Automatic adjustment method for environmental adaptability of power distribution terminal

Abstract

The present invention relates to the field of power distribution terminal management technology, and specifically to an automatic adjustment method for environmental adaptability of a power distribution terminal, S1: setting a reference value of a power distribution terminal operating parameter; S2: collecting external environmental data of the power distribution terminal; S3: using data analysis technology to perform comparative analysis to identify abnormal environmental changes; S4: based on the data analysis results in S3, predicting the impact of environmental changes on the operation of the power distribution terminal; S5: according to the prediction results of S4, using a preset parameter optimization algorithm to calculate new operating parameters; S6: analyzing data to verify the effectiveness of the adjustment; when the adjustment result meets the expected effect, the parameter reference value is updated; if the effect does not meet expectations, the prediction model and the parameter optimization algorithm are adjusted. The present invention significantly improves the operating efficiency and reliability of the system by real-time monitoring of environmental changes and automatically adjusting the operating parameters of the power distribution terminal, while reducing energy consumption and maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution terminal management, and in particular to a method for automatically adjusting the environmental adaptability of a power distribution terminal. Background Art

[0002] In the power system, the stable and efficient operation of distribution terminals is crucial to the reliability of the entire power grid. Traditional distribution terminal operations mainly rely on preset fixed parameters, which are rarely adjusted according to environmental changes after the initial configuration of the equipment. However, environmental conditions such as temperature, humidity, air pressure and electromagnetic interference can significantly affect the performance and life of distribution equipment. For example, high temperature may increase power loss, and humidity changes may affect the insulation performance of the equipment. These factors are often not effectively monitored and responded to in real time in traditional systems.

[0003] Existing technologies have significant limitations in dynamic environmental adaptability. Although some systems attempt to adjust operating parameters through simple sensor feedback, these adjustments are usually not flexible enough to reflect actual needs under complex environmental conditions in real time. In addition, the lack of highly integrated data analysis and predictive models makes it impossible for existing systems to predict the potential impact of environmental changes on distribution terminal operations, making it difficult to adjust parameters in advance to prevent efficiency degradation or equipment failure.

[0004] Therefore, there is an urgent need to develop a distribution terminal management system that can monitor environmental changes in real time and automatically optimize operating parameters to adapt to these changes in order to improve the overall efficiency and reliability of the power system. Summary of the invention

[0005] Based on the above objectives, the present invention provides a method for automatically adjusting the environmental adaptability of a power distribution terminal.

[0006] The method for automatically adjusting the environmental adaptability of a power distribution terminal comprises the following steps:

[0007] S1: Initialize the adjustment process and set the reference values ​​of the operating parameters of the power distribution terminal, which include voltage, current and frequency;

[0008] S2: Through the preset sensor network, real-time monitoring and collection of external environmental data of the power distribution terminal, including temperature, humidity, air pressure and electromagnetic field strength;

[0009] S3: The real-time environmental data collected in step S2 is integrated with the historical data, and data analysis techniques are used for comparative analysis to identify abnormal environmental changes;

[0010] S4: Based on the data analysis results in S3, use the preset environmental impact prediction model to predict the impact of environmental changes on the operation of distribution terminals;

[0011] S5: According to the prediction results of S4, a preset parameter optimization algorithm is used to calculate new operating parameters, and the operating parameters are automatically adjusted;

[0012] S6: Record the operating parameters and their effects after adjustment in S5, and analyze the data to verify the effectiveness of the adjustment; when the adjustment results meet the expected effects, update the parameter baseline values; if the effects do not meet expectations, adjust the prediction model and parameter optimization algorithm.

[0013] Furthermore, the S1 specifically includes:

[0014] S11: Collecting historical operation data of the power distribution terminal, wherein the historical operation data includes long-term records of parameters such as voltage, current, frequency, ambient temperature, humidity and air pressure;

[0015] S12: Analyze historical operation data and identify environmental factors that affect the performance of distribution terminals through data mining technology. Specifically, regression analysis technology is used to count the variation range and frequency of the factors under different conditions, and the analysis results are used as output;

[0016] S13: Based on the statistical results of S12, a multivariate regression model is established to determine the optimal operating parameters under different environmental conditions, and the regression equation formula is set as: ,in, is the operation parameter, is a constant term, is the ambient temperature, For humidity, is the air pressure, is the electromagnetic field strength, is the regression coefficient. The multivariable regression model takes historical data as input, provides preset reference values ​​of parameters of voltage, current and frequency through regression analysis, and generates model parameters as output;

[0017] S14: Using the multivariate regression model established in S13, a simulation test is performed on the operating parameters of the power distribution terminal to verify the accuracy and stability of the model;

[0018] S15: According to the simulation results of S14, the multivariable regression model is fine-tuned and optimized to set the final operating parameter benchmark values.

