A time series data feature extraction method based on frequency GINI coefficient

Abstract

The application relates to the field of artificial intelligence, in particular to a time series data feature extraction method based on a frequency GINI coefficient, which comprises the following steps: acquiring an input data stream collected by a sensor, extracting feature parameters in the data stream, recording the numerical value of each feature parameter at each time point, arranging time series data of each feature parameter, and acquiring a feature parameter set. The application introduces an automatic feature engineering method with a standard GINI coefficient as the core, thereby improving the efficiency and precision of data preprocessing. The GINI coefficient calculation and threshold setting can identify and mark abnormal features in the data, thereby reducing errors caused by data abnormalities. In addition, the automatic feature parameter marking and abnormal isolation operation reduces the dependence on expert intervention and improves the ability to process large data sets. Not only does the application accelerate the feature engineering processing flow, but also improves the generalization ability of the model, because it can continuously adapt to new data inputs.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for extracting time series data features based on frequency GINI coefficients. Background Art

[0002] Automated feature engineering is a technique used in artificial intelligence, particularly machine learning. Its primary purpose is to improve the performance of data analysis models by automatically identifying, creating, and optimizing features in data. Feature engineering is a key step in building effective machine learning models because good features provide a more accurate representation of the data, leading to more accurate and efficient models.

[0003] However, existing technologies lack automation and real-time data processing capabilities. In particular, without the support of automated tools, data processing is susceptible to human bias and struggles to maintain efficiency and accuracy on dynamically changing datasets. Furthermore, existing technologies often fail to update feature selection and optimization criteria in real time when processing large-scale data, limiting model adaptability and accuracy. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose a time series data feature extraction method based on frequency GINI coefficient.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a time series data feature extraction method based on frequency GINI coefficient, comprising the following steps:

[0006] Obtain the input data stream collected by the sensor, extract the characteristic parameters in the data stream, extract feature X, feature Y, feature Z and feature W, record the value of each characteristic parameter at each time point, organize the time series data of each characteristic parameter, and obtain the characteristic parameter set;

[0007] Calculating the distribution frequency of each characteristic parameter in the characteristic parameter set, counting the number of occurrences of each characteristic parameter in each value range, and calculating the frequency value of each value range to obtain a characteristic distribution set;

[0008] Based on the feature distribution set, a standard GINI coefficient of each feature parameter is calculated, and for each feature parameter, the standard GINI coefficient is sorted from small to large, and the cumulative distribution frequency is calculated. The GINI coefficient is then calculated based on the cumulative distribution frequency to generate a feature GINI coefficient set;

[0009] Set a GINI coefficient threshold range, compare the GINI coefficient values ​​in the feature GINI coefficient set with the threshold range, and for each feature parameter, determine whether its GINI coefficient exceeds the threshold range. If so, mark the feature parameter as abnormal, and obtain the abnormal feature parameter set;

[0010] For the abnormal characteristic parameter set, data correction, alarm triggering and data point isolation operations are performed to correct the values ​​of the abnormal characteristic parameters, trigger alarm information, isolate abnormal data points, and obtain a processed characteristic set.

[0011] Preferably, the steps of obtaining the characteristic parameter set are:

[0012] Obtain the input data stream collected by the sensor, extract the feature parameters in the data stream, including feature X, feature Y, feature Z and feature W, and obtain a preliminary feature parameter list;

[0013] Based on the preliminary feature parameter list, record the value of each feature parameter at each time point, organize the values ​​to form time series data, and generate a time series data set;

[0014] According to the time series data set, the formula is used:

[0015] ;

[0016] Calculate the time-series weighted average of each feature parameter, where represents the weighted average of the feature parameter set, is the characteristic parameter at time point The numerical value of is the corresponding time weight, n is the total number of data points (time points), and the feature parameter set is obtained.

[0017] Preferably, the steps of obtaining the feature distribution set are:

[0018] Acquire time series data of each feature parameter from the feature parameter set to obtain a time series data set for each feature;

[0019] Calculate the number of occurrences of the feature parameter in each value range to obtain the frequency distribution data of each feature;

[0020] According to the frequency distribution data of each feature, the formula is used:

[0021] ;

[0022] Calculate the frequency value of each value range and generate a feature distribution set; represents the frequency value, It is The number of occurrences within a value range, is the center value of the range, is the mean of the values, is a small positive number, ensuring that the denominator is non-zero, is the total number of occurrences of the value, and m is the total number of value ranges of the feature parameter.

