Expressway patrol path optimization method and device and storage medium

Abstract

The invention provides an expressway patrol path optimization method and device and a storage medium. The method comprises the following steps: acquiring basic data of expressways and patrol road sections in an area under administration to construct a basic data set; generating an initial patrol path based on the basic data set and the traversal demand of the patrol road section; the inspection vehicle inspects according to the initial inspection path and starts to receive sudden abnormal event data; if the sudden abnormal event belongs to an instant response event, directly inserting the sudden abnormal event into the current patrol path, and if the sudden abnormal event belongs to a to-be-responded event, leaving the event to be processed; after the inspection vehicle continuously receives the sudden abnormal event data for T time, stopping receiving the sudden abnormal event data; and generating a dynamic patrol path according to the real-time position data of the patrol vehicle, the remaining patrol road section data and the unprocessed sudden abnormal event data at present. According to the method, the balance between the operation time and the priority of the sudden abnormal events in the path can be considered under the condition that the path is optimal.

Description

Technical Field

[0001] The present invention relates to the technical field of path optimization, and particularly to a method, device and storage medium for optimizing the inspection path of expressways. Background Art

[0002] Highway inspection is an important measure to ensure road safety, improve service quality, prevent maintenance and respond to emergencies. At present, the traditional inspection method is still the mainstream inspection method. As one of the traditional inspection methods, manual driving inspection has a large workload and a high risk factor. At present, most studies focus on the construction of inspection systems and inspection systems. The highway inspection path mainly depends on the personal experience of inspectors, and it is difficult to ensure the rationality and efficiency of the inspection path.

[0003] In addition to completing the inspection work of fixed sections, highway inspectors also need to respond and handle emergencies that occur in real time within the jurisdiction during the inspection process. With the continuous increase in the number of automobiles, the frequency of highway traffic accidents has also increased year by year. A series of emergencies that affect road traffic safety, such as component scattering caused by traffic accidents, damage to road facilities, highway waterlogging caused by bad weather, slope water damage, and broken roadside trees, all require the rapid response and handling of inspectors. Highways are long closed roads, and efficient handling of emergencies is crucial for reducing traffic jams and ensuring the traffic efficiency of expressways. Therefore, the inspection path needs to be continuously adjusted during the highway inspection process. This problem can be abstracted as a dynamic vehicle path problem. Due to different application scenarios, the methods in the existing technologies cannot be directly transplanted, and it is necessary to closely combine the handling requirements and characteristics of highway emergencies to formulate targeted dynamic path optimization models and dynamic response strategies to scientifically and reasonably adjust the dynamic path to respond to emergencies during the inspection process.

[0004] In summary, there is an urgent need for a method, device and storage medium for optimizing the inspection path of expressways to solve the problems existing in the prior art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the inspection path of expressways, which realizes the balance between operation time and the priority of emergencies in the path under the condition of the optimal path. The specific technical solutions are as follows: A method for optimizing the inspection path of expressways includes: S01. Obtain the basic data of the expressway and the inspection sections within the jurisdiction to construct a basic data set; S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections; S03. The inspection vehicle conducts inspections according to the initial inspection path and starts to receive emergency data; S04. If burst abnormal event data is received, determine whether the burst abnormal event belongs to an immediate response event or a pending response event: if it belongs to an immediate response event, directly insert it into the current inspection path; if it belongs to a pending response event, leave it for processing; S05. The inspection vehicle continuously receives burst abnormal event data After a certain time, stop receiving burst abnormal event data; S06. Construct a dynamic optimization data set based on the real-time position data of the inspection vehicle, the remaining inspection section data, and the current unprocessed burst abnormal event data, and generate a dynamic inspection path based on the dynamic optimization data set; S07. The inspection vehicle travels according to the dynamic inspection path and processes burst abnormal events; S08. If all burst abnormal events have been processed, the inspection vehicle starts to receive burst abnormal event data again; S09. Repeat steps S04 - S08 until the inspection tasks for all inspection sections are completed.

[0006] Preferably, the method for generating the initial inspection path in step S02 is specifically: S21. According to the highway network topology structure Construct a directed graph ; where, a highway interchange is used as a node, and a road in one driving direction of a highway section is used as an edge, represents the set of all nodes in the directed graph, represents the set of all edges in the directed graph; S22. Calculate the road length between the starting mileage of the th inspection section and the highway interchange at the reverse end of the road where it is located, , and the road length between the ending mileage of this inspection section and the highway interchange at the forward end of the road where it is located, , where is an integer and its value is , is the total number of inspection sections; S23. Based on the and obtained for each inspection section, obtain the origin node and the shortest path between any two of the inspection sections that make up the set , and further obtain the shortest path distance matrix and the set of road segments composed of all shortest paths; S24. Using the shortest path distance matrix and the set composed of all the shortest paths that form road segments As input parameters, the genetic algorithm is used to solve the initial inspection path optimization model to obtain the initial inspection path.

[0007] Preferably, the objective function of the initial inspection path optimization model is:[[]] (1.2), In formula (1.2): represents the starting node and the set composed of and both belong to and , is from to the distance of the shortest path of represents a 0-1 decision variable. If the inspection vehicle travels from to , then , otherwise ; is the specified driving speed of the inspection vehicle; is the adjustment speed, used to represent the delay impact of the operation process of the inspection vehicle on the driving time in the inspection section; is the destination 's weight value. When represents the starting node takes the value of 0. When represents the inspection section takes the value of the length of the inspection section.

[0008] Preferably, the method for determining whether a sudden abnormal event belongs to an immediate response event or a pending response event is:[[]] If the sudden abnormal event is located on the path between the inspection vehicle and the next uninspected inspection section in the current inspection path, then the sudden abnormal event is regarded as an immediate response event, immediately responded to and inserted into the current inspection path; otherwise, the sudden abnormal event is regarded as a pending response event to be processed.

[0009] Preferably, when constructing the dynamic optimization data set:[[]] If a sudden abnormal event is located in an inspection section, the inspection section where it is located is divided into two inspection sections with the sudden abnormal event as the dividing point. The two newly obtained inspection sections are included in the dynamic optimization data set, and at the same time, the inspected section is deleted; If the inspection vehicle is inspecting a certain inspection section, then the remaining uninspected section of the inspection section is taken as a new inspection section and included in the dynamic optimization dataset.

