A data processing method and system for a flexible control terminal

Abstract

The present invention relates to the field of data processing technology, and specifically to a data processing method and system for a flexible control terminal, wherein the method determines the fuzzy entropy value of each voltage data point in the voltage data according to a fuzzy entropy calculation method, and performs abnormality judgment according to the fuzzy entropy value, wherein the sub-window length in the fuzzy entropy calculation method determines the voltage data point window where the voltage data is located at the sampling moment; calculates the first stability level according to the interquartile range and the number of extreme points of the voltage data in the voltage data point window; calculates the second stability level according to the similarity of the voltage data segment and the current data segment in the voltage data point window; and calculates the sub-window length in the voltage data point window according to the second stability level. According to the scheme of the present invention, the accuracy of the fuzzy entropy value calculation and the reliability of abnormality detection are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a data processing method and system for a flexible control terminal. Background Art

[0002] As the power system becomes more complex and intelligent, the operating environment of electrical equipment has become more diverse and dynamic. Especially with the popularization of smart grids and renewable energy, the voltage fluctuation and instability of power networks have become more prominent. Traditional power control methods are often unable to respond quickly to these complex changes, so new technical solutions are urgently needed to improve the stability and reliability of power systems.

[0003] As a device that integrates advanced sensing technology, real-time data analysis and intelligent decision-making functions, the flexible control terminal can efficiently and flexibly adjust the operating status of the power system. By monitoring electrical equipment in real time, the flexible control terminal can promptly detect abnormal conditions such as excessively high or low voltage fluctuations, and respond quickly based on the detection results, automatically adjust the power flow or send early warning signals, effectively preventing power equipment from malfunctioning or being damaged due to voltage problems.

[0004] At present, the main method for detecting abnormal voltage data of electrical equipment is the fuzzy entropy method. Its strong robustness and strong discrimination ability make it a powerful tool for detecting abnormal voltage data of electrical equipment. For example, in the Chinese invention patent application with application publication number CN115455842A, a method for estimating the state of charge of supercapacitors based on weighted fusion of variable temperature model is disclosed. By weighted fusion of three kinds of Kalman filter estimated state of charge values, the fuzzy entropy formula is used to allocate weights according to the residual difference between the measured terminal voltage and the estimated terminal voltage, thereby improving the accuracy and stability of the state of charge estimation.

[0005] However, the problem with this method is that when the fuzzy entropy method calculates the fuzzy entropy value for all voltage data, a sub-window of the same size is selected within the window range of each voltage data point for analysis. If a sub-window of the same size is selected within the window range of each voltage data point for analysis, it is not suitable for voltage data with different data volatility in different time periods. A large sub-window will cause the voltage data to appear simpler and more consistent at a larger scale, resulting in a lower fuzzy entropy value. A small sub-window may be easily affected by noise, resulting in deviations in the calculation of fuzzy entropy, which will also increase the complexity of the algorithm.

[0006] Then, when optimizing the sub-window length according to the data change characteristics within a voltage data point window, the electromagnetic interference that may exist in the power grid will enter the measurement system, resulting in voltage noise data, which in turn causes deviations in the optimization results of the sub-window length within each voltage data point window, making the anomaly detection results inaccurate.

[0007] Based on this, how to solve the problem that the current flexible control terminal cannot accurately analyze voltage data anomalies is one of the current research focuses. Summary of the invention

[0008] In view of the technical problem that the above-mentioned flexible control terminal cannot accurately analyze voltage data anomalies, the present invention provides solutions in the following aspects.

[0009] In a first aspect, the present invention provides a data processing method for a flexible control terminal, comprising: acquiring voltage data and current data of an electrical device; determining a fuzzy entropy value of each voltage data point in the voltage data according to a fuzzy entropy calculation method, and performing an abnormality judgment according to the fuzzy entropy value, wherein a method for determining the sub-window length in the fuzzy entropy calculation method comprises: determining a voltage data point window in which the voltage data at the sampling moment is located; calculating a first degree of stability according to the interquartile range and the number of extreme points of the voltage data in the voltage data point window, wherein the first degree of stability is negatively correlated with the interquartile range and the number of extreme points; calculating a second degree of stability according to the similarity of a voltage data segment and a current data segment in the voltage data point window, wherein the second degree of stability is positively correlated with the similarity and the first degree of stability; calculating a sub-window length in the voltage data point window according to the second degree of stability, wherein the sub-window length is positively correlated with the second degree of stability.

