A circuit-oriented data acquisition method, device and system

Abstract

The present invention relates to the field of data processing, and in particular to a circuit-oriented data acquisition method, device and system, the method comprising: using a first sampling frequency to acquire data from a target circuit to obtain first sampling data; calculating a second sampling frequency according to the degree of change of the first sampling data and the first sampling frequency; acquiring data from the target circuit according to the second sampling frequency in a second time window to obtain second sampling data, the second time window including multiple sub-time windows, the second sampling data including multiple sub-sampling data acquired in multiple sub-time windows; calculating a third sampling frequency according to the degree of difference between the sub-sampling data and a target sampling frequency, the target sampling frequency being the sampling frequency corresponding to the last sub-time window in the second time window; and acquiring data from the target circuit using the third sampling frequency. Analyzing the degree of change of real-time data and adaptively adjusting the sampling frequency can better balance the accuracy and efficiency of signal processing and ensure efficient operation of the system.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a circuit-oriented data acquisition method, device and system. Background Art

[0002] With the rapid development of modern electronic devices and intelligent systems, the demand for real-time monitoring and data collection of circuit performance is increasing, especially in the fields of industrial automation, smart grid and Internet of Things. Existing technologies usually use multi-channel data acquisition combined with signal conditioning modules to collect and analyze parameters such as voltage, current and temperature in real time. These devices convert analog signals into digital signals, allowing computers or embedded systems to perform more complex data processing and storage.

[0003] In addition, with the help of advanced communication technology, the collected data can be transmitted to the cloud in real time to achieve remote monitoring and data sharing.

[0004] The collected data plays a vital role in the subsequent analysis, and the frequency of data collection will affect the accuracy of the circuit analysis. A higher sampling frequency can provide better time resolution, making the capture of rapidly changing signals (such as pulse signals or transient phenomena) more accurate, but a sampling frequency that is too high can easily lead to a large amount of calculation. A lower sampling frequency has a relatively low amount of calculation, and may also reduce the impact of high-frequency noise and provide a better signal-to-noise ratio, but a sampling frequency that is too low will not be able to accurately capture rapidly changing signals, and it is easy to lose information, which will lead to inaccurate subsequent analysis. It can be seen that how to collect data for circuits through a more appropriate sampling frequency has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide a circuit-oriented data acquisition method, comprising:

[0006] Using a first sampling frequency to collect data from a target circuit in a first time window to obtain first sampling data; calculating a second sampling frequency according to the degree of change of the first sampling data and the first sampling frequency; collecting data from the target circuit in a second time window according to the second sampling frequency to obtain second sampling data, wherein the second time window includes a plurality of sub-time windows, and the second sampling data includes a plurality of sub-sampling data collected in the plurality of sub-time windows, and each sub-time window corresponds to one sub-sampling data; calculating a third sampling frequency according to the degree of difference between the plurality of sub-sampling data and a target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window in the second time window; and collecting data from the target circuit using the third sampling frequency.

[0007] Optionally, before calculating the third sampling frequency based on the degree of difference between the multiple sub-sampling data and the target sampling frequency, the method further includes: for the multiple sub-sampling data, calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data.

[0008] Optionally, if the target circuit includes a main circuit and at least one branch circuit, each sub-sampling data includes main circuit data and at least one branch circuit data corresponding to the at least one branch circuit, and the step of calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and the variance difference between adjacent sub-sampling data includes: calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch circuit data and the main circuit data, and the average amplitude difference and the variance difference between each branch circuit data and the adjacent branch circuit data.

[0009] Optionally, before calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data, the method also includes: for each branch data, calculating the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data.

[0010] Optionally, before calculating the synchronization between each branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data, the method also includes: for each branch data, calculating the stability of the branch data based on the maximum amplitude, minimum amplitude and mean amplitude in the branch data; calculating the stability of the main data based on the maximum amplitude, minimum amplitude and mean amplitude in the main data.

[0011] Optionally, calculating the second sampling frequency according to the change degree of the first sampling data and the first sampling frequency includes: calculating the second sampling frequency according to the change degree of the first sampling data, the change degree of historical sampling data, and the first sampling frequency.

[0012] Optionally, before calculating the second sampling frequency based on the degree of change of the first sampling data, the degree of change of historical sampling data and the first sampling frequency, the method also includes: calculating the degree of change of the first sampling data and the degree of change of historical sampling data; wherein the degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0013] Optionally, before calculating the degree of change of the first sampling data and the degree of change of the historical sampling data, the method further includes: for each data point in the first sampling data and the historical sampling data, calculating the volatility of the data point based on the amplitude difference between the data point and its neighboring data points.

