Active operation and maintenance work order driven method based on meteorological impact analysis

Abstract

The present invention discloses an active operation and maintenance work order driven method based on meteorological impact analysis, including a meteorological impact analysis module and an operation and maintenance work order generation module. The meteorological impact analysis module predicts the fault impact and trend of meteorological events on the distribution network by establishing a meteorological impact model. The operation and maintenance work order generation module generates corresponding operation and maintenance work orders based on the analysis results of the meteorological impact analysis module. By analyzing the meteorological impact model, this method can identify and locate possible fault points in advance, thereby reducing power outage events caused by weather factors, reducing the failure rate, and ensuring the stable operation of the distribution network. At the same time, the analysis results of the meteorological impact analysis module are used to automatically generate work orders in the operation and maintenance work order generation module, reducing the time of manual decision-making, improving the operation and maintenance response speed, realizing the optimal allocation of resources, and improving the operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault analysis, and particularly to an active operation and maintenance work order driven method based on meteorological impact analysis. Background Art

[0002] As a key link in power supply, the distribution network connects power plants and end-users and is an important part of the power system. The stable operation of the distribution network is crucial. In modern society, people's dependence on electricity is increasing continuously, and faults in the distribution network will directly affect users' lives and economic activities.

[0003] For example, the Chinese invention patent with the application number 201910399119.3 proposes a power grid active operation and maintenance early warning method based on big data, including the following steps: A) obtaining historical operation and maintenance data of each component of the regional power grid; B) processing the historical operation and maintenance data of each component into Boolean data, obtaining the operation and maintenance vector Vs of the component, and obtaining the set F of historical operation and maintenance vectors; C) pairwise associating the fields in the operation and maintenance vector Vs, counting the fields that are always the same value or always different values in the historical operation and maintenance data, and establishing an association set G; D) periodically monitoring the operation and maintenance data of the component, converting the operation and maintenance data into the latest operation and maintenance vector, and if so, sending out an early warning message; E) if in the latest operation and maintenance vector, the equality relationship of the fields recorded in the association set G is different from the historical equality relationship, then sending out an early warning message. This technical solution mainly generates early warning conditions by combining historical data of the regional power grid to achieve early warning, but does not mention relevant measures after early warning.

[0004] In order to enable the distribution network to continue to operate stably under extreme meteorological events and ensure the normal production of residents in the areas affected by extreme meteorological events, corresponding measures need to be taken in advance to deal with the occurrence of disasters and deploy relevant personnel and materials. Summary of the Invention

[0005] In order to overcome the above problems, the object of the present invention is to provide an active operation and maintenance work order driven method based on meteorological impact analysis, including a meteorological impact analysis module and an operation and maintenance work order generation module. This method uses the meteorological impact analysis module to establish a meteorological impact model, and by analyzing this meteorological impact model, it can identify and locate possible fault points in advance, thereby reducing power outage events caused by weather factors, reducing the failure rate, ensuring the stable operation of the distribution network, and at the same time automatically generating work orders in the operation and maintenance work order generation module based on the analysis results of the meteorological impact analysis module, reducing the time of manual decision-making, improving the operation and maintenance response speed, realizing the optimal allocation of resources, and improving the operation and maintenance efficiency.

[0006] The technical solution adopted by the present invention is:

[0007] Active operation and maintenance work order driven method based on meteorological impact analysis, including a meteorological impact analysis module and an operation and maintenance work order generation module. The meteorological impact analysis module predicts the fault impact and trend of meteorological events on the distribution network by establishing a meteorological impact model. The operation and maintenance work order generation module generates corresponding operation and maintenance work orders based on the analysis results of the meteorological impact analysis module;

[0008] The steps for constructing the meteorological impact model are as follows:

[0009] S1: Data collection and preprocessing;

[0010] S2: Establish a linear regression model for the correlation between meteorological factors and fault occurrence;

[0011] S3: Model inspection and optimization, inspect and optimize the linear regression model in step S2;

[0012] S4: Establish a logistic regression model to analyze the binary relationship between meteorological factors and fault occurrence;

[0013] S5: Integrate multiple factors to establish a meteorological impact model;

[0014] S6: Model solution to obtain the occurrence probability and trend of distribution network faults.

