Route control method, control device and routing inspection control system for substation routing inspection

Abstract

The invention provides a route control method and device for substation inspection and an inspection control system, and the method comprises the steps: obtaining data, such as environment parameters, historical records and equipment conditions, of all to-be-inspected point locations of a substation in real time, and calculating a static weight and a dynamic weight based on the information, wherein the dynamic weight considers the waiting and maintenance duration of the inspection point, the equipment emergency degree and external influence factors; a random inspection path is generated to serve as an initial population of a genetic algorithm, and a fitness function is constructed in combination with weights for optimization so as to determine a priority and a penalty value, so that an optimal inspection path is obtained; and finally, the route is issued to terminal equipment of inspection personnel, and dynamic updating of the inspection route is realized. According to the method, the problem of low inspection efficiency caused by incapability of effectively adjusting the inspection route in case of emergency or equipment problems in the inspection process of the transformer substation in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment inspection, and in particular to a route control method, a control device, a computer-readable storage medium and an inspection control system for substation inspection. Background Art

[0002] The stability and reliability of the power system operation require that the substation equipment must be kept in good condition, and the operation and maintenance of substation equipment requires a large number of personnel inspections. The inspection work of the substation mainly focuses on the inspection, maintenance and recording of the equipment, so as to timely discover the potential failures of the equipment and provide guarantees for the normal operation of the equipment. The traditional substation inspection work mainly relies on manual inspections. The inspection personnel go to the site to check the equipment one by one, and rely on the appearance of the equipment to judge the failure. The inspection work is completely dependent on manual experience, and the inspection efficiency is low and it is easy to miss inspections. With the development of technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, the digital transformation of the power industry is urgent, and the intelligent inspection of substations has also become an inevitable trend in the development of the industry.

[0003] At present, a large number of literatures have adopted methods based on machine learning, graph theory, genetic algorithms, trajectory planning, etc. to realize intelligent inspection. In the machine learning method, some literatures use logistic regression to analyze the relationship between the fault of substation equipment and the inspection data to predict the failure of the equipment; some literatures have built a fault diagnosis model based on deep learning to realize the fault detection of equipment. In the graph theory method, some literatures connect the substations, lines and switch stations of power grid equipment into a graph to realize the fault location of the power grid; some literatures have built a fault diagnosis model for substation equipment based on the graph. In the genetic algorithm and trajectory planning method, some literatures abstract the optimization problem of the inspection path into the optimization problem of the genetic algorithm; some literatures abstract the optimization problem of the inspection path into the optimization problem of trajectory planning.

[0004] However, the substation inspection methods in the above literature all have certain shortcomings. For machine learning methods, although they can detect faults in substation equipment, the fault diagnosis of the equipment only relies on the fault data of the equipment itself, while ignoring the impact of external factors such as the operating environment of the substation on the equipment. For graph theory methods, although they can locate faults in the power grid, the inspection path optimization problem of substation equipment has not been widely used in graph theory. For genetic algorithms and trajectory planning methods, although they can optimize the inspection path, they usually rely on a large amount of historical inspection data, and the equipment inspection data of substations is difficult to obtain. In summary, the current substation intelligent inspection method is difficult to achieve real-time dynamic optimization of the inspection path. Summary of the invention

[0005] The main purpose of the present application is to provide a route control method, a control device, a computer-readable storage medium and an inspection control system for substation inspection, so as to at least solve the problem in the prior art that when encountering emergencies or equipment problems during substation inspection, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a route control method for substation inspection is provided, including: acquiring relevant data of each inspection point to be inspected in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operating parameters of the inspection point to be inspected; determining the static weight of each inspection point to be inspected based on the first target data, and adjusting the dynamic weight of each inspection point to be inspected in real time based on the first target data, wherein the static weight is based on the historical inspection records, the real-time operating parameters and The equipment parameters are determined, and the dynamic weight is based on the inspection waiting time, inspection consumption time, equipment operating parameters, fault information and external impact score of the inspection point to be inspected, wherein the external impact score is determined by the inspection point to be inspected before the inspection; a random path is generated based on each of the inspection points as the initial population, a fitness function is constructed based on the static weight and the dynamic weight corresponding to each of the inspection points to be inspected, and a genetic algorithm is used to perform optimization to obtain a target inspection path; the target inspection path is sent to the terminal device carried by the inspection personnel to realize real-time update of the inspection route of the operating personnel.

[0007] Optionally, the static weight of each of the points to be inspected is determined based on the first target data, including: determining the historical failure rate of the points to be inspected based on the historical inspection records, and estimating the fault repair time of the points to be inspected based on the real-time operating parameters; scoring based on the historical failure rate and the fault repair time, and performing weighted calculation based on the scoring results to obtain the degree of urgency; determining the fault impact scope, fault impact degree and equipment availability of the points to be inspected based on the equipment parameters, determining the equipment operation record of the points to be inspected according to the equipment operating parameters, and obtaining the equipment cost of the points to be inspected; scoring based on the fault impact scope, fault impact degree, equipment availability, equipment operation record and equipment cost, and performing weighted calculation based on the scoring results to obtain the equipment importance; performing weighted calculation based on the degree of urgency and the equipment importance to obtain the static weight.

[0008] Optionally, the dynamic weight of each of the points to be inspected is adjusted in real time based on the first target data, including: determining the estimated time it takes for the operator to reach the point to be inspected from the current position based on the current inspection route, and obtaining the inspection waiting time; determining the average maintenance time of the point to be inspected based on the historical inspection records, and obtaining the inspection consumption time; determining the damage probability of the point to be inspected based on the equipment operating parameters, and determining the urgency of the point to be inspected according to the damage probability; determining the point in the current inspection route that has changed before the point to be inspected and in the previous inspection route, and obtaining the target point, and determining the external impact score based on the emergency event corresponding to the target point; scoring based on the inspection waiting time, the inspection consumption time and the urgency, and performing weighted calculation based on the scoring result and the external impact score to obtain the dynamic weight.

[0009] Optionally, a random path is generated based on each of the points to be inspected as an initial population, and a fitness function is constructed based on the static weight and the dynamic weight corresponding to each of the points to be inspected, including: randomly encoding each of the points to be inspected in the substation to obtain a plurality of coding sequences, and determining each of the coding sequences as a chromosome, wherein the coding sequence includes the codes corresponding to all of the points to be inspected in the substation; each of the chromosomes is used as an alternative inspection path to obtain an initial population; based on the static weight and the dynamic weight, a first preset formula p(v i )=w 静态 (v i )*w 动态 (v i ), get the priority of the inspection point, w 静态 (v i ) is the static weight of node vi, w 动态 (v i ) is the dynamic weight of node vi at iteration number t, w 动态 (v i )satisfy where w 动态 (v i , 0) is the initial dynamic weight of node vi, t is the current number of iterations, tmax is the maximum number of iterations; based on the priority, the priority penalty is substituted into the second preset formula Get the priority penalty, where x is chromosome x, v i is the ith node in the path corresponding to chromosome x, and n is the total number of nodes included in chromosome x; the fitness function is constructed based on the chromosome and the priority penalty: fitness Among them, the path length (x) is the total length of the inspection path corresponding to the chromosome x, ε is a positive number less than the first threshold, and α is the weight coefficient.

[0010] Optionally, optimization is performed through a genetic algorithm to obtain a target inspection path, including: a selection step, performing a selection operation based on the current population, and selecting a preset number of the chromosomes from the current population based on the fitness as parent individuals; a crossover step, performing a crossover operation based on the parent individuals to obtain child individuals; a mutation step, performing a mutation operation based on the parent individuals and the child individuals to obtain an updated population; a calculation step, calculating the fitness based on all the chromosomes in the updated population; repeating the selection step, the crossover step, the mutation step and the mutation step at least once in sequence until the deviation of the maximum fitness value in the updated population of two adjacent iterations is less than a preset ratio or the maximum number of iterations is reached.

[0011] Optionally, the crossover operation is performed within a preset range centered on the point to be inspected where the dynamic weight difference in two adjacent iterations is greater than a second threshold.

[0012] Optionally, the variation operation is performed at the point to be inspected corresponding to the priority level being less than a third threshold.