[0014] The present invention also provides a circuit-oriented data acquisition device, comprising a memory, at least one processor, and at least one program stored in the memory and executable by the at least one processor, wherein the at least one program, when executed by the at least one processor, implements the steps in any one of the above-mentioned circuit-oriented data acquisition methods.

[0015] The present invention also provides a circuit-oriented data acquisition system, the system comprising: a sampling module, used to perform data acquisition on a target circuit using a first sampling frequency within a first time window to obtain first sampling data; a calculation module, used to calculate a second sampling frequency according to a degree of change of the first sampling data and the first sampling frequency; the sampling module is also used to perform data acquisition on the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes a plurality of sub-time windows, and the second sampling data includes a plurality of sub-sampling data collected within the plurality of sub-time windows, and each sub-time window corresponds to a sub-sampling data; the calculation module is also used to calculate a third sampling frequency according to a degree of difference between the plurality of sub-sampling data and a target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window within the second time window; the sampling module is also used to perform data acquisition on the target circuit using the third sampling frequency.

[0016] The present invention has the following beneficial effects: the circuit-oriented data acquisition method analyzes the data collected in real time, and adaptively adjusts the sampling frequency according to the degree of change of the data. It can increase the sampling frequency when the data changes rapidly, and reduce the sampling frequency when the data changes slowly. This flexibility not only improves the accuracy and completeness of data acquisition, but also effectively reduces storage requirements and consumption of computing resources. Especially in real-time monitoring and analysis applications, it can better balance the accuracy and efficiency of signal processing to ensure efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A first schematic diagram of a circuit-oriented data acquisition method provided by an embodiment of the present invention;

[0019] Figure 2 A second schematic diagram of a circuit-oriented data acquisition method provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of a circuit-oriented data acquisition device provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of a circuit-oriented data acquisition system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a circuit-oriented data acquisition method proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] 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 invention belongs.

[0024] The present invention provides a circuit-oriented data acquisition method, and the scheme of the present invention is described in detail below in conjunction with the accompanying drawings.

[0025] See also Figure 1 , which shows a first schematic diagram of a circuit-oriented data acquisition method provided by an embodiment of the present invention, the circuit-oriented data acquisition method (hereinafter referred to as "the method") comprises the following steps:

[0026] Step 101: Collect data from a target circuit using a first sampling frequency within a first time window to obtain first sampled data.

[0027] In this step, the method uses the first sampling frequency to collect data from the target circuit within the first time window to obtain first sampled data. The method can collect signals from the target circuit through an oscilloscope to obtain sampled data. The first time window can be a time window of default duration, or a time window of corresponding duration set by the user according to actual conditions. The first sampling frequency can be the sampling frequency used when the target circuit was last collected for data, or it can be an initial sampling frequency of the target circuit by default. The embodiment of the present invention does not make specific limitations on this, and only requires that the first sampling frequency meet the basic requirements of subsequent signal processing (for example, according to the Nyquist sampling theorem, in order to accurately restore the continuous signal, the sampling frequency must be at least twice the signal writing bandwidth). For example, in one embodiment, the first time window duration can be 2 minutes, and the first sampling frequency can be 0.02 seconds.

[0028] Step 102: Calculate a second sampling frequency according to the variation degree of the first sampling data and the first sampling frequency.

[0029] It can be understood that the degree of change of the real-time data can more accurately reflect whether the sampling frequency is reasonable. In this step, the method calculates the second sampling frequency according to the degree of change of the first sampling data and the first sampling frequency, and adjusts the current first sampling frequency according to the degree of change of the first sampling data collected in real time to obtain the second sampling frequency. Specifically, the degree of change of the first sampling data can be used as a weight to adjust the first sampling frequency to obtain the second sampling frequency.

[0030] In some embodiments, in addition to adjusting the sampling frequency according to the first sampling data collected in real time, the method can also adjust the sampling frequency in combination with historical sampling data. Specifically, the method can calculate the second sampling frequency according to the degree of change of the first sampling data, the degree of change of the historical sampling data, and the first sampling frequency. For example, the second sampling frequency can be calculated based on the difference between the degree of change of the first sampling data and the historical sampling data and the first sampling frequency. For the degree of change of the sampling data, the method can calculate it based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0031] Step 103: collect data on the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes multiple sub-time windows, and the second sampling data includes multiple sub-sampling data collected within the multiple sub-time windows, and each sub-time window corresponds to one sub-sampling data.

[0032] After preliminarily adjusting the acquisition frequency according to the first acquisition data acquired in real time, the method uses the preliminarily adjusted sampling frequency to acquire data from the target circuit to obtain sampling data with the preliminarily adjusted sampling frequency.

[0033] Specifically, in this step, after calculating the second sampling frequency, the method collects data on the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes multiple sub-time windows, and the second sampling data includes multiple sub-sampling data collected within the multiple sub-time windows, and each sub-time window corresponds to one sub-sampling data.

