Ant colony algorithm-based vehicle insurance road rescue planning method and related device

Abstract

The embodiment of the application belongs to the technical field of financial science and technology, and relates to a vehicle insurance road rescue planning method based on an ant colony algorithm and a related device thereof, which comprises the following steps: identifying position information of an accident site; screening a minimum-time first-stage rescue path according to all dispatchable resource position information, the position information of the accident site, dynamic road network data within a first specific range and an ant colony algorithm; screening a minimum-time second-stage rescue path according to the position information of the accident site, a rescue point set, dynamic road network data within a second specific range and the ant colony algorithm; and sending the screened first-stage rescue path and the second-stage rescue path to a preset rescue planning and dispatch platform for rescue planning. The search model constructed by the ant colony algorithm and the rescue hindering factor are introduced twice to obtain the first-stage rescue path and the second-stage rescue path respectively, so that the rescue site can be reached in time and the wounded can be sent to a target medical institution in time.

Description

Technical Field

[0001] This application relates to the field of financial technology, and in particular to a method for planning roadside assistance for vehicle insurance based on ant colony algorithm and related equipment. Background Technology

[0002] With socio-economic development, people's mode of transportation is gradually shifting from traditional public buses to private cars. The rapid increase in private cars often leads to road congestion and frequent traffic accidents. This places high demands on insurance companies' car insurance services. To reduce direct losses from traffic accidents, insurance companies typically provide roadside assistance services. However, for a vast urban transportation network, the efficient dispatch of personnel and vehicles for roadside assistance within a short period is extremely challenging.

[0003] To address the difficulties in roadside assistance within the aforementioned insurance services, existing rescue dispatch solutions often require service providers to establish multiple rescue networks in different areas, with unified dispatch from a central rescue center. This approach has several drawbacks. Firstly, it requires significant manpower and resources; secondly, information transmission takes considerable time, hindering rapid rescue operations. Therefore, current technology has limitations in planning roadside assistance for auto insurance, preventing rescue personnel from reaching the accident scene promptly. Summary of the Invention

[0004] The purpose of this application is to propose a roadside assistance planning method for vehicle insurance based on ant colony algorithm and related equipment, so as to solve the shortcomings of the existing technology in planning roadside assistance for vehicle insurance, and enable rescuers to arrive at the accident scene in a timely manner to carry out rescue.

[0005] To address the aforementioned technical problems, this application provides a vehicle insurance roadside assistance planning method based on ant colony optimization, employing the following technical solution:

[0006] A method for planning roadside assistance in vehicle insurance based on ant colony optimization includes the following steps:

[0007] Identify the location of the accident site and use it as the primary target point;

[0008] Locate all schedulable resource location information within a first specific range of the first target point, and obtain dynamic road network data within the first specific range;

[0009] Based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm, the front-end rescue path with the shortest time consumption is selected.

[0010] Locate all rescue point locations within a second specific range of the first target point, and obtain dynamic road network data within the second specific range to construct a second target point set;

[0011] Based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm, the rescue route with the shortest time consumption is selected.

[0012] The selected front-end rescue path and the selected rear-end rescue path are sent to a preset rescue planning and dispatching platform for rescue planning.

[0013] Furthermore, the step of identifying the location information of the accident site and using it as the first target point specifically includes:

[0014] The latitude and longitude values ​​of the accident site are determined using a preset GPS positioning component, and these values ​​are used as the first target point.

[0015] Furthermore, the steps of finding all schedulable resource location information within a first specific range of the first target point and obtaining dynamic road network data within the first specific range specifically include:

[0016] Using the first specific range as the search radius and the first target point as the search origin, search for all schedulable resources within the first specific range of the first target point, and obtain the location information of all schedulable resources. The schedulable resources include schedulable rescue vehicles, and the location information of all schedulable resources can be represented by latitude and longitude values ​​determined by a preset GPS positioning component.

[0017] According to the preset GIS visualization system, dynamic road network data within the first specific range is obtained, wherein the dynamic road network data includes dynamic real-time traffic signal scheduling data, real-time road condition and vehicle data, and static road grid data.

[0018] The steps of finding the location information of all rescue points within a second specific range of the first target point, and obtaining dynamic road network data within the second specific range to construct a second target point set, specifically include:

[0019] Using the second specific range as the search radius and the first target point as the search origin, search for all rescue points within the second specific range of the first target point, and obtain the location information of all rescue points. The rescue points include medical institutions, and the location information of all rescue points can be represented by latitude and longitude values ​​measured by a preset GPS positioning component.

[0020] Based on the preset GIS visualization system, obtain dynamic road network data within the second specific range.