[0023] Preferably, the steps of obtaining the characteristic GINI coefficient set are:

[0024] Extracting frequency distribution data of each feature from the feature distribution set to obtain a frequency distribution data set;

[0025] For each feature parameter, sort the values ​​from small to large, calculate the cumulative distribution frequency of each feature, and generate a cumulative distribution frequency set;

[0026] Based on the cumulative distribution frequency set, the formula is adopted:

[0027] ;

[0028] Calculate the GINI coefficient of each feature parameter to generate a feature GINI coefficient set; Representative characteristics The GINI coefficient, Representative The frequency value of the data point, is the total number of data points, is the average of all frequency values, It is a small amount added to avoid the denominator being zero.

[0029] Preferably, the steps of obtaining the abnormal characteristic parameter set are:

[0030] Obtaining the GINI coefficient of each feature parameter from the feature GINI coefficient set to obtain a GINI coefficient data set;

[0031] Set the GINI coefficient threshold range, compare the GINI coefficient of each feature with the set threshold, determine whether each feature parameter exceeds the threshold, and generate a list of over-threshold features;

[0032] Based on the super-threshold feature list, according to:

[0033] ;

[0034] Judge each characteristic parameter to obtain an abnormal characteristic parameter set; Represents the abnormal feature parameter set, is the characteristic parameter The GINI coefficient, and are the lower and upper thresholds of the GINI coefficient respectively.

[0035] Preferably, the steps of obtaining the processed feature set are:

[0036] Acquire characteristic parameters marked as abnormal from the abnormal characteristic parameter set to obtain an abnormal characteristic parameter list;

[0037] Data correction, alarm triggering, and data point isolation operations are performed on abnormal characteristic parameters to generate a preliminary corrected data set.

[0038] Preferably, the step of obtaining the processed feature set further comprises:

[0039] Integrate the preliminary correction data set using the formula:

[0040] ;

[0041] Re-judge the corrected feature parameter values ​​to obtain the processed feature set; represents the processed feature set, It is Correction data of characteristic parameters, is a correction factor used to adjust the influence of each data point.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] This paper improves the efficiency and accuracy of data preprocessing by introducing an automated feature engineering method based on the standard GINI coefficient. Using GINI coefficient calculation and threshold setting, it is possible to identify and label abnormal features in the data, reducing errors caused by data anomalies. Furthermore, automated feature parameter labeling and anomaly isolation reduce reliance on expert intervention and improve the ability to process large data sets. This not only accelerates the feature engineering process but also improves the model's generalization ability, as it can continuously adapt to new data inputs and promptly adjust and optimize feature selection criteria. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] See also Figure 1The present invention provides a technical solution, a method for extracting time series data features based on frequency GINI coefficient, comprising the following steps:

[0047] Obtain the input data stream collected by the sensor, extract the characteristic parameters in the data stream, extract feature X, feature Y, feature Z and feature W, record the value of each characteristic parameter at each time point, organize the time series data of each characteristic parameter, and obtain the characteristic parameter set.

[0048] Calculate the distribution frequency of each feature parameter in the feature parameter set, count the number of occurrences of each feature parameter in each value range, calculate the frequency value of each value range, and obtain the feature distribution set.

[0049] Based on the feature distribution set, the standard GINI coefficient of each feature parameter is calculated. For each feature parameter, the values ​​are sorted from small to large, the cumulative distribution frequency is calculated, and the GINI coefficient is calculated based on the cumulative distribution frequency to generate a feature GINI coefficient set.

[0050] A GINI coefficient threshold range is set, and the GINI coefficient values ​​in the feature GINI coefficient set are compared with the threshold range. For each feature parameter, it is determined whether its GINI coefficient exceeds the threshold range. If so, the feature parameter is marked as abnormal, and an abnormal feature parameter set is obtained.

[0051] For the abnormal feature parameter set, data correction, alarm triggering and data point isolation operations are performed to correct the values ​​of the abnormal feature parameters, trigger alarm information, isolate abnormal data points, and obtain the processed feature set.