[0013] According to another aspect of the present application, a route control device for substation inspection is provided, the device comprising: a first acquisition unit, used to acquire relevant data of each to-be-inspected point in the substation in real time, and obtain corresponding first target data, the first target data including environmental parameters, historical inspection records, equipment parameters, fault information and real-time operating parameters of the to-be-inspected point; a first calculation unit, used to determine the static weight of each to-be-inspected point based on the first target data, and at the same time adjust the dynamic weight of each to-be-inspected point in real time based on the first target data, the static weight being based on the historical inspection records, the real-time operating parameters and the The equipment parameters are determined, and the dynamic weight is based on the inspection waiting time, inspection consumption time, equipment operation parameters, fault information and external impact score of the inspection point to be inspected, wherein the external impact score is determined by the inspection point to be inspected before the inspection; the second calculation unit is used to generate a random path based on each of the inspection points as the initial population, and to construct a fitness function based on the static weight and the dynamic weight corresponding to each of the inspection points to be inspected, and to obtain a target inspection path by optimizing through a genetic algorithm; the sending unit is used to send the target inspection path to the terminal device carried by the inspection personnel to realize the real-time update of the inspection route of the operating personnel.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0015] According to another aspect of the present application, a patrol control system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0016] By applying the technical solution of the present application, in the route control method for the above-mentioned substation inspection, first, the relevant data of each inspection point to be inspected in the substation is acquired in real time to obtain the corresponding first target data, and the above-mentioned first target data includes the environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the above-mentioned inspection points; then, the static weight of each of the above-mentioned inspection points is determined based on the above-mentioned first target data, and the dynamic weight of each of the above-mentioned inspection points to be inspected is adjusted in real time based on the above-mentioned first target data, and the above-mentioned static weight is determined based on the above-mentioned historical inspection records, the above-mentioned real-time operation parameters and the above-mentioned equipment parameters. The above dynamic weight is based on the inspection waiting time, inspection consumption time, equipment operation parameters, fault information and external impact score of the above inspection points, wherein the external impact score is determined by the inspection points before the inspection; then, a random path is generated based on each of the above inspection points as the initial population, and a fitness function is constructed based on the static weight and dynamic weight corresponding to each of the above inspection points, and the target inspection path is obtained by genetic algorithm optimization; finally, the target inspection path is sent to the terminal device carried by the inspection personnel to realize the real-time update of the inspection route of the above operating personnel. This application adjusts the dynamic weight of the inspection point in real time and combines the genetic algorithm for optimal route planning. It not only takes into account the static importance of the equipment, but also flexibly responds to the real-time changes in the status of the equipment in the substation, ensures the optimization of the inspection route, reduces the impact of equipment failure on the operation of the power grid, improves the work efficiency of the operating personnel and the operation and maintenance management level of the substation, and solves the problem that in the prior art, when encountering emergencies or equipment problems during the substation inspection process, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a mobile terminal according to a route control method for substation inspection provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a route control method for substation inspection provided according to an embodiment of the present application is shown;

[0019] Figure 3 A structural block diagram of a route control device for substation inspection provided according to an embodiment of the present application is shown.

[0020] The above drawings include the following reference numerals:

[0021] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, it is difficult for the intelligent inspection method of substation in the prior art to achieve real-time dynamic optimization of the inspection path. In order to solve the problem that when encountering emergencies or equipment problems during the substation inspection process in the prior art, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency, the embodiments of the present application provide a substation inspection route control method, a control device, a computer-readable storage medium and an inspection control system.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a route control method for substation inspection according to an embodiment of the present invention. Figure 1As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a route control method for substation inspection running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0030] Figure 2 FIG. 1 is a flow chart of a route control method for substation inspection according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0031] Step S201, obtaining relevant data of each inspection point in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point;

[0032] Specifically, the environmental information, historical inspection information, basic equipment information, fault information, real-time operation status and other data related to the inspection points are obtained. The above-mentioned first target data is obtained.

[0033] In the specific implementation, the system collects the internal environmental information of the substation in real time through an integrated sensor network (including but not limited to temperature sensors, humidity sensors, vibration sensors, cameras, etc.), such as temperature, humidity, equipment vibration, etc. This information can reflect the environmental conditions in which the equipment is currently operating. Secondly, the historical inspection records are queried through the database, including the equipment's failure history, maintenance records, equipment operation time, etc. These data are used to assess the potential failure risk of the equipment. Furthermore, basic equipment information (such as equipment type, importance level, location, etc.) and current fault information (such as equipment status, fault alarm) are also collected to determine the importance and urgency of the equipment. Finally, the system also obtains the operating status of the substation equipment in real time, including key parameters such as voltage, current, and temperature, to ensure the real-time and accuracy of the data.

[0034] Step S202, determining the static weight of each of the above-mentioned points to be inspected based on the above-mentioned first target data, and adjusting the dynamic weight of each of the above-mentioned points to be inspected in real time based on the above-mentioned first target data, wherein the above-mentioned static weight is determined based on the above-mentioned historical inspection records, the above-mentioned real-time operation parameters and the above-mentioned equipment parameters, and the above-mentioned dynamic weight is based on the inspection waiting time of the above-mentioned points to be inspected, the inspection consumption time, the above-mentioned equipment operation parameters, the above-mentioned fault information and the external impact score, wherein the external impact score is determined by the above-mentioned points to be inspected before the inspection;

[0035] Specifically, the static weight and dynamic weight of the inspection point are determined based on the relevant data of the inspection point. The static weight is calculated based on the urgency and importance of the equipment; the dynamic weight is adjusted based on the waiting time, urgency, inspection time and external influencing factors, and is updated and calculated every time the inspection point changes.

[0036] In specific implementations, the calculation of static weights is based on the inherent properties and historical data of the equipment. For example, equipment A is assigned higher w1 (urgency weight) and w2 (equipment importance weight) because of its key functions and failure frequency, thus obtaining a higher static weight. The dynamic weight is more complex, and it will be adjusted according to the real-time situation of the inspection operation, such as the current waiting time, the real-time urgency of equipment A (based on fault information and real-time operating status), the estimated inspection time (taking into account the complexity of the equipment and the difficulty of inspection), and the impact of external emergencies on equipment A (through recurrent neural network RNN). After each inspection point is completed, the system will recalculate the dynamic weights of all uninspected points based on the latest information to ensure the optimality of route planning.

[0037] Step S203, generating a random path as an initial population based on each of the above-mentioned points to be inspected, constructing a fitness function based on the above-mentioned static weights and the above-mentioned dynamic weights corresponding to each of the above-mentioned points to be inspected, and performing optimization through a genetic algorithm to obtain a target inspection path;

[0038] Specifically, a genetic algorithm is used to generate an optimal inspection route according to static weights and dynamic weights.

[0039] In the specific implementation, the process of the genetic algorithm includes initializing the population, selection, crossover and mutation operations, and evaluation of the fitness function. Specifically, the system first generates an initial population, and each individual represents a possible inspection path. The fitness function comprehensively considers the length of the path and the priority of each inspection point to evaluate the quality of the path. The selection operation is based on the result of the fitness function, so that the path with higher fitness has a greater chance of being selected for genetic operation. The crossover operation increases the intelligence and adaptability of the algorithm by intelligently selecting intersections and using the dynamic weight change range to guide the selection of intersections. The mutation operation selects the path with lower fitness for local adjustment under the new dynamic weight to accelerate the finding of the optimal path. Through multiple generations of genetic operations, it finally converges to an optimal inspection route.

[0040] Step S204: Send the target inspection path to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the inspection personnel.

[0041] Specifically, during the inspection process, the inspection route is updated in real time according to the dynamic adjustment of external emergencies.

[0042] In the specific implementation, during the actual inspection process, the system collects the status information of each inspection point in real time through the terminal devices worn by the operators. If an external emergency occurs in the substation, such as power grid fluctuations, extreme weather conditions, etc., the recurrent neural network predicts the external impact of the emergency on the subsequent uninspected points. Based on the updated dynamic weights (new scores taking into account external impacts), the system recalculates the priorities of all uninspected points and uses genetic algorithms to quickly generate new optimal inspection routes. After the operator's terminal device receives the adjusted route, it immediately conducts inspections along the new route to ensure inspection efficiency and equipment safety under the influence of emergencies.

