A method, device and equipment for collecting anti-shake measurement data and a storage medium

Abstract

The application discloses a kind of anti-jitter measurement data acquisition method, device, equipment and storage medium, belong to data processing technical field, the method is: the bandwidth time series data, delay time series data and packet loss time series data of power grid system are respectively collected;For each time series data, utilize preset sliding window to divide time series data and form multiple time series segments;The range value and average value of each time series segment are calculated respectively;The difference between the range value of adjacent time series segment is determined as fluctuation amplitude;Then the instantaneous fluctuation rate of each time series segment is calculated;The instantaneous fluctuation rate of each time series segment is summarized to form instantaneous fluctuation rate curve, the corresponding time series segment in instantaneous fluctuation rate curve that meets preset jitter characteristic is determined as jitter time series segment;Jitter time series segment in bandwidth time series data, delay time series data and packet loss time series data is respectively rejected, and anti-jitter measurement data for monitoring power grid system anomaly is formed.The application can solve the problem of low accuracy of measurement data by implementation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for anti-shake measurement data acquisition. Background Technology

[0002] Current methods for monitoring power grid systems based on measurement data often rely on simple rules and manually set thresholds, which cannot adapt to the complex nonlinear relationships and variable network operating environments in power grid data. When network transmission jitter is caused by peak instantaneous trading activity, monitoring characteristic data is prone to delays or out-of-order arrival. Current monitoring methods often misinterpret this simple "network transmission jitter" as anomalies such as "data transaction failure" or "business node downtime," leading to high false alarm and false negative rates. Therefore, ensuring the high stability and timing accuracy of the underlying data stream input to the anomaly detection end, thereby fundamentally reducing the false alarm and false negative rates, is a goal that those skilled in the art are dedicated to developing. Summary of the Invention

[0003] This invention provides a method, apparatus, device, and storage medium for acquiring anti-shake measurement data, which can solve the problem of low accuracy of measurement data in the prior art.

[0004] To address the aforementioned technical problems, this invention provides a method for acquiring anti-shake measurement data, comprising: Collect time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data; For each time series data, a preset sliding window is used to divide the time series data into several time series segments; Calculate the range and average value for each time period; The difference between the range values ​​of adjacent time periods is defined as the fluctuation range; Based on the average value of the earlier time series in adjacent time series, the fluctuation amplitude of adjacent time series is converted into the instantaneous volatility of the later time series in adjacent time series. The instantaneous volatility of each time series is summarized to form an instantaneous volatility curve. The corresponding time series in the instantaneous volatility curve that meets the preset jitter characteristics is determined as the jitter time series. Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant measurement data for monitoring power grid system anomalies.

[0005] As a preferred embodiment, the acquisition of time-series data of the power grid system includes: Data acquisition equipment is deployed at several business nodes in the power grid system. Within a preset time period, the data acquisition device is controlled to collect bandwidth data, latency data, and packet loss data of the corresponding service nodes; Based on a preset reference timestamp, the bandwidth data collected by each data acquisition device is processed for time-series synchronization to obtain bandwidth time-series data. Based on a preset reference timestamp, the delayed data collected by each data acquisition device is processed for time synchronization to obtain delayed time series data. Based on a preset baseline timestamp, the packet loss data collected by each data acquisition device is processed for time-series synchronization to obtain packet loss time-series data.

[0006] As a preferred method, the instantaneous volatility is calculated using the following formula: In the formula, This represents the instantaneous volatility of the later time segment among adjacent time segments; This represents the fluctuation amplitude between adjacent time segments; It is the average value of the earlier time series among adjacent time series.

[0007] As a preferred option, the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics is determined as the jitter time segment, including: Identify several first time segments in the instantaneous volatility curve where the instantaneous volatility is greater than a first preset volatility threshold; Traverse all first time segments, merge consecutive first time segments in time, and determine the merged consecutive time segments as second time segments; Both the first time segment and the second time segment that were not merged into the second time segment are identified as time segments to be analyzed. The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the instantaneous volatility of the corresponding time series segment to be analyzed. The instantaneous volatility of each time series to be analyzed is compared with the second preset volatility threshold. The time series segment to be analyzed where the instantaneous volatility is determined to be less than the second preset volatility threshold is defined as the jitter time series segment; wherein, the second preset volatility threshold is determined according to the preset ratio value of the first preset volatility threshold.

[0008] As a preferred embodiment, the step of removing jitter time segments from bandwidth time-series data, delay time-series data, and packet loss time-series data respectively to form jitter-resistant measurement data for monitoring power grid system anomalies includes: Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant bandwidth time series data, jitter-resistant delay time series data, and jitter-resistant packet loss time series data; Several time-domain feature values ​​were extracted from the anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data to form a time-domain feature dataset; The time-domain feature dataset is compressed to obtain anti-jitter measurement data.

