A method and system for monitoring environment safety inspection planning based on the shortest path

Abstract

The present invention relates to a method for planning safety inspections in a regulatory environment based on the shortest path, comprising the following steps: obtaining data information of a point to be inspected, including coordinate data; constructing a spatial distance matrix based on the acquired coordinate data; obtaining an inspection path planning model based on the constructed spatial distance matrix; defining constraints based on the inspection path planning model, and obtaining a model objective function based on the constraints; and traversing the points to be inspected using an ant colony algorithm to obtain inspection results based on the model objective function. The present invention provides a method for planning safety inspections in a regulatory environment based on the shortest path, proposes a planning method based on an ant colony algorithm as an optimization algorithm, takes into account the daily requirements of regulatory environment personnel during safety inspections, takes short inspection distances, less time, and fewer personnel as the goals, rationally plans inspection plans, effectively improves the inspection efficiency of inspection personnel, and also improves the work enthusiasm of inspection personnel.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for planning safety inspections in a regulatory environment based on the shortest path. Background Art

[0002] The safety of the regulatory environment is the basic premise for the regulatory environment to achieve its goals and tasks. Maintaining the safety of the regulatory environment is a large-scale systematic project. It is necessary to comprehensively use theoretical means such as human defense, material defense, and technical defense to build a long-term and effective security prevention system for the regulatory environment to ensure the long-term stability of the regulatory environment. In recent years, with the continuous maturity of new technologies such as big data and machine learning, artificial intelligence has begun to be widely used in smart cities, smart regulatory environments and other fields. Intelligent security systems will be more helpful in ensuring the safe management of the regulatory environment.

[0003] At present, the safety inspection work of the supervision environment mainly relies on the subjective judgment of the supervision environment personnel. There is no reasonable route planning scheme, and there are problems such as strong subjectivity, low inspection efficiency, and poor inspection quality. Specifically, it manifests in the following three aspects: (1) The administrator responsible for the inspection work cannot supervise the actual work of the safety inspection personnel in real time, resulting in a decline in the inspection quality; (2) Safety inspection personnel generally use paper records to conduct inspections, which can easily lead to the possibility of missed inspections; (3) There are many inspection tasks, and it is difficult for inspection personnel to use the best inspection plan to complete the work during the inspection process, which takes up more resources and is inefficient.

[0004] Therefore, how to reasonably plan inspection routes and improve the efficiency and quality of personnel's inspections on the safety of the regulatory environment is an issue that needs to be addressed urgently. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method and system for planning safety inspections in a regulatory environment based on the shortest path. The present invention integrates an optimization algorithm, which can quickly and efficiently plan inspection routes for personnel, greatly improving the inspection efficiency. At the same time, it also avoids the occurrence of problems such as missed inspections and forgotten inspections, thereby ensuring the inspection quality.

[0006] Terminology explanation:

[0007] ACO: Ant Colony Optimization (ACO) was proposed by Italian scholar Dorigo Maniezzo and others in the 1990s. The idea of ​​the ant colony algorithm is to imitate the foraging process of ants. In a complex and changing environment, the ant colony can still find the shortest path to the food source. In the process of looking for food, ants will leave a special substance on the road they pass through. The ants in the ant colony can sense the existence of this substance. We call this substance "pheromone". The ants will move towards the path with high "pheromone" concentration, and then the ants walking along this path will continue to release pheromones. Under the action of such a positive feedback mechanism, the "pheromone" concentration of this path continues to increase, and all ants will eventually find food through this path. This is why the ant colony can find food through the shortest path. This algorithm is essentially a heuristic global optimization algorithm with the characteristics of information positive feedback, information search and distributed computing.

[0008] The technical solution of the present invention is:

[0009] A method for planning a safety inspection of a regulatory environment based on the shortest path includes the following steps:

[0010] S1. Obtain data information of the inspection point, including coordinate data;

[0011] S2. construct a spatial distance matrix based on the acquired coordinate data;

[0012] S3. Obtain an inspection path planning model based on the constructed spatial distance matrix;

[0013] S4. According to the inspection path planning model, define the constraint conditions, and obtain the model objective function according to the constraint conditions;

[0014] S5. According to the model objective function, the ant colony algorithm is used to traverse the inspection points to obtain the inspection results;

[0015] Among them, in the ant colony algorithm, ants are used to select the location of the next inspection point according to the pheromone concentration of the inspection point, and the transfer probability of the path being selected is obtained according to the total pheromone concentration and total cost between the inspection points. The path corresponding to the maximum transfer probability is the optimal path.

