Inspection scheme generation method and device, equipment and storage medium

Abstract

Embodiments of the present disclosure provide an inspection scheme generation method, device and equipment and a storage medium. The method comprises: step 1: using a clustering algorithm and a greedy algorithm, based on the topology structure information of each site, determining a path that can be completed by a single person and meets a constraint condition; step 2: according to the nearest principle, assigning a site not on the determined path to the corresponding path; step 3: for the path of any new site, performing path optimization according to the site in the path to obtain a corresponding optimal path; step 4: for each path of a site not added and any path in each optimal path, determining the number of required workers according to the constraint condition; generating a set comprising any path and the number of required workers; step 5: repeatedly performing steps 1-4 to obtain multiple sets, and determining a set with the minimum target function value from the multiple sets according to a target function, and generating an inspection scheme based on the set, thereby effectively improving the inspection scheme generation effect.

Description

Technical Field

[0001] This disclosure relates to the field of inspection technology, and in particular to an inspection plan generation method, apparatus, equipment, and storage medium. Background Technology

[0002] Site inspections (such as those for sewage and power plants) can effectively ensure the normal operation of the sites, reduce malfunctions, facilitate the discovery of potential hazards, enable timely rectification, and prevent or eliminate accidents. However, when sites are distributed across multiple geographically different locations, there are challenges in designing the inspection plan.

[0003] Current inspection plans are typically designed manually or generated using inspection planning algorithms. However, these methods generally suffer from poor performance when dealing with a large number of inspection sites. Therefore, improving the effectiveness of inspection plan generation has become a pressing technical challenge. Summary of the Invention

[0004] Embodiments of this disclosure provide a method, apparatus, device, and storage medium for generating inspection plans.

[0005] In a first aspect, embodiments of this disclosure provide a method for generating an inspection plan, the method comprising:

[0006] Step 1: Using clustering and greedy algorithms, based on the topological information of each station, determine the path that can be completed by a single person and meets the constraints;

[0007] Step 2: Based on the principle of proximity, assign the stations that are not on the determined paths to the corresponding paths;

[0008] Step 3: For any newly added site's path, perform path optimization based on the sites within it to obtain the corresponding optimal path;

[0009] Step 4: For each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; generate a set including each path without new stations and any path among the optimal paths and the required number of workers.

[0010] Step 5: Repeat steps 1-4 multiple times to obtain multiple sets, and determine the set with the smallest objective function value from the multiple sets according to the objective function, and generate the inspection plan based on it.

[0011] Among the possible implementations of the first aspect, clustering and greedy algorithms are employed to determine paths that can be completed by a single person and meet the constraints, based on the topological information of each station. These include:

[0012] Step 1.1: If the number of stations in the site set to be clustered is greater than or equal to the number of clusters k, then the clustering algorithm is used to cluster the stations into k clusters based on the topological information of the stations in the site set to be clustered; otherwise, proceed to step 2. The site set to be clustered initially includes all stations.

[0013] Step 1.2: Filter the k clusters respectively. If none of the k clusters meet the filtering conditions, then k = k + 1 and execute step 1.1; otherwise, execute step 1.3.

[0014] Step 1.3: Use a greedy algorithm to optimize the paths of clusters that meet the screening criteria to obtain the corresponding optimal paths;

[0015] Step 1.4: If there is a path that meets the constraints in the optimal path obtained in Step 1.3, then identify it as a path that can be completed by a single person and meets the constraints, delete the site corresponding to the path in the site set to be clustered, and let k return to the initial value, then execute Step 1.1; otherwise, k = k + 1, then execute Step 1.1.

[0016] Among the possible implementations of the first aspect, the clustering algorithm is the K-means algorithm, and the greedy algorithm is the Traveling Salesman Problem algorithm.

[0017] Among some possible implementations of the first aspect, sites not located on the determined paths are assigned to the corresponding paths according to the proximity principle, including:

[0018] Stations not on any of the defined paths are assigned to the path of the nearest station that is already on a defined path.