[0052] The steps to obtain the feature parameter set are:

[0053] Obtain the input data stream collected by the sensor, extract the feature parameters in the data stream, including feature X, feature Y, feature Z and feature W, and obtain a preliminary feature parameter list;

[0054] Based on the preliminary feature parameter list, record the value of each feature parameter at each time point, organize the values ​​to form time series data, and generate a time series dataset;

[0055] According to the time series data set, the formula is used:

[0056] ;

[0057] Calculate the time-series weighted average of each feature parameter, where represents the weighted average of the feature parameter set, is the characteristic parameter at time point The numerical value of is the corresponding time weight, n is the total number of data points (time points), and the feature parameter set is obtained.

[0058] Specifically, the input data stream collected by the sensors is obtained, and the feature parameters including feature X, feature Y, feature Z, and feature W are extracted through real-time monitoring equipment. Based on the real-time readings of each feature parameter, a preliminary feature parameter list is formed. This process involves analysis of various sensor inputs and preliminary data formatting. The data provided by each sensor needs to be filtered for noise and preliminarily verified to ensure data accuracy and usability, which includes checking the range, type, and frequency of the data. The purpose is to provide clean and structured input for subsequent data processing steps, resulting in a preliminarily processed feature parameter list.

[0059] The numerical value of each feature parameter at each time point is recorded, and these numerical values are arranged to form time series data. This involves time synchronization of data collection points and data integration to ensure that all data is correctly recorded in chronological order. The arrangement process includes unification of time labels and alignment of data, as well as handling of missing data, such as through interpolation or using the average of previous and subsequent data points to fill in. In addition, detection and handling of outliers are necessary to ensure the continuity and accuracy of the time series, generating a time series data set.

[0060] The calculation process of the formula is as follows:

[0061] The formula is used to calculate the time series weighted average of the feature parameter set, where represents the weighted average of the feature parameter set, is the value of the feature parameter at time point , and is the weight of that time point.

[0062] Through actual data collection, let be the parameter values at time points 1, 2, and 3, which are 10, 20, and 30 respectively, and the time weights be 1, 2, and 3 respectively. The calculation process is as follows:

[0063] ;

[0064] The result shows that through weighted averaging, we can obtain a comprehensive consideration of the time effect and parameter value of the feature parameter set overall representation value, which is 140, reflecting the weighted feature influence of all considered time points.

[0065] The steps for obtaining the feature distribution set are as follows:

[0066] Obtain the time series data of each feature parameter from the feature parameter set to obtain the time series data set of each feature.

[0067] ​The frequency distribution data of each feature is obtained by counting the number of occurrences of the feature parameter in each value range.

[0068] According to the frequency distribution data of each feature, the formula is:

[0069] ;

[0070] The frequency value of each value range is calculated to generate a feature distribution set; wherein represents the frequency value, is the number of occurrences in the th value range, is the center value of the value range, is the average value of the value, is a small positive number to ensure that the denominator is not zero, is the total number of occurrences of the value, and m is the total number of value ranges of the feature parameter.

[0071] Specifically, the time series data of each feature parameter is obtained from the feature parameter set, and these data are sorted to construct a time series data set. This step involves collecting data streams and extracting the specific values of features X, features Y, features Z and features W from them. During the data collection process, the integrity and accuracy of the data need to be ensured. The values of each feature are indexed by time labels to ensure that the time series of the data are complete and correspond to each other, so that the data set can be effectively utilized in the subsequent steps, and the time series data set of each feature is obtained.

[0072] The number of occurrences of the feature parameter in each value range is calculated. According to the collected time series data set, statistical methods are used to segment the data of each feature. Each segment represents a value range. The data points in each segment are counted, and the number of occurrences in these value ranges is calculated. This process involves basic frequency statistical methods of statistics. Through frequency statistics, the distribution trend of the data can be observed, and the frequency distribution data is sorted to obtain the frequency distribution data of each feature.