[0010] According to the solution of the present invention, the variation characteristics of voltage and current data are analyzed and the stability within each voltage data point window is evaluated, thereby optimizing the sub-window length, which can significantly improve the accuracy of fuzzy entropy value calculation and the reliability of anomaly detection.

[0011] Preferably, determining the voltage data point window where the voltage data at the sampling moment is located includes: acquiring a set number of voltage data points immediately before the voltage data point sampling moment to form a data segment corresponding to the voltage data point window.

[0012] Preferably, the calculation formula for the first stability level is:

[0013] ;

[0014] Where: Indicates the first stability of the data in the mth voltage data point window, represents the interquartile range of the voltage data set composed of the mth voltage data point window, It indicates the number of extreme value points in the corresponding voltage data segment in the mth voltage data point window. Represents the total number of data points within each voltage data point window.

[0015] In the present invention, by analyzing the change characteristics of the corresponding voltage data segment in each voltage data point window, the first stability of the data in each voltage data point window is obtained, and the data change characteristics in the voltage data segment are quantified. When analyzing this indicator, the smaller the interquartile range and the fewer the number of extreme points in the voltage data segment corresponding to each voltage data point window, the higher the stability of the voltage data segment, so that the size of the sub-window can be accurately adjusted according to the change characteristics of the voltage data.

[0016] Preferably, the similarity calculation formula is:

[0017] ;

[0018] Where: It represents the similarity between the voltage data segment and the current data segment in the time period corresponding to the mth voltage data point window. Represents the total number of data points in each voltage data point window, and They represent the maximum and minimum values ​​in the voltage data segment in the time period corresponding to the mth voltage data point window, respectively. and They represent the value of the voltage data point and the value of the current data point corresponding to the hth sampling moment in the time period corresponding to the mth voltage data point window, respectively. and They respectively represent the maximum and minimum values ​​in the current data segment in the time period corresponding to the mth voltage data point window.

[0019] Preferably, the calculation formula for the second stability level is:

[0020] ;

[0021] In the formula, Indicates the second stability of the data in the mth voltage data point window, Indicates the first stability level of the data in the mth voltage data point window.

[0022] The present invention characterizes the noise in the voltage data segment through the similarity of the change characteristics between the current data and the voltage data, and can distinguish the noise data from the real data, so that the reliability of the voltage data is higher, thereby making the setting of the sub-window size more accurate.

[0023] Preferably, the calculation formula of the sub-window length is:

[0024] ;

[0025] Where: represents the sub-window length within the m-th voltage data point window, Indicates the second stability of the data in the mth voltage data point window, Indicates the initial preset sub-window length, Indicates a rounding operation.

[0026] According to the solution of the present invention, by optimizing the sub-window length within each voltage data point window, the window size can be dynamically adjusted according to the stability of the signal, so as to more flexibly and accurately capture the dynamic characteristics of the data and reduce the introduction of noise, thereby effectively improving the accuracy of the data processing process.

[0027] Preferably, the fuzzy entropy value of each voltage data point in the voltage data is determined according to the fuzzy entropy calculation method, including: determining the corresponding window length of each voltage data point window, and determining the number of sub-windows according to the sub-window length; determining the number of sub-windows based on the window length and the sub-window length, the number of sub-windows is: window length-each sub-window length+1; using the sub-window to calculate the fuzzy entropy value of the voltage data point.

[0028] In the present invention, the optimized sub-window is used to perform fuzzy entropy calculation, which can perform more accurate abnormality detection on voltage data and ensure the safety of the flexible control terminal.

[0029] Preferably, abnormality judgment is performed based on the fuzzy entropy value, including: calculating the absolute value of the difference in fuzzy entropy values ​​between a voltage data point and its immediately previous voltage data point; in response to the absolute value of the difference being greater than a set threshold, an abnormality exists at the corresponding voltage data point, and an alarm is issued.