[0034] The second time window is a time window after the first time window. The method can use the second sampling frequency to collect data from the target circuit in multiple sub-time windows of the second time window to obtain second sampling data including multiple sub-sampled data, wherein each sub-sampled data is sampled data obtained by collecting data in each sub-time window of the second time window using the second sampling frequency. In some embodiments, the method can adjust the sampling frequency of each sub-time window based on the second sampling frequency, and the sampling frequency corresponding to each sub-time window is the sampling frequency adjusted according to the sub-sampled data corresponding to the previous sub-time window. In this case, in the second time window, the sampling frequency can be continuously updated according to the collected sub-sampled data based on the second sampling frequency, and the sampling frequencies corresponding to different sub-time windows may be the same or different.

[0035] Step 104: Calculate a third sampling frequency according to the difference between the plurality of sub-sampled data and a target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window in the second time window.

[0036] After obtaining the sampling data after the sampling frequency is initially adjusted, the method further analyzes the sampling data collected after the sampling frequency is adjusted to determine whether the sampling frequency after the initial adjustment is reasonable. Specifically, in this step, the method calculates the third sampling frequency according to the degree of difference between the multiple sub-sampling data and the target sampling frequency.

[0037] For the plurality of sub-sampled data, the method may calculate the degree of difference between the plurality of sub-sampled data according to the average amplitude difference and variance difference between adjacent sub-sampled data, and then further calculate the third sampling frequency according to the degree of difference between the plurality of sub-sampled data and the target sampling frequency. In an embodiment where the target circuit includes a trunk and at least one branch, each sub-sampled data includes trunk data and at least one branch data corresponding to the at least one branch, and the method may calculate the degree of difference between the plurality of sub-sampled data according to the synchronization between each branch data and the trunk data, and the average amplitude difference and variance difference between each branch data and adjacent branch data. The adjacent branch data of the branch data are the branch data corresponding to the same branch in adjacent time windows.

[0038] Step 105: Use the third sampling frequency to collect data from the target circuit.

[0039] In this step, the method uses the third sampling frequency to collect data from the target circuit to obtain monitoring data of the target circuit, so as to further analyze and process the monitoring data of the target circuit. In this embodiment, the preliminarily adjusted frequency is further optimized by the sampling data collected after the preliminarily adjusted frequency, and the obtained optimal third sampling frequency is used to collect data from the target circuit, which can ensure the accuracy and efficiency of data collection while balancing signal characteristics and system resources.

[0040] The method may continue to use the third sampling frequency by default to collect data from the target circuit in subsequent sampling until a preset trigger condition is met and the sampling frequency of the target circuit is adjusted again. The preset trigger condition may include readjusting at a preset time point (for example, after a sampling cycle has passed) or receiving a frequency adjustment operation triggered by a user. That is, the circuit-oriented data acquisition method provided by the present invention may be repeatedly executed in real time, or the circuit-oriented data acquisition method provided by the present invention may be repeatedly executed with a preset time length as a cycle, or the circuit-oriented data acquisition method provided by the present invention may be executed after receiving a trigger operation from the user. When repeatedly executing the circuit-oriented data acquisition method provided by the present invention, the method continuously adjusts the sampling frequency in real time according to the sampling data collected in the time window before the current time window.

[0041] In some embodiments of the present invention, the method may use the third sampling frequency to collect data from the target circuit within a third time window. The third time window is a time window located after the second time window. The durations of the first time window, the second time window (or a sub-time window of the second time window), and the third time window may be partially the same, completely the same, or different, and the embodiments of the present invention do not specifically limit this. It is understandable that in order to perform frequency adjustment more evenly, the first time window, the second time window (or a sub-time window of the second time window), and the third time window may be time windows of the same length.

[0042] The first time window, the second time window (or a sub-time window of the second time window), and the third time window may be time windows of fixed duration or time windows of non-fixed duration, for example, time windows for collecting a first preset number of data points. For example, assuming that the first sampled data to be collected includes 20 data points, the first time window may be the sampling duration required to collect the 20 data points.

[0043] In an embodiment of the present invention, in this embodiment, the circuit-oriented data acquisition method uses a first sampling frequency to collect data from a target circuit in a first time window to obtain first sampling data; calculates a second sampling frequency according to the degree of change of the first sampling data and the first sampling frequency; collects data from the target circuit according to the second sampling frequency in a second time window to obtain second sampling data, wherein the second time window includes multiple sub-time windows, and the second sampling data includes multiple sub-sampled data collected in the multiple sub-time windows, and each sub-time window corresponds to one sub-sampled data; calculates a third sampling frequency according to the degree of difference between the multiple sub-sampled data and the target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window in the second time window; and collects data from the target circuit using the third sampling frequency. The circuit-oriented data acquisition method analyzes the data collected in real time, and adaptively adjusts the sampling frequency according to the degree of change of the data, and can increase the sampling frequency when the data changes rapidly, and reduce the sampling frequency when the data changes slowly. This flexibility not only improves the accuracy and integrity of data acquisition, but also effectively reduces storage requirements and consumption of computing resources. Especially in real-time monitoring and analysis applications, it can better balance the accuracy and efficiency of signal processing and ensure the efficient operation of the system.