[0019] Furthermore, the S2 specifically includes:

[0020] S21: deploying a sensor network, wherein the sensor network includes a plurality of high-precision sensors, which are installed around the power distribution terminal to form a comprehensive coverage of environmental parameters, and the sensor types include temperature sensors, humidity sensors, air pressure sensors, and electromagnetic field sensors;

[0021] S22: Real-time monitoring of ambient temperature through a temperature sensor. The temperature sensor uses thermocouple or thermistor technology to measure ambient temperature changes and transmit the temperature data as an input signal to a data acquisition unit;

[0022] S23: Real-time monitoring of environmental humidity through a humidity sensor. The humidity sensor uses capacitive humidity sensing technology to capture humidity changes in the air and transmits humidity data as an input signal to a data acquisition unit;

[0023] S24: monitor the ambient air pressure in real time through the air pressure sensor, and transmit the air pressure data as an input signal to the data acquisition unit;

[0024] S25: Real-time monitoring of the electromagnetic field strength of the environment through an electromagnetic field sensor. The electromagnetic field sensor adopts Hall effect sensing technology, can detect changes in the electromagnetic field in the environment, and transmits the electromagnetic field strength data as an input signal to the data acquisition unit;

[0025] S26: The data of temperature, humidity, air pressure and electromagnetic field strength are collected into a data acquisition unit. The data acquisition unit adopts a multi-channel data acquisition card to ensure the synchronous collection and transmission of various types of data; and the collected data are preliminarily processed, including denoising, filtering and calibration.

[0026] Furthermore, the S3 specifically includes:

[0027] S31: Establish a data repository for storing historical environmental data and real-time collected data of the power distribution terminal;

[0028] S32: transmits the real-time environmental data collected in S2 to the data repository to ensure real-time data update, while retaining historical environmental data for long-term trend analysis and immediate anomaly comparison;

[0029] S33: Data fusion processing, which integrates real-time data and historical data in the data repository. Specifically, the timestamp alignment method is used to ensure data consistency. That is, for each real-time data point, the historical data point closest to its time is found and merged.

[0030] S34: Use data analysis techniques to conduct comparative analysis, including anomaly detection algorithms and trend analysis. The anomaly detection algorithm is based on a machine learning model, specifically preset as an isolation forest-based algorithm, which is used to identify data points that are significantly different from historical data patterns; trend analysis uses sliding window average and linear regression models to analyze data change trends;

[0031] S35: Identify abnormal changes and trend deviations. When the value of a parameter of real-time data or its change trend is abnormal or has trend deviations compared with historical data, the abnormal or trend deviation data will be automatically marked and an abnormal report will be generated.

[0032] Furthermore, the S34 specifically includes:

[0033] S341: Isolation forest algorithm is used to identify abnormal data points. Isolation forest is an unsupervised learning algorithm that isolates a single observation point by randomly selecting a feature and its random value. This process is repeated to build multiple decision trees to form a forest. The degree of abnormality of a data point is determined by the length of the path in which it is isolated in the tree. The shorter the path, the more likely the data point is to be abnormal. The specific calculation formula is: ,in, It is a data point The average path length in all trees, is the normalization factor, which depends on the number of samples ;

[0034] S342: Use sliding window average to analyze the change trend of data, and select a window size for each environmental parameter , and then calculate the average of the data in the window to smooth out short-term fluctuations and reveal long-term trends. The calculation formula is: ,in, It's time point The data value of is the current time point, is the window size;

[0035] S343: Apply the linear regression model to determine the specific direction and strength of the trend. The linear regression fits the data model by minimizing the sum of squared errors. The calculation formula is: ,in, is the dependent variable, is the independent variable, and is the regression coefficient, is the error term; the coefficient The slope of the trend is used to quantify the speed and direction of change;

[0036] S344: Combine the results of S341 and S343 to mark abnormal data points and trend changes.

[0037] Furthermore, the S4 specifically includes:

[0038] S41: receiving the analysis results transmitted from S3, including the identified abnormal data points and trend change reports, and the received data and reports are used as inputs to the environmental impact prediction model;

[0039] S42: Deploy an environmental impact prediction model, which uses a regression analysis method based on machine learning, specifically a support vector machine regression model, which can establish the relationship between environmental changes and distribution terminal operating parameters based on historical data;

[0040] S43: Input the data received in S41 into the support vector machine regression model to calculate the potential impact of environmental changes on the operating parameters of the distribution terminal. The specific formula is: ,in, is the predicted operating parameter impact value; are the coefficients learned by the model; is the kernel function used to calculate the training data points and new data points the relationship between; is the bias term; is the number of support vectors;

[0041] S44: Analyze the prediction results output by the model to determine the specific impact level and its potential impact on the operation of distribution terminals.

[0042] Furthermore, the S44 specifically includes:

[0043] S441: receiving the prediction results generated by the support vector machine regression model in step S43, including the predicted impact values ​​of the distribution terminal operation parameters current, voltage and frequency;

[0044] S442: Calculate the prediction error to evaluate the accuracy of the model output. The error calculation formula is:

[0045] , where the actual value is the operating parameter value obtained from the actual operation data, and the predicted value is the predicted value of the corresponding parameter given by the model;

[0046] S443: judging the accuracy of the prediction according to the size of the error, specifically setting a threshold, when the error is less than the threshold, the prediction result is considered to be reliable; when the error is greater than the threshold, the cause of the error needs to be analyzed;

[0047] S444: For prediction results with errors greater than the threshold, factor analysis is performed to identify variables that lead to inaccurate predictions, including environmental factors that are not fully considered in the model, data collection errors, or model overfitting problems;

[0048] S445: Based on the factor analysis results, adjusting the parameters of the support vector machine regression model or retraining the model to optimize the prediction accuracy, including adjusting the kernel function parameters of the model, introducing or removing characteristic variables;

[0049] S446: Conduct a new round of testing on the adjusted model to verify the effectiveness of the improvement measures by repeating S442 to S445 until the prediction error meets the preset threshold.