[0015] The work order generation steps of the operation and maintenance work order generation module are as follows:

[0016] The first step: Set the generation rules of the operation and maintenance work order,

[0017] including equipment inspection and maintenance work orders, equipment fault warning work orders, operation parameter adjustment work orders, and environmental risk assessment work orders;

[0018] The second step: Train the model through historical work order data and historical meteorological data, and continuously optimize the generation rules,

[0019] The optimization of the generation rules is carried out according to the priority, and the priority order is:

[0020] Equipment inspection and maintenance work order Equipment fault warning work order Operation parameter adjustment work order Environmental risk assessment work order,

[0021] The third step: Generate operation and maintenance work orders.

[0022] As a further description of the present invention, the data collection in S1 of the meteorological impact model includes meteorological data, equipment data, operation parameters, and environmental parameters.

[0023] The meteorological data is wind speed, light time, air pressure, temperature, humidity, and precipitation.

[0024] The device data includes device age, type, and historical failure records.

[0025] The operating parameters are load, current, and voltage.

[0026] The environmental parameters are terrain, vegetation cover, and altitude.

[0027] As a further description of the present invention, the data preprocessing in S1 of the meteorological impact model is performed by cleaning data, data transformation, data standardization, or normalization.

[0028] As a further description of the present invention, in the linear regression model of the meteorological impact model S2, meteorological factors are used as independent variables, the failure incidence rate is used as the dependent variable, and operating parameters and environmental parameters are used as regression coefficients to describe the linear relationship between meteorological factors and the occurrence of failures, that is

[0029]

[0030] Among them, represents the failure incidence rate,

[0031] represents meteorological factors,

[0032] represents the regression coefficient of operating parameters,

[0033] represents the regression coefficient of environmental parameters.

[0034] As a further description of the present invention, in the meteorological impact model S3, the influence degree of each meteorological factor on the occurrence of failures is judged, the model is optimized, variables are added or deleted, and the model structure is adjusted to improve the prediction accuracy of the model.

[0035] As a further description of the present invention, in the logistic regression model of the meteorological impact model S4, the occurrence of failures is regarded as a binary classification problem, with occurrence or non-occurrence as the result, meteorological data, device data, operating parameters, and environmental parameters are used as independent variables, and the failure occurrence status is used as the dependent variable to establish a logistic regression equation to describe the binary relationship between the independent variable and the dependent variable, that is:

[0036]

[0037] Among them, represents the probability that the value of the dependent variable Y is 1 under the condition of the given independent variable ,

[0038] is the base of the natural logarithm,

[0039] They are the regression coefficients of meteorological data, equipment data, operating parameters, and environmental parameters respectively,

[0040] They are the values of equipment data, operating parameters, and environmental parameters respectively,

[0041] Use the significance level test to screen important variables, exclude insignificant variables, optimize the model, adjust the coefficients of the logistic regression equation, add regularization terms, and improve the classification accuracy of the model.

[0042] As a further description of the present invention, in the meteorological impact model S5, a meteorological impact model is established by integrating various factors: combining the results of linear regression and logistic regression, selecting variables that have a significant impact on the occurrence of faults, integrating the selected variables into the model, and using historical data to verify the model.

[0043] As a further description of the present invention, in the work order generation step of the operation and maintenance work order generation module:

[0044] The equipment inspection and maintenance work order corresponds to the situation of abnormal meteorological factors, and the operation and maintenance personnel check whether the equipment is affected by meteorological factors.

[0045] The equipment fault warning work order combines meteorological factors and equipment operation data to predict the probability of possible equipment failures, reminding the operation and maintenance personnel to take preventive measures in advance to prevent the occurrence of equipment failures.

[0046] The operating parameter adjustment work order gives optimization suggestions for operating parameters based on real-time monitoring data of meteorological factors and operating parameters. The operation and maintenance personnel adjust the operating parameters of the equipment according to the suggestions to improve the operating efficiency and stability of the equipment.

[0047] The environmental risk assessment work order combines environmental parameters and meteorological factors to evaluate the risk level of the environment where the equipment is located, waking up the operation and maintenance personnel to pay attention to the risk changes of the environment where the equipment is located and take corresponding protective measures.

