A method, device and storage medium for optimizing the inspection path of an expressway

Abstract

The present invention provides a method, device and storage medium for optimizing the inspection path of an expressway, including: obtaining the basic data of the expressway and the inspection sections within the jurisdiction to construct a basic data set; generating an initial inspection path based on the basic data set and the traversal requirements of the inspection sections; the inspection vehicle conducts inspections according to the initial inspection path and starts receiving data on sudden abnormal events; if a sudden abnormal event belongs to an immediate response event, it is directly inserted into the current inspection path, and if it belongs to a pending response event, it is left for processing; the inspection vehicle continuously receives data on sudden abnormal events T After a certain time, it stops receiving data on sudden abnormal events; a dynamic inspection path is generated based on the real-time position data of the inspection vehicle, the remaining inspection section data and the current unprocessed sudden abnormal event data. The present invention can balance the operation time and the priority of sudden abnormal events in the path under the condition of the optimal path.

Description

Technical Field

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

[0002] Expressway 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 coefficient. At present, most research focuses on the construction of inspection systems and inspection systems. The inspection path of expressways 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, expressway inspectors also need to respond and handle sudden abnormal events that occur in real time within the jurisdiction during the inspection process. With the continuous increase in the number of automobiles, the frequency of traffic accidents on expressways has also increased year by year. A series of sudden abnormal events that affect road traffic safety, such as component scattering caused by traffic accidents, damage to road facilities, and highway waterlogging, slope water damage, and broken roadside trees caused by bad weather, all require the rapid response and handling of inspectors. Expressways are long and closed roads. Efficient handling of sudden abnormal events is crucial for reducing traffic congestion and ensuring the traffic efficiency of expressways. Therefore, the inspection path needs to be continuously adjusted during the expressway 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 sudden abnormal events on expressways to formulate targeted dynamic path optimization models and dynamic response strategies, and use scientific and reasonable dynamic path adjustments to respond to sudden abnormal events during the inspection process.

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

[0005] The object of the present invention is to provide a method for optimizing the inspection path of an expressway, which realizes the balance between the operation time and the priority of sudden abnormal events in the path under the condition of the optimal path. The specific technical solutions are as follows:

[0006] A method for optimizing the inspection path of an expressway includes:

[0007] S01. Obtain the basic data of the expressway and the inspection sections within the jurisdiction to construct a basic data set;

[0008] S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections;

[0009] S03. The inspection vehicle conducts inspections along the initial inspection path and starts receiving data on sudden abnormal events;

[0010] 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;

[0011] S05. The inspection vehicle continuously receives data on sudden abnormal events After a certain time, stop receiving data on sudden abnormal events;

[0012] 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 data on sudden abnormal events that have not been processed yet, and generate a dynamic inspection path based on the dynamic optimization data set;

[0013] S07. The inspection vehicle travels along the dynamic inspection path and processes sudden abnormal events;

[0014] S08. If all sudden abnormal events have been processed, the inspection vehicle starts to receive data on sudden abnormal events again;

[0015] S09. Repeat steps S04 - S08 until the inspection tasks for all inspection sections are completed.

[0016] Preferably, the method for generating the initial inspection path in step S02 is specifically:

[0017] S21. According to the highway network topology structure Construct a directed graph ; where, a highway interchange is used as a node, and the 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;

[0018] S22. Calculate the road length between the starting stake number 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 stake number 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;

[0019] S23. Based on the and to obtain the starting node and a set composed of inspection sections the shortest path between any two of them, and further obtain the shortest path distance matrix and a set composed of road sections of all shortest paths ;

[0020] S24. Using the shortest path distance matrix and a set composed of road sections 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.

[0021] Preferably, the objective function of the initial inspection path optimization model is:[[]]

[0022] (1.2),

[0023] In formula (1.2): represents the starting node and a set composed of inspection sections, and both belong to and , is the distance of the shortest path from to 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 weight of the destination . 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.

[0024] Preferably, the method for determining whether a sudden abnormal event belongs to an immediate response event or a pending response event is:[[]]

[0025] If a 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 later.

[0026] Preferably, when constructing the dynamic optimization data set:

[0027] If a sudden abnormal event is located in an inspection section, then the inspection section where it is located is divided into two inspection sections with the sudden abnormal event as the splitting point, the newly obtained two inspection sections are included in the dynamic optimization data set, and at the same time the inspected inspection section is deleted;

[0028] If the inspection vehicle is inspecting an inspection section, then the remaining uninspected section of the inspection section is used as a new inspection section and included in the dynamic optimization data set.

