A planning method for power distribution line inspection

Abstract

This invention relates to the field of inspection planning technology, and discloses a planning method for power distribution line inspection. The method includes: dividing the overall inspection route of the power distribution line into multiple inspection sub-routes; performing cluster analysis on the inspection sub-routes to obtain similar inspection units of the overall inspection route; acquiring historical environmental factor sampling data of the similar inspection units; dividing the sampling stages of the similar inspection units to obtain environmental fluctuation stages and environmental stability stages of the similar inspection units; performing high-frequency dynamic sampling on the environmental fluctuation stages to obtain a first set of unknown environmental factors for the similar inspection units; and performing mixed dynamic sampling on the environmental stability stages. This invention improves the accuracy of power distribution line inspection planning.

Description

Technical Field

[0001] This invention relates to the field of inspection planning technology, and in particular to a planning method for power distribution line inspection. Background Technology

[0002] In the field of power distribution line inspection planning, with the continuous expansion of power system scale and the advancement of intelligent development, formulating reasonable planning schemes is becoming increasingly important in inspection. Existing technologies for power distribution line inspection planning have the following shortcomings:

[0003] First, in traditional sampling processes, the sampling frequency is often fixed. Fixed-frequency sampling is ill-suited to dynamic environmental changes, especially during periods of environmental fluctuation. When unknown environmental factors appear at short intervals, a fixed sampling frequency may miss these factors, making them difficult to sample. In stable environmental phases, the low-frequency sampling mode further reduces the probability of detecting unknown environmental factors. Existing technologies lack mechanisms to adjust sampling strategies in real-time based on changes in environmental factors. When encountering unknown environmental factors, it's difficult to promptly increase the sampling frequency to obtain more data, leading to missing sampling of unknown environmental factors. This makes the generated inspection plan difficult to meet actual needs, reducing the accuracy of power distribution line inspection planning. Furthermore, existing methods typically rely on experience-based judgment regarding personnel, tool allocation, and inspection cycle determination, lacking scientific predictive models. This makes it difficult to accurately estimate the required number of inspection personnel, types and quantities of tools, and a reasonable inspection cycle when facing different environmental characteristics and fluctuating environmental factors. This results in insufficient resource allocation, failing to meet actual inspection needs, leading to inadequate inspection work and further reducing the accuracy of power distribution line inspection planning. Summary of the Invention

[0004] This invention provides a planning method for power distribution line inspection, the main purpose of which is to solve the problem of low accuracy in the existing technology for planning power distribution line inspection.

[0005] To achieve the above objectives, the present invention provides a planning method for power distribution line inspection, comprising:

[0006] S1. Divide the main inspection route of the power distribution line into multiple inspection sub-routes, perform cluster analysis on the inspection sub-routes, and obtain the same type of inspection units of the main inspection route.

[0007] S2. Obtain historical environmental factor sampling data of the same type of inspection unit, divide the sampling stage of the same type of inspection unit, and obtain the environmental fluctuation stage and environmental stability stage of the same type of inspection unit, wherein the historical environmental factors include historical fluctuation environmental factors and historical stable environmental factors.

[0008] S3. Perform high-frequency dynamic sampling during the environmental fluctuation phase to obtain the first set of unknown environmental factors for the same type of inspection unit. Perform mixed dynamic sampling during the environmental stability phase, which includes low-frequency sampling and high-frequency sampling. When an unknown environmental factor that does not belong to the historical fluctuation environmental factors is sampled during the environmental stability phase, switch the low-frequency sampling to the high-frequency sampling to obtain the second set of unknown environmental factors for the same type of inspection unit. Generate the real-time fluctuation environmental factor set for the same type of inspection unit based on the first set of unknown environmental factors and the second set of unknown environmental factors.

[0009] S4. Based on the real-time fluctuation environmental factor set, the pre-acquired environmental data, and the preset prediction model, generate the inspection parameters of the same type of inspection unit;

[0010] S5. Based on the inspection parameters, formulate an inspection plan for the same type of inspection unit, and inspect the line equipment of the same type of inspection unit based on the inspection plan.

[0011] Optionally, the step of dividing the sampling phase of the same type of inspection units to obtain the environmental fluctuation phase and the environmental stability phase of the same type of inspection units includes:

[0012] The historical environmental factor sampling data is divided into stages according to a fixed time window to obtain multiple detection stages for the same type of inspection unit;

[0013] The total number of historical fluctuation environmental factors during the detection phase is counted one by one:

[0014] When the total number of factors exceeds a preset factor threshold, the detection phase is defined as the environmental fluctuation phase.

[0015] When the total number of factors does not exceed the preset factor threshold, the detection phase is determined as the environmental stability phase.

[0016] Optionally, the high-frequency dynamic sampling of the environmental fluctuation phase to obtain the first set of unknown environmental factors for the same type of inspection unit includes:

[0017] During the high-frequency dynamic sampling process in the environmental fluctuation phase, when the unknown environmental factor is sampled, the first sampling frequency of the high-frequency dynamic sampling is increased until the unknown environmental factor can no longer be sampled. When a known environmental factor belonging to the historical fluctuation environmental factors is sampled, the first sampling frequency is kept unchanged.

[0018] High-frequency dynamic sampling is performed during the environmental fluctuation phase to obtain the first unknown environmental factor of the same type of inspection unit;

[0019] The first unknown environmental factors are aggregated into a set of first unknown environmental factors for the same type of inspection unit.