[0048] As a further description of the present invention, the content in the work order includes the name, number, location, current status, equipment historical fault records, maintenance plan, required tools, and material list of the equipment to be repaired.

[0049] The beneficial effects of the present invention:

[0050] The active operation and maintenance work order driven method based on meteorological impact analysis of the present invention includes a meteorological impact analysis module and an operation and maintenance work order generation module. This method uses the meteorological impact analysis module to establish a meteorological impact model. By analyzing this meteorological impact model, potential fault points can be identified and located in advance, thereby reducing power outage events caused by weather factors, reducing the failure rate, ensuring the stable operation of the distribution network. At the same time, work orders are automatically generated in the operation and maintenance work order generation module based on the analysis results of the meteorological impact analysis module, reducing the time for manual decision-making, improving the operation and maintenance response speed, realizing the optimal allocation of resources, and improving the operation and maintenance efficiency. Description of the Drawings

[0051] Figure 1 It is a structural block diagram of the active operation and maintenance work order driven method based on meteorological impact analysis proposed by the present invention;

[0052] Figure 2 It is a flowchart for constructing the meteorological impact model of the meteorological impact analysis module of the active operation and maintenance work order driven method based on meteorological impact analysis proposed by the present invention. Detailed Embodiments

[0053] The following describes the detailed embodiments of the present invention in conjunction with the drawings and embodiments:

[0054] It should be noted that the structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed by the present invention.

[0055] At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration, and are not used to limit the scope in which the present invention can be implemented. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope in which the present invention can be implemented.

[0056] As Figures 1 - 2 shown, it shows the detailed embodiments of the present invention:

[0057] Embodiment

[0058] The active operation and maintenance work order driven method based on meteorological impact analysis includes a meteorological impact analysis module and an operation and maintenance work order generation module. The meteorological impact analysis module predicts the fault impact and trend of meteorological events on the distribution network by establishing a meteorological impact model. The operation and maintenance work order generation module generates corresponding operation and maintenance work orders based on the analysis results of the meteorological impact analysis module;

[0059] In this embodiment, as Figure 1 shown, this method uses a meteorological impact analysis module to establish a meteorological impact model. By analyzing this meteorological impact model, potential fault points can be identified and located in advance, thereby reducing power outage events caused by weather factors, reducing the failure rate, ensuring the stable operation of the distribution network. At the same time, work orders are automatically generated in the operation and maintenance work order generation module based on the analysis results of the meteorological impact analysis module, reducing the time for manual decision-making, improving the operation and maintenance response speed, achieving optimal allocation of resources, and improving operation and maintenance efficiency.

[0060] The steps for constructing the meteorological impact model are as follows:

[0061] S1: Data collection and preprocessing;

[0062] S2: Establish a linear regression model for the correlation between meteorological factors and the occurrence of faults;

[0063] S3: Model testing and optimization, testing and optimizing the linear regression model in step S2;

[0064] S4: Establish a logistic regression model to analyze the binary relationship between meteorological factors and the occurrence of faults;

[0065] S5: Integrate various factors to establish a meteorological impact model;

[0066] S6: Model solution to obtain the occurrence probability and trend of distribution network faults.

[0067] In this embodiment, as Figure 2 shown, the meteorological impact model comprehensively uses two methods, namely the linear regression model and the logistic regression model, to obtain the final meteorological impact model, improving the accuracy of the model in predicting the occurrence probability of faults, and timely responding to ensure the stable operation of the distribution network.

[0068] The steps for generating work orders in the operation and maintenance work order generation module are as follows:

[0069] The first step: Set the generation rules for operation and maintenance work orders,

[0070] including equipment inspection and maintenance work orders, equipment fault warning work orders, operation parameter adjustment work orders, and environmental risk assessment work orders;

[0071] The second step: Train the model through historical work order data and historical meteorological data to continuously optimize the generation rules,

[0072] The optimization of the generation rules is carried out according to the priority, and the priority order is:

[0073] Equipment inspection and maintenance work orders Equipment fault warning work orders Operation parameter adjustment work orders Environmental risk assessment work orders,

[0074] Step 3: Generate operation and maintenance work orders.