[0044] Optionally, before calculating the third sampling frequency based on the degree of difference between the multiple sub-sampling data and the target sampling frequency, the method further includes: for the multiple sub-sampling data, calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data.

[0045] In this embodiment, for the plurality of sub-sampled data, the method calculates the degree of difference between the plurality of sub-sampled data according to the average amplitude difference and variance difference between adjacent sub-sampled data.

[0046] Optionally, calculating the second sampling frequency according to the change degree of the first sampling data and the first sampling frequency includes: calculating the second sampling frequency according to the change degree of the first sampling data, the change degree of historical sampling data, and the first sampling frequency.

[0047] In this embodiment, the method calculates the second sampling frequency according to the degree of change of the first sampling data, the degree of change of the historical sampling data, and the first sampling frequency. Specifically, the method can calculate the degree of change of the first sampling data and the degree of change of the historical sampling data, and then calculate the second sampling frequency according to the degree of change of the first sampling data, the degree of change of the historical sampling data, and the first sampling frequency. The degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0048] It can be understood that if the difference between the change degree of the first sampling data and the change degree of the historical sampling data is large, it may be caused by the large sampling frequency; on the contrary, if the difference between the change degree of the first sampling data and the change degree of the historical sampling data is small, it may be caused by the small sampling frequency. Therefore, it is necessary to calculate the second sampling frequency according to the difference between the change degree of the first sampling data and the change degree of the historical sampling data. Specifically, the following formula can be used to calculate the second sampling frequency :

[0049]

[0050] in, represents the second sampling frequency, represents the degree of change of the first sampling data, Indicates the degree of change of the historical sampling data, exp represents the natural exponential function, Indicates the first sampling frequency.

[0051] In the above formula, It indicates the difference between the change degree of the first sampling data and the change degree of the historical sampling data. The larger the absolute value of the difference is, the greater the degree to which the sampling frequency needs to be adjusted. When the difference is positive, it means that the sampling frequency needs to be increased. When the difference is negative, it means that the sampling frequency needs to be reduced.

[0052] Optionally, before calculating the second sampling frequency based on the degree of change of the first sampling data, the degree of change of historical sampling data and the first sampling frequency, the method also includes: calculating the degree of change of the first sampling data and the degree of change of historical sampling data; wherein the degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0053] In this embodiment, the method calculates the degree of change of the sampled data based on the volatility of each data point in the sampled data and the average amplitude of the sampled data. Specifically, the following formula can be used to calculate the degree of change of a certain sampled data: :

[0054]

[0055] in, Indicates the degree of change of the sampled data. Indicates the sampling data The volatility of the data points, represents the volatility variance of all data points in the sampled data, , represents the maximum value of volatility of all data points in the sampled data, Indicates the minimum amplitude of volatility of all data points in the sampled data. Represents the average amplitude of all data points of the sampled data.

[0056] In the above formula, It represents the volatility variance of all data points in the sampled data, and represents the overall volatility of the sampled data. When the variance is large, it means that the current sampling frequency may be high, and the capture of data details is too sensitive, so the sampling frequency needs to be reduced accordingly. It indicates the range of the sampled data. The larger the range, the more dispersed the distribution of the data points in the sampled data and the greater the abnormality.

[0057] Optionally, before calculating the degree of change of the first sampling data and the degree of change of the historical sampling data, the method further includes: for each data point in the first sampling data and the historical sampling data, calculating the volatility of the data point based on the amplitude difference between the data point and its neighboring data points.

[0058] In this embodiment, for each data point, the method can calculate the volatility of the data point based on the amplitude difference between the data point and its neighboring data points. The neighboring data points of the data point include a preset number of data points centered on the data point. For example, the neighboring data points of a data point may include 5 data points before the data point and 5 data points after the data point. Specifically, for the first data points, the method can use the following formula to calculate the The volatility of the data points :

[0059]

[0060] in, Indicates the sampling data The volatility of the data points, Indicates that The average amplitude of a data point and its neighboring data points, Indicates that The number of neighboring data points of a data point, Indicates that The data point Neighborhood data points, Indicates that The data point The amplitude of the neighboring data points.