[0073] The calculation process of the formula is as follows:

[0074] During the collection of the feature parameter, for feature X, the number of occurrences in 10 different value ranges is:

[0075] ;

[0076] The center value of each value range is:

[0077] ;

[0078] For example, the average value of all feature values is:

[0079] ;

[0080] The added small amount is:

[0081] ;

[0082] Total number of occurrences:

[0083] ;

[0084] In the calculation process, for each , the and are brought into the formula to obtain the frequency value of each range , which is calculated as follows:

[0085] ;

[0086] Continue this process until . Specifically, considering the deviation of the center value of each value range from the average value, the influence of each value range is more accurately evaluated, and the final feature distribution set will reflect the actual distribution of different feature values.

[0087] The steps for obtaining the feature GINI coefficient set are:

[0088] Extract the frequency distribution data of each feature from the feature distribution set to obtain the frequency distribution data set;

[0089] For each feature parameter, sort the values from small to large, calculate the cumulative distribution frequency of each feature, and generate a cumulative distribution frequency set;

[0090] Based on the cumulative distribution frequency set, the formula:

[0091] ;

[0092] Calculate the GINI coefficient of each feature parameter to generate a feature GINI coefficient set; wherein represents the GINI coefficient of the feature , represents the frequency value of the th data point, is the total number of data points, is the average value of all frequency values, is a small amount added to avoid a zero denominator.

[0093] Specifically, data is extracted from the feature parameter set to generate a time series data set. During this process, the numerical changes of each feature parameter at different time points are analyzed in detail, and these changes are recorded to construct a time series representation of the data. This method can comprehensively evaluate the volatility of feature parameters over time, which is crucial for understanding the performance of features under different conditions. Especially when processing data involving time-sensitive analysis, time series analysis of feature data can more accurately capture the dynamic changes of data and provide the necessary basic information for subsequent data analysis. The core purpose of this stage is to establish a comprehensive and detailed time series data set for more in-depth statistical analysis and pattern recognition to obtain time series data sets for each feature.

[0094] Calculate the number of occurrences of the feature parameter in each value range. This process identifies and records the frequency of each value range by analyzing the distribution of each feature parameter value. This operation requires accurate classification and counting of each feature value to ensure the integrity and accuracy of the data. The generation of frequency distribution data of feature parameters involves careful sorting and classification of the data. The accurate classification and counting of these data is to ensure that the analysis of the data in subsequent steps can reflect the actual distribution. This classification and counting process requires not only the accurate execution of the algorithm, but also a reasonable data processing strategy to avoid any deviation or error in the data processing process, thereby generating frequency distribution data for each feature.

[0095] formula:

[0096] ;

[0097] Formula calculation process: First, in order to calculate the GINI coefficient of the characteristic parameter , we must get the frequency distribution of each feature from the dataset Collect data that reflects the frequency of occurrence of eigenvalues ​​in various ranges. Calculate the frequency of each eigenvalue With the average frequency The difference, and by adding a small amount , usually 0.01, to avoid the denominator being zero and increase the stability of the calculation. Apply the square root and absolute value processing to each frequency value to ensure that all values ​​are non-negative and take into account the size of the difference.

[0098] Next, using the weighted sum method, each frequency value The corresponding adjusted serial number Weighting takes into account the position of the data points, increasing the weight of data points at the ends of the sequence, and emphasizing the unevenness of the distribution. The denominator is the sum of the adjusted frequency values, which is used to normalize the GINI coefficient to ensure that the result is between 0 and 1, better reflecting the degree of inequality of the feature.

[0099] By calculating a given frequency distribution and substituting the data into the above formula, the GINI coefficient of each feature can be calculated, providing a powerful tool for measuring inequality or diversity for data analysis and further statistical processing.

[0100] The steps to obtain the abnormal feature parameter set are:

[0101] Obtain the GINI coefficient of each feature parameter from the feature GINI coefficient set to obtain a GINI coefficient data set;

[0102] Set the GINI coefficient threshold range, compare the GINI coefficient of each feature with the set threshold, determine whether each feature parameter exceeds the threshold, and generate a list of over-threshold features;

[0103] Based on the super-threshold feature list, according to:

[0104] ;

[0105] Judge each characteristic parameter to obtain an abnormal characteristic parameter set; Represents the abnormal feature parameter set, is the characteristic parameter The GINI coefficient, and are the lower and upper thresholds of the GINI coefficient respectively.