[0010] Preferably, the specific method for generating the dynamic inspection path in step S06 includes: S61. Construct a priority evaluation model for events to be responded to, and calculate the priority of each event to be responded to ; S62. Obtain the real-time coordinates of the inspection vehicle , the starting node The shortest path between any two of the set composed of all unprocessed sudden abnormal events and the remaining inspection sections is obtained, and further the shortest path distance matrix and the set composed of road sections formed by all shortest paths ; S63. Using the shortest path distance matrix and the set composed of road sections formed by all shortest paths as input parameters, solve the global dynamic path optimization model to obtain the dynamic inspection path; Among them, the global dynamic path optimization model takes minimizing the total cost as the first objective function, and taking maximizing the total priority value of the events to be responded to included in the inspection path G as the second objective function, which are respectively: (1.8), (1.9), In formulas (1.8) and (1.9): represents the total time cost of the inspection vehicle driving and operating, represents the total penalty cost generated when the inspection vehicle arrives at each event to be responded to beyond the optimal arrival time limit; represents the real-time coordinates of the inspection vehicle , the starting node the set composed of all unprocessed sudden abnormal events and the remaining inspection sections; represents the set composed of all unprocessed sudden abnormal events, , is the set composed of all unprocessed immediate response events, is the set composed of all events to be responded to; , represents any element within the set and , the set represents the set excluding the starting node A set composed of elements other than, the set represents the set excluding the real-time coordinates of the inspection vehicle from the elements it is composed of, the set represents the set composed of all unprocessed sudden abnormal events and the real-time coordinates of the inspection vehicle ; is a 0-1 decision variable. If the inspection vehicle travels from to , then , otherwise ; is to the distance of the shortest path; is the priority value of the event to be responded to ; is the operation time of the inspection vehicle at , is the penalty cost generated when the inspection vehicle arrives at the event to be responded to exceeding the optimal arrival time limit; is the specified driving speed of the inspection vehicle.

[0011] Preferably, the values in different cases are expressed as: (1.10), In formula (1.10): represents the length of the inspection section , represents the set composed of all remaining inspection sections, represents the remaining processing time of the sudden abnormal event ; is the speed adjustment; Furthermore, the calculation method of is: In formula (1.11): is a fixed value, representing the estimated processing time of the sudden abnormal event ; is the processed time of the sudden abnormal event ; The values in different cases are expressed as: (1.12), In formula (1.12): is the distance that the inspection vehicle travels from the real-time coordinates to the event to be responded to Time spent; Optimal arrival time limit for events to be responded to Indicates the time for the inspection vehicle to travel from the real-time coordinates To the event to be responded to The time length limit, expressed as: (1.13), In formula (1.13): Is a fixed value, representing the optimal time range from the occurrence of the event to be responded to until an inspection vehicle arrives for handling Is the event to be responded to The time that the inspection vehicle has traveled within the current time period when the event occurs Within

[0012] Preferably, the priority evaluation model for the event to be responded to is: (1.3), In formula (1.3): , , , And Are all weight coefficients, ; Is The normalized value, Is The normalized value, Is The normalized value, Is The normalized value, Is The normalized value; Is the road type of the 100-meter section within the stake number interval where the event to be responded to is located; Is the real-time traffic flow within the stake number interval where the sudden abnormal event is located; Indicates the occurrence order of the event to be responded to, sorted from small to large according to the occurrence time of the sudden abnormal event. If there are events to be responded to that occur simultaneously, the same value is taken; Is the location coordinate of the event to be responded to And the real-time coordinates of the inspection vehicle The Euclidean distance between; Is the type of the event to be responded to

[0013] The present invention also provides a device for optimizing the inspection path of an expressway. The device adopts the above-mentioned method for optimizing the inspection path of an expressway. The device includes: An information storage module for storing basic data of the highway, basic data of the patrol section, sudden abnormal event data, real-time position data of the patrol vehicle, remaining patrol section data, and current unprocessed sudden abnormal event data; A real-time data acquisition module for real-time acquiring real-time position data of the patrol vehicle, sudden abnormal event data, and remaining patrol section data; An initial path generation module for generating an initial patrol path according to the basic data of the highway, the basic data of the patrol section, and the traversal requirements of all patrol sections; A dynamic path generation module for generating a dynamic patrol path according to a dynamically optimized data set; An information feedback module for feeding back the generated patrol path to a remote control terminal and displaying it on the screen of the patrol vehicle.

[0014] The present invention also provides a storage medium, in which a computer program is stored, and when the computer program runs, it executes the highway patrol path optimization method described above.

[0015] Applying the technical solution of the present invention has the following beneficial effects: By prioritizing sudden abnormal events, the present invention enables high-priority sudden abnormal events to be preferentially responded to and promptly processed, optimizes the dispatching of patrol vehicles, reduces the impact of sudden abnormal events on highway traffic operation, and avoids secondary accidents.

[0016] The present invention preferentially processes sudden abnormal events and then considers the patrol tasks of the patrol section, and can process sudden abnormal events in the order of their respective priorities in the first place, reducing the impact of sudden abnormal events on the highway. At the same time, when the first sudden abnormal event is received, the patrol vehicle only continuously performs data reception for a certain period of time, which can take into account the processing capacity of a single patrol vehicle and avoid excessive accumulation of sudden abnormal events, resulting in the inability to process sudden abnormal events within an appropriate time.

[0017] Based on the initial patrol path plan, when the patrol personnel encounter sudden abnormal events during the operation process, the present invention dynamically adjusts the patrol path through a hybrid response strategy mode combining immediate response and pending response with sudden abnormal events as key points, and takes into account the balance between operation time and the priority of sudden abnormal events in the path under the condition of optimal path.

[0018] The present invention proposes the best arrival time limit for sudden abnormal events, and sets a penalty cost for exceeding the time limit in the objective function value of the dynamic path optimization model based on the best arrival time limit, so that the generated dynamic path can fully consider the urgency of the processing requirements of each sudden abnormal event.