[0016] Furthermore, constructing a spatial distance matrix according to the acquired coordinate data includes constructing a Euclidean spatial distance matrix according to the acquired coordinate data, and the calculation formula is:

[0017]

[0018]

[0019] |X| is the Euclidean distance from the point (x2, y2, z2) to the origin; d is the Euclidean distance between the point (x1, y1, z1) and the point (x2, y2, z2);

[0020] The Euclidean space distance matrix is ​​expressed as:

[0021]

[0022] Among them, the d in the matrix ij This is the Euclidean distance between two points.

[0023] Furthermore, the inspection path planning model is obtained according to the constructed spatial distance matrix, including establishing a VRP model based on the characteristics of the safety inspection of the regulatory environment, and defining the safety inspection path planning model parameters, wherein the safety inspection path planning model parameters include the decision variables x ij k and i k :

[0024]

[0025]

[0026] Furthermore, the constraints include inspection point constraints, i.e. ensuring that each inspection point is inspected; inspection personnel constraints, i.e. ensuring that each inspection personnel leaves the inspection point after completing an inspection; inspection task constraints, i.e. ensuring that the number of arrivals and departures from any inspection point is 1 in each inspection task; inspection route duration constraints, i.e. the total inspection duration of the inspection personnel cannot exceed the maximum inspection working duration; and inspection route distance constraints, i.e. the total inspection distance of the inspection personnel cannot exceed the maximum inspection working distance.

[0027] Furthermore, the objective function of the model is constructed according to the constraint conditions, including defining the total cost of the model as F, wherein the sub-costs are F1 and F2, wherein F1 is the inspection time cost, and F2 is the inspection personnel cost, and F1 and F2 are defined as:

[0028]

[0029]

[0030] Among them, t ij is the time from inspection point i to inspection point j within a unit distance; d ij is the spatial distance from inspection point i to inspection point j; x ij k and i kis the decision variable;

[0031] The total cost function is:

[0032] Min: F = F1 + F2.

[0033] Furthermore, the transfer probability of the path being selected is obtained according to the total pheromone concentration and the total cost between the inspection points, including using ants to select the position of the next inspection point according to the pheromone concentration between the inspection points, obtaining the probability of the ants moving from the next inspection point at that moment, and using the pheromone volatility coefficient. When all ants complete a traversal, the pheromone concentration between each inspection node is updated. The update formula is as follows:

[0034] τ ij (t) = (1-ρ)τ ij (t)+Δτ ij ;

[0035]

[0036] Among them, Δτ ij k represents the pheromone concentration released by the kth ant between patrol nodes i and j, Δτ ij represents the sum of the total pheromone concentrations released by all ants between patrol nodes i and j, and ρ is the pheromone volatility coefficient;

[0037]

[0038] Among them, Q represents the total pheromone concentration left by the ants after one traversal, L k Represents the total distance of all routes traversed by the kth ant in this traversal.

[0039] Furthermore, the transition probability is:

[0040]

[0041] in, F is the total cost of safety inspection. The smaller the total cost of inspection, the greater the ij (t) is larger, p′ ij k The larger (t) is, α is the pheromone factor parameter, and β is the heuristic function factor parameter.

[0042] A shortest path-based supervisory environment safety inspection planning system, comprising:

[0043] The data acquisition module is configured to acquire data information of the points to be inspected, including coordinate data;

[0044] The path planning module is configured to construct a spatial distance matrix according to the acquired coordinate data, obtain an inspection path planning model according to the constructed spatial distance matrix; define constraint conditions according to the inspection path planning model, and obtain a model objective function according to the constraint conditions;

[0045] The inspection module is configured to traverse the inspection points using the ant colony algorithm according to the model objective function to obtain the inspection results;

[0046] Among them, in the ant algorithm, ants are used to select the location of the next inspection point according to the pheromone concentration of the inspection point, and the transition probability of the path being selected is obtained according to the total pheromone concentration between the inspection points. The path corresponding to the maximum transition probability is the optimal path.

