Automatic equipment data acquisition method and system

Abstract

The invention relates to the technical field of data processing, in particular to an automatic equipment data acquisition method and system, and the method comprises the steps: obtaining the voltage data and temperature data of each piece of equipment; dividing the voltage data and the temperature data of each device into a plurality of time windows; acquiring a change trend coefficient of each device in each time window, and acquiring a temperature influence coefficient of each device in each time window; acquiring a voltage transient coefficient of each device in each time window; fractal dimensions of all the devices in all the time windows are obtained; acquiring an electromagnetic disturbance coefficient of each device in each time window; acquiring a data acquisition disturbance value of each device in each time window; and the disturbance coupling index of each device in each time window is obtained, and then the filtering window parameter of each device in each time window is obtained, so that adaptive filtering of the voltage data is realized. By analyzing the environmental interference degrees of different devices in different time periods, adaptive filtering is performed on the acquired data, so that the accuracy of data acquisition is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for collecting data from automated equipment. Background Art

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

[0003] The electric control cabinet is the main control unit used for centralized control and protection of various electrical equipment. It realizes the automated operation of the equipment through the combination of various electrical components and controllers. The electric control cabinet usually contains power switches, controllers, relays, sensors, inverters and other components. In the process of monitoring each electrical component, the accuracy and stability of data acquisition are the key to achieving precise control. During the data acquisition process, the various components in the electric controller are prone to electromagnetic interference, and the intensity of electromagnetic interference varies at different times. In addition, the temperature of each component will also change at different times, which can easily cause different noise content in the data collected in different time periods. In the process of filtering data, the existing method usually filters the entire data with the same filter window parameters, resulting in low accuracy of the filtered data, and then the problem of inaccurate data acquisition. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for collecting data from automated equipment. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a method for collecting data from an automated device, the method comprising the following steps: Obtain voltage and temperature data of each device; The voltage data and temperature data of each device are divided into multiple time windows; the trend item data corresponding to the temperature data of each device in each time window is obtained; the change trend coefficient of each device in each time window is obtained based on the change trend of the temperature data of each device in each time window, and the numerical value and dispersion degree of the slope value at all moments after curve fitting are combined with the average level of the temperature data of each device in each time window and the trend item data of the temperature data of the corresponding time window to obtain the temperature influence coefficient of each device in each time window; The voltage transient coefficient of each device in each time window is obtained based on the difference between the voltage at the mutation point and the voltage at the non-mutation 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; According to the difference in the data acquisition disturbance values ​​between devices in 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 realize adaptive filtering of voltage data.

[0005] Preferably, the calculation formula for the change trend coefficient of each device in each time window is: , where Indicates the change trend coefficient of the jth device in the i-th time window, The last data of the trend item data of the j-th device in the i-th time window, The first data in the trend item data of the j-th device in the i-th time window.

[0006] Preferably, the method for determining the temperature influence coefficient of each device in each time window is: Obtain a fitting curve of the trend item data of each device in each time window, calculate the slope value corresponding to each moment in the fitting curve, and use 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 ​​as the oscillation coefficient of the j-th device in the i-th time window; The calculation formula for the temperature influence coefficient of each device in each time window is: , where represents the temperature influence coefficient of the jth device in the i-th time window, represents the mean value of all temperature data of the jth device in the i-th time window, represents the exponential function with the natural constant e as the base, Indicates the change trend coefficient of the jth device in the i-th time window, represents the oscillation coefficient of the jth device in the i-th time window.

[0007] Preferably, the method for determining the voltage transient coefficient of each device in each time window is: Obtain the mutation point of the voltage data of each device in each time window; Calculate the mean of the voltage data corresponding to all non-mutation points in the voltage data of each device in each time window, calculate the absolute value of the difference between each mutation point in the voltage data of each device in each time window and the corresponding mean, and take 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.

[0008] Preferably, the method for determining the fractal dimension of each device in each time window is: obtaining the frequency amplitude spectrum of the voltage data of each time window; recording the sequence composed of all amplitudes in the frequency amplitude spectrum of each device in each time window that are less than the 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.

[0009] Preferably, the electromagnetic disturbance coefficient of each device in each time window is the product of the voltage transient coefficient of each device in each time window and the fractal dimension.