[0061] In the above formula, Indicates that The overall weight of the neighborhood data points of a data point in the sampled data. The larger the value, the The greater the volatility of each data point, Indicates that The degree of data change between a data point and its neighboring data points. The larger the value, the The greater the data variation between a data point and its neighboring data points, the The greater the volatility of each data point.

[0062] Please read further Figure 2 , Figure 2 is a second schematic diagram of the circuit-oriented data acquisition method provided by an embodiment of the present invention, such as Figure 2 As shown, the method comprises the following steps:

[0063] Step 201: Collect data from a target circuit using a first sampling frequency within a first time window to obtain first sampling data, wherein the target circuit includes a trunk circuit and at least one branch circuit.

[0064] In this embodiment, the target circuit includes a trunk and at least one branch. In step 201, the method uses a first sampling frequency to collect data from the target circuit within a first time window. Step 201 is similar to Figure 1 Step 101 in the illustrated embodiment is similar and will not be described again. It should be emphasized that in the present embodiment, when the target circuit includes a trunk and at least one branch, the sampled data collected will include trunk data and branch data corresponding to each branch.

[0065] Step 202: Calculate a second sampling frequency according to the variation degree of the first sampling data and the first sampling frequency.

[0066] Step 203: collect data on the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes multiple sub-time windows, and the second sampling data includes multiple sub-sampling data collected within the multiple sub-time windows, each sub-time window corresponds to one sub-sampling data, and each sub-sampling data includes main line data and at least one branch data corresponding to the at least one branch.

[0067] Steps 202 and 203 are Figure 1 Steps 102 and 103 in the illustrated embodiment are the same or similar and will not be described in detail herein.

[0068] Step 204: Calculate the degree of difference between the plurality of sub-sampled data according to the synchronization between each branch data and the trunk data, and the average amplitude difference and variance difference between each branch data and adjacent branch data.

[0069] When there are branches (especially a large number of branches) in the target circuit, when adjusting the sampling frequency, the operating power of the components in the branches may be different, and the resulting data fluctuations may also be different. In order to more accurately reflect the impact of branch data on the data acquisition of the entire circuit, it is necessary to analyze the changing relationship between branch data and main data, and then further adjust the sampling frequency accordingly.

[0070] In this step, the method calculates the degree of difference between the multiple sub-sampled data according to the synchronization between each branch data and the main data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data. Specifically, the following formula can be used to calculate the degree of difference between the multiple sub-sampled data: :

[0071]

[0072] in, Indicates the degree of difference between multiple sub-sampled data. Indicates the first Branch road, represents the number of branches in the target circuit, Indicates Branch data and trunk data The synchronization between trunk data, Indicates the first sub-time windows, represents the number of sub-time windows in the second time window minus 1, Indicates The sub-time window corresponds to the The average amplitude of sub-sampled data, Indicates The sub-time window corresponds to the The average amplitude of sub-sampled data, Indicates The sub-time window corresponds to the The amplitude variance of the sub-sampled data, Indicates The sub-time window corresponds to the The amplitude variance of the sub-sampled data.

[0073] In the above formula, The value indicates the amplitude difference of adjacent sub-sampling data. The larger the value, the higher the degree of data change caused by the change of sampling frequency, resulting in a higher probability of abnormal monitoring data of the target circuit. It indicates the variance difference of adjacent sub-sampling data. The larger the difference, the greater the degree of data change caused by the change in sampling frequency.

[0074] Step 205: Calculate a third sampling frequency according to the difference between the plurality of sub-sampled data and a target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window in the second time window.

[0075] In the process of adjusting the sampling frequency, the change of sampling frequency may also cause the collected sampling data to change. For example, assuming that the sampling frequency at the previous moment is 65HZ and the sampling frequency at the next moment is 70HZ, the sampling frequency of the data has changed, and the amplitude of the corresponding sampling data will also have some errors. If the data itself is abnormal at this time, it is impossible to accurately distinguish whether the data abnormality is caused by the change of sampling frequency or the abnormality caused by the change of data itself. In this way, it is impossible to accurately describe the abnormality of the sampling data. Therefore, it is necessary to limit the sampling frequency according to the change of sampling data to minimize the sampling data error caused by the change of sampling frequency.

[0076] In this step, the method calculates the third sampling frequency according to the difference between the plurality of sub-sampled data and the target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window in the second time window. Specifically, the third sampling frequency can be calculated using the following formula: :

[0077]

[0078] in, represents the third sampling frequency, represents the degree of difference between the plurality of sub-sampled data in the second sampled data, represents the target sampling frequency, exp represents the natural exponential function.

[0079] Step 206: Use the third sampling frequency to collect data from the target circuit.

[0080] The step 206 and Figure 1 Step 105 in the illustrated embodiment is the same and will not be described again here.