[0043] Through this embodiment, first, the relevant data of each inspection point to be inspected in the substation is obtained in real time to obtain the corresponding first target data, and the first target data includes the environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point to be inspected; then, the static weight of each inspection point to be inspected is determined based on the first target data, and the dynamic weight of each inspection point to be inspected is adjusted in real time based on the first target data, the static weight is determined based on the historical inspection records, the real-time operation parameters and the equipment parameters, and the dynamic weight is determined based on the The inspection waiting time, inspection consumption time, the above equipment operating parameters, the above fault information and external impact score of the inspection point to be inspected, wherein the external impact score is determined by the inspection point to be inspected before the inspection; then, a random path is generated based on each of the above inspection points as the initial population, and a fitness function is constructed based on the above static weights and the above dynamic weights corresponding to each of the above inspection points, and the target inspection path is obtained by optimizing through a genetic algorithm; finally, the above target inspection path is sent to the terminal device carried by the inspection personnel to realize the real-time update of the inspection route of the above operating personnel. This application adjusts the dynamic weights of the inspection points in real time and combines genetic algorithms for optimal route planning. It not only takes into account the static importance of the equipment, but also flexibly responds to the real-time changes in the status of the equipment in the substation, ensures the optimization of the inspection route, reduces the impact of equipment failures on the operation of the power grid, improves the work efficiency of the operators and the operation and maintenance management level of the substation, and solves the problem that in the prior art, when encountering emergencies or equipment problems during the substation inspection process, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency.

[0044] In order to determine the static weight, in an optional implementation, the step S202 includes:

[0045] Step S20201, determining the historical failure rate of the above-mentioned points to be inspected based on the above-mentioned historical inspection records, and estimating the fault repair time of the above-mentioned points to be inspected based on the above-mentioned real-time operation parameters;

[0046] Specifically, based on historical inspection records, determine the historical failure rate of each inspection point. The historical failure rate refers to the ratio of the number of failures at the point (equipment) to the total number of inspections in the past certain period of time (such as one year or half a year), which can reflect the reliability of the equipment. The fault repair time of the inspection point is estimated through real-time operating parameters (such as the operating temperature, voltage, current, etc. of the equipment). The estimation method can combine the equipment type, historical maintenance records and current fault conditions, and use statistical models or machine learning algorithms for prediction.

[0047] Step S20202, scoring based on the above historical failure rate and the above failure repair time, and performing weighted calculation based on the scoring results to obtain the urgency level;

[0048] Specifically, the historical failure rate and fault repair time are used as the core indicators of the urgency score. The higher the historical failure rate, the more likely the equipment will fail again in the short term, and the higher the score; the longer the fault repair time, the greater the impact of the equipment failure, and the higher the score.

[0049] In the specific implementation, a weighted calculation is performed to obtain an emergency score. The weighting coefficient can be adjusted according to the type of equipment, importance, and potential impact on the stability of the power system to ensure that the score can accurately reflect the emergency level of the equipment. For example, emergency level = w3*historical failure rate + w4*fault repair time, where w3 and w4 are the weight parameters of the historical failure rate and fault repair time, respectively.

[0050] Step S20203, determining the fault impact range, fault impact degree and equipment availability of the above-mentioned points to be inspected based on the above-mentioned equipment parameters, determining the equipment operation record of the above-mentioned points to be inspected according to the above-mentioned equipment operation parameters, and obtaining the equipment cost of the above-mentioned points to be inspected;

[0051] Specifically, determine the fault impact range, fault impact degree, and equipment availability of the points to be inspected based on the equipment parameters. The fault impact range refers to the range of other devices or areas that may be affected when the equipment fails; the fault impact degree reflects the impact of the fault on the system operation; equipment availability refers to the probability that the equipment can operate normally within the prediction period. Determine the equipment operation record based on the equipment operation parameters. The operation record includes the equipment's operating time, maintenance records, failure frequency, etc. This information can reflect the equipment's operating status and maintenance requirements. Obtain the equipment cost of the points to be inspected. The equipment cost includes the purchase cost and maintenance cost. The higher the cost, the more important the equipment is.

[0052] Step S20204, scoring based on the fault impact range, the fault impact degree, the equipment availability, the equipment operation record and the equipment cost, and performing weighted calculation based on the scoring results to obtain the equipment importance;

[0053] Specifically, the above indicators are combined for weighted calculation to obtain the score of equipment importance. For example, equipment importance = w5*fault impact range + w6*fault impact degree + w7*equipment availability + w8*equipment cost, where w5, w6, w7 and w8 are weight parameters of fault impact range, fault impact degree, equipment availability and equipment cost respectively.

[0054] Step S20205, performing weighted calculation based on the above urgency and the above equipment importance to obtain the above static weight.

[0055] Specifically, the urgency score and the equipment importance score are weighted to obtain a static weight. For example, static weight = w1*urgency+w2*equipment importance, where w1 and w2 are weight parameters of urgency and equipment importance, which can be adjusted according to specific circumstances.

[0056] In order to obtain the above dynamic weight, in an optional implementation manner, the above step S202 further includes:

[0057] Step S20206, determining the estimated time it takes for the operator to reach the inspection point from the current position based on the current inspection route, and obtaining the inspection waiting time;

[0058] Specifically, based on the current location of the operator and the estimated travel speed, the system calculates the estimated time it will take to reach each inspection point from the current location, i.e. the inspection waiting time. This calculation takes into account the layout of the substation and the shortest path for the operator to move to each equipment point, ensuring the accuracy of the time estimate.

[0059] Step S20207, determining the average maintenance time of the above-mentioned points to be inspected based on the above-mentioned historical inspection records, and obtaining the above-mentioned inspection consumption time;

[0060] Specifically, based on historical inspection records, the system analyzes and determines the average inspection time for each inspection point, that is, the inspection time. This average value reflects the time required for equipment inspection, maintenance or minor repairs that may be encountered during actual inspections.

[0061] Step S20208, determining the damage probability of the points to be inspected based on the equipment operation parameters, and determining the urgency of the points to be inspected according to the damage probability;

[0062] Specifically, based on the real-time equipment operating parameters, the system estimates the probability of damage at each inspection point, that is, the possibility of equipment failure in the current or future period. Based on the probability of damage, the system further determines the urgency score of each point, and a high probability of damage will result in a high urgency score. The calculation of urgency also needs to be combined with the importance of the equipment and the scope of the failure to comprehensively evaluate the potential consequences of equipment failure.

[0063] Step S20209, determining the point in the current inspection route that has changed between the point to be inspected and the point in the previous inspection route, obtaining the target point, and determining the external impact score based on the emergency event corresponding to the target point;

[0064] Specifically, the system identifies the points in the current inspection route that have changed from the previous inspection route to the points to be inspected, i.e., the target points. For each target point, the external impact score is calculated by analyzing the impact of the emergency events related to it. The external impact score reflects the impact of the current emergency event on the uninspected equipment, including but not limited to the impact of power grid fluctuations and severe weather.

[0065] Step S20210, scoring is performed based on the inspection waiting time, the inspection consumption time and the urgency level, and weighted calculation is performed based on the scoring result and the external impact score to obtain the dynamic weight.

[0066] Specifically, the inspection waiting time, inspection consumption time, urgency score and external impact score are quantified and scored respectively, and the scoring criteria can be set based on historical data and expert experience. According to the scoring results of each factor, a weighted formula is used to calculate and obtain the dynamic weight of the inspection point. The dynamic weight formula is: dynamic weight = w9 inspection waiting time + w10 inspection consumption time + w11 urgency score + w12 external impact score, where w9, w10, w11 and w12 are the weight parameters of the inspection waiting time, inspection consumption time, urgency score and external impact score respectively.

[0067] In the above embodiment, the real-time update of dynamic weights will affect the priority sorting of the points to be inspected in the genetic algorithm and the calculation of the fitness function. The system combines dynamic weights with static weights, re-evaluates the fitness of all inspection routes, and selects and generates a better inspection route.

