A method and system for data acquisition of automated equipment

Abstract

This application relates to the field of data processing technology, specifically to a method and system for data acquisition of automated equipment. The method includes: acquiring voltage and temperature data from each device; dividing the voltage and temperature data into multiple time windows; acquiring the trend coefficients of each device's changes within each time window; acquiring the temperature influence coefficients of each device within each time window; acquiring the voltage transient coefficients of each device within each time window; acquiring the fractal dimension of each device within each time window; acquiring the electromagnetic disturbance coefficients of each device within each time window; acquiring the data acquisition disturbance values ​​of each device within each time window; acquiring the disturbance coupling index of each device within each time window; and further acquiring the filtering window parameters of each device within each time window, thereby achieving adaptive filtering of the voltage data. By analyzing the degree of environmental interference experienced by different devices at different time periods, adaptive filtering is applied to the acquired data, thus improving the accuracy of data acquisition.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and system for data acquisition from automated equipment. Background Technology

[0002] As a critical hub for power distribution, equipment control, and signal transmission in industrial production, the operating status of the electrical control cabinet directly affects the stability and energy efficiency of the production system. By integrating high-precision sensors and intelligent gateway devices, multi-dimensional status monitoring of core components such as circuit breakers, contactors, and PLCs inside the electrical control cabinet can be achieved.

[0003] An electrical control cabinet is a main control unit used for centralized control and protection of various electrical equipment. It achieves automated operation of the equipment through the combination of various electrical components and controllers. Electrical control cabinets typically contain power switches, controllers, relays, sensors, frequency converters, and other components. During the monitoring of each electrical component, the accuracy and stability of data acquisition are crucial for achieving precise control. During data acquisition, various components within the electrical control unit are prone to electromagnetic interference, and the intensity of electromagnetic interference varies at different times. Furthermore, the temperature of each component changes over time, easily causing variations in noise content in the acquired data at different times. In the data filtering process, existing methods typically use the same filtering window parameters to filter the entire data, resulting in low accuracy of the filtered data and consequently, insufficient data acquisition precision. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for data acquisition from automated equipment, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide an automated equipment data acquisition method, which includes the following steps:

[0006] Acquire voltage and temperature data for each device;

[0007] The voltage and temperature data of each device are divided into multiple time windows; the trend data of the temperature data of each device in each time window is obtained; the trend coefficient of the change of the temperature data of each device in each time window is obtained according to the change trend of the temperature data of each device in each time window; the average level of the temperature data of each device in each time window and the trend data of the temperature data of the corresponding time window are combined with the slope values ​​and dispersion of all time points after curve fitting, and the temperature influence coefficient of each device in each time window is obtained.

[0008] The voltage transient coefficient of each device in each time window is obtained based on the difference between the voltage at abrupt change point and the voltage at non-abrupt change point in the voltage data of each device in each time window; the fractal dimension of each device in each time window is obtained based on the frequency characteristics of the voltage data of each device in each time window; the electromagnetic disturbance coefficient of each device in each time window is obtained based on the voltage transient coefficient and fractal dimension of each device in each time window; the data acquisition disturbance value of each device in each time window is obtained based on the temperature influence coefficient and electromagnetic disturbance coefficient of each device in each time window.

[0009] Based on the degree of difference in data acquisition disturbance values ​​between devices within the same time window and the data acquisition disturbance values ​​of each device in each time window, the disturbance coupling index of each device in each time window is obtained, and then the filtering window parameters of each device in each time window are obtained to achieve adaptive filtering of voltage data.

[0010] Preferably, the formula for calculating the trend coefficient of each device in each time window is as follows: In the formula, This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the last data point in the trend data for the j-th device during the i-th time window. This is the first data point in the trend data of the j-th device during the i-th time window.

[0011] Preferably, the method for determining the temperature influence coefficient of each device in each time window is as follows:

[0012] The fitting curves of the trend data of each device in each time window are obtained respectively, and the slope value of the fitting curve at each time point is calculated respectively. The product of the absolute value of the sum of all the slope values ​​of the j-th device in the i-th time window and the standard deviation of all the slope values ​​is taken as the oscillation coefficient of the j-th device in the i-th time window.

