Active operation and maintenance work order driving method based on meteorological influence analysis

Abstract

The invention discloses an active operation and maintenance work order driving method based on meteorological influence analysis, which comprises a meteorological influence analysis module and an operation and maintenance work order generation module, and is characterized in that the meteorological influence analysis module predicts the fault influence and trend of a meteorological event on a power distribution network by establishing a meteorological influence model; the operation and maintenance work order generation module generates a corresponding operation and maintenance work order on the basis of the analysis result of the meteorological influence analysis module, and the method analyzes the meteorological influence model, identifies and positions possible fault points in advance, thereby reducing power failure events caused by weather factors, reducing the fault rate, guaranteeing the stable operation of the power distribution network, and improving the reliability of the power distribution network. And meanwhile, the work order is automatically generated in the operation and maintenance work order generation module according to the analysis result of the meteorological influence analysis module, so that the manual decision-making time is shortened, the operation and maintenance response speed is improved, the optimal configuration of resources is realized, and the operation and maintenance efficiency is improved.

Description

Technical Field

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

[0002] Since the Industrial Revolution, with the development of the economy and the over-exploitation of the earth's resources, carbon dioxide emissions have increased sharply, leading to an enhanced greenhouse effect, which has had a significant impact on the earth's environment, causing global warming, an increase in extreme weather events and other phenomena.

[0003] 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. Failures in the distribution network will directly affect users' lives and economic activities.

[0004] However, extreme weather events pose a huge threat to the stable and reliable operation of the power system. For example, the heavy rainfall and flooding in southern China in 2019 caused large-scale power outages, showing the vulnerability of the distribution network under severe weather conditions. In order to improve the operational reliability of the distribution network under extreme weather events, relevant power workers made corresponding deployments in advance based on meteorological forecasts to deal with the threats posed by meteorological events to the safety of the distribution network.

[0005] For example, the Chinese invention patent with application number 201910399119.3 proposes a method for active power grid operation and maintenance warning 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 a set F of historical operation and maintenance vectors; C) associating the fields in the operation and maintenance vector Vs with each other, and 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, and converting the operation and maintenance data into the latest operation and maintenance vector. If, then issue a 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 issue a warning message. This technical solution mainly generates warning conditions by combining the historical data of the regional power grid to achieve early warning, but does not mention the relevant measures after the warning.

[0006] In order to ensure that the distribution network can continue to operate stably during extreme weather events and to ensure the normal sound field for residents in areas experiencing extreme weather events, it is necessary to take corresponding measures in advance to respond to the occurrence of disasters and deploy relevant personnel and materials. Summary of the invention

[0007] In order to overcome the above problems, the purpose of the present invention is to provide an active operation and maintenance work order driving method based on meteorological impact analysis, including a meteorological impact analysis module and an operation and maintenance work order generation module. The method uses the meteorological impact analysis module to establish a meteorological impact model, and by analyzing the meteorological impact model, identifies and locates possible fault points in advance, thereby reducing power outages 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, thereby 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.

[0008] The technical solution adopted by the present invention is:

[0009] An active operation and maintenance work order driving 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 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 result of the meteorological impact analysis module.

[0010] The steps for constructing the meteorological impact model are:

[0011] S1: Data collection and preprocessing;

[0012] S2: Establish a linear regression model of the correlation between meteorological factors and fault occurrence;

[0013] S3: Model testing and optimization, testing and optimizing the linear regression model in step S2;

[0014] S4: Establish a logistic regression model to analyze the binary relationship between meteorological factors and fault occurrence;

[0015] S5: Integrate multiple factors to establish a meteorological impact model;

[0016] S6: Solve the model to obtain the probability and trend of distribution network failure.

[0017] The steps of generating a work order in the operation and maintenance work order generating module are as follows:

[0018] Step 1: Set the rules for generating operation and maintenance work orders.

[0019] Including equipment inspection and maintenance work orders, equipment failure warning work orders, operating parameter adjustment work orders, and environmental risk assessment work orders;

[0020] Step 2: Train the model through historical work order data and historical meteorological data, and continuously optimize the generation rules.