[0075] In this embodiment, as Figure 1 shown, the generation rules of the operation and maintenance work orders are set according to the impacts on the distribution network. The four types of work orders, namely, equipment inspection and maintenance work orders, equipment failure warning work orders, operation parameter adjustment work orders, and environmental risk assessment work orders, respectively correspond to different meteorological factors, equipment failures, equipment operation parameter failures, or environmental factors.

[0076] Specifically, in the work order generation steps of the operation and maintenance work order generation module:

[0077] The equipment inspection and maintenance work order corresponds to the situation of abnormal meteorological factors. The operation and maintenance personnel check whether the equipment is affected by meteorological factors.

[0078] The equipment failure warning work order combines meteorological factors and equipment operation data to predict the probability of possible equipment failures, reminding the operation and maintenance personnel to take preventive measures in advance to prevent the occurrence of equipment failures.

[0079] The operation parameter adjustment work order gives optimization suggestions for operation parameters based on real-time monitoring data of meteorological factors and operation parameters. The operation and maintenance personnel adjust the operation parameters of the equipment according to the suggestions to improve the operation efficiency and stability of the equipment.

[0080] The environmental risk assessment work order combines environmental parameters and meteorological factors to evaluate the risk level of the environment where the equipment is located, waking up the operation and maintenance personnel to pay attention to the risk changes in the environment where the equipment is located and take corresponding protective measures.

[0081] Specifically, the content in the work order includes the name, number, location, current status, equipment historical failure records, maintenance plan, required tools, and material list of the equipment to be repaired.

[0082] In this embodiment, the equipment inspection and maintenance work order corresponds to the situation of abnormal meteorological factors. When meteorological parameters such as temperature, humidity, air pressure, and wind speed exceed the preset thresholds or show abnormal fluctuations, the system determines it as abnormal meteorological factors. For example, when the temperature is abnormal, that is, when the temperature exceeds the normal working range of the equipment, it may cause the equipment to overheat or the performance to decline. When the humidity is too high, that is, too high humidity may cause short circuits or corrosion in the internal circuits of the equipment, increasing the risk of failures. When the air pressure fluctuates, the violent fluctuations in air pressure may affect the sealing and stability of the equipment. When the wind speed is too high, it may cause physical damage to the equipment or affect the normal operation of the equipment. For abnormal meteorological factors, an equipment inspection and maintenance work order is generated, requiring the operation and maintenance personnel to check whether the equipment is affected by meteorological factors and perform necessary maintenance.

[0083] The equipment failure warning work order combines meteorological factors and equipment operation data, mainly predicting the probability of equipment failure based on equipment type, operation years, etc. For example, when equipment ages, i.e., has been in operation for a long time, its performance may gradually decline and it is more vulnerable to meteorological factors. An equipment failure warning work order is generated based on equipment operation data to remind maintenance personnel to take preventive measures in advance to prevent equipment failures.

[0084] The operation parameter adjustment work order gives optimization suggestions for operation parameters based on meteorological factors and real-time monitoring data of operation parameters, mainly parameters such as voltage, current, and load. For example, when the load is too large, i.e., the equipment load exceeds its design capacity, it may cause equipment overheating, damage, or performance degradation. An operation parameter adjustment work order is generated for operation parameters, and maintenance personnel adjust the equipment operation parameters according to the suggestions to improve the operation efficiency and stability of the equipment.

[0085] The environmental risk assessment work order combines environmental parameters and meteorological factors, mainly terrain, vegetation coverage, altitude, etc., to assess the risk level of the environment where the equipment is located, generate an environmental risk assessment work order, and remind maintenance personnel to pay attention to the risk changes in the environment where the equipment is located and take corresponding protective measures.

[0086] Specifically, the content in the work order includes the name, number, location, current status, equipment historical failure records, maintenance plan, required tools, and material list of the equipment to be repaired.

[0087] In this embodiment, during actual maintenance, it should be dynamically adjusted according to weather forecasts and equipment status to ensure that maintenance is completed before extreme weather and avoid losses in extreme weather. The required tools and material list for maintenance in the work order are directly included to ensure the efficiency of on-site maintenance during actual use and reduce work order delays caused by lack of tools.