[0106] Specifically, the GINI coefficient of each characteristic parameter is obtained from the characteristic GINI coefficient set. First, data collection is performed to organize the GINI coefficient of each characteristic parameter under different conditions. These data usually come from a detailed database or are obtained through real-time monitoring. The GINI coefficient of each characteristic parameter is calculated by analyzing the characteristic distribution data, which involves the central tendency and dispersion of the data. The GINI coefficient data of each characteristic parameter reflects the unequal distribution of the feature in the population. Through statistical methods, such as calculating the frequency of the feature and the integral of the frequency, the GINI coefficient data set can be accurately obtained. This process ensures the accuracy and reliability of the data and obtains the GINI coefficient data set.

[0107] The threshold range of the GINI coefficient is set by using historical data and industry standards provided by the data analysis team. This process involves a large amount of data comparison and historical record analysis to ensure that the set threshold can reflect abnormal characteristic parameters without being too sensitive to cause frequent false alarms. The GINI coefficient of each feature is compared with the set threshold. Through calculation and logical judgment, the characteristic parameters that exceed the normal range are identified and a list of super-threshold features is generated. This process is implemented through programming and algorithms to ensure processing speed and accuracy and generate a list of super-threshold features.

[0108] According to the super-threshold feature list, the set GINI coefficient threshold range is used to detect anomalies in feature parameters, where Represents characteristic parameters The GINI coefficient, and Represent the lower and upper thresholds of the GINI coefficient respectively.

[0109] Sure The value of , the threshold is set to and ,if falls within this range, such as and ,but Mark as abnormal features. Ensure that only features that are truly outside the normal range of variation are marked, and ensure that each feature parameter is accurately calculated to obtain the abnormal feature parameter set.

[0110] The steps to obtain the processed feature set are:

[0111] Acquire the feature parameters marked as abnormal from the abnormal feature parameter set to obtain an abnormal feature parameter list;

[0112] Perform data correction, alarm triggering, and data point isolation operations on abnormal characteristic parameters to generate a preliminary corrected data set;

[0113] Integrate the preliminary correction data set using the formula:

[0114] ;

[0115] Re-judge the corrected feature parameter values ​​to obtain the processed feature set; represents the processed feature set, It is Correction data of characteristic parameters, is a correction factor used to adjust the influence of each data point.

[0116] Specifically, the formula The calculation process:

[0117] When processing abnormal characteristic parameter sets, each corrected data Re-evaluation is performed using a logistic regression model, which is represented by the above formula. In the formula, represents the sum of the processed feature set, It is Correction data of characteristic parameters, is the corresponding correction coefficient, which is used to adjust the influence of the data point.

[0118] There is a simplified data set where , correction factor , the calculation process is as follows:

[0119] ;

[0120] The result shows that the weighted sum of the comprehensive corrected feature parameters is 0.8748, which represents the comprehensive characteristics of the corrected data and reflects the statistical performance of the adjusted data points in the feature set.

[0121] The characteristic parameters extracted from the abnormal characteristic parameter set are confirmed to be parameters outside the normal range after detailed inspection. This operation ensures that only parameters that really need attention are marked and enter the subsequent correction process. By accumulating this data, a list containing all abnormal characteristics is established. This list not only reflects the abnormal status of each parameter, but also provides a direct input source for subsequent processing.

[0122] The acquired list of abnormal characteristic parameters is used as input and processed through a series of predetermined data correction measures. Each characteristic parameter not only undergoes data correction, but also triggers corresponding alarms and data point isolation operations. These measures ensure the quality of the data and the responsiveness of the system. The corrected data set collects all these adjusted parameters, providing a basis for further data integration and analysis.

