Remote monitoring method for operation data of power equipment

Abstract

The invention discloses a remote monitoring method for operation data of power equipment, and relates to the technical field of data analysis and artificial intelligence. Preprocessing the power equipment operation data to obtain a power equipment operation data set; performing feature extraction on the power equipment operation data set to obtain a power equipment operation feature data set; inputting the power equipment operation characteristic data set into an operation state prediction model to predict the operation state of the power equipment; comparing the predicted operation state of the power equipment with a preset operation state threshold value of the power equipment, and judging whether the operation state of the power equipment reaches the standard or not; if the operation state of the power equipment reaches the standard, continuously monitoring the operation data of the power equipment; if the operation state of the power equipment does not reach the standard, giving an alarm and notifying related personnel to take countermeasures; the method is combined with a big data technology to realize prediction of the operation state of the power equipment and warning of potential hidden dangers.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring technology, and in particular to a method for remote monitoring of power equipment operation data. Background Art

[0002] With the continuous expansion and complexity of the power network, the safe operation of power equipment has become particularly important. Remote monitoring can grasp the operating status of equipment in real time, discover potential safety hazards in a timely manner, avoid major accidents, and improve the operating efficiency and safety of the power system; at the same time, remote monitoring also greatly reduces the frequency and scope of manual inspections, reducing labor costs and time costs; in addition, through data analysis and predictive maintenance, it can also reduce the failure rate of equipment and reduce maintenance costs. In the digital age, the digital transformation of power companies has become an inevitable trend. As an important part of digital transformation, remote monitoring methods help improve the overall operational efficiency and competitiveness of enterprises, and are necessary for enterprises to successfully embark on the road of transformation.

[0003] The patent application publication number CN 115864661 B discloses a method for remote monitoring of power equipment operation data, including obtaining power equipment operation data collected by a power equipment operation data collection terminal within a preset time period; selecting part of the power equipment operation data as target power equipment operation data, and storing the target power equipment operation data in a target storage module; the power equipment data collection terminal supports minute-level data collection. The present invention improves the communication efficiency between the power equipment data collection terminal and the power server, facilitates power equipment monitoring personnel to package and send power equipment operation data to the power server in the form of files, and also reduces the storage capacity of the power server, thereby saving power server resources, and when the network is abnormal or the terminal is down, the power equipment operation data that has not been uploaded to the power server will not be lost, thereby reducing the data loss rate.

[0004] The traditional way of monitoring the operation data of power equipment has the following problems: Relying on manual inspections is not only inefficient, but also prone to problems such as missed inspections and false inspections, and cannot ensure the accuracy and timeliness of operation and maintenance; it is limited to on-site operations and difficult to achieve remote monitoring and management, which limits the scope and flexibility of the operation and maintenance personnel and increases the complexity and difficulty of operation and maintenance; it has not been combined with big data technology, and has only achieved preliminary integration of the power equipment operation status data, without in-depth exploration of the intrinsic value, unable to predict the operation status of the power equipment, and unable to predict and warn of potential hidden dangers; the power equipment operation status data has not been extracted, which is likely to cause data redundancy and dimensionality explosion, which will not only take up more storage space and processing resources, increase the company's operating costs, but may also interfere with normal data flow and processing processes, affecting the operating efficiency of the entire data ecosystem.

[0005] In view of this, the present invention proposes a method for remote monitoring of power equipment operation data to solve the above problems. Summary of the invention

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for remote monitoring of power equipment operation data, comprising the following steps: S1. Collecting power equipment operation data; S2. Preprocess the power equipment operation data to obtain a power equipment operation data set; perform feature extraction on the power equipment operation data set to obtain a power equipment operation feature data set; S3, inputting the power equipment operation characteristic data set into the operation state prediction model to predict the operation state of the power equipment; comparing the predicted operation state of the power equipment with the preset power equipment operation state threshold value to determine whether the operation state of the power equipment meets the standard; S4. If the operating status of the power equipment meets the standard, the operating data of the power equipment will continue to be monitored; if the operating status of the power equipment does not meet the standard, an alarm will be triggered and relevant personnel will be notified to take countermeasures.