[0019] In addition to the purposes, features, and advantages described above, the present invention has other purposes, features, and advantages. The present invention will be further described in detail below with reference to the drawings. Description of the Drawings

[0020] The drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of the method for optimizing the highway patrol route in Embodiment 1; Figure 2 is a topological structure diagram of the highway network in Embodiment 2; Figure 3 is a directed graph of the highway network in Embodiment 2; Figure 4 is the schematic diagram of the positions of each patrol section in the directed graph in Embodiment 2 in Embodiment 2; Figure 5 is the schematic diagram of the remaining patrol sections, the real-time coordinates of the patrol vehicle, and the positions of sudden abnormal events on the highway at 14:25 in Embodiment 2; Figure 6 is the schematic diagram of the numbers and positions of sudden abnormal events at 14:30 in Embodiment 2; Figure 7 is the schematic diagram of the remaining patrol sections, the real-time coordinates of the patrol vehicle, and the positions of sudden abnormal events at 14:30 in Embodiment 2. Detailed Embodiments

[0021] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below, and preferred embodiments of the present invention are given. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure content of the present invention more thorough and comprehensive.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] Embodiment 1: Referring to Figure 1 , this embodiment provides a method for optimizing the highway patrol route, which specifically includes the following steps: S01. Obtain the basic data of the highways and patrol sections within the jurisdiction and construct a basic data set; Preferably, the basic data of the highway and the inspection section in step S01 are specifically as follows: The basic data of the highway include: highway network topology , road name , origin highway interchange , two-way road lengths of each highway section (here, the highway section refers to the section between two adjacent highway interchanges, and between two adjacent highway interchanges, there are two roads with opposite driving directions), road mileage intervals of each road , types of 100-meter road sections between each mileage .

[0024] The basic data of the inspection section include: starting mileage of the inspection section , ending mileage of the inspection section , length of the inspection section , road name where the inspection section is located and driving direction of the road where the inspection section is located .

[0025] Furthermore, the highway interchange includes two types: hub interchange and landing interchange. Among them, the hub interchange is the position where different highways intersect (i.e., one highway can enter another highway), and the landing interchange is the entrance and exit connecting to local ordinary roads (i.e., getting off the highway and making a U-turn to enter another driving direction). These two different types of highway interchanges have different impacts on the driving path planning of the inspection vehicle. When performing path planning, the actual type of highway interchange shall prevail. Among them, the origin highway interchange refers to the starting point of the inspection vehicle during inspection.

[0026] S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections; Furthermore, the method for generating the initial inspection path in step S02 of this embodiment is specifically as follows: S21. Construct a directed graph according to the highway network topology ; where, a highway interchange serves as a node, and a road in one driving direction of a highway section serves as an edge, represents the set of all nodes in the directed graph, represents the set of all edges in the directed graph; S22. Calculate the road length between the starting mileage of the th inspection section and the highway interchange at the reverse end of the road where it is located (i.e., the starting mileage The road length between it and the nearest highway interchange behind it on the road where it is located ), and the end stake number of this inspection section The road length between it and the highway interchange at the positive end of the road where it is located (i.e., the end stake number The road length between it and the nearest highway interchange in front of it on the road where it is located ), where is an integer and its value is , is the total number of inspection sections.

[0027] As is well known, the roads on highways can only be traveled in one direction. Here, the reverse direction refers to the direction opposite to the driving direction of this road, and the positive direction refers to the direction the same as the driving direction of this road.

[0028] S23. Based on the two-way road lengths of each highway section and those obtained for each inspection section and , use the Floyd algorithm (node-insertion method) to obtain the shortest path between any two of the origin nodes and in the set composed of inspection sections, and further obtain the shortest path distance matrix and the set composed of the road sections that make up all the shortest paths ; Preferably, in the directed graph, use the origin highway interchange as the origin node , that is, when the inspection vehicle conducts inspections, it starts from the origin node to start the inspection.

[0029] Preferably, the set composed of the road sections that make up all the shortest paths , represents the set of road sections that make up the th shortest path, is an integer and its value is , is the total number of shortest paths; further, the set of road sections that make up the shortest paths includes all the component road sections of the th shortest path. The road sections in a shortest path are segmented by highway interchanges (i.e., nodes in the directed graph). For example, in the shortest path from the origin node to the 1st inspection section, only the 2nd node needs to be passed through. Then the set of its component road sections includes the origin node The road section from node 0 to node 2 and the road section from node 2 to the inspection section 1.

[0030] Preferably, since the inspection vehicle departs from the starting node and there are inspection sections, it is necessary to generate a shortest path distance matrix . The shortest path distance matrix is expressed as: (1.1), wherein, taking , both belong to the set and . In the set , 0 represents the starting node, 1 to each represents an inspection section, represents the to shortest path distance. When , takes an infinite value. When , takes the to actual road length of the shortest path.

[0031] S24. Using the shortest path distance matrix and the set consisting of all shortest paths as input parameters, use the genetic algorithm to solve the initial inspection path optimization model to obtain the initial inspection path; Specifically, the objective function of the initial inspection path optimization model is to minimize the time cost of the path, and the objective function is expressed as: (1.2), In formula (1.2): represents the set composed of the starting node and inspection sections, and both belong to and , is the to shortest path distance; represents a 0-1 decision variable. If the inspection vehicle travels from to , then , otherwise ; is the specified driving speed of the inspection vehicle; To adjust the speed, , is used to represent the delay impact of the operation process of the inspection vehicle on the driving time in the inspection section; is the destination The weight value of, when represents the starting node, the value of is 0, when represents the inspection section, the value of is the length of the inspection section.

[0032] Preferably, when using the genetic algorithm to solve for the initial inspection path, the parameters that need to be input include: the shortest path distance matrix , the set composed of the road segments formed by all the shortest paths , the specified driving speed of the inspection vehicle , the adjusted speed and the lengths of each inspection section .

[0033] Furthermore, the specific method of solving by the genetic algorithm is common knowledge in the art. In this embodiment, only a simple description of the solving process of the genetic algorithm is given: First, obtain the generated shortest path distance matrix and the set composed of the road segments formed by all the shortest paths , restrict the inspection vehicle to start from the starting node , each inspection section is inspected and only inspected once, and finally returns to the starting node ; Secondly, set the population size , the number of chromosome genes , the number of iterations , the crossover probability and the mutation probability ; Then, initialize the population, calculate the fitness value of each individual (i.e., the time cost of each inspection path plan), perform selection, crossover, and mutation operations to generate a new population; Finally, determine whether the termination condition is met. If it is met, the iteration ends and the final initial inspection path is output. Otherwise, continue the iterative optimization.

