Traffic emergency big data research and judgment system and data fusion method

Abstract

The invention discloses a traffic emergency big data research and judgment system and a data fusion method, and relates to the technical field of data fusion, and the method comprises the steps: integrating the real-time data of a target electric vehicle, meteorological monitoring data and traditional traffic detection data, achieving the omnibearing improvement of the traffic emergency management capability, and improving the traffic emergency management capability. Firstly, the space limitation of traditional fixed detection equipment is broken through, non-blind area coverage of an urban road network is achieved through the high-frequency moving characteristic of an electric vehicle, particularly for non-motorized vehicle lanes and branch alleys which are difficult to cover through traditional monitoring means, the traffic situation perception fineness is remarkably improved, and in the aspect of data processing, the detection accuracy is improved. The problem of heterogeneous data fusion is effectively solved, the real-time performance and the accuracy of traffic incident detection are qualitatively improved, and traffic anomalies under severe weather conditions such as rainfall and the like can be accurately recognized based on an improved behavior analysis model and a congestion evaluation algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a traffic emergency big data analysis and judgment system and a data fusion method. Background Art

[0002] With the acceleration of urbanization and the continuous growth of traffic demand, urban traffic congestion is becoming increasingly serious. Especially under severe weather conditions, the risk of traffic congestion and accidents has increased significantly. Traditional traffic emergency management methods mainly rely on fixed traffic detection equipment, and there is relatively little monitoring of non-motorized vehicle lanes. Therefore, it is necessary to conduct emergency data analysis on non-motorized vehicle lanes.

[0003] Existing technology, such as the invention patent application CN120032512A, discloses an intelligent traffic condition analysis method for uncertain modal loss. This method includes: utilizing accurately represented modal fusion traffic data for traffic data analysis to improve the accuracy of traffic condition analysis results. Furthermore, based on the traffic condition analysis results, traffic emergency response analysis is performed, traffic emergency response recommendations are determined, and the traffic emergency response recommendations are transmitted to a traffic management terminal.

[0004] Existing technology, such as the invention patent application with publication number CN118606775A, discloses a method and system for emergency event identification based on multi-source data fusion. The method includes: obtaining a multi-source data training sample set for a target emergency event; obtaining feature information; obtaining target fused data; and inputting the fused data into a constructed emergency event identification model to obtain an emergency event identification result. This invention can effectively improve the accuracy and timeliness of emergency event identification.

[0005] According to the above scheme, the target traffic emergency data fusion method includes: (1) The deployment of major fixed detection equipment is concentrated on main roads and key intersections, with limited coverage of urban branch roads, non-motorized vehicle lanes and other areas, resulting in monitoring blind spots. In particular, in non-motorized vehicle lanes, there is a lack of effective real-time data collection methods, making it difficult for traffic management departments to grasp the operating status of the entire road network in a timely manner.

[0006] (2) Traditional methods rely on manual analysis and periodic data reporting from fixed detection equipment, and it usually takes several minutes or even longer from the occurrence of an event to the response.

[0007] (3) Existing systems are mainly based on structured data (such as vehicle flow and speed) and lack the integrated analysis of unstructured data (such as electric vehicle behavior and trajectory dynamics).

[0008] (4) Occupations that transport goods and require the platform to plan delivery routes in real time frequently adjust routes in rainy weather to avoid congestion or dangerous areas, but these dynamic route data have not been effectively integrated into traffic management. Summary of the Invention

[0009] The purpose of the present invention is to provide a traffic emergency big data analysis system and a data fusion method to solve the problems existing in the background technology.

[0010] In order to solve the above technical problems, the present invention adopts the following technical solutions: A traffic emergency data fusion method, comprising: Step 1. During rainfall, obtain the characteristic values ​​of lane planning and traffic data for each target electric vehicle in the city during the target time period, and obtain the rainfall amount for each non-motorized vehicle lane in the city at each monitoring time point during the target time period; Step 2: Conduct behavioral analysis on each target electric vehicle and analyze the congestion feedback from electric vehicle data on each non-motorized vehicle lane in the city; Step 3. Based on the feedback congestion data of electric vehicles on each non-motorized vehicle lane in the city and the rainfall at each monitoring time point, the actual congestion level of each non-motorized vehicle lane in the city is evaluated, and the estimated congestion areas of each non-motorized vehicle lane in the city are analyzed; Step 4. Send the actual congestion level of each non-motorized vehicle lane in the city and the estimated congestion areas to the traffic terminal and display them visually.