[0019] In some possible implementations of the first aspect, for each path without newly added stations and for any path among the optimal paths, the required number of personnel is determined according to constraints, including:

[0020] For each path without newly added stations and any path among the optimal paths, the number of manual inspectors on the path will be increased sequentially starting from 1. Each time, the time required to complete the path inspection will be checked to see if the constraint condition is met. If the constraint condition is met, the increase will stop, and the current number of manual inspectors will be determined as the number of manual inspectors required for the path.

[0021] Among some possible implementations of the first aspect, the method also includes:

[0022] Step 6: Based on the total number of actual workers, the paths in the inspection plans that each actual worker can complete, and the maximum working time of workers within the inspection period, perform maximum matching between the actual number of workers and the inspection plans.

[0023] In some feasible ways of implementing the first aspect, the actual number of workers and the inspection plan are matched to the maximum extent based on the total number of workers, the paths in the inspection plan that each worker can complete, and the maximum working time of workers within the inspection period. This includes:

[0024] Connect each actual worker to its complete path to form a bipartite graph between actual workers and paths, and transform it into a maximum flow problem. In the maximum flow problem, the maximum inflow of each actual worker is its maximum working time, the maximum flow between an actual worker and a path is 1, and the maximum outflow of each path is the number of workers required.

[0025] The maximum flow problem is solved using the augmenting path algorithm, and the maximum matching is performed between the actual manual inspection and the inspection plan.

[0026] Secondly, embodiments of this disclosure provide an inspection plan generation apparatus, the apparatus comprising:

[0027] The determination module is used to execute step 1: using clustering and greedy algorithms, based on the topological information of each station, to determine the path that can be completed by a single person and meets the constraints.

[0028] The allocation module is used to perform step 2: based on the principle of proximity, allocate the stations that are not on the determined paths to the corresponding paths;

[0029] The optimization module is used to execute step 3: for any newly added site's path, optimize the path based on the sites within it to obtain the corresponding optimal path;

[0030] The set generation module is used to perform step 4: for each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; and generate a set including each path without new stations and any path among the optimal paths and the required number of workers.

[0031] The inspection plan generation module is used to execute step 5: based on the objective function, determine the set with the smallest objective function value from the multiple sets obtained by repeatedly iterating through steps 1-4, and generate an inspection plan accordingly.

[0032] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0033] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.

[0034] In the embodiments of this disclosure, clustering algorithms and greedy algorithms can be used to determine paths that can be completed by a single person and meet the constraints based on the topological information of each station. According to the proximity principle, stations not on the determined paths are assigned to the corresponding paths. For any newly added station's path, path optimization is performed based on the stations within it to obtain the corresponding optimal path. For each path without newly added stations and any path among the optimal paths, the required number of workers is determined according to the constraints. A set including any path and its required number of workers is generated. The above steps are iterated multiple times to obtain multiple sets. Based on the objective function, the set with the smallest objective function value is determined from the multiple sets, and this set is used to quickly generate an inspection plan, thereby effectively improving the inspection plan generation effect.

[0035] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0036] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0037] Figure 1 A flowchart of an inspection plan generation method provided by an embodiment of this disclosure is shown;

[0038] Figure 2 A detailed flowchart of an inspection plan generation method provided by an embodiment of this disclosure is shown;

[0039] Figure 3 A flowchart of yet another inspection scheme generation method provided by an embodiment of this disclosure is shown;

[0040] Figure 4 A detailed flowchart of another inspection scheme generation method provided by an embodiment of this disclosure is shown;

[0041] Figure 5 A structural diagram of an inspection scheme generation device provided by an embodiment of the present disclosure is shown;