[0050] Furthermore, the S5 specifically includes:

[0051] S51: receiving the prediction result in S4, including the predicted impact values ​​on the operating parameters of the power distribution terminal, namely, current, voltage and frequency;

[0052] S52: Deploy parameter optimization algorithm. The specific optimization algorithm used is particle swarm optimization, which is used to solve continuous nonlinear optimization problems and find the optimal solution by simulating the social behavior of bird flocks.

[0053] S53: The predicted target operating parameter value is used as the objective function of the particle swarm optimization algorithm to form an optimization problem. In the particle swarm optimization algorithm, each particle represents a potential solution, that is, a set of set values ​​of operating parameters. The particle updates its position by following the current optimal particle. The position update formula is:

[0054] ;

[0055] ;in, The particle is The speed of iterations; The particle is The position of the iteration represents a set of operation parameters; is the optimal position that the particle has ever experienced; is the global optimal position; is the inertia weight, which is used to control the influence of the previous speed on the current speed; is the learning factor; is a random number used to increase the randomness of the search;

[0056] S54: executing a particle swarm optimization algorithm to calculate new operating parameter settings, and after multiple iterations, until a parameter setting that optimizes the objective function is found;

[0057] S55: Apply the calculated optimal operating parameters, including current, voltage and frequency, to the power distribution terminal to adjust its operating state to adapt to the predicted environmental changes.

[0058] Furthermore, the S6 specifically includes:

[0059] S61: recording the operating parameters adjusted in S5 and their actual operating effects, including collecting performance data after adjustment, including energy efficiency ratio, system stability and failure rate;

[0060] S62: Analyze the adjusted data and compare the operating results before and after the adjustment to verify the effectiveness of the adjustment of the operating parameters. Specifically, the statistical analysis method of the T-test is used to determine whether the performance difference before and after the adjustment is significant. The specific formula is: ,in represent The statistic of the test; represents the sample mean of the performance indicator related to the operating parameter before adjustment; represents the sample mean of the performance indicator related to the adjusted operating parameters; represents the standard deviation of the sample differences in the performance index before and after adjustment; represents the sample size, i.e., the number of data points for performance evaluation;

[0061] S63: Determine the degrees of freedom for the T-test ,use Statistics and degrees of freedom are calculated by table lookup or statistical software. The value is used to express the probability of the observed data or extreme data occurring when the null hypothesis is true;

[0062] S64: According to The value is used to judge the significance of the adjustment effect. When the value is less than the pre-set significance level, the adjustment result is considered significant, and the baseline value of the operating parameter will be updated to the new optimal value;

[0063] S65: If When the value is not significant or the improvement target is not achieved, the prediction model and parameter optimization algorithm are adjusted;

[0064] S66: Implement new prediction models and parameter optimization algorithms, and repeat steps S5 and S6 to test the effectiveness of the adjustment measures to ensure that the distribution terminals can achieve optimal performance under new environmental conditions.

[0065] Furthermore, the S65 specifically includes:

[0066] S651: Analyze the potential factors of prediction error. First, perform statistical analysis on the error predicted by the model, including calculating the mean and variance of the error. The specific formula is:

[0067] Mean Error ;

[0068] Error variance ;in, is the sample size, and They are The predicted and actual values ​​of the samples are then analyzed for sensitivity to the model input characteristics to determine which input variables contribute most to the prediction error, thereby identifying model parameters that need to be readjusted or optimized.

[0069] S652: Adjust the prediction model based on the analysis results of S651. When it is found that some feature variables have a great impact on the model and exceed the preset threshold, it is necessary to update the processing method of the variables, including introducing new data features or modifying the weights of existing features;

[0070] S563: Adjust the particle swarm optimization algorithm, including changing the inertia weight , learning factor and , as well as particle initialization and velocity update strategies to improve the search efficiency and adaptability of the algorithm.

[0071] Beneficial effects of the present invention:

[0072] The present invention can significantly improve the operating efficiency and system reliability of the distribution terminal by real-time monitoring of environmental conditions and automatically adjusting operating parameters. Real-time monitoring includes continuous tracking of key environmental parameters such as temperature, humidity, air pressure and electromagnetic field strength, ensuring that any environmental changes that may potentially affect the performance of the distribution terminal can be identified and responded to in a timely manner. Through this method, the specific impact of environmental conditions on system operation can be predicted before they change, thereby adjusting operating parameters in advance to prevent performance degradation or equipment failure.

[0073] The present invention utilizes isolation forest anomaly detection and particle swarm optimization algorithms to optimize the adjustment of operating parameters, which not only enhances the automation of the system but also minimizes energy waste and maintenance costs through precise and dynamic parameter management. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 A schematic diagram of an automatic adjustment method for environmental adaptability according to an embodiment of the present invention;

[0076] Figure 2 Schematic diagram of the process of predicting the impact of environmental changes according to an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0078] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The words "include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0079] like Figure 1-Figure 2 As shown, the method for automatically adjusting the environmental adaptability of the power distribution terminal includes the following steps:

[0080] S1: Initialize the adjustment process and set the reference values ​​of the operating parameters of the power distribution terminal, which include voltage, current and frequency;

[0081] S2: Through the preset sensor network, real-time monitoring and collection of external environmental data of the power distribution terminal, including temperature, humidity, air pressure and electromagnetic field strength, ensures the accuracy and real-time nature of the data;

[0082] S3: The real-time environmental data collected in step S2 is integrated with the historical data, and data analysis techniques are used for comparative analysis to identify abnormal environmental changes;

[0083] S4: Based on the data analysis results in S3, use the preset environmental impact prediction model to predict the impact of environmental changes on the operation of distribution terminals;

[0084] S5: Based on the prediction results of S4, new operating parameters are calculated using a preset parameter optimization algorithm, and the operating parameters are automatically adjusted to adapt to environmental changes and optimize equipment performance;

[0085] S6: Record the operating parameters and their effects after adjustment in S5, and analyze the data to verify the effectiveness of the adjustment; when the adjustment results meet the expected effects, update the parameter baseline values; if the effects do not meet expectations, adjust the prediction model and parameter optimization algorithm.