[0011] Based on the same inventive concept, a traffic emergency big data analysis and judgment system for executing the aforementioned traffic emergency data fusion method is also proposed, comprising: The road information acquisition module is used to obtain the characteristic values ​​of lane planning and traffic data of each target electric vehicle in the city within the target time period during rainfall, and to obtain the rainfall of each non-motorized vehicle lane in the city at each monitoring time point within the target time period.

[0012] The electric vehicle behavior analysis module is used to analyze the behavior of each target electric vehicle and analyze the feedback congestion of electric vehicle data on each non-motorized vehicle lane in the city.

[0013] The data fusion evaluation module is used to evaluate the actual congestion level of each non-motorized vehicle lane in the city based on the feedback congestion data of electric vehicles in the city, and according to the rainfall in each non-motorized vehicle lane in the city at each monitoring time point, and analyze the estimated congestion areas of each non-motorized vehicle lane in the city.

[0014] The visualization processing module is used to send the actual congestion level and estimated congestion areas of each non-motorized vehicle lane in the city to the traffic terminal for visual display.

[0015] The beneficial effects of the present invention are: The present invention integrates real-time data of target electric vehicles, meteorological monitoring data and traditional traffic detection data, achieving an all-round improvement in traffic emergency management capabilities. First, it breaks through the spatial limitations of traditional fixed detection equipment and uses the high-frequency mobility characteristics of electric vehicles to achieve blind-spot coverage of urban road networks, especially for non-motorized vehicle lanes and branch alleys that are difficult to cover with traditional monitoring methods. It significantly improves the precision of traffic situation perception. At the data processing level, it effectively solves the problem of heterogeneous data fusion, making a qualitative leap in the real-time and accuracy of traffic event detection. Based on the improved behavior analysis model and congestion assessment algorithm, it can accurately identify traffic anomalies under severe weather conditions such as rain. It also ensures the scalability and cross-platform compatibility of the system through standardized data interfaces and modular design. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 Schematic diagram of the method of the present invention.

[0018] Figure 2 Schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, the present invention provides a traffic emergency data fusion method, comprising: Step 1. When it rains, obtain the characteristic values ​​of lane planning and traffic data of each target electric vehicle in the city during the target time period, and obtain the rainfall of each non-motorized vehicle lane in the city at each monitoring time point during the target time period.

[0021] In a specific embodiment of the present invention, the lane planning of each target electric vehicle in the city and the characteristic values ​​of each traffic data are obtained during the target time period, and the rainfall of each non-motorized vehicle lane in the city at each monitoring time point during the target time period is obtained. The specific acquisition method is: obtaining the lane planning of each target electric vehicle in the city during the target time period from the data monitoring platform.

[0022] It should be noted that the target electric vehicle described in the present invention is an electric vehicle used for transporting goods and requiring the platform to plan the transportation route in real time, including but not limited to express delivery, catering service distribution and other occupations.

[0023] It should also be noted that the lane planning of each target electric vehicle is planned by the data monitoring platform. Each target electric vehicle has an initial lane planning. If it deviates from the route of the initial lane planning, the second lane planning is updated. If it deviates from the route of the second lane planning, the third lane planning is updated, and so on, so as to obtain the lane planning of each target electric vehicle in the city within the target time period.

[0024] Determine the stable position of each target electric vehicle, install each sensor at the stable position of each target electric vehicle, and obtain the characteristic values ​​of each traffic data of each target electric vehicle in the city during the target time period through each sensor. The traffic data includes: the speed value and GPS position at each monitoring time point.

[0025] In a specific embodiment, the stable position of each target electric vehicle is determined by the following method: the existing electric vehicle stable position analysis technology is relatively mature, and the stable position of each target electric vehicle can be determined by the existing electric vehicle stable position analysis technology.

[0026] It should be noted that by installing sensors in a unified manner, the credibility of the acquired data is ensured to be high, and large errors in the acquired data due to the differences in target electric vehicles are prevented.

[0027] The rainfall of each non-motorized vehicle lane in the city at each monitoring time point is obtained from the meteorological platform.

[0028] Step 2: Conduct behavioral analysis on each target electric vehicle and analyze the feedback congestion of electric vehicle data on each non-motorized vehicle lane in the city.