[0009] As a preferred embodiment, the time-domain feature value includes skewness, which measures the degree of asymmetry in data distribution; The formula for calculating the skewness includes: In the formula, To prevent skewness in timing data due to jitter; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0010] As a preferred embodiment, the time-domain feature value includes kurtosis, which measures the degree of peak in the data distribution; The formula for calculating kurtosis includes: In the formula, To prevent jitter in time-series data kurtosis; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0011] Accordingly, the present invention provides a jitter reduction measurement data acquisition device, comprising: a time series data acquisition module, a time series data partitioning module, a data distribution characteristic statistics module, an absolute fluctuation degree analysis module, a relative fluctuation degree analysis module, a jitter recognition module, and a jitter reduction data generation module; The time-series data acquisition module is used to acquire time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data; The time series data partitioning module is used to partition each time series data using a preset sliding window to form several time series segments. The data distribution characteristic statistics module is used to calculate the range and average value of each time segment respectively; The absolute fluctuation degree analysis module is used to determine the difference between the range values ​​of adjacent time segments as the fluctuation amplitude; The relative volatility analysis module is used to convert the volatility of adjacent time segments into the instantaneous volatility of the later time segments based on the average value of the earlier time segments among adjacent time segments. The jitter identification module is used to summarize the instantaneous volatility of each time segment to form an instantaneous volatility curve, and to determine the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as the jitter time segment; The jitter reduction data generation module is used to remove jitter time segments from bandwidth time-series data, delay time-series data and packet loss time-series data respectively, to form jitter reduction measurement data for monitoring power grid system anomalies.

[0012] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the anti-shake measurement data acquisition method of the present invention.

[0013] The present invention also provides a computer-readable storage medium item, comprising: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the anti-shake measurement data acquisition method of the present invention.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides a method for acquiring anti-jitter measurement data, which involves acquiring bandwidth time-series data, delay time-series data, and packet loss time-series data of a power grid system. For any acquired time-series data, a preset sliding window is used to divide the time-series data into several time segments. The range and average value of each time segment are calculated. The difference between the range values ​​of adjacent time segments is determined as the fluctuation amplitude. Based on the average value of the earlier time segment among adjacent time segments, the fluctuation amplitude of adjacent time segments is converted into the instantaneous volatility of the later time segment among adjacent time segments. The instantaneous volatility of each time segment is summarized to form an instantaneous volatility curve. The corresponding time segments in the instantaneous volatility curve that meet the preset jitter characteristics are determined as jitter time segments. The jitter time segments in the bandwidth time-series data, delay time-series data, and packet loss time-series data are removed respectively to form anti-jitter measurement data for monitoring power grid system anomalies. This invention divides the acquired time-series data into several time segments, calculates the instantaneous volatility of each time segment to generate an instantaneous volatility curve, and identifies the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as a jitter time segment. The jitter time segment is then removed from the collected time-series data, thereby generating time-series data that does not contain jitter characteristics. This improves the accuracy of the measurement data and avoids the situation where subsequent monitoring mistakenly identifies network jitter as an abnormal event. Attached Figure Description

[0015] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating one embodiment of the anti-shake measurement data acquisition method provided by the present invention; Figure 2 This is a schematic diagram of one embodiment of the anti-shake measurement data acquisition device provided by the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0019] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0022] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0023] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0024] See Figure 1 To address the problem of low accuracy in measurement data in existing technologies, an embodiment of the present invention provides a method for acquiring anti-shake measurement data. This method includes steps 101 to 107, each of which is detailed below: Step 101: Collect time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data.

[0025] In this embodiment of the invention, the measurement data used to detect whether the power grid system is abnormal includes bandwidth data, delay data, and packet loss data. Therefore, generating measurement data for the power grid system requires acquiring bandwidth time-series data, delay time-series data, and packet loss time-series data respectively.

[0026] As a preferred embodiment, collecting time-series data of the power grid system includes: Data acquisition equipment is deployed at several business nodes in the power grid system. Within a preset time period, the data acquisition device is controlled to collect bandwidth data, latency data, and packet loss data of the corresponding service nodes; Based on a preset reference timestamp, the bandwidth data collected by each data acquisition device is processed for time-series synchronization to obtain bandwidth time-series data. Based on a preset reference timestamp, the delayed data collected by each data acquisition device is processed for time synchronization to obtain delayed time series data. Based on a preset baseline timestamp, the packet loss data collected by each data acquisition device is processed for time-series synchronization to obtain packet loss time-series data.

[0027] In this embodiment of the invention, for the complex and diverse application scenarios of measurement data centers, a distributed acquisition architecture can be adopted, in which acquisition agents are deployed on different business nodes to collect bandwidth data, latency data and packet loss data of different business nodes, thereby performing time-series synchronization processing on the collected data to obtain the corresponding time-series data.

[0028] Specifically, data acquisition devices are first deployed at each business node of the power grid system. When power grid system anomaly detection is required, the data acquisition devices at each business node are controlled to collect bandwidth data, latency data, and packet loss data respectively. Since the data collected by each business node may differ due to factors such as latency, it is necessary to use a unified timestamp as a benchmark to perform time-series synchronization processing on each type of data, thereby obtaining bandwidth time-series data, latency time-series data, and packet loss time-series data respectively.

[0029] Step 102: For each time series data, the time series data is divided into several time series segments using a preset sliding window.

[0030] In this embodiment of the invention, a preset sliding window is set with a fixed window length. Assuming the window length is 5, the timing data is divided using the preset sliding window, and the resulting timing segments include data at 5 time points. Therefore, by dividing the bandwidth timing data, delay timing data, and packet loss timing data separately, several bandwidth timing segments, several delay timing segments, and several packet loss timing segments can be obtained.

[0031] Step 103: Calculate the range and average value for each time period.

[0032] In this embodiment of the invention, the range value reflects the absolute amplitude of data fluctuations in each time segment, and the average value reflects the baseline level of data fluctuations in each time segment. When the fluctuation situation matches the fluctuation characteristics corresponding to jitter, the time segment can be removed to avoid identifying jitter as an anomaly during anomaly detection, which would affect the accuracy of power grid detection.

[0033] Step 104: Determine the difference between the range values ​​of adjacent time segments as the fluctuation range.