[0034] S03. The inspection vehicle conducts inspections according to the initial inspection path and starts to receive data on sudden abnormal events; Preferably, the data on sudden abnormal events includes: the name of the road where the sudden abnormal event is located , the driving direction of the road where the sudden abnormal event is located , the stake number interval where the sudden abnormal event is located , the occurrence time of the sudden abnormal event , the location coordinates of the sudden abnormal event and types of sudden abnormal events 。

[0035] Further, the types of sudden abnormal events include landslides, falling objects, damage to road facilities, car accidents, etc. The sudden abnormal events in this embodiment refer to events that affect the normal passage of the highway.

[0036] Further, when receiving sudden abnormal event data, number them in ascending order according to the occurrence time of the sudden abnormal events from earliest to latest; if multiple sudden abnormal events occur at the same time, random numbering can be used.

[0037] S04. If sudden abnormal event data is received, determine whether the sudden abnormal event is an immediate response event or a pending response event: if it is an immediate response event, directly insert it into the current patrol route; if it is a pending response event, leave it for processing. Specifically, the method for determining whether a sudden abnormal event is an immediate response event or a pending response event is as follows: If the sudden abnormal event is on the path between the patrol vehicle and the next unpatrolled patrol section in the current patrol route, then regard the sudden abnormal event as an immediate response event, immediately respond and insert it into the current patrol route; otherwise, regard the sudden abnormal event as a pending response event and leave it for processing.

[0038] Further, when determining whether a sudden abnormal event is an immediate response event or a pending response event, if the patrol vehicle is patrolling a certain section, after determining that there is an immediate response event, immediately stop the current section patrol task and go to handle the immediate response event; if it is determined that there is no event data for the immediate response event, then continue the current section patrol task and wait for subsequent dynamic route optimization.

[0039] S05. After the patrol vehicle continuously receives sudden abnormalities, stop receiving sudden abnormal event data; Specifically, in order to take into account the event processing capacity of a single patrol vehicle, the sudden abnormal event data reception time is set in this embodiment , that is, after receiving the first sudden abnormal event data, the patrol vehicle continuously receives sudden abnormal event data for a time, reaching time, after which the patrol vehicle no longer accepts sudden abnormal event data, and the patrol vehicle fully processes the currently unprocessed sudden abnormal events to ensure that the sudden abnormal events can be processed in a timely and efficient manner. Further, the value of can be set by those skilled in the art according to the situation, and can be 5 minutes, 10 minutes, 15 minutes, or even longer.

[0040] S06. Construct a dynamic optimization dataset based on the real-time position data of the inspection vehicle, the remaining inspection section data, and the data of the current unprocessed sudden abnormal events, and generate a dynamic inspection path based on the dynamic optimization dataset; wherein, the current unprocessed sudden abnormal events include unprocessed immediate response events and all pending response events; Preferably, the real-time position data of the inspection vehicle includes: the real-time coordinates of the inspection vehicle , the name of the road where the inspection vehicle is located , the distance between the inspection vehicle and the high-speed interchange at the positive end of the road where it is located (that is, the distance between the inspection vehicle and the nearest high-speed interchange in the driving direction of the road where it is located).

[0041] Further, the remaining inspection section data includes: the starting stake number of the inspection section and the ending stake number , the length of the inspection section , the name of the road where the inspection section is located and the driving direction of the road where the inspection section is located .

[0042] Further, the data of the current unprocessed sudden abnormal events includes: the name of the road where the sudden abnormal event is located , the driving direction of the road where the sudden abnormal event is located , the stake number interval where the sudden abnormal event is located , the occurrence time of the sudden abnormal event , the position coordinates of the sudden abnormal event , the type of the sudden abnormal event , the processed time of the sudden abnormal event , the real-time traffic flow within the stake number interval where the sudden abnormal event is located and the road type of every 100-meter section within the stake number interval where the sudden abnormal event is located . Among them, the processed time of the sudden abnormal event mainly considers that when regenerating the dynamic inspection path, there may be a situation where a sudden abnormal event (generally an immediate response event) is being processed but not yet completed. Introducing the processed time of the sudden abnormal event can accurately consider the impact of this sudden abnormal event on the regeneration of the dynamic inspection path.

[0043] Preferably, when generating the dynamic inspection path, if the inspection vehicle is inspecting a certain inspection section, then use the remaining uninspected sections of this inspection section as a new inspection section and include it in the dynamic optimization dataset.

[0044] Preferably, when constructing the dynamically optimized data set, if a sudden abnormal event is located in the inspection section, the inspection section where the sudden abnormal event is located is divided into two inspection sections with the sudden abnormal event as the segmentation point. The newly obtained two inspection sections are included in the dynamically optimized data set, and the inspected section that has been divided is deleted. Thus, after processing the last sudden abnormal event in the dynamic inspection path, the inspection task can be carried out immediately, preventing a long detour between the last sudden abnormal event in the dynamic inspection path and the first inspection section, and reducing the time required to complete the inspection.

[0045] Preferably, the specific method for generating the dynamic inspection path includes: S61. Construct a priority evaluation model for events to be responded to, and calculate the priority of each event to be responded to ; Specifically, the priority evaluation model for events to be responded to is: (1.3), In formula (1.3): , , , and are all weight coefficients, ; is the normalized value, is the normalized value, is the normalized value, is the normalized value, is the normalized value; is the road type of the 100-meter section within the stake number interval where the event to be responded to is located, is an integer discrete variable, and different values correspond to different road types of the 100-meter section. In this embodiment, the value 0 is set to represent a normal section, 1 represents a landslide-prone section, 2 represents a bridge, and 3 represents a tunnel; is the real-time traffic flow within the stake number interval where the sudden abnormal event is located; is an integer discrete variable representing the occurrence order of the event to be responded to, and is sorted from small to large according to the occurrence time of the sudden abnormal event. If there are events to be responded to that occur simultaneously, the same value is taken; is the position coordinate of the event to be responded to and the real-time coordinate of the inspection vehicle is the type of the event to be responded to, is an integer discrete variable, and different numerical values correspond to different event types. In this embodiment, the numerical value 0 is set to represent falling objects, 1 to represent damage to road facilities, 2 to represent car accidents, and 3 to represent landslides.