[0029] In a specific embodiment of the present invention, the behavior analysis of each target electric vehicle and the feedback congestion degree of the electric vehicle data of each non-motorized vehicle lane in the city are analyzed. The specific analysis method is: based on the position of each target electric vehicle in the city on the non-motorized vehicle lane at each monitoring time point during the target time period, the speed reduction congestion assessment coefficient of each target electric vehicle in the city on each non-motorized vehicle lane during the target time period is analyzed, and the feedback congestion degree of the electric vehicle data of each non-motorized vehicle lane in the city is calculated.

[0030] In a specific embodiment, the feedback congestion degree of electric vehicle data of each non-motorized vehicle lane in the city is calculated by mapping the speed reduction congestion assessment coefficient of each target electric vehicle in each non-motorized vehicle lane in the city within the target time period to obtain the speed reduction congestion assessment coefficient of each target electric vehicle in each non-motorized vehicle lane in the city within the target time period. , where x represents the number of each target electric vehicle, , y is a positive integer greater than 2, n represents the number of each non-motorized vehicle lane, , m is a positive integer greater than 2, and the feedback congestion degree of electric vehicle data of each non-motorized vehicle lane in the city is calculated .

[0031] In a specific embodiment of the present invention, the speed reduction congestion assessment coefficient of each target electric vehicle belonging to the city in each non-motorized vehicle lane is analyzed within the target time period, and the specific analysis method is: based on the position of each target electric vehicle belonging to the city on the non-motorized vehicle lane at each monitoring time point within the target time period, the monitoring time points of each target electric vehicle belonging to the city in each non-motorized vehicle lane within the target time period are mapped, and based on the speed value of each target electric vehicle belonging to the city at each monitoring time point within the target time period, the speed value of each target electric vehicle belonging to the city in each non-motorized vehicle lane within the target time period is extracted.

[0032] The rainfall intervals, the speed values ​​and rainfall of each target electric vehicle in the city at each historical monitoring time point are obtained from the local database, and the appropriate speed values ​​of each target electric vehicle in the city at each monitoring time point are calculated based on them.

[0033] It should be noted that the local database is used to store each rainfall interval, the rate value and rainfall of each target electric vehicle belonging to the city at each historical monitoring time point, the non-motor vehicle lane type and road width of each non-motor vehicle lane belonging to the city, the actual congestion level corresponding to each comprehensive congestion assessment coefficient interval, the appropriate floating traffic flow of each non-motor vehicle lane type in each road width interval and each rainfall interval, each segmented area of ​​each non-motor vehicle lane belonging to the city, the estimated congestion coefficient threshold, and the display color corresponding to each actual congestion level.

[0034] Based on the correspondence between the location and each monitoring time point, and based on the speed value of each target electric vehicle in the city at each monitoring time point on each non-motorized vehicle lane within the target time period, the speed value of each target electric vehicle at each location on each non-motorized vehicle lane within the target time period is mapped, and based on the appropriate speed value of each target electric vehicle in the city at each monitoring time point, the speed reduction congestion assessment coefficient of each target electric vehicle in the city on each non-motorized vehicle lane within the target time period is calculated.

[0035] It should be noted that the correspondence between the position and each monitoring time point is that each position on the non-motor vehicle lane corresponds to a certain position at different monitoring time points.

[0036] In a specific embodiment, the speed reduction congestion assessment coefficient of each target electric vehicle in each non-motor vehicle lane in the city during the target time period is calculated by: the speed value of each target electric vehicle at each location in each non-motor vehicle lane in the city is calculated as follows: , where i represents the number of each position, , j is a positive integer greater than 2, and the speed fluctuation hazard coefficient of each target electric vehicle at each location on each non-motorized vehicle lane in the city is calculated , where j represents the number of positions.

[0037] According to the appropriate speed value of each target electric vehicle in the city at each monitoring time point, and according to the corresponding relationship between the location and each monitoring time point, the appropriate speed value of each target electric vehicle at each location in each non-motorized vehicle lane in the city is mapped. , calculate the speed reduction comparison coefficient of each target electric vehicle at each location on each non-motorized vehicle lane in the city , and calculate the speed reduction congestion assessment coefficient of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period .