[0034] In this embodiment of the invention, data transmission jitter causes uneven sampling times of time-series data, resulting in a shift in the statistical characteristics of data within adjacent time segments, which directly leads to an increase in the fluctuation amplitude between time segments. Therefore, whether jitter occurred during network transmission in each time segment can be determined by the fluctuation amplitude of adjacent time segments.

[0035] The fluctuation range can be calculated using the following formula: In the formula, This represents the fluctuation range between time segment i and time segment i+1; It represents the maximum value of time segment i+1; It is the minimum value of time segment i+1; This represents the maximum value of time segment i; This represents the minimum value of time segment i. It represents the extreme value of time segment i+1; Let i be the extreme value of time segment i.

[0036] Step 105: Based on the average value of the earlier time series in the adjacent time series, convert the fluctuation amplitude of the adjacent time series into the instantaneous volatility of the later time series in the adjacent time series.

[0037] As a preferred embodiment, the instantaneous volatility is calculated using the following formula: In the formula, This represents the instantaneous volatility of the later time segment among adjacent time segments; This represents the fluctuation amplitude between adjacent time segments; It is the average value of the earlier time series among adjacent time series.

[0038] In this embodiment of the invention, the fluctuation amplitude of adjacent time segments is the difference in the range between adjacent time segments, reflecting the absolute degree of fluctuation between adjacent time segments. The average value of the earlier time segment among adjacent time segments is the benchmark reference level, reflecting the stable state or initial level before the fluctuation occurs. Therefore, based on the above formula, the absolute degree of fluctuation can be normalized to the benchmark level to obtain the relative volatility, making the fluctuations of time segments of different magnitudes and different benchmarks comparable, thereby making it easier to determine whether jitter occurred in the data transmission of the later time segment among adjacent time segments.

[0039] Step 106: Summarize the instantaneous volatility of each time series to form an instantaneous volatility curve, and determine the corresponding time series that meets the preset jitter characteristics in the instantaneous volatility curve as the jitter time series.

[0040] In this embodiment of the invention, the jitter generated by data transmission is characterized by a data curve that peaks instantaneously and decays rapidly. Therefore, after determining the instantaneous volatility of each time segment, an instantaneous volatility curve is generated by summarizing the instantaneous volatility of each time segment. The existence of a curve segment that conforms to the preset jitter characteristics is then identified within the instantaneous volatility curve. If such a curve segment exists, it is considered that the data in the time segment corresponding to that curve segment experienced jitter during transmission. If no curve segment conforms to the preset jitter characteristics exists, it is considered that the time series data did not experience jitter during transmission.

[0041] As a preferred embodiment, the corresponding time segment in the instantaneous volatility curve that conforms to the preset jitter characteristics is determined as the jitter time segment, including: Identify several first time segments in the instantaneous volatility curve where the instantaneous volatility is greater than a first preset volatility threshold; Traverse all first time segments, merge consecutive first time segments in time, and determine the merged consecutive time segments as second time segments; Both the first time segment and the second time segment that were not merged into the second time segment are identified as time segments to be analyzed. The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the instantaneous volatility of the corresponding time series segment to be analyzed. The instantaneous volatility of each time series to be analyzed is compared with the second preset volatility threshold. The time series segment to be analyzed where the instantaneous volatility is determined to be less than the second preset volatility threshold is defined as the jitter time series segment; wherein, the second preset volatility threshold is determined according to the preset ratio value of the first preset volatility threshold.

[0042] In this embodiment of the invention, since the data curve exhibiting data transmission jitter is characterized by an instantaneous peak followed by rapid decay, the instantaneous volatility of each time segment is first compared with a first preset volatility threshold to determine whether there is high volatility in the instantaneous volatility curve. The time segment with an instantaneous volatility greater than the first preset volatility threshold is determined as the first time segment.

[0043] Since a sustained high volatility curve is consistent with the fault characteristics of a power grid system, after identifying the time series of high instantaneous volatility, it is also necessary to determine whether the instantaneous volatility will drop rapidly after the occurrence of high instantaneous volatility.

[0044] First, the adjacent first time series segments are merged into the second time series segment. The second time series segment is then analyzed as a whole to determine whether the instantaneous volatility is rapidly decaying. This determination is based on the instantaneous volatility of the time series segment following the second time series segment.

[0045] Therefore, the second time segment obtained by merging the first time segment and the first time segment without merging are both identified as time segments to be analyzed. The time segments to be analyzed may be time segments with data transmission jitter.

[0046] The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the judgment instantaneous volatility of the corresponding time series segment to be analyzed. Based on this judgment instantaneous volatility, it is determined whether the time series segment to be analyzed meets the jitter characteristics of instantaneous volatility spike and rapid decay.

[0047] Specifically, the instantaneous volatility is compared with the second preset volatility threshold, and the time series to be analyzed where the instantaneous volatility is less than the second preset volatility threshold is determined as the jitter time series.

[0048] Since the jitter characteristic is an instantaneous spike in volatility followed by a rapid decay, the difference between the first preset volatility threshold and the second preset volatility threshold should be set relatively large. The second preset volatility threshold can be determined as a preset percentage of the first preset volatility threshold. For example, the second preset volatility threshold can be set to 20% or 30% of the first preset volatility threshold.

[0049] Step 107: Remove jitter time segments from the bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant measurement data for monitoring power grid system anomalies.