[0021] Furthermore, the step of selecting the shortest initial rescue path based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm specifically includes:

[0022] By analyzing the dynamic road network data within the first specific range, data corresponding to all rescue obstruction factors within the first specific range are obtained. The rescue obstruction factors include the number of road sections with saturated traffic flow, and the number of turning intersections and straight intersections from each schedulable resource location information to the first target point.

[0023] The location information of each schedulable resource is used as the starting location information of the ant colony.

[0024] Set the first target point as the target location information that a single ant colony member needs to reach;

[0025] Based on the dynamic road network data within the first specific range, obtain all road intersection information within the first specific range, and use the road intersection information as the filtering node information of the ant colony;

[0026] Based on the starting position information of the ant colony, the target position information, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from each schedulable resource location information to the first target point.

[0027] Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor.

[0028] According to the preset algorithm formula: Obtain the rescue time corresponding to the shortest path from each schedulable resource location information to the first target point, where i represents the number of each schedulable resource location information, and s i Let represent the shortest path length corresponding to the location information of the schedulable resource with ID i, v represent the travel speed of the schedulable resource, j represent the ID information of each rescue obstacle factor in the shortest path corresponding to the location information of the schedulable resource with ID i, and t represent the shortest path length of the schedulable resource with ID i. j t represents the duration of each obstacle hindering rescue efforts. i This indicates the rescue time from the location information of the schedulable resource numbered i to the first target point;

[0029] The shortest path corresponding to the minimum rescue time is obtained as the initial rescue path.

[0030] Furthermore, the step of predicting the shortest path from each schedulable resource location to the first target point using the ant colony algorithm based on the ant colony's starting position information, the target location information, and the ant colony's filtering node information specifically includes:

[0031] The starting position information of the current ant colony is used as the search starting point, and the target position information is used as the search ending point, which are then deployed into the search model constructed by the ant colony algorithm.

[0032] The filtering node information of the ant colony is used as training data and input into the search model. The filtering node information of the ant colony is a set of point value data composed of all road intersection information within the first specific range.

[0033] Through the search model, an iterative search is performed to obtain the probability value of the next candidate node searched each time from the search starting point to the search ending point.

[0034] From each of the next candidate nodes found in the search, select the node with the highest probability value, and add the node with the highest probability value to the preset node set in turn;

[0035] Until the iteration is complete, road intersections are connected according to the sequence of elements in the node set, and the connection path from the current ant colony location information to the target location information is obtained as the shortest path from the current schedulable resource location information to the first target point.

[0036] Furthermore, the step of selecting the least time-consuming subsequent rescue route based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm specifically includes:

[0037] By analyzing the dynamic road network data within the second specific range, data corresponding to all rescue obstruction factors within the second specific range are obtained;

[0038] The first target point is used as the starting position information of the ant colony;

[0039] Set the position information of each element in the second target point set as the target position information that each individual ant colony member will reach;

[0040] Based on the dynamic road network data within the second specific range, obtain all road intersection information within the second specific range, and use the road intersection information as the filtering node information of the ant colony;

[0041] Based on the starting position information of the ant colony, the target position information to be reached by each individual ant, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from the first target point to each element in the set of the second target points.

[0042] Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor.

[0043] According to the preset algorithm formula: Obtain the rescue time corresponding to the shortest path from the first target point to each element in the set of second target points, where l represents the number of each element in the set of second target points, and s l Let represent the shortest path length corresponding to element l in the second target point set, v represent the travel speed of the schedulable resource, n represent the numbering information of each rescue obstacle factor in the shortest path corresponding to element l in the second target point set, and t represent the shortest path length. n t represents the duration of each obstacle hindering rescue efforts. l This indicates the rescue time corresponding to the location information of the element numbered l in the set of the second target points from the first target point;

[0044] The shortest path corresponding to the minimum rescue time is obtained as the subsequent rescue path.

[0045] Furthermore, the step of predicting the shortest path from the first target point to each element in the second target point set using the ant colony algorithm based on the starting position information of the ant colony, the target position information to be reached by each individual ant, and the screening node information of the ant colony, specifically includes:

[0046] The starting position information of the ant colony is used as the search starting point, and the target position information to be reached by each individual ant colony member is used as the search endpoint and deployed into the search model.

[0047] The filtering node information of the ant colony is used as training data and input into the search model. The filtering node information of the ant colony is a set of point value data composed of all road intersection information within the second specific range.

[0048] Through the search model, an iterative search is performed to obtain the probability value of the next candidate node searched each time from the search starting point to the current search endpoint.