[0042] Figure 6 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0044] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0045] To address the problems in the background art, embodiments of this disclosure provide a method, apparatus, device, and storage medium for generating inspection plans. Specifically, clustering and greedy algorithms are employed to determine paths that can be completed by a single person and meet constraints based on the topology information of each station. Stations not located on the determined paths are assigned to corresponding paths according to the proximity principle. For any newly added station's path, path optimization is performed based on the stations within it to obtain the corresponding optimal path. For each path without newly added stations and any path among the optimal paths, the required number of workers is determined according to the constraints. A set including any path and its required number of workers is generated. The above steps are iterated multiple times to obtain multiple sets. Based on the objective function, the set with the smallest objective function value is determined from the multiple sets, and this set is used to quickly generate an inspection plan, thereby effectively improving the inspection plan generation effect.

[0046] The following detailed description, with reference to the accompanying drawings and specific embodiments, illustrates the inspection scheme generation method, apparatus, equipment, and storage medium provided in the embodiments of this disclosure.

[0047] Figure 1 A flowchart of an inspection plan generation method provided by an embodiment of this disclosure is shown, such as... Figure 1 As shown, the inspection plan generation method 100 may include the following steps:

[0048] S110 uses clustering and greedy algorithms to determine a path that can be completed by a single person and meets the constraints based on the topological information of each station.

[0049] S120, based on the principle of proximity, assigns stations not located on the determined paths to the corresponding paths.

[0050] S130: For any newly added site's path, perform path optimization based on the sites to obtain the corresponding optimal path.

[0051] S140, for each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; generate a set including each path without new stations and any path among the optimal paths and the required number of workers.

[0052] S150, iterates through S110-S140 multiple times to obtain multiple sets, and based on the objective function, determines the set with the smallest objective function value from the multiple sets, and uses this set to generate the inspection plan.

[0053] This method allows for the rapid generation of inspection plans, thereby effectively improving the quality of inspection plan generation.

[0054] In some embodiments, the objective function can be obtained by merging objective function 1 and objective function 2, as shown below:

[0055] Objective function 1:

[0056] Objective function 2:

[0057] Objective function 1 represents minimizing cost, where the first equation represents the cost of travel and the second equation represents the cost of labor. Objective function 2 represents minimizing the difference between the longest and shortest travel times.

[0058] The objective function obtained by merging objective function 1 and objective function 2 can be:

[0059]

[0060] The constraints can be as follows:

[0061]

[0062] The parameters of the above formula can be shown in Table 1:

[0063] Table 1

[0064]

[0065]

[0066] In some embodiments, S110 may specifically include, for example: Figure 2 The following steps are shown:

[0067] S111: Determine if the number of sites in the site set to be clustered is greater than or equal to the number of clusters k. If the number of sites in the site set to be clustered is greater than or equal to the number of clusters k, then execute S112; otherwise, execute S120. The site set to be clustered initially includes all sites, and the initial value of k is 2.

[0068] S112 uses a clustering algorithm (such as K-means algorithm, KNN algorithm, community detection algorithm) to cluster the sites in the set of sites to be clustered into k clusters based on the topological structure information of the sites.

[0069] S113, filter each of the k clusters and determine whether all k clusters do not meet the filtering conditions. If all k clusters do not meet the filtering conditions, then k = k + 1 and execute S111; otherwise, execute S114.

[0070] For example, since each manual inspection route has a time limit, the maximum number of stations in a cluster can be obtained by dividing the upper bound of the inspection time (ub) by the time (t) for a single manual inspection of a single station. For any cluster, if the number of stations in the cluster is greater than the maximum, it is determined that it does not meet the screening criteria; otherwise, it is determined that it meets the screening criteria.

[0071] S114 uses a greedy algorithm (such as the Traveling Salesman Problem algorithm or Dijkstra's algorithm) to optimize the path of the clusters that meet the selection criteria, and obtains the corresponding optimal path.

[0072] S115: Determine if there is a path that satisfies the constraints in the optimal path obtained by executing S114. If there is a path that satisfies the constraints in the optimal path obtained by executing S114, then execute S116; otherwise, k = k + 1, and execute S111.