[0050] In this embodiment of the invention, after identifying the jitter time segments of each time series data, it is necessary to remove the identified jitter time segments from each time series data to prevent subsequent anomaly detection from identifying data transmission jitter as an anomaly in the power grid system.

[0051] As a preferred embodiment, jitter time segments are removed from bandwidth time-series data, delay time-series data, and packet loss time-series data, respectively, to form jitter-resistant measurement data for monitoring power grid system anomalies, including: Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant bandwidth time series data, jitter-resistant delay time series data, and jitter-resistant packet loss time series data; Several time-domain feature values ​​were extracted from the anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data to form a time-domain feature dataset; The time-domain feature dataset is compressed to obtain anti-jitter measurement data.

[0052] In this embodiment of the invention, in order to avoid misidentifying jitter generated by data transmission as a power grid anomaly, it is necessary to remove jitter time sequence segments from the bandwidth time sequence data to anti-jitter bandwidth time sequence data, remove jitter time sequence segments from the delay time sequence data to anti-jitter delay time sequence data, and remove jitter time sequence segments from the packet loss time sequence data to anti-jitter packet loss time sequence data.

[0053] To address the issue of network congestion caused by massive real-time data transmission, after generating anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data, it is necessary to perform dimensionality reduction and compression processing on each anti-jitter time-series data before transmitting it to reduce network bandwidth consumption.

[0054] In this process, the anti-jitter time series data can be dimensionality reduced by feature extraction. Specifically, several time-domain feature values ​​of the anti-jitter bandwidth time series data, anti-jitter delay time series data, and anti-jitter packet loss time series data are extracted to form a time-domain feature dataset. In subsequent data transmission, only the time-domain features are transmitted, and the original detailed data is no longer uploaded in full, so as to reduce the amount of data transmitted.

[0055] After dimensionality reduction, the temporal feature dataset is compressed to obtain jitter reduction measurement data. Only the jitter reduction measurement data is transmitted to the backend anomaly detection model, further reducing network bandwidth usage during data transmission.

[0056] As a preferred embodiment, the time-domain feature value includes skewness, which measures the degree of asymmetry in data distribution. The formula for calculating the skewness includes: In the formula, To prevent skewness in timing data due to jitter; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0057] In this embodiment of the invention, the extracted time-domain feature values ​​include mean, variance, maximum value, minimum value, skewness (used to measure the degree of asymmetry in data distribution), and kurtosis (used to measure the degree of peaks in data distribution).

[0058] The formula for calculating skewness involves first standardizing the deviation of each data point from the overall average level according to the overall fluctuation range of the data, then amplifying the impact of extreme deviations through a cubic operation, and finally performing unbiased correction based on the sample size. The result is a skewness value that directly reflects the direction of the data distribution's tilt. A positive skewness indicates that the data is biased towards the larger side, meaning there are more extreme values ​​above the average level; a negative skewness indicates that the data is biased towards the smaller side, meaning there are more extreme values ​​below the average level; and a skewness close to 0 indicates that the data distribution is approximately symmetrical.

[0059] As a preferred embodiment, the time-domain feature value includes kurtosis, which is used to measure the peak degree of the data distribution; The formula for calculating kurtosis includes: In the formula, To prevent jitter in time-series data kurtosis; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0060] In this embodiment of the invention, kurtosis is a physical indicator that quantifies the steepness and thickness of the tails of data. The formula for calculating kurtosis first standardizes the deviation of each data point from the overall average level according to the overall fluctuation range, then amplifies the influence of extreme deviations through fourth power operation, performs unbiased correction based on sample size, and finally subtracts the baseline kurtosis value corresponding to the normal distribution to obtain the kurtosis value that can intuitively reflect the shape of the data distribution. If the kurtosis is positive, it indicates that the data distribution is steeper and the tails are thicker than the normal distribution, meaning that after shaking, the data is more concentrated near the mean, and there are more extreme values ​​far from the mean; if the kurtosis is negative, it indicates that the data distribution is flatter and the tails are thinner than the normal distribution, meaning that after shaking, the data is more dispersed and there are fewer extreme values; if the kurtosis is close to the mean, it indicates that the data distribution shape is consistent with the normal distribution.

[0061] In this embodiment of the invention, monitoring data can explode exponentially during periods of extreme high-concurrency data transactions or large-scale power grid anomalies. To address the surge in monitoring data caused by extreme high-concurrency transactions or large-scale failures, a high-throughput buffer layer can be introduced to establish a resilient defense between the data acquisition end and the central cloud platform. This ensures stable and reliable transmission of monitoring data during peak data periods and completely eliminates the loss of critical anomaly data.

[0062] Specifically, asynchronous data transmission is achieved using message queue technology (such as Kafka). Edge node acquisition agents write processed feature data and high-priority transaction monitoring status into topics in a Kafka distributed message queue. The message queue acts as an isolation buffer layer between the central cloud platform's anomaly detection engine and the front-end acquisition agent, effectively absorbing and temporarily storing instantaneous data surges. This asynchronous transmission mechanism ensures the stability and reliability of data transmission, completely avoiding data loss or transmission delays caused by instantaneous data spikes. Backend big data processing or anomaly detection platforms (such as Flink / Spark) can smoothly pull data from the queue for consumption and computation based on their own computing power, thus ensuring the absolute stability of the full-stack monitoring system in high-concurrency transaction scenarios.