[0049] From each of the next candidate nodes found in the search, select the node with the highest probability value, and add the node with the highest probability value to the preset node set in turn;

[0050] Until the iteration is complete, road intersections are connected according to the sequence of elements in the node set to obtain the connection path from the starting position information of the ant colony to the target position information corresponding to the current search endpoint. The connection path is used as the shortest path from the first target point to the target rescue point.

[0051] To address the aforementioned technical problems, this application also provides a vehicle insurance roadside assistance planning device based on ant colony algorithm, employing the following technical solution:

[0052] A vehicle insurance roadside assistance planning device based on ant colony algorithm, comprising:

[0053] The first target point identification module is used to identify the location information of the accident site and use it as the first target point.

[0054] The first data acquisition module is used to find all schedulable resource location information within a first specific range of the first target point, and to acquire dynamic road network data within the first specific range.

[0055] The front-end rescue path filtering module is used to filter out the front-end rescue path with the least time consumption based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm.

[0056] The second data acquisition module is used to find the location information of all rescue points within a second specific range of the first target point, and to acquire dynamic road network data within the second specific range to construct a second target point set;

[0057] The downstream rescue route filtering module is used to filter out the downstream rescue route with the least time based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm.

[0058] The path filtering result sending module is used to send the filtered front-end rescue path and the rear-end rescue path to a preset rescue planning and dispatching platform for rescue planning.

[0059] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0060] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the vehicle insurance roadside assistance planning method based on the ant colony algorithm described above.

[0061] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0062] A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the vehicle insurance roadside assistance planning method based on the ant colony algorithm described above.

[0063] Compared with the prior art, the embodiments of this application have the following main advantages:

[0064] The vehicle insurance road rescue planning method based on ant colony optimization (ACO) described in this application identifies the location information of the accident site as the first target point; searches for all schedulable resource locations within a first specific range of the first target point and obtains dynamic road network data within that range; based on the schedulable resource locations, the first target point, the dynamic road network data within the first specific range, and the ACO algorithm, selects the initial rescue path with the shortest travel time; searches for all rescue point locations within a second specific range of the first target point and obtains dynamic road network data within that range to construct a second target point set; based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ACO algorithm, selects the subsequent rescue path with the shortest travel time; and sends the selected initial and subsequent rescue paths to a preset rescue planning and scheduling platform for rescue planning. By employing a search model constructed using the ACO algorithm twice and introducing a rescue obstacle factor, the initial and subsequent rescue paths are obtained respectively, ensuring timely arrival at the rescue site and timely transport of the injured to the target medical institution. Attached Figure Description

[0065] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0067] Figure 2 A flowchart of an embodiment of the vehicle insurance roadside assistance planning method based on the ant colony algorithm according to this application;

[0068] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 203 shown;

[0069] Figure 4 yes Figure 3A flowchart of a specific embodiment of step 305 shown;

[0070] Figure 5 yes Figure 2 A flowchart of a specific embodiment of step 205 shown;

[0071] Figure 6 yes Figure 5 A flowchart of a specific embodiment of step 505 shown;

[0072] Figure 7 A schematic diagram of the structure of an embodiment of the vehicle insurance roadside assistance planning device based on ant colony algorithm according to this application;

[0073] Figure 8 A schematic diagram of the structure of an embodiment of the computer device according to this application. Detailed Implementation

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0077] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0078] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0079] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0080] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0081] It should be noted that the vehicle insurance roadside assistance planning method based on ant colony algorithm provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the vehicle insurance roadside assistance planning device based on ant colony algorithm is generally set in the server / terminal device.

[0082] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0083] Continue to refer to Figure 2 The diagram illustrates a flowchart of an embodiment of the ant colony algorithm-based roadside assistance planning method for vehicle insurance according to this application. The ant colony algorithm-based roadside assistance planning method for vehicle insurance includes the following steps:

[0084] Step 201: Identify the location information of the accident site and use it as the first target point.

[0085] In this embodiment, the step of identifying the location information of the accident site and using it as the first target point specifically includes: using a preset GPS positioning component to determine the latitude and longitude values ​​of the accident site, and using the latitude and longitude values ​​as the first target point.

[0086] Step 202: Locate all schedulable resource location information within a first specific range of the first target point, and obtain dynamic road network data within the first specific range.

[0087] In this embodiment, the steps of finding all schedulable resource location information within a first specific range of the first target point and obtaining dynamic road network data within the first specific range specifically include: using the first specific range as the search radius and the first target point as the search origin, searching for all schedulable resources within the first specific range of the first target point and obtaining the location information of all schedulable resources; and obtaining dynamic road network data within the first specific range according to a preset GIS visualization system.