[0088] Specifically, the data collection in S1 of the meteorological impact model includes meteorological data, equipment data, operation parameters, and environmental parameters.

[0089] The meteorological data is wind speed, sunshine duration, air pressure, temperature, humidity, and precipitation.

[0090] The equipment data is equipment age, type, and historical failure records.

[0091] The operation parameters are load, current, and voltage.

[0092] The environmental parameters are terrain, vegetation coverage, and altitude.

[0093] Specifically, the data preprocessing in S1 of the meteorological impact model is carried out by cleaning data, data conversion, data standardization, or normalization.

[0094] In this embodiment, the cleaning data is to remove duplicates and error values, fill in missing values, identify and remove outliers in the data using the box plot method or the Z-score method to ensure the accuracy of the analysis results, and use linear interpolation or KNN (K-Nearest Neighbor) to fill in missing values to ensure the integrity of the data set, thereby ensuring the effective use of the collected data.

[0095] The data conversion is to convert the data type into a form suitable for analysis, specifically converting categorical variables into numerical variables.

[0096] The purpose of data standardization or normalization is to ensure that the dimensions of different variables are consistent, facilitating subsequent analysis.

[0097] Specifically, in the linear regression model of the meteorological impact model S2, meteorological factors are used as independent variables, the failure rate is used as the dependent variable, and operating parameters and environmental parameters are used as regression coefficients to describe the linear relationship between meteorological factors and the occurrence of failures, that is

[0098]

[0099] Among them, represents the failure rate,

[0100] represents meteorological factors,

[0101] represents the regression coefficient of operating parameters,

[0102] represents the regression coefficient of environmental parameters.

[0103] In this embodiment, both the operating parameters and environmental parameters can cause failures in the distribution network. However, under the condition that meteorological factors are the main influencing factors, a linear regression equation is established to analyze the relationship between meteorological factors and the occurrence of failures.

[0104] Specifically, in the meteorological impact model S3, the influence degree of each meteorological factor on the occurrence of failures is judged, the model is optimized, variables are added or deleted, and the model structure is adjusted to improve the prediction accuracy of the model.

[0105] Specifically, in the meteorological impact model S4, the logistic regression model regards the occurrence of failures as a binary classification problem, with occurrence or non-occurrence as the result, uses meteorological data, equipment data, operating parameters and environmental parameters as independent variables, and the failure occurrence status as the dependent variable to establish a logistic regression equation to describe the binary relationship between the independent variable and the dependent variable, that is:

[0106]

[0107] Among them, represents the probability that the value of the dependent variable Y is 1 under the condition of a given independent variable .

[0108] is the base of the natural logarithm,

[0109] are the regression coefficients of meteorological data, equipment data, operating parameters, and environmental parameters respectively,

[0110] are the values of equipment data, operating parameters, and environmental parameters respectively,

[0111] Use the significance level test to screen important variables, exclude insignificant variables, optimize the model, adjust the coefficients of the logistic regression equation, add regularization terms, and improve the classification accuracy of the model.

[0112] Specifically, in the meteorological impact model S5, a meteorological impact model is established by integrating multiple factors: combining the results of linear regression and logistic regression, selecting variables that have a significant impact on the occurrence of faults, integrating the selected variables into the model, and using historical data to verify the model.

[0113] Generally speaking, this proactive operation and maintenance work order-driven method analyzes the meteorological impact model, identifies and locates possible fault points in advance, thereby reducing power outage events caused by weather factors, reducing the failure rate, ensuring the stable operation of the distribution network. At the same time, work orders are automatically generated in the operation and maintenance work order generation module based on the analysis results of the meteorological impact analysis module, reducing the time for manual decision-making, improving the operation and maintenance response speed, realizing the optimal allocation of resources, and improving the operation and maintenance efficiency.

[0114] The preferred embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention.

