Vehicle service project order management method and system

Abstract

The present invention relates to the technical field of vehicle services, and specifically discloses a vehicle service project order management method and system, the method comprising the following steps: S1: establishing a city model, S2: obtaining vehicle rescue service order information, S3: analyzing the vehicle rescue emergency state, S4: analyzing the driving path of the rescue vehicle, S5: managing the vehicle rescue service order, S6: evaluating the rescue service quality, and S7: establishing a human-computer interaction module; the present invention optimizes the processing flow of automobile rescue services through intelligent and automated means, improves the rescue response speed and processing efficiency, thereby effectively alleviating traffic pressure, improving urban traffic operation efficiency, and improving user satisfaction and experience; by establishing a city model to simulate and optimize the driving path of the rescue vehicle, the rescue efficiency is improved; and the rescue service quality is evaluated and fed back, providing strong support for the continuous improvement of service quality.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle services, and in particular to a vehicle service project order management method and system. Background Art

[0002] With the rapid development of modern urban transportation, the number of vehicles has increased dramatically, and the probability of vehicles encountering unexpected situations such as breakdowns and accidents during driving has also increased accordingly. As a key link in ensuring the smooth operation of urban transportation and the safety of drivers and passengers, vehicle rescue services are becoming increasingly important.

[0003] Traditional methods of managing vehicle rescue service project orders mainly rely on manual labor and traditional communications. First, rescue requests are received through phone calls, text messages or websites, and the fault type, location, contact information, etc. are recorded. Then, managers manually dispatch the nearest rescue resources based on experience. During the process, frequent phone calls are required to track progress. After the rescue is completed, the personnel feedback the results, and the managers record and mark the order as completed. Although this method is effective, it also has many disadvantages.

[0004] First, the allocation of rescue service orders often lacks reasonable planning, relying on manual scheduling or simple geographic information systems (GIS) for task allocation. There is a lack of intelligent order management and path optimization mechanisms, which not only leads to uneven distribution of rescue resources, but also leads to problems such as resource waste, long response time and low customer satisfaction. Secondly, traditional automobile rescue services often have problems such as slow response speed and low processing efficiency. Especially during peak traffic hours, failure to handle vehicle rescue service orders in a timely manner will not only affect the normal use of faulty vehicles, but may also aggravate traffic congestion and bring greater pressure to urban traffic. In addition, there is a lack of effective means and standards for controlling rescue costs and evaluating service quality, making it difficult to meet the car owners' growing demand for efficient, high-quality and economical rescue services. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a vehicle service item order management method and system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a vehicle service item order management method, comprising the following steps: S1: Establishing a city model: acquiring multi-source information data of the city, integrating and preprocessing the multi-source information data, and establishing a city model based on the integrated and preprocessed data, wherein the multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; S2: Obtaining vehicle rescue service order information: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; S3: Analyze the emergency status of vehicle rescue: Based on the rescue service order information, weather condition information and traffic information in the city model, analyze the rescue emergency situation assessment coefficient of each monitoring sub-area, and classify the rescue emergency level of each vehicle rescue service order; S4: Analyze the driving path of the rescue vehicle: simulate the driving path of the rescue vehicle based on the city model, output the driving path of the rescue vehicle, optimize the driving path by calculating the driving path optimization coefficient, and output the optimal driving path of the rescue vehicle; S5: Manage vehicle rescue service orders: Match rescue vehicles and rescue plans based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, and start vehicle rescue services for each monitoring sub-area; S6: Evaluate the rescue service quality: obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; S7: Establish a human-computer interaction module: Display the location information, estimated arrival time information and rescue personnel information of the rescue vehicle in real time on the user's mobile device.

[0007] Preferably, the specific content of establishing the city model is: using big data analysis technology and data fusion technology to integrate multi-source information data, and using 3D modeling software, spatial analysis and modeling technology to construct the city model.

[0008] Preferably, the order information includes but is not limited to basic vehicle information, vehicle owner contact information, vehicle fault information and vehicle location information.