[0013] The formula for calculating the temperature influence coefficient of each device in each time window is as follows: In the formula, This represents the temperature influence coefficient of the j-th device in the i-th time window. This represents the mean of all temperature data for the j-th device within the i-th time window. This represents an exponential function with base e. This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the oscillation coefficient of the j-th device in the i-th time window.

[0014] Preferably, the method for determining the voltage transient coefficient of each device in each time window is as follows:

[0015] Obtain the abrupt change points in the voltage data of each device in each time window;

[0016] Calculate the mean of voltage data corresponding to all non-abrupt points in the voltage data of each device in each time window. Calculate the absolute value of the difference between each abrupt point and the corresponding mean in the voltage data of each device in each time window. Use the mean of all the absolute values ​​of each device in each time window as the voltage transient coefficient of each device in each time window.

[0017] Preferably, the method for determining the fractal dimension of each device in each time window is as follows: obtaining the frequency amplitude spectrum of the voltage data in each time window; recording the sequence of all amplitudes in the frequency amplitude spectrum of each device in each time window that are less than its fundamental frequency amplitude as the amplitude sequence of each device in each time window; and recording the fractal dimension of the amplitude sequence of each device in each time window as the fractal dimension of each device in each time window.

[0018] Preferably, the electromagnetic disturbance coefficient of each device in each time window is the product of the voltage transient coefficient and the fractal dimension of each device in each time window.

[0019] Preferably, the calculation formula for the data acquisition disturbance value of each device in each time window is as follows: In the formula, This represents the data acquisition disturbance value of the j-th device in the i-th time window. This represents the temperature influence coefficient of the j-th device in the i-th time window. Let represent the electromagnetic disturbance coefficient of the j-th device in the i-th time window. As the first preset weight, As the second preset weight, and .

[0020] Preferably, the method for determining the disturbance coupling index is as follows: calculate the absolute value of the difference between the data acquisition disturbance value of a single device and all other devices in a single time window, and take the sum of the mean of all the absolute values ​​of the single device in a single time window and the data acquisition disturbance value as the disturbance coupling index of the single device in the single time window.

[0021] Preferably, the calculation formula for the filter window parameters of each device in each time window is as follows: In the formula, Let be the filter window parameters for the j-th device in the i-th time window. This is an odd-number function used to find the nearest odd number from the input data. This is the normalized result of the disturbance coupling index of the j-th device in the i-th time window.

[0022] Secondly, embodiments of this application also provide an automated equipment data acquisition system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described automated equipment data acquisition methods.

[0023] This application has at least the following beneficial effects:

[0024] This application addresses the issue that sensors are susceptible to temperature and electromagnetic interference during automated data acquisition, causing fluctuations in the acquired data at different times. Furthermore, using a single filter window parameter in filtering algorithms can lead to inaccurate results. This application provides an in-depth analysis of temperature and electromagnetic interference, and calculates different filter window parameters for each time period based on the degree of temperature and electromagnetic interference experienced by the acquired data at different times, as well as the coupling relationships between devices. This ensures that the final filtering result more accurately reflects the true state of the data, thereby improving the accuracy of data acquisition. Attached Figure Description

[0025] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating the steps of an automated equipment data acquisition method according to one embodiment of this application;

[0027] Figure 2 This is a flowchart illustrating the process of obtaining filter window parameters according to one embodiment of this application. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automated equipment data acquisition method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automated equipment data acquisition method and system provided in this application.

[0031] Please see Figure 1 The diagram illustrates a flowchart of an automated equipment data acquisition method according to an embodiment of this application. The method includes the following steps:

[0032] Step 1: Obtain voltage and temperature data for each device.

[0033] This embodiment uses automated voltage data acquisition as an example for analysis. Various devices exist within the electrical control cabinet, including but not limited to power switches, controllers, relays, and frequency converters. Voltage data from each device within the cabinet is acquired in real time using voltage sensors. To account for the impact of internal temperature on the accuracy of the acquired data, temperature data from each device within the cabinet is acquired using temperature sensors. In this embodiment, the acquisition time interval for each voltage data point is set to 0.1 seconds, and the acquisition time interval for each temperature data point is set to 0.5 seconds. This allows for the acquisition of voltage and temperature data from multiple devices.