[0068] In order to construct the initial population and the fitness function, in an optional implementation, the above step S203 includes:

[0069] Step S20301, randomly encoding each of the points to be inspected in the substation to obtain a plurality of coding sequences, and determining each of the coding sequences as a chromosome, wherein the coding sequence includes codes corresponding to all of the points to be inspected in the substation;

[0070] Specifically, random path generation and encoding,First, the system generates multiple coding sequences containing all the inspection points through randomization.,Each coding sequence represents a possible inspection route, and the position order in the sequence reflects the order of,the inspection points to be visited. These coding sequences are then converted into chromosomes.

[0071] Step S20302, taking each of the above chromosomes as an alternative inspection path to obtain an initial population;

[0072] Specifically, an initial population is formed based on the above chromosomes. Each chromosome in the population represents an inspection route plan, which contains information about all points to be inspected in the substation.

[0073] Step S20303: Substitute the static weight and the dynamic weight into the first preset formula p(v i )=w 静态 (v i )*w 动态 (v i ), get the priority of the above inspection points, w 静态 (v i ) is the above static weight of node vi, w 动态 (v i ) is the dynamic weight of node vi at iteration number t, w 动态 (v i )satisfy where w 动态 (v i , 0) is the initial dynamic weight of node vi, t is the current iteration number, and tmax is the maximum iteration number;

[0074] Specifically, the integration system of static weight and dynamic weight integrates the static weight and dynamic weight of each inspection point into the priority calculation. The dynamic weight changes with the real-time status of the inspection point, reflecting the current urgency and maintenance needs of the point. The calculation of the dynamic weight takes into account the influence of the number of iterations t. As t increases, the dynamic weight wdynamic(vi,t) gradually approaches the static weight wstatic(vi), ensuring that in the later stage of the iteration process, the influence of the static weight on the priority gradually increases, which helps to stabilize the search for the optimal path.

[0075] Step S20304: Substitute the priority into the second preset formula priority penalty based on the priority Get the priority penalty, where x is chromosome x, vi is the i-th node in the path corresponding to chromosome x, and n is the total number of nodes included in chromosome x;

[0076] Specifically, for each chromosome x in the population, the system calculates the priority p(vi), which combines the static weight and the dynamic weight, reflecting the comprehensive priority of the inspection point vi in ​​the current iteration. The priority penalty P(x) is calculated based on the priority order of all nodes in chromosome x. If a node with a higher priority in path x is placed behind a node with a lower priority, the penalty term will increase to reflect the non-ideal nature of path planning.

[0077] Step S20305, construct the above fitness function based on the chromosome and the above priority penalty:

[0078]

[0079] Among them, the path length (x) is the total length of the inspection path corresponding to the chromosome x, ε is a positive number less than the first threshold, and α is the weight coefficient.

[0080] Specifically, based on the path length and priority penalty, the system constructs a fitness function F(x). The path length (x) is the total length of the inspection path corresponding to chromosome x, which reflects the physical distance of the inspection route. The design of the fitness function F(x) ensures a balance between path length and priority penalty. α is a weight coefficient, which is used to adjust the relative importance of path length and priority penalty, and can be adjusted according to actual needs. ∈ is a very small positive number, which is used to avoid division by zero errors in the division operation in the fitness function when the priority penalty term is zero. This small value ensures that even if the path priority is extremely good, the fitness function will not be infinite, but will remain within a reasonable range.

[0081] In the above embodiment, a method for constructing a genetic algorithm fitness function that comprehensively considers static weights and dynamic weights is provided, which effectively balances the relationship between the length of the inspection route and the priority of the points, and ensures the efficiency and flexibility of the inspection operation. When dealing with the problem of substation inspection route planning, this method can dynamically adjust the priority according to the real-time status of the equipment and changes in the external environment, and quickly find the inspection route that meets the optimal conditions through genetic algorithm iteration.

[0082] In order to determine the optimal path, the target inspection path is obtained by optimizing through a genetic algorithm. In an optional implementation, the step S203 further includes:

[0083] Step S20306, a selection step, performing a selection operation based on the current population, and selecting a preset number of the chromosomes from the current population based on the fitness as parent individuals;

[0084] Specifically, in the current population, the system performs a selection operation based on the fitness value of each chromosome (i.e., patrol path). The higher the fitness value of a chromosome, the greater the probability of being selected as a parent individual. The selection operation usually adopts a roulette selection method or a tournament selection method. In the roulette selection method, the probability of each chromosome being selected is proportional to its fitness value; while in the tournament selection method, several chromosomes are randomly selected from the population for comparison, and the chromosome with the highest fitness value is selected as the parent individual. A preset number of chromosomes are selected as parent individuals to ensure the diversity of the population, while reducing the population size and improving the execution efficiency of the algorithm.

[0085] Step S20307, ​​a crossover step, performing a crossover operation based on the above-mentioned parent individuals to obtain child individuals;

[0086] Specifically, for the selected parent individuals, the system performs a crossover operation to generate new offspring individuals. The crossover operation can be a single-point crossover, a multi-point crossover, or a uniform crossover, and the specific method depends on the chromosome encoding method and the characteristics of the problem. The crossover operation generates two or more new offspring individuals by exchanging part of the chromosome information between the parent individuals. These new individuals contain the features inherited from the parent, but in a different order or combination, thereby exploring new solution spaces.

[0087] Step S20308, a mutation step, performing a mutation operation based on the above-mentioned parent individual and the above-mentioned child individual to obtain an updated population;

[0088] Specifically, mutation operations are performed on parent individuals and offspring individuals, introducing new changes by randomly changing certain genes in the chromosomes to improve the exploration ability of the population. The frequency and intensity of mutation operations need to be moderate to avoid premature convergence or falling into local optimality of the population, while promoting the search process for the global optimal solution.

[0089] Step S20309, a calculation step, calculating the fitness respectively based on all the chromosomes in the updated population;

[0090] Specifically, after the crossover and mutation operations are completed, the system recalculates the fitness values ​​of all chromosomes and evaluates the inspection route quality of the newly generated offspring individuals and the mutated individuals. Based on the newly calculated fitness values, the system combines the parent individuals and offspring individuals into an updated population. This process may involve the elimination of some individuals to ensure that the individuals with higher fitness are retained in the population.

[0091] Step S20310, repeating the selection step, the crossover step, the mutation step and the mutation step at least once in sequence until the deviation of the maximum fitness value in the updated population of two adjacent iterations is less than a preset ratio or the maximum number of iterations is reached.

[0092] Specifically, the system repeatedly performs the operations of selection, crossover, mutation, and updating the population, and recalculates the fitness values ​​of all chromosomes in the updated population after each iteration. The optimization process will continue until the stopping condition is met. The stopping conditions include: the deviation of the maximum fitness value in the updated population of two consecutive iterations is less than the preset ratio, which indicates that the algorithm has approached the optimal solution; or the maximum number of iterations is reached to prevent the algorithm from running indefinitely.

[0093] Through the specific implementation of the above steps, the genetic algorithm can effectively find the optimal inspection path that adapts to dynamic environmental changes, which not only improves the flexibility and efficiency of substation inspection operations, but also ensures the timely maintenance of key equipment and the stable operation of the power system. This process fully considers the real-time status, historical data and external environmental factors of the equipment, and realizes the intelligence and adaptability of inspection route planning through intelligent selection and optimization mechanisms.

[0094] In the above embodiment, as a preferred solution, the above cross operation is performed within a preset range centered on the above inspection point where the difference of the above dynamic weights in two adjacent iterations is greater than a second threshold.

[0095] Specifically, intelligent selection is guided by the change amplitude of dynamic weights. Dynamic weight difference analysis In each iteration, the system calculates the dynamic weights of all points to be inspected and compares them with the dynamic weights of the previous iteration. A large difference in the dynamic weights of a point indicates that the priority of the point has changed significantly in a short period of time, which may be caused by abnormal equipment status, the influence of external emergencies, or other important factors. Preset range determination The system sets a preset range, usually centered on the point with the largest dynamic weight change. The size of this range is determined by the second threshold. The second threshold is a pre-set parameter used to determine whether the dynamic weight change is sufficient to trigger an intelligent crossover operation. The setting of the preset range ensures that the crossover operation can be concentrated near the points with the largest weight changes, and these change information is used to optimize the inspection route. Intelligent selection of crossover points When the system detects that the dynamic weight difference of a point to be inspected is greater than the second threshold, it will preferentially perform the crossover operation within the preset range centered on the point. This selection mechanism can respond quickly to weight changes and generate offspring individuals that better meet the current priority requirements by exchanging information about related nodes on the chromosome.