[0046] Further, in this embodiment, the weight coefficients , , , and are determined as follows: S61.1. Use different methods to calculate the weight coefficients , , , and respectively, where is an integer greater than or equal to 2; S61.2. Construct a comprehensive weight calculation model based on the minimum information entropy theory; Specifically, the objective function of the comprehensive weight calculation model is expressed as: (1.4), In formula (1.4): is the deviation function of the combined weight; is the combined weight of the th index; is the weight of the th way to obtain the th index; is the number of indices. In this embodiment, the weight coefficients include , , , and , so .

[0047] Further, use the result value constraint to limit the values of the combined weights obtained by solving the comprehensive weight calculation model, specifically including: (1.5), (1.6), (1.7), S61.3. The weight coefficients calculated by , , , and Input it into the comprehensive weight calculation model, and solve the comprehensive weight calculation model through the genetic algorithm to obtain the combined weight coefficients , , , and .

[0048] Furthermore, when determining the weight coefficients , , , and , it is also possible not to adopt S61.1 - S61.3, but directly adopt a calculation method (i.e., equals 1) to obtain the weight coefficients , , , and , and then substitute them into formula (1.3) for calculation.

[0049] S62. Use the Floyd algorithm to obtain the real - time coordinates of the inspection vehicle , the starting node the set composed of all unprocessed sudden abnormal events and the remaining inspection sections, and further obtain the shortest - path distance matrix and the set composed of road segments of all shortest paths ; Specifically, the methods for obtaining the shortest path, the shortest - path distance matrix and the set composed of road segments of all shortest paths in step S62 are the same as those in step S23. The only difference between the two is the number of elements in the set and the set and the set . Therefore, the details of step S62 will not be described here again.

[0050] S63. Using the shortest - path distance matrix and the set composed of road segments of all shortest paths as input parameters, use the NSGA - II algorithm (multi - objective genetic algorithm) to solve the global dynamic path optimization model to obtain the dynamic inspection path.

[0051] Specifically, the global dynamic path optimization model in this embodiment takes minimizing the total cost as the first objective function, and taking maximizing the total priority value of the events to be responded in the inspection path as the second objective function, which are respectively (1.8), (1.9), In formulas (1.8) and (1.9): represents the total time cost of the inspection vehicle during driving and operation, represents the total penalty cost generated when the inspection vehicle exceeds the optimal arrival time limit when reaching each event to be responded; represents the real-time coordinates of the inspection vehicle , the starting node the set composed of all unhandled sudden abnormal events and the remaining inspection sections; represents the set composed of all unhandled sudden abnormal events, , is the set composed of all unhandled immediate response events, is the set composed of all events to be responded; , represents any element within the affiliated set and , the set represents the set after removing the starting node from the elements, the set represents the set after removing the real-time coordinates of the inspection vehicle from the elements, the set represents the set composed of all unhandled sudden abnormal events and the real-time coordinates of the inspection vehicle ; is a 0-1 decision variable. If the inspection vehicle travels from to , then , otherwise ; is the distance of the shortest path from to ; is the priority value of the event to be responded ; is the operation time of the inspection vehicle at . The value of in different cases is: If represents the starting node, then , if represents the inspection section, then , if represents an unhandled sudden abnormal event, then is the remaining processing time of this sudden abnormal event , The specific value of (1.10) Wherein, represents the length of the inspection section , represents the set composed of all the remaining inspection sections, represents the starting node, represents the set composed of all unprocessed sudden abnormal events; Furthermore, the calculation method of is: In formula (1.11): is a fixed value, representing the expected processing time of the sudden abnormal event , is the processed time of the sudden abnormal event .

[0052] is the penalty cost generated when the inspection vehicle arrives at the event to be responded beyond the optimal arrival time limit. The value of in different cases is: if the inspection vehicle travels to the event to be responded (where ), and the time spent is within the optimal arrival time limit of this event to be responded, then the value is 0; otherwise, the value is . The value of in different cases is specifically expressed as: The optimal arrival time limit of the event to be responded represents the time length limit for the inspection vehicle to travel from the real-time coordinate to the event to be responded , and is expressed as: (1.13). In formula (1.13): is a fixed value, representing the optimal time range from the occurrence of the event to be responded to the arrival of an inspection vehicle for processing, is the time (where ) that the inspection vehicle has traveled within the current time period when the event to be responded occurs.

[0053] Preferably, the method of using the NSGA-II algorithm (multi-objective genetic algorithm) to solve the global dynamic path optimization model belongs to the common knowledge in the field. In this embodiment, only a simple description is given: First, obtain the generated shortest path distance matrix and the set composed of all shortest paths forming road segments , restrict starting from the real-time coordinates of the inspection vehicle , first insert the unprocessed immediate response events in the order from near to far from the real-time coordinates of the inspection vehicle , then insert a random number of the remaining pending response events in the order of the priorities of each pending response event (i.e., it is allowed not to insert all the pending response events, but it is required that the inserted pending response events conform to the priority order), then traverse the remaining inspection sections and each inspection section is only inspected once, and finally return to the starting node; secondly, set the population size , the number of chromosome genes , the number of iterations , the crossover probability and the mutation probability ; then, initialize the population, perform non-dominated sorting and crowding degree calculation according to the fitness value of each individual (here the fitness value is the opposite of the total cost of the inspection path and the total priority value of the pending response events), execute selection, crossover, and mutation operations, merge the populations and then perform non-dominated sorting and crowding degree calculation again to generate a new population; finally, judge whether the termination condition is satisfied, if satisfied, the iteration ends, perform normalization processing on the values of the objective function formulas (1.8) and (1.9), set the weight coefficients and , use to calculate the weighted sum of the two objective function values, and output the dynamic inspection path with the smallest value, otherwise continue iterative optimization; among them, is the normalized value of the output value of formula (1.8) for the path scheme , is the normalized value of the output value of formula (1.9) for the path scheme .

[0054] Preferably, when the NSGA-II algorithm (multi-objective genetic algorithm) solves the global dynamic path optimization model, the parameters that need to be input include the shortest path distance matrix , the set composed of all shortest paths forming road segments , the specified driving speed of the inspection vehicle , the adjustment speed , the length of the inspection section , the priority value of the pending response event and sorting, the best arrival time limit of the pending response event , the remaining processing time of the sudden abnormal event .