[0034] Step 2: Divide the voltage and temperature data of each device into multiple time windows; obtain the trend data corresponding to the temperature data of each device in each time window; obtain the trend coefficient of each device in each time window based on the changing trend of the temperature data of each device in each time window; combine the average level of the temperature data of each device in each time window with the trend data of the temperature data of the corresponding time window to obtain the magnitude and dispersion of the slope value of all moments after curve fitting, and obtain the temperature influence coefficient of each device in each time window.

[0035] The electrical control cabinet contains numerous devices. When multiple devices operate under high load or overload conditions, the cabinet temperature rises rapidly, leading to abnormally high temperatures. Sustained high temperatures can trigger multiple mechanisms affecting sensor accuracy, including self-heating, resistance drift, and uneven temperature field, resulting in decreased sensitivity. Insufficient arc-extinguishing performance of high-voltage electrical appliances within the cabinet and electromagnetic interference generated during high-frequency switching of high-power equipment can further reduce data acquisition accuracy. Therefore, a thorough analysis of the potential impact characteristics of temperature and electromagnetic interference during data acquisition is necessary to improve data acquisition accuracy.

[0036] Firstly, regarding the impact of temperature changes on data acquisition, the temperature distribution of different devices within the electrical control cabinet typically varies. This is because temperature changes with load and environment; near devices operating under high load, the temperature rises rapidly, and deteriorating heat dissipation also leads to localized temperature increases. Furthermore, the temperature distribution within the electrical control cabinet is dynamic and influenced by various factors, such as equipment operating status, heat dissipation efficiency, and airflow. Therefore, temperature changes within the electrical control cabinet exhibit complex dynamic fluctuation characteristics. As temperature increases, the impact on data acquisition accuracy becomes greater. Sensors operating at high temperatures for extended periods experience performance drift in their internal electronic components; for example, resistance increases with temperature. Thus, it is necessary to analyze the dynamic fluctuation characteristics and long-term temperature characteristics of the temperature. A time window of 20 seconds was set, dividing the acquired voltage and temperature data from each device into several time windows in chronological order. It should be noted that the i-th time window for voltage data corresponds to the same time period as the i-th time window for temperature data.

[0037] To obtain the trend of temperature data under dynamic fluctuations, this embodiment uses the temperature data of each device in each time window as input to the Singular Spectrum Analysis (SSA) algorithm, and outputs the trend data corresponding to the temperature data of each device in each time window. The SSA algorithm is a well-known technology, and the specific process will not be described in detail. The SSA algorithm can reduce the impact of short-term complex changes in temperature data on the extraction of its overall trend features, so as to obtain the long-term changes in temperature data more accurately. Then, the trend coefficient of each device in each time window is calculated, and the trend coefficient of the j-th device in the i-th time window is denoted as... Its specific expression is: In the formula, This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the last data point in the trend data for the j-th device during the i-th time window. This is the first data point in the trend data for the j-th device during the i-th time window. The result is... The larger the value, the more significant the increasing trend of the temperature data of the j-th device in the i-th time window. Furthermore, the temperature data also exhibits oscillations and fluctuations. Therefore, this embodiment uses polynomial fitting technology to obtain the fitting curves of the trend term data for each device in each time window, and calculates the slope value of the fitting curve at each corresponding moment. The product of the absolute value of the sum of all the slope values ​​of the j-th device in the i-th time window and the standard deviation of all the slope values ​​is taken as the oscillation coefficient of the j-th device in the i-th time window, denoted as [equation missing]. The result The larger the value, the less stable the temperature data of the j-th device is in the i-th time window.

[0038] Furthermore, by combining the overall temperature data, the temperature influence coefficient of each device in each time window is obtained, and its expression is as follows: In the formula, This represents the temperature influence coefficient of the j-th device in the i-th time window. This represents the mean of all temperature data for the j-th device within the i-th time window. This represents an exponential function with base e. This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the oscillation coefficient of the j-th device in the i-th time window. The obtained... The larger the value, the greater the impact of the temperature state of the j-th device on the accuracy of voltage data acquisition during the i-th time window. The resulting temperature influence coefficient takes into account the complex dynamic changes in temperature within the electrical control cabinet, the overall temperature trend characteristics, and the impact of its magnitude on data acquisition.