[0038] In a specific embodiment of the present invention, the suitable speed value of each target electric vehicle belonging to the city at each monitoring time point is calculated by the following specific calculation method: if the rainfall of a non-motorized vehicle lane belonging to the city at a certain monitoring time point is included in a certain rainfall interval, then the rainfall interval is used as the target rainfall interval of the non-motorized vehicle lane belonging to the city at the monitoring time point, thereby obtaining the target rainfall interval of each non-motorized vehicle lane belonging to the city at each monitoring time point.

[0039] If the rainfall of a target electric vehicle belonging to the city at a certain historical monitoring time point is included in the target rainfall range of a non-motorized vehicle lane at a certain monitoring time point, then the historical monitoring time point will be used as the target historical monitoring time point of the target electric vehicle belonging to the city at that monitoring time point, thereby screening out the target historical monitoring time points of each target electric vehicle belonging to the city at each monitoring time point, and based on the rate values ​​of each target electric vehicle belonging to the city at each historical monitoring time point, the rate values ​​of each target electric vehicle belonging to the city at each monitoring time point are mapped to obtain the rate values ​​of each target historical monitoring time point of each monitoring time point, thereby calculating the appropriate rate values ​​of each target electric vehicle belonging to the city at each monitoring time point.

[0040] In a specific embodiment, the suitable speed value of each target electric vehicle belonging to the city at each monitoring time point is calculated by the following specific calculation method: based on the speed values ​​of each target electric vehicle belonging to the city at each target historical monitoring time point at each monitoring time point, the speed values ​​are accumulated and the average value is calculated to obtain the suitable speed value of each target electric vehicle belonging to the city at each monitoring time point.

[0041] Step 3. Based on the feedback congestion data of electric vehicles in each non-motorized vehicle lane in the city, and according to the rainfall in each non-motorized vehicle lane in the city at each monitoring time point, the actual congestion level of each non-motorized vehicle lane in the city is evaluated, and the estimated congestion areas of each non-motorized vehicle lane in the city are analyzed.

[0042] It should be noted that the actual congestion level is divided into: level 1 congestion level, level 2 congestion level, level 3 congestion level and level 4 congestion level, among which level 1 congestion level is greater than level 2 congestion level, level 2 congestion level is greater than level 3 congestion level, and level 3 congestion level is greater than level 4 congestion level.

[0043] In a specific embodiment of the present invention, the actual congestion level of each non-motorized vehicle lane in the city is evaluated by a specific evaluation method: the non-motorized vehicle lane type and road width of each non-motorized vehicle lane in the city are obtained from a local database, and the vehicle number congestion coefficient of each non-motorized vehicle lane in the city is analyzed accordingly.

[0044] It should be noted that there are three different types of non-motor vehicle lanes: one is an exclusive non-motor vehicle lane, in which case the non-motor vehicle lane type is exclusive; the second is no exclusive non-motor vehicle lane, and the motor vehicle lane in the direction of travel is not a single lane, then the rightmost motor vehicle lane is used as a non-motor vehicle lane, in which case the non-motor vehicle lane type is motor vehicle lane occupation; the third is no exclusive non-motor vehicle lane, and the motor vehicle lane in the direction of travel is a single lane, in which case the non-motor vehicle lane type is mixed use for motor vehicles and electric vehicles.

[0045] It should also be noted that, with respect to the road width, if the non-motor vehicle lane type is exclusive, the lane width of the exclusive non-motor vehicle lane shall be used as the road width; if the non-motor vehicle lane type is motor vehicle lane occupation, the lane width of the rightmost motor vehicle lane shall be used as the road width; if the non-motor vehicle lane type is mixed for motor vehicles and electric vehicles, the lane width of the single-lane motor vehicle lane shall be used as the road width.

[0046] Based on the feedback congestion data of electric vehicles in each non-motorized vehicle lane in the city, and combined with the vehicle number congestion coefficient of each non-motorized vehicle lane in the city, the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city is calculated.

[0047] In a specific embodiment, the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city is calculated by the following method: based on the feedback congestion degree of electric vehicle data of each non-motorized vehicle lane in the city , and combined with the congestion coefficient of the number of vehicles in each non-motorized vehicle lane in the city , calculate the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city .

[0048] The actual congestion level corresponding to each comprehensive congestion assessment coefficient interval is obtained from the local database, and the actual congestion level of each non-motorized vehicle lane in the city is mapped based on the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city.