[0009] Preferably, the specific content of analyzing the rescue emergency situation assessment coefficient of each monitoring sub-area is: S31: Calculate the deviation rate of each weather condition index , the calculation model is: ,in, W j represents the index value of the jth weather condition index, represents the preset ideal index value corresponding to the jth weather condition, j represents the number of each weather condition, j=1,2,...m; m is the total number of weather conditions; The weather conditions include but are not limited to weather conditions visibility, rainfall, wind speed, snowfall and road ice thickness; S32: Calculate the accident risk rate under various weather conditions Ar j , the calculation model is as follows: ,in, on j represents the number of historical accidents under the jth weather condition, on represents the number of historical accidents under ideal weather conditions, Other factors affecting the accident risk; S33: Calculate the emergency situation assessment coefficient of each monitoring sub-area EAC i , the calculation model is as follows: ,in, EAC i represents the rescue emergency situation assessment coefficient of the i-th monitoring sub-area, represents the deviation rate of the jth weather condition index, Indicates the preset maximum deviation rate of the corresponding weather condition indicator. Ar j represents the accident risk rate under the jth weather condition, fd i represents the degree of vehicle failure in the i-th monitoring sub-area, ft i represents the vehicle failure duration in the i-th monitoring sub-area, At i represents the estimated arrival time of the rescue vehicle in the ith monitoring sub-area, where , D i represents the distance between the vehicle in the ith monitoring sub-area and the rescue vehicle, V represents the average speed of the rescue vehicle, It represents other influencing factors of the rescue emergency situation assessment coefficient, i is the number of each monitoring sub-area, and e represents a natural constant.

[0010] Preferably, the specific content of analyzing the driving path of the rescue vehicle is: S41: Obtain the estimated travel time of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path T ik , substituting it into the formula Get the time optimization coefficient of the i-th monitoring sub-area Ct i ,in, T i0 represents the preset minimum estimated driving time of the rescue vehicle in the ith monitoring sub-area, represents the exponential function; S42: Obtain the total number of nodes affected by traffic on the kth path selected by the rescue vehicle in the i-th monitoring sub-area M ik , substituting it into the formula Get the traffic condition optimization coefficient of the i-th monitoring sub-area Ctc i , where q represents the number of the node affected by traffic, It represents the interference factor of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path affected by the q-th traffic node; S43: Calculate the optimal driving path optimization coefficient, and the calculation model is specifically as follows: ,in, POC i represents the optimal driving path optimization coefficient of the rescue vehicle in the i-th monitoring sub-area, Co i represents the comprehensive cost coefficient of the ith monitoring sub-area, w1, w2, and w3 represent the weight factors of the time optimization coefficient, traffic condition optimization coefficient, and comprehensive cost coefficient, respectively, and w1+w2+w3=1; S44: Optimizing the rescue vehicle driving path by using the optimal driving path optimization coefficient, and outputting the optimal driving path of the rescue vehicle in each monitoring sub-area.

[0011] Preferably, the calculation steps of the vehicle rescue service quality evaluation index of each monitoring sub-area are: S61: Obtaining the actual rescue time of the i-th monitoring sub-area and the estimated rescue time for the i-th monitoring sub-area , substituting it into the formula Get the vehicle rescue progress deviation rate of the i-th monitoring sub-area ; S62: Obtaining the actual driving path length of the rescue vehicle in the i-th monitoring sub-area and the planned driving path length of the rescue vehicle in the ith monitoring sub-area , substituting it into the formula Get the rescue vehicle path deviation of the i-th monitoring sub-area ; S63: Calculate the vehicle rescue service quality evaluation index, and the calculation model is: ,in, QEI i represents the vehicle rescue service quality evaluation index of the i-th monitoring sub-area, pe i represents the order processing efficiency of the i-th monitoring sub-area, where , hti represents the order processing time of the i-th monitoring sub-area, rt i Indicates the order response time of the i-th monitoring sub-area, pe 0 represents the preset ideal order processing efficiency. Represents other influencing factors of vehicle rescue service quality evaluation index, Represents an exponential function.

[0012] Preferably, the specific content of the rescue service quality evaluation is: extracting the vehicle rescue service quality evaluation index of each monitoring sub-area and comparing it with the preset quality threshold; if the vehicle rescue service quality evaluation index of the i-th monitoring sub-area is less than the preset quality threshold, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be abnormal, and an early warning message is issued for the abnormal data and sent to the management personnel; otherwise, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be normal.