[0086] S1 specifically includes:

[0087] S11: Collect historical operation data of the power distribution terminal, the historical operation data includes long-term records of parameters such as voltage, current, frequency, ambient temperature, humidity and air pressure, and the data is used as input;

[0088] S12: Analyze historical operation data and identify environmental factors that affect the performance of distribution terminals through data mining technology. Specifically, regression analysis technology is used to count the variation range and frequency of the factors under different conditions, and the analysis results are used as output;

[0089] S13: Based on the statistical results of S12, a multivariate regression model is established to determine the optimal operating parameters under different environmental conditions, and the regression equation formula is set as: ,in, are operating parameters (such as voltage, current, frequency, etc.), is a constant term, is the ambient temperature, For humidity, is the air pressure, is the electromagnetic field strength, is the regression coefficient. The multivariable regression model takes historical data as input, provides preset reference values ​​of parameters of voltage, current and frequency through regression analysis, and generates model parameters as output;

[0090] S14: using the multivariable regression model established in S13, a simulation test is performed on the operating parameters of the distribution terminal to verify the accuracy and stability of the model. The input of the simulation test is the model parameters and actual operation data, and the output is the simulation result, including the accuracy of the model and optimization suggestions.

[0091] S15: According to the simulation results of S14, the multivariable regression model is fine-tuned and optimized to set the final operating parameter baseline values; through the above steps, the baseline values ​​of the distribution terminal operating parameters are systematically set to ensure the stable and efficient operation of the distribution terminal under different environmental conditions.

[0092] S2 specifically includes:

[0093] S21: Deploy a sensor network, which includes multiple high-precision sensors installed around the power distribution terminal to provide comprehensive coverage of environmental parameters. Sensor types include temperature sensors, humidity sensors, air pressure sensors, and electromagnetic field sensors.

[0094] S22: Real-time monitoring of ambient temperature through a temperature sensor. The temperature sensor uses thermocouple or thermistor technology to measure ambient temperature changes and transmit the temperature data as an input signal to a data acquisition unit;

[0095] S23: Real-time monitoring of environmental humidity through a humidity sensor. The humidity sensor adopts capacitive humidity sensing technology, which can capture humidity changes in the air and transmit humidity data as an input signal to a data acquisition unit;

[0096] S24: Real-time monitoring of ambient air pressure through an air pressure sensor. The air pressure sensor uses micro-electromechanical system (MEMS) technology, has high precision and stability, can accurately reflect air pressure changes, and transmits air pressure data as an input signal to the data acquisition unit;

[0097] S25: Real-time monitoring of the electromagnetic field strength of the environment through an electromagnetic field sensor. The electromagnetic field sensor adopts Hall effect sensing technology, can detect changes in the electromagnetic field in the environment, and transmits the electromagnetic field strength data as an input signal to the data acquisition unit;

[0098] S26: The data of temperature, humidity, air pressure and electromagnetic field strength are collected in a data acquisition unit. The data acquisition unit adopts a multi-channel data acquisition card to ensure the synchronous collection and transmission of various types of data. The collected data are preliminarily processed, including denoising, filtering and calibration, to ensure the accuracy and reliability of the data. The processed data are transmitted as output signals to the central processing unit for further data fusion and analysis.

[0099] S3 specifically includes:

[0100] S31: Establish a data repository for storing historical environmental data and real-time collected data of the power distribution terminal. The data repository is designed as an efficient time series database that can efficiently handle the storage and query of a large amount of time series data;

[0101] S32: transmits the real-time environmental data collected in S2 to the data repository to ensure real-time data update, while retaining historical environmental data for long-term trend analysis and immediate anomaly comparison;

[0102] S33: Data fusion processing, which integrates real-time data and historical data in the data repository. Specifically, the timestamp alignment method is used to ensure data consistency. That is, for each real-time data point, the historical data point closest to its time is found and merged.

[0103] S34: Use data analysis techniques to conduct comparative analysis, including anomaly detection algorithms and trend analysis. The anomaly detection algorithm is based on a machine learning model, specifically preset as an isolation forest-based algorithm, which is used to identify data points that are significantly different from historical data patterns; trend analysis uses sliding window average and linear regression models to analyze data change trends;

[0104] S35: Identify abnormal changes and trend deviations. When the value of a parameter of real-time data or its change trend is abnormal or has trend deviations compared with historical data, the abnormal or trend deviation data will be automatically marked and an abnormal report will be generated; the marked abnormal data and analysis report will be output to the central processing unit for use in S4 to predict the impact of environmental changes on the operation of the distribution terminal and adjust the operating parameters accordingly; the above steps describe in detail how to merge real-time environmental data with historical data and use data analysis technology for comparative analysis to identify abnormal environmental changes.