[0073] S116, identify the path as one that can be completed by a single person and meets the constraints, delete the site corresponding to the path from the set of sites to be clustered, and let k return to the initial value, then execute S111.

[0074] In some embodiments, S120 may specifically include the following steps:

[0075] Stations not on any of the defined paths are assigned to the path of the nearest station that is already on a defined path.

[0076] In other words, the stations in the set to be clustered after S110 are assigned to the path of the nearest station that is already on a determined path.

[0077] In some embodiments, S140 may specifically include the following steps:

[0078] For each path without newly added stations and any path among the optimal paths, the number of manual inspectors on the path will be increased sequentially starting from 1. Each time, the time required to complete the path inspection will be checked to see if the constraint condition is met. If the constraint condition is met, the increase will stop, and the current number of manual inspectors will be determined as the number of manual inspectors required for the path.

[0079] After determining the number of workers required for each path, a set is generated that includes each path and its required number of workers.

[0080] In some embodiments, S150 may specifically include the following steps:

[0081] Repeat steps S110-S140 multiple times to obtain multiple sets. Calculate the objective function value for each set based on the objective function, and determine the set with the smallest objective function value to generate an inspection plan.

[0082] In some embodiments, such as Figure 3 As shown, in order to improve the matching between actual manual inspection and the inspection plan, the inspection plan generation method 100 may further include the following steps:

[0083] S160: Based on the total number of actual workers, the paths in the inspection plans that each actual worker can complete, and the maximum working time of workers within the inspection period, perform maximum matching between actual workers and inspection plans.

[0084] For example, such as Figure 4 As shown, S160 may specifically include the following steps:

[0085] S161 connects each actual worker with its achievable path to form a bipartite graph between actual workers and paths, and transforms it into a maximum flow problem.

[0086] In the maximum flow problem, the maximum inflow of each actual worker is its maximum working time S within the inspection time period T, the maximum flow between the actual worker and the path is 1, and the maximum outflow of each path is the number of workers required.

[0087] S162, solve the maximum flow problem using the augmenting path algorithm, and perform maximum matching between the actual manual inspection and the inspection plan.

[0088] In the embodiments of this disclosure, all stations can be inspected within a specified time T. Inspection costs can be minimized, including expenses incurred during travel (minimizing total distance) and daily wages for workers (minimizing man-days). Furthermore, since the stations are widely distributed, the limited number of existing workers can only be responsible for stations in a certain area, and each worker can work a maximum of S days within time T, thus matching actual manpower with the routes.

[0089] Meanwhile, the embodiments of this disclosure make the following contributions:

[0090] (1) Added time constraints for each path taken by the traveler to the original traveling salesman problem.

[0091] (2) Most path planning using the Traveling Salesman Problem algorithm usually only considers one traveler (even with multiple travelers, a path is completed by only one traveler). The embodiments of this disclosure take into account that different numbers of people in reality will lead to different inspection times, and add the number of people as a decision variable.

[0092] (3) It not only considers the goal of the shortest total distance, but also includes the goal of the minimum number of people per day.

[0093] (4) Unlike other solutions that homogenize human resources, the embodiments of this disclosure take into account the heterogeneity of human resources, that is, human resources can only be responsible for some areas of the path, rather than all paths.

[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0095] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0096] Figure 5 A structural diagram of an inspection plan generation device provided in an embodiment of this disclosure is shown, as follows: Figure 5 As shown, the inspection plan generation device 500 may include:

[0097] The determination module 510 is used to execute step 1: using clustering and greedy algorithms, based on the topology information of each station, to determine a path that can be completed by a single person and meets the constraints.

[0098] The allocation module 520 is used to perform step 2: according to the principle of proximity, the stations that are not on the determined paths are allocated to the corresponding paths.