[0021] The optimization of the generation rules is performed according to the priority, and the priority is sorted as follows:

[0022] Equipment inspection and maintenance work order > Equipment failure warning work order > Operation parameter adjustment work order > Environmental risk assessment work order,

[0023] Step 3: Generate an operation and maintenance work order.

[0024] As a further description of the present invention, the data collected in S1 in the meteorological impact model includes meteorological data, equipment data, operating parameters, and environmental parameters.

[0025] The meteorological data include wind speed, sunshine time, air pressure, temperature, humidity, and precipitation.

[0026] The equipment data includes equipment age, type, and historical fault records.

[0027] The operating parameters are load, current, and voltage.

[0028] The environmental parameters are topography, vegetation cover, and altitude.

[0029] As a further description of the present invention, the data preprocessing in S1 in the meteorological impact model adopts data cleaning, data conversion, data standardization or normalization.

[0030] As a further description of the present invention, the linear regression model in the meteorological impact model S2 takes meteorological factors as independent variables, fault occurrence rate as dependent variable, and operation parameters and environmental parameters as regression coefficients to describe the linear relationship between meteorological factors and fault occurrence, that is,

[0031] F(t)=λ1t+λ2t

[0032] Where F(t) represents the failure rate,

[0033] t represents meteorological factors,

[0034] λ1 represents the regression coefficient of the operating parameters,

[0035] λ2 represents the regression coefficient of environmental parameters.

[0036] As a further description of the present invention, the meteorological impact model S3 determines the degree of influence of each meteorological factor on the occurrence of a fault, optimizes the model, adds or deletes variables, adjusts the model structure, and improves the prediction accuracy of the model.

[0037] As a further description of the present invention, 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 state as the dependent variable, and establishes a logistic regression equation to describe the binary relationship between the independent variable and the dependent variable, that is:

[0038]

[0039] Among them, P(Y=1 / X) represents the probability that the dependent variable Y takes the value of 1 under the condition of a given independent variable x.

[0040] e is the base of natural logarithms,

[0041] β0, β1, β2, and β3 are the regression coefficients of meteorological data, equipment data, operating parameters, and environmental parameters, respectively.

[0042] x1, x2, and x3 are the values ​​of device data, operating parameters, and environmental parameters, respectively.

[0043] 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.

[0044] As a further description of the present invention, the meteorological impact model S5 integrates multiple factors to establish a meteorological impact model: combining the results of linear regression and logistic regression, selecting variables that have a significant impact on the occurrence of faults, integrating the screened variables into the model, and using historical data to verify the model.

[0045] As a further description of the present invention, in the work order generation step of the operation and maintenance work order generation module:

[0046] The equipment inspection and maintenance work order corresponds to abnormal weather factors. The operation and maintenance personnel check whether the equipment is affected by weather factors.

[0047] The equipment failure warning worksheet combines meteorological factors and equipment operation data to predict the probability of equipment failure and remind operation and maintenance personnel to take measures in advance to prevent equipment failure.

[0048] The operating parameter adjustment worksheet gives optimization suggestions for the operating parameters based on meteorological factors and real-time monitoring data of the 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.

[0049] The environmental risk assessment worksheet combines environmental parameters and meteorological factors to assess the risk level of the environment in which the equipment is located, reminding operation and maintenance personnel to pay attention to risk changes in the environment in which the equipment is located and take corresponding protective measures.

[0050] As a further description of the present invention, the content of the work order includes the name, number, location, current status, historical fault record of the equipment to be repaired, maintenance plan, required tools, and material list.