[0081] In this embodiment, for a target circuit including a trunk and at least one branch, the circuit-oriented data acquisition method uses a first sampling frequency to acquire data from the target circuit within a first time window to obtain first sampling data; calculates a second sampling frequency according to a degree of change of the first sampling data and the first sampling frequency; acquires data from the target circuit according to the first sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes a plurality of sub-time windows, the second sampling data includes a plurality of sub-sampled data acquired within the plurality of sub-time windows, each sub-time window corresponds to a sub-sampled data, and each sub-sampled data includes trunk data and at least one branch data corresponding to the at least one branch; calculates the degree of difference between the plurality of sub-sampled data according to the synchronization between each branch data and the trunk data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data; calculates a third sampling frequency according to the degree of difference between the plurality of sub-sampled data and the target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window within the second time window, and the target circuit is acquired using the third sampling frequency. In this way, the sampling frequency is adaptively adjusted based on the degree of data change and the synchronization between branch data and main data. The sampling frequency can be increased when the data changes rapidly, and reduced when the data changes slowly. This flexibility not only improves the accuracy and completeness of data acquisition, but also effectively reduces storage requirements and computing resource consumption. Especially in real-time monitoring and analysis applications, it can better balance the accuracy and efficiency of signal processing to ensure efficient operation of the system.

[0082] Optionally, before calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data, the method also includes: for each branch data, calculating the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data.

[0083] Specifically, the following formula can be used to calculate the first Branch data and trunk data The pace between the trunk data :

[0084]

[0085] in, Indicates Branch data and trunk data The synchronization between trunk data, Indicates The Pearson correlation coefficient between the branch road data and the trunk road data of trunk road a, Indicates The stability of the branch data, Indicates the stability of the trunk data, exp represents the natural exponential function.

[0086] If the branch data changes and the trunk data also changes, it means that the change of the branch data has a greater impact on the trunk data, and the synchronization between the branch data and the trunk data should also be greater. Therefore, in the above formula, the Pearson correlation coefficient between the branch data and the trunk data is used to describe the correlation between the branch data change and the trunk data change. Indicates The stability difference between the branch data and the trunk data. The smaller the value, the better. The higher the synchronization between the branch data and the trunk data.

[0087] Optionally, before calculating the synchronization between each branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data, the method also includes: for each branch data, calculating the stability of the branch data based on the maximum amplitude, minimum amplitude and mean amplitude in the branch data; calculating the stability of the main data based on the maximum amplitude, minimum amplitude and mean amplitude in the main data.

[0088] Specifically, for example, The following formula can be used to calculate the branch data. Stability of branch data :

[0089]

[0090] in, Indicates The stability of the branch data, Indicates that The maximum value in the branch data, Indicates that The minimum amplitude in the branch data, Indicates that The mean amplitude of the branch data. ) indicates the The smaller the value, the greater the range of the branch data. The lower the variation of data points in the branch data, the better the data stability. The higher the value, exp() represents an exponential function with a natural constant as base.

[0091] The present invention also provides a circuit-oriented data acquisition device, please refer to Figure 3 The circuit-oriented data acquisition device 300 includes a memory 301, at least one processor 302, and at least one program stored in the memory 301 and executable by the at least one processor 302. When the at least one program is executed by the at least one processor 302, the following steps are implemented:

[0092] Collecting data from a target circuit using a first sampling frequency within a first time window to obtain first sampling data;

[0093] Calculating a second sampling frequency according to the variation degree of the first sampling data and the first sampling frequency;

[0094] Collecting data on the target circuit according to the second sampling frequency in a second time window to obtain second sampling data, wherein the second time window includes a plurality of sub-time windows, and the second sampling data includes a plurality of sub-sampled data collected in the plurality of sub-time windows, and each sub-time window corresponds to one sub-sampled data;

[0095] Calculating a third sampling frequency according to the difference between the plurality of sub-sampled data and a target sampling frequency, wherein the target sampling frequency is a sampling frequency corresponding to the last sub-time window in the second time window;

[0096] Data is collected from the target circuit using the third sampling frequency.

[0097] Optionally, before calculating the third sampling frequency based on the degree of difference between the multiple sub-sampling data and the target sampling frequency, the method further includes: for the multiple sub-sampling data, calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data.

[0098] Optionally, if the target circuit includes a main circuit and at least one branch circuit, each sub-sampling data includes main circuit data and at least one branch circuit data corresponding to the at least one branch circuit, and the step of calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and the variance difference between adjacent sub-sampling data includes: calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch circuit data and the main circuit data, and the average amplitude difference and the variance difference between each branch circuit data and the adjacent branch circuit data.

[0099] Optionally, before calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data, the method also includes: for each branch data, calculating the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data.