[0029] Preferably, the specific method for generating the dynamic inspection path in step S06 includes:

[0030] S61. Construct a priority evaluation model for pending response events and calculate the priority of each pending response event ;

[0031] 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 ;

[0032] 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;

[0033] Among them, the global dynamic path optimization model takes minimizing the total cost as the first objective function and maximizing the total priority value of the pending response events included in the inspection path G as the second objective function, which are respectively:

[0034] (1.8),

[0035] (1.9),

[0036] In formulas (1.8) and (1.9): Represents the total time cost of the inspection vehicle's 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 to; 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 of all events to be responded to; , Represents any element within the set and , the set Represents the set Excluding the starting node Outside elements, the set Represents the set Excluding the real-time coordinates of the inspection vehicle Outside 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 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 exceeds the optimal arrival time limit when reaching the event to be responded to ; Is the specified driving speed of the inspection vehicle.

[0037] Preferably, The values in different situations are expressed as:

[0038] (1.10),

[0039] In formula (1.10): Represents the length of the inspection section , Represents the set composed of all remaining inspection sections, Indicates the remaining processing time of a sudden abnormal event , and is the adjustment speed; For further information,

[0040] The calculation method of is as follows:

[0041] (1.11),

[0042] In formula (1.11): is a fixed value, indicating the estimated processing time of a sudden abnormal event , and is the processed time of the sudden abnormal event ;

[0043] The values in different cases are expressed as:

[0044] (1.12),

[0045] In formula (1.12): is the time taken for the inspection vehicle to travel from the real-time coordinate to the event to be responded to ; the best arrival time limit of the event to be responded to indicates the time length limit for the inspection vehicle to travel from the real-time coordinate to the event to be responded to , and is expressed as:

[0046] (1.13),

[0047] In formula (1.13): is a fixed value, indicating the best time range from the occurrence of the event to be responded to until an inspection vehicle arrives for processing, is the event to be responded to when the inspection vehicle has traveled within the current time period ;

[0048] Preferably, the priority evaluation model for the event to be responded to is:

[0049] (1.3),

[0050] 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 is located; is the real-time traffic volume within the stake number interval where the sudden abnormal event is located; represents the occurrence order of the event to be responded, sorted from small to large according to the occurrence time of the sudden abnormal event. If there are events to be responded that occur simultaneously, the same value is taken; is the position coordinate of the event to be responded and the real-time coordinate of the inspection vehicle The Euclidean distance between them; is the type of the event to be responded.

[0051] The present invention also provides a device for optimizing the inspection path of an expressway. The device adopts the method for optimizing the inspection path of an expressway, and the device includes:

[0052] An information storage module for storing the basic data of the expressway, the basic data of the inspection section, 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;

[0053] A real-time data acquisition module for acquiring the real-time position data of the inspection vehicle, the sudden abnormal event data, and the remaining inspection section data in real time;

[0054] An initial path generation module for generating an initial inspection path according to the basic data of the expressway, the basic data of the inspection section, and the traversal requirements of all inspection sections;

[0055] A dynamic path generation module for generating a dynamic inspection path according to the dynamic optimization data set;

[0056] 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.

[0057] The present invention also provides a storage medium, in which a computer program is stored. When the computer program runs, it executes the method for optimizing the inspection path of an expressway.

[0058] Applying the technical solution of the present invention has the following beneficial effects:

[0059] The present invention prioritizes sudden abnormal events, enabling high-priority sudden abnormal events to be preferentially responded to and promptly processed, optimizing the patrol vehicle scheduling, reducing the impact of sudden abnormal events on highway traffic operation, and avoiding secondary accidents.

[0060] The present invention preferentially processes sudden abnormal events and then considers the patrol tasks of the patrol sections. It can process sudden abnormal events in the first instance 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 patrol vehicle only continuously performs data reception for a certain period of time, taking into account the processing capacity of a single patrol vehicle, and avoiding the accumulation of excessive sudden abnormal events, resulting in the inability to complete the processing of sudden abnormal events within an appropriate time.

[0061] Based on the initial patrol path plan, when the patrol personnel encounter sudden abnormal events during the operation, the present invention dynamically adjusts the patrol path through a hybrid response strategy mode that combines immediate response and pending response with the 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.

[0062] 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, enabling the generated dynamic path to fully consider the urgency of the processing requirements of each sudden abnormal event.