[0099] The optimization module 530 is used to perform step 3: for any newly added site's path, optimize the path based on the sites to obtain the corresponding optimal path.

[0100] The set generation module 540 is used to perform step 4: for each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; and generate a set including each path without new stations and any path among the optimal paths and the required number of workers.

[0101] The inspection plan generation module 550 is used to execute step 5: based on the objective function, determine the set with the smallest objective function value from the multiple sets obtained by repeatedly iterating through steps 1-4, and generate an inspection plan accordingly.

[0102] In some embodiments, the determining module 510 is specifically used to perform the following steps:

[0103] Step 1.1: If the number of sites in the set of sites to be clustered is greater than the number of clusters k, then a clustering algorithm is used to cluster the sites into k clusters based on the topological information of the sites in the set of sites to be clustered; otherwise, proceed to Step 2. The initial set of sites to be clustered includes all the sites.

[0104] Step 1.2: Filter the k clusters respectively. If none of the k clusters meet the filtering conditions, then k = k + 1 and execute step 1.1; otherwise, execute step 1.3.

[0105] Step 1.3: Use a greedy algorithm to optimize the paths of clusters that meet the screening criteria to obtain the corresponding optimal paths.

[0106] Step 1.4: If there is a path that meets the constraints in the optimal path obtained in Step 1.3, then identify it as a path that can be completed by a single person and meets the constraints, delete the site corresponding to the path in the site set to be clustered, and let k return to the initial value, then execute Step 1.1; otherwise, k = k + 1, then execute Step 1.1.

[0107] In some embodiments, the allocation module 520 is specifically used for:

[0108] Stations not on any of the defined paths are assigned to the path of the nearest station that is already on a defined path.

[0109] In some embodiments, the set generation module 540 is specifically used for:

[0110] For each path without newly added stations and any path among the optimal paths, the number of manual inspectors on the path will be increased sequentially starting from 1. Each time, the time required to complete the path inspection will be checked to see if the constraint condition is met. If the constraint condition is met, the increase will stop, and the current number of manual inspectors will be determined as the number of manual inspectors required for the path.

[0111] In some embodiments, the inspection plan generation device 500 further includes:

[0112] The matching module is used to maximize the matching between actual manpower and inspection plans based on the total number of actual manpower, the paths in the inspection plans that each actual manpower can complete, and the maximum working time of manpower within the inspection period.

[0113] In some embodiments, the matching module is specifically used for:

[0114] Connect each actual worker to its achievable path to form a bipartite graph between actual workers and paths, and transform it into a maximum flow problem. In the maximum flow problem, the maximum inflow to each actual worker is its maximum working time, the maximum flow between an actual worker and a path is 1, and the maximum outflow to each path is the number of workers required.

[0115] The maximum flow problem is solved using the augmenting path algorithm, and the maximum matching is performed between the actual manual inspection and the generated inspection plan.

[0116] Understandable, Figure 5 Each module / unit in the inspection plan generation device 500 shown has the function of implementing each step in the inspection plan generation method 100 shown above, and can achieve its corresponding technical effect. For the sake of brevity, it will not be described in detail here.

[0117] Figure 6 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 600 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 600 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0118] like Figure 6 As shown, the electronic device 600 may include a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0119] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0120] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0121] The various embodiments described above can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this disclosure, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0124] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the embodiments of this disclosure in executing the method. For the sake of brevity, these will not be elaborated here.

[0125] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.