[0007] Furthermore, the power equipment operation data includes electrical parameters, equipment status parameters, environmental status parameters and fault maintenance data; The electrical parameters include voltage, current and power factor; the equipment status parameters include switch status, operation status and accumulated operation time; the environmental status parameters include ambient temperature, ambient humidity and dust content; the fault maintenance data include fault records and maintenance records; The power equipment operation data is monitored by sensors and collected by data collectors, and each module is connected by wire and / or wireless means.

[0008] Furthermore, the method of preprocessing the power equipment operation data to obtain the power equipment operation data set includes: Check and process missing values. For missing data points, use the interpolation method to fill in the missing data points with the average of the non-missing values ​​of the data points before and after the data point. Detect and process outliers. Use clustering algorithms to identify outliers in the power equipment operation data and remove them, and replace them with the average value. Standardize and normalize the power equipment operation data to convert it into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the impact of dimension. After the preprocessing in the above steps, the power equipment operation data set is obtained.

[0009] Furthermore, the method of extracting features from the power equipment operation data set to obtain the power equipment operation feature data set includes: Defining the Matrix and the target variable ; Construct the matrix ,in is the sample size, is the number of features, is a set of real numbers; the matrix Each row corresponds to a sample, and each column corresponds to a feature; the matrix The data source is the power equipment operation data set; Use Lasso regression on the matrix Perform feature extraction; the objective function of the Lasso regression is ;in, is the sample size, is the number of features, For the The target value of samples, For the The feature vector of the samples, is the regression coefficient vector, is the regularization parameter, is the index of the sample, is the index of the feature; Choosing the best regularization parameter , by changing the regularization parameter The size of controls the complexity of regression and the strength of feature selection; use the data set to train the Lasso regression model, and extract the regression coefficients after training. ; Eliminate regression coefficient The feature is 0, and the regression coefficient is retained The non-zero features are used to construct the power equipment operation feature dataset.

[0010] Furthermore, the method for selecting the optimal regularization parameter includes: Constructing a limit function The regularization parameter is adjusted; the restriction function The mathematical expression is: ;in, is the noise level, is the sample size, is the number of features, and is the influencing factor of the regularization parameter.

[0011] Furthermore, the method of inputting the power equipment operation characteristic data set into the operation status prediction model to predict the operation status of the power equipment includes: Construct an operation status prediction model, including an input layer, an LSTM layer, a fully connected layer, and an output layer; divide the data set into multiple sequence samples, each sample containing a fixed-length time window as input, and the power equipment operation status data in the subsequent time period as output; divide the processed data set into a training set, a test set, and a validation set for model training and evaluation of model performance; The input data of the operation status prediction model is the power equipment operation feature data set, and the output label is the power equipment operation status; the mean square error loss function is selected to measure the difference between the model prediction result and the true label; the operation status prediction model is a long short-term memory network model; The mean square error loss function is: ;in, It is True value, It is Prediction values, is the number of samples of different categories; The running status prediction model is trained using the training set, and the Adam algorithm is selected as the optimizer to minimize the loss function. The Adam algorithm dynamically adjusts the learning rate according to the first-order and second-order moments of the gradient to perform deviation correction and parameter update. The weights are adjusted through back-propagation, and the model is trained iteratively. The performance of the model is evaluated using the validation set and measured by calculating the accuracy metric; the accuracy metric is: ;in, The number of data samples to predict the correct operating status; The number of operating status data samples for prediction errors; Based on the performance feedback of the validation set accuracy, the model structure and hyperparameters are adjusted and retrained to obtain the operation status prediction model.