[0047] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device, for a method for planning safety inspections in a regulatory environment based on the shortest path.

[0048] A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the method for planning safety inspections in a regulatory environment based on the shortest path.

[0049] The beneficial effects of the present invention are:

[0050] 1. The present invention provides a method for planning safety inspections in a regulatory environment based on the shortest path, and proposes a planning method based on an ant colony algorithm as an optimization algorithm. It takes into account the daily requirements of regulatory environment personnel during safety inspections, and takes short inspection distances, less time, and fewer personnel as the goals. It rationally plans inspection plans, effectively improves the inspection efficiency of inspection personnel, and also improves the work enthusiasm of inspection personnel.

[0051] 2. The present invention provides a regulatory environment safety inspection planning system based on the shortest path, which includes a data acquisition module, a path planning module, a data management module, and an inspection query module. By combining the regulatory environment database, terminal, and algorithm model, a systematic regulatory environment safety inspection planning system is formed, which brings great convenience to the daily management of regulatory environment safety and effectively reduces the occurrence of various safety hazards in the regulatory environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0053] Figure 1 is a diagram of the implementation steps of the ant colony algorithm provided in this embodiment;

[0054] Figure 2 It is a flow chart of a method for planning a safety inspection of a regulatory environment based on the shortest path of the present invention;

[0055] Figure 3 It is a structural block diagram of the regulatory environment safety inspection planning system based on the shortest path of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further defined below in conjunction with the accompanying drawings and embodiments, but is not limited thereto.

[0057] Example 1

[0058] like Figure 1 As shown, a method for planning safety inspections in a regulatory environment based on the shortest path specifically includes the following steps:

[0059] S1. Use GPS positioning technology to obtain the spatial geographic location information of the equipment, instruments, personnel, etc. that need to be inspected, mainly obtaining the X, Y, Z three-dimensional coordinate data of the location.

[0060] S2. Construct a spatial distance matrix based on the acquired coordinate data.

[0061] The present invention uses the most commonly used Euclidean space distance matrix in mathematics. Euclidean distance is also called Euclidean distance or Euclidean metric. It is a commonly used distance definition. It is the real distance between two points in m-dimensional space. The Euclidean distance in two-dimensional and three-dimensional space is the distance between two points. Using this distance, the Euclidean space becomes a metric space, and the associated norm is called the Euclidean norm. The calculation formula is as follows:

[0062]

[0063]

[0064] |X| is the Euclidean distance from the point (x2, y2, z2) to the origin; ρ is the Euclidean distance between the point (x1, y1, z1) and the point (x2, y2, z2);

[0065] The Euclidean space distance matrix is ​​expressed as:

[0066]

[0067] Among them, the d in the matrix ij This is the Euclidean distance between two points.

[0068] S3. Establish a safety inspection path planning model for the regulatory environment

[0069] The spatial distance matrix represents the spatial position relationship between different inspection nodes i and j, that is, the inspection nodes i and j in the safety inspection path planning model, which is the premise for constructing the safety inspection path planning model.

[0070] Among them, building a regulatory environment safety inspection path planning model includes the following steps:

[0071] 1. First, a VRP model is established based on the characteristics of safety inspections in the regulatory environment, with two main considerations as optimization goals:

[0072] (1) In order to enable personnel to complete inspection work quickly and efficiently, it is necessary to develop an inspection route with the shortest total distance to ensure that the time spent on inspection is the shortest.

[0073] (2) When formulating inspection tasks, considering the rationality of personnel allocation, allocating as few inspection personnel as possible to complete the inspection work is also the goal of improving inspection efficiency. The primary optimization goal should be the total distance and total time spent on the inspection, followed by optimizing the allocation of inspection personnel. In order to reasonably construct a mathematical model for safety inspection of the regulatory environment, the following basic assumptions need to be made:

[0074] A. The specific parameters in the model and the locations that need to be inspected are known;

[0075] B. Each inspection route starts and ends at the guardhouse;

[0076] C. Each inspector is responsible for only one inspection route;

[0077] D. Each inspection location is inspected by only one inspector, and each inspection location can only be inspected once;

[0078] E. Each inspection location has an equal probability of being inspected;

[0079] F. Inspection personnel are subject to restrictions on the total distance and duration of their inspection work, which must not exceed the specified requirements.