[0051] Beneficial effects of the present invention:

[0052] The present invention discloses an active operation and maintenance work order driving method based on meteorological impact analysis, comprising a meteorological impact analysis module and an operation and maintenance work order generation module. The method uses the meteorological impact analysis module to establish a meteorological impact model, and by analyzing the meteorological impact model, identifies and locates possible fault points in advance, thereby reducing power outages caused by weather factors, reducing the failure rate, and ensuring stable operation of the distribution network. At the same time, the analysis result of the meteorological impact analysis module is used to automatically generate a work order in the operation and maintenance work order generation module, thereby reducing the time for manual decision-making, improving the operation and maintenance response speed, achieving optimal allocation of resources, and improving the operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural diagram of the active operation and maintenance work order driving method based on meteorological impact analysis proposed by the present invention;

[0054] Figure 2 This is a flow chart for building a meteorological impact model for the meteorological impact analysis module of the active operation and maintenance work order driving method based on meteorological impact analysis proposed in the present invention. DETAILED DESCRIPTION

[0055] The specific implementation of the present invention is described below in conjunction with the accompanying drawings and embodiments:

[0056] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0057] 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 description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0058] like Figures 1-2 As shown, it shows a specific embodiment of the present invention:

[0059] Example

[0060] An active operation and maintenance work order driving 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 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 result of the meteorological impact analysis module.

[0061] In this embodiment, Figure 1 As shown, the method uses a meteorological impact analysis module to establish a meteorological impact model. By analyzing the meteorological impact model, possible fault points can be identified and located in advance, thereby reducing power outages 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, thereby reducing the time for manual decision-making, improving the operation and maintenance response speed, achieving optimal resource allocation, and improving operation and maintenance efficiency.

[0062] The steps for constructing the meteorological impact model are:

[0063] S1: Data collection and preprocessing;

[0064] S2: Establish a linear regression model of the correlation between meteorological factors and fault occurrence;

[0065] S3: Model testing and optimization, testing and optimizing the linear regression model in step S2;

[0066] S4: Establish a logistic regression model to analyze the binary relationship between meteorological factors and fault occurrence;

[0067] S5: Integrate multiple factors to establish a meteorological impact model;

[0068] S6: Solve the model to obtain the probability and trend of distribution network failure.

[0069] In this embodiment, Figure 2 As shown, the meteorological impact model adopts a linear regression model and a logistic regression model to obtain the final meteorological impact model, thereby improving the accuracy of the model in predicting the probability of fault occurrence, responding in a timely manner, and ensuring the stable operation of the distribution network.

[0070] The steps of generating a work order in the operation and maintenance work order generating module are as follows:

[0071] Step 1: Set the rules for generating operation and maintenance work orders.

[0072] Including equipment inspection and maintenance work orders, equipment failure warning work orders, operating parameter adjustment work orders, and environmental risk assessment work orders;

[0073] Step 2: Train the model through historical work order data and historical meteorological data, and continuously optimize the generation rules.

[0074] The optimization of the generation rules is performed according to the priority, and the priority is sorted as follows:

[0075] Equipment inspection and maintenance work order > Equipment failure warning work order > Operation parameter adjustment work order > Environmental risk assessment work order,

[0076] Step 3: Generate an operation and maintenance work order.

[0077] In this embodiment, Figure 1 As shown, the generation rules of the operation and maintenance work order are set based on the impact on the distribution network. The four types of work orders, namely, equipment inspection and maintenance work order, equipment failure warning work order, operation parameter adjustment work order, and environmental risk assessment work order, correspond to different meteorological factors or equipment failures or equipment operation parameter failures or environmental factors.

[0078] Specifically, in the work order generation step of the operation and maintenance work order generation module:

[0079] The equipment inspection and maintenance work order corresponds to abnormal weather factors. The operation and maintenance personnel check whether the equipment is affected by weather factors.

[0080] The equipment failure warning worksheet combines meteorological factors and equipment operation data to predict the probability of equipment failure and remind operation and maintenance personnel to take measures in advance to prevent equipment failure.

[0081] The operating parameter adjustment worksheet gives optimization suggestions for the operating parameters based on meteorological factors and real-time monitoring data of the 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.

[0082] The environmental risk assessment worksheet combines environmental parameters and meteorological factors to assess the risk level of the environment in which the equipment is located, reminding operation and maintenance personnel to pay attention to risk changes in the environment in which the equipment is located and take corresponding protective measures.