[0088] In this embodiment, the schedulable resources include schedulable rescue vehicles, and the location information of all schedulable resources can be represented using latitude and longitude values ​​determined by a preset GPS positioning component.

[0089] In this embodiment, the dispatchable rescue vehicles may include road clearing vehicles, medical rescue vehicles, fire rescue vehicles, accident tow trucks, or cranes, depending on the nature of the vehicle accident.

[0090] In this embodiment, the preset GIS visualization system is based on geospatial data, uses geographic model analysis methods, and provides various spatial and dynamic geographic information in a timely manner. It is a system for collecting, storing, analyzing, and visualizing various geospatial information.

[0091] In this embodiment, the dynamic road network data includes dynamic real-time traffic signal scheduling data, real-time road condition and vehicle data, and static road grid data.

[0092] The GIS visualization system allows for an intuitive observation of real-time road conditions. By acquiring the dynamic road network data, it can further provide data support for rescue route planning, enabling the rational planning of the optimal route, i.e., the route with the least time consumption.

[0093] Step 203: Based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm, select the front-end rescue path with the shortest time consumption.

[0094] Continue to refer to Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 203 shown includes:

[0095] Step 301: By analyzing the dynamic road network data within the first specific range, data corresponding to all rescue obstruction factors within the first specific range are obtained. The rescue obstruction factors include the number of road sections with saturated traffic flow, and the number of turning intersections and straight intersections from each schedulable resource location information to the first target point.

[0096] Step 302: Use the location information of each schedulable resource as the starting location information of the ant colony;

[0097] Step 303: Set the first target point as the target location information that the individual ant colony member needs to reach;

[0098] Step 304: Based on the dynamic road network data within the first specific range, obtain all road intersection information within the first specific range, and use the road intersection information as the filtering node information of the ant colony;

[0099] Step 305: Based on the starting position information of the ant colony, the target position information, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from each schedulable resource location information to the first target point.

[0100] Continue to refer to Figure 4 , Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 305 shown includes:

[0101] Step 401: Deploy the current ant colony's starting position information as the search starting point and the target position information as the search ending point into the search model constructed by the ant colony algorithm;

[0102] Step 402: Input the ant colony's filtering node information as training data into the search model, wherein the ant colony's filtering node information is a set of point value data composed of all road intersection information within the first specific range.

[0103] Step 403: Using the search model, perform an iterative search to sequentially obtain the probability value of the next candidate node searched each time from the search starting point to the search ending point;

[0104] Step 404: Select the node with the highest probability value from the next candidate node found in each search, and add the node with the highest probability value to the preset node set in turn.

[0105] Step 405: Until the iteration is complete, connect the road intersections according to the sequence of elements in the node set, and obtain the connection path from the current ant colony location information to the target location information as the shortest path from the current schedulable resource location information to the first target point.

[0106] The shortest path from the current schedulable resource location information to the first target point is obtained by using the ant colony algorithm. Different schedulable resource location information is used as the current schedulable resource location information in turn, and steps 401 to 405 are repeated to finally obtain the shortest path from each schedulable resource location information to the first target point. Here, since the probability prediction formula for obtaining the next arrival node is the traditional ant colony algorithm formula, the implementation principle of the formula will not be elaborated.

[0107] Step 306: Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor.

[0108] Step 307, according to the preset algorithm formula: Obtain the rescue time corresponding to the shortest path from each schedulable resource location information to the first target point, where i represents the number of each schedulable resource location information, and s i Let represent the shortest path length corresponding to the location information of the schedulable resource with ID i, v represent the travel speed of the schedulable resource, j represent the ID information of each rescue obstacle factor in the shortest path corresponding to the location information of the schedulable resource with ID i, and t represent the shortest path length of the schedulable resource with ID i. j t represents the duration of each obstacle hindering rescue efforts. i This indicates the rescue time from the location information of the schedulable resource numbered i to the first target point;

[0109] Step 308: Obtain the shortest path corresponding to the minimum rescue time as the initial rescue path.

[0110] By introducing rescue obstacle factors and ant colony algorithms to select the shortest initial rescue path, it is ensured that schedulable resources arrive at the accident site in a timely manner.

[0111] Step 204: Locate all rescue point locations within the second specific range of the first target point, and obtain dynamic road network data within the second specific range to construct a second target point set.

[0112] In this embodiment, the steps of finding the location information of all rescue points within a second specific range of the first target point, and obtaining dynamic road network data within the second specific range to construct a second target point set specifically include: using the second specific range as the search radius and the first target point as the search origin, searching for all rescue points within the second specific range of the first target point, and obtaining the location information of all rescue points; and obtaining dynamic road network data within the second specific range according to a preset GIS visualization system.