[0096] The above implementation method improves the efficiency and adaptability of the algorithm. The crossover operation based on weight changes can more efficiently utilize information in substation inspection route planning, reduce unnecessary searches, speed up convergence, and maintain the diversity of the population to avoid the trap of local optimal solutions. However, the system should also consider using alternative solutions in some cases. For example, when the dynamic weight changes of all points are not sufficient to trigger intelligent crossover, random crossover based on fixed probability or crossover based on chromosome fitness can be used to maintain population diversity and avoid premature convergence of the algorithm.

[0097] In the above embodiment, as a preferred solution, the above variation operation is performed at the above inspection point corresponding to the above priority level less than the third threshold.

[0098] The third threshold is used to filter out inspection points with lower priority. After each dynamic weight update, the system recalculates the priority scores of all inspection points. The priority score takes into account both static and dynamic weights, reflecting the current urgency and importance of the equipment. Points with lower scores may be identified because the equipment is stable, maintenance requirements are not high, or they are less affected by external factors. Based on the priority scores of the inspection points, the system selects those points with scores lower than the third threshold as the targets of mutation operations. This selection mechanism ensures that the algorithm can give priority to optimizing those points with lower current priorities, improve their priority scores through mutation operations, or adjust their positions in the inspection route, so as to better adapt to changes in inspection needs.

[0099] In specific implementations, the algorithm may adopt one of the following mutation methods:

[0100] a. Point replacement: Replace a point with a lower priority with another point with a higher priority to improve the priority balance of the overall route.

[0101] b. Position adjustment: In the chromosome, move the points with lower priority to a more forward position and conduct inspections in advance to cope with possible changes in device status.

[0102] c. Attribute modification: By adjusting certain attributes of the point (such as the score of a factor in the dynamic weight), the priority of the point is indirectly improved, which may involve the prediction or rule adjustment of the machine learning model.

[0103] The existing technology usually adopts random mutation or mutation operation based on fixed probability, while the present invention optimizes the mutation operation by introducing priority threshold to guide mutation. It not only takes into account the dynamic state and external influence of the equipment, but also avoids the population degradation that may be caused by blind mutation by intelligently selecting mutation points, making the genetic algorithm more efficient and intelligent in solving the problem of substation inspection route planning.

[0104] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the route control method for substation inspection of the present application will be described in detail below in combination with specific embodiments.

[0105] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0106] The embodiment of the present application also provides a route control device for substation inspection. It should be noted that the route control device for substation inspection in the embodiment of the present application can be used to execute the route control method for substation inspection provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0107] The following is an introduction to the route control device for substation inspection provided in an embodiment of the present application.

[0108] Figure 3 1 is a structural block diagram of a route control device for substation inspection according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0109] The first acquisition unit 10 is used to acquire relevant data of each inspection point in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point;

[0110] Specifically, the environmental information, historical inspection information, basic equipment information, fault information, real-time operation status and other data related to the inspection points are obtained. The above-mentioned first target data is obtained.

[0111] In the specific implementation, the system collects the internal environmental information of the substation in real time through an integrated sensor network (including but not limited to temperature sensors, humidity sensors, vibration sensors, cameras, etc.), such as temperature, humidity, equipment vibration, etc. This information can reflect the environmental conditions in which the equipment is currently operating. Secondly, the historical inspection records are queried through the database, including the equipment's failure history, maintenance records, equipment operation time, etc. These data are used to assess the potential failure risk of the equipment. Furthermore, basic equipment information (such as equipment type, importance level, location, etc.) and current fault information (such as equipment status, fault alarm) are also collected to determine the importance and urgency of the equipment. Finally, the system also obtains the operating status of the substation equipment in real time, including key parameters such as voltage, current, and temperature, to ensure the real-time and accuracy of the data.

[0112] The first calculation unit 20 is used to determine the static weight of each of the above-mentioned points to be inspected based on the above-mentioned first target data, and at the same time adjust the dynamic weight of each of the above-mentioned points to be inspected in real time based on the above-mentioned first target data, the above-mentioned static weight is determined based on the above-mentioned historical inspection records, the above-mentioned real-time operation parameters and the above-mentioned equipment parameters, and the above-mentioned dynamic weight is based on the inspection waiting time of the above-mentioned points to be inspected, the inspection consumption time, the above-mentioned equipment operation parameters, the above-mentioned fault information and the external impact score, wherein the external impact score is determined by the above-mentioned points to be inspected before the inspection;

[0113] Specifically, the static weight and dynamic weight of the inspection point are determined based on the relevant data of the inspection point. The static weight is calculated based on the urgency and importance of the equipment; the dynamic weight is adjusted based on the waiting time, urgency, inspection time and external influencing factors, and is updated and calculated every time the inspection point changes.

[0114] In specific implementations, the calculation of static weights is based on the inherent properties and historical data of the equipment. For example, equipment A is assigned higher w1 (urgency weight) and w2 (equipment importance weight) because of its key functions and failure frequency, thus obtaining a higher static weight. The dynamic weight is more complex, and it will be adjusted according to the real-time situation of the inspection operation, such as the current waiting time, the real-time urgency of equipment A (based on fault information and real-time operating status), the estimated inspection time (taking into account the complexity of the equipment and the difficulty of inspection), and the impact of external emergencies on equipment A (through recurrent neural network RNN). After each inspection point is completed, the system will recalculate the dynamic weights of all uninspected points based on the latest information to ensure the optimality of route planning.

[0115] The second calculation unit 30 is used to generate a random path as an initial population based on each of the above-mentioned points to be inspected, construct a fitness function based on the above-mentioned static weights and the above-mentioned dynamic weights corresponding to each of the above-mentioned points to be inspected, and perform optimization through a genetic algorithm to obtain a target inspection path;

[0116] Specifically, a genetic algorithm is used to generate an optimal inspection route according to static weights and dynamic weights.

[0117] In the specific implementation, the process of the genetic algorithm includes initializing the population, selection, crossover and mutation operations, and evaluation of the fitness function. Specifically, the system first generates an initial population, and each individual represents a possible inspection path. The fitness function comprehensively considers the length of the path and the priority of each inspection point to evaluate the quality of the path. The selection operation is based on the result of the fitness function, so that the path with higher fitness has a greater chance of being selected for genetic operation. The crossover operation increases the intelligence and adaptability of the algorithm by intelligently selecting intersections and using the dynamic weight change range to guide the selection of intersections. The mutation operation selects the path with lower fitness for local adjustment under the new dynamic weight to accelerate the finding of the optimal path. Through multiple generations of genetic operations, it finally converges to an optimal inspection route.

[0118] The sending unit is used to send the above-mentioned target inspection path to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the above-mentioned operating personnel.

[0119] Specifically, during the inspection process, the inspection route is updated in real time according to the dynamic adjustment of external emergencies.

[0120] In the specific implementation, during the actual inspection process, the system collects the status information of each inspection point in real time through the terminal devices worn by the operators. If an external emergency occurs in the substation, such as power grid fluctuations, extreme weather conditions, etc., the recurrent neural network predicts the external impact of the emergency on the subsequent uninspected points. Based on the updated dynamic weights (new scores taking into account external impacts), the system recalculates the priorities of all uninspected points and uses genetic algorithms to quickly generate new optimal inspection routes. After the operator's terminal device receives the adjusted route, it immediately conducts inspections along the new route to ensure inspection efficiency and equipment safety under the influence of emergencies.