[0049] In a specific embodiment of the present invention, the specific analysis method for analyzing the vehicle number congestion coefficient of each non-motorized vehicle lane belonging to the city is: according to the non-motorized vehicle lane type of each non-motorized vehicle lane belonging to the city, the floating traffic flow of the corresponding non-motorized vehicle lanes in the city within the target time period is obtained according to different non-motorized vehicle lane types, and according to the road width of each non-motorized vehicle lane belonging to the city and the rainfall at each monitoring time point, the vehicle number congestion coefficient of each non-motorized vehicle lane belonging to the city is calculated.

[0050] In a specific embodiment, the floating traffic flow of the non-motorized vehicle lanes corresponding to the city within the target time period is obtained respectively according to different non-motorized vehicle lane types. The specific acquisition method is: if the non-motorized vehicle lane type of a non-motorized vehicle lane belonging to the city is exclusive, the floating traffic flow of the non-motorized vehicle lane corresponding to the city within the target time period is obtained through the camera at the entrance of the exclusive non-motorized vehicle lane; if the non-motorized vehicle lane type of a non-motorized vehicle lane belonging to the city is motor vehicle lane occupation, the floating traffic flow of the non-motorized vehicle lane corresponding to the city within the target time period is obtained through the camera at the entrance direction of the rightmost lane of the intersection; if the non-motorized vehicle lane type of a non-motorized vehicle lane belonging to the city is mixed for motor vehicles and electric vehicles, the floating traffic flow of the non-motorized vehicle lane corresponding to the city within the target time period is obtained through the camera at the entrance direction of the single lane of the intersection.

[0051] In a specific embodiment, the vehicle congestion coefficient of each non-motorized vehicle lane in the city is calculated by obtaining the appropriate floating traffic flow of each non-motorized vehicle lane type in each road width interval and each rainfall interval from a local database, and mapping the appropriate floating traffic flow of each non-motorized vehicle lane in the city within the target time period based on the non-motorized vehicle lane type of each non-motorized vehicle lane in the city, the road width of each non-motorized vehicle lane in the city, and the rainfall at each monitoring time point. , based on the floating traffic volume of each non-motorized vehicle lane in the city , calculate the congestion coefficient of the number of vehicles in each non-motorized vehicle lane in the city , where e is a natural constant.

[0052] It should be noted that the suitable floating traffic flow of each type of non-motorized vehicle lane in each road width range and each rainfall range is set by scientific researchers according to actual conditions, and needs to satisfy the relationship: the larger the road width, the larger the corresponding suitable floating traffic flow, and the greater the rainfall, the smaller the corresponding suitable floating traffic flow.

[0053] In a specific embodiment of the present invention, the estimated congestion areas of each non-motorized vehicle lane in the city are analyzed, and the specific analysis method is: based on the GPS position of each target electric vehicle in the city at each monitoring time point within the target time period, the position of each target electric vehicle in the city on the non-motorized vehicle lane at each monitoring time point within the target time period is obtained, and the actual travel route of each target electric vehicle in the city within the target time period is analyzed.

[0054] According to the lane planning and actual travel routes of each target electric vehicle in the city during the target time period, the route change nodes of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period are analyzed, and the route change nodes of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period are mapped.

[0055] The segmented areas of each non-motorized vehicle lane in the city are obtained from the local database. If the route change node of a target electric vehicle in a non-motorized vehicle lane in the city is included in a segmented area, the estimated congestion coefficient of the segmented area is increased by 1, and so on, to obtain the estimated congestion coefficient of each segmented area of ​​each non-motorized vehicle lane in the city.

[0056] The estimated congestion coefficient threshold is obtained from the local database. If the estimated congestion coefficient of a segmented area of ​​a non-motorized vehicle lane in the city is greater than the estimated congestion coefficient threshold, the segmented area is marked as an estimated congestion area, thereby screening the estimated congestion areas of each non-motorized vehicle lane in the city.