[0113] In this embodiment, the aid points include medical institutions, and the location information of all aid points can be represented by latitude and longitude values ​​measured by a preset GPS positioning component.

[0114] Step 205: Based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm, select the rescue route with the shortest time consumption.

[0115] Continue to refer to Figure 5 , Figure 5 yes Figure 2 A flowchart of a specific embodiment of step 205 shown includes:

[0116] Step 501: By analyzing the dynamic road network data within the second specific range, data corresponding to all rescue obstruction factors within the second specific range are obtained;

[0117] Step 502: Use the first target point as the starting position information of the ant colony;

[0118] Step 503: Set the position information of each element in the second target point set as the target position information that each individual ant colony member will reach;

[0119] Step 504: Based on the dynamic road network data within the second specific range, obtain all road intersection information within the second specific range, and use the road intersection information as the filtering node information of the ant colony;

[0120] Step 505: Based on the starting position information of the ant colony, the target position information to be reached by each individual ant, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from the first target point to each element in the set of the second target points.

[0121] Continue to refer to Figure 6 , Figure 6 yes Figure 5 A flowchart of a specific embodiment of step 505 shown includes:

[0122] Step 601: Deploy the starting position information of the ant colony as the search starting point and the target position information to be reached by each individual ant in the colony as the search endpoint into the search model;

[0123] Step 602: Input the ant colony's filtering node information as training data into the search model, wherein the ant colony's filtering node information is aggregate point value data composed of all road intersection information within the second specific range;

[0124] Step 603: Using the search model, perform iterative search to obtain the probability value of the next candidate node searched each time from the search starting point to the current search endpoint.

[0125] Step 604: Select the node with the highest probability value from the next candidate nodes found in each search, and add the node with the highest probability value to the preset node set in turn.

[0126] Step 605: Until the iteration is complete, connect the road intersections according to the sequence of elements in the node set, obtain the connection path from the starting position information of the ant colony to the target position information corresponding to the current search endpoint, and use the connection path as the shortest path from the first target point to the target rescue point.

[0127] Similarly, by using the ant colony algorithm to obtain the shortest path from the first target point to the target rescue point, different rescue points are successively used as the current search endpoints, and steps 601 to 605 are repeated to finally obtain the shortest path from the search starting point to each search endpoint.

[0128] Step 506: Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor.

[0129] Step 507, according to the preset algorithm formula: Obtain the rescue time corresponding to the shortest path from the first target point to each element in the set of second target points, where l represents the number of each element in the set of second target points, and s l Let represent the shortest path length corresponding to element l in the second target point set, v represent the travel speed of the schedulable resource, n represent the numbering information of each rescue obstacle factor in the shortest path corresponding to element l in the second target point set, and t represent the shortest path length. n t represents the duration of each obstacle hindering rescue efforts. l This indicates the rescue time corresponding to the location information of the element numbered l in the set of the second target points from the first target point;

[0130] Step 508: Obtain the shortest path corresponding to the minimum rescue time as the subsequent rescue path.

[0131] By introducing rescue obstacle factors and ant colony algorithms to select the shortest rescue route, it was ensured that the injured could be transferred to hospitals with the necessary conditions for treatment in a timely manner.

[0132] Step 206: Send the selected front-end rescue path and the rear-end rescue path to the preset rescue planning and dispatching platform for rescue planning.

[0133] By employing a search model constructed using the ant colony algorithm twice and introducing a rescue obstacle factor, the initial rescue path and the subsequent rescue path were obtained respectively, ensuring timely arrival at the rescue site and timely delivery of the injured to the target medical institution. Moreover, the two model searches and path selections are independent of each other and do not interfere with each other. Furthermore, only one search model needs to be constructed, and it can be efficiently applied at different stages of road rescue planning by changing the parameters, thus achieving the iterative applicability of the search model.

[0134] This application identifies the location information of the accident site as the first target point; it then searches for all schedulable resource locations within a first specific range of the first target point and obtains dynamic road network data within that range; based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and an ant colony algorithm, it selects the initial rescue path with the shortest travel time; it then searches for all rescue point locations within a second specific range of the first target point and obtains dynamic road network data within that range to construct a second target point set; based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ant colony algorithm, it selects the subsequent rescue path with the shortest travel time; and finally, it sends the selected initial and subsequent rescue paths to a preset rescue planning and scheduling platform for rescue planning. By employing a search model constructed using an ant colony algorithm twice and introducing a rescue obstacle factor, the initial and subsequent rescue paths are obtained respectively, ensuring timely arrival at the rescue site and timely transport of the injured to the target medical institution.