[0010] Preferably, the calculation formula for the data acquisition disturbance value of each device in each time window is: , where represents the data acquisition disturbance value of the jth device in the i-th time window, represents the temperature influence coefficient of the jth device in the i-th time window, represents the electromagnetic disturbance coefficient of the jth device in the i-th time window, is the first preset weight, is the second preset weight, and .

[0011] Preferably, the method for determining the disturbance coupling index is: respectively calculating 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 taking the mean of all the absolute values ​​of the single device in a single time window and the sum of the data acquisition disturbance value as the disturbance coupling index of the single device in a single time window.

[0012] Preferably, the calculation formula for the filter window parameters of each device in each time window is: , where is the filter window parameter of the jth device in the i-th time window, is an odd function, which is used to take the nearest odd number from the input data. is the normalized result of the disturbance coupling index of the jth device in the i-th time window.

[0013] In a second aspect, an embodiment of the present application further provides an automated equipment data acquisition system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned automated equipment data acquisition methods are implemented.

[0014] This application has at least the following beneficial effects: This application addresses the problem that in the process of automated data acquisition, sensors are susceptible to temperature interference and electromagnetic interference, which causes the collected data to fluctuate to varying degrees in different time periods. When filtering with a filtering algorithm, using a single filtering window parameter will result in inaccurate filtering results. This application conducts an in-depth analysis of temperature interference and electromagnetic interference, and calculates different filtering window parameters for each time period based on the degree of temperature interference and electromagnetic interference in different time periods in the collected data, as well as the coupling relationship between devices, so that the final filtering result can more accurately reflect the true state of the data, thereby improving the accuracy of data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A flowchart of a method for collecting data from an automated device according to an embodiment of the present application; Figure 2 A flowchart for obtaining filter window parameters provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a method and system for automated equipment data acquisition proposed in this application. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, 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 belongs.

[0019] The following describes in detail a specific solution of an automated equipment data collection method and system provided by the present application with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flowchart of a method for collecting data from an automated device provided by an embodiment of the present application, the method comprising the following steps: Step 1: Obtain voltage and temperature data of each device.

[0021] This example uses automated voltage data collection as an example. An electrical control cabinet contains various devices, including but not limited to power switches, controllers, relays, and inverters. Voltage sensors are used to collect real-time voltage data from each device within the cabinet. To account for the impact of internal temperature on the accuracy of collected data, temperature sensors are used to collect temperature data from each device within the cabinet. This example sets the interval for collecting voltage data to 0.1 seconds and the interval for collecting temperature data to 0.5 seconds. This allows the collection of voltage and temperature data for multiple devices.

[0022] Step 2: Divide the voltage data and temperature data of each device into multiple time windows; obtain the trend item data corresponding to the temperature data of each device in each time window; obtain the change trend coefficient of each device in each time window according to the change trend of the temperature data of each device in each time window, and combine the average level of the temperature data of each device in each time window and the numerical size and discrete degree of the slope value at all moments after curve fitting based on the trend item data of the temperature data of the corresponding time window to obtain the temperature influence coefficient of each device in each time window.

[0023] Electrical control cabinets contain numerous devices. When multiple devices operate under high load or overload, the cabinet temperature rises rapidly, reaching abnormally high temperatures. Sustained high temperatures can lead to sensor self-heating, resistance drift, and uneven temperature fields. These multiple mechanisms affect sensor accuracy and reduce sensitivity. Inadequate arc extinguishing performance of high-voltage electrical equipment within the cabinet and high-frequency switching of high-power equipment can also cause electromagnetic interference, reducing data acquisition accuracy. Therefore, in-depth analysis of the temperature and electromagnetic interference characteristics that may affect data acquisition is necessary to improve data collection accuracy.

[0024] First, regarding the temperature fluctuations affecting data acquisition, the temperature distribution of different devices within an electrical control cabinet typically varies. This is because temperature varies with load and environment. Temperatures rise faster near devices operating at high loads, and deteriorating heat dissipation conditions can also cause localized temperature increases. Furthermore, the temperature distribution within the electrical control cabinet is dynamic and influenced by multiple factors, such as device operating status, heat dissipation, and air flow. Therefore, temperature fluctuations within the electrical control cabinet exhibit complex dynamic fluctuations. As temperature rises, the impact on data acquisition accuracy increases. Sensors exposed to high temperatures for extended periods of time experience drift in the performance of their internal electronic components, such as increasing resistance with increasing temperature. Therefore, it is necessary to analyze the dynamic temperature fluctuations and long-term temperature state characteristics. The time window length is set to 20 seconds, and the collected voltage and temperature data for each device is divided into several time windows in chronological order. It should be noted that the i-th time window of the voltage data corresponds to the same time period as the i-th time window of the temperature data.