[0013] To achieve the above object, the present invention provides the following technical solution: a vehicle service item order management system, implementing the above vehicle service item order management method, comprising: City model building module: used to obtain multi-source information data of the city, integrate and pre-process the multi-source information data, and build a city model based on the integrated and pre-processed data. The multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; Obtaining vehicle rescue service order module: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; Vehicle rescue emergency status analysis module: Based on the rescue service order information, weather conditions and traffic information in the city model, it analyzes the rescue emergency situation assessment coefficient of each monitoring sub-area and divides each vehicle rescue service order into rescue emergency levels; Rescue vehicle driving path analysis module: simulates the rescue vehicle driving path based on the city model, outputs the rescue vehicle driving path, optimizes the driving path by calculating the driving path optimization coefficient, and outputs the optimal driving path for the rescue vehicle; Vehicle rescue service order management module: Based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, the rescue vehicle and rescue plan are matched, and the vehicle rescue service is initiated for each monitoring sub-area; Rescue service quality evaluation module: used to obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; Human-computer interaction interface module: used to display the rescue vehicle's location information, estimated arrival time information and rescue personnel information in real time on the user's mobile device.

[0014] Technical effects and advantages of the present invention: The present invention optimizes the processing flow of automobile rescue services through intelligent and automated means, improves rescue response speed and processing efficiency, thereby effectively alleviating traffic pressure, improving urban traffic operation efficiency, and enhancing user satisfaction and experience; the present invention can simulate and optimize the driving path of rescue vehicles by establishing a city model, and ensure that rescue vehicles arrive at the scene as quickly as possible by calculating the driving path optimization coefficient, thereby significantly improving rescue efficiency; the present invention can evaluate the quality of rescue services by obtaining key indicators such as vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, and feed back the evaluation results to management personnel, thereby providing strong support for continuous improvement of service quality. BRIEF DESCRIPTION OF THE DRAWINGS The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0015] Figure 1 The present invention is a flowchart of a vehicle service item order management method.

[0016] Figure 2 The present invention is a structural schematic diagram of a vehicle service item order management system. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1As shown, the present invention provides a vehicle service project order management method, comprising the following steps: S1: establishing a city model, S2: obtaining vehicle rescue service order information, S3: analyzing the vehicle rescue emergency state, S4: analyzing the rescue vehicle driving path, S5: managing the vehicle rescue service order, S6: evaluating the rescue service quality and S7: establishing a human-computer interaction module; S1: Establishing a city model: acquiring multi-source information data of the city, integrating and preprocessing the multi-source information data, and establishing a city model based on the integrated and preprocessed data, wherein the multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; In this embodiment, it should be specifically explained that the specific content of establishing the city model is: Obtaining geographic information data: Obtaining geographic information data from geographic information system (GIS) databases, archives of urban planning departments, and commercial map data suppliers. The geographic information data includes but is not limited to the city's road layout, building outlines, and topography. Satellite images and aerial photogrammetry data are used to supplement and verify geographic information. Satellite images are used to identify large buildings, roads, and natural terrain features. Through image processing software, road centerlines and building boundary information are extracted, and compared and integrated with existing geographic information system data to ultimately form geographic information data; Obtaining traffic information data: Obtaining traffic flow statistics, traffic light setting information and traffic rules documents from the city's traffic management department, the traffic light setting information including but not limited to the location of traffic lights and the duration of traffic lights; installing induction coils and radar speedometer traffic sensors on the road to collect traffic flow and speed; installing video surveillance cameras at road intersections and traffic hubs to collect real-time urban traffic status information, and based on the data of the real-time traffic information platform, obtaining traffic event information including but not limited to the latest traffic accidents and road construction, and finally forming traffic information data; Obtain rescue resource information data: extract relevant information about rescue sites, rescue vehicles and rescue personnel from the rescue company's internal management system, including but not limited to the address and contact information of the rescue site, the model, license plate number, purchase date, maintenance record of the rescue vehicle, and the name and contact information of the rescue personnel; Big data analysis technology and data fusion technology are used to integrate multi-source information data, and 3D modeling software, spatial analysis and modeling technology are used to construct urban models.