[0100] Optionally, before calculating the synchronization between each branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data, the method also includes: for each branch data, calculating the stability of the branch data based on the maximum amplitude, minimum amplitude and mean amplitude in the branch data; calculating the stability of the main data based on the maximum amplitude, minimum amplitude and mean amplitude in the main data.

[0101] Optionally, calculating the second sampling frequency according to the change degree of the first sampling data and the first sampling frequency includes: calculating the second sampling frequency according to the change degree of the first sampling data, the change degree of historical sampling data, and the first sampling frequency.

[0102] Optionally, before calculating the second sampling frequency based on the degree of change of the first sampling data, the degree of change of historical sampling data and the first sampling frequency, the method also includes: calculating the degree of change of the first sampling data and the degree of change of historical sampling data; wherein the degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0103] Optionally, before calculating the degree of change of the first sampling data and the degree of change of the historical sampling data, the method further includes: for each data point in the first sampling data and the historical sampling data, calculating the volatility of the data point based on the amplitude difference between the data point and its neighboring data points.

[0104] In this embodiment, the circuit-oriented data acquisition device analyzes the data collected in real time, and adaptively adjusts the sampling frequency according to the degree of change of the data. It can increase the sampling frequency when the data changes rapidly, and reduce the sampling frequency when the data changes slowly. This flexibility not only improves the accuracy and completeness of data acquisition, but also effectively reduces storage requirements and computing resource consumption. Especially in real-time monitoring and analysis applications, it can better balance the accuracy and efficiency of signal processing to ensure efficient operation of the system.

[0105] The present invention also provides a circuit-oriented data acquisition system. Figure 4 , the circuit-oriented data acquisition system 400 comprises:

[0106] The sampling module 401 is used to collect data from the target circuit using a first sampling frequency within a first time window to obtain first sampled data;

[0107] A calculation module 402, configured to calculate a second sampling frequency according to a change degree of the first sampling data and the first sampling frequency;

[0108] The sampling module 401 is further configured to collect data from the target circuit according to the second sampling frequency within a second time window to obtain second sampled data, wherein the second time window includes a plurality of sub-time windows, and the second sampled data includes a plurality of sub-sampled data collected within the plurality of sub-time windows, and each sub-time window corresponds to one sub-sampled data;

[0109] The calculation module 402 is further configured to calculate a third sampling frequency according to the difference between the plurality of sub-sampled data and a target sampling frequency, wherein the target sampling frequency is a sampling frequency corresponding to the last sub-time window in the second time window;

[0110] The sampling module 401 is further configured to collect data from the target circuit using the third sampling frequency.

[0111] Optionally, before calculating the third sampling frequency based on the degree of difference between the multiple sub-sampling data and the target sampling frequency, the calculation module 402 is further used to: for the multiple sub-sampling data, calculate the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data.

[0112] Optionally, if the target circuit includes a main line and at least one branch line, each sub-sampling data includes main line data and at least one branch line data corresponding to the at least one branch line, and the calculation module 402 calculates the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data, including: calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main line data, and the average amplitude difference and variance difference between each branch data and the adjacent branch data.

[0113] Optionally, before calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main data, the average amplitude difference and the variance difference between each branch data and the adjacent branch data, the calculation module 402 is also used to: for each branch data, calculate the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data.

[0114] Optionally, for each branch data, before calculating the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data, the calculation module 402 is also used to: for each branch data, calculate the stability of the branch data based on the maximum amplitude, minimum amplitude and mean amplitude in the branch data; calculate the stability of the main data based on the maximum amplitude, minimum amplitude and mean amplitude in the main data.

[0115] Optionally, the calculation module 402 calculates the second sampling frequency according to the change degree of the first sampling data and the first sampling frequency, including: calculating the second sampling frequency according to the change degree of the first sampling data, the change degree of historical sampling data and the first sampling frequency.

[0116] Optionally, before calculating the second sampling frequency based on the degree of change of the first sampling data, the degree of change of the historical sampling data and the first sampling frequency, the calculation module 402 is also used to: calculate the degree of change of the first sampling data and the degree of change of the historical sampling data; wherein the degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

[0117] Optionally, before calculating the degree of change of the first sampling data and the degree of change of the historical sampling data, the calculation module 402 is also used to: for each data point in the first sampling data and the historical sampling data, calculate the volatility of the data point based on the amplitude difference between the data point and its neighboring data points.

[0118] In this embodiment, the circuit-oriented data acquisition system analyzes the data collected in real time, and adaptively adjusts the sampling frequency according to the degree of change of the data. It can increase the sampling frequency when the data changes rapidly, and reduce the sampling frequency when the data changes slowly. This flexibility not only improves the accuracy and completeness of data acquisition, but also effectively reduces storage requirements and computing resource consumption. Especially in real-time monitoring and analysis applications, it can better balance the accuracy and efficiency of signal processing to ensure efficient operation of the system.