[0020] Optionally, hybrid dynamic sampling is performed during the stable environmental phase to obtain a second set of unknown environmental factors for the same type of inspection units, including:

[0021] The low-frequency sampling is performed during the environmental stabilization phase.

[0022] During the low-frequency sampling process in the environmental stabilization phase, when the unknown environmental factor is sampled, the high-frequency sampling is performed in the environmental stabilization phase until the unknown environmental factor is not detected within a preset time period, and then the low-frequency sampling is performed in the environmental stabilization phase.

[0023] Hybrid dynamic sampling is performed during the stable environmental phase to obtain a second unknown environmental factor for the same type of inspection unit;

[0024] The second unknown environmental factor is aggregated into a set of second unknown environmental factors for the same type of inspection unit.

[0025] Optionally, generating the real-time fluctuation environmental factor set of the same type of inspection unit based on the first set of unknown environmental factors and the second set of unknown environmental factors includes:

[0026] The first set of unknown environmental factors and the second set of unknown environmental factors are combined to obtain the real-time fluctuating environmental factors of the same type of inspection unit.

[0027] The real-time fluctuation environmental factors are aggregated into a set of real-time fluctuation environmental factors for the same type of inspection unit.

[0028] Optionally, the inspection parameters include inspection cycle, number of personnel, and tool type, and the prediction model includes an inspection cycle prediction algorithm, a number of personnel prediction algorithm, and a tool type prediction algorithm.

[0029] Optionally, the calculation formula for the inspection cycle prediction algorithm is as follows:

[0030]

[0031] In the formula, This indicates the inspection cycle. , and This represents the adjustment coefficient. The set of real-time fluctuating environmental factors represents the first... Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors The first environmental data represents the An environmental characteristic, Indicates the first The weight of each environmental feature, This represents the maximum value of the inspection cycle. Represents the sigmoid activation function. Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time.

[0032] Optionally, the calculation formula for the personnel number prediction algorithm is as follows:

[0033]

[0034] In the formula, This indicates the number of people. Indicates the first Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first The weight of each environmental feature, Indicates the basic number of personnel. Indicates the first The adjustment coefficient of a real-time fluctuating environmental factor. Indicates the first The adjustment coefficient for each environmental characteristic, Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Represents the hyperbolic tangent function. Indicates from Multiplication begins when the value is 1.

[0035] Optionally, the calculation formula for the tool type prediction algorithm is as follows:

[0036]

[0037] In the formula, Indicates the first in the tool library A tool, Indicates the first The probability of using each tool This indicates the number of tools in the tool library. Indicates the first Real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first Real-time fluctuating environmental factors The influence coefficient of each tool Indicates the first Environmental characteristics The influence coefficient of each tool Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Indicates the tool's identifier. This represents the hyperbolic tangent function.

[0038] Optionally, the inspection planning scheme includes:

[0039] Based on the number of personnel, personnel are allocated to the same type of inspection units;

[0040] Based on the tool type, allocate tools to the same type of inspection units;

[0041] Mark the line equipment of the aforementioned similar inspection units;

[0042] The inspection duration of the line equipment is determined based on the inspection cycle.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This invention divides the sampling stages of similar inspection units into environmental fluctuation stages and environmental stability stages by statistically analyzing the total number of historical fluctuating environmental factors in each detection phase. It clarifies the different characteristics of the environmental fluctuation stage and the environmental stability stage. When the total number of factors in the environmental fluctuation stage exceeds a preset threshold, high-frequency dynamic sampling is required to capture rapidly changing environmental factors, promptly identify unknown environmental factors, and avoid missing sampling opportunities due to fixed-frequency sampling. When the total number of factors in the environmental stability stage does not exceed the threshold, hybrid dynamic sampling is adopted. Hybrid dynamic sampling mainly uses low-frequency sampling, while switching to high-frequency sampling when encountering unknown environmental factors. This balances sampling efficiency and resource utilization, providing a basis for generating a real-time fluctuating environmental factor set. As a result, when generating inspection parameters, the impact of environmental factors can be considered more accurately, making the inspection planning scheme more in line with actual needs and improving the accuracy of inspection planning for power distribution lines.

[0045] 2. This invention constructs a prediction model comprising three algorithms: an inspection cycle prediction algorithm, a personnel quantity prediction algorithm, and a tool type prediction algorithm. The inspection cycle prediction algorithm comprehensively considers real-time fluctuating environmental factors, environmental characteristics in the environmental data, and the maximum inspection cycle value, avoiding the subjectivity of relying solely on experience to determine the inspection cycle. It fully considers the dynamic changes of environmental factors, providing a scientific basis for determining a reasonable inspection cycle. The personnel quantity prediction algorithm, based on the basic personnel quantity and combined with real-time fluctuating environmental factors and environmental characteristics, accurately estimates the required personnel quantity according to the environmental conditions of different similar inspection units, changing the previous unreasonable situation of allocating personnel solely based on experience. The tool type prediction algorithm comprehensively considers the impact of real-time fluctuating environmental factors and environmental characteristics in the environmental data on the probability of tool use, ensuring that the types and quantities of tools provided meet actual inspection needs, thus improving the accuracy of power distribution line inspection planning. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a planning method for power distribution line inspection according to an embodiment of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] This application provides a planning method for power distribution line inspection. The execution entity of the power distribution line inspection planning method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the power distribution line inspection planning method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0050] Reference Figure 1 The diagram shown is a flowchart illustrating a planning method for power distribution line inspection according to an embodiment of the present invention. In this embodiment, the planning method for power distribution line inspection includes:

[0051] S1. Divide the main inspection route of the power distribution line into multiple inspection sub-routes, perform cluster analysis on the inspection sub-routes, and obtain the same type of inspection units of the main inspection route.