[0126] To provide interaction with a user, the embodiments described above can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0127] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0128] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0129] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0130] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for generating inspection plans, characterized in that, The method includes: Step 1: Using clustering and greedy algorithms, based on the topological information of each station, determine the path that can be completed by a single person and meets the constraints; Step 2: Based on the principle of proximity, assign the stations that are not on the determined paths to the corresponding paths; Step 3: For any newly added site's path, perform path optimization based on the sites within it to obtain the corresponding optimal path; Step 4: For each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; generate a set including each path without new stations and any path among the optimal paths and their required number of workers. Step 5: Repeat steps 1-4 multiple times to obtain multiple sets, and determine the set with the smallest objective function value from the multiple sets according to the objective function, and generate the inspection plan based on it; The method employs clustering and greedy algorithms to determine paths that can be completed by a single person and meet the constraints, based on the topological information of each station. This includes: Step 1.1: If the number of stations in the site set to be clustered is greater than or equal to the number of clusters k, then the clustering algorithm is used to cluster the stations into k clusters based on the topological information of the stations in the site set to be clustered; otherwise, proceed to step 2; wherein, the site set to be clustered initially includes each station. Step 1.2: Filter the k clusters respectively. If none of the k clusters meet the filtering conditions, then k = k + 1 and execute step 1.1; otherwise, execute step 1.

3. Step 1.3: Using the greedy algorithm, perform path optimization on the clusters that meet the screening conditions to obtain the corresponding optimal path; Step 1.4: If there is a path in the optimal path obtained in Step 1.3 that satisfies the constraints, then determine it as a path that can be completed by a single person and satisfies the constraints, delete the site corresponding to the path in the set of sites to be clustered, and let k return to the initial value, then execute Step 1.1; otherwise, k = k + 1, then execute Step 1.

1.

2. The method according to claim 1, characterized in that, The clustering algorithm is the K-means algorithm, and the greedy algorithm is the Traveling Salesman Problem algorithm.

3. The method according to claim 1, characterized in that, The process of assigning stations not located on each determined path to the corresponding path based on the proximity principle includes: Stations not on any of the defined paths are assigned to the path of the nearest station that is already on a defined path.

4. The method according to claim 1, characterized in that, The determination of the required manpower for each path without newly added stations and any path among the optimal paths, based on the constraints, includes: For each path without newly added stations and any path among the optimal paths, the number of manual inspectors on the path will be increased sequentially starting from 1. Each time, the time required to complete the path inspection will be checked to see if the constraint condition is met. If the constraint condition is met, the increase will stop, and the current number of manual inspectors will be determined as the number of manual inspectors required for the path.

5. The method according to claim 1, characterized in that, The method further includes: Step 6: Based on the total number of actual workers, the paths in the inspection plan that each actual worker can complete, and the maximum working time of the workers within the inspection period, perform maximum matching between the actual workers and the inspection plan.

6. The method according to claim 5, characterized in that, The step of maximizing the matching between actual manpower and the inspection plan based on the total number of actual manpower, the paths that each actual manpower can complete in the inspection plan, and the maximum working time of manpower within the inspection time period includes: Connect each actual worker to its complete path to form a bipartite graph between actual workers and paths, and transform it into a maximum flow problem. In the maximum flow problem, the maximum inflow of each actual worker is its maximum working time, the maximum flow between actual workers and paths is 1, and the maximum outflow of each path is the number of workers required. The maximum flow problem is solved using the augmenting path algorithm, and the actual manual inspection is matched with the inspection scheme to achieve maximum matching.

7. An inspection plan generation device, characterized in that, The apparatus is used to perform the method according to any one of claims 1-6, comprising: The determination module is used to execute step 1: using clustering and greedy algorithms, based on the topological information of each station, to determine the path that can be completed by a single person and meets the constraints. The allocation module is used to perform step 2: based on the principle of proximity, allocate the stations that are not on the determined paths to the corresponding paths; The optimization module is used to execute step 3: for any newly added site's path, optimize the path based on the sites within it to obtain the corresponding optimal path; The set generation module is used to perform step 4: for each path without new stations and any path among the optimal paths, determine the required number of workers according to the constraints; and generate a set including each path without new stations and any path among the optimal paths and the required number of workers. The inspection plan generation module is used to execute step 5: based on the objective function, determine the set with the smallest objective function value from the multiple sets obtained by repeatedly iterating through steps 1-4, and generate an inspection plan accordingly.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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