[0030] Preferably, the threshold is set to 0.1.

[0031] In a second aspect, the present invention further provides a data processing system for a flexible control terminal, comprising: a processor; a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data processing method for a flexible control terminal as described above is implemented.

[0032] The beneficial effects of the present invention are as follows: by analyzing the data change characteristics of voltage data and current data, the stability of the data in each voltage data point window is obtained, and the sub-window length in each voltage data point window is optimized based on the index, so as to obtain a more accurate fuzzy entropy value and anomaly detection result for each voltage data point, thereby achieving more reliable safety monitoring of the flexible control terminal and ensuring the safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0034] Figure 1 is a flow chart showing a data processing method of a flexible control terminal of the present invention;

[0035] Figure 2 is a flow chart showing a method for determining a sub-window length in a fuzzy entropy calculation method of the present invention;

[0036] Figure 3 It is a structural diagram showing the data processing system of the flexible control terminal of the present invention. DETAILED DESCRIPTION

[0037] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0038] The present invention obtains the stability of the data in each voltage data point window by analyzing the data change characteristics of the voltage data and the current data, and then optimizes the sub-window length in each voltage data point window according to the index to obtain a more accurate fuzzy entropy value and anomaly detection result for each voltage data point.

[0039] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] Figure 1 1 is a flow chart showing a data processing method 100 of a flexible control terminal according to the present invention.

[0041] like Figure 1 As shown, at step S101, the voltage data and current data of the electrical equipment are obtained. In the present invention, the voltage data of the electrical equipment can be collected by a voltage sensor, and the current data of the electrical equipment can be collected by a current sensor. Specifically, the collection time of the two types of data can be, for example, one hour, and the collection frequency is ten times per second, and the collection time periods of the two types of data need to be consistent. Subsequently, an analog-to-digital conversion device is used to perform digital conversion on the above two types of data to obtain digital representations of the above two types of data. Subsequently, the existing curve fitting technology is used to perform curve fitting on the above two types of data for subsequent analysis.

[0042] At step S102, the fuzzy entropy value of each voltage data point in the voltage data is determined according to the fuzzy entropy calculation method, and an abnormality judgment is performed according to the fuzzy entropy value. In some embodiments, when calculating the fuzzy entropy value, the corresponding window length of each voltage data point window is determined, and the number of sub-windows is determined according to the sub-window length. The number of sub-windows is determined based on the window length and the sub-window length, and the number of sub-windows is: window length-each sub-window length+1. The fuzzy entropy value of the voltage data point is calculated using the sub-window.

[0043] After obtaining the fuzzy entropy value, the absolute value of the difference between the fuzzy entropy values ​​of a voltage data point and its immediately adjacent previous voltage data point is calculated. In response to the absolute value of the difference being greater than a set threshold, an abnormality exists at the corresponding voltage data point, and an alarm is issued.

[0044] The present invention determines the abnormal probability of voltage data by fuzzy entropy calculation. When calculating the fuzzy entropy of voltage data points, a data segment in the voltage data can be selected using a voltage data point window, and a subwindow can be set in the voltage data point window, so that the data in the voltage data point window is divided into different sequences, and the distance between each sequence and other sequences is calculated, and the fuzzy membership is calculated according to the distance, and then the calculation is repeated by adjusting the window, and the fuzzy entropy is finally calculated. In the present invention, by combining the data stability to adaptively adjust the size of the subwindow, the data volatility can be more accurately modeled in different time periods, avoiding the simplification caused by too large a subwindow or the noise interference caused by too small a subwindow, so as to more truly reflect the complexity of the voltage data and enhance the sensitivity and accuracy of abnormal detection.

[0045] Based on this, the focus of the present invention is to analyze the data change characteristics of voltage data and current data, and optimize the sub-window length when calculating the fuzzy entropy value in the window for each voltage data point. Specifically, anomaly detection is performed on each voltage data point by calculating the fuzzy entropy value of each voltage data point. During the detection process, for each voltage data point, the sub-window length will be optimized when calculating the fuzzy entropy value in the window. This process is mainly based on the analysis of the data change characteristics of voltage data and current data.