[0052] In detail, basic information about the power distribution lines is collected, including the geographical coordinates of the power distribution lines, the distribution of equipment (such as the location and quantity of transformers, circuit breakers, insulators, etc.), and historical inspection records (including the inspection time of each section of the power distribution line, the types of problems found, etc.). The collected data is cleaned to remove duplicate, erroneous or incomplete data, and the geographical coordinates and other data are converted into a format suitable for analysis, laying the foundation for subsequent steps.

[0053] In detail, based on factors such as the route of the power distribution lines, geographical environment, and equipment distribution density, the main inspection route is divided into multiple relatively independent inspection sub-routes of reasonable length. For example, it can be divided by region, with power distribution lines in different areas of the city being divided into different inspection sub-routes; or based on the complexity of the power distribution lines, complex lines can be broken down into multiple simple sub-routes, ensuring a balanced workload for each sub-route and ease of operation. Next, key features for cluster analysis are selected, such as the length of the inspection sub-routes, the type and number of equipment passed through, equipment failure rate, and inspection frequency. These features will serve as the basis for measuring the similarity of the inspection sub-routes.

[0054] In detail, the specific steps for using the K-means clustering algorithm to perform cluster analysis on the inspection sub-routes and obtain the same type of inspection units for the overall inspection route are as follows: Determine the number of clusters N, randomly select N inspection sub-routes as initial cluster centers, use iterative calculation to assign each inspection sub-routes to the nearest cluster center based on Euclidean distance, then recalculate the mean of the cluster centers and update the center point positions, repeat the cycle until the cluster centers stabilize and no longer change, and finally output N categories of the same type of inspection units with similar characteristics.

[0055] S2. Obtain historical environmental factor sampling data of the same type of inspection unit, divide the sampling stage of the same type of inspection unit, and obtain the environmental fluctuation stage and environmental stability stage of the same type of inspection unit, wherein the historical environmental factors include historical fluctuation environmental factors and historical stable environmental factors.

[0056] In this embodiment of the invention, the step of dividing the sampling phase of the same type of inspection unit into an environmental fluctuation phase and an environmental stability phase for the same type of inspection unit includes:

[0057] The historical environmental factor sampling data is divided into stages according to a fixed time window to obtain multiple detection stages for the same type of inspection unit;

[0058] The total number of historical fluctuation environmental factors during the detection phase is counted one by one:

[0059] When the total number of factors exceeds a preset factor threshold, the detection phase is defined as the environmental fluctuation phase.

[0060] When the total number of factors does not exceed the preset factor threshold, the detection phase is determined as the environmental stability phase.

[0061] In detail, the sampling phase is the time period for data collection in the time dimension, and it is the basic time unit for subsequent analysis of environmental conditions. "Dividing the phase according to a fixed time window" means that when processing historical environmental factor sampling data, a fixed time interval (such as 1 hour, this duration is the fixed time window) is selected. Using this fixed duration as the unit, the historical data is divided into multiple consecutive time periods in chronological order. Each time period is a detection phase. In this way, continuous historical environmental factor data is cut into smaller segments that are easy to analyze.

[0062] Specifically, if the total number of historical fluctuation environmental factors exceeds a preset threshold during a certain detection phase of a similar inspection unit, it means that the environmental factors change drastically during that phase, which may have a significant impact on the operation of the power distribution line equipment. When the total number of historical fluctuation environmental factors does not exceed the preset threshold during a detection phase of a similar inspection unit, it indicates that the environmental factors are relatively stable during that phase, and the operation of the power distribution line equipment is less affected by environmental interference.

[0063] In detail, "environmental factors" refer to environmental factors or conditions that may affect the operating status of power distribution line equipment or the inspection work during the inspection of power distribution lines. Environmental factors include natural environmental factors (such as temperature, humidity, wind speed, electromagnetic interference, vegetation cover, etc.) and human environmental factors (such as industrial interference, terrain complexity, etc.). They can be continuous (such as long-term stable climate conditions) or intermittent (such as sudden strong winds).

[0064] In detail, "historical environmental factor sampling data" refers to historical data of various environmental factors collected through periodic or continuous monitoring during past power distribution line inspections. "Historical fluctuating environmental factors" refers to environmental factors that appear intermittently and change drastically in the historical environmental factor sampling data (such as sudden strong winds or instantaneous electromagnetic interference), while "historical stable environmental factors" refers to environmental factors that are stable or appear regularly in the historical environmental factor sampling data over a long period of time (such as topographic features or average temperature).

[0065] In detail, the "preset factor threshold" is a standard value set by humans to determine whether the detection phase is a period of environmental fluctuation or a stable environmental phase. Experts in the power system field evaluate and recommend the preset factor threshold based on their practical experience in historical fault statistical analysis, environmental monitoring data calibration, and simulation verification. Taking a certain urban area as an example, if there is frequent industrial activity in the surrounding area and diverse environmental factors such as electromagnetic interference and temperature and humidity changes, the preset factor threshold ranges from 25 to 30; while for power distribution lines in remote mountainous areas with simpler environments, the preset factor threshold ranges from 6 to 8.

[0066] S3. Perform high-frequency dynamic sampling during the environmental fluctuation phase to obtain the first set of unknown environmental factors for the same type of inspection unit. Perform mixed dynamic sampling during the environmental stability phase, which includes low-frequency sampling and high-frequency sampling. When an unknown environmental factor that does not belong to the historical fluctuation environmental factors is sampled during the environmental stability phase, switch the low-frequency sampling to the high-frequency sampling to obtain the second set of unknown environmental factors for the same type of inspection unit. Generate the real-time fluctuation environmental factor set for the same type of inspection unit based on the first set of unknown environmental factors and the second set of unknown environmental factors.