[0063] In addition to the purposes, features, and advantages described above, the present invention has other purposes, features, and advantages. The following will refer to the drawings for a further detailed description of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0065] Figure 1 is a flowchart of the highway patrol path optimization method in Embodiment 1;

[0066] Figure 2 is a highway network topology structure diagram in Embodiment 2;

[0067] Figure 3 is a directed graph of the highway network in Embodiment 2;

[0068] Figure 4 is a schematic diagram of the positions of each patrol section in the directed graph in Embodiment 2 ;

[0069] Figure 5 It is a schematic diagram of the remaining highway inspection sections, the real-time coordinates of the inspection vehicle, and the locations of sudden abnormal events at 14:25 in Embodiment 2;

[0070] Figure 6 It is a schematic diagram of the numbers and locations of sudden abnormal events at 14:30 in Embodiment 2;

[0071] Figure 7 It is a schematic diagram of the remaining inspection sections, the real-time coordinates of the inspection vehicle, and the locations of sudden abnormal events at 14:30 in Embodiment 2. Specific Embodiment

[0072] 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, these embodiments are provided to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill 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.

[0074] Embodiment 1:

[0075] Refer to Figure 1 , this embodiment provides a method for optimizing the highway inspection path, which specifically includes the following steps:

[0076] S01. Obtain the basic data of the highways and inspection sections within the jurisdiction, and construct a basic data set;

[0077] Preferably, the basic data of the highways and inspection sections in step S01 are specifically:

[0078] The basic data of the highway include: the highway network topology , road name , the starting highway interchange , the two-way road lengths of each highway section (here, the highway section refers to the section between two adjacent highway interchanges, and there are two roads with opposite driving directions between two adjacent highway interchanges), the road stake number intervals of each road (here, the highway section refers to the section between two adjacent highway interchanges, and there are two roads with opposite driving directions between two adjacent highway interchanges), the types of 100-meter road sections between each stake number , the types of 100-meter road sections between each stake number .

[0079] The basic data of the inspection section include: the starting stake number of the inspection section , the ending stake number of the inspection section , 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 .

[0080] Furthermore, the highway interchange includes two types: hub interchange and landing interchange. Among them, the hub interchange is the location where different highways intersect (i.e., one highway can enter another highway), and the landing interchange is the entrance and exit connecting the local ordinary road (i.e., it can realize getting off the highway and turning around to enter another driving direction). The two different types of highway interchanges have different impacts on the driving path planning of the inspection vehicle. When conducting path planning, the actual type of highway interchange shall prevail. Among them, the originating highway interchange refers to the starting point of the inspection vehicle during inspection.

[0081] S02. Generate an initial inspection path based on the basic data set and the traversal requirements of all inspection sections;

[0082] Furthermore, the method for generating the initial inspection path in step S02 of this embodiment is specifically as follows:

[0083] S21. Construct a directed graph according to the highway network topology ; among them, a highway interchange serves as a node, and the road in one driving direction of the 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;

[0084] S22. Calculate the road length between the starting stake number of theth inspection section and the highway interchange at the reverse end of the road where it is located (that is, the road length between the starting stake number of this inspection section and the nearest highway interchange behind it on the road where it is located), and the road length between the ending stake number of this inspection section and the highway interchange at the forward end of the road where it is located (that is, the road length between the ending stake number and the nearest highway interchange in front of it and closest to it on the road where it is located), where, is an integer and its value is , is the total number of inspection sections.

[0085] As is well known, the roads on expressways can only be traveled in one direction. Here, the reverse direction refers to the direction opposite to the driving direction of the road, and the forward direction refers to the direction the same as the driving direction of the road.

[0086] S23. Based on the two-way road lengths of each expressway section and those obtained from each inspection section and , use the Floyd algorithm (node-insertion method) to obtain the shortest paths between any two of the set and consisting of the origin node and the inspection sections, and further obtain the shortest path distance matrix as well as the set composed of the road sections of all the shortest paths;

[0087] 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 and begins the inspection.

[0088] Preferably, for the set , represents the set of road sections of the th shortest path, is an integer and its value range is , is the total number of the shortest paths; further, the set of the road sections of 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, its set of component road sections includes the road section from the origin node

[0089] to the 2nd node and the road section from the 2nd node to the 1st inspection section. Preferably, since the inspection vehicle starts from the origin node and there are inspection sections, a shortest path distance matrix needs to be generated. The shortest path distance matrix

[0090] is expressed as:

[0091] where, take , All belong to the set and , the set in which 0 represents the starting node, 1 to each represents an inspection section, represents from to the distance of the shortest path. When , the value of is infinite. When the value of is from to

[0092] S24. Using the shortest path distance matrix and the set composed of all the road sections of the shortest paths as input parameters, the initial inspection path optimization model is solved using the genetic algorithm to obtain the initial inspection path;

[0093] 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:

[0094] (1.2),

[0095] In formula (1.2): represents the starting node and the set composed of and both belong to and , is from to 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 weight of the destination . When represents the starting node the value of is 0. When represents the inspection section the value of is the length of this inspection section.

[0096] Preferably, when using the genetic algorithm to solve for the initial inspection path, the parameters to be input include: the shortest path distance matrix , the set composed of all the road segments that make up the shortest paths , the specified driving speed of the inspection vehicle , the adjustment speed and the lengths of each inspection section .