[0115] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. An active operation and maintenance work order driving method based on meteorological impact analysis is characterized by: It includes a meteorological impact analysis module and an operation and maintenance work order generation module. The meteorological impact analysis module predicts the impact and trend of meteorological events on the distribution network by establishing a meteorological impact model. The operation and maintenance work order generation module generates a corresponding operation and maintenance work order based on the analysis results of the meteorological impact analysis module. The steps for constructing the meteorological impact model are: S1: Data collection and preprocessing, the data collection includes meteorological data, equipment data, operating parameters, and environmental parameters. The meteorological data includes wind speed, sunshine time, air pressure, temperature, humidity, and precipitation. The equipment data includes equipment age, type, and historical fault records. The operating parameters include load, current, and voltage. The environmental parameters include terrain, vegetation coverage, and altitude. The data preprocessing adopts data cleaning, data conversion, data standardization, or normalization. S2: Establish a linear regression model of the correlation between meteorological factors and fault occurrence. The linear regression model uses meteorological factors as independent variables, fault occurrence rate as dependent variable, operation parameters and environmental parameters as regression coefficients to describe the linear relationship between meteorological factors and fault occurrence, that is, ， in, represents the failure rate, Indicates meteorological factors, represents the regression coefficient of the operating parameter, represents the regression coefficient of environmental parameters; S3: Model testing and optimization: testing and optimizing the linear regression model in step S2, determining the influence of various meteorological factors on the occurrence of faults, optimizing the model, adding or deleting variables, adjusting the model structure, and improving the prediction accuracy of the model; S4: Establish a logistic regression model to analyze the binary relationship between meteorological factors and fault occurrence; S5: Integrate multiple factors to establish a meteorological impact model; S6: Solve the model to obtain the probability and trend of distribution network failure; The steps of generating a work order in the operation and maintenance work order generating module are as follows: Step 1: Set the rules for generating operation and maintenance work orders. Including equipment inspection and maintenance work orders, equipment failure warning work orders, operating parameter adjustment work orders, and environmental risk assessment work orders; Step 2: Train the model through historical work order data and historical meteorological data, and continuously optimize the generation rules. The optimization of the generation rules is performed according to the priority, and the priority is sorted as follows: Equipment inspection and maintenance work order Equipment failure warning work order Operation parameter adjustment work order Environmental risk assessment work order, The equipment inspection and maintenance work order corresponds to the situation of abnormal meteorological factors. The operation and maintenance personnel check whether the equipment is affected by meteorological factors. The equipment failure warning work order combines meteorological factors and equipment operation data to predict the probability of possible equipment failure, and reminds the operation and maintenance personnel to take measures in advance to prevent equipment failure. The operation parameter adjustment work order gives optimization suggestions for the operation parameters based on the real-time monitoring data of meteorological factors and operation parameters. The operation and maintenance personnel adjust the equipment's operating parameters according to the suggestions to improve the equipment's operating efficiency and stability. The environmental risk assessment work order combines environmental parameters and meteorological factors to assess the risk level of the equipment's environment, reminding the operation and maintenance personnel to pay attention to the risk changes in the equipment's environment and take corresponding protective measures; Step 3: Generate an operation and maintenance work order, which includes the name, number, location, current status, historical equipment failure records, maintenance plan, required tools, and material list of the equipment to be repaired.

2. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The logistic regression model in the meteorological impact model S4 regards the occurrence of a fault as a binary classification problem, takes occurrence or non-occurrence as the result, takes meteorological data, equipment data, operating parameters and environmental parameters as independent variables, and the fault occurrence status as the dependent variable, and establishes a logistic regression equation to describe the binary relationship between the independent variable and the dependent variable, namely: ， in, Indicates that given the independent variable Under the condition of , the probability that the dependent variable Y takes the value of 1 is, is the base of natural logarithms, are the regression coefficients of meteorological data, equipment data, operating parameters and environmental parameters, respectively. are the values ​​of device data, operating parameters and environmental parameters respectively. The significance level test is used to screen important variables, exclude insignificant variables, optimize the model, adjust the coefficients of the logistic regression equation, add regularization terms, and improve the classification accuracy of the model.

3. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The meteorological impact model S5 integrates multiple factors to establish the meteorological impact model by combining the results of linear regression and logistic regression, selecting variables that have a significant impact on the occurrence of faults, integrating the selected variables into the model, and using historical data to verify the model.

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