[0135] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0136] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0137] In this embodiment, by employing a search model constructed using the ant colony algorithm twice and introducing a rescue obstacle factor, the initial rescue path and the subsequent rescue path are obtained respectively, ensuring timely arrival at the rescue site and timely delivery of the injured to the target medical institution. Moreover, the two model searches and path selections are independent of each other and do not interfere with each other. Furthermore, only one search model needs to be constructed, and it can be efficiently applied at different stages of road rescue planning by changing the parameters, thus achieving the iterative applicability of the search model.

[0138] Further reference Figure 7 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of a vehicle insurance roadside assistance planning device based on ant colony algorithm. This device embodiment is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0139] like Figure 7 As shown, the vehicle insurance roadside assistance planning device 700 based on ant colony algorithm described in this embodiment includes: a first target point identification module 701, a first data acquisition module 702, a front-end rescue path filtering module 703, a second data acquisition module 704, a rear-end rescue path filtering module 705, and a path filtering result sending module 706. Wherein:

[0140] The first target point identification module 701 is used to identify the location information of the accident site and use it as the first target point.

[0141] The first data acquisition module 702 is used to find all schedulable resource location information within a first specific range of the first target point, and to acquire dynamic road network data within the first specific range.

[0142] The front-end rescue path filtering module 703 is used to filter out the front-end rescue path with the least time consumption based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm.

[0143] The second data acquisition module 704 is used to find the location information of all rescue points within a second specific range of the first target point, and to acquire dynamic road network data within the second specific range to construct a second target point set;

[0144] The downstream rescue route filtering module 705 is used to filter out the downstream rescue route with the least time based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ant colony algorithm.

[0145] The path filtering result sending module 706 is used to send the filtered front-end rescue path and the rear-end rescue path to a preset rescue planning and dispatching platform for rescue planning.

[0146] This application identifies the location information of the accident site as the first target point; it then searches for all schedulable resource locations within a first specific range of the first target point and obtains dynamic road network data within that range; based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and an ant colony algorithm, it selects the initial rescue path with the shortest travel time; it then searches for all rescue point locations within a second specific range of the first target point and obtains dynamic road network data within that range to construct a second target point set; based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ant colony algorithm, it selects the subsequent rescue path with the shortest travel time; and finally, it sends the selected initial and subsequent rescue paths to a preset rescue planning and scheduling platform for rescue planning. By employing a search model constructed using an ant colony algorithm twice and introducing a rescue obstacle factor, the initial and subsequent rescue paths are obtained respectively, ensuring timely arrival at the rescue site and timely transport of the injured to the target medical institution.

[0147] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0148] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0149] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0150] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected via a system bus. It should be noted that only the computer device 8 with components 8a-8c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0151] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0152] The memory 8a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 8a may be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 8a may also be an external storage device of the computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 8. Of course, the memory 8a may include both the internal storage unit and its external storage device of the computer device 8. In this embodiment, the memory 8a is typically used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for a roadside assistance planning method for vehicle insurance based on ant colony algorithm. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or will be output.

[0153] In some embodiments, the processor 8b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 8b is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to execute computer-readable instructions stored in the memory 8a or to process data, for example, to execute computer-readable instructions for the ant colony algorithm-based roadside assistance planning method for vehicle insurance.

[0154] The network interface 8c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 8 and other electronic devices.

[0155] The computer device proposed in this embodiment belongs to the field of financial technology. This application identifies the location information of the accident site as the first target point; searches for all schedulable resource location information within a first specific range of the first target point, and obtains dynamic road network data within the first specific range; based on all schedulable resource location information, the first target point, the dynamic road network data within the first specific range, and an ant colony algorithm, it selects the initial rescue path with the shortest time consumption; searches for all rescue point location information within a second specific range of the first target point, and obtains dynamic road network data within the second specific range, constructing a second target point set; based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ant colony algorithm, it selects the subsequent rescue path with the shortest time consumption; and sends the selected initial and subsequent rescue paths to a preset rescue planning and scheduling platform for rescue planning. By employing a search model constructed using the ant colony algorithm twice and introducing a rescue obstacle factor, the initial and subsequent rescue paths are obtained respectively, ensuring timely arrival at the rescue site and timely transport of the injured to the target medical institution.

[0156] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to perform the steps of the vehicle insurance roadside assistance planning method based on the ant colony algorithm described above.