[0097] 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 all the road segments that make up the shortest paths , restrict the inspection vehicle to start from the starting node , each inspection section is inspected and 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, 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.

[0098] S03. The inspection vehicle conducts inspections according to the initial inspection path and starts to receive data on sudden abnormal events;

[0099] Preferably, the data on sudden abnormal events includes: the name of the road where the sudden abnormal event occurs , the driving direction of the road where the sudden abnormal event occurs , the stake number interval where the sudden abnormal event occurs , the time when the sudden abnormal event occurs , the location coordinates of the sudden abnormal event and the type of the sudden abnormal event .

[0100] Furthermore, the types of sudden abnormal events include landslides, falling objects, road facility damage, car accidents, etc. The sudden abnormal events in this embodiment refer to events that affect the normal passage of the highway.

[0101] Further, when receiving sudden abnormal event data, number them in ascending order according to the occurrence time of the sudden abnormal events from early to late; if multiple sudden abnormal events occur at the same time, random numbering can be adopted.

[0102] 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 inspection path; if it is a pending response event, leave it for processing.

[0103] Specifically, the method for determining whether a sudden abnormal event is an immediate response event or a pending response event is as follows:

[0104] If the sudden abnormal event is on the path between the inspection vehicle and the next uninspected inspection section in the current inspection path, then regard the sudden abnormal event as an immediate response event, immediately respond and insert it into the current inspection path; otherwise, regard the sudden abnormal event as a pending response event and leave it for processing.

[0105] Further, when determining whether a sudden abnormal event is an immediate response event or a pending response event, if the inspection vehicle is inspecting a certain section, after determining that there is an immediate response event, immediately stop the current section inspection task 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 inspection task and wait for subsequent dynamic path optimization.

[0106] S05. After the inspection vehicle continuously receives sudden abnormalities, stop receiving sudden abnormal event data;

[0107] Specifically, in order to take into account the event processing ability of a single inspection vehicle, in this embodiment, the receiving time of sudden abnormal event data is set , that is, after receiving the first sudden abnormal event data, the inspection vehicle continuously receives sudden abnormal event data for a time of . After reaching the time of , the inspection vehicle no longer accepts sudden abnormal event data, and the inspection vehicle fully processes the current unprocessed sudden abnormal events to ensure that the sudden abnormal events can be processed in a timely and efficient manner. Further,

[0108] 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; among them, the current unprocessed sudden abnormal events include unprocessed immediate response events and all pending response events.

[0109] 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 (i.e., the distance between the inspection vehicle and the nearest high-speed interchange in the driving direction of the road where it is located).

[0110] 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 .

[0111] 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 certain 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.

[0112] Preferably, when generating the dynamic inspection path, if the inspection vehicle is inspecting a certain inspection section, then the remaining uninspected sections of this inspection section are used as a new inspection section and included in the dynamic optimization dataset.

[0113] 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 splitting point, the newly obtained two inspection sections are included in the dynamically optimized data set, and the inspected section is deleted. In this way, 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.

[0114] Preferably, the specific method for generating the dynamic inspection path includes:

[0115] S61. Construct a priority evaluation model for events to be responded to, and calculate the priority of each event to be responded to ;

[0116] Specifically, the priority evaluation model for events to be responded to is:

[0117] (1.3),

[0118] 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 the Euclidean distance between them; is the type of 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 represents road facility damage, 2 represents traffic accidents, and 3 represents landslides.

[0119] Furthermore, in this embodiment, the weight coefficients , , , and are determined as follows:

[0120] S61.1. Use different methods to calculate the values of the weight coefficients , , , and respectively, where is an integer greater than or equal to 2;

[0121] S61.2. Build a comprehensive weight calculation model based on the minimum information entropy theory;

[0122] Specifically, the objective function of the comprehensive weight calculation model is expressed as:

[0123] (1.4),

[0124] In formula (1.4): is the deviation function of the combined weight; is the combined weight of the rd 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 .

[0125] Furthermore, use the result value constraint to limit the values of the combined weights obtained by solving the comprehensive weight calculation model, specifically including:

[0126] (1.5),

[0127] (1.6),

[0128] (1.7),

[0129] S61.3. The weight coefficients obtained by calculating in , , , and are input into the comprehensive weight calculation model, and the comprehensive weight calculation model is solved by the genetic algorithm to obtain the combined weight coefficients , , , and .

[0130] Further, 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., is equal to 1) to obtain the weight coefficients , , , and , and then substitute them into formula (1.3) for calculation.