[0105] S34 specifically includes:

[0106] S341: Isolation forest algorithm is used to identify abnormal data points. Isolation forest is an unsupervised learning algorithm that isolates a single observation point by randomly selecting a feature and its random value. This process is repeated to build multiple decision trees to form a forest. The degree of abnormality of a data point is determined by the length of the path in which it is isolated in the tree. The shorter the path, the more likely the data point is to be abnormal. The specific calculation formula is: ,in, It is a data point The average path length in all trees, is the normalization factor, which depends on the number of samples ;

[0107] S342: Use sliding window average to analyze the change trend of data, and select a window size for each environmental parameter , and then calculate the average of the data in the window to smooth out short-term fluctuations and reveal long-term trends. The calculation formula is: ,in, It's time point The data value of is the current time point, is the window size, which determines the number of data points considered in the average;

[0108] S343: Apply the linear regression model to determine the specific direction and strength of the trend. The linear regression fits the data model by minimizing the sum of squared errors. The calculation formula is: ,in, are dependent variables (e.g. environmental parameter values ​​in a time series), is the independent variable (usually time), and is the regression coefficient, is the error term; the coefficient The slope of the trend is used to quantify the speed and direction of change;

[0109] S344: Combine the results of S341 and S343 to mark abnormal data points and trend changes. This information will be used for analysis and decision-making. By combining the isolation forest algorithm and trend analysis technology, it is possible to effectively identify abnormal points and important change trends from a large amount of environmental data. This method improves the response speed and accuracy to environmental fluctuations, allowing distribution terminals to more accurately adjust their operating parameters to adapt to environmental changes, thereby enhancing the stability and efficiency of the system.

[0110] S4 specifically includes:

[0111] S41: receiving the analysis results transmitted from S3, including the identified abnormal data points and trend change reports, and the received data and reports are used as inputs to the environmental impact prediction model;

[0112] S42: Deploy an environmental impact prediction model, which uses a regression analysis method based on machine learning, specifically the Support Vector Machine (SVM) regression model, which is a technology that effectively handles nonlinear problems and can establish the relationship between environmental changes and distribution terminal operating parameters based on historical data;

[0113] S43: Input the data received in S41 into the support vector machine regression model to calculate the potential impact of environmental changes on the operating parameters of the distribution terminal. The specific formula is: ,in, is the predicted operating parameter impact value; are the coefficients learned by the model; is the kernel function used to calculate the training data points and new data points the relationship between; is the bias term; is the number of support vectors;

[0114] S44: Analyze the prediction results output by the model to determine the specific degree of impact and its potential impact on the operation of the distribution terminal. For example, if the model predicts that an increase in ambient temperature will lead to an increase in current demand, adjust the current setting accordingly to adapt to the predicted change; by using a preset environmental impact prediction model, this method can scientifically predict the specific impact of different environmental changes on the operation of the distribution terminal. Such predictions not only improve the operating efficiency of the distribution terminal, but also can quickly adjust operating parameters when environmental conditions change drastically, thereby ensuring the stability and safety of the system.

[0115] S44 specifically includes:

[0116] S441: receiving the prediction results generated by the support vector machine (SVM) regression model in step S43, including the predicted impact values ​​of the distribution terminal operation parameters of current, voltage and frequency;

[0117] S442: Calculate the prediction error to evaluate the accuracy of the model output. The error calculation formula is:

[0118] Among them, the actual value is the operating parameter value obtained from the actual operation data, and the predicted value is the predicted value of the corresponding parameter given by the model;

[0119] S443: judging the accuracy of the prediction according to the size of the error, and specifically setting a threshold (e.g., 5%). When the error is less than the threshold, the prediction result is considered reliable; when the error is greater than the threshold, the cause of the error needs to be analyzed;

[0120] S444: For prediction results with errors greater than the threshold, factor analysis is performed to identify variables that lead to inaccurate predictions, including environmental factors that are not fully considered in the model, data collection errors, or model overfitting problems;

[0121] S445: Based on the factor analysis results, adjusting the parameters of the support vector machine regression model or retraining the model to optimize the prediction accuracy, including adjusting the kernel function parameters of the model, introducing or removing feature variables;

[0122] S446: Conduct a new round of testing on the adjusted model to verify the effectiveness of the improvement measures, by repeating S442 to S445 until the prediction error meets the preset threshold; through the implementation of the above steps, the output of the environmental impact prediction model can be effectively analyzed and optimized to ensure the high accuracy and practicality of the prediction results, which not only improves the responsiveness of the distribution terminal to environmental changes, but also helps to reduce energy waste and equipment loss caused by improper parameter settings. In addition, by continuously optimizing the prediction model, the overall efficiency and reliability of the system can be gradually improved.

[0123] S5 specifically includes:

[0124] S51: receiving the prediction results in S4, including the predicted impact values ​​on the operating parameters of the power distribution terminal, namely, current, voltage and frequency, which indicate the target values ​​that the operating parameters need to reach under future environmental conditions;

[0125] S52: Deployment of parameter optimization algorithm, which aims to adjust the operating parameters based on the prediction results to optimize the performance of the distribution terminal. The specific optimization algorithm used is Particle Swarm Optimization (PSO), which is an optimization technology based on swarm intelligence and is used to solve continuous nonlinear optimization problems. It simulates the social behavior of bird flocks to find the optimal solution;

[0126] S53: The predicted target operating parameter value is used as the objective function of the particle swarm optimization algorithm to form an optimization problem. In the particle swarm optimization algorithm, each particle represents a potential solution, that is, a set of set values ​​of operating parameters. The particle updates its position by following the current optimal particle. The position update formula is:

[0127] ;