[0067] In this embodiment of the invention, the step of performing high-frequency dynamic sampling during the environmental fluctuation phase to obtain the first set of unknown environmental factors for the same type of inspection unit includes:

[0068] During the high-frequency dynamic sampling process in the environmental fluctuation phase, when the unknown environmental factor is sampled, the first sampling frequency of the high-frequency dynamic sampling is increased until the unknown environmental factor can no longer be sampled. When a known environmental factor belonging to the historical fluctuation environmental factors is sampled, the first sampling frequency is kept unchanged.

[0069] High-frequency dynamic sampling is performed during the environmental fluctuation phase to obtain the first unknown environmental factor of the same type of inspection unit;

[0070] In detail, data such as temperature, humidity, wind speed, and pollutant concentration are collected through a fixed sensor network (such as temperature, humidity, wind speed, and salt density monitoring devices) deployed in similar inspection units. Real-time meteorological data (such as lightning location systems and rainfall radar) are simultaneously accessed as auxiliary references. During the sampling process, the collected environmental factors are compared with a historical fluctuation environmental factor database to determine whether they are known or unknown environmental factors. When an unknown environmental factor is sampled, the first sampling frequency of high-frequency dynamic sampling is increased from the normal period (such as once every 10 minutes) to the minute level (such as once every 5 minutes) through the fixed sensor network until the unknown environmental factor can no longer be sampled. The environmental factors in the environmental data collected by the fixed sensor network are taken as the first unknown environmental factor of the similar inspection unit. The value range of the first sampling frequency is once every 1-20 minutes.

[0071] It is important to note that the first sampling frequency of "high-frequency dynamic sampling" is the same as the sampling frequency of the historical environmental fluctuation stage. The historical environmental fluctuation stage is obtained by dividing the historical environmental factor sampling data into stages using historical methods. The "dynamic" in "high-frequency dynamic sampling" is reflected in the fact that when an unknown environmental factor is sampled, the first sampling frequency will be increased.

[0072] In detail, continuous high-frequency sampling is conducted until no unknown environmental factors can be sampled. This allows for a complete record of the entire process of the appearance, development, and disappearance of unknown environmental factors, avoiding the loss of key information due to insufficient sampling frequency. This provides reliable data support for further analysis of the impact of unknown environmental factors on similar inspection units.

[0073] The first unknown environmental factors are aggregated into a set of first unknown environmental factors for the same type of inspection unit.

[0074] In detail, high-frequency dynamic sampling refers to the continuous collection of environmental factors at a higher time frequency during environmental fluctuations. Compared with conventional sampling, it can capture environmental changes more timely and in greater detail.

[0075] In detail, the first set of unknown environmental factors refers to the set of unknown environmental factor data obtained through high-frequency dynamic sampling during the environmental fluctuation phase, arranged and combined according to the sampling time order.

[0076] In detail, unknown environmental factors refer to newly discovered environmental factors during the sampling process that are not included in the historical fluctuation environmental factor data. These environmental factors may be newly emerging or previously unmonitored. Known environmental factors refer to those already recorded in the historical fluctuation environmental factor data, which have been identified and understood through past monitoring and analysis. Because unknown environmental factors are not recorded, their variation patterns, impact range, and intensity are unclear. By increasing the initial sampling frequency, data on the environmental factor at different time points can be collected more intensively, quickly obtaining its complete variation trend. Increasing the sampling frequency can record details such as intensity fluctuations and frequency changes in a timely manner, helping maintenance personnel to fully understand the characteristics of the environmental factor and avoid missing key data due to excessively large sampling intervals.

[0077] In detail, high-frequency dynamic sampling is performed during the environmental fluctuation phase to obtain a first environmental factor time series set and a first unknown environmental factor set for the same type of inspection unit. Hybrid dynamic sampling is then performed during the stable environmental phase to obtain a second environmental factor time series set and a second unknown environmental factor set for the same type of inspection unit. This collaborative strategy of high-frequency dynamic sampling and hybrid dynamic sampling improves the accuracy and efficiency of power distribution line inspection planning. During the environmental fluctuation phase, high-frequency dynamic sampling ensures the capture of the complete change process of sudden unknown environmental factors (such as instantaneous strong winds), avoiding data omissions caused by traditional fixed-frequency sampling. During the stable environmental phase, a hybrid mode of low-frequency sampling is initiated and triggered to switch to high-frequency sampling, saving ineffective sampling resources and enabling rapid response to occasional anomalies (such as sudden electromagnetic interference). The real-time fluctuating environmental factor set generated by integrating the two types of sampling results provides data support for the intelligent prediction of subsequent inspection parameters, ultimately enabling the inspection plan to dynamically adapt to environmental changes.

[0078] In this embodiment of the invention, hybrid dynamic sampling is performed during the environmental stabilization phase to obtain a second set of unknown environmental factors for the same type of inspection unit, including:

[0079] The low-frequency sampling is performed during the environmental stabilization phase, wherein the second sampling frequency of the low-frequency sampling is calculated using the following formula:

[0080]

[0081] In the formula, This indicates the second sampling frequency of the low-frequency sampling. This indicates the base sampling frequency for the low-frequency sampling. This represents the attenuation coefficient, and the sampling interval corresponding to the second sampling frequency is approximately 60-120 minutes.