[0131] S62. Use the Floyd algorithm to obtain the real - time coordinates of the inspection vehicle , the starting node and the shortest paths between any two of all the unprocessed sudden abnormal events and the remaining inspection sections, and further obtain the shortest - path distance matrix and the set composed of the road segments formed by all the shortest paths ; ;

[0132] Specifically, the ways to obtain the shortest paths, the shortest - path distance matrix and the set composed of the road segments formed by all the shortest paths in step S62 are the same as those in step S23. The only difference between the two is that the number of elements in the set and the set is different. Therefore, the details of step S62 will not be elaborated here.

[0133] S63. Using the shortest - path distance matrix and the set composed of the road segments formed by all the shortest paths As the input parameters, the NSGA-II algorithm (multi-objective genetic algorithm) is used to solve the global dynamic path optimization model to obtain the dynamic inspection path.

[0134] Specifically, the global dynamic path optimization model in this embodiment minimizes 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:

[0135] (1.8),

[0136] (1.9) ,

[0137] 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 The operation time of the inspection vehicle at is as follows. In different cases, its value is: If represents the starting node, then , if represents the inspection section, then , if represents an unprocessed sudden abnormal event, then is the remaining processing time of this sudden abnormal event , , In different cases, its value is specifically expressed as:

[0138] (1.10)

[0139] Among them, represents the length of the inspection section , represents the set composed of all remaining inspection sections, represents the starting node, represents the set composed of all unprocessed sudden abnormal events;

[0140] Furthermore, is calculated as:

[0141] (1.11),

[0142] 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 .

[0143] is the penalty cost generated when the inspection vehicle arrives at the event to be responded and exceeds the optimal arrival time limit. In different cases, its value is: If the time (where ) spent by the inspection vehicle when driving to the event to be responded is within the optimal arrival time limit of this event to be responded, then the value is 0; otherwise, the value is , In different cases, its value is specifically expressed as:

[0144] (1.12),

[0145] The optimal arrival time limit of the event to be responded represents the inspection vehicle from the real-time coordinates Time length limit for traveling to the event to be responded, expressed as:

[0146] (1.13),

[0147] 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 the inspection vehicle for processing. is the event to be responded (where ) is the time that the inspection vehicle has traveled within the current time period when the occurs.

[0148] Preferably, the method of solving the global dynamic path optimization model by the NSGA-II algorithm (multi-objective genetic algorithm) belongs to the common knowledge in the art. In this embodiment, only a simple description is given:

[0149] First, obtain the generated shortest path distance matrix and the set of road segments composed of all shortest paths , starting from the real-time coordinates of the inspection vehicle, insert the unprocessed immediate response events in the order from near to far from the real-time coordinates of the inspection vehicle, and then insert a random number of the remaining events to be responded according to the priority order of each event to be responded (that is, it is allowed not to insert all events to be responded, but it is required that the inserted events to be responded conform to the priority order). Subsequently, traverse the remaining inspection sections and each inspection section is only inspected once, and finally return to the starting node; second, 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 events to be responded), perform selection, crossover, and mutation operations, merge the population and then perform non-dominated sorting and crowding degree calculation again to generate a new population; finally, determine whether the termination condition is met. If it is met, the iteration ends, normalize 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 the iterative optimization; where is for formula (1.8) for the path scheme​ Normalized value of the output value is the normalized value of the output value of formula (1.9) for the path plan Normalized value of the output value

[0150] 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 road segments formed by all the shortest paths , the specified driving speed of the inspection vehicle , adjustment speed , the length of the inspection section , the priority value of the event to be responded and sorting, the best arrival time limit of the event to be responded , the remaining processing time of the sudden abnormal event .

[0151] S07. The inspection vehicle travels along the dynamic inspection path and processes the sudden abnormal events within the dynamic inspection path;

[0152] S08. If all the sudden abnormal events have been processed, the inspection vehicle starts to receive the sudden abnormal event data again;

[0153] S09. Repeat steps S04 - S08 until the inspection tasks of all inspection sections are completed.

[0154] 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 processed, optimizing the inspection vehicle scheduling, reducing the impact of sudden abnormal events on the highway traffic operation, and avoiding secondary accidents.

[0155] The method of this embodiment preferentially processes the sudden abnormal events and then considers the inspection tasks of the inspection sections. It can process the 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, when the first sudden abnormal event is received, the inspection vehicle only continuously performs data reception for a certain period of time, which can take into account the processing capacity of a single inspection vehicle and avoid the accumulation of too many sudden abnormal events, resulting in the inability to process the sudden abnormal events within a suitable time.

[0156] Based on the initial inspection path plan, when the inspection personnel encounter sudden abnormal events during the operation process, the method of this embodiment dynamically adjusts the inspection path through a hybrid response strategy mode that combines immediate response and response to be left with the sudden abnormal events as key points, taking into account the balance between the operation time and the priority of sudden abnormal events in the path under the condition of the optimal path.