[0025] In order to obtain the trend of temperature data under dynamic fluctuations, this embodiment uses the temperature data of each device in each time window as the input of the singular spectrum analysis (SSA) algorithm, and outputs the trend item 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 repeated here. The SSA algorithm can reduce the impact of short-term complex changes in temperature data on its overall trend feature extraction, so as to more accurately obtain the long-term changes in temperature data. Then calculate the change trend coefficient of each device in each time window, and record the change trend coefficient of the jth device in the i-th time window as , its specific expression is: , where Indicates the change trend coefficient of the jth device in the i-th time window, The last data of the trend item data of the j-th device in the i-th time window, is the first data in the trend item data of the jth device in the i-th time window. The larger the value, the more significant the increase trend of the temperature data of the jth device in the i-th time window. Furthermore, the temperature data also shows an oscillating state. Therefore, this embodiment uses polynomial fitting technology to obtain the fitting curve of the trend item data of each device in each time window, and calculates the slope value corresponding to each moment in the fitting curve. The product of the absolute value of the sum of all the slope values ​​of the jth device in the i-th time window and the standard deviation of all the slope values ​​is used as the oscillation coefficient of the jth device in the i-th time window, which is recorded as . Income The larger it is, the more unstable the temperature data of the j-th device in the i-th time window is.

[0026] Furthermore, combined with the overall temperature data, the temperature influence coefficient of each device in each time window is obtained, and its expression is: , where represents the temperature influence coefficient of the jth device in the i-th time window, represents the mean value of all temperature data of the jth device in the i-th time window, represents the exponential function with the natural constant e as the base, Indicates the change trend coefficient of the jth device in the i-th time window, represents the oscillation coefficient of the jth device in the i-th time window. The larger the value, the greater the impact of the temperature state of the jth device in the i-th time window on the accuracy of voltage data collection. The resulting temperature impact coefficient deeply considers the complex dynamic changes in temperature in the electrical control cabinet, the overall temperature trend characteristics, and the impact of its magnitude on data collection.

[0027] Step 3: Obtain the voltage transient coefficient of each device in each time window based on the difference between the voltage at the mutation point and the voltage at the non-mutation 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.

[0028] Furthermore, regarding the characteristics of data acquisition affected by electromagnetic interference, in electrical control cabinets, switchgear generates transient high voltages and large voltage pulses during operation, which can cause strong electromagnetic interference to surrounding equipment. Frequency converters and servo controllers generate a large number of high-frequency harmonics during operation, while transformers, motors, and other equipment generate electromagnetic fields during operation, all of which 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 when equipment generates electromagnetic interference, transient high voltages are often accompanied. Therefore, the greater the transient high voltage, the more glitches and harmonic components in the voltage data, indicating a greater impact of harmonic interference during the data acquisition process.

[0029] Therefore, to obtain the instantaneous degree of voltage data change, this embodiment uses the voltage data of each device in each time window as input to a mutation point detection method based on an adaptive sliding window, and outputs the location of the mutation point in the voltage data of each device in each time window. This mutation point detection method based on an adaptive sliding window is well known in the art, and the specific process is not described in detail here. Furthermore, a voltage transient coefficient is constructed for each device in each time window to characterize the intensity of the transient high voltage in the voltage data of each device in each time window. The specific process is as follows: calculating the mean of all voltage data corresponding to non-mutation points in the voltage data of each device in each time window, calculating the absolute value of the difference between each mutation point in the voltage data of each device in each time window and the corresponding mean value, and taking the mean of all these absolute values ​​for each device in each time window as the voltage transient coefficient for each device in each time window. A larger voltage transient coefficient indicates a greater intensity of the transient high voltage in the voltage data of the corresponding device in the corresponding time window.