[0080] Define the safety inspection path planning model parameters:

[0081] G = (V, A) complete graph;

[0082] V = {0, 1, 2, ..., n} each inspection location, where 0 represents the guard room;

[0083] V′={1,2,3,…,n} all task points that can be inspected;

[0084] A={(i,j)|i,j∈V,i≠j} represents an isolated set consisting of interconnected vertices in the complete graph;

[0085] t ij The time from inspection point i to inspection point j within a unit distance;

[0086] d ij The spatial distance from inspection point i to inspection point j;

[0087] D. The longest inspection distance that an inspector can take in one inspection mission;

[0088] The maximum acceptable duration of a patrol task for a patrol inspector;

[0089] mThe number of inspection personnel;

[0090] R = {1,2,…,m} inspection personnel set;

[0091] R i ={0,i1,…,i n ,0} inspection route, i1,…,i n ∈V′,i∈R;

[0092] Define the decision variable x ij k and i k :

[0093]

[0094]

[0095] 2. Create constraints and objective functions for the safety inspection path planning model

[0096] Combined with the actual situation of personnel's safety inspection in the supervision environment, there are various subjective or objective constraints in the inspection process. The following constraints are defined in this model:

[0097] (1) Inspection point constraints

[0098] Each inspection point should be inspected, and j represents any point to be inspected.

[0099]

[0100] (2) Inspection personnel constraints

[0101] Every inspector should leave the inspection point after completing the inspection.

[0102]

[0103] (3) Inspection point uniqueness constraint

[0104] Formula 1.1 ensures that every inspection point can be inspected. In addition, it must also ensure that each inspection point can only be inspected by one inspector.

[0105]

[0106] (4) Uniqueness constraint of the overall inspection task

[0107] In each inspection task, it is necessary to ensure that the number of arrivals and departures at any inspection point is 1.

[0108]

[0109]

[0110] (5) Inspection route duration constraints

[0111] The total inspection time of the inspector cannot exceed the maximum inspection working time

[0112]

[0113] (6) Inspection route constraints

[0114] The total inspection distance of the inspection personnel cannot exceed the maximum inspection working distance

[0115]

[0116] 3. Through the parameters and related constraints of the regulatory environment safety inspection planning model defined above, the objective function of the model is now constructed, and the total cost of the model is defined as F, where the sub-costs are F1 and F2, respectively, where F1 is the inspection time cost, F2 is the inspection personnel cost, G is a very large positive real number, and F1 and F2 are defined as:

[0117]

[0118]

[0119] Finally, the total cost function is defined as:

[0120] Min:F=F1+F2 (1.10)

[0121] 4. Set the ant colony algorithm as the optimization algorithm for this model

[0122] Suppose the number of ants is m, the number of inspection points is n, and the distance between inspection point i and inspection point j is d. ij , the pheromone concentration between inspection point i and inspection point j at time t is τ ij(t), at the initial moment, the pheromone concentration between each inspection point is τ ij (0) = C, where C is a constant.

[0123] Ant k selects the next location to be inspected based on the pheromone concentration between each inspection point. The probability of ant k moving from inspection point i to inspection point j at time t is p ij k (t), the formula is as follows:

[0124]

[0125] In formula (1.11), allow k =(1, 2, ..., n)-tabu k , is the set of inspection nodes that ant k can traverse next when it is at inspection node i, and the taboo table tabu k (k=1,2,…,n) is the set of inspection nodes that ant k has traversed, tabu k will be adjusted continuously during the evolution process, η ij (t) is the heuristic function, α is the information heuristic factor, and β is the expectation heuristic factor.

[0126] Assume parameter ρ (0<ρ<1) as the pheromone volatility coefficient. When all ants finish a traversal, the pheromone concentration between each inspection node needs to be updated in time. The update formula is as follows:

[0127] τ ij (t) = (1-ρ)τ ij (t)+Δτ ij (1.12)

[0128]

[0129] In formula (1.12), Δτ ij k represents the pheromone concentration released by the kth ant between patrol nodes i and j. In formula (1.13), Δτ ij It represents the total concentration of pheromones released by all ants between patrol nodes i and j.

[0130]

[0131] In formula (1.14), Q represents the total pheromone concentration left by the ants after one traversal, L k Represents the total distance of all routes traversed by the kth ant in this traversal.