[0019] S2: Obtaining vehicle rescue service order information: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; In this embodiment, it should be specifically explained that the order information includes but is not limited to basic vehicle information, owner contact information, vehicle fault information and vehicle location information; the basic vehicle information includes but is not limited to license plate number, vehicle model, and vehicle identification code; the owner contact information includes but is not limited to name, telephone number, and email address; the vehicle fault information includes but is not limited to vehicle fault type and fault duration.

[0020] S3: Analyze the emergency status of vehicle rescue: Based on the rescue service order information, weather condition information and traffic information in the city model, analyze the rescue emergency situation assessment coefficient of each monitoring sub-area, and classify the rescue emergency level of each vehicle rescue service order; In this embodiment, it should be specifically explained that the specific content of analyzing the rescue emergency situation assessment coefficient of each monitoring sub-area is: S31: Calculate the deviation rate of each weather condition index , the calculation model is: ,in, W j represents the index value of the jth weather condition index, represents the preset ideal index value corresponding to the jth weather condition, j represents the number of each weather condition, j=1,2,...m; m is the total number of weather conditions; In this embodiment, it should be specifically noted that the weather conditions include but are not limited to weather conditions visibility, rainfall, wind speed, snowfall, and road ice thickness; S32: Calculate the accident risk rate under various weather conditions Ar j , the calculation model is as follows: ,in, on j represents the number of historical accidents under the jth weather condition, on represents the number of historical accidents under ideal weather conditions, Other factors affecting the accident risk; S33: Calculate the emergency situation assessment coefficient of each monitoring sub-area EAC i , the calculation model is as follows: ,in, EAC i represents the rescue emergency situation assessment coefficient of the i-th monitoring sub-area, represents the deviation rate of the jth weather condition index, Indicates the preset maximum deviation rate of the corresponding weather condition indicator. Ar j represents the accident risk rate under the jth weather condition, fd i represents the degree of vehicle failure in the i-th monitoring sub-area, ft i represents the vehicle failure duration in the i-th monitoring sub-area, At i represents the estimated arrival time of the rescue vehicle in the ith monitoring sub-area, where , D i represents the distance between the vehicle in the ith monitoring sub-area and the rescue vehicle, V represents the average speed of the rescue vehicle, It represents other influencing factors of the rescue emergency situation assessment coefficient, i is the number of each monitoring sub-area, and e represents a natural constant.

[0021] In this embodiment, it should be specifically explained that the degree of fault is quantified by vehicle technicians using a scoring method, and the scoring level is 1 to 10 points, among which 8 to 10 points are severe fault levels, 4 to 7 points are moderate fault levels, and 1 to 3 points are minor fault levels.

[0022] In this embodiment, it should be specifically explained that the specific content of the rescue emergency level classification of each vehicle rescue service order is: Extract the rescue emergency situation assessment coefficient of each monitoring sub-area, and compare them with the preset rescue level range thresholds respectively. If the rescue emergency situation assessment coefficient of the ith monitoring sub-area is within the first rescue level range threshold, the vehicle rescue in the ith monitoring sub-area is determined to be a first-level emergency, that is, the rescue level is high; if the rescue emergency situation assessment coefficient of the ith monitoring sub-area is within the second rescue level range threshold, the vehicle rescue in the ith monitoring sub-area is determined to be a second-level emergency, that is, the rescue level is medium; if the rescue emergency situation assessment coefficient of the ith monitoring sub-area is within the third rescue level range threshold, the vehicle rescue in the ith monitoring sub-area is determined to be a third-level emergency, that is, the rescue level is low; the management personnel give different rescue plans according to different rescue levels; S4: Analyze the driving path of the rescue vehicle: simulate the driving path of the rescue vehicle based on the city model, output the driving path of the rescue vehicle, optimize the driving path by calculating the driving path optimization coefficient, and output the optimal driving path of the rescue vehicle; In this embodiment, it should be specifically explained that the specific content of analyzing the driving path of the rescue vehicle is: S41: Obtain the estimated travel time of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path T ik , substituting it into the formula Get the time optimization coefficient of the i-th monitoring sub-area Ct i ,in, Ti0 represents the preset minimum estimated driving time of the rescue vehicle in the ith monitoring sub-area, represents the exponential function; S42: Obtain the total number of nodes affected by traffic on the kth path selected by the rescue vehicle in the i-th monitoring sub-area M ik , substituting it into the formula Get the traffic condition optimization coefficient of the i-th monitoring sub-area Ctc i , where q represents the number of the node affected by traffic, It represents the interference factor of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path affected by the q-th traffic node; In this embodiment, it should be specifically explained that: The acquisition method is: obtain the traffic congestion coefficient, road construction impact coefficient and traffic accident impact coefficient, and add the three together to get value.