[0030] To capture the high-frequency glitches and harmonic components of voltage data, this embodiment uses a discrete Fourier transform (DFT) to obtain the frequency-amplitude spectrum of the voltage data for each device in each time window. The frequency with the largest amplitude in the frequency-amplitude spectrum is the fundamental frequency. Discrete Fourier transforms are a well-known technique, and the detailed process is omitted. Because the frequencies corresponding to glitches and harmonic components are highly random, the amplitude distribution of the resulting frequency-amplitude spectrum becomes more unstable when the collected voltage data contains more glitches and harmonic components. The sequence consisting of all amplitudes in the frequency-amplitude spectrum for each device in each time window that are less than the fundamental frequency amplitude is recorded as the amplitude sequence for each device in each time window. This embodiment uses the amplitude sequence for each device in each time window as input to the Higuchi algorithm to obtain the fractal dimension for each device in each time window. A larger fractal dimension indicates a more chaotic distribution of the frequency components in the voltage data for the corresponding device in that time window. The Higuchi algorithm is a well-known technique, and the detailed process is omitted.

[0031] Then, the electromagnetic disturbance coefficient of each device in each time window is constructed to characterize the degree to which the voltage data of each device in each time window is affected by electromagnetic interference. In this embodiment, the product of the voltage transient coefficient of each device in each time window and the fractal dimension is used as the electromagnetic disturbance coefficient of each device in each time window. It represents the electromagnetic disturbance coefficient of the jth device in the i-th time window. The larger the value, the more the voltage data collected by the jth device in the ith time window is affected by electromagnetic interference. The obtained electromagnetic disturbance coefficient accurately assesses the impact of electromagnetic disturbance on data collection by analyzing the transient change characteristics of the voltage data and the content of high-frequency glitches and harmonic components in the affected state.

[0032] Through the above steps, we can obtain the characteristics of the voltage data corresponding to each device in each time window affected by temperature and electromagnetic interference. The degree of influence of the two on the accuracy of data acquisition is usually inconsistent. To comprehensively evaluate the degree of influence, we construct the data acquisition disturbance value of each device in each time window. The specific expression is: , where represents the data acquisition disturbance value of the jth device in the i-th time window, represents the temperature influence coefficient of the jth device in the i-th time window, represents the electromagnetic disturbance coefficient of the jth device in the i-th time window, is the first preset weight, is the second preset weight, and In this embodiment 、 Take 0.3 and 0.7 respectively. The larger the value is, the more the voltage data of the j-th device in the i-th time window is affected by temperature and electromagnetic interference.

[0033] Step 4: Based on the difference in the data acquisition disturbance values ​​between the devices in 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 filter window parameters of each device in each time window are obtained to realize adaptive filtering of the voltage data.

[0034] In addition, the disturbances in different locations in the electrical control cabinet have mutual influences, which can be reflected in electromagnetic induction coupling interference. When a device is disturbed, its interference signal can be transmitted to other parts through coupling. The greater the degree of interference on the device and the higher the accuracy requirements for data acquisition, the more obvious the coupling characteristics are. Therefore, in order to obtain the mutual influence of different devices during automatic data collection, the absolute value of the difference between the data acquisition disturbance value of the jth device and all other devices in the i-th time window is calculated respectively. The mean of all the absolute values ​​and the sum of the data acquisition disturbance value of the jth device in the i-th time window are used as the disturbance coupling index of the jth device in the i-th time window, which is recorded as . Income The larger the value is, the greater the external interference degree is on the voltage data of the j-th device in the i-th time window.

[0035] After the above analysis, the disturbance coupling index of each device in each time window is obtained. This value reflects the degree of external interference to the collected voltage data. In order to obtain more accurate voltage data, this embodiment uses the Savitzky-Golay algorithm for filtering. Specifically, when the disturbance coupling index is larger, it means that the collected voltage data is subject to greater external interference and the data is more unstable. In the filtering process, a larger filter window parameter should be set to provide a better smoothing effect; otherwise, a smaller filter window parameter should be set to retain more data details. The commonly used value range of the filter window parameter is an odd number in [5,23]. According to the above characteristics, the filter window parameters of each device in each time window are optimized, and the formula is: , where is the filter window parameter of the jth device in the i-th time window, is an odd function, which is used to take the nearest odd number from the input data. The normalized result of the disturbance coupling index of the jth device in the i-th time window is obtained. The normalization process is implemented by the sigmoid function in this embodiment. The flow chart for obtaining the filter window parameters is as follows: Figure 2 shown.