[0012] Furthermore, the method for adjusting the structure and hyperparameters of the model includes: Bayesian optimization is used to adjust the hyperparameters of the operation status prediction model. The specific steps are as follows: S1. Define the objective function ,in Represents a set of hyperparameters; randomly collects several sets of hyperparameters in the hyperparameter space as initial observation points, and calculates the corresponding objective function values; S2. construct a probability model using the initial observation points, wherein the probability model is a Gaussian process regression model; S3. Define the acquisition function based on the probability model and determine the next evaluation point ; At data point Evaluate the objective function at ; The data points And the corresponding objective function value Add to the training set to get the updated dataset; S4. Retrain the probability model using the updated dataset; S5. Repeat steps 3 and 4 and stop after reaching the preset number of iterations.

[0013] Furthermore, the method of comparing the predicted power equipment operating state with a preset power equipment operating state threshold to determine whether the power equipment operating state meets the standard includes: If the predicted operating state of the electric equipment matches the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment meets the standard; If the predicted operating state of the electric equipment does not match the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment does not meet the standard.

[0014] Furthermore, if the operating state of the electric equipment meets the standard, the operating data of the electric equipment is continuously monitored; if the operating state of the electric equipment does not meet the standard, an alarm is triggered and relevant personnel are notified to take countermeasures, including: If the result of the determination is that the operating status of the power equipment meets the standard, the corresponding operating status data is recorded through the power equipment operation remote monitoring terminal, and the power equipment operation data continues to be monitored; If the result of the determination is that the operating status of the power equipment does not meet the standards, an alarm will be issued through the power equipment operation remote monitoring terminal to notify relevant personnel to remotely control the power equipment and adjust the operating parameters of the power equipment or perform emergency shutdown according to the equipment fault information.

[0015] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects: It realizes remote monitoring of power equipment operation data without relying on manual inspections, which improves efficiency while greatly reducing errors and omissions, ensuring the accuracy and timeliness of operation and maintenance; it realizes remote monitoring and management, improves the work scope and flexibility of operation and maintenance personnel, and reduces the complexity and difficulty of operation and maintenance; combined with big data technology, it deeply explores the intrinsic value of power equipment operation status data, realizes the prediction of power equipment operation status and prejudgment and warning of potential hidden dangers; it extracts features from power equipment operation status data to prevent data redundancy and dimensionality explosion problems, which not only does not take up more storage space and processing resources, but also reduces the operating costs of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for remote monitoring of operating data of power equipment; DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1 Please refer to Figure 1 The present invention provides a method for remote monitoring of power equipment operation data, comprising the following steps: S1. Collecting power equipment operation data; S2. Preprocess the power equipment operation data to obtain a power equipment operation data set; perform feature extraction on the power equipment operation data set to obtain a power equipment operation feature data set; S3, inputting the power equipment operation characteristic data set into the operation state prediction model to predict the operation state of the power equipment; comparing the predicted operation state of the power equipment with the preset power equipment operation state threshold value to determine whether the operation state of the power equipment meets the standard; S4. If the operating status of the power equipment meets the standard, the operating data of the power equipment will continue to be monitored; if the operating status of the power equipment does not meet the standard, an alarm will be triggered and relevant personnel will be notified to take countermeasures.

[0019] The power equipment operation data includes electrical parameters, equipment status parameters, environmental status parameters and fault maintenance data; The electrical parameters include voltage, current and power factor; the equipment status parameters include switch status, operation status and accumulated operation time; the environmental status parameters include ambient temperature, ambient humidity and dust content; the fault maintenance data include fault records and maintenance records; The power equipment operation data is monitored by sensors and collected by data collectors, and each module is connected by wire and / or wireless means.

[0020] The method of preprocessing the power equipment operation data to obtain the power equipment operation data set includes: Check and process missing values. For missing data points, use the interpolation method to fill in the missing data points with the average of the non-missing values ​​of the data points before and after the data point. Detect and process outliers. Use clustering algorithms to identify outliers in the power equipment operation data and remove them, and replace them with the average value. Standardize and normalize the power equipment operation data to convert it into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the impact of dimension. After the preprocessing in the above steps, the power equipment operation data set is obtained.