[0082] During the low-frequency sampling process in the environmental stabilization phase, when the unknown environmental factor is sampled, the high-frequency sampling is performed in the environmental stabilization phase until the unknown environmental factor is not detected within a preset time period, and then the low-frequency sampling is performed in the environmental stabilization phase.

[0083] Hybrid dynamic sampling is performed during the stable environmental phase to obtain a second unknown environmental factor for the same type of inspection unit;

[0084] In detail, data such as temperature, humidity, wind speed, and pollutant concentration are collected through a fixed sensor network deployed in similar inspection units. During low-frequency sampling in the environmental fluctuation phase, the collected environmental factors are compared with a historical environmental factor database to determine whether they are known or unknown environmental factors. When an unknown environmental factor is sampled, high-frequency sampling is performed in the environmental stability phase until no unknown environmental factor is detected within a preset time period. Then, low-frequency sampling is performed in the environmental stability phase. The environmental factors in the environmental data collected by the fixed sensor network in the environmental stability phase are used as the second unknown environmental factor for similar inspection units. The sampling interval corresponding to the high-frequency sampling is 20-60 minutes.

[0085] It should be noted that the second sampling frequency of "low-frequency sampling" is the same as the sampling frequency of the historical stable environmental phase. The historical stable environmental phase is obtained by dividing the historical environmental factor sampling data into phases using historical methods.

[0086] In detail, the sampling frequency is switched according to whether unknown environmental factors are detected, which realizes the rational allocation of sampling resources. When there are no unknown environmental factors, low-frequency sampling is used to reduce the workload and data processing volume of the fixed sensor network, reduce energy consumption and storage costs, while the sampling frequency is increased when unknown environmental factors are detected to ensure that key data can be obtained and improve the utilization efficiency of resources.

[0087] The second unknown environmental factor is aggregated into a set of second unknown environmental factors for the same type of inspection unit.

[0088] In detail, hybrid dynamic sampling is a sampling method used during the environmentally stable phase. It combines low-frequency and high-frequency sampling, dynamically adjusting the sampling frequency based on the actual situation of environmental factors to achieve efficient data acquisition. Low-frequency sampling is one mode in hybrid dynamic sampling, collecting environmental factors at a relatively low frequency. It is used when the environment is stable and no special circumstances occur, aiming to obtain necessary environmental data while reducing resource consumption. High-frequency sampling is another mode in hybrid dynamic sampling, activated when unknown environmental factors are detected during low-frequency sampling in the environmentally stable phase. It can collect environmental factor data more intensively to capture the changing characteristics of unknown factors.

[0089] In detail, the second set of unknown environmental factors refers to the set of unknown environmental factor data obtained through mixed dynamic sampling during the environmental stabilization phase, which is organized in chronological order of sampling time.

[0090] In detail, the preset time period is a manually set standard of time length used to determine whether unknown environmental factors have disappeared. If no unknown environmental factors are detected within the time period, low-frequency sampling is resumed.

[0091] In detail, the calculation formula for the second sampling frequency uses a base sampling frequency determined by referencing historical data and industry standards. This involves analyzing historical environmental factor sampling data from similar inspection units during periods of stable environmental conditions. If, over a long period under similar environmental conditions, sampling at a fixed frequency has effectively acquired historical environmental factor sampling data without encountering problems, this frequency can be used as a reference for the base sampling frequency. Furthermore, the power industry has relevant standards and specifications for distribution line environmental monitoring, stipulating sampling frequency requirements under specific stable environments, which can be used to determine the base sampling frequency. The attenuation coefficient is determined through experiments conducted at different environmental stability stages, setting different values ​​accordingly. The value was then used to observe the effect of adjusting the second sampling frequency as environmental factors changed. For example, different levels of environmental factor fluctuations were simulated, and the effects were compared with different... Whether the second sampling frequency under the given value can reasonably respond to fluctuations, obtain historical environmental factor sampling data without oversampling, and after multiple experiments, determine a sampling strategy that can adapt to environmental changes without excessively consuming resources. value.

[0092] In this embodiment of the invention, generating a set of real-time fluctuating environmental factors for the same type of inspection unit based on the first set of unknown environmental factors and the second set of unknown environmental factors includes:

[0093] The first set of unknown environmental factors and the second set of unknown environmental factors are combined to obtain the real-time fluctuating environmental factors of the same type of inspection unit.

[0094] The real-time fluctuation environmental factors are aggregated into a set of real-time fluctuation environmental factors for the same type of inspection unit.

[0095] In detail, real-time fluctuation environmental factors are environmental factors that appear intermittently, selected from the first set of unknown environmental factors and the second set of unknown environmental factors in the power distribution line inspection planning.

[0096] In detail, "union processing" refers to merging the elements in the first and second sets of unknown environmental factors and removing duplicates to generate a set of real-time fluctuating environmental factors. The time, frequency and intensity of the real-time fluctuating environmental factors in the set are uncertain. They may suddenly appear in a short period of time and affect the operation of power distribution line equipment, and then disappear for a period of time. Examples include sudden local electromagnetic interference and instantaneous strong winds caused by short-term severe convective weather.

[0097] S4. Based on the real-time fluctuation environmental factor set, the pre-acquired environmental data, and the preset prediction model, generate the inspection parameters of the same type of inspection unit;

[0098] In this embodiment of the invention, the inspection parameters include inspection cycle, number of personnel, and tool type, and the prediction model includes an inspection cycle prediction algorithm, a number of personnel prediction algorithm, and a tool type prediction algorithm.