[0055] S07. The inspection vehicle travels along the dynamic inspection path and handles the sudden abnormal events within the dynamic inspection path; S08. If all the sudden abnormal events have been handled, the inspection vehicle starts to receive the data of sudden abnormal events again; S09. Repeat steps S04 - S08 until the inspection tasks of all inspection sections are completed.

[0056] The method of this embodiment prioritizes the sudden abnormal events through priority sorting, enabling high - priority sudden abnormal events to be preferentially responded to and timely handled, optimizing the inspection vehicle scheduling, reducing the impact of sudden abnormal events on the highway traffic operation, and avoiding secondary accidents.

[0057] The method of this embodiment preferentially handles sudden abnormal events and then considers the inspection tasks of the inspection sections. It can handle sudden abnormal events in the first time according to their respective priority orders, reducing the impact of sudden abnormal events on the highway. At the same time, after receiving the first sudden abnormal event, the inspection vehicle only continuously receives data for a certain period of time, which can take into account the processing capacity of a single inspection vehicle and avoid excessive accumulation of sudden abnormal events, resulting in the inability to complete the handling of sudden abnormal events within an appropriate time.

[0058] Based on the initial inspection path plan, when the inspection personnel encounter sudden abnormal events during the operation, the method of this embodiment dynamically adjusts the inspection path through a hybrid response strategy mode that combines immediate response and pending response with sudden abnormal events as key points, taking into account the balance between operation time and the priority of sudden abnormal events in the path under the condition of the optimal path.

[0059] The method of this embodiment proposes the best arrival time limit for sudden abnormal events and sets a penalty cost for exceeding the time limit in the objective function value of the dynamic path optimization model based on the best arrival time limit, so that the generated dynamic path can fully consider the urgency of the handling requirements of each sudden abnormal event.

[0060] Embodiment 2: Based on some expressways around Changsha City, this embodiment uses the expressway inspection path optimization method in Embodiment 1 to plan the inspection path, specifically as follows: S01. Obtain the basic data of the expressways and inspection sections within the jurisdiction to construct a basic data set, specifically as follows: This embodiment obtains the highway network topology structure As Figure 2 shown, the starting expressway interchange for inspection is set as Jinqiao Interchange. The basic data of the expressways in this embodiment are shown in Table 1 and Table 2: In this embodiment, the upward direction refers to the north-to-south and west-to-east directions, and the downward direction refers to the south-to-north and east-to-west directions; the conventional road section refers to the road section other than bridges, tunnels and sections prone to landslides.

[0061] The basic data of the patrol section in this embodiment is shown in Table 3: S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections; S21.Based on Figure 2 The highway network topology shown Constructing a directed graph , the resulting directed graph is Figure 3 As shown in the figure, the weight of each edge is the road length RL (km) of the expressway section in one driving direction. A expressway interchange is regarded as a node and the nodes are numbered as - , the road in one driving direction of the expressway section is taken as an edge, and the corresponding road information of each edge is shown in Table 4: Among them, due to the origin of high-speed interchange For Jinqiao Interconnection, the originating node is determined as .

[0062] Furthermore, each patrol section is numbered according to the basic data of the patrol section, wherein the patrol sections with IDs 1-10 in Table 3 are numbered as follows: , each patrol section in the directed graph The position in Figure 4 shown.

[0063] S22. According to Length of patrol section Calculate the starting pile number respectively The length of the road between the highway interchange at the opposite end of the road where it is located , its end pile number The length of the road between the freeway interchange at the positive end of the road where it is located ; To patrol the road section For example, the starting pile number 229 and the node Distance between pile numbers 94 It is 13.6km long, and its terminal number is 241 and node Distance between pile numbers 292 It is 5.2km.

[0064] S23. Based on the and obtained for each inspection section, use the Floyd algorithm (node-insertion method) to obtain the shortest paths between any two of the origin node and and the set consisting of inspection sections, and further obtain the shortest path distance matrix ; Specifically, since the inspection vehicle departs from the road node and there are 10 inspection sections in the current road network, a 11×11 shortest path distance matrix needs to be generated. Represent the origin node with 0, then this shortest path distance matrix is expressed as: (2.1), S24. Using the shortest path distance matrix and the set consisting of all road segments that form the shortest paths as input parameters, use the genetic algorithm to solve the initial inspection path optimization model to obtain the initial inspection path; Specifically, in this embodiment, it is assumed that the specified driving speed of the inspection vehicle is 80 km / h, and the adjustment speed is 40 km / h. The finally obtained initial path is expressed as: → → → → → → → → → → → .

[0065] S03. The inspection vehicle conducts inspections according to the initial inspection path and starts receiving data on sudden abnormal events; S04. If data on sudden abnormal events is received, determine whether the sudden abnormal event is an immediate response event or a pending response event: if it is an immediate response event, directly insert it into the current inspection path; if it is a pending response event, leave it for processing; In this embodiment, it is assumed that the inspection vehicle starts the inspection work at 13:50 pm and receives data on 2 sudden abnormal events in the jurisdiction at 14:25. For the convenience of calculation, this embodiment is based on Figure 3 with Taking the origin as the origin and 1m as a unit to construct a rectangular coordinate system. Assuming that the interval between two-way lanes is 2m, the received sudden abnormal event data is shown in Table 5: Furthermore, as Figure 5 shown, assuming that when a sudden abnormal event and data is received at 14:25, the inspection vehicle is located on the Changchang North Line expressway section corresponding to the directed graph edge . At this time, the remaining path of the inspection vehicle is → → → → → → → → → .

[0066] After judgment, the sudden abnormal events and are not located between the paths → . Therefore, the sudden abnormal events and are left as events to be responded to for further processing.

[0067] S05. The inspection vehicle continues to receive sudden abnormal event data for a certain period of time and then stops receiving sudden abnormal event data; In this embodiment, assuming that the time = 5min, since the inspection vehicle received sudden abnormal events and at 14:25, it continues to receive sudden abnormal event data in the time period from 14:25 to 14:30.