[0132] 5. Design of safety inspection model based on ant colony algorithm

[0133] The transition probability is linked to the total inspection cost. The transition probability refers to the probability that an ant transfers from node i to the next node j. The path with a larger transition probability will be chosen by more ants. By calculating the transition probability, we can determine where the next node is. The transition probability formula is:

[0134]

[0135] In formula (1.15), F is the total cost of safety inspection. The smaller the total cost of inspection, the greater the ij (t) is larger, p′ ij k The larger (t) is, the higher the pheromone concentration between inspection nodes i and j is, and the higher the probability that the path will be selected is;

[0136] The η in the transition probability formula ij (t) More specific, The total cost of the safety inspection is added, and the probability of the path being selected is obtained by optimizing the transfer probability. The transfer probability is the probability of the path being selected, and the path with the largest probability is the final optimal path.

[0137] Example 2

[0138] A shortest path-based supervisory environment safety inspection planning system, comprising:

[0139] The data acquisition module is configured to acquire data information of the points to be inspected, including coordinate data;

[0140] The path planning module is configured to construct a spatial distance matrix according to the acquired coordinate data, obtain an inspection path planning model according to the constructed spatial distance matrix; define constraint conditions according to the inspection path planning model, and obtain a model objective function according to the constraint conditions;

[0141] The inspection module is configured to traverse the inspection points to be inspected using an ant colony algorithm according to the model objective function to obtain the inspection results;

[0142] Among them, in the ant algorithm, ants are used to select the location of the next inspection point according to the pheromone concentration of the inspection point, and the transfer probability of the path being selected is obtained according to the total pheromone concentration and total cost between the inspection points. The path with the largest transfer probability is the optimal path.

[0143] Example 3

[0144] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device. A method for planning a safety inspection of a regulatory environment based on the shortest path provided in this embodiment.

[0145] Example 4

[0146] A terminal device includes a processor and a computer-readable storage medium, the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor. A method for planning safety inspections in a regulatory environment based on the shortest path provided in this embodiment.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0152] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for planning safety inspections in a regulatory environment based on the shortest path, characterized in that: The following steps are involved: S1. Obtain data information of the inspection point, including coordinate data; S2. construct a spatial distance matrix based on the acquired coordinate data; S3. Obtain an inspection path planning model based on the constructed spatial distance matrix; S4. According to the inspection path planning model, define the constraint conditions, and obtain the model objective function according to the constraint conditions; S5. According to the model objective function, the ant colony algorithm is used to traverse the inspection points to obtain the inspection results; Among them, in the ant colony algorithm, ants are used to select the location of the next inspection point according to the pheromone concentration of the inspection point, and the transition probability of the path being selected is obtained according to the total pheromone concentration and total cost between the inspection points. The path with the largest transition probability is the optimal path; The spatial distance matrix is ​​constructed according to the acquired coordinate data, including constructing a Euclidean spatial distance matrix according to the acquired coordinate data, and the calculation formula is: |X| is the Euclidean distance from the point (x2, y2, z2) to the origin; d is the Euclidean distance between the point (x1, y1, z1) and the point (x2, y2, z2); The Euclidean space distance matrix is ​​expressed as: Among them, the d in the matrix ij That is the Euclidean distance between two points; The inspection path planning model is obtained according to the constructed spatial distance matrix, including establishing a VRP model based on the characteristics of the safety inspection of the regulatory environment, and defining the safety inspection path planning model parameters. The safety inspection path planning model parameters include the decision variables x ij k and i k : The constraints include inspection point constraints, i.e. ensuring that each inspection point is inspected; inspection personnel constraints, i.e. ensuring that each inspection personnel leaves the inspection point after completing an inspection; inspection task constraints, i.e. ensuring that the number of times of arriving at and leaving any inspection point is 1 in each inspection task; inspection route duration constraints, i.e. the total inspection duration of the inspection personnel cannot exceed the maximum inspection working duration; inspection route distance constraints, i.e. the total inspection distance of the inspection personnel cannot exceed the maximum inspection working distance; The objective function of the model is constructed according to the constraint conditions, including defining the total cost of the model as F, wherein the sub-costs are F1 and F2, wherein F1 is the inspection time cost, and F2 is the inspection personnel cost. F1 and F2 are defined as: Among them, t ij is the time from inspection point i to inspection point j within a unit distance; d ij is the spatial distance from inspection point i to inspection point j; x ij k and i k is the decision variable; The total cost function is: Min: F = F1 + F2.