[0039] Step 3: Obtain the voltage transient coefficient of each device in each time window based on the difference between the voltage at the abrupt change point and the voltage at the non-abrupt change point in the voltage data of each device in each time window; obtain the fractal dimension of each device in each time window based on the frequency characteristics of the voltage data of each device in each time window; obtain the electromagnetic disturbance coefficient of each device in each time window based on the voltage transient coefficient and fractal dimension of each device in each time window; obtain the data acquisition disturbance value of each device in each time window based on the temperature influence coefficient and electromagnetic disturbance coefficient of each device in each time window.

[0040] Furthermore, regarding the characteristics of data acquisition affected by electromagnetic interference, in electrical control cabinets, switching equipment generates instantaneous high voltage and large voltage pulses during operation. These pulses can cause strong electromagnetic interference to surrounding equipment. Inverters and servo controllers generate numerous high-frequency harmonics during operation, while transformers, motors, and other equipment generate electromagnetic fields. All of these can interfere with data acquisition instruments. Under the influence of electromagnetic interference, the acquired voltage data will exhibit high-frequency glitches and increased harmonic components. The frequencies corresponding to these components are random, and the generation of electromagnetic interference is usually accompanied by instantaneous high voltage. Therefore, the greater the instantaneous high voltage, the more glitches and harmonic components are present in the voltage data, indicating a greater impact from harmonic interference during data acquisition.

[0041] Therefore, to obtain the instantaneous change level of voltage data, this embodiment uses the voltage data of each device in each time window as input to the abrupt change point detection method based on an adaptive sliding window, and outputs the abrupt change point position of each device in the voltage data of each time window. The abrupt change point detection method based on an adaptive sliding window is a known technology, and the specific process will not be described in detail. Further, a voltage transient coefficient for each device in each time window is constructed to characterize the intensity of the instantaneous high voltage of each device in the voltage data of each time window. The specific process is as follows: calculate the mean value of the voltage data corresponding to all non-abrupt points in the voltage data of each device in each time window; calculate the absolute value of the difference between each abrupt point in the voltage data of each device in each time window and the corresponding mean value; and use the mean value of all such absolute values ​​for each device in each time window as the voltage transient coefficient for each device in each time window. The larger the voltage transient coefficient, the greater the intensity of the instantaneous high voltage of the corresponding device in the voltage data of the corresponding time window.

[0042] To obtain the high-frequency glitches and harmonic components of voltage data, this embodiment uses Discrete Fourier Transform (DFT) to obtain the frequency amplitude spectrum of voltage data for each device in each time window, where the frequency with the largest amplitude in the frequency amplitude spectrum is the fundamental frequency. DFT is a well-known technique, and its specific process will not be elaborated further. Since the frequencies corresponding to glitches and harmonic components have high randomness, the amplitude distribution of the obtained frequency amplitude spectrum is more unstable when the collected voltage data contains a large number of glitches and harmonic components. The sequence of all amplitudes less than the fundamental frequency amplitude in the frequency amplitude spectrum of each device in each time window is denoted as the amplitude sequence of each device in each time window. In this embodiment, the amplitude sequences of each device in each time window are used as inputs to the Higuchi algorithm to obtain the fractal dimension of each device in each time window. The larger the obtained fractal dimension value, the more chaotic the frequency component distribution of the voltage data of the corresponding device in the corresponding time window. The Higuchi algorithm is a well-known technique, and its specific process will not be elaborated further.

[0043] Then, the electromagnetic disturbance coefficients for each device in each time window are constructed to characterize the degree to which the voltage data of each device is affected by electromagnetic interference in each time window. In this embodiment, the product of the voltage transient coefficient and the fractal dimension of each device in each time window is used as the electromagnetic disturbance coefficient of each device in each time window. The obtained value represents the electromagnetic disturbance coefficient of the j-th device in the i-th time window. The larger the value, the greater the impact of electromagnetic interference on the voltage data acquired by the j-th device in the i-th time window. The obtained electromagnetic disturbance coefficient is used to accurately assess the degree of electromagnetic disturbance on data acquisition by analyzing the transient change characteristics of the voltage data and the content of high-frequency glitches and harmonic components under the affected state.