[0128] ;in, The particle is The speed of iterations; The particle is The position of the iteration represents a set of operation parameters; is the optimal position that the particle has ever experienced; is the global optimal position; is the inertia weight, which is used to control the influence of the previous speed on the current speed; It is a learning factor that regulates individual and social learning behaviors; is a random number used to increase the randomness of the search;

[0129] S54: executing a particle swarm optimization algorithm to calculate new operating parameter settings, and after multiple iterations, until a parameter setting that optimizes the objective function is found;

[0130] S55: Apply the calculated optimal operating parameters, including current, voltage and frequency, to the distribution terminal to adjust its operating state to adapt to the predicted environmental changes; by using the particle swarm optimization algorithm to automatically calculate and adjust the operating parameters, it can ensure that the distribution terminal always maintains the optimal operating state when facing environmental changes. This automated adjustment not only improves the system's response speed and efficiency, but also helps to reduce energy consumption and enhance the overall reliability of the system.

[0131] S6 specifically includes:

[0132] S61: recording the operating parameters (such as current, voltage and frequency, etc.) adjusted in S5 and their actual operating effects, including collecting performance data after adjustment, including energy efficiency ratio, system stability and failure rate;

[0133] S62: Analyze the adjusted data and compare the operating results before and after the adjustment to verify the effectiveness of the adjustment of the operating parameters. Specifically, the statistical analysis method of the T-test is used to determine whether the performance difference before and after the adjustment is significant. The specific formula is: ,in represent The statistic of the test is used to compare whether the average performance difference of the distribution terminal operating parameters before and after the adjustment is statistically significant; Represents the sample mean of the performance indicators related to the operating parameters (such as current, voltage and frequency) before adjustment, such as the average value of energy efficiency ratio or system stability before adjustment; Represents the sample mean of a performance indicator related to adjusted operating parameters, such as the average of adjusted energy efficiency ratio or system stability represents the standard deviation of the sample differences between the performance indicators before and after adjustment, which measures the dispersion of performance changes in different measurements; represents the sample size, i.e., the number of data points for performance evaluation, which reflect the impact of operating parameters on system performance before and after adjustment;

[0134] S63: Determine the degrees of freedom for the T-test ,use Statistics and degrees of freedom are calculated by table lookup or statistical software. The value is used to express the probability of the observed data or extreme data occurring when the null hypothesis is true;

[0135] S64: According to The value is used to judge the significance of the adjustment effect. When the value is less than the pre-set significance level (e.g. 0.05), the adjustment result is considered significant, and the baseline value of the operating parameter is updated to the new optimal value;

[0136] S65: If When the value is not significant or the improvement target is not achieved, the prediction model and parameter optimization algorithm are adjusted;

[0137] S66: Implement a new prediction model and parameter optimization algorithm, and repeat steps S5 and S6 to test the effectiveness of the adjustment measures to ensure that the distribution terminal can achieve optimal performance under the new environmental conditions; through the implementation of the above steps, it can be ensured that the operating parameters of the distribution terminal are always optimized based on the latest environmental conditions and system performance data. This dynamic update and continuous verification mechanism not only improves the operating efficiency and reliability of the system, but also enhances the system's adaptability to environmental changes, reducing energy consumption and potential operation and maintenance costs.

[0138] S65 specifically includes:

[0139] S651: Analyze the potential factors of prediction error. First, perform statistical analysis on the error predicted by the model, including calculating the mean and variance of the error. The specific formula is:

[0140] Mean Error ;

[0141] Error variance ;in, is the sample size, and They are The predicted and actual values ​​of the samples are then analyzed for sensitivity to the model input characteristics to determine which input variables contribute most to the prediction error, thereby identifying model parameters that need to be readjusted or optimized.

[0142] S652: Adjust the prediction model based on the analysis results of S651. When it is found that some feature variables have a great impact on the model and exceed the preset threshold, it is necessary to update the processing method of the variables, including introducing new data features or modifying the weights of existing features. In addition, you can try to use different machine learning algorithms to improve the prediction ability of the model. For example, if a linear regression model is currently used and the data shows nonlinear characteristics, you can consider using more complex models such as support vector machines or neural networks. The adjustment of model parameters can be optimized through cross-validation to ensure the generalization ability of the new model under various environmental conditions;

[0143] S563: Adjust the particle swarm optimization algorithm, including changing the inertia weight , learning factor and , as well as particle initialization and velocity update strategies to improve the search efficiency and adaptability of the algorithm. The adjusted velocity and position update formulas are as follows:

[0144] ;

[0145] ,in, ,and are the adjusted inertia weights and learning factors. These parameter adjustments are based on the analysis results of the previous steps, aiming to more effectively explore the solution space and avoid local optimality. Through the adjustment of the above steps, the improvement of the model and algorithm aims to reduce the prediction error and improve the accuracy of parameter optimization, thereby ensuring that the distribution terminal can operate in the optimal state under various environmental conditions.