[0063] Implementing the above embodiments has the following effects: This invention provides a method for acquiring anti-jitter measurement data, which involves acquiring bandwidth time-series data, delay time-series data, and packet loss time-series data of a power grid system. For any acquired time-series data, a preset sliding window is used to divide the time-series data into several time segments. The range and average value of each time segment are calculated. The difference between the range values ​​of adjacent time segments is determined as the fluctuation amplitude. Based on the average value of the earlier time segment among adjacent time segments, the fluctuation amplitude of adjacent time segments is converted into the instantaneous volatility of the later time segment among adjacent time segments. The instantaneous volatility of each time segment is summarized to form an instantaneous volatility curve. The corresponding time segments in the instantaneous volatility curve that meet the preset jitter characteristics are determined as jitter time segments. The jitter time segments in the bandwidth time-series data, delay time-series data, and packet loss time-series data are removed respectively to form anti-jitter measurement data for monitoring power grid system anomalies. This invention divides the acquired time-series data into several time segments, calculates the instantaneous volatility of each time segment to generate an instantaneous volatility curve, and identifies the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as a jitter time segment. The jitter time segment is then removed from the collected time-series data, thereby generating time-series data that does not contain jitter characteristics. This improves the accuracy of the measurement data and avoids the situation where subsequent monitoring mistakenly identifies network jitter as an abnormal event.

[0064] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a jitter reduction measurement data acquisition device, comprising: a time series data acquisition module, a time series data partitioning module, a data distribution characteristic statistics module, an absolute fluctuation degree analysis module, a relative fluctuation degree analysis module, a jitter recognition module, and a jitter reduction data generation module; The time-series data acquisition module is used to acquire time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data; The time series data partitioning module is used to partition each time series data using a preset sliding window to form several time series segments. The data distribution characteristic statistics module is used to calculate the range and average value of each time segment respectively; The absolute fluctuation degree analysis module is used to determine the difference between the range values ​​of adjacent time segments as the fluctuation amplitude; The relative volatility analysis module is used to convert the volatility of adjacent time segments into the instantaneous volatility of the later time segments based on the average value of the earlier time segments among adjacent time segments. The jitter identification module is used to summarize the instantaneous volatility of each time segment to form an instantaneous volatility curve, and to determine the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as the jitter time segment; The jitter reduction data generation module is used to remove jitter time segments from bandwidth time-series data, delay time-series data and packet loss time-series data respectively, to form jitter reduction measurement data for monitoring power grid system anomalies.

[0065] In this embodiment of the invention, the measurement data used to detect whether the power grid system is abnormal includes bandwidth data, delay data, and packet loss data. Therefore, generating measurement data for the power grid system requires acquiring bandwidth time-series data, delay time-series data, and packet loss time-series data respectively.

[0066] In this embodiment of the invention, a preset sliding window is set with a fixed window length. Assuming the window length is 5, the timing data is divided using the preset sliding window, and the resulting timing segments include data at 5 time points. Therefore, by dividing the bandwidth timing data, delay timing data, and packet loss timing data separately, several bandwidth timing segments, several delay timing segments, and several packet loss timing segments can be obtained.

[0067] In this embodiment of the invention, the range value reflects the absolute amplitude of data fluctuations in each time segment, and the average value reflects the baseline level of data fluctuations in each time segment. When the fluctuation situation matches the fluctuation characteristics corresponding to jitter, the time segment can be removed to avoid identifying jitter as an anomaly during anomaly detection, which would affect the accuracy of power grid detection.

[0068] In this embodiment of the invention, data transmission jitter causes uneven sampling times of time-series data, resulting in a shift in the statistical characteristics of data within adjacent time segments, which directly leads to an increase in the fluctuation amplitude between time segments. Therefore, whether jitter occurred during network transmission in each time segment can be determined by the fluctuation amplitude of adjacent time segments.

[0069] The fluctuation range can be calculated using the following formula: In the formula, This represents the fluctuation range between time segment i and time segment i+1; It represents the maximum value of time segment i+1; It is the minimum value of time segment i+1; This represents the maximum value of time segment i; This represents the minimum value of time segment i. It represents the extreme value of time segment i+1; Let i be the extreme value of time segment i.

[0070] In this embodiment of the invention, the jitter generated by data transmission is characterized by a data curve that peaks instantaneously and decays rapidly. Therefore, after determining the instantaneous volatility of each time segment, an instantaneous volatility curve is generated by summarizing the instantaneous volatility of each time segment. The existence of a curve segment that conforms to the preset jitter characteristics is then identified within the instantaneous volatility curve. If such a curve segment exists, it is considered that the data in the time segment corresponding to that curve segment experienced jitter during transmission. If no curve segment conforms to the preset jitter characteristics exists, it is considered that the time series data did not experience jitter during transmission.

[0071] In this embodiment of the invention, after identifying the jitter time segments of each time series data, it is necessary to remove the identified jitter time segments from each time series data to prevent subsequent anomaly detection from identifying data transmission jitter as an anomaly in the power grid system.

[0072] As a preferred embodiment, collecting time-series data of the power grid system includes: Data acquisition equipment is deployed at several business nodes in the power grid system. Within a preset time period, the data acquisition device is controlled to collect bandwidth data, latency data, and packet loss data of the corresponding service nodes; Based on a preset reference timestamp, the bandwidth data collected by each data acquisition device is processed for time-series synchronization to obtain bandwidth time-series data. Based on a preset reference timestamp, the delayed data collected by each data acquisition device is processed for time synchronization to obtain delayed time series data. Based on a preset baseline timestamp, the packet loss data collected by each data acquisition device is processed for time-series synchronization to obtain packet loss time-series data.

