A network data self-adaptive collection method and system based on error detection

Abstract

This invention discloses an adaptive network data acquisition method and system based on error detection, belonging to the field of power distribution communication network operation monitoring and data acquisition technology. The method includes: acquiring error detection results based on a preset multi-error detection algorithm and pre-acquired power distribution network data; triggering frequency adjustment when a network error is detected; acquiring the number of errors based on a preset sliding time window and the error detection results; acquiring the error severity based on the number of errors and a preset anomaly threshold; acquiring a target acquisition frequency based on preset frequency adjustment parameters when a preset frequency adjustment condition is met; updating the current acquisition frequency to the target acquisition frequency, and acquiring the current power distribution network data based on the target acquisition frequency, thus achieving adaptive network data acquisition. This invention discloses an adaptive network data acquisition method and system based on error detection, which can solve the technical problem of the lack of adaptive capability in existing acquisition strategies.

Description

Technical Field

[0001] This invention relates to the field of power distribution communication network operation monitoring and data acquisition technology, and in particular to a network data adaptive acquisition method and system based on error detection. Background Technology

[0002] Due to the complex transmission environment of power distribution communication networks, data transmission is susceptible to errors such as bit flips and data loss caused by various factors including link noise, equipment failure, electromagnetic interference, buffer overflows, and protocol anomalies. To ensure communication reliability and improve the correctness and stability of data transmission, existing communication systems widely incorporate error detection and control technologies. Parity check, CRC, and Hamming codes are widely used at the physical, data link, and transport layers. In the field of power distribution communication network operation monitoring and data acquisition, existing systems often use fixed periods or fixed frequencies for data acquisition and analysis. They assess network operation status by collecting indicators such as interface traffic and packet loss rate at preset time intervals. This fixed-frequency acquisition method has significant advantages in terms of simplicity and low deployment cost, enabling basic network operation status assessment. Simultaneously, various underlying error detection technologies effectively ensure the correctness of data transmission, providing support for the basic communication operation of power distribution communication networks.

[0003] However, although error detection technologies such as parity checking, CRC, and Hamming codes have been widely used in communication processes, these technologies mainly focus on ensuring the correctness of data transmission and have not yet formed an effective linkage with network data acquisition and monitoring strategies. This makes it impossible for acquisition strategies to adaptively adjust according to error conditions. When the network is stable, high-frequency acquisition generates a large amount of redundant data, resulting in a serious waste of network bandwidth, computing, and storage resources. When the network is abnormal, fixed low-frequency acquisition is prone to missing key abnormal data, affecting fault location and handling. At the same time, fixed acquisition methods are difficult to adapt to the staged and regional characteristics of network errors, and cannot capture local anomalies with fine granularity, easily masking potential risks. In summary, the existing technologies have the following technical problems that urgently need to be solved: the acquisition strategy lacks adaptive capability and cannot be dynamically adjusted according to the actual error status of the network; the error detection results are disconnected from the acquisition decisions, and the acquisition system cannot perceive changes in the underlying network quality; resource utilization efficiency is low, and fixed high-frequency acquisition is prone to causing performance bottlenecks in the monitoring system, and may even cause reverse interference to the monitored network; the anomaly capture accuracy is insufficient, making it difficult to accurately capture the characteristics of error occurrence and failing to meet the fine-grained monitoring needs of high reliability and high security scenarios. Summary of the Invention

[0004] This invention provides a network data adaptive acquisition method and system based on error detection, which can solve the technical problems in the prior art that lead to resource waste when the network is stable, insufficient accuracy of anomaly capture when the network is abnormal, and lack of adaptive capability of acquisition strategy when acquiring network data at a fixed frequency, thereby realizing adaptive acquisition of network data.

[0005] This invention provides an adaptive network data acquisition method based on error detection, comprising: Data from the power distribution network is acquired periodically based on the current acquisition frequency. Layered error detection is performed based on a preset multi-error detection algorithm and the distribution network data to obtain error detection results; When a network error is determined to exist based on the error detection result, a frequency adjustment action is performed according to the error detection result: Based on a preset sliding time window and the error detection results, error statistics are performed on the distribution network data to obtain the number of errors within the preset sliding time window; The severity of errors in the power distribution network data is obtained based on the number of errors and a preset anomaly threshold. When the error severity is determined to meet the preset frequency adjustment conditions, the current acquisition frequency is adjusted according to the preset frequency adjustment parameters to obtain the target acquisition frequency; The current acquisition frequency is updated to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency.