[0057] In a specific embodiment of the present invention, the analysis of the route change nodes of each target electric vehicle in the city in each non-motorized vehicle lane within the target time period is performed by the following specific analysis method: the actual travel route of a target electric vehicle in the city within the target time period is compared with the corresponding first lane planning, and the position of the non-motorized vehicle lane where the actual travel route of the target electric vehicle in the city is inconsistent with the first lane planning is extracted, and the subsequent travel route of the target electric vehicle is compared with the second lane planning, and the position of the non-motorized vehicle lane where the subsequent travel route of the target electric vehicle in the city is inconsistent with the second lane planning is extracted, and so on, and the position of the non-motorized vehicle lane where the inconsistent selection occurs in the above derivation process is used as the route change node of the target electric vehicle in the city in the corresponding non-motorized vehicle lane, thereby summarizing the route change nodes of each target electric vehicle in the city in each non-motorized vehicle lane within the target time period.

[0058] It should be noted that the subsequent route is the remaining route after subtracting the route consistent with the first lane planning from the actual route.

[0059] Step 4. Send the actual congestion level of each non-motorized vehicle lane in the city and the estimated congestion areas to the traffic terminal and display them visually.

[0060] In a specific embodiment, the visual display is performed in the following manner: the display color corresponding to each actual congestion level is obtained from the local database, and the display color of each non-motorized vehicle lane in the city is mapped according to the actual congestion level of each non-motorized vehicle lane in the city, and the display color is displayed on the screen, and the estimated congestion area of ​​each non-motorized vehicle lane in the city is marked with a warning.

[0061] The present invention integrates the real-time data of target electric vehicles, meteorological monitoring data and traditional traffic detection data, achieving an all-round improvement in traffic emergency management capabilities. First, it breaks through the spatial limitations of traditional fixed detection equipment and uses the high-frequency mobility characteristics of target electric vehicles to achieve blind-spot coverage of urban road networks, especially for non-motorized vehicle lanes and branch alleys that are difficult to cover with traditional monitoring methods. It significantly improves the precision of traffic situation perception. At the data processing level, it effectively solves the problem of heterogeneous data fusion, making the real-time and accuracy of traffic event detection a qualitative leap. Based on the improved behavior analysis model and congestion assessment algorithm, it can accurately identify traffic anomalies under severe weather conditions such as rain. It also ensures the scalability and cross-platform compatibility of the system through standardized data interfaces and modular design.

[0062] Based on the same inventive concept, a traffic emergency big data analysis and judgment system for executing the aforementioned traffic emergency data fusion method is also proposed, comprising: Road information acquisition module, electric vehicle behavior analysis module, data fusion evaluation module, visualization processing module and local database.

[0063] It should be noted that the road information acquisition module is connected to the electric vehicle behavior analysis module, the electric vehicle behavior analysis module is connected to the data fusion evaluation module, the data fusion evaluation module is connected to the visualization processing module, and the local database is connected to the electric vehicle behavior analysis module, the data fusion evaluation module, and the visualization processing module.

[0064] The road information acquisition module is used to obtain the characteristic values ​​of lane planning and traffic data of each target electric vehicle in the city within the target time period during rainfall, and to obtain the rainfall of each non-motorized vehicle lane in the city at each monitoring time point within the target time period.

[0065] The electric vehicle behavior analysis module is used to perform behavior analysis on each target electric vehicle and analyze the feedback congestion degree of electric vehicle data on each non-motorized vehicle lane in the city.

[0066] The data fusion evaluation module is used to evaluate the actual congestion level of each non-motorized vehicle lane in the city based on the feedback congestion data of electric vehicle data on each non-motorized vehicle lane in the city and the rainfall on each non-motorized vehicle lane in the city at each monitoring time point, and analyze the estimated congestion areas of each non-motorized vehicle lane in the city.

[0067] The visualization processing module is used to send the actual congestion level of each non-motorized vehicle lane in the city and each estimated congestion area to the traffic terminal and perform visual display.

[0068] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A traffic emergency data fusion method, characterized in that: include: Step 1. During rainfall, obtain the characteristic values ​​of lane planning and traffic data for each target electric vehicle in the city during the target time period, and obtain the rainfall amount for each non-motorized vehicle lane in the city at each monitoring time point during the target time period; Step 2: Conduct behavioral analysis on each target electric vehicle and analyze the congestion feedback from electric vehicle data on each non-motorized vehicle lane in the city; Step 3. Based on the feedback congestion data of electric vehicles on each non-motorized vehicle lane in the city and the rainfall at each monitoring time point, the actual congestion level of each non-motorized vehicle lane in the city is evaluated, and the estimated congestion areas of each non-motorized vehicle lane in the city are analyzed; Step 4. Send the actual congestion level of each non-motorized vehicle lane in the city and the estimated congestion areas to the traffic terminal and display them visually.