[0073] In this embodiment of the invention, for the complex and diverse application scenarios of measurement data centers, a distributed acquisition architecture can be adopted, in which acquisition agents are deployed on different business nodes to collect bandwidth data, latency data and packet loss data of different business nodes, thereby performing time-series synchronization processing on the collected data to obtain the corresponding time-series data.

[0074] Specifically, data acquisition devices are first deployed at each business node of the power grid system. When power grid system anomaly detection is required, the data acquisition devices at each business node are controlled to collect bandwidth data, latency data, and packet loss data respectively. Since the data collected by each business node may differ due to factors such as latency, it is necessary to use a unified timestamp as a benchmark to perform time-series synchronization processing on each type of data, thereby obtaining bandwidth time-series data, latency time-series data, and packet loss time-series data respectively.

[0075] As a preferred embodiment, the instantaneous volatility is calculated using the following formula: In the formula, This represents the instantaneous volatility of the later time segment among adjacent time segments; This represents the fluctuation amplitude between adjacent time segments; It is the average value of the earlier time series among adjacent time series.

[0076] In this embodiment of the invention, the fluctuation amplitude of adjacent time segments is the difference in the range between adjacent time segments, reflecting the absolute degree of fluctuation between adjacent time segments. The average value of the earlier time segment among adjacent time segments is the benchmark reference level, reflecting the stable state or initial level before the fluctuation occurs. Therefore, based on the above formula, the absolute degree of fluctuation can be normalized to the benchmark level to obtain the relative volatility, making the fluctuations of time segments of different magnitudes and different benchmarks comparable, thereby making it easier to determine whether jitter occurred in the data transmission of the later time segment among adjacent time segments.

[0077] As a preferred embodiment, the corresponding time segment in the instantaneous volatility curve that conforms to the preset jitter characteristics is determined as the jitter time segment, including: Identify several first time segments in the instantaneous volatility curve where the instantaneous volatility is greater than a first preset volatility threshold; Traverse all first time segments, merge consecutive first time segments in time, and determine the merged consecutive time segments as second time segments; Both the first time segment and the second time segment that were not merged into the second time segment are identified as time segments to be analyzed. The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the instantaneous volatility of the corresponding time series segment to be analyzed. The instantaneous volatility of each time series to be analyzed is compared with the second preset volatility threshold. The time series segment to be analyzed where the instantaneous volatility is determined to be less than the second preset volatility threshold is defined as the jitter time series segment; wherein, the second preset volatility threshold is determined according to the preset ratio value of the first preset volatility threshold.

[0078] In this embodiment of the invention, since the data curve exhibiting data transmission jitter is characterized by an instantaneous peak followed by rapid decay, the instantaneous volatility of each time segment is first compared with a first preset volatility threshold to determine whether there is high volatility in the instantaneous volatility curve. The time segment with an instantaneous volatility greater than the first preset volatility threshold is determined as the first time segment.

[0079] Since a sustained high volatility curve is consistent with the fault characteristics of a power grid system, after identifying the time series of high instantaneous volatility, it is also necessary to determine whether the instantaneous volatility will drop rapidly after the occurrence of high instantaneous volatility.

[0080] First, the adjacent first time series segments are merged into the second time series segment. The second time series segment is then analyzed as a whole to determine whether the instantaneous volatility is rapidly decaying. This determination is based on the instantaneous volatility of the time series segment following the second time series segment.

[0081] Therefore, the second time segment obtained by merging the first time segment and the first time segment without merging are both identified as time segments to be analyzed. The time segments to be analyzed may be time segments with data transmission jitter.

[0082] The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the judgment instantaneous volatility of the corresponding time series segment to be analyzed. Based on this judgment instantaneous volatility, it is determined whether the time series segment to be analyzed meets the jitter characteristics of instantaneous volatility spike and rapid decay.

[0083] Specifically, the instantaneous volatility is compared with the second preset volatility threshold, and the time series to be analyzed where the instantaneous volatility is less than the second preset volatility threshold is determined as the jitter time series.

[0084] Since the jitter characteristic is an instantaneous spike in volatility followed by a rapid decay, the difference between the first preset volatility threshold and the second preset volatility threshold should be set relatively large. The second preset volatility threshold can be determined as a preset percentage of the first preset volatility threshold. For example, the second preset volatility threshold can be set to 20% or 30% of the first preset volatility threshold.

[0085] As a preferred embodiment, jitter time segments are removed from bandwidth time-series data, delay time-series data, and packet loss time-series data, respectively, to form jitter-resistant measurement data for monitoring power grid system anomalies, including: Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant bandwidth time series data, jitter-resistant delay time series data, and jitter-resistant packet loss time series data; Several time-domain feature values ​​were extracted from the anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data to form a time-domain feature dataset; The time-domain feature dataset is compressed to obtain anti-jitter measurement data.

[0086] In this embodiment of the invention, in order to avoid misidentifying jitter generated by data transmission as a power grid anomaly, it is necessary to remove jitter time sequence segments from the bandwidth time sequence data to anti-jitter bandwidth time sequence data, remove jitter time sequence segments from the delay time sequence data to anti-jitter delay time sequence data, and remove jitter time sequence segments from the packet loss time sequence data to anti-jitter packet loss time sequence data.

[0087] To address the issue of network congestion caused by massive real-time data transmission, after generating anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data, it is necessary to perform dimensionality reduction and compression processing on each anti-jitter time-series data before transmitting it to reduce network bandwidth consumption.