2. A method for planning a safety inspection of a regulatory environment based on the shortest path as claimed in claim 1, characterized in that: The transfer probability of the path being selected is obtained according to the total pheromone concentration and the total cost between the inspection points, including using ants to select the position of the next inspection point according to the pheromone concentration between the inspection points, obtaining the probability of the ants moving from the next inspection point at that moment, and using the pheromone volatility coefficient. When all ants complete a traversal, the pheromone concentration between each inspection node is updated. The update formula is as follows: t ij (t)=(1-ρ)τ ij (t)+Δτ ij ; Among them, Δτ ij k represents the pheromone concentration released by the kth ant between patrol nodes i and j, Δτ ij represents the sum of the total pheromone concentrations released by all ants between patrol nodes i and j, and ρ is the pheromone volatility coefficient; Among them, Q represents the total pheromone concentration left by the ants after one traversal, L k Represents the total distance of all routes traversed by the kth ant in this traversal.

3. A method for planning a safety inspection of a regulatory environment based on the shortest path as claimed in claim 1, characterized in that: The transition probability is: in, F is the total cost of safety inspection. The smaller the total cost of inspection, the higher the cost of safety inspection. ij The larger (t) is, The larger it is, α is the pheromone factor parameter, and β is the heuristic function factor parameter.

4. A shortest path-based supervisory environment safety inspection planning system, characterized in that: include: The data acquisition module is configured to acquire data information of the points to be inspected, including coordinate data; The path planning module is configured to construct a spatial distance matrix according to the acquired coordinate data, obtain an inspection path planning model according to the constructed spatial distance matrix; define constraint conditions according to the inspection path planning model, and obtain a model objective function according to the constraint conditions; The inspection module is configured to traverse the inspection points using the ant colony algorithm according to the model objective function to obtain the inspection results; Among them, in the ant colony algorithm, ants are used to select the location of the next inspection point according to the pheromone concentration of the inspection point, and the transfer probability of the path selected is obtained according to the total pheromone concentration and total cost between the inspection points. The path corresponding to the maximum transfer probability is the optimal path; The spatial distance matrix is ​​constructed according to the acquired coordinate data, including constructing a Euclidean spatial distance matrix according to the acquired coordinate data, and the calculation formula is: |X| is the Euclidean distance from the point (x2, y2, z2) to the origin; d is the Euclidean distance between the point (x1, y1, z1) and the point (x2, y2, z2); The Euclidean space distance matrix is ​​expressed as: Among them, the d in the matrix ij That is the Euclidean distance between two points; The inspection path planning model is obtained according to the constructed spatial distance matrix, including establishing a VRP model based on the characteristics of the safety inspection of the regulatory environment, and defining the safety inspection path planning model parameters. The safety inspection path planning model parameters include the decision variables x ij k and i k : The constraints include inspection point constraints, i.e. ensuring that each inspection point is inspected; inspection personnel constraints, i.e. ensuring that each inspection personnel leaves the inspection point after completing an inspection; inspection task constraints, i.e. ensuring that the number of times of arriving at and leaving any inspection point is 1 in each inspection task; inspection route duration constraints, i.e. the total inspection duration of the inspection personnel cannot exceed the maximum inspection working duration; inspection route distance constraints, i.e. the total inspection distance of the inspection personnel cannot exceed the maximum inspection working distance; The objective function of the model is constructed according to the constraint conditions, including defining the total cost of the model as F, wherein the sub-costs are F1 and F2, wherein F1 is the inspection time cost, and F2 is the inspection personnel cost. F1 and F2 are defined as: Among them, t ij is the time from inspection point i to inspection point j within a unit distance; d ij is the spatial distance from inspection point i to inspection point j; x ij k and i k is the decision variable; The total cost function is: Min: F = F1 + F2.

5. A computer-readable storage medium, characterized in that: A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing a method for planning safety inspection of a regulatory environment based on the shortest path according to any one of claims 1-3.

6. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executed by a regulatory environment security inspection planning method based on the shortest path as described in any one of claims 1-3.

Images