2. A traffic emergency data fusion method according to claim 1, characterized in that: The method for obtaining the characteristic values ​​of lane planning and traffic data of each target electric vehicle in the city during the target time period, and obtaining the rainfall of each non-motorized vehicle lane in the city at each monitoring time point during the target time period, is as follows: Obtain the lane planning for each target electric vehicle in the city during the target time period from the data monitoring platform; Determine the stable position of each target electric vehicle, install each sensor at the stable position of each target electric vehicle, and obtain characteristic values ​​of each traffic data of each target electric vehicle in the city during a target time period through each sensor, wherein the traffic data includes: speed value and GPS position at each monitoring time point; The rainfall of each non-motorized vehicle lane in the city at each monitoring time point is obtained from the meteorological platform.

3. A traffic emergency data fusion method according to claim 2, characterized in that: The behavior analysis of each target electric vehicle is performed, and the feedback congestion degree of electric vehicle data of each non-motorized vehicle lane in the city is analyzed. The specific analysis method is as follows: Based on the positions of each target electric vehicle in the city on the non-motorized vehicle lane at each monitoring time point during the target time period, the speed reduction congestion assessment coefficient of each target electric vehicle in the city on each non-motorized vehicle lane during the target time period is analyzed, and the feedback congestion degree of the electric vehicle data of each non-motorized vehicle lane in the city is calculated.

4. A traffic emergency data fusion method according to claim 3, characterized in that: The analysis is performed on the speed reduction congestion assessment coefficient of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period. The specific analysis method is as follows: Based on the position of each target electric vehicle in the city on the non-motorized vehicle lane at each monitoring time point during the target time period, the monitoring time points of each target electric vehicle in the city on each non-motorized vehicle lane during the target time period are mapped, and based on the speed value of each target electric vehicle in the city at each monitoring time point during the target time period, the speed value of each target electric vehicle in the city on each non-motorized vehicle lane during the target time period is extracted; Obtain the speed values ​​and rainfall of each target electric vehicle in each rainfall interval and city at each historical monitoring time point from the local database, and calculate the appropriate speed value of each target electric vehicle in the city at each monitoring time point based on the obtained data; Based on the correspondence between the location and each monitoring time point, and based on the speed value of each target electric vehicle in the city at each monitoring time point on each non-motorized vehicle lane within the target time period, the speed value of each target electric vehicle at each location on each non-motorized vehicle lane within the target time period is mapped, and based on the appropriate speed value of each target electric vehicle in the city at each monitoring time point, the speed reduction congestion assessment coefficient of each target electric vehicle in the city on each non-motorized vehicle lane within the target time period is calculated.

5. A traffic emergency data fusion method according to claim 4, characterized in that: The specific calculation method for calculating the appropriate speed value of each target electric vehicle in the city at each monitoring time point is: If the rainfall of a non-motorized vehicle lane in the city at a certain monitoring time point is included in a certain rainfall interval, then the rainfall interval is used as the target rainfall interval of the non-motorized vehicle lane in the city at the monitoring time point, thereby obtaining the target rainfall interval of each non-motorized vehicle lane in the city at each monitoring time point; If the rainfall of a target electric vehicle belonging to the city at a certain historical monitoring time point is included in the target rainfall range of a non-motorized vehicle lane at a certain monitoring time point, then the historical monitoring time point will be used as the target historical monitoring time point of the target electric vehicle belonging to the city at that monitoring time point, thereby screening out the target historical monitoring time points of each target electric vehicle belonging to the city at each monitoring time point, and based on the rate values ​​of each target electric vehicle belonging to the city at each historical monitoring time point, the rate values ​​of each target electric vehicle belonging to the city at each monitoring time point are mapped to obtain the rate values ​​of each target historical monitoring time point of each monitoring time point, thereby calculating the appropriate rate values ​​of each target electric vehicle belonging to the city at each monitoring time point.