[0044] After the above steps, the influence characteristics of voltage data from each device on temperature and electromagnetic interference at each time window were obtained. The degree of impact of these two factors on the accuracy of data acquisition is usually inconsistent. To comprehensively assess the degree of influence, a data acquisition disturbance value for each device at each time window is constructed, the specific expression of which is: In the formula, This represents the data acquisition disturbance value of the j-th device in the i-th time window. This represents the temperature influence coefficient of the j-th device in the i-th time window. Let represent the electromagnetic disturbance coefficient of the j-th device in the i-th time window. As the first preset weight, As the second preset weight, and In this embodiment , We took values ​​of 0.3 and 0.7 respectively. The results were... The larger the value, the greater the influence of temperature and electromagnetic interference on the voltage data of the j-th device in the i-th time window.

[0045] Step 4: Based on the degree of difference in data acquisition disturbance values ​​between devices under the same time window and the data acquisition disturbance values ​​of each device in each time window, obtain the disturbance coupling index of each device in each time window, and then obtain the filtering window parameters of each device in each time window to achieve adaptive filtering of voltage data.

[0046] Furthermore, disturbances experienced at different locations within the electrical control cabinet can interact with each other, manifesting as electromagnetic induction coupling interference. When one device is disturbed, its interference signal can be transmitted to other parts through coupling. The greater the degree of disturbance to a device and the higher the accuracy requirements for data acquisition, the more pronounced the coupling characteristics become. Therefore, to obtain the mutual influence of different devices during automated data acquisition, the absolute value of the difference between the data acquisition disturbance values ​​of the j-th device and all other devices in the i-th time window is calculated. The sum of the mean of all such absolute values ​​and the data acquisition disturbance value of the j-th device in the i-th time window is taken as the disturbance coupling index of the j-th device in the i-th time window, denoted as [equation missing]. The result The larger the value, the greater the degree of external interference affecting the voltage data of the j-th device in the i-th time window.

[0047] Based on the above analysis, the disturbance coupling index of each device within each time window is obtained. This value reflects the degree of external interference affecting the collected voltage data. To obtain more accurate voltage data, this embodiment uses the Savitzky-Golay algorithm for filtering. Specifically, the larger the disturbance coupling index, the greater the external interference affecting the collected voltage data, and the more unstable the data. A larger filter window parameter should be set in the filtering process to provide better smoothing; conversely, a smaller filter window parameter should be set to retain more data details. The commonly used range for the filter window parameter is an odd number in [5, 23]. Based on the above characteristics, the filter window parameters of each device within each time window are optimized using the following formula: In the formula, Let be the filter window parameters for the j-th device in the i-th time window. This is an odd-number function used to find the nearest odd number from the input data. This is the normalized result of the disturbance coupling index of the j-th device in the i-th time window. In this embodiment, the normalization process is implemented using the sigmoid function. The flowchart for obtaining the filter window parameters is as follows: Figure 2 As shown.

[0048] At this point, the filtering window parameters for voltage data across all time windows from all devices have been obtained. Filtering the acquired voltage data using adaptive filtering window parameters can improve the accuracy of the voltage data.

[0049] Based on the same inventive concept as the above method, this application also provides an automated equipment data acquisition system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described automated equipment data acquisition methods.

[0050] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0052] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for data acquisition in automated equipment, characterized in that, The method includes the following steps: Acquire voltage and temperature data for each device; The voltage and temperature data of each device are divided into multiple time windows; the trend data of the temperature data of each device in each time window is obtained; the trend coefficient of the change of the temperature data of each device in each time window is obtained according to the change trend of the temperature data of each device in each time window; the average level of the temperature data of each device in each time window and the trend data of the temperature data of the corresponding time window are combined with the slope values ​​and dispersion of all time points after curve fitting, and the temperature influence coefficient of each device in each time window is obtained. The voltage transient coefficient of each device in each time window is obtained based on the difference between the voltage at abrupt change point and the voltage at non-abrupt change point in the voltage data of each device in each time window; the fractal dimension of each device in each time window is obtained based on the frequency characteristics of the voltage data of each device in each time window; the electromagnetic disturbance coefficient of each device in each time window is obtained based on the voltage transient coefficient and fractal dimension of each device in each time window; the data acquisition disturbance value of each device in each time window is obtained based on the temperature influence coefficient and electromagnetic disturbance coefficient of each device in each time window. Based on the degree of difference in data acquisition disturbance values ​​between devices within the same time window and the data acquisition disturbance values ​​of each device in each time window, the disturbance coupling index of each device in each time window is obtained, and then the filtering window parameters of each device in each time window are obtained to achieve adaptive filtering of voltage data.