[0046] The process of optimizing the sub-window length when calculating the fuzzy entropy value in the window for each voltage data point in the present invention is as follows:

[0047] a. Analyze the change characteristics of the corresponding voltage data segment in each voltage data point window to obtain the first stability level of the data in each voltage data point window.

[0048] b. Analyze the similarity of the change characteristics of the voltage data segment and the current data segment in the corresponding time period in each voltage data point window, and optimize the first stability of the data in each voltage data point window to obtain the second stability of the data in each voltage data point window.

[0049] c. Calculating the optimized sub-window length in each voltage data point window according to the second stability level of the data in each voltage data point window.

[0050] Next, the method for setting the size of the sub-window in the present invention will be described in detail. Figure 2 2 is a flow chart showing a method 200 for determining a sub-window length in the fuzzy entropy calculation method of the present invention.

[0051] like Figure 2 As shown, at step S201, a voltage data point window where the voltage data at the sampling moment is located is determined. In some embodiments, a set number of voltage data points immediately before the voltage data point sampling moment may be acquired to form a data segment corresponding to the voltage data point window.

[0052] For example, when calculating the fuzzy entropy value of the data in the window corresponding to a voltage data point, select 500 data points immediately before the sampling time of the voltage data point for analysis. If the number of data points before the sampling time of a voltage data point is less than 500, you can choose to collect the voltage data one minute in advance for its window establishment and subsequent analysis.

[0053] At step S202, the first stability level is calculated according to the interquartile range and the number of extreme value points of the voltage data in the voltage data point window. The first stability level is negatively correlated with the interquartile range and the number of extreme value points. When analyzing this indicator, the smaller the interquartile range and the fewer the number of extreme value points in the voltage data segment corresponding to each voltage data point window, the higher the stability level of the voltage data segment.

[0054] In one embodiment, the first stability level of data in each voltage data point window can be calculated by the following formula:

[0055]

[0056] In the formula, Indicates the first stability of the data in the mth voltage data point window, represents the interquartile range in the data set composed of the data in the mth voltage data point window, It indicates the number of extreme value points in the corresponding voltage data segment in the mth voltage data point window. Represents the total number of data points within each voltage data point window.

[0057] In the above formula The smaller it is, the more stable the data change characteristics in the voltage data segment are, and the greater the first stability level in the corresponding voltage data segment is. It indicates the relative proportion of the number of extreme value points in a voltage data segment in this data segment. The smaller the value is, the smaller the number of extreme value points in a voltage data segment is. The more consistent the data change characteristics in this voltage data segment will be. The more stable the data change characteristics in the corresponding voltage data segment are, the greater the credibility is, that is, the greater the first degree of stability is.

[0058] After the above steps are analyzed, the first stability of the data in each voltage data point window is determined. This step preliminarily quantifies the data change characteristics in a voltage data segment, but due to the presence of noise data in the voltage data segment. The first stability of the data in each voltage data point window obtained based only on the analysis of the voltage data point numerical performance level may not be able to distinguish between noise data and real data. Through further scene analysis, it is known that the authenticity of the voltage data of electrical equipment in the flexible control system can be reflected to a certain extent by the current data. This is because the resistance in the circuit remains constant, and there will be a certain proportional relationship between the current and voltage, that is, the change characteristics of the two have a certain similarity in the same time period. Under normal circumstances (that is, when there is no noise data point in a voltage data segment), the similarity between the two data will be infinite, but if there is more noise data in a voltage data segment, the pixels of the two will be lower.

[0059] In this embodiment, the similarity of the change characteristics of the voltage data segment and the current data segment in the corresponding time period in each voltage data point window is analyzed, and the first stability of the data in each voltage data point window is optimized to obtain the second stability of the data in each voltage data point window. Among them, the greater the similarity between the voltage data segment and the current data segment in the corresponding time period of each voltage data point window, it can be explained that the noise data in the voltage data segment may be less, then the reliability of the first stability obtained by the above calculation will be greater, and the second stability corresponding to this voltage data segment will be greater.

[0060] In step S203, a second stability level is calculated based on the similarity between the voltage data segment and the current data segment in the voltage data point window, wherein the second stability level is positively correlated with the similarity and the first stability level.