[0021] The method of extracting features from a power equipment operation data set to obtain a power equipment operation feature data set comprises: Defining the Matrix and the target variable ; Construct the matrix ,in is the sample size, is the number of features, is a set of real numbers; each row of the matrix corresponds to a sample, and each column corresponds to a feature; the matrix The data source is the power equipment operation data set; Lasso regression is used to extract features from the matrix; the objective function of the Lasso regression is ;in, is the sample size, is the number of features, For the The target value of samples, For the The feature vector of the samples, is the regression coefficient vector, which represents the weight of each feature in the model. is the regularization parameter, is the index of the sample, is the index of the feature; is the residual sum of squares, which represents the error between the predicted value and the actual value; is the L1 norm, which sums the absolute values ​​of all feature coefficients and is used to achieve feature selection; Choosing the best regularization parameter , by changing the regularization parameter The size of controls the complexity of regression and the strength of feature selection; use the data set to train the Lasso regression model, and extract the regression coefficients after training. ; Eliminate regression coefficient The feature is 0, and the regression coefficient is retained The non-zero features are used to construct the power equipment operation feature dataset.

[0022] The method for selecting the optimal regularization parameter includes: Constructing a limit function The regularization parameter is adjusted; the restriction function The mathematical expression is: ;in, is the noise level, is the sample size, is the number of features, and is the influencing factor of the regularization parameter.

[0023] For example: Assuming the noise level , sample size , the number of features , impact factor , impact factor , then according to the formula .

[0024] The method of inputting the power equipment operation characteristic data set into the operation state prediction model to predict the operation state of the power equipment includes: Construct an operation status prediction model, including an input layer, an LSTM layer, a fully connected layer, and an output layer; divide the data set into multiple sequence samples, each sample containing a fixed-length time window as input, and the power equipment operation status data in the subsequent time period as output; divide the processed data set into a training set, a test set, and a validation set for model training and evaluation of model performance; The input data of the operation status prediction model is the power equipment operation feature data set, and the output label is the power equipment operation status; the mean square error loss function is selected to measure the difference between the model prediction result and the true label; the operation status prediction model is a long short-term memory network model; The mean square error loss function is: ;in, It is True value, It is Prediction values, is the number of samples of different categories; The running status prediction model is trained using the training set, and the Adam algorithm is selected as the optimizer to minimize the loss function. The Adam algorithm dynamically adjusts the learning rate according to the first-order and second-order moments of the gradient to perform deviation correction and parameter update. The weights are adjusted through back-propagation, and the model is trained iteratively. The performance of the model is evaluated using the validation set and measured by calculating the accuracy metric; the accuracy metric is: ;in, The number of data samples to predict the correct operating status; The number of operating status data samples for prediction errors; For example, suppose there is a data set containing 100 running status data samples, of which there are 60 positive samples and 40 negative samples. The model is used to predict these 100 samples. The model prediction results are as follows: Among the samples predicted as positive, there are 50 samples that are actually positive and 5 samples that are actually negative; among the samples predicted as negative, there are 10 samples that are actually positive and 30 samples that are actually negative; The number of samples predicted correctly = the number of samples predicted as positive + the number of samples predicted as negative = 50 + 30 = 80; the number of all samples = the total number of samples = 100; the accuracy rate = 80 / 100 × 100% = 80%; From this, we can conclude that the model’s accuracy is 80%, meaning that the model correctly predicted 80% of the samples.

[0025] Based on the performance feedback of the validation set accuracy, the model structure and hyperparameters are adjusted and retrained to obtain the operation status prediction model.

[0026] The method for adjusting the structure and hyperparameters of the model includes: Bayesian optimization is used to adjust the hyperparameters of the operation status prediction model. The specific steps are as follows: S1. Define the objective function ,in Represents a set of hyperparameters; randomly collects several sets of hyperparameters in the hyperparameter space as initial observation points, and calculates the corresponding objective function values ​​to construct the prior distribution of the objective function; S2. construct a probability model using the initial observation points, wherein the probability model is a Gaussian process regression model; S3. Define the acquisition function based on the probability model and determine the next evaluation point ; At data point Evaluate the objective function at ; The data points And the corresponding objective function value Add to the training set to get the updated dataset; S4. Retrain the probability model using the updated dataset; S5. Repeat steps 3 and 4 and stop after reaching the preset number of iterations.