6. A traffic emergency data fusion method according to claim 2, characterized in that: The specific evaluation method for evaluating the actual congestion level of each non-motorized vehicle lane in the city is as follows: Obtain the non-motorized vehicle lane type and road width of each non-motorized vehicle lane in the city from the local database, and analyze the vehicle congestion coefficient of each non-motorized vehicle lane in the city based on this; Based on the feedback congestion data of electric vehicles in each non-motorized vehicle lane in the city and the vehicle number congestion coefficient of each non-motorized vehicle lane in the city, the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city is calculated; The actual congestion level corresponding to each comprehensive congestion assessment coefficient interval is obtained from the local database, and the actual congestion level of each non-motorized vehicle lane in the city is mapped based on the comprehensive congestion assessment coefficient of each non-motorized vehicle lane in the city.

7. A traffic emergency data fusion method according to claim 6, characterized in that: The specific analysis method for analyzing the vehicle congestion coefficient of each non-motorized vehicle lane in the city is as follows: According to the non-motorized vehicle lane types of the city, the floating traffic volume of the corresponding non-motorized vehicle lanes in the city during the target time period is obtained according to the different non-motorized vehicle lane types, and the vehicle number congestion coefficient of each non-motorized vehicle lane in the city is calculated based on the road width of each non-motorized vehicle lane in the city and the rainfall at each monitoring time point.

8. A traffic emergency data fusion method according to claim 2, characterized in that: The specific analysis method for each estimated congestion area of ​​each non-motorized vehicle lane in the analysis city is as follows: Based on the GPS position of each target electric vehicle in the city at each monitoring time point during the target time period, the position of each target electric vehicle in the city on the non-motorized vehicle lane at each monitoring time point during the target time period is obtained, and the actual travel route of each target electric vehicle in the city during the target time period is analyzed; Based on the lane planning and actual travel routes of each target electric vehicle in the city during the target time period, the route change nodes of each target electric vehicle in the city in each non-motorized vehicle lane during the target time period are analyzed, and the route change nodes of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period are mapped; Obtain each segmented area of ​​each non-motorized vehicle lane in the city from the local database. If the route change node of a target electric vehicle in a non-motorized vehicle lane in the city is included in a segmented area, then increase the estimated congestion coefficient of the segmented area by 1. Similarly, obtain the estimated congestion coefficient of each segmented area of ​​each non-motorized vehicle lane in the city. The estimated congestion coefficient threshold is obtained from the local database. If the estimated congestion coefficient of a segmented area of ​​a non-motorized vehicle lane in the city is greater than the estimated congestion coefficient threshold, the segmented area is marked as an estimated congestion area, thereby screening the estimated congestion areas of each non-motorized vehicle lane in the city.

9. A traffic emergency data fusion method according to claim 8, characterized in that: The analysis is performed on the route change nodes of each target electric vehicle in each non-motorized vehicle lane in the city during the target time period. The specific analysis method is as follows: The actual travel route of a target electric vehicle in the city during the target time period is compared with the corresponding first lane planning, and the position of the non-motorized vehicle lane where the actual travel route of the target electric vehicle in the city is inconsistent with the first lane planning is extracted, and the subsequent travel route of the target electric vehicle is compared with the second lane planning, and the position of the non-motorized vehicle lane where the subsequent travel route of the target electric vehicle in the city is inconsistent with the second lane planning is extracted, and so on. The position of the non-motorized vehicle lane where the inconsistent selection occurs in the above derivation process is used as the route change node of the target electric vehicle in the city in the corresponding non-motorized vehicle lanes, so as to summarize the route change nodes of each target electric vehicle in the city in each non-motorized vehicle lane during the target time period.

10. A traffic emergency big data analysis and judgment system, used to execute a traffic emergency data fusion method according to any one of claims 1 to 9, characterized in that: include: The road information acquisition module is used to obtain the characteristic values ​​of lane planning and traffic data of each target electric vehicle in the city during the target time period during rainfall, and obtain the rainfall amount of each non-motorized vehicle lane in the city at each monitoring time point during the target time period; The electric vehicle behavior analysis module is used to analyze the behavior of each target electric vehicle and analyze the feedback congestion of electric vehicle data on each non-motorized vehicle lane in the city; The data fusion assessment module is used to evaluate the actual congestion level of each non-motorized vehicle lane in the city based on the feedback congestion data of electric vehicles in the city and the rainfall in each non-motorized vehicle lane at each monitoring time point, and analyze the estimated congestion areas of each non-motorized vehicle lane in the city; The visualization processing module is used to send the actual congestion level and estimated congestion areas of each non-motorized vehicle lane in the city to the traffic terminal for visual display.

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