[0006] In the above scheme, firstly, distribution network data is acquired periodically based on the current acquisition frequency to ensure the continuity of distribution network data acquisition. Then, layered error detection is carried out based on a preset multi-error detection algorithm to achieve multi-dimensional detection of network errors and ensure the comprehensiveness of error detection. Furthermore, frequency adjustment is triggered only when a network error is detected, avoiding meaningless frequency adjustment operations and useless operating conditions. Error statistics are performed on the error detection results through a preset sliding time window to concisely and effectively quantify the occurrence of errors within a preset time period. Combined with a preset abnormal threshold, the severity of errors is obtained, providing a clear basis for frequency adjustment. When the frequency adjustment conditions are met, the current acquisition frequency is adjusted to obtain the target acquisition frequency. The acquisition frequency is then updated to carry out subsequent data acquisition, so that the acquisition frequency can dynamically and adaptively change with the network error status of the distribution network. This abandons the fixed frequency acquisition mode and realizes adaptive acquisition of network data, making the data acquisition behavior compatible with the actual operating state of the distribution network.

[0007] Furthermore, the preset multi-error detection algorithm includes a parity check algorithm, a cyclic redundancy check algorithm, and a Hamming code check algorithm; the step of performing hierarchical error detection based on the preset multi-error detection algorithm and the distribution network data to obtain error detection results includes: Single-bit error detection is performed based on the aforementioned power distribution network data and parity check algorithm to obtain parity detection results; Based on the power distribution network data and the cyclic redundancy check algorithm, sudden error detection is performed to obtain the sudden error detection results. Error location detection and correction detection are performed based on the aforementioned power distribution network data and Hamming code verification algorithm to obtain error location detection results; Error detection results are generated based on the odd / even detection results, sudden error detection results, and error location detection results.

[0008] In the above scheme, the present invention uses parity check algorithm, cyclic redundancy check algorithm and Hamming code check algorithm as preset multi-error detection algorithms, which respectively carry out single-bit error detection, burst error detection, error location detection and correction detection. The three types of algorithms perform their respective functions to achieve accurate identification of different types of network errors. Then, the detection results of each algorithm are integrated to form the final error detection result, so that the error detection result fully covers all types of error in the data transmission process of the distribution network, and realizes error location and corrective detection, thereby improving the accuracy and completeness of hierarchical error detection.

[0009] Furthermore, after performing hierarchical error detection based on the preset multi-error detection algorithm and the distribution network data to obtain the error detection result, the method further includes: when it is determined based on the error detection result that there is no network error, returning to the process of periodically acquiring distribution network data based on the current acquisition frequency.

[0010] In the above scheme, after the hierarchical error detection step, if it is determined that there is no network error, it directly returns to the step of periodically acquiring distribution network data based on the current acquisition frequency. There is no need to perform a series of operations such as error statistics, error severity determination, and frequency adjustment. This simplifies the method execution process in the absence of network errors, reduces unnecessary calculation and operation steps in the system, reduces the system's computing resource consumption, and keeps the current acquisition frequency unchanged. This avoids unnecessary adjustment of the acquisition frequency in the stable and error-free state of the distribution network, ensuring the stability and continuity of data acquisition in a stable state.

[0011] Furthermore, the step of obtaining the error severity of the distribution network data based on the number of errors and a preset anomaly threshold includes: The preset abnormal threshold includes a first abnormal preset threshold and a second abnormal preset threshold, wherein the first abnormal preset threshold is less than the second abnormal preset threshold; When the number of errors is less than the first preset threshold for anomalies, the error severity of the power distribution network data is determined to be no anomaly. When the number of errors is not less than the first preset threshold for anomalies and the number of errors is less than the second preset threshold for anomalies, the severity of the error in the distribution network data is determined to be a minor anomaly. When the number of errors is not less than the second preset threshold for anomalies, the severity of the errors in the power distribution network data is determined to be a serious anomaly.

[0012] In the above scheme, based on obtaining the severity of the error through the number of errors and the preset abnormal threshold, the preset abnormal threshold is divided into a first abnormal preset threshold and a second abnormal preset threshold, with the first abnormal preset threshold set to be less than the second abnormal preset threshold. According to the comparison results of the number of errors and the two types of thresholds, the severity of the error is divided into three levels: no abnormality, minor abnormality, and severe abnormality. This transforms the abstract error state of the distribution network into a quantifiable and clearly identifiable level indicator, making the judgment criteria for the severity of the error clearer and more uniform, avoiding subjectivity in the judgment process, and providing a specific and executable grading basis for subsequent judgment of whether the preset frequency adjustment conditions are met, making the trigger judgment of frequency adjustment more targeted.