[0157] The method of this embodiment proposes the optimal 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, so that the generated dynamic path can fully consider the urgency of the handling requirements of each sudden abnormal event.

[0158] Embodiment 2:

[0159] Based on some expressways around Changsha City, this embodiment uses the expressway patrol path optimization method in Embodiment 1 to plan the patrol path, which is specifically as follows:

[0160] S01. Obtain the basic data of the expressways and patrol sections in the jurisdiction to construct a basic data set, which is specifically as follows:

[0161] This embodiment obtains the expressway network topology As Figure 2 shown, the starting expressway interchange for patrol is set as Jinqiao Interchange. The basic data of the expressways in this embodiment are shown in Tables 1 and 2:

[0162]

[0163]

[0164] In this embodiment, the upstream direction always refers to the north-south and west-east directions, and the downstream direction always refers to the south-north and east-west directions; the conventional section refers to the section except for bridges, tunnels and landslide-prone sections.

[0165] The basic data of the patrol sections in this embodiment are shown in Table 3:

[0166]

[0167] S02. Generate an initial patrol path based on the basic data set and the traversal requirements of all patrol sections;

[0168] S21. Based on Figure 2 the expressway network topology shown construct a directed graph , and the obtained directed graph is as Figure 3 shown. The weight value on each edge is the road length RL (km) of one driving direction of the expressway section. Take an expressway interchange as a node and number the nodes as - , take the road of one driving direction of the expressway section as an edge, and the corresponding road information of each edge is shown in Table 4:

[0169]

[0170] Among them, since the originating highway interchange is the Jinqiao interchange, the originating node is determined to be .

[0171] Furthermore, each inspection section is numbered according to the basic data of the inspection section. Among them, the inspection sections with IDs 1 - 10 in Table 3 are numbered as , and the positions of each inspection section in the directed graph are as Figure 4 shown.

[0172] S22. According to the th inspection section length , calculate the road length between its starting stake number and the highway interchange at the reverse end of the road where it is located , and the road length between its ending stake number and the highway interchange at the forward end of the road where it is located ; Taking the inspection section as an example, the distance between its starting stake number 229 and the stake number 94 of the node is 13.6 km, and the distance between its ending stake number 241 and the stake number 292 of the node is 5.2 km.

[0173] S23. Based on the and obtained for each inspection section, use the Floyd algorithm (point - insertion method) to obtain the shortest path between any two of the originating node and and the set composed of inspection sections, and further obtain the shortest - path distance matrix ;

[0174] 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 originating node with 0, then this shortest - path distance matrix is expressed as:

[0175] (2.1),

[0176] S24. Using the shortest - path distance matrix And all the shortest paths constitute the set of road segments As input parameters, the initial inspection path optimization model is solved using genetic algorithm to obtain the initial inspection path;

[0177] Specifically, this embodiment assumes that the specified driving speed of the inspection vehicle is Adjust the speed to 80km / h The final initial path is expressed as: → → → → → → → → → → → .

[0178] S03. The inspection vehicle conducts inspections along the initial inspection route and begins to receive data on sudden abnormal events;

[0179] 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;

[0180] In this embodiment, it is assumed that the patrol car starts patrolling at 13:50 in the afternoon and receives data on two sudden abnormal events in the jurisdiction at 14:25. Figure 3 by As the origin, a rectangular coordinate system is constructed with 1m as a unit. Assuming that the interval between two-way lanes is 2m, the received sudden abnormal event data is shown in Table 5:

[0181]

[0182] Further, such as Figure 5 As shown, suppose that when a sudden abnormal event is received at 14:25 , When the data is On the corresponding Changchang North Line Expressway section, the remaining path of the inspection vehicle is → → → → → → → → → .

[0183] After judgment, the sudden abnormal event and are not located on the path → Therefore, the sudden abnormal event and are left as events to be responded to for further processing.

[0184] S05. The inspection vehicle continuously receives the data of sudden abnormal events After a certain period of time, it stops receiving the data of sudden abnormal events;

[0185] In this embodiment, assume that the time = 5 min. Since the inspection vehicle received the sudden abnormal events , at 14:25, it continues to receive the data of sudden abnormal events in the time period from 14:25 to 14:30.