[0027] The method of comparing the predicted power equipment operating state with a preset power equipment operating state threshold to determine whether the power equipment operating state meets the standard includes: If the predicted operating state of the electric equipment matches the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment meets the standard; If the predicted operating state of the electric equipment does not match the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment does not meet the standard.

[0028] If the operating state of the power equipment meets the standard, the operating data of the power equipment is continuously monitored; if the operating state of the power equipment does not meet the standard, an alarm is triggered and relevant personnel are notified to take countermeasures, including: If the result of the determination is that the operating status of the power equipment meets the standard, the corresponding operating status data is recorded through the power equipment operation remote monitoring terminal, and the power equipment operation data continues to be monitored; If the result of the determination is that the operating status of the power equipment does not meet the standards, an alarm will be issued through the power equipment operation remote monitoring terminal to notify relevant personnel to remotely control the power equipment and adjust the operating parameters of the power equipment or perform emergency shutdown according to the equipment fault information.

[0029] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0030] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division of a waterway underwater terrain change analysis system and method. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0031] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0032] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0033] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0034] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for remote monitoring of power equipment operation data, characterized in that: The following steps are involved: S1. Collecting power equipment operation data; S2. Preprocess the power equipment operation data to obtain a power equipment operation data set; perform feature extraction on the power equipment operation data set to obtain a power equipment operation feature data set; S3, inputting the power equipment operation characteristic data set into the operation status prediction model to predict the operation status of the power equipment; comparing the predicted operation status of the power equipment with the preset power equipment operation status threshold value to determine whether the operation status of the power equipment meets the standard; S4. If the operating status of the power equipment meets the standard, continue to monitor the operating data of the power equipment; If the operating status of the power equipment does not meet the standards, an alarm will be triggered and relevant personnel will be notified to take countermeasures.

2. A method for remote monitoring of power equipment operation data according to claim 1, characterized in that: The power equipment operation data includes electrical parameters, equipment status parameters, environmental status parameters and fault maintenance data; The electrical parameters include voltage, current and power factor; the equipment status parameters include switch status, operation status and accumulated operation time; the environmental status parameters include ambient temperature, ambient humidity and dust content; the fault maintenance data include fault records and maintenance records; The power equipment operation data is monitored by sensors and collected by data collectors, and each module is connected by wire and / or wireless means.

3. A method for remote monitoring of power equipment operation data according to claim 2, characterized in that: The method of preprocessing the power equipment operation data to obtain the power equipment operation data set includes: Check and process missing values. For missing data points, use the interpolation method to fill in the missing data points with the average of the non-missing values ​​of the data points before and after the data point. Detect and process outliers. Use clustering algorithms to identify outliers in the power equipment operation data and remove them, and replace them with the average value. Standardize and normalize the power equipment operation data to convert them into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the impact of dimensions. After the preprocessing in the above steps, the power equipment operation data set is obtained.

4. A method for remote monitoring of power equipment operation data according to claim 3, characterized in that: The method of extracting features from a power equipment operation data set to obtain a power equipment operation feature data set comprises: Defining the Matrix and the target variable ; Construct the matrix ,in is the sample size, is the number of features, is a set of real numbers; the matrix Each row corresponds to a sample, and each column corresponds to a feature; the matrix The data source is the power equipment operation data set; Use Lasso regression on the matrix Perform feature extraction; the objective function of the Lasso regression is ;in, is the sample size, is the number of features, For the The target value of samples, For the The feature vector of the samples, is the regression coefficient vector, is the regularization parameter, is the index of the sample, is the index of the feature; Choosing the best regularization parameter , by changing the regularization parameter The size of controls the complexity of regression and the strength of feature selection; use the data set to train the Lasso regression model, and extract the regression coefficients after training. ; Eliminate regression coefficient The feature is 0, and the regression coefficient is retained The non-zero features are used to construct the power equipment operation feature dataset.