[0013] Furthermore, after obtaining the error severity of the distribution network data based on the number of errors and the preset anomaly threshold, the method further includes: When the error severity is no abnormality, it is determined that the preset frequency adjustment conditions are not met based on the error severity. At this time, the process returns to the point of periodically acquiring distribution network data based on the current acquisition frequency.

[0014] In the above scheme, when the severity of the error is determined to be no abnormality, it is directly determined that the preset frequency adjustment conditions are not met and the process returns to the step of periodically acquiring distribution network data based on the current acquisition frequency. This further simplifies the method execution process in the absence of network errors, avoids invalid calculations of the system, and maintains the current acquisition frequency without adjustment. Under the condition that the distribution network is stable and error-free, unnecessary frequency adjustment operations are avoided, resource waste is reduced, and the continuity and stability of distribution network data acquisition in a stable state are ensured.

[0015] Further, when the error severity is determined to meet the preset frequency adjustment conditions, adjusting the current acquisition frequency according to the preset frequency adjustment parameters to obtain the target acquisition frequency includes: The preset frequency adjustment parameters include preset mild abnormality adjustment parameters; When the severity of the error is mild, it is determined that the preset first frequency adjustment condition is met in the preset frequency adjustment conditions. At this time, the current acquisition frequency is dynamically adjusted proportionally based on the preset mild error adjustment parameters to obtain the target acquisition frequency.

[0016] In the above scheme, for minor anomaly error states, a preset minor anomaly adjustment parameter is used as the basis for frequency adjustment. It is determined that the preset first frequency adjustment condition is met, and the current acquisition frequency is dynamically proportionally adjusted based on this parameter. This allows the acquisition frequency adjustment to accurately adapt to the minor anomaly network error state of the distribution network, avoiding drastic changes in the acquisition frequency caused by over-adjustment. At the same time, the dynamic proportional adjustment method makes the frequency adjustment more flexible and can adapt to different minor anomaly application scenarios according to the preset parameters. This ensures the acquisition of distribution network data under minor anomaly conditions, saving network and computing resources while maintaining basic error detection accuracy.

[0017] Further, when the error severity is determined to meet the preset frequency adjustment conditions, adjusting the current acquisition frequency according to the preset frequency adjustment parameters to obtain the target acquisition frequency includes: The preset frequency adjustment parameters include preset severe anomaly adjustment parameters; When the severity of the error is severe abnormal, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met. At this time, the current acquisition frequency is dynamically proportionally adjusted based on the preset severe abnormality adjustment parameter to obtain the target acquisition frequency.

[0018] In the above scheme, for severe abnormal error states, a preset severe abnormality adjustment parameter is used as the basis for frequency adjustment. It is determined that the preset second frequency adjustment condition is met, and the current acquisition frequency is dynamically proportionally adjusted based on this parameter. This allows the acquisition frequency adjustment to accurately match the severe abnormal network error state of the distribution network. By adjusting the ratio, the data acquisition frequency can be quickly increased, ensuring that the data acquisition frequency can be increased in the case of high network error incidence, accurately capturing more network error data, and providing sufficient and effective data sources for the location and handling of distribution network faults. At the same time, the dynamic proportional adjustment method can adapt to different severe abnormality scenarios, making the frequency adjustment more targeted.

[0019] Further, updating the current acquisition frequency to the target acquisition frequency, so as to obtain the current distribution network data based on the target acquisition frequency, includes: The current acquisition frequency is progressively updated based on the target acquisition frequency and a preset progressive method until the current acquisition frequency is updated to the target acquisition frequency. The current power distribution network data is acquired based on the target acquisition frequency to achieve adaptive acquisition of network data.

[0020] In the above scheme, the current acquisition frequency is updated gradually based on the target acquisition frequency and a preset gradual method, rather than jumping directly to the target acquisition frequency. This avoids drastic oscillations in the acquisition frequency, ensures the stability of the data acquisition frequency adjustment in the distribution network, prevents instability in the acquisition system due to sudden frequency changes, and allows the acquisition frequency to gradually adapt to the actual network error state of the distribution network. This makes the frequency adjustment more in line with the dynamic changes in the network, further improving the rationality of adaptive acquisition of network data and the stability of system operation.

[0021] Furthermore, before updating the current acquisition frequency to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency, the method further includes: When the target acquisition frequency is greater than the preset maximum acquisition frequency protection threshold, the preset maximum acquisition frequency protection threshold is used as the target acquisition frequency. When the target acquisition frequency is less than the preset minimum acquisition frequency protection threshold, the preset minimum acquisition frequency protection threshold is used as the target acquisition frequency.