[0083] Specifically, the content of the work order includes the name, number, location, current status, historical fault record of the equipment to be repaired, maintenance plan, required tools, and material list.

[0084] 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 preset thresholds or show abnormal fluctuations, the system determines that the meteorological factors are abnormal. For example, when the temperature is abnormal, that is, the temperature exceeds the normal operating range of the equipment, it may cause the equipment to overheat or performance degradation. When the humidity is too high, that is, too high humidity may cause internal circuit short circuit or corrosion of the equipment, increasing the risk of failure. When the air pressure fluctuates, the drastic fluctuation of the 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. An equipment inspection and maintenance work order is generated for the meteorological factor agenda, requiring operation and maintenance personnel to check whether the equipment is affected by meteorological factors and perform necessary maintenance.

[0085] The equipment failure warning work order combines meteorological factors and equipment operation data, mainly the type of equipment, operating years, etc. to predict the probability of equipment failure. For example, when the equipment is aged, that is, when the equipment has been in operation for a long time, its performance may gradually decline and be more susceptible to meteorological factors. An equipment failure warning work order is generated based on the equipment operation data to remind operation and maintenance personnel to take measures in advance to prevent the occurrence of equipment failures.

[0086] The operating parameter adjustment work order gives optimization suggestions for the operating parameters based on meteorological factors and real-time monitoring data of operating parameters, mainly voltage, current, compliance and other parameters. For example, when the load is too large and the load of the equipment exceeds its design capacity, it may cause the equipment to overheat, be damaged or have performance degraded. An operating parameter adjustment work order is generated for the operating parameters, and 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.

[0087] 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 in which the equipment is located, generate an environmental risk assessment work order, and alert operation and maintenance personnel to pay attention to risk changes in the environment in which the equipment is located and take corresponding protective measures.

[0088] Specifically, the content of the work order includes the name, number, location, current status, historical fault record of the equipment to be repaired, maintenance plan, required tools, and material list.

[0089] In this embodiment, when actually carrying out maintenance, dynamic adjustments should be made based on weather forecasts and equipment status to ensure that maintenance is completed before extreme weather and to avoid losses in extreme weather. The list of tools and materials required for maintenance in the work order is directly included, which ensures the efficiency of on-site maintenance during actual use and reduces work order delays caused by lack of tools.

[0090] Specifically, the data collected in S1 in the meteorological impact model includes meteorological data, equipment data, operating parameters, and environmental parameters.

[0091] The meteorological data include wind speed, sunshine time, air pressure, temperature, humidity, and precipitation.

[0092] The equipment data includes equipment age, type, and historical fault records.

[0093] The operating parameters are load, current, and voltage.

[0094] The environmental parameters are topography, vegetation cover, and altitude.

[0095] Specifically, the data preprocessing in S1 in the meteorological impact model adopts data cleaning, data conversion, data standardization or normalization.

[0096] In this embodiment, the data cleaning is to remove duplicates and erroneous values, fill in missing values, use box plot method or Z-score method to identify and remove outliers in the data, ensure the accuracy of the analysis results, use linear interpolation method and KNN (K nearest neighbors) to fill in missing values, ensure the integrity of the data set, and thus ensure the effective use of the collected data.

[0097] The data conversion is to convert the data type into a form suitable for analysis, specifically to convert the categorical variables into numerical variables.

[0098] The purpose of data standardization or normalization is to ensure that the dimensions of different variables are consistent to facilitate subsequent analysis.

[0099] Specifically, the linear regression model in the meteorological impact model S2 uses meteorological factors as independent variables, failure rate as dependent variables, and operating parameters and environmental parameters as regression coefficients to describe the linear relationship between meteorological factors and failure occurrence, that is,

[0100] F(t)=λ1t+λ2t

[0101] Where F(t) represents the failure rate,

[0102] t represents meteorological factors,

[0103] λ1 represents the regression coefficient of the operating parameters,

[0104] λ2 represents the regression coefficient of environmental parameters.