[0157] The computer-readable storage medium proposed in this embodiment belongs to the field of financial technology. This application identifies the location information of the accident site as the first target point; searches for all schedulable resource location information within a first specific range of the first target point, and obtains dynamic road network data within the first specific range; based on all schedulable resource location information, the first target point, the dynamic road network data within the first specific range, and an ant colony algorithm, it selects the initial rescue path with the shortest time consumption; searches for all rescue point location information within a second specific range of the first target point, and obtains dynamic road network data within the second specific range, constructing a second target point set; based on the first target point, the second target point set, the dynamic road network data within the second specific range, and the ant colony algorithm, it selects the subsequent rescue path with the shortest time consumption; and sends the selected initial and subsequent rescue paths to a preset rescue planning and scheduling platform for rescue planning. By employing a search model constructed using the ant colony algorithm twice and introducing a rescue obstacle factor, the initial and subsequent rescue paths are obtained respectively, ensuring timely arrival at the rescue site and timely transport of the injured to the target medical institution.

[0158] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0159] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A roadside assistance planning method for vehicle insurance based on ant colony algorithm, characterized in that, Includes the following steps: Identify the location of the accident site and use it as the primary target point; Locate all schedulable resource location information within a first specific range of the first target point, and obtain dynamic road network data within the first specific range; Based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm, the shortest initial rescue path is selected, specifically including: By analyzing the dynamic road network data within the first specific range, data corresponding to all rescue obstruction factors within the first specific range are obtained. The rescue obstruction factors include the number of road sections with saturated traffic flow, and the number of turning intersections and straight intersections from each schedulable resource location information to the first target point. The location information of each schedulable resource is used as the starting location information of the ant colony. Set the first target point as the target location information that a single ant colony member needs to reach; Based on the dynamic road network data within the first specific range, obtain all road intersection information within the first specific range, and use the road intersection information as the filtering node information of the ant colony; Based on the starting position information of the ant colony, the target position information, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from each schedulable resource location information to the first target point. Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor. According to the preset algorithm formula: The rescue time corresponding to the shortest path from each schedulable resource location to the first target point is obtained, wherein... This indicates the number representing the location information of each schedulable resource. Indicates the number is The shortest path length corresponding to the location information of schedulable resources. This indicates the travel speed of the schedulable resource. Indicates the number is The location information of schedulable resources corresponds to the numbering information of each rescue obstacle factor in the shortest path. This indicates the duration of each obstacle hindering rescue efforts. Indicates the number is The available location information of the schedulable resources and the rescue time from the first target point; The shortest path corresponding to the minimum rescue time is obtained as the initial rescue path; Locate all rescue point locations within a second specific range of the first target point, and obtain dynamic road network data within the second specific range to construct a second target point set; Based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm, the rescue route with the shortest time consumption is selected. The selected front-end rescue path and the selected rear-end rescue path are sent to a preset rescue planning and dispatching platform for rescue planning.

2. The vehicle insurance roadside assistance planning method based on ant colony algorithm according to claim 1, characterized in that, The step of identifying the location information of the accident site and using it as the first target point specifically includes: The latitude and longitude values ​​of the accident site are determined using a preset GPS positioning component, and these values ​​are used as the first target point.

3. The vehicle insurance roadside assistance planning method based on ant colony algorithm according to claim 1, characterized in that, The steps of finding all schedulable resource location information within a first specific range of the first target point and obtaining dynamic road network data within the first specific range specifically include: Using the first specific range as the search radius and the first target point as the search origin, search for all schedulable resources within the first specific range of the first target point, and obtain the location information of all schedulable resources. The schedulable resources include schedulable rescue vehicles, and the location information of all schedulable resources can be represented by latitude and longitude values ​​determined by a preset GPS positioning component. According to the preset GIS visualization system, dynamic road network data within the first specific range is obtained, wherein the dynamic road network data includes dynamic real-time traffic signal scheduling data, real-time road condition and vehicle data, and static road grid data. The steps of finding the location information of all rescue points within a second specific range of the first target point, and obtaining dynamic road network data within the second specific range to construct a second target point set, specifically include: Using the second specific range as the search radius and the first target point as the search origin, search for all rescue points within the second specific range of the first target point, and obtain the location information of all rescue points. The rescue points include medical institutions, and the location information of all rescue points can be represented by latitude and longitude values ​​measured by a preset GPS positioning component. Based on the preset GIS visualization system, obtain dynamic road network data within the second specific range.