5. A method for remote monitoring of power equipment operation data according to claim 4, characterized in that: The method for selecting the optimal regularization parameter includes: Constructing a limit function The regularization parameter is adjusted; the restriction function The mathematical expression is: ;in, is the noise level, is the sample size, is the number of features, and is the influencing factor of the regularization parameter.

6. A method for remote monitoring of power equipment operation data according to claim 5, characterized in that: The method of inputting the power equipment operation characteristic data set into the operation state prediction model to predict the operation state of the power equipment includes: Construct an operation status prediction model, including an input layer, an LSTM layer, a fully connected layer, and an output layer; divide the data set into multiple sequence samples, each sample containing a fixed-length time window as input, and the power equipment operation status data in the subsequent time period as output; divide the processed data set into a training set, a test set, and a validation set for model training and evaluation of model performance; The input data of the operation status prediction model is the power equipment operation feature data set, and the output label is the power equipment operation status; the mean square error loss function is selected to measure the difference between the model prediction result and the true label; the operation status prediction model is a long short-term memory network model; The mean square error loss function is: ;in, It is True value, It is Prediction values, is the number of samples of different categories; The running status prediction model is trained using the training set, and the Adam algorithm is selected as the optimizer to minimize the loss function. The Adam algorithm dynamically adjusts the learning rate according to the first-order and second-order moments of the gradient to perform deviation correction and parameter update. The weights are adjusted through back-propagation, and the model is trained iteratively. The performance of the model is evaluated using the validation set and measured by calculating the accuracy metric; the accuracy metric is: ;in, The number of data samples to predict the correct operating status; The number of operating status data samples for prediction errors; Based on the performance feedback of the validation set accuracy, the model structure and hyperparameters are adjusted and retrained to obtain the operation status prediction model.

7. A method for remote monitoring of power equipment operation data according to claim 6, characterized in that: The method for adjusting the structure and hyperparameters of the model includes: Bayesian optimization is used to adjust the hyperparameters of the operation status prediction model. The specific steps are as follows: S1. Define the objective function ,in Represents a set of hyperparameters; randomly collects several sets of hyperparameters in the hyperparameter space as initial observation points, and calculates the corresponding objective function values; S2. construct a probability model using the initial observation points, wherein the probability model is a Gaussian process regression model; S3. Define the acquisition function based on the probability model and determine the next evaluation point ; At data point Evaluate the objective function at ; The data points And the corresponding objective function value Add to the training set to get the updated dataset; S4. Retrain the probability model using the updated dataset; S5. Repeat steps 3 and 4 and stop after reaching the preset number of iterations.

8. A method for remote monitoring of power equipment operation data according to claim 7, characterized in that: The method of comparing the predicted power equipment operating state with a preset power equipment operating state threshold to determine whether the power equipment operating state meets the standard includes: If the predicted operating state of the electric equipment matches the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment meets the standard; If the predicted operating state of the electric equipment does not match the preset operating state threshold of the electric equipment, it is determined that the operating state of the electric equipment does not meet the standard.

9. A method for remote monitoring of power equipment operation data according to claim 8, characterized in that: If the operating status of the power equipment meets the standard, the operating data of the power equipment continues to be monitored; If the operating status of the power equipment does not meet the standards, the methods of triggering an alarm and notifying relevant personnel to take countermeasures include: If the result of the determination is that the operating status of the power equipment meets the standard, the corresponding operating status data is recorded through the power equipment operation remote monitoring terminal, and the power equipment operation data continues to be monitored; If the result of the determination is that the operating status of the power equipment does not meet the standards, an alarm will be issued through the power equipment operation remote monitoring terminal to notify relevant personnel to remotely control the power equipment and adjust the operating parameters of the power equipment or perform emergency shutdown according to the equipment fault information.

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