[0036] At this point, the filter window parameters for the voltage data in all time windows of all devices are obtained. By filtering the collected voltage data with adaptive filter window parameters, the accuracy of the voltage data can be improved.

[0037] Based on the same inventive concept as the above method, an embodiment of the present 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, the steps of any one of the above-mentioned automated equipment data acquisition methods are implemented.

[0038] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0039] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0040] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for collecting data from an automated device, characterized in that: The method comprises the following steps: Obtain voltage and temperature data of each device; The voltage data and temperature data of each device are divided into multiple time windows; the trend item data corresponding to the temperature data of each device in each time window is obtained; the change trend coefficient of each device in each time window is obtained based on the change trend of the temperature data of each device in each time window, and the numerical value and dispersion degree of the slope value at all moments after curve fitting are combined with the average level of the temperature data of each device in each time window and the trend item data of the temperature data of the corresponding time window to obtain the temperature influence coefficient of each device in each time window; The voltage transient coefficient of each device in each time window is obtained based on the difference between the voltage at the mutation point and the voltage at the non-mutation 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; According to the difference in the data acquisition disturbance values ​​between devices in 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 realize adaptive filtering of voltage data.

2. The method for collecting data from an automated device according to claim 1, wherein: The calculation formula for the change trend coefficient of each device in each time window is: , where Indicates the change trend coefficient of the jth device in the i-th time window, The last data of the trend item data of the j-th device in the i-th time window, The first data in the trend item data of the j-th device in the i-th time window.

3. The method for collecting data from an automated device according to claim 1, wherein: The method for determining the temperature influence coefficient of each device in each time window is: Obtain a fitting curve of the trend item data of each device in each time window, calculate the slope value corresponding to each moment in the fitting curve, and use 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 ​​as the oscillation coefficient of the j-th device in the i-th time window; The calculation formula for the temperature influence coefficient of each device in each time window is: , where represents the temperature influence coefficient of the jth device in the i-th time window, represents the mean value of all temperature data of the jth device in the i-th time window, represents the exponential function with the natural constant e as the base, Indicates the change trend coefficient of the jth device in the i-th time window, represents the oscillation coefficient of the jth device in the i-th time window.

4. The method for collecting data from an automated device according to claim 1, wherein: The method for determining the voltage transient coefficient of each device in each time window is: Obtain the mutation point of the voltage data of each device in each time window; Calculate the mean of the voltage data corresponding to all non-mutation points in the voltage data of each device in each time window, calculate the absolute value of the difference between each mutation point in the voltage data of each device in each time window and the corresponding mean, and take 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 method for collecting data from an automated device according to claim 1, wherein: 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 of each time window; recording the sequence composed of all amplitudes in the frequency amplitude spectrum of each device in each time window that are less than the 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.

6. The method for collecting data from an automated device according to claim 1, wherein: The electromagnetic disturbance coefficient of each device in each time window is the product of the voltage transient coefficient of each device in each time window and the fractal dimension.

7. The method for collecting data from an automated device according to claim 1, wherein: The calculation formula for the data acquisition disturbance value of each device in each time window is: , where represents the data acquisition disturbance value of the jth device in the i-th time window, represents the temperature influence coefficient of the jth device in the i-th time window, represents the electromagnetic disturbance coefficient of the jth device in the i-th time window, is the first preset weight, is the second preset weight, and .

8. The method for collecting data from an automated device according to claim 1, wherein: The method for determining the disturbance coupling index is as follows: 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 is calculated respectively, and the mean of all the absolute values ​​of the single device in a single time window and the sum of the data acquisition disturbance value are used as the disturbance coupling index of the single device in the single time window.

9. The method for collecting data from an automated device according to claim 1, wherein: The calculation formula of the filter window parameters of each device in each time window is: , where is the filter window parameter of the jth device in the i-th time window, is an odd function, which is used to take the nearest odd number from the input data. is the normalized result of the disturbance coupling index of the jth device in the i-th time window.

10. An automation 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, the steps of the automated equipment data acquisition method according to any one of claims 1 to 9 are implemented.

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