[0123] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A time series data feature extraction method based on frequency GINI coefficient, characterized in that: The following steps are involved: Obtain the input data stream collected by the sensor, extract the characteristic parameters in the data stream, extract feature X, feature Y, feature Z and feature W, record the value of each characteristic parameter at each time point, organize the time series data of each characteristic parameter, and obtain the characteristic parameter set; Calculating the distribution frequency of each characteristic parameter in the characteristic parameter set, counting the number of occurrences of each characteristic parameter in each value range, and calculating the frequency value of each value range to obtain a characteristic distribution set; Based on the feature distribution set, a standard GINI coefficient of each feature parameter is calculated, and for each feature parameter, the standard GINI coefficient is sorted from small to large, and the cumulative distribution frequency is calculated. The GINI coefficient is then calculated based on the cumulative distribution frequency to generate a feature GINI coefficient set; Set a GINI coefficient threshold range, compare the GINI coefficient values ​​in the feature GINI coefficient set with the threshold range, and for each feature parameter, determine whether its GINI coefficient exceeds the threshold range. If so, mark the feature parameter as abnormal, and obtain the abnormal feature parameter set; For the abnormal characteristic parameter set, performing data correction, alarm triggering and data point isolation operations, correcting the values ​​of the abnormal characteristic parameters, triggering alarm information, isolating abnormal data points, and obtaining a processed characteristic set; The steps of obtaining the feature distribution set are as follows: Acquire time series data of each feature parameter from the feature parameter set to obtain a time series data set for each feature; Calculate the number of occurrences of the feature parameter within each value range to obtain the frequency distribution data of each feature; According to the frequency distribution data of each feature, the formula is used: ； Calculate the frequency value of each value range and generate a feature distribution set; represents the frequency value, It is The number of occurrences within a value range, is the center value of the range, is the mean of the values, is a small positive number, ensuring that the denominator is non-zero, is the total number of occurrences of the value, and m is the total number of value ranges of the characteristic parameter; The steps for obtaining the characteristic GINI coefficient set are: Extracting frequency distribution data of each feature from the feature distribution set to obtain a frequency distribution data set; For each feature parameter, sort the values ​​from small to large, calculate the cumulative distribution frequency of each feature, and generate a cumulative distribution frequency set; Based on the cumulative distribution frequency set, the formula is adopted: ； Calculate the GINI coefficient of each feature parameter to generate a feature GINI coefficient set; Representative characteristics The GINI coefficient, Representative The frequency value of the data point, is the total number of data points, is the average of all frequency values, It is a small amount added to avoid the denominator being zero.

2. The method for extracting time series data features based on frequency GINI coefficient according to claim 1, characterized in that: The steps for obtaining the characteristic parameter set are: Obtain the input data stream collected by the sensor, extract the feature parameters in the data stream, including feature X, feature Y, feature Z and feature W, and obtain a preliminary feature parameter list; Based on the preliminary feature parameter list, record the value of each feature parameter at each time point, organize the values ​​to form time series data, and generate a time series data set; According to the time series data set, the formula is used: ； Calculate the time-series weighted average of each feature parameter, where represents the weighted average of the feature parameter set, is the characteristic parameter at time point The numerical value of is the corresponding time weight, n is the total number of time points, and the feature parameter set is obtained.

3. The method for extracting time series data features based on frequency GINI coefficient according to claim 1, characterized in that: The steps for obtaining the abnormal feature parameter set are: Obtaining the GINI coefficient of each characteristic parameter from the characteristic GINI coefficient set to obtain a GINI coefficient data set; Set the GINI coefficient threshold range, compare the GINI coefficient of each feature with the set threshold, determine whether each feature parameter exceeds the threshold, and generate a list of over-threshold features; Based on the super-threshold feature list, according to: ； Judge each characteristic parameter to obtain an abnormal characteristic parameter set; Represents the abnormal feature parameter set, is the characteristic parameter The GINI coefficient, and are the lower and upper thresholds of the GINI coefficient respectively.

4. The method for extracting time series data features based on frequency GINI coefficient according to claim 1, characterized in that: The steps for obtaining the processed feature set are: Acquire characteristic parameters marked as abnormal from the abnormal characteristic parameter set to obtain an abnormal characteristic parameter list; Data correction, alarm triggering, and data point isolation operations are performed on abnormal characteristic parameters to generate a preliminary corrected data set.

5. The method for extracting time series data features based on frequency GINI coefficient according to claim 4, characterized in that: The step of obtaining the processed feature set further includes: Integrate the preliminary correction data set using the formula: ； Re-judge the corrected feature parameter values ​​to obtain the processed feature set; represents the processed feature set, It is Correction data of characteristic parameters, is a correction factor used to adjust the influence of each data point.

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