[0186] 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 data of sudden abnormal events that have not been processed yet, and generate a dynamic inspection path based on the dynamic optimization data set;

[0187] S61. Construct a priority evaluation model for events to be responded to, and calculate the priorities of each event to be responded to ;

[0188] 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:

[0189]

[0190] Specifically, the real-time position data of the inspection vehicle at 14:30 is shown in Table 7:

[0191]

[0192] As Figure 6 shown, assume that a total of 4 sudden abnormal events occurred in the time period from 14:25 to 14:30, and the numbers are respectively , , , . Specifically, the data of sudden abnormal events that have not been processed yet in this embodiment are shown in Table 8 and Table 9:

[0193]

[0194]

[0195] Due to sudden abnormal events and are respectively located in the inspection section and in the middle. Therefore, based on and the inspection section 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:

[0196]

[0197] Furthermore, please refer to Figure 7 . At 14:30, the inspection vehicle is located at the mileage number 43 in the down - ward direction of the Changchang North Line Expressway. Therefore, the current unprocessed sudden abnormal events include the immediate response event and the event to be responded , .

[0198] 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:

[0199]

[0200] Solve the comprehensive weight calculation model through the genetic algorithm to obtain the combined weight coefficients , , , and as shown in Table 12:

[0201]

[0202] Further, substitute the obtained combined weight coefficients , , , and into formula (1.3) to obtain the priority values , , of the to-be-responded events and the priority ranking is shown in Table 13 as follows:

[0203]

[0204] S62. Use the Floyd algorithm to obtain the real-time coordinates of the inspection vehicle , the starting node and the shortest paths between any two of all the unprocessed sudden abnormal events and the set composed of the remaining inspection sections , and further obtain the shortest path distance matrix and the set composed of the road sections formed by all the shortest paths ;

[0205] In this embodiment, the shortest path distance matrix and the set composed of the road sections formed by all the shortest paths are obtained in the manner of step S23 in Embodiment 1 ; specifically, the end 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 distances between the real-time coordinates of the inspection vehicle and and and respectively, and the distances between and and are all 0 km.

[0206] S63. Using the shortest path distance matrix and the set composed of the road sections formed by 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.

[0207] Specifically, when using the NSGA-II algorithm (multi-objective genetic algorithm) to solve the global dynamic path optimization model to obtain the dynamic inspection path in this embodiment, it is restricted to start from the real-time coordinates Start by inserting the unprocessed immediate response events in the corresponding order , and then according to → → Insert a random number of the three pending response events in sequence. Subsequently, traverse the remaining inspection sections and each inspection section is only inspected once, and finally return to the starting node. The final output dynamic inspection path is expressed as: → → → → → → → → → → → → → → .

[0208] S07. The inspection vehicle travels along the dynamic inspection path and processes sudden abnormal events;

[0209] S08. If all sudden abnormal events have been processed, the inspection vehicle starts to receive sudden abnormal event data again;

[0210] In this embodiment, before the inspection vehicle processes the sudden abnormal events , , , it does not receive sudden abnormal event data until after processing the sudden abnormal event and then starts to receive sudden abnormal event data again.

[0211] S09. Repeat steps S04 - S08 until the inspection tasks of all inspection sections are completed.

[0212] Embodiment 3:

[0213] This embodiment provides a device for optimizing the inspection path of an expressway. The device in this embodiment adopts the method for optimizing the inspection path of an expressway in Embodiment 1, and specifically includes:

[0214] An information storage module for storing the basic data of the expressway, 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;

[0215] A real-time data acquisition module for acquiring the real-time position data of the inspection vehicle, the sudden abnormal event data, and the remaining inspection section data in real time;

[0216] An initial path generation module, configured to generate an initial inspection path according to the basic data of the highway, the basic data of the inspection section, and the traversal requirements of all inspection sections;

[0217] A dynamic path generation module, configured to generate a dynamic inspection path according to the dynamic optimization data set;

[0218] An information feedback module, configured to feedback the generated inspection path to the remote control terminal and display it on the screen of the inspection vehicle.

[0219] Embodiment 4:

[0220] 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.

[0221] 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 may 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. An optimization method for the inspection path of an expressway, characterized in that, It includes the following steps: S01. Obtain the basic data of highways and 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 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; S05. The inspection vehicle continuously receives the data of sudden abnormal events After a certain time, it stops receiving the data of sudden abnormal events; 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 data on sudden abnormal events that have not been processed yet, 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 sudden abnormal events; S08. If all sudden abnormal events have been processed, the inspection vehicle starts to receive data on sudden abnormal events again; S09. Repeat steps S04 - S08 until the inspection tasks for all inspection sections are completed; 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 priorities of each event to be responded to ; S62. 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 , the shortest path between any two of them, and further obtain the shortest path distance matrix and the set composed of the road sections formed by all the shortest paths ; S63. With the shortest path distance matrix and the set composed of all the shortest paths that form road segments as input parameters, solve the global dynamic path optimization model to obtain the dynamic patrol path; Among them, the global dynamic path optimization model aims to minimize the total cost as the first objective function, and aims to maximize the total priority value of the events to be responded to included 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 incurred 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 is 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; , represents any element within the set and , the set represents the set excluding the starting node from the elements that make it up, the set represents the set excluding the real-time coordinates of the inspection vehicle from the elements that make it up, 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 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 , is the penalty cost incurred when the inspection vehicle exceeds the optimal arrival time limit when reaching the event to be responded ; is the specified driving speed of the inspection vehicle.