[0119] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A circuit-oriented data acquisition method, characterized in that: The method includes: using a first sampling frequency to collect data from a target circuit within a first time window to obtain first sampling data; calculating a second sampling frequency according to the degree of change of the first sampling data and the first sampling frequency; collecting data from the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes multiple sub-time windows, and the second sampling data includes multiple sub-sampling data collected within the multiple sub-time windows, and each sub-time window corresponds to one sub-sampling data; calculating a third sampling frequency according to the degree of difference between the multiple sub-sampling data and a target sampling frequency, wherein the target sampling frequency is the sampling frequency corresponding to the last sub-time window within the second time window; and using the third sampling frequency to collect data from the target circuit.

2. The circuit-oriented data acquisition method according to claim 1, characterized in that: Before calculating the third sampling frequency based on the degree of difference between the multiple sub-sampling data and the target sampling frequency, the method further includes: for the multiple sub-sampling data, calculating the degree of difference between the multiple sub-sampling data based on the average amplitude difference and variance difference between adjacent sub-sampling data.

3. The circuit-oriented data acquisition method according to claim 2, characterized in that: If the target circuit includes a trunk and at least one branch, each sub-sampled data includes trunk data and at least one branch data corresponding to the at least one branch, and the calculating the degree of difference between the multiple sub-sampled data based on the average amplitude difference and the variance difference between adjacent sub-sampled data includes: calculating the degree of difference between the multiple sub-sampled data based on the synchronization between each branch data and the trunk data, and the average amplitude difference and the variance difference between each branch data and adjacent branch data.

4. The circuit-oriented data acquisition method according to claim 3, characterized in that: Before calculating the degree of difference between the multiple sub-sampling data based on the synchronization between each branch data and the main data, the average amplitude difference and variance difference between each branch data and the adjacent branch data, the method also includes: for each branch data, calculating the synchronization between the branch data and the main data based on the Pearson correlation coefficient between the branch data and the main data, and the stability difference between the branch data and the main data.

5. The circuit-oriented data acquisition method according to claim 4, characterized in that: Before calculating the synchronization between the branch data and the trunk data for each branch data based on the Pearson correlation coefficient between the branch data and the trunk data, and the stability difference between the branch data and the trunk data, the method further includes: for each branch data, calculating the stability of the branch data based on the maximum amplitude, minimum amplitude and mean amplitude in the branch data; calculating the stability of the trunk data based on the maximum amplitude, minimum amplitude and mean amplitude in the trunk data.

6. The circuit-oriented data acquisition method according to any one of claims 1 to 5, characterized in that: Calculating the second sampling frequency according to the change degree of the first sampling data and the first sampling frequency includes: calculating the second sampling frequency according to the change degree of the first sampling data, the change degree of historical sampling data and the first sampling frequency.

7. The circuit-oriented data acquisition method according to claim 6, characterized in that: Before calculating the second sampling frequency based on the degree of change of the first sampling data, the degree of change of the historical sampling data and the first sampling frequency, the method also includes: calculating the degree of change of the first sampling data and the degree of change of the historical sampling data; wherein the degree of change of the sampling data is calculated based on the volatility of each data point in the sampling data and the average amplitude of the sampling data.

8. The circuit-oriented data acquisition method according to claim 7, characterized in that: Before calculating the degree of change of the first sampling data and the degree of change of the historical sampling data, the method further includes: for each data point in the first sampling data and the historical sampling data, calculating the volatility of the data point based on the amplitude difference between the data point and its neighboring data points.

9. A circuit-oriented data acquisition device, comprising a memory, at least one processor, and at least one program stored in the memory and executable on the at least one processor, characterized in that: When the at least one program is executed by the at least one processor, the steps of the circuit-oriented data acquisition method described in any one of claims 1 to 8 are implemented.

10. A circuit-oriented data acquisition system, characterized in that: The system includes: a sampling module, which is used to collect data from a target circuit using a first sampling frequency within a first time window to obtain first sampling data; a calculation module, which is used to calculate a second sampling frequency according to a degree of change of the first sampling data and the first sampling frequency; the sampling module is also used to collect data from the target circuit according to the second sampling frequency within a second time window to obtain second sampling data, wherein the second time window includes a plurality of sub-time windows, and the second sampling data includes a plurality of sub-sampled data collected within the plurality of sub-time windows, and each sub-time window corresponds to a sub-sampled data; the calculation module is also used to calculate a third sampling frequency according to a degree of difference between the plurality of sub-sampled data and a target sampling frequency, wherein the target sampling frequency is a sampling frequency corresponding to the last sub-time window within the second time window; the sampling module is also used to collect data from the target circuit using the third sampling frequency.

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