[0023] S43: Calculate the optimal driving path optimization coefficient, and the calculation model is specifically as follows: ,in, POC i represents the optimal driving path optimization coefficient of the rescue vehicle in the i-th monitoring sub-area, Co i represents the comprehensive cost coefficient of the ith monitoring sub-area, w1, w2, and w3 represent the weight factors of the time optimization coefficient, traffic condition optimization coefficient, and comprehensive cost coefficient, respectively, and w1+w2+w3=1; S44: Optimizing the rescue vehicle driving path by using the optimal driving path optimization coefficient, and outputting the optimal driving path of the rescue vehicle in each monitoring sub-area.

[0024] S5: Manage vehicle rescue service orders: Match rescue vehicles and rescue plans based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, and start vehicle rescue services for each monitoring sub-area; S6: Evaluate the rescue service quality: obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; In this embodiment, it should be specifically explained that the calculation steps of the vehicle rescue service quality evaluation index of each monitoring sub-area are: S61: Obtaining the actual rescue time of the i-th monitoring sub-area and the estimated rescue time for the ith monitoring sub-area , substituting it into the formula Get the vehicle rescue progress deviation rate of the i-th monitoring sub-area ; S62: Obtaining the actual driving path length of the rescue vehicle in the i-th monitoring sub-area and the planned driving path length of the rescue vehicle in the ith monitoring sub-area , substituting it into the formula Get the rescue vehicle path deviation of the i-th monitoring sub-area ; S63: Calculate the vehicle rescue service quality evaluation index, and the calculation model is: ,in, QEI i represents the vehicle rescue service quality evaluation index of the i-th monitoring sub-area, pe i represents the order processing efficiency of the i-th monitoring sub-area, where , ht i represents the order processing time of the i-th monitoring sub-area, rt i Indicates the order response time of the i-th monitoring sub-area, pe 0 represents the preset ideal order processing efficiency. Represents other influencing factors of vehicle rescue service quality evaluation index, represents the exponential function; In this embodiment, it should be specifically explained that the specific content of the rescue service quality evaluation is: extracting the vehicle rescue service quality evaluation index of each monitoring sub-area and comparing it with the preset quality threshold. If the vehicle rescue service quality evaluation index of the i-th monitoring sub-area is less than the preset quality threshold, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be abnormal, and an early warning message is issued for the abnormal data and sent to the management personnel. Otherwise, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be normal.

[0025] S7: Establish a human-computer interaction module: Display the location information, estimated arrival time information and rescue personnel information of the rescue vehicle in real time on the user's mobile device.

[0026] Example 2 See also Figure 2 As shown, the present invention provides a vehicle service project order management system, including: a city model building module, a vehicle rescue service order acquisition module, a vehicle rescue emergency state analysis module, a rescue vehicle driving path analysis module, a vehicle rescue service order management module, a rescue service quality evaluation module and a human-computer interaction interface module; City model building module: used to obtain multi-source information data of the city, integrate and pre-process the multi-source information data, and build a city model based on the integrated and pre-processed data. The multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; Obtaining vehicle rescue service order module: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; Vehicle rescue emergency status analysis module: Based on the rescue service order information, weather conditions and traffic information in the city model, it analyzes the rescue emergency situation assessment coefficient of each monitoring sub-area and divides each vehicle rescue service order into rescue emergency levels; Rescue vehicle driving path analysis module: simulates the rescue vehicle driving path based on the city model, outputs the rescue vehicle driving path, optimizes the driving path by calculating the driving path optimization coefficient, and outputs the optimal driving path for the rescue vehicle; Vehicle rescue service order management module: Based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, the rescue vehicle and rescue plan are matched, and the vehicle rescue service is initiated for each monitoring sub-area; Rescue service quality evaluation module: used to obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; Human-computer interaction interface module: used to display the rescue vehicle's location information, estimated arrival time information and rescue personnel information in real time on the user's mobile device.