[0061] In one embodiment, the calculation formula for the second stability level of data in each voltage data point window is:

[0062]

[0063]

[0064] In the formula, Indicates the similarity between the voltage data segment and the current data segment in the time period corresponding to the mth voltage data point window. Represents the total number of data points in each voltage data point window, that is, the total number of sampling moments. and They respectively represent the maximum and minimum values ​​in the voltage data segment in the time period corresponding to the mth voltage data point window. It represents the value of the voltage data point corresponding to the hth sampling moment in the time period corresponding to the mth voltage data point window. and They respectively represent the maximum and minimum values ​​in the current data segment in the time period corresponding to the mth voltage data point window. Indicates the value of the current data point corresponding to the hth sampling moment in the time period corresponding to the mth voltage data point window.

[0065] In the above formula Indicates the second stability of the data in the mth voltage data point window, Indicates the first stability level of the data in the mth voltage data point window.

[0066] for In the calculation formula, and The relative values ​​of the voltage data points corresponding to each sampling moment in the window corresponding to the mth voltage data point and the current data points in their respective corresponding voltage data segments and current data segments are quantified respectively. The difference between these two indicators reflects the similarity of the voltage data and the current data at a sampling moment. The smaller it is, the greater the similarity between the voltage data segment and the current data segment in the time period corresponding to the window of the mth voltage data point.

[0067] for In the calculation formula, The larger the value is, the more stable the data change characteristics in the mth voltage data point window are, and the greater the corresponding second stability level is. The larger it is, the less noise data may exist in the voltage data segment in the time period corresponding to the m-th voltage data point window. Then the reliability of the first degree of stability calculated above will be greater, and the second degree of stability of the voltage data segment in the time period corresponding to the m-th voltage data point window will be greater.

[0068] In step S204, the sub-window length within the voltage data point window is calculated according to the second stability level, wherein the sub-window length is positively correlated with the second stability level.

[0069] Through the analysis of the above steps, this step needs to calculate the optimized sub-window length in each voltage data point window based on the second stability of the data in each voltage data point window. Among them, the greater the second stability of the data in each voltage data point window, the larger the corresponding sub-window length will be. Since the stable signal changes more smoothly, a larger window can be tolerated to capture the overall dynamic characteristics without introducing too much noise. On the contrary, if the data in the voltage data point window changes greatly (that is, the lower the second stability), the smaller the sub-window length will be more suitable, thereby more accurately capturing the dynamic changes in the short term while avoiding unnecessary noise interference. According to the above logic, the optimized sub-window length in each voltage data point window is calculated:

[0070]

[0071] In the formula, represents the optimized sub-window length within the m-th voltage data point window, Indicates the second stability of the data in the mth voltage data point window, Indicates the initial preset sub-window length, .use The optimized sub-window length within each voltage data point window is rounded off.

[0072] After completing the calculation of the sub-window length using the above process, the fuzzy entropy value of each voltage data point can be calculated according to the optimized fuzzy entropy method, and a threshold can be set to perform abnormal judgment on the fuzzy entropy value of each voltage data point to achieve abnormal detection of voltage data.

[0073] Through the above analysis, the optimized sub-window length in each voltage data point window is obtained. In this step, the fuzzy entropy value of each voltage data point is calculated according to the optimized fuzzy entropy method, and anomaly detection is performed on the voltage data. In an application scenario, anomaly detection can be achieved through the following process. Specifically:

[0074] 1. Initial parameters are preset for the fuzzy entropy method, where the window length corresponding to each voltage data point is preset to an empirical value of 500, and the similarity tolerance of each sub-window in each voltage data point window when calculating the fuzzy membership with other sub-windows is preset to an empirical value of 0.2;

[0075] 2. Calculate the sub-window length in each voltage data point window using the method in the above embodiment. After obtaining the sub-window length, the number of sub-windows is calculated as follows: window length - length of each sub-window + 1;

[0076] 3. According to the existing known steps in the fuzzy entropy method, the fuzzy entropy value of each voltage data point is calculated. Perform calculations;

[0077] 4. Set the threshold for abnormal detection of each voltage data point The absolute value of the difference between the fuzzy entropy value of a voltage data point and its immediately previous voltage data point Make a judgment, if , it can be explained that the fuzzy entropy value at the mth voltage data point has a mutation, that is, an abnormality has occurred at the mth voltage data point and it is abnormal data. It is necessary to notify the relevant technical personnel to make adjustments based on the flexible control end to ensure the safe operation of the power grid.