[0121] Through this embodiment, the first acquisition unit acquires the relevant data of each inspection point in the substation in real time to obtain the corresponding first target data, and the above-mentioned first target data includes the environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the above-mentioned inspection points; the first calculation unit determines the static weight of each of the above-mentioned inspection points based on the above-mentioned first target data, and at the same time adjusts the dynamic weight of each of the above-mentioned inspection points in real time based on the above-mentioned first target data, the above-mentioned static weight is determined based on the above-mentioned historical inspection records, the above-mentioned real-time operation parameters and the above-mentioned equipment parameters, and the above-mentioned dynamic weight is determined based on the above-mentioned The inspection waiting time, inspection consumption time, the above-mentioned equipment operating parameters, the above-mentioned fault information and external impact score of the inspection point to be inspected, wherein the external impact score is determined by the inspection point to be inspected before the inspection; the second calculation unit generates a random path as the initial population based on each of the above-mentioned inspection points, constructs a fitness function based on the above-mentioned static weights and the above-mentioned dynamic weights corresponding to each of the above-mentioned inspection points, and optimizes through a genetic algorithm to obtain a target inspection path; the sending unit sends the above-mentioned target inspection path to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the above-mentioned operating personnel. This application adjusts the dynamic weights of the inspection points in real time and combines genetic algorithms for optimal route planning. It not only takes into account the static importance of the equipment, but also flexibly responds to the real-time changes in the status of the equipment in the substation, ensures the optimization of the inspection route, reduces the impact of equipment failures on the operation of the power grid, improves the work efficiency of the operating personnel and the operation and maintenance management level of the substation, and solves the problem that in the prior art, when encountering emergencies or equipment problems during the inspection of the substation, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency.

[0122] In order to determine the static weight, in an optional implementation manner, the first calculation unit includes:

[0123] A first determination module is used to determine the historical failure rate of the above-mentioned points to be inspected based on the above-mentioned historical inspection records, and estimate the failure repair time of the above-mentioned points to be inspected based on the above-mentioned real-time operation parameters;

[0124] Specifically, based on historical inspection records, determine the historical failure rate of each inspection point. The historical failure rate refers to the ratio of the number of failures at the point (equipment) to the total number of inspections in the past certain period of time (such as one year or half a year), which can reflect the reliability of the equipment. The fault repair time of the inspection point is estimated through real-time operating parameters (such as the operating temperature, voltage, current, etc. of the equipment). The estimation method can combine the equipment type, historical maintenance records and current fault conditions, and use statistical models or machine learning algorithms for prediction.

[0125] A first calculation module is used to score based on the above historical failure rate and the above failure repair time, and perform weighted calculation based on the scoring result to obtain the urgency level;

[0126] Specifically, the historical failure rate and fault repair time are used as the core indicators of the urgency score. The higher the historical failure rate, the more likely the equipment will fail again in the short term, and the higher the score; the longer the fault repair time, the greater the impact of the equipment failure, and the higher the score.

[0127] In the specific implementation, a weighted calculation is performed to obtain an emergency score. The weighting coefficient can be adjusted according to the type of equipment, importance, and potential impact on the stability of the power system to ensure that the score can accurately reflect the emergency level of the equipment. For example, emergency level = w3*historical failure rate + w4*fault repair time, where w3 and w4 are the weight parameters of the historical failure rate and fault repair time, respectively.

[0128] A second determination module is used to determine the fault impact range, fault impact degree and equipment availability of the above-mentioned points to be inspected based on the above-mentioned equipment parameters, determine the equipment operation record of the above-mentioned points to be inspected according to the above-mentioned equipment operation parameters, and obtain the equipment cost of the above-mentioned points to be inspected;

[0129] Specifically, determine the fault impact range, fault impact degree, and equipment availability of the points to be inspected based on the equipment parameters. The fault impact range refers to the range of other devices or areas that may be affected when the equipment fails; the fault impact degree reflects the impact of the fault on the system operation; equipment availability refers to the probability that the equipment can operate normally within the prediction period. Determine the equipment operation record based on the equipment operation parameters. The operation record includes the equipment's operating time, maintenance records, failure frequency, etc. This information can reflect the equipment's operating status and maintenance requirements. Obtain the equipment cost of the points to be inspected. The equipment cost includes the purchase cost and maintenance cost. The higher the cost, the more important the equipment is.

[0130] A second calculation module is used to score based on the fault impact range, the fault impact degree, the equipment availability, the equipment operation record and the equipment cost, and perform weighted calculation based on the scoring result to obtain the equipment importance;

[0131] Specifically, the above indicators are combined for weighted calculation to obtain the score of equipment importance. For example, equipment importance = w5*fault impact range + w6*fault impact degree + w7*equipment availability + w8*equipment cost, where w5, w6, w7 and w8 are weight parameters of fault impact range, fault impact degree, equipment availability and equipment cost respectively.

[0132] The third calculation module is used to perform weighted calculation based on the above urgency and the above equipment importance to obtain the above static weight.

[0133] Specifically, the urgency score and the equipment importance score are weighted to obtain a static weight. For example, static weight = w1*urgency+w2*equipment importance, where w1 and w2 are weight parameters of urgency and equipment importance, which can be adjusted according to specific circumstances.

[0134] In order to obtain the above dynamic weight, in an optional implementation manner, the above first calculation unit further includes:

[0135] The third determination module is used to determine the estimated time it takes for the operator to reach the inspection point from the current position based on the current inspection route, and obtain the inspection waiting time;

[0136] Specifically, based on the current location of the operator and the estimated travel speed, the system calculates the estimated time it will take to reach each inspection point from the current location, i.e. the inspection waiting time. This calculation takes into account the layout of the substation and the shortest path for the operator to move to each equipment point, ensuring the accuracy of the time estimate.

[0137] A fourth determination module is used to determine the average maintenance time of the above-mentioned inspection points based on the above-mentioned historical inspection records to obtain the above-mentioned inspection consumption time;

[0138] Specifically, based on historical inspection records, the system analyzes and determines the average inspection time for each inspection point, that is, the inspection time. This average value reflects the time required for equipment inspection, maintenance or minor repairs that may be encountered during actual inspections.

[0139] A fifth determination module, used to determine the damage probability of the to-be-inspected point based on the above equipment operation parameters, and determine the urgency of the above to-be-inspected point according to the above damage probability;

[0140] Specifically, based on the real-time equipment operating parameters, the system estimates the probability of damage at each inspection point, that is, the possibility of equipment failure in the current or future period. Based on the probability of damage, the system further determines the urgency score of each point, and a high probability of damage will result in a high urgency score. The calculation of urgency also needs to be combined with the importance of the equipment and the scope of the failure to comprehensively evaluate the potential consequences of equipment failure.

[0141] A sixth determination module is used to determine the point in the current inspection route that has changed before the point to be inspected and in the previous inspection route, obtain the target point, and determine the external impact score based on the emergency event corresponding to the target point;

[0142] Specifically, the system identifies the points in the current inspection route that have changed from the previous inspection route to the points to be inspected, i.e., the target points. For each target point, the external impact score is calculated by analyzing the impact of the emergency events related to it. The external impact score reflects the impact of the current emergency event on the uninspected equipment, including but not limited to the impact of power grid fluctuations and severe weather.

[0143] The fourth calculation module is used to score based on the above inspection waiting time, the above inspection consumption time and the above urgency, and perform weighted calculation based on the scoring result and the above external influence score to obtain the above dynamic weight.

[0144] Specifically, the inspection waiting time, inspection consumption time, urgency score and external impact score are quantified and scored respectively, and the scoring criteria can be set based on historical data and expert experience. According to the scoring results of each factor, a weighted formula is used to calculate and obtain the dynamic weight of the inspection point. The dynamic weight formula is: dynamic weight = w9 inspection waiting time + w10 inspection consumption time + w11 urgency score + w12 external impact score, where w9, w10, w11 and w12 are the weight parameters of the inspection waiting time, inspection consumption time, urgency score and external impact score respectively.

[0145] In the above embodiment, the real-time update of dynamic weights will affect the priority sorting of the points to be inspected in the genetic algorithm and the calculation of the fitness function. The system combines dynamic weights with static weights, re-evaluates the fitness of all inspection routes, and selects and generates a better inspection route.

[0146] In order to construct the initial population and the fitness function, in an optional implementation, the second calculation unit includes:

[0147] A seventh determination module is used to randomly encode each of the above-mentioned points to be inspected in the above-mentioned substation to obtain multiple coding sequences, and determine each of the above-mentioned coding sequences as a chromosome, wherein the above-mentioned coding sequence includes the codes corresponding to all the above-mentioned points to be inspected in the above-mentioned substation;

[0148] Specifically, random path generation and encoding,First, the system generates multiple coding sequences containing all the inspection points through randomization.,Each coding sequence represents a possible inspection route, and the position order in the sequence reflects the order of,the inspection points to be visited. These coding sequences are then converted into chromosomes.