[0027] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0028] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle service item order management method, characterized in that: The following steps are involved: S1: Establishing a city model: acquiring multi-source information data of the city, integrating and preprocessing the multi-source information data, and establishing a city model based on the integrated and preprocessed data, wherein the multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; S2: Obtaining vehicle rescue service order information: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; S3: Analyze the emergency status of vehicle rescue: Based on the rescue service order information, weather condition information and traffic information in the city model, analyze the rescue emergency situation assessment coefficient of each monitoring sub-area, and classify the rescue emergency level of each vehicle rescue service order; S4: Analyze the driving path of the rescue vehicle: simulate the driving path of the rescue vehicle based on the city model, output the driving path of the rescue vehicle, optimize the driving path by calculating the driving path optimization coefficient, and output the optimal driving path of the rescue vehicle; S5: Manage vehicle rescue service orders: Match rescue vehicles and rescue plans based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, and start vehicle rescue services for each monitoring sub-area; S6: Evaluate the rescue service quality: obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; S7: Establish a human-computer interaction module: Display the location information, estimated arrival time information and rescue personnel information of the rescue vehicle in real time on the user's mobile device.

2. A vehicle service item order management method according to claim 1, characterized in that: The specific contents of establishing the city model are: using big data analysis technology and data fusion technology to integrate multi-source information data, and using 3D modeling software, spatial analysis and modeling technology to construct the city model.

3. A vehicle service item order management method according to claim 1, characterized in that: The order information includes but is not limited to basic vehicle information, owner contact information, vehicle fault information and vehicle location information.

4. A vehicle service item order management method according to claim 1, characterized in that: The specific content of analyzing the rescue emergency situation assessment coefficient of each monitoring sub-area is: S31: Calculate the deviation rate of each weather condition index , the calculation model is: ,in, W j represents the index value of the jth weather condition index, represents the preset ideal index value corresponding to the jth weather condition, j represents the number of each weather condition, j=1,2,...m; m is the total number of weather conditions; The weather conditions include but are not limited to weather conditions visibility, rainfall, wind speed, snowfall and road ice thickness; S32: Calculate the accident risk rate under various weather conditions Ar j , the calculation model is as follows: ,in, on j represents the number of historical accidents under the jth weather condition, on represents the number of historical accidents under ideal weather conditions, Other factors affecting the accident risk; S33: Calculate the emergency situation assessment coefficient of each monitoring sub-area EAC i , the calculation model is as follows: ,in, EAC i represents the rescue emergency situation assessment coefficient of the i-th monitoring sub-area, represents the deviation rate of the jth weather condition index, Indicates the preset maximum deviation rate of the corresponding weather condition indicator. Ar j represents the accident risk rate under the jth weather condition, fd i represents the degree of vehicle failure in the i-th monitoring sub-area, ft i represents the vehicle failure duration in the i-th monitoring sub-area, At i represents the estimated arrival time of the rescue vehicle in the ith monitoring sub-area, where , D i represents the distance between the vehicle in the ith monitoring sub-area and the rescue vehicle, V represents the average speed of the rescue vehicle, It represents other influencing factors of the rescue emergency situation assessment coefficient, i is the number of each monitoring sub-area, and e represents a natural constant.