[0088] In this process, the anti-jitter time series data can be dimensionality reduced by feature extraction. Specifically, several time-domain feature values ​​of the anti-jitter bandwidth time series data, anti-jitter delay time series data, and anti-jitter packet loss time series data are extracted to form a time-domain feature dataset. In subsequent data transmission, only the time-domain features are transmitted, and the original detailed data is no longer uploaded in full, so as to reduce the amount of data transmitted.

[0089] After dimensionality reduction, the temporal feature dataset is compressed to obtain jitter reduction measurement data. Only the jitter reduction measurement data is transmitted to the backend anomaly detection model, further reducing network bandwidth usage during data transmission.

[0090] As a preferred embodiment, the time-domain feature value includes skewness, which measures the degree of asymmetry in data distribution. The formula for calculating the skewness includes: In the formula, To prevent skewness in timing data due to jitter; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0091] In this embodiment of the invention, the extracted time-domain feature values ​​include mean, variance, maximum value, minimum value, skewness (used to measure the degree of asymmetry in data distribution), and kurtosis (used to measure the degree of peaks in data distribution).

[0092] The formula for calculating skewness involves first standardizing the deviation of each data point from the overall average level according to the overall fluctuation range of the data, then amplifying the impact of extreme deviations through a cubic operation, and finally performing unbiased correction based on the sample size. The result is a skewness value that directly reflects the direction of the data distribution's tilt. A positive skewness indicates that the data is biased towards the larger side, meaning there are more extreme values ​​above the average level; a negative skewness indicates that the data is biased towards the smaller side, meaning there are more extreme values ​​below the average level; and a skewness close to 0 indicates that the data distribution is approximately symmetrical.

[0093] As a preferred embodiment, the time-domain feature value includes kurtosis, which is used to measure the peak degree of the data distribution; The formula for calculating kurtosis includes: In the formula, To prevent jitter in time-series data kurtosis; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

[0094] In this embodiment of the invention, kurtosis is a physical indicator that quantifies the steepness and thickness of the tails of data. The formula for calculating kurtosis first standardizes the deviation of each data point from the overall average level according to the overall fluctuation range, then amplifies the influence of extreme deviations through fourth power operation, performs unbiased correction based on sample size, and finally subtracts the baseline kurtosis value corresponding to the normal distribution to obtain the kurtosis value that can intuitively reflect the shape of the data distribution. If the kurtosis is positive, it indicates that the data distribution is steeper and the tails are thicker than the normal distribution, meaning that after shaking, the data is more concentrated near the mean, and there are more extreme values ​​far from the mean; if the kurtosis is negative, it indicates that the data distribution is flatter and the tails are thinner than the normal distribution, meaning that after shaking, the data is more dispersed and there are fewer extreme values; if the kurtosis is close to the mean, it indicates that the data distribution shape is consistent with the normal distribution.

[0095] In this embodiment of the invention, monitoring data can explode exponentially during periods of extreme high-concurrency data transactions or large-scale power grid anomalies. To address the surge in monitoring data caused by extreme high-concurrency transactions or large-scale failures, a high-throughput buffer layer can be introduced to establish a resilient defense between the data acquisition end and the central cloud platform. This ensures stable and reliable transmission of monitoring data during peak data periods and completely eliminates the loss of critical anomaly data.

[0096] Specifically, asynchronous data transmission is achieved using message queue technology (such as Kafka). Edge node acquisition agents write processed feature data and high-priority transaction monitoring status into topics in a Kafka distributed message queue. The message queue acts as an isolation buffer layer between the central cloud platform's anomaly detection engine and the front-end acquisition agent, effectively absorbing and temporarily storing instantaneous data surges. This asynchronous transmission mechanism ensures the stability and reliability of data transmission, completely avoiding data loss or transmission delays caused by instantaneous data spikes. Backend big data processing or anomaly detection platforms (such as Flink / Spark) can smoothly pull data from the queue for consumption and computation based on their own computing power, thus ensuring the absolute stability of the full-stack monitoring system in high-concurrency transaction scenarios.

[0097] Implementing the above embodiments has the following effects: This invention provides a jitter-resistant measurement data acquisition device, which collects bandwidth time-series data, delay time-series data, and packet loss time-series data of a power grid system. For any collected time-series data, a preset sliding window is used to divide the time-series data into several time segments. The range and average value of each time segment are calculated. The difference between the range values ​​of adjacent time segments is determined as the fluctuation amplitude. Based on the average value of the earlier time segment among adjacent time segments, the fluctuation amplitude of adjacent time segments is converted into the instantaneous volatility of the later time segment among adjacent time segments. The instantaneous volatility of each time segment is summarized to form an instantaneous volatility curve. The corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics is determined as the jitter time segment. The jitter time segments in the bandwidth time-series data, delay time-series data, and packet loss time-series data are removed respectively to form jitter-resistant measurement data for monitoring power grid system anomalies. This invention divides the acquired time-series data into several time segments, calculates the instantaneous volatility of each time segment to generate an instantaneous volatility curve, and identifies the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as a jitter time segment. The jitter time segment is then removed from the collected time-series data, thereby generating time-series data that does not contain jitter characteristics. This improves the accuracy of the measurement data and avoids the situation where subsequent monitoring mistakenly identifies network jitter as an abnormal event.

[0098] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the anti-shake measurement data acquisition method provided by any of the above-described method embodiments of the present invention.

[0099] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0100] Based on the above embodiments of the anti-shake measurement data acquisition method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the anti-shake measurement data acquisition method of any embodiment of the present invention.

[0101] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0102] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0103] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0104] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the anti-shake measurement data acquisition method described in any of the above-described method embodiments of the present invention.