[0146] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically adjusting the environmental adaptability of a power distribution terminal, characterized in that: The following steps are involved: S1: Initialize the adjustment process and set the reference values ​​of the operating parameters of the power distribution terminal, which include voltage, current and frequency; S2: Through the preset sensor network, real-time monitoring and collection of external environmental data of the power distribution terminal, including temperature, humidity, air pressure and electromagnetic field strength; S3: The real-time environmental data collected in step S2 is integrated with the historical data, and data analysis techniques are used for comparative analysis to identify abnormal environmental changes; S4: Based on the data analysis results in S3, use the preset environmental impact prediction model to predict the impact of environmental changes on the operation of distribution terminals; S5: According to the prediction results of S4, a preset parameter optimization algorithm is used to calculate new operating parameters, and the operating parameters are automatically adjusted; S6: Record the operating parameters and their effects after adjustment in S5, and analyze the data to verify the effectiveness of the adjustment; if the adjustment results meet the expected effects, update the parameter baseline values; if the effects do not meet expectations, adjust the prediction model and parameter optimization algorithm; The S1 specifically includes: S11: Collecting historical operation data of the power distribution terminal, wherein the historical operation data includes long-term records of parameters such as voltage, current, frequency, ambient temperature, humidity and air pressure; S12: Analyze historical operation data and identify environmental factors that affect the performance of distribution terminals through data mining technology. Specifically, regression analysis technology is used to count the variation range and frequency of the factors under different conditions, and the analysis results are used as output; S13: Based on the statistical results of S12, a multivariate regression model is established to determine the optimal operating parameters under different environmental conditions, and the regression equation formula is set as: ,in, is the operation parameter, is a constant term, is the ambient temperature, For humidity, is the air pressure, is the electromagnetic field strength, is the regression coefficient. The multivariable regression model takes historical data as input, provides preset reference values ​​of parameters of voltage, current and frequency through regression analysis, and generates model parameters as output; S14: Using the multivariate regression model established in S13, a simulation test is performed on the operating parameters of the distribution terminal to verify the accuracy and stability of the model; S15: fine-tune and optimize the multivariable regression model according to the simulation results of S14, and set the final operating parameter benchmark value; The S4 specifically includes: S41: receiving the analysis results transmitted from S3, including the identified abnormal data points and trend change reports, and the received data and reports are used as inputs to the environmental impact prediction model; S42: Deploy an environmental impact prediction model, which uses a regression analysis method based on machine learning, specifically a support vector machine regression model, which can establish the relationship between environmental changes and distribution terminal operating parameters based on historical data; S43: Input the data received in S41 into the support vector machine regression model to calculate the potential impact of environmental changes on the operating parameters of the distribution terminal. The specific formula is: ,in, is the predicted operating parameter impact value; are the coefficients learned by the model; is the kernel function used to calculate the training data points and new data points the relationship between; is the bias term; is the number of support vectors; S44: Analyze the prediction results output by the model to determine the specific impact level and its potential impact on the operation of the distribution terminal; The S44 specifically includes: S441: receiving the prediction results generated by the support vector machine regression model in step S43, including the predicted impact values ​​of the distribution terminal operation parameters current, voltage and frequency; S442: Calculate the prediction error to evaluate the accuracy of the model output. The error calculation formula is: , where the actual value is the operating parameter value obtained from the actual operation data, and the predicted value is the predicted value of the corresponding parameter given by the model; S443: judging the accuracy of the prediction according to the size of the error, specifically setting a threshold, when the error is less than the threshold, the prediction result is considered to be reliable; when the error is greater than the threshold, the cause of the error needs to be analyzed; S444: For prediction results with errors greater than the threshold, factor analysis is performed to identify variables that lead to inaccurate predictions, including environmental factors that are not fully considered in the model, data collection errors, or model overfitting problems; S445: Based on the factor analysis results, adjusting the parameters of the support vector machine regression model or retraining the model to optimize the prediction accuracy, including adjusting the kernel function parameters of the model, introducing or removing characteristic variables; S446: Conduct a new round of testing on the adjusted model to verify the effectiveness of the improvement measures by repeating S442 to S445 until the prediction error meets the preset threshold.

2. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 1, characterized in that: The S2 specifically includes: S21: deploying a sensor network, wherein the sensor network includes a plurality of high-precision sensors, which are installed around the power distribution terminal to form a comprehensive coverage of environmental parameters, and the sensor types include temperature sensors, humidity sensors, air pressure sensors, and electromagnetic field sensors; S22: Real-time monitoring of ambient temperature through a temperature sensor. The temperature sensor uses thermocouple or thermistor technology to measure ambient temperature changes and transmit the temperature data as an input signal to a data acquisition unit; S23: Real-time monitoring of environmental humidity through a humidity sensor. The humidity sensor uses capacitive humidity sensing technology to capture humidity changes in the air and transmits humidity data as an input signal to a data acquisition unit; S24: monitor the ambient air pressure in real time through the air pressure sensor, and transmit the air pressure data as an input signal to the data acquisition unit; S25: Real-time monitoring of the electromagnetic field strength of the environment through an electromagnetic field sensor. The electromagnetic field sensor adopts Hall effect sensing technology, can detect changes in the electromagnetic field in the environment, and transmits the electromagnetic field strength data as an input signal to the data acquisition unit; S26: The data of temperature, humidity, air pressure and electromagnetic field strength are collected into a data acquisition unit. The data acquisition unit adopts a multi-channel data acquisition card to ensure the synchronous collection and transmission of various types of data; and the collected data are preliminarily processed, including denoising, filtering and calibration.

3. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 1, characterized in that: The S3 specifically includes: S31: Establish a data repository for storing historical environmental data and real-time collected data of the power distribution terminal; S32: transmits the real-time environmental data collected in S2 to the data repository to ensure real-time data update, while retaining historical environmental data for long-term trend analysis and immediate anomaly comparison; S33: Data fusion processing, which integrates real-time data and historical data in the data repository. Specifically, the timestamp alignment method is used to ensure data consistency. That is, for each real-time data point, the historical data point closest to its time is found and merged. S34: Use data analysis techniques to conduct comparative analysis, including anomaly detection algorithms and trend analysis. The anomaly detection algorithm is based on a machine learning model, specifically preset as an isolation forest-based algorithm, which is used to identify data points that are significantly different from historical data patterns; trend analysis uses sliding window average and linear regression models to analyze data change trends; S35: Identify abnormal changes and trend deviations. When the value of a parameter of real-time data or its change trend is abnormal or has trend deviations compared with historical data, the abnormal or trend deviation data will be automatically marked and an abnormal report will be generated.

4. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 3, characterized in that: The S34 specifically includes: S341: Isolation forest algorithm is used to identify abnormal data points. Isolation forest is an unsupervised learning algorithm that isolates a single observation point by randomly selecting a feature and its random value. This process is repeated to build multiple decision trees to form a forest. The degree of abnormality of a data point is determined by the length of the path in which it is isolated in the tree. The shorter the path, the more likely the data point is to be abnormal. The specific calculation formula is: ,in, It is a data point The average path length in all trees, is the normalization factor, which depends on the number of samples ; S342: Use sliding window average to analyze the change trend of data, and select a window size for each environmental parameter , and then calculate the average of the data in the window to smooth out short-term fluctuations and reveal long-term trends. The calculation formula is: ,in, It's time point The data value of is the current time point, is the window size; S343: Apply the linear regression model to determine the specific direction and strength of the trend. The linear regression fits the data model by minimizing the sum of squared errors. The calculation formula is: ,in, is the dependent variable, is the independent variable, and is the regression coefficient, is the error term; the coefficient The slope of the trend is used to quantify the speed and direction of change; S344: Combine the results of S341 and S343 to mark abnormal data points and trend changes.

5. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 1, characterized in that: The S5 specifically includes: S51: receiving the prediction result in S4, including the predicted impact values ​​on the operating parameters of the power distribution terminal, namely, current, voltage and frequency; S52: Deploy parameter optimization algorithm. The specific optimization algorithm used is particle swarm optimization, which is used to solve continuous nonlinear optimization problems and find the optimal solution by simulating the social behavior of bird flocks. S53: The predicted target operating parameter value is used as the objective function of the particle swarm optimization algorithm to form an optimization problem. In the particle swarm optimization algorithm, each particle represents a potential solution, that is, a set of set values ​​of operating parameters. The particle updates its position by following the current optimal particle. The position update formula is: ； ;in, The particle is The speed of iterations; The particle is The position of the iteration represents a set of operation parameters; is the optimal position that the particle has ever experienced; is the global optimal position; is the inertia weight, which is used to control the influence of the previous speed on the current speed; is the learning factor; is a random number used to increase the randomness of the search; S54: executing a particle swarm optimization algorithm to calculate new operating parameter settings, and after multiple iterations, until a parameter setting that optimizes the objective function is found; S55: Apply the calculated optimal operating parameters, including current, voltage and frequency, to the power distribution terminal to adjust its operating state to adapt to the predicted environmental changes.

6. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 1, characterized in that: The S6 specifically includes: S61: recording the operating parameters adjusted in S5 and their actual operating effects, including collecting performance data after adjustment, including energy efficiency ratio, system stability and failure rate; S62: Analyze the adjusted data and compare the operating results before and after the adjustment to verify the effectiveness of the adjustment of the operating parameters. Specifically, the statistical analysis method of the T-test is used to determine whether the performance difference before and after the adjustment is significant. The specific formula is: ,in represent The statistic of the test; represents the sample mean of the performance indicator related to the operating parameter before adjustment; represents the sample mean of the performance indicator related to the adjusted operating parameters; represents the standard deviation of the sample differences in the performance index before and after adjustment; represents the sample size, i.e., the number of data points for performance evaluation; S63: Determine the degrees of freedom for the T-test ,use Statistics and degrees of freedom are calculated by table lookup or statistical software. The value is used to express the probability of the observed data or extreme data occurring when the null hypothesis is true; S64: According to The value is used to judge the significance of the adjustment effect. When the value is less than the pre-set significance level, the adjustment result is considered significant, and the baseline value of the operating parameter will be updated to the new optimal value; S65: If When the value is not significant or the improvement target is not achieved, the prediction model and parameter optimization algorithm are adjusted; S66: Implement new prediction models and parameter optimization algorithms, and repeat steps S5 and S6 to test the effectiveness of the adjustment measures to ensure that the distribution terminals can achieve optimal performance under new environmental conditions.

7. The method for automatically adjusting the environmental adaptability of a power distribution terminal according to claim 6, characterized in that: The S65 specifically includes: S651: Analyze the potential factors of prediction error. First, perform statistical analysis on the error predicted by the model, including calculating the mean and variance of the error. The specific formula is: Mean Error ; Error variance ;in, is the sample size, and They are The predicted and actual values ​​of the samples are then analyzed for sensitivity to the model input characteristics to determine which input variables contribute most to the prediction error, thereby identifying model parameters that need to be readjusted or optimized. S652: Adjust the prediction model based on the analysis results of S651. When it is found that some feature variables have a great impact on the model and exceed the preset threshold, it is necessary to update the processing method of the variables, including introducing new data features or modifying the weights of existing features; S563: Adjust the particle swarm optimization algorithm, including changing the inertia weight , learning factor and , as well as particle initialization and velocity update strategies to improve the search efficiency and adaptability of the algorithm.

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