5. A vehicle service item order management method according to claim 1, characterized in that: The specific content of analyzing the driving path of the rescue vehicle is as follows: S41: Obtain the estimated travel time of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path T ik , substituting it into the formula Get the time optimization coefficient of the i-th monitoring sub-area Ct i ,in, T i0 represents the preset minimum estimated driving time of the rescue vehicle in the ith monitoring sub-area, represents the exponential function; S42: Obtain the total number of nodes affected by traffic on the kth path selected by the rescue vehicle in the i-th monitoring sub-area M ik , substituting it into the formula Get the traffic condition optimization coefficient of the i-th monitoring sub-area Ctc i , where q represents the number of the node affected by traffic, It represents the interference factor of the rescue vehicle in the i-th monitoring sub-area selecting the k-th path affected by the q-th traffic node; S43: Calculate the optimal driving path optimization coefficient, and the calculation model is specifically as follows: ,in, POC i represents the optimal driving path optimization coefficient of the rescue vehicle in the i-th monitoring sub-area, Co i represents the comprehensive cost coefficient of the i-th monitoring sub-area, w1, w2, and w3 represent the weight factors of the time optimization coefficient, traffic condition optimization coefficient, and comprehensive cost coefficient, respectively, and w1+w2+w3=1; S44: Optimizing the rescue vehicle driving path by using the optimal driving path optimization coefficient, and outputting the optimal driving path of the rescue vehicle in each monitoring sub-area.

6. A vehicle service item order management method according to claim 1, characterized in that: The calculation steps of the vehicle rescue service quality evaluation index of each monitoring sub-area are as follows: S61: Obtaining the actual rescue time of the i-th monitoring sub-area and the estimated rescue time for the ith monitoring sub-area , substituting it into the formula Get the vehicle rescue progress deviation rate of the i-th monitoring sub-area ; S62: Obtaining the actual driving path length of the rescue vehicle in the i-th monitoring sub-area and the planned driving path length of the rescue vehicle in the ith monitoring sub-area , substituting it into the formula Get the rescue vehicle path deviation of the i-th monitoring sub-area ; S63: Calculate the vehicle rescue service quality evaluation index, and the calculation model is: ,in, QEI i represents the vehicle rescue service quality evaluation index of the i-th monitoring sub-area, pe i represents the order processing efficiency of the i-th monitoring sub-area, where , ht i represents the order processing time of the i-th monitoring sub-area, rt i Indicates the order response time of the i-th monitoring sub-area, pe 0 represents the preset ideal order processing efficiency. Represents other influencing factors of vehicle rescue service quality evaluation index, Represents an exponential function.

7. A vehicle service item order management method according to claim 1, characterized in that: The specific content of the rescue service quality evaluation is: extracting the vehicle rescue service quality evaluation index of each monitoring sub-area and comparing it with the preset quality threshold. If the vehicle rescue service quality evaluation index of the i-th monitoring sub-area is less than the preset quality threshold, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be abnormal, and an early warning message is issued for the abnormal data and sent to the management personnel. Otherwise, the vehicle rescue service quality of the i-th monitoring sub-area is judged to be normal.

8. A vehicle service item order management system, used to implement a vehicle service item order management method as described in any one of claims 1 to 7, characterized in that: include: City model building module: used to obtain multi-source information data of the city, integrate and pre-process the multi-source information data, and build a city model based on the integrated and pre-processed data. The multi-source information data includes geographic information data, traffic information data, weather condition information data, rescue resource information data, and vehicle rescue information data; Obtaining vehicle rescue service order module: Obtaining vehicle rescue service order information based on natural language processing technology, and dividing each order into n monitoring sub-areas, i=1, 2, ..., n, i is the number of each monitoring sub-area; Vehicle rescue emergency status analysis module: Based on the rescue service order information, weather conditions and traffic information in the city model, it analyzes the rescue emergency situation assessment coefficient of each monitoring sub-area and divides each vehicle rescue service order into rescue emergency levels; Rescue vehicle driving path analysis module: simulates the rescue vehicle driving path based on the city model, outputs the rescue vehicle driving path, optimizes the driving path by calculating the driving path optimization coefficient, and outputs the optimal driving path for the rescue vehicle; Vehicle rescue service order management module: Based on the rescue emergency situation assessment coefficient and the optimal driving path of the rescue vehicle, the rescue vehicle and rescue plan are matched, and the vehicle rescue service is initiated for each monitoring sub-area; Rescue service quality evaluation module: used to obtain the vehicle rescue progress deviation rate, rescue vehicle path deviation and order processing efficiency, analyze and obtain the vehicle rescue service quality evaluation index of each monitoring sub-area, evaluate the rescue service quality, and send the evaluation results to the management personnel; Human-computer interaction interface module: used to display the rescue vehicle's location information, estimated arrival time information and rescue personnel information in real time on the user's mobile device.

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