[0105] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0106] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for collecting anti-jitter metrology data, comprising: include: Collect time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data; For each time series data, a preset sliding window is used to divide the time series data into several time series segments; Calculate the range and average value for each time period; The difference between the range values ​​of adjacent time periods is defined as the fluctuation range; Based on the average value of the earlier time series in adjacent time series, the fluctuation amplitude of adjacent time series is converted into the instantaneous volatility of the later time series in adjacent time series. The instantaneous volatility of each time series is summarized to form an instantaneous volatility curve. The corresponding time series in the instantaneous volatility curve that meets the preset jitter characteristics is determined as the jitter time series. Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant measurement data for monitoring power grid system anomalies.

2. The method of claim 1, wherein, The time-series data of the power grid system collected includes: Data acquisition equipment is deployed at several business nodes in the power grid system. Within a preset time period, the data acquisition device is controlled to collect bandwidth data, latency data, and packet loss data of the corresponding service nodes; Based on a preset reference timestamp, the bandwidth data collected by each data acquisition device is processed for time-series synchronization to obtain bandwidth time-series data. Based on a preset reference timestamp, the delayed data collected by each data acquisition device is processed for time synchronization to obtain delayed time series data. Based on a preset baseline timestamp, the packet loss data collected by each data acquisition device is processed for time-series synchronization to obtain packet loss time-series data.

3. The anti-shake measurement data acquisition method according to claim 2, characterized in that, Instantaneous volatility is calculated using the following formula: In the formula, This represents the instantaneous volatility of the later time segment among adjacent time segments; This represents the fluctuation amplitude between adjacent time segments; It is the average value of the earlier time series among adjacent time series.

4. The anti-shake measurement data acquisition method according to claim 3, characterized in that, The time segments in the instantaneous volatility curve that meet the preset jitter characteristics are identified as jitter time segments, including: Identify several first time segments in the instantaneous volatility curve where the instantaneous volatility is greater than a first preset volatility threshold; Traverse all first time segments, merge consecutive first time segments in time, and determine the merged consecutive time segments as second time segments; Both the first time segment and the second time segment that were not merged into the second time segment are identified as time segments to be analyzed. The instantaneous volatility of the next time series segment after the time series segment to be analyzed is determined as the instantaneous volatility of the corresponding time series segment to be analyzed. The instantaneous volatility of each time series to be analyzed is compared with the second preset volatility threshold. The time series segment to be analyzed where the instantaneous volatility is determined to be less than the second preset volatility threshold is defined as the jitter time series segment; wherein, the second preset volatility threshold is determined according to the preset ratio value of the first preset volatility threshold.

5. The anti-shake measurement data acquisition method according to claim 4, characterized in that, The process involves removing jitter time segments from bandwidth time-series data, delay time-series data, and packet loss time-series data to form jitter-resistant measurement data for monitoring power grid system anomalies, including: Jitter time segments are removed from bandwidth time series data, delay time series data, and packet loss time series data respectively to form jitter-resistant bandwidth time series data, jitter-resistant delay time series data, and jitter-resistant packet loss time series data; Several time-domain feature values ​​were extracted from the anti-jitter bandwidth time-series data, anti-jitter delay time-series data, and anti-jitter packet loss time-series data to form a time-domain feature dataset; The time-domain feature dataset is compressed to obtain anti-jitter measurement data.

6. The anti-shake measurement data acquisition method according to claim 5, characterized in that, The time-domain feature values ​​include skewness, which measures the degree of asymmetry in data distribution; The formula for calculating the skewness includes: In the formula, To prevent skewness in timing data due to jitter; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

7. The anti-shake measurement data acquisition method according to claim 5, characterized in that, The time-domain feature values ​​include kurtosis, which measures the peak size of the data distribution; The formula for calculating kurtosis includes: In the formula, To prevent jitter in time-series data kurtosis; To prevent jitter in the i-th data in the timing data; To prevent jitter in the mean of time-series data; To prevent jitter in the standard deviation of time-series data; The number of data points in the time series data to prevent jitter.

8. A vibration-resistant measurement data acquisition device, characterized in that, include: The system includes a time-series data acquisition module, a time-series data partitioning module, a data distribution characteristic statistics module, an absolute fluctuation degree analysis module, a relative fluctuation degree analysis module, a jitter recognition module, and a jitter-resistant data generation module. The time-series data acquisition module is used to acquire time-series data of the power grid system; wherein, the time-series data includes bandwidth time-series data, delay time-series data, and packet loss time-series data; The time series data partitioning module is used to partition each time series data using a preset sliding window to form several time series segments. The data distribution characteristic statistics module is used to calculate the range and average value of each time segment respectively; The absolute fluctuation degree analysis module is used to determine the difference between the range values ​​of adjacent time segments as the fluctuation amplitude; The relative volatility analysis module is used to convert the volatility of adjacent time segments into the instantaneous volatility of the later time segments based on the average value of the earlier time segments among adjacent time segments. The jitter identification module is used to summarize the instantaneous volatility of each time segment to form an instantaneous volatility curve, and to determine the corresponding time segment in the instantaneous volatility curve that meets the preset jitter characteristics as the jitter time segment; The jitter reduction data generation module is used to remove jitter time segments from bandwidth time-series data, delay time-series data and packet loss time-series data respectively, to form jitter reduction measurement data for monitoring power grid system anomalies.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the anti-shake measurement data acquisition method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the anti-shake measurement data acquisition method as described in any one of claims 1-7.

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