[0099] In detail, inspection parameters are indicators used to describe the relevant characteristics and requirements of power distribution line inspection work. They are important bases for formulating inspection plans, allocating resources, and evaluating inspection work. The inspection cycle refers to the time interval for a comprehensive inspection of the power distribution line. A reasonable inspection cycle can ensure the safe operation of line equipment while avoiding excessive inspections that waste resources. Personnel quantity refers to the number of personnel required to perform power distribution line inspection tasks. Tool types refer to the quantity of various tools needed to carry and use during power distribution line inspection work. Tools include testing instruments (such as infrared thermometers, insulation resistance testers, etc.) and maintenance tools (wrenches, screwdrivers, etc.).

[0100] In detail, the prediction model is a mathematical model based on certain algorithms and a large amount of historical data, used to predict relevant future situations and to assist in the scientific planning of inspection work in the scenario of power distribution line inspection.

[0101] In this embodiment of the invention, the calculation formula of the inspection cycle prediction algorithm is as follows:

[0102]

[0103] In the formula, This indicates the inspection cycle. , and This represents the adjustment coefficient. The set of real-time fluctuating environmental factors represents the first... Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors The first environmental data represents the An environmental characteristic, Indicates the first The weight of each environmental feature, This represents the maximum value of the inspection cycle. Represents the sigmoid activation function. Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time.

[0104] For example, setting , , The set of real-time fluctuating environmental factors includes A series of intermittent, real-time fluctuating environmental factors, environmental characteristics There are 3;

[0105] The first real-time fluctuating environmental factor , Indicates intermittent strong winds, measured in m / s. The wind speed at one occurrence was 20 m / s, weight , The event occurred intermittently 3 times within this calculation period. Considering the intermittent occurrence, the overall value is [value to be filled in]. ;

[0106] The second real-time fluctuation environmental factor , Indicates the intensity of intermittent electromagnetic interference, dimensionless. The interference strength is 0.8, and the weight is... , This occurred intermittently twice within this calculation period, and the overall value was [value missing]. ;

[0107] The first environmental feature , Indicates the complexity of the terrain, with a value of 0.7, and a weight. ;

[0108] The second environmental feature , Indicates the degree of disturbance from surrounding industries, with a value of 0.5 and a weight. ;

[0109] The third environmental feature , This represents the vegetation coverage rate, with a value of 0.6 and a weight. ;

[0110] Maximum value of inspection cycle sky;

[0111]

[0112]

[0113]

[0114]

[0115] In detail, based on their long-accumulated professional knowledge and practical experience, power industry experts believe that in the operating environment of power distribution lines in this region, natural meteorological factors (such as strong winds) have a more critical and direct impact on line equipment than electromagnetic interference factors. Based on the experts' judgment on the degree of influence of various environmental factors, intermittent strong winds are given greater weight to highlight their importance in determining the inspection cycle.

[0116] In this embodiment of the invention, the calculation formula of the personnel number prediction algorithm is as follows:

[0117]

[0118] In the formula, This indicates the number of people. Indicates the first Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first The weight of each environmental feature, Indicates the basic number of personnel. Indicates the first The adjustment coefficient of a real-time fluctuating environmental factor. Indicates the first The adjustment coefficient for each environmental characteristic, Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Represents the hyperbolic tangent function. Indicates from Multiplication begins when the value is 1.

[0119] In detail, The range of its value is (-1, 1). The calculation results may fluctuate within a large range, after After processing, The output value is limited to the range (-1, 1), which avoids certain extreme values ​​from having too much impact on the final number of people prediction results, making the prediction model more robust.

[0120] For example, setting a basic number of personnel. The basic number of personnel is determined based on past experience, and the set of real-time fluctuating environmental factors includes... A series of intermittent, real-time fluctuating environmental factors, environmental characteristics There are 3;

[0121] The first real-time fluctuating environmental factor , Indicates intermittent strong winds, measured in m / s. The wind speed at one occurrence was 20 m / s, weight , The adjustment coefficient appeared intermittently 3 times during this calculation period. The comprehensive value is ;

[0122] The second real-time fluctuation environmental factor , Indicates the intensity of intermittent electromagnetic interference, dimensionless. The interference strength is 0.8, and the weight is... , The adjustment coefficient appeared intermittently twice during this calculation period. The comprehensive value is ;

[0123] The first environmental feature , Indicates the complexity of the terrain, with a value of 0.7, and is an adjustment coefficient. Weight ;

[0124] The second environmental feature , Indicates the degree of disturbance from surrounding industries, with a value of 0.5, and is an adjustment coefficient. Weight ;

[0125] The third environmental feature , This represents the vegetation coverage rate, with a value of 0.6, and is an adjustment coefficient. Weight ;

[0126]

[0127] Calculate intermittent strong winds The corresponding part of the value:

[0128]

[0129] Calculate the intensity of intermittent electromagnetic interference The corresponding part of the value:

[0130]

[0131] Calculate the product:

[0132]

[0133] Calculate the final number of people:

[0134]

[0135] Therefore, according to the personnel number prediction algorithm, in this case, 9 people are needed to carry out the inspection work of this type of inspection unit.

[0136] In this embodiment of the invention, the calculation formula of the tool type prediction algorithm is as follows:

[0137]

[0138] In the formula, Indicates the first in the tool library A tool, Indicates the first The probability of using each tool This indicates the number of tools in the tool library. Indicates the first Real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first Real-time fluctuating environmental factors The influence coefficient of each tool Indicates the first Environmental characteristics The influence coefficient of each tool Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Indicates the tool's identifier. This represents the hyperbolic tangent function.