[0149] An eighth determination module is used to use each of the above chromosomes as a candidate inspection path to obtain an initial population;

[0150] Specifically, an initial population is formed based on the above chromosomes. Each chromosome in the population represents an inspection route plan, which contains information about all points to be inspected in the substation.

[0151] A fifth calculation module is used to substitute the static weight and the dynamic weight into the first preset formula p(v i )=w 静态 (v i )*w 动态 ( i v), get the priority of the above inspection points, w 静态 (v i ) is the above static weight of node vi, w 动态 (v i ) is the dynamic weight of node vi at iteration number t, w 动态 (v i )satisfy where w 动态 (v i , 0) is the initial dynamic weight of node vi, t is the current iteration number, and tmax is the maximum iteration number;

[0152] Specifically, the integration system of static weight and dynamic weight integrates the static weight and dynamic weight of each inspection point into the priority calculation. The dynamic weight changes with the real-time status of the inspection point, reflecting the current urgency and maintenance needs of the point. The calculation of the dynamic weight takes into account the influence of the number of iterations t. As t increases, the dynamic weight wdynamic(vi,t) gradually approaches the static weight wstatic(vi), ensuring that in the later stage of the iteration process, the influence of the static weight on the priority gradually increases, which helps to stabilize the search for the optimal path.

[0153] The sixth calculation module is used to substitute the priority penalty into the second preset formula based on the priority Get the priority penalty, where x is chromosome x, v i is the i-th node in the path corresponding to chromosome x, and n is the total number of nodes included in chromosome x;

[0154] Specifically, for each chromosome x in the population, the system calculates the priority p(vi), which combines the static weight and the dynamic weight, reflecting the comprehensive priority of the inspection point vi in ​​the current iteration. The priority penalty P(x) is calculated based on the priority order of all nodes in chromosome x. If a node with a higher priority in path x is placed behind a node with a lower priority, the penalty term will increase to reflect the non-ideal nature of path planning.

[0155] Building module for building the above fitness function based on chromosomes and the above priority penalty:

[0156]

[0157] Among them, the path length (x) is the total length of the inspection path corresponding to the chromosome x, ε is a positive number less than the first threshold, and α is the weight coefficient.

[0158] Specifically, based on the path length and priority penalty, the system constructs a fitness function F(x). The path length (x) is the total length of the inspection path corresponding to chromosome x, which reflects the physical distance of the inspection route. The design of the fitness function F(x) ensures a balance between path length and priority penalty. α is a weight coefficient, which is used to adjust the relative importance of path length and priority penalty, and can be adjusted according to actual needs. ∈ is a very small positive number, which is used to avoid division by zero errors in the division operation in the fitness function when the priority penalty term is zero. This small value ensures that even if the path priority is extremely good, the fitness function will not be infinite, but will remain within a reasonable range.

[0159] In the above embodiment, a method for constructing a genetic algorithm fitness function that comprehensively considers static weights and dynamic weights is provided, which effectively balances the relationship between the length of the inspection route and the priority of the points, and ensures the efficiency and flexibility of the inspection operation. When dealing with the problem of substation inspection route planning, this method can dynamically adjust the priority according to the real-time status of the equipment and changes in the external environment, and quickly find the inspection route that meets the optimal conditions through genetic algorithm iteration.

[0160] In order to determine the optimal path, the target inspection path is obtained by optimizing through a genetic algorithm. In an optional implementation, the second calculation unit further includes:

[0161] A selection module is used to perform a selection step, perform a selection operation based on the current population, and select a preset number of the chromosomes from the current population based on the fitness as parent individuals;

[0162] Specifically, in the current population, the system performs a selection operation based on the fitness value of each chromosome (i.e., patrol path). The higher the fitness value of a chromosome, the greater the probability of being selected as a parent individual. The selection operation usually adopts a roulette selection method or a tournament selection method. In the roulette selection method, the probability of each chromosome being selected is proportional to its fitness value; while in the tournament selection method, several chromosomes are randomly selected from the population for comparison, and the chromosome with the highest fitness value is selected as the parent individual. A preset number of chromosomes are selected as parent individuals to ensure the diversity of the population, while reducing the population size and improving the execution efficiency of the algorithm.

[0163] The first processing module is used to perform a crossover step, performing a crossover operation based on the above-mentioned parent individuals to obtain offspring individuals;

[0164] Specifically, for the selected parent individuals, the system performs a crossover operation to generate new offspring individuals. The crossover operation can be a single-point crossover, a multi-point crossover, or a uniform crossover, and the specific method depends on the chromosome encoding method and the characteristics of the problem. The crossover operation generates two or more new offspring individuals by exchanging part of the chromosome information between the parent individuals. These new individuals contain the features inherited from the parent, but in a different order or combination, thereby exploring new solution spaces.

[0165] The second processing module is used to execute the mutation step, performing a mutation operation based on the above-mentioned parent individuals and the above-mentioned child individuals to obtain an updated population;

[0166] Specifically, mutation operations are performed on parent individuals and offspring individuals, introducing new changes by randomly changing certain genes in the chromosomes to improve the exploration ability of the population. The frequency and intensity of mutation operations need to be moderate to avoid premature convergence or falling into local optimality of the population, while promoting the search process for the global optimal solution.

[0167] A seventh calculation module, used for executing a calculation step, and calculating the fitness respectively based on all the chromosomes in the updated population;

[0168] Specifically, after the crossover and mutation operations are completed, the system recalculates the fitness values ​​of all chromosomes and evaluates the inspection route quality of the newly generated offspring individuals and the mutated individuals. Based on the newly calculated fitness values, the system combines the parent individuals and offspring individuals into an updated population. This process may involve the elimination of some individuals to ensure that the individuals with higher fitness are retained in the population.

[0169] A repetition module is used to sequentially repeat the selection step, the crossover step, the mutation step and the mutation step at least once until the deviation of the maximum fitness value in the updated population of two adjacent iterations is less than a preset ratio or the maximum number of iterations is reached.

[0170] Specifically, the system repeatedly performs the operations of selection, crossover, mutation, and updating the population, and recalculates the fitness values ​​of all chromosomes in the updated population after each iteration. The optimization process will continue until the stopping condition is met. The stopping conditions include: the deviation of the maximum fitness value in the updated population of two consecutive iterations is less than the preset ratio, which indicates that the algorithm has approached the optimal solution; or the maximum number of iterations is reached to prevent the algorithm from running indefinitely.

[0171] Through the specific implementation of the above steps, the genetic algorithm can effectively find the optimal inspection path that adapts to dynamic environmental changes, which not only improves the flexibility and efficiency of substation inspection operations, but also ensures the timely maintenance of key equipment and the stable operation of the power system. This process fully considers the real-time status, historical data and external environmental factors of the equipment, and realizes the intelligence and adaptability of inspection route planning through intelligent selection and optimization mechanisms.

[0172] The route control device for substation inspection includes a processor and a memory. The first acquisition unit, the first calculation unit, the second calculation unit and the sending unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0173] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the communication efficiency can be improved by adjusting the kernel parameters.

[0174] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0175] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the route control method for substation inspection.

[0176] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the route control method for substation inspection is executed when the program is run.

[0177] An embodiment of the present invention provides a communication system, the communication system includes a primary communication domain, a secondary communication domain processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the steps of the route control method for substation inspection are implemented:

[0178] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the steps of the route control method for substation inspection described above.

[0179] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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 A step that specifies a function in one or more boxes.