2. The automated equipment data acquisition method as described in claim 1, characterized in that, The formula for calculating the trend coefficient of each device in each time window is as follows: In the formula, This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the last data point in the trend data for the j-th device during the i-th time window. This is the first data point in the trend data of the j-th device during the i-th time window.

3. The automated equipment data acquisition method as described in claim 1, characterized in that, The method for determining the temperature influence coefficient of each device in each time window is as follows: The fitting curves of the trend data of each device in each time window are obtained respectively, and the slope value of the fitting curve at each time point is calculated respectively. The product of the absolute value of the sum of all the slope values ​​of the j-th device in the i-th time window and the standard deviation of all the slope values ​​is taken as the oscillation coefficient of the j-th device in the i-th time window. The formula for calculating the temperature influence coefficient of each device in each time window is as follows: In the formula, This represents the temperature influence coefficient of the j-th device in the i-th time window. This represents the mean of all temperature data for the j-th device within the i-th time window. This represents an exponential function with base e. This represents the coefficient representing the trend of the j-th device during the i-th time window. This represents the oscillation coefficient of the j-th device in the i-th time window.

4. The automated equipment data acquisition method as described in claim 1, characterized in that, The method for determining the voltage transient coefficient of each device in each time window is as follows: Obtain the abrupt change points in the voltage data of each device in each time window; Calculate the mean of voltage data corresponding to all non-abrupt points in the voltage data of each device in each time window. Calculate the absolute value of the difference between each abrupt point and the corresponding mean in the voltage data of each device in each time window. Use the mean of all the absolute values ​​of each device in each time window as the voltage transient coefficient of each device in each time window.

5. The automated equipment data acquisition method as described in claim 1, characterized in that, The method for determining the fractal dimension of each device in each time window is as follows: obtain the frequency amplitude spectrum of the voltage data in each time window; denote the sequence of all amplitudes in the frequency amplitude spectrum of each device in each time window that are less than its fundamental frequency amplitude as the amplitude sequence of each device in each time window; denote the fractal dimension of the amplitude sequence of each device in each time window as the fractal dimension of each device in each time window.

6. The automated equipment data acquisition method as described in claim 1, characterized in that, The electromagnetic disturbance coefficient of each device in each time window is the product of the voltage transient coefficient and the fractal dimension of each device in each time window.

7. The automated equipment data acquisition method as described in claim 1, characterized in that, The formula for calculating the data acquisition disturbance value of each device in each time window is as follows: In the formula, This represents the data acquisition disturbance value of the j-th device in the i-th time window. This represents the temperature influence coefficient of the j-th device in the i-th time window. Let represent the electromagnetic disturbance coefficient of the j-th device in the i-th time window. As the first preset weight, As the second preset weight, and .

8. The automated equipment data acquisition method as described in claim 1, characterized in that, The method for determining the disturbance coupling index is as follows: calculate the absolute value of the difference between the data acquisition disturbance value of a single device and all other devices in a single time window, and take the sum of the mean of all the absolute values ​​of a single device in a single time window and the data acquisition disturbance value as the disturbance coupling index of the single device in a single time window.

9. The automated equipment data acquisition method as described in claim 1, characterized in that, The formulas for calculating the filter window parameters of each device in each time window are as follows: In the formula, Let be the filter window parameters for the j-th device in the i-th time window. This is an odd-number function used to find the nearest odd number from the input data. This is the normalized result of the disturbance coupling index of the j-th device in the i-th time window.

10. An automated equipment data acquisition system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the automated equipment data acquisition method as described in any one of claims 1-9.

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