[0139] For example, suppose there are 7 tools in a tool library:

[0140]

[0141] The set of real-time fluctuating environmental factors includes A real-time fluctuating environmental factor that appears intermittently:

[0142] Indicates intermittent strong winds, measured in m / s. The wind speed was 20 m / s at one point. This occurred intermittently 3 times within this calculation period, and the overall value was [value missing]. , A value of 0.5 represents the influence coefficient of intermittent strong winds on the infrared thermometer;

[0143] Indicates the intensity of intermittent electromagnetic interference, dimensionless. The interference intensity was 0.8. This occurred intermittently twice within this calculation period, and the overall value was [value missing]. , A value of 0.3 indicates the influence coefficient of electromagnetic interference on the infrared thermometer;

[0144] Environmental characteristics There are 3, with influence coefficients of... For example:

[0145] Indicates the complexity of the terrain, with a value of 0.7. The value is 0.4, which represents the influence coefficient of terrain complexity on the infrared thermometer.

[0146] This indicates the degree of disturbance from surrounding industries, with a value of 0.5. A value of 0.2 indicates the influence coefficient of industrial interference on the infrared thermometer;

[0147] This represents the vegetation coverage rate, with a value of 0.6. A value of 0.3 indicates the influence coefficient of vegetation coverage on the infrared thermometer.

[0148] Calculation tools usage probability :

[0149] 1. Calculate the contribution of real-time fluctuating environmental factors:

[0150]

[0151] 2. Contribution of computational environment characteristics:

[0152]

[0153] 3. Calculate the hyperbolic tangent:

[0154]

[0155] because Then the tool Selected, and following the above method, ultimately chosen. , , and Based on the personnel number prediction algorithm results calculated above, assuming that the current similar inspection unit needs to be equipped with 9 inspection personnel, the 9 inspection personnel will be divided into three groups, with each group having one unit. , and One per person .

[0156] S5. Based on the inspection parameters, formulate an inspection plan for the same type of inspection unit, and inspect the line equipment of the same type of inspection unit based on the inspection plan.

[0157] In this embodiment of the invention, the inspection planning scheme includes:

[0158] Based on the number of personnel, personnel are allocated to the same type of inspection units: based on the number of personnel N obtained by the above personnel number prediction algorithm, and combined with the distribution and complexity of lines and equipment within the same type of inspection unit, the inspection personnel of the number N are reasonably arranged to different types of inspection units.

[0159] In detail, rationally allocating N inspection personnel to different similar inspection units can ensure that the inspection work can be carried out efficiently and comprehensively, avoiding situations where unreasonable personnel allocation leads to inadequate inspection of some similar inspection units or idle personnel, making full use of human resources and improving the quality and efficiency of inspections.

[0160] Based on the tool type, allocate tools to the same type of inspection units;

[0161] For example: ultimately chose , , and Based on the personnel number prediction algorithm results calculated above, assuming that the current similar inspection unit needs to be equipped with 9 inspection personnel, the 9 inspection personnel will be divided into three groups, with each group having one unit. , and One per person .

[0162] In detail, the reasonable allocation of various tools to inspection personnel can ensure that they can use the appropriate tools at any time during the inspection process, so as to smoothly complete the inspection and maintenance of line equipment and avoid affecting the inspection progress or failing to deal with the problems found in a timely manner due to improper allocation of tools.

[0163] Mark the line equipment of the same type of inspection unit: affix labels to the equipment, make electronic marks on the map or management system, and give each line equipment in the same type of inspection unit a unique identifier. The identifier may include information such as equipment name, model, number, installation location, and last inspection time.

[0164] The inspection duration of the line equipment is determined based on the inspection cycle: the inspection cycle T is calculated according to the inspection cycle prediction algorithm, and the inspection duration of each line equipment is determined by combining factors such as the importance of the line equipment and historical fault conditions.

[0165] For example, the inspection time will be relatively long for important substation equipment or line sections with frequent historical faults; while for some relatively stable and less important equipment, the inspection time can be appropriately shortened. The inspection cycle T allows for the reasonable scheduling of inspection time. Within the specified inspection cycle, it ensures a comprehensive and detailed inspection of important and problem-prone equipment, while also taking other equipment into account. This ensures that all line equipment within the same inspection unit receives effective inspection, allowing for the timely detection and handling of potential problems, and guaranteeing the safe and stable operation of the power distribution lines.

[0166] In the several embodiments provided by this invention, it should be understood that the disclosed method can be implemented in other ways.

[0167] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A planning method for power distribution line inspection, characterized in that, The method includes: The main inspection route of the power distribution line is divided into multiple inspection sub-routes. Cluster analysis is performed on the inspection sub-routes to obtain the same type of inspection units of the main inspection route. Historical environmental factor sampling data of the same type of inspection units are obtained, and the sampling stages of the same type of inspection units are divided to obtain the environmental fluctuation stage and the environmental stability stage of the same type of inspection units. The historical environmental factors include historical fluctuation environmental factors and historical stable environmental factors. High-frequency dynamic sampling is performed during the environmental fluctuation phase to obtain a first set of unknown environmental factors for the same type of inspection unit. Mixed dynamic sampling is performed during the environmental stability phase, which includes low-frequency sampling and high-frequency sampling. When an unknown environmental factor that does not belong to the historical fluctuation environmental factors is sampled during the environmental stability phase, the low-frequency sampling is switched to the high-frequency sampling to obtain a second set of unknown environmental factors for the same type of inspection unit. Based on the first set of unknown environmental factors and the second set of unknown environmental factors, a real-time fluctuation environmental factor set for the same type of inspection unit is generated. Based on the real-time fluctuation environmental factor set, the pre-acquired environmental data, and the preset prediction model, the inspection parameters of the same type of inspection unit are generated. Based on the inspection parameters, an inspection planning scheme is formulated for the same type of inspection unit, and the line equipment of the same type of inspection unit is inspected based on the inspection planning scheme.

