acquire the highway gantry data of the adjacent gantry corresponding to each video point through the adjacent relationship; A determination module, used for determining a high-value time period according to the highway roadside video and the highway gantry data; A generation module, used to obtain floating vehicle data on the expressway section in the key monitoring area, and generate abnormal clues according to the vehicle flow data of the high-value time period or the floating vehicle data, wherein the abnormal clues include abnormal flow clues or abnormal speed clues; The sending module is used to send the traffic anomaly clue and the speed anomaly clue to a client for emergency investigation to determine whether there is an emergency in the abnormal road section.

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