2. The highway patrol route optimization method according to claim 1, characterized in that The specific method for generating the initial inspection path in step S02 is: S21. According to the highway network topology Construct a directed graph ; where a highway interchange is used as a node, and the 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 starting stake number of the th inspection section and the road length between the highway interchange at the reverse end of the road where it is located , as well as the ending stake number of this inspection section and the road length between the highway interchange at the forward end of the road where it is located . Among them, is an integer and its value is , is the total number of inspection sections; S23. Based on and obtained for each inspection section, obtain the set and composed of the starting node and inspection sections, and further obtain the shortest path between any two of them, and further obtain the shortest path distance matrix and the set composed of road segments of all shortest paths; S24. With the shortest path distance matrix and the set of road segments composed of all the shortest paths as input parameters, use the genetic algorithm to solve the initial inspection path optimization model to obtain the initial inspection path.

3. The highway patrol route optimization method according to claim 2, characterized in that The objective function of the initial inspection path optimization model is: （1.2）， In formula (1.2): represents the origin node and a set composed of [[number]] inspection sections and both belong to and , is from to the distance of the shortest path; 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 weight of the destination . When represents the origin node takes a value of 0. When represents the inspection section takes a value of the length of the inspection section.

4. The highway patrol route optimization method according to claim 1, characterized in that The method for determining whether a sudden abnormal event is an immediate response event or a pending response event is: If the sudden abnormal event is on the path between the inspection vehicle and the next uninspected inspection section in the current inspection path, then regard this sudden abnormal event as an immediate response event, immediately respond and insert it into the current inspection path; Otherwise, regard this sudden abnormal event as a pending response event and leave it for processing.

5. The highway patrol route optimization method according to claim 1, wherein When constructing the dynamic optimization data set: If a sudden abnormal event is in an inspection section, use this sudden abnormal event as a segmentation point to divide the inspection section where it is located into two inspection sections, include the newly obtained two inspection sections in the dynamic optimization data set, and at the same time delete the inspected section; If the inspection vehicle is inspecting an inspection section, use the remaining uninspected section of this inspection section as a new inspection section and include it in the dynamic optimization data set.

6. The freeway patrol route optimization method according to claim 1, characterized in that The values taken in different cases are represented 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 adjusted speed; Further, The calculation method is as follows: （1.11）， In formula (1.11): is a fixed value, representing a sudden abnormal event of the expected processing time, is the processed time of the sudden abnormal event ; The values in different cases are represented as: （1.12）， In formula (1.12): is the time taken for the inspection vehicle to travel from the real-time coordinates to the event to be responded ; Optimal arrival time limit for events to be responded to Indicates the time length limit for the inspection vehicle to travel from the real-time coordinates to the event to be responded to which 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 until the arrival of the inspection vehicle for processing, is the event to be responded to is the time that the inspection vehicle has traveled within the current time period when the event occurs.

7. The freeway patrol route optimization method according to claim 1, characterized in that The priority evaluation model for the pending response event is: （1.3）， In formula (1.3): , , , and are all weight coefficients, ; is the value after normalization, is the value after normalization, is the value after normalization, is the value after normalization, is the value after normalization; is the road type of the 100 - meter section within the stake number range of the event to be responded; is the real - time traffic flow within the stake number range of the sudden abnormal event; represents the occurrence order of the event to be responded, sorted from small to large according to the occurrence time of the sudden abnormal event. If there are events to be responded that occur simultaneously, they take the same value; is the location coordinate of the event to be responded and the Euclidean distance between the real - time coordinate of the inspection vehicle; is the type of the event to be responded.

8. An optimized device for highway inspection paths, characterized in that, The device adopts the highway inspection path optimization method described in any one of claims 1 - 7. The device includes: An information storage module for storing the basic data of highways, the basic data of inspection sections, data on sudden abnormal events, the real-time position data of the inspection vehicle, the remaining inspection section data, and the data on sudden abnormal events that have not been processed yet; A real-time data acquisition module for acquiring the real-time position data of the inspection vehicle, data on sudden abnormal events, and the remaining inspection section data in real time; An initial path generation module for generating an initial inspection path according to the basic data of highways, the basic data of 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.

9. A storage medium, characterized in that, A computer program is stored in the storage medium. When the computer program runs, it executes the highway patrol route optimization method according to any one of claims 1-7.

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