2. The planning method for power distribution line inspection as described in claim 1, characterized in that, The sampling phases of the same type of inspection units are divided to obtain the environmental fluctuation phase and the environmental stability phase of the same type of inspection units. The historical environmental factors include historical fluctuation environmental factors and historical stable environmental factors, including: The historical environmental factor sampling data is divided into stages according to a fixed time window to obtain multiple detection stages for the same type of inspection unit; The total number of historical fluctuation environmental factors during the detection phase is counted one by one: When the total number of factors exceeds a preset factor threshold, the detection phase is defined as the environmental fluctuation phase. When the total number of factors does not exceed the preset factor threshold, the detection phase is determined as the environmental stability phase.

3. The planning method for power distribution line inspection as described in claim 2, characterized in that, The high-frequency dynamic sampling during the environmental fluctuation phase yields a first set of unknown environmental factors for the same type of inspection unit, including: During the high-frequency dynamic sampling process in the environmental fluctuation phase, when the unknown environmental factor is sampled, the first sampling frequency of the high-frequency dynamic sampling is increased until the unknown environmental factor can no longer be sampled. When a known environmental factor belonging to the historical fluctuation environmental factors is sampled, the first sampling frequency is kept unchanged. High-frequency dynamic sampling is performed during the environmental fluctuation phase to obtain the first unknown environmental factor of the same type of inspection unit; The first unknown environmental factors are aggregated into a set of first unknown environmental factors for the same type of inspection unit.

4. The planning method for power distribution line inspection as described in claim 3, characterized in that, Hybrid dynamic sampling is performed during the stable environmental phase to obtain a second set of unknown environmental factors for the same type of inspection unit, including: The low-frequency sampling is performed during the environmental stabilization phase. During the low-frequency sampling process in the environmental stabilization phase, when the unknown environmental factor is sampled, the high-frequency sampling is performed in the environmental stabilization phase until the unknown environmental factor is not detected within a preset time period, and then the low-frequency sampling is performed in the environmental stabilization phase. Hybrid dynamic sampling is performed during the stable environmental phase to obtain a second unknown environmental factor for the same type of inspection unit; The second unknown environmental factor is aggregated into a set of second unknown environmental factors for the same type of inspection unit.

5. The planning method for power distribution line inspection as described in claim 1, characterized in that, The step of generating a real-time fluctuation environmental factor set for the same type of inspection unit based on the first set of unknown environmental factors and the second set of unknown environmental factors includes: The first set of unknown environmental factors and the second set of unknown environmental factors are combined to obtain the real-time fluctuating environmental factors of the same type of inspection unit. The real-time fluctuation environmental factors are aggregated into a set of real-time fluctuation environmental factors for the same type of inspection unit.

6. The planning method for power distribution line inspection as described in claim 1, characterized in that, The inspection parameters include inspection cycle, number of personnel, and tool type, and the prediction model includes an inspection cycle prediction algorithm, a number of personnel prediction algorithm, and a tool type prediction algorithm.

7. The planning method for power distribution line inspection as described in claim 6, characterized in that, The calculation formula for the inspection cycle prediction algorithm is as follows: In the formula, This indicates the inspection cycle. , and This represents the adjustment coefficient. The set of real-time fluctuating environmental factors represents the first... Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors The first environmental data represents the An environmental characteristic, Indicates the first The weight of each environmental feature, This represents the maximum value of the inspection cycle. Represents the sigmoid activation function. Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time.

8. The planning method for power distribution line inspection as described in claim 7, characterized in that, The calculation formula for the personnel number prediction algorithm is as follows: In the formula, This indicates the number of people. Indicates the first Real-time fluctuating environmental factors Indicates the first The weights of real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first The weight of each environmental feature, Indicates the basic number of personnel. Indicates the first The adjustment coefficient of a real-time fluctuating environmental factor. Indicates the first The adjustment coefficient for each environmental characteristic. Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Represents the hyperbolic tangent function. Indicates from Multiplication begins when the value is 1.

9. The planning method for power distribution line inspection as described in claim 7, characterized in that, The calculation formula for the tool type prediction algorithm is as follows: In the formula, Indicates the first in the tool library A tool, Indicates the first The probability of using each tool This indicates the number of tools in the tool library. Indicates the first Real-time fluctuating environmental factors Indicates the first An environmental characteristic, Indicates the first Real-time fluctuating environmental factors The influence coefficient of each tool Indicates the first Environmental characteristics The influence coefficient of each tool Identifiers representing real-time fluctuating environmental factors. Labels indicating environmental characteristics, Indicates the quantity of environmental characteristics. This indicates the number of environmental factors that fluctuate in real time. Indicates the tool's identifier. This represents the hyperbolic tangent function.

10. The planning method for power distribution line inspection as described in claim 6, characterized in that, The inspection planning scheme includes: Based on the number of personnel, personnel are allocated to the same type of inspection units; Based on the tool type, allocate tools to the same type of inspection units; Mark the line equipment of the aforementioned similar inspection units; The inspection duration of the line equipment is determined based on the inspection cycle.

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