[0022] In the above scheme, the target acquisition frequency is limited by setting maximum and minimum acquisition frequency protection thresholds. When the target acquisition frequency exceeds the threshold range, the corresponding protection threshold is used as the target acquisition frequency for subsequent updates. This avoids excessive consumption of network bandwidth and computing resources due to excessively high target acquisition frequencies, while preventing the problem of missed network error data and insufficient accuracy in anomaly capture due to excessively low target acquisition frequencies. It sets reasonable adjustment boundaries for the acquisition frequency, ensuring that the dynamic adjustment of the acquisition frequency is always within a controllable range, taking into account both resource utilization efficiency and the need for anomaly data capture, further optimizing the effect of adaptive network data acquisition, and ensuring the effectiveness of distribution network monitoring.

[0023] Furthermore, it also includes: Based on the error detection results, the error type and the corresponding terminal in the distribution network data are obtained, and the error type is transmitted to the corresponding terminal for error alarm.

[0024] In the above solution, the error type and corresponding terminal of the distribution network data are extracted from the error detection results, and the error type is transmitted to the corresponding terminal for error alarm. This enables distribution network operation and maintenance personnel to know the terminal location and specific error type of the error in a timely manner, realizing the precision and targeting of network error alarms, avoiding indiscriminate global alarms, improving the response efficiency of operation and maintenance personnel to distribution network errors, and facilitating the rapid location of the terminal where the error occurred, thus saving time for subsequent network fault handling.

[0025] Furthermore, it also includes: Based on the error detection results, obtain the error network data from the distribution network data; The error network data and error detection results are visualized to obtain visualized error monitoring results, and the visualized error monitoring results are transmitted to the visualized monitoring platform.

[0026] In the above solution, by visualizing the error network data and error detection results and transmitting them to the visualization monitoring platform, the abstract error data and detection results are transformed into an intuitive visualization form. This allows maintenance personnel to clearly and intuitively grasp the network error situation of the distribution network without having to perform tedious analysis of the original error data, thus improving the efficiency of network error status perception. At the same time, transmitting the visualized error monitoring results to the monitoring platform enables centralized management and display of error information, facilitating maintenance personnel to conduct global network error monitoring and analysis of the distribution network.

[0027] This invention provides an adaptive network data acquisition method based on error detection. By periodically acquiring distribution network data and employing parity check, cyclic redundancy check, and Hamming code verification algorithms for hierarchical error detection, it achieves comprehensive and accurate identification of various network errors in the distribution network, providing reliable error data support for subsequent frequency regulation. The frequency regulation process is triggered only when a network error is detected; otherwise, it directly returns to the acquisition step, significantly reducing unnecessary system operations and lowering computing resource consumption. By using a sliding time window to count the number of errors and combining this with dual preset anomaly thresholds, the severity of errors is divided into three levels: no anomaly, minor anomaly, and severe anomaly. This provides a quantitative and unified standard for error judgment, offering a clear and targeted triggering basis for frequency regulation. Dedicated adjustment parameters are configured for different anomaly levels to dynamically adjust the proportional frequency. Simultaneously, a gradual approach is used to update the acquisition frequency, and maximum and minimum acquisition frequency protection thresholds are set. This avoids drastic oscillations in the acquisition frequency, ensuring the smoothness of frequency adjustment and system stability. It also defines reasonable adjustment boundaries for the acquisition frequency, preventing excessive consumption of network and computing resources due to excessively high frequencies and missed acquisition of critical anomaly data due to excessively low frequencies. Meanwhile, the method pushes error types to the corresponding terminals for precise alarms and visualizes the error data and detection results before transmitting them to the monitoring platform. This enables maintenance personnel to quickly locate the error and intuitively understand the network error situation, improving the response efficiency of network error handling. Overall, the method achieves dynamic adaptive adjustment of the data acquisition frequency of the distribution network according to the network error state, completely abandoning the traditional fixed-frequency acquisition mode. It breaks down the barriers between error detection results and acquisition decisions, achieving deep integration of error detection and network monitoring. When the network is stable, it reduces redundant data acquisition and saves various resources; when the network is abnormal, it increases the acquisition frequency and accurately captures error data. This effectively solves the problems of low resource utilization efficiency and insufficient anomaly capture accuracy in traditional acquisition methods, significantly improving the timeliness and accuracy of anomaly detection and fault location, and meeting the actual needs of distribution communication networks for refined and intelligent perception of