[0078] Figure 3 It is a structural diagram showing the data processing system of the flexible control terminal of the present invention.

[0079] The present invention also provides a data processing system for a flexible control terminal. Figure 3 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a data processing method for a flexible control terminal as described above is implemented.

[0080] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0081] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0082] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0083] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A data processing method for a flexible control terminal, characterized in that: include: Obtain voltage and current data of electrical equipment; The fuzzy entropy value of each voltage data point in the voltage data is determined according to the fuzzy entropy calculation method, and an abnormality judgment is performed according to the fuzzy entropy value, wherein the determination method of the subwindow length in the fuzzy entropy calculation method includes: Determine the voltage data point window where the voltage data is located at the sampling moment; Calculate the first stability level, the calculation formula is: ; Where: Indicates the first stability of the data in the mth voltage data point window, represents the interquartile range of the voltage data set composed of the mth voltage data point window, It indicates the number of extreme value points in the corresponding voltage data segment in the mth voltage data point window. Indicates the total number of data points in each voltage data point window; Calculating a second stability level according to the similarity of the voltage data segment and the current data segment in the voltage data point window, wherein the second stability level is positively correlated with the similarity and the first stability level; Calculate the sub-window length within the voltage data point window using the following formula: ; Where: represents the sub-window length within the m-th voltage data point window, Indicates the second stability of the data in the mth voltage data point window, Indicates the initial preset sub-window length, Indicates a rounding operation.

2. The data processing method of the flexible control terminal according to claim 1, characterized in that: Determine the voltage data point window where the voltage data is located at the sampling time, including: A set number of voltage data points immediately before a voltage data point sampling moment is acquired to form a data segment corresponding to a voltage data point window.

3. The data processing method of the flexible control terminal according to claim 1, characterized in that: The similarity calculation formula is: ; Where: It represents the similarity between the voltage data segment and the current data segment in the time period corresponding to the mth voltage data point window. Represents the total number of data points in each voltage data point window, and They represent the maximum and minimum values ​​in the voltage data segment in the time period corresponding to the mth voltage data point window, respectively. and They represent the value of the voltage data point and the value of the current data point corresponding to the hth sampling moment in the time period corresponding to the mth voltage data point window, respectively. and They respectively represent the maximum and minimum values ​​in the current data segment in the time period corresponding to the mth voltage data point window.

4. The data processing method of the flexible control terminal according to claim 3 is characterized in that: The calculation formula for the second degree of stability is: ; In the formula, is the standard normalization function, Indicates the second stability of the data in the mth voltage data point window, Indicates the first stability level of the data in the mth voltage data point window.

5. The data processing method of the flexible control terminal according to claim 1, characterized in that: The fuzzy entropy value of each voltage data point in the voltage data is determined according to the fuzzy entropy calculation method, including: Determine the corresponding window length of each voltage data point window, and determine the number of sub-windows according to the sub-window length; Determine the number of sub-windows based on the window length and the sub-window length, the number of sub-windows being: window length - length of each sub-window + 1; The sub-window is used to calculate the fuzzy entropy value of the voltage data point.

6. The data processing method of the flexible control terminal according to claim 1, characterized in that: Anomaly judgment is performed based on fuzzy entropy values, including: Calculate the absolute value of the difference between the fuzzy entropy value of a voltage data point and its immediately previous voltage data point; In response to the absolute value of the difference being greater than a set threshold, an abnormality exists at the corresponding voltage data point, and an alarm is issued.

7. The data processing method of the flexible control terminal according to claim 6, characterized in that: The threshold is set to 0.

1.

8. A data processing system for a flexible control terminal, characterized in that: include: processor; A memory storing computer program instructions, wherein when the computer program instructions are executed by the processor, a data processing method for a flexible control terminal according to any one of claims 1 to 7 is implemented.

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