[0068] S06. Construct a dynamic optimization data set based on the real-time position data of the inspection vehicle, the remaining inspection section data, and the current unprocessed sudden abnormal event data, and generate a dynamic inspection path based on the dynamic optimization data set; S61. Construct a priority evaluation model for events to be responded to, and calculate the priorities of each event to be responded to ; Specifically, among the inspection sections shown in Table 3 at 14:30, the inspection sections and have been inspected, and the section is being inspected. Then the remaining inspection section data is shown in Table 6: Specifically, the real-time position data of the inspection vehicle at 14:30 is shown in Table 7: As Figure 6 shown, it is assumed that a total of 4 sudden abnormal events occurred during the period from 14:25 to 14:30, and the numbers are respectively 、 、 、 . Specifically, the data of the current unprocessed sudden abnormal events in this embodiment are shown in Table 8 and Table 9: Since the sudden abnormal events and are respectively located in the middle of the inspection sections and , therefore, based on and , the inspection sections and are segmented. The in → and → are segmented into two inspection sections and numbered respectively as and . The in → and → are segmented into two inspection sections and numbered respectively as and , as Figure 7 shown; the remaining inspection section data after segmentation is shown in Table 10: Furthermore, please refer to Figure 7 . At 14:30, the inspection vehicle is located at the mileage marker 43 in the downstream direction of the Changchang North Line Expressway. Therefore, the current unprocessed sudden abnormal events include the immediate response event and the pending response events 、 .

[0069] Preferably, in this embodiment, the entropy weight method, the CRITIC method, and the coefficient of variation method are used to calculate the weight coefficients 、 、 、 and in formula (1.3) respectively, and the specific results are shown in Table 11: Solve the comprehensive weight calculation model through the genetic algorithm to obtain the combined weight coefficients 、 、 、 and as shown in Table 12: Furthermore, substitute the obtained combined weight coefficients 、 、 、 and into formula (1.3) to obtain the priority values 、 、 of the events to be responded to and the priority ranking as shown in Table 13: S62. Use the Floyd algorithm to obtain the real-time coordinates of the inspection vehicle 、the starting node All unprocessed sudden abnormal events and the set composed of the remaining inspection sections the shortest path between any two of them, and further obtain the shortest path distance matrix and the set composed of the road sections of all the shortest paths ; In this embodiment, the shortest path distance matrix and the set composed of the road sections of all the shortest paths are obtained in the same way as in step S23 of Embodiment 1 ; specifically, the termination node, i.e., the starting node is represented as the number 0, so a 16×16 shortest path distance matrix should be constructed. In the matrix the distance between the real-time coordinates of the inspection vehicle and and respectively and the distance between and respectively and are all 0 km.

[0070] S63. Using the shortest path distance matrix and the set composed of the road sections of all the shortest paths as input parameters, use the NSGA-II algorithm (multi-objective genetic algorithm) to solve the global dynamic path optimization model to obtain the dynamic inspection path.

[0071] Specifically, when using the NSGA-II algorithm (multi-objective genetic algorithm) in this embodiment to solve the global dynamic path optimization model to obtain the dynamic inspection path, it is restricted to start from the real-time coordinates of the inspection vehicle and first insert the unprocessed immediate response events in the corresponding order , and then insert a random number of the three pending response events in the order of → → . Subsequently, traverse the remaining inspection sections and each inspection section is only inspected once, and finally return to the starting node. The finally output dynamic inspection path is expressed as: → → → → → → → → → → → → → → .

[0072] S07. The inspection vehicle travels according to the dynamic inspection path and processes the sudden abnormal events; S08. If all the sudden abnormal events have been processed, the inspection vehicle starts to receive the sudden abnormal event data again; In this embodiment, before the inspection vehicle processes the sudden abnormal events , , , it will not receive the sudden abnormal event data until after processing the sudden abnormal event and then starts to receive the sudden abnormal event data again.

[0073] S09. Repeat steps S04 - S08 until the inspection tasks of all inspection sections are completed.

[0074] Embodiment 3: This embodiment provides a highway inspection path optimization device. The device in this embodiment adopts the highway inspection path optimization method in Embodiment 1, and specifically includes: An information storage module, which is used to store the basic data of the highway, the basic data of the inspection sections, the sudden abnormal event data, the real-time position data of the inspection vehicle, the remaining inspection section data, and the current unprocessed sudden abnormal event data; A real-time data acquisition module for acquiring real-time position data of the inspection vehicle, sudden abnormal event data, and remaining inspection section data in real time; An initial path generation module for generating an initial inspection path according to the basic data of the highway, the basic data of the inspection sections, and the traversal requirements of all inspection sections; A dynamic path generation module for generating a dynamic inspection path according to the dynamic optimization data set; An information feedback module for feeding back the generated inspection path to the remote control terminal and displaying it on the screen of the inspection vehicle.

[0075] Embodiment 4: This embodiment provides a storage medium in which a computer program is stored. When the computer program runs, it executes the highway inspection path optimization method in Embodiment 1.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing a highway inspection path, characterized in that: The following steps are involved: S01. Obtain basic data of expressways and patrol sections within the jurisdiction to build a basic data set; S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections; S03. The inspection vehicle conducts inspections along the initial inspection route and begins to receive data on sudden abnormal events; S04. If the sudden abnormal event data is received, it is determined whether the sudden abnormal event is an immediate response event or a pending response event: if it is an immediate response event, it is directly inserted into the current inspection path; if it is a pending response event, it is left for processing; S05. Inspection vehicles continue to receive data on sudden abnormal events After the time, stop receiving data on sudden abnormal events; S06. Construct a dynamic optimization data set based on the real-time location data of the inspection vehicle, the remaining inspection section data, and the currently unprocessed abnormal event data, and generate a dynamic inspection path based on the dynamic optimization data set; S07. The inspection vehicle drives along the dynamic inspection route and handles sudden abnormal events; S08. If all the sudden abnormal events have been processed, the inspection vehicle starts to receive sudden abnormal event data again; S09. Repeat steps S04-S08 until the inspection tasks of all inspection sections are completed.

2. The highway inspection path optimization method according to claim 1, characterized in that: The method for generating the initial inspection path in step S02 is specifically: S21. Based on the highway network topology Constructing a directed graph ; Among them, a highway interchange is regarded as a node, and the road in one driving direction of the highway section is regarded as an edge. represents the set of all nodes in a directed graph, Represents the set of all edges in a directed graph; S22. Calculate the The starting point number of the patrol section The length of the road between the highway interchange at the opposite end of the road where it is located , and the terminal pile number of the patrol section The length of the road between the freeway interchange at the positive end of the road where it is located ,in, is an integer and its value is , is the total number of road sections inspected; S23. Based on the information obtained from each patrol section and , get the originating node and A collection of patrol sections The shortest path between any two, and further obtain the shortest path distance matrix And all the shortest paths constitute the set of road segments ; S24. Using the shortest path distance matrix And all the shortest paths constitute the set of road segments As the input parameters, the genetic algorithm is used to solve the initial patrol path optimization model to obtain the initial patrol path.