[0105] In this embodiment, both the operating parameters and environmental parameters may 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.

[0106] Specifically, the meteorological influence model S3 determines the influence degree of each meteorological factor on the occurrence of the fault, optimizes the model, adds or deletes variables, adjusts the model structure, and improves the prediction accuracy of the model.

[0107] Specifically, 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 state as the dependent variable, and establishes a logistic regression equation to describe the binary relationship between the independent variable and the dependent variable, that is:

[0108]

[0109] Among them, P(Y=1 / X) represents the probability that the dependent variable Y takes the value of 1 under the condition of a given independent variable x.

[0110] e is the base of natural logarithms,

[0111] β0, β1, c2, and β3 are the regression coefficients of meteorological data, equipment data, operating parameters, and environmental parameters, respectively.

[0112] x1, x2, and x3 are the values ​​of device data, operating parameters, and environmental parameters, respectively.

[0113] 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.

[0114] Specifically, 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.

[0115] In general, the active operation and maintenance work order driven method analyzes the meteorological impact model to identify and locate possible fault points in advance, thereby reducing power outages 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 for manual decision-making, improving the operation and maintenance response speed, achieving optimal resource allocation, and improving operation and maintenance efficiency.

[0116] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0117] Many other changes and modifications may 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 the 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; S2: Establish a linear regression model of the correlation between meteorological factors and fault occurrence; S3: Model testing and optimization, testing and optimizing the linear regression model in step S2; 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, Step 3: Generate an operation and maintenance work order.

2. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The data collected in S1 of the meteorological impact model includes meteorological data, equipment data, operating parameters, and environmental parameters. The meteorological data include wind speed, sunshine time, air pressure, temperature, humidity, and precipitation. The equipment data includes equipment age, type, and historical fault records. The operating parameters are load, current, and voltage. The environmental parameters are topography, vegetation cover, and altitude.

3. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The data preprocessing in S1 in the meteorological impact model is carried out by cleaning data, converting data, standardizing data or normalizing data.

4. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The linear regression model in the meteorological impact model S2 takes meteorological factors as independent variables, fault occurrence rate as dependent variables, and operation parameters and environmental parameters as regression coefficients to describe the linear relationship between meteorological factors and fault occurrence, that is, F(t)=λ1t+λ2t Where F(t) represents the failure rate, t represents meteorological factors, λ1 represents the regression coefficient of the operating parameters, λ2 represents the regression coefficient of environmental parameters.

5. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: The meteorological influence model S3 determines the influence of each meteorological factor on the occurrence of a fault, optimizes the model, adds or deletes variables, adjusts the model structure, and improves the prediction accuracy of the model.

6. 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: Among them, P(Y=1 / X) represents the probability that the dependent variable Y takes the value of 1 under the condition of a given independent variable x. e is the base of natural logarithms, β0, β1, β2, and β3 are the regression coefficients of meteorological data, equipment data, operating parameters, and environmental parameters, respectively. x1, x2, and x3 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.

7. 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.

8. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 1 is characterized in that: In the work order generation step of the operation and maintenance work order generation module: The equipment inspection and maintenance work order corresponds to abnormal weather factors. The operation and maintenance personnel check whether the equipment is affected by weather factors. The equipment failure warning worksheet combines meteorological factors and equipment operation data to predict the probability of equipment failure and remind operation and maintenance personnel to take measures in advance to prevent equipment failure. The operating parameter adjustment worksheet gives optimization suggestions for the operating parameters based on meteorological factors and real-time monitoring data of the 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. The environmental risk assessment worksheet combines environmental parameters and meteorological factors to assess the risk level of the environment in which the equipment is located, reminding operation and maintenance personnel to pay attention to risk changes in the environment in which the equipment is located and take corresponding protective measures.

9. The active operation and maintenance work order driving method based on meteorological impact analysis according to claim 8 is characterized in that: The contents of the work order include the name, number, location, current status, historical fault record of the equipment to be repaired, maintenance plan, required tools, and material list.

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