4. The vehicle insurance roadside assistance planning method based on ant colony algorithm according to claim 3, characterized in that, The step of predicting the shortest path from each schedulable resource location to the first target point using the ant colony algorithm based on the ant colony's starting position information, the target location information, and the ant colony's filtering node information specifically includes: The starting position information of the current ant colony is used as the search starting point, and the target position information is used as the search ending point, which are then deployed into the search model constructed by the ant colony algorithm. The filtering node information of the ant colony is used as training data and input into the search model. The filtering node information of the ant colony is a set of point value data composed of all road intersection information within the first specific range. Through the search model, an iterative search is performed to obtain the probability value of the next candidate node searched each time from the search starting point to the search ending point. From each of the next candidate nodes found in the search, select the node with the highest probability value, and add the node with the highest probability value to the preset node set in turn; Until the iteration is complete, road intersections are connected according to the sequence of elements in the node set, and the connection path from the current ant colony location information to the target location information is obtained as the shortest path from the current schedulable resource location information to the first target point.

5. The vehicle insurance roadside assistance planning method based on ant colony algorithm according to claim 4, characterized in that, The step of selecting the shortest rescue route based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm specifically includes: By analyzing the dynamic road network data within the second specific range, data corresponding to all rescue obstruction factors within the second specific range are obtained; The first target point is used as the starting position information of the ant colony; Set the position information of each element in the second target point set as the target position information that each individual ant colony member will reach; Based on the dynamic road network data within the second specific range, obtain all road intersection information within the second specific range, and use the road intersection information as the filtering node information of the ant colony; Based on the starting position information of the ant colony, the target position information to be reached by each individual ant, and the screening node information of the ant colony, the ant colony algorithm is used to predict the shortest path from the first target point to each element in the set of the second target points. Obtain the number of rescue obstruction factors contained in each shortest path, and the obstruction time of each rescue obstruction factor. According to the preset algorithm formula: Obtain the rescue time corresponding to the shortest path from the first target point to each element in the set of second target points, where... This represents the number of each element in the second set of target points. This indicates that the second set of target points is numbered as follows. The shortest path length corresponding to the element. This indicates the travel speed of the schedulable resource. This indicates that the second set of target points is numbered as follows. The element corresponds to the numbering information of each rescue obstacle factor in the shortest path. This indicates the duration of each obstacle hindering rescue efforts. This indicates that the set of points from the first target point to the second target point is numbered as follows. The location information of the element corresponds to the rescue time; The shortest path corresponding to the minimum rescue time is obtained as the subsequent rescue path.

6. The vehicle insurance roadside assistance planning method based on ant colony algorithm according to claim 5, characterized in that, The step of predicting the shortest path from the first target point to each element in the second target point set using the ant colony algorithm, based on the ant colony's starting position information, the target position information to be reached by each individual ant, and the ant colony's selection node information, specifically includes: The starting position information of the ant colony is used as the search starting point, and the target position information to be reached by each individual ant colony member is used as the search endpoint and deployed into the search model. The filtering node information of the ant colony is used as training data and input into the search model. The filtering node information of the ant colony is a set of point value data composed of all road intersection information within the second specific range. Through the search model, an iterative search is performed to obtain the probability value of the next candidate node searched each time from the search starting point to the current search endpoint. From each of the next candidate nodes found in the search, select the node with the highest probability value, and add the node with the highest probability value to the preset node set in turn; Until the iteration is complete, road intersections are connected according to the sequence of elements in the node set to obtain the connection path from the starting position information of the ant colony to the target position information corresponding to the current search endpoint. The connection path is used as the shortest path from the first target point to the target rescue point.

7. A vehicle insurance roadside assistance planning device based on ant colony algorithm, characterized in that, The ant colony algorithm-based vehicle insurance roadside assistance planning device implements the steps of the ant colony algorithm-based vehicle insurance roadside assistance planning method as described in any one of claims 1 to 6, wherein the ant colony algorithm-based vehicle insurance roadside assistance planning device comprises: The first target point identification module is used to identify the location information of the accident site and use it as the first target point. The first data acquisition module is used to find all schedulable resource location information within a first specific range of the first target point, and to acquire dynamic road network data within the first specific range. The front-end rescue path filtering module is used to filter out the front-end rescue path with the least time consumption based on the location information of all schedulable resources, the first target point, the dynamic road network data within the first specific range, and the ant colony algorithm. The second data acquisition module is used to find the location information of all rescue points within a second specific range of the first target point, and to acquire dynamic road network data within the second specific range to construct a second target point set; The downstream rescue route filtering module is used to filter out the downstream rescue route with the least time based on the first target point, the second set of target points, the dynamic road network data within the second specific range, and the ant colony algorithm. The path filtering result sending module is used to send the filtered front-end rescue path and the rear-end rescue path to a preset rescue planning and dispatching platform for rescue planning.

8. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, and the processor, when executing the computer-readable instructions, implements the steps of the vehicle insurance roadside assistance planning method based on the ant colony algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the vehicle insurance roadside assistance planning method based on the ant colony algorithm as described in any one of claims 1 to 6.

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