3. The highway inspection path optimization method according to claim 2 is characterized in that: The objective function of the initial patrol path optimization model is: （1.2）， In formula (1.2): Indicates the originating node and A collection of patrol sections. and All belong to and , for arrive The distance of the shortest path; represents a 0-1 decision variable. If the inspection vehicle is Drive to ,but ,otherwise ; The prescribed speed for inspection vehicles; To adjust the speed, It is used to indicate the impact of the inspection vehicle's operation process on the travel time on the inspection section; For destination The weight of Indicates the originating node The value of is 0. Indicates when patrolling a road section The value of is the length of the inspection section.

4. The highway inspection path optimization method according to claim 1, characterized in that: The method to determine whether an emergency abnormal event is an immediate response event or a pending response event is: If the sudden abnormal event is located on the path between the inspection vehicle and the next uninspected inspection section in the current inspection path, the sudden abnormal event is treated as an immediate response event, responded to immediately and inserted into the current inspection path; Otherwise, the sudden abnormal event is treated as a pending response event and is left for processing.

5. The highway inspection path optimization method according to claim 1, characterized in that: When building a dynamically optimized dataset: If a sudden abnormal event is located in a patrol section, the patrol section where the sudden abnormal event is located is split into two patrol sections using the sudden abnormal event as the split point, and the two newly obtained patrol sections are included in the dynamic optimization data set, while the split patrol section is deleted; If the inspection vehicle is inspecting a certain inspection section, the remaining uninspected section of the inspection section will be taken as a new inspection section and included in the dynamic optimization data set.

6. The highway inspection route optimization method according to claim 1, characterized in that: The specific method of generating a dynamic inspection path in step S06 includes: S61. Construct a priority evaluation model for pending events and calculate the priority of each pending event ; S62. Get the real-time coordinates of the inspection vehicle , Originating Node , the set of all unprocessed sudden abnormal events and the remaining patrol sections The shortest path between any two, and further obtain the shortest path distance matrix And all the shortest paths constitute the set of road segments ; S63. Using the shortest path distance matrix And all the shortest paths constitute the set of road segments As input parameters, the global dynamic path optimization model is solved to obtain the dynamic inspection path; Among them, the global dynamic path optimization model is used to minimize the total cost The first objective function is to maximize the total priority value of the events to be responded to in the patrol path. is the second objective function, respectively: （1.8）， （1.9）， In formulas (1.8) and (1.9): represents the total time cost of the inspection vehicle's driving and operation, It represents the total penalty cost caused by the inspection vehicle exceeding the optimal arrival time when arriving at each event to be responded to; Indicates the real-time coordinates of the inspection vehicle , Originating Node A collection of all unprocessed sudden abnormal events and remaining patrol sections; Represents the set of all unprocessed sudden abnormal events. , A collection of all unprocessed immediate response events. A collection of all events to be responded to; , represents any element in the set to which it belongs and ,gather Representing a collection Remove the originating node A collection consisting of elements other than Representing a collection Remove the real-time coordinates of the inspection vehicle A collection consisting of elements other than Indicates all unprocessed sudden abnormal events and the real-time coordinates of the inspection vehicle The set of components; is a 0-1 decision variable. If the inspection vehicle is Drive to ,but ,otherwise ; for arrive The distance of the shortest path; For events to be responded The priority value of For patrol vehicles The working time of The patrol vehicle arrives and waits for response event The penalty cost of exceeding the optimal arrival time; The specified driving speed for patrol vehicles.

7. The highway inspection route optimization method according to claim 6, characterized in that: The values ​​in different situations are expressed as: （1.10）， In formula (1.10): Indicates patrol section Length, represents the set of all remaining patrol sections, Indicates sudden abnormal events The remaining processing time, To adjust the speed; Furthermore, The calculation method is: （1.11）， In formula (1.11): A fixed value, indicating a sudden abnormal event Estimated processing time, For unexpected events Processed time; The values ​​in different situations are expressed as: （1.12）， In formula (1.12): For inspection vehicles from real-time coordinates Travel to the event to be responded to the time spent; The optimal arrival time limit for events to be responded to Indicates that the inspection vehicle is from the real-time coordinates Travel to the event to be responded to The time length limit is expressed as: （1.13）， In formula (1.13): is a fixed value, indicating the optimal time range from the occurrence of the event to be responded to to the arrival of the inspection vehicle for processing. For events to be responded When the inspection vehicle is in the current time period The time that has been traveled.

8. The highway inspection route optimization method according to claim 6, characterized in that: The priority evaluation model of the pending response event is: （1.3）， In formula (1.3): , , , and are weight coefficients, ; for The normalized value, for The normalized value, for The normalized value, for The normalized value, for Normalized value; The road type of the 100-meter section within the stake number interval where the event to be responded to is located; The real-time traffic flow in the pile number interval where the sudden abnormal event occurs; Indicates the order of occurrence of pending response events, which are sorted from small to large according to the time of occurrence of sudden abnormal events. If there are pending response events occurring at the same time, the same value is taken; The coordinates of the event location to be responded to Real-time coordinates with inspection vehicles The Euclidean distance between The event type to be responded to.

9. A highway inspection route optimization device, characterized in that: The device adopts the highway inspection path optimization method according to any one of claims 1 to 8, and the device comprises: The information storage module is used to store basic data of the expressway, basic data of the inspection section, data of sudden abnormal events, real-time location data of the inspection vehicle, data of the remaining inspection section, and data of sudden abnormal events that have not been processed yet; Real-time data acquisition module, used to obtain the real-time location data of the inspection vehicle, data on sudden abnormal events, and data on the remaining inspection sections; An initial path generation module is used to generate an initial inspection path based on the basic data of the expressway, the basic data of the inspection section, and the traversal requirements of all inspection sections; A dynamic path generation module is used to generate a dynamic inspection path based on a dynamic optimization data set; The information feedback module is used to feed back the generated inspection path to the remote control terminal and display it on the screen of the inspection vehicle.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is run, the highway inspection path optimization method according to any one of claims 1 to 8 is executed.

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