[0184] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0185] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0186] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0187] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0188] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0189] 1) The route control method for substation inspection of the present application first obtains relevant data of each inspection point in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point; then, the static weight of each inspection point is determined based on the first target data, and the dynamic weight of each inspection point is adjusted in real time based on the first target data, wherein the static weight is determined based on the historical inspection records, the real-time operation parameters and the equipment parameters, and the dynamic weight is adjusted based on the first target data. The weight is based on the inspection waiting time, inspection consumption time, equipment operation parameters, fault information and external impact score of the above-mentioned inspection points, wherein the external impact score is determined by the inspection points before the inspection; then, a random path is generated based on each of the above-mentioned inspection points as the initial population, and a fitness function is constructed based on the static weight and dynamic weight corresponding to each of the above-mentioned inspection points, and the target inspection path is obtained by optimization through genetic algorithm; finally, the target inspection path is sent to the terminal device carried by the inspection personnel to realize the real-time update of the inspection route of the above-mentioned operators. This application adjusts the dynamic weight of the inspection points in real time and combines the genetic algorithm for optimal route planning. It not only takes into account the static importance of the equipment, but also flexibly responds to the real-time changes in the status of the equipment in the substation, ensures the optimization of the inspection route, reduces the impact of equipment failures on the operation of the power grid, improves the work efficiency of the operators and the operation and maintenance management level of the substation, and solves the problem that in the prior art, when encountering emergencies or equipment problems during the inspection of the substation, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency.

[0190] 2) In the route control device for substation inspection of the present application, the first acquisition unit acquires the relevant data of each inspection point in the substation in real time to obtain the corresponding first target data, and the first target data includes the environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point; the first calculation unit determines the static weight of each inspection point based on the first target data, and adjusts the dynamic weight of each inspection point in real time based on the first target data, the static weight is determined based on the historical inspection records, the real-time operation parameters and the equipment parameters, and the dynamic weight is determined based on the historical inspection records, the real-time operation parameters and the equipment parameters. The static weight is based on the inspection waiting time, inspection consumption time, equipment operation parameters, fault information and external impact score of the above-mentioned inspection points, wherein the external impact score is determined by the inspection points before the inspection; the second calculation unit generates a random path as the initial population based on each of the above-mentioned inspection points, and constructs a fitness function based on the static weight and dynamic weight corresponding to each of the above-mentioned inspection points, and optimizes through a genetic algorithm to obtain a target inspection path; the sending unit sends the target inspection path to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the above-mentioned operators. This application adjusts the dynamic weight of the inspection points in real time and combines the genetic algorithm for optimal route planning. It not only takes into account the static importance of the equipment, but also flexibly responds to the real-time changes in the status of the equipment in the substation, ensures the optimization of the inspection route, reduces the impact of equipment failures on the operation of the power grid, improves the work efficiency of the operators and the operation and maintenance management level of the substation, and solves the problem that in the prior art, when encountering emergencies or equipment problems during the inspection of the substation, the inspection route cannot be effectively adjusted, resulting in low inspection efficiency.

[0191] 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.

Claims

1. A route control method for substation inspection, characterized in that: include: Acquire relevant data of each inspection point in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point; Determine the static weight of each of the points to be inspected based on the first target data, and adjust the dynamic weight of each of the points to be inspected in real time based on the first target data, wherein the static weight is determined based on the historical inspection records, the real-time operating parameters and the equipment parameters, and the dynamic weight is based on the inspection waiting time, inspection consumption time, equipment operating parameters, fault information and external impact score of the points to be inspected, wherein the external impact score is determined by the points to be inspected before the inspection; Generate a random path as the initial population based on each of the points to be inspected, construct a fitness function based on the static weight and the dynamic weight corresponding to each of the points to be inspected, and perform optimization through a genetic algorithm to obtain a target inspection path; The target inspection path is sent to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the operating personnel.

2. The method according to claim 1, characterized in that Determining the static weight of each of the points to be inspected based on the first target data includes: Determine the historical failure rate of the point to be inspected based on the historical inspection records, and estimate the fault repair time of the point to be inspected based on the real-time operation parameters; Scoring is performed based on the historical failure rate and the failure repair time, and weighted calculation is performed based on the scoring result to obtain the degree of urgency; Determine the fault impact range, fault impact degree and equipment availability of the point to be inspected based on the equipment parameters, determine the equipment operation record of the point to be inspected according to the equipment operation parameters, and obtain the equipment cost of the point to be inspected; Scoring is performed based on the fault impact scope, the fault impact degree, the equipment availability, the equipment operation record and the equipment cost, and weighted calculation is performed based on the scoring result to obtain the equipment importance; weighted calculation is performed based on the urgency and the equipment importance to obtain the static weight.

3. The method according to claim 1, characterized in that Adjusting the dynamic weight of each of the points to be inspected in real time based on the first target data includes: Determine the estimated time it takes for the operator to reach the inspection point from the current position based on the current inspection route, and obtain the inspection waiting time; Determine the average maintenance time of the inspection point based on the historical inspection records to obtain the inspection time consumption; Determine the damage probability of the point to be inspected based on the equipment operation parameters, and determine the urgency of the point to be inspected according to the damage probability; Determine a point in the current inspection route that has changed between the point to be inspected and the point in the previous inspection route, obtain a target point, and determine the external impact score based on the emergency event corresponding to the target point; Scoring is performed based on the inspection waiting time, the inspection consumption time and the urgency respectively, and weighted calculation is performed according to the scoring result and the external influence score to obtain the dynamic weight.

4. The method according to claim 1, characterized in that: Generating a random path as an initial population based on each of the points to be inspected, and constructing a fitness function based on the static weight and the dynamic weight corresponding to each of the points to be inspected, including: Randomly encode each of the points to be inspected in the substation to obtain a plurality of encoding sequences, and determine each of the encoding sequences as a chromosome, wherein the encoding sequence includes the codes corresponding to all of the points to be inspected in the substation; Taking each of the chromosomes as an alternative inspection path, an initial population is obtained; Based on the static weight and the dynamic weight, a first preset formula p(v i )=w 静态 (v i )*w 动态 (v i ), get the priority of the inspection point, w 静态 (v i ) is the static weight of node vi, w 动态 (v i ) is the node v i Dynamic weight at iteration t, w 动态 (v i )satisfy where w 动态 (v i , 0) is the node v i The initial dynamic weight, t is the current iteration number, t max is the maximum number of iterations; Substitute the second preset formula priority penalty based on priority Get the priority penalty, where x is chromosome x, v i is the i-th node in the path corresponding to chromosome x, and n is the total number of nodes included in chromosome x; The fitness function is constructed based on the chromosome and the priority penalty: Among them, the path length (x) is the total length of the inspection path corresponding to the chromosome x, ε is a positive number less than the first threshold, and α is the weight coefficient.

5. The method according to claim 4, characterized in that The target inspection path is obtained by optimizing through genetic algorithm, including: A selection step, performing a selection operation based on the current population, and selecting a preset number of chromosomes from the current population based on the fitness as parent individuals; A crossover step, performing a crossover operation based on the parent individuals to obtain offspring individuals; A mutation step, performing a mutation operation based on the parent individual and the child individual to obtain an updated population; A calculation step, calculating the fitness respectively based on all the chromosomes in the update population; The selection step, the crossover step, the mutation step and the mutation step are sequentially repeated at least once until the deviation of the maximum fitness value in the update population of two adjacent iterations is less than a preset ratio or the maximum number of iterations is reached.

6. The method according to claim 5, characterized in that The crossover operation is performed within a preset range centered on the point to be inspected where the dynamic weight difference between two adjacent iterations is greater than a second threshold.

7. The method according to claim 5, characterized in that The variation operation is performed at the to-be-inspected point corresponding to the priority level being less than a third threshold.

8. A route control device for substation inspection, characterized in that: The device comprises: A first acquisition unit is used to acquire relevant data of each inspection point in the substation in real time to obtain corresponding first target data, wherein the first target data includes environmental parameters, historical inspection records, equipment parameters, fault information and real-time operation parameters of the inspection point; A first calculation unit is used to determine the static weight of each of the points to be inspected based on the first target data, and to adjust the dynamic weight of each of the points to be inspected in real time based on the first target data, wherein the static weight is determined based on the historical inspection records, the real-time operation parameters and the equipment parameters, and the dynamic weight is based on the inspection waiting time, inspection consumption time, equipment operation parameters, fault information and external impact score of the points to be inspected, wherein the external impact score is determined by the points to be inspected before the inspection; A second calculation unit is used to generate a random path as an initial population based on each of the points to be inspected, construct a fitness function based on the static weight and the dynamic weight corresponding to each of the points to be inspected, and perform optimization through a genetic algorithm to obtain a target inspection path; The sending unit is used to send the target inspection path to the terminal device carried by the inspection personnel to achieve real-time update of the inspection route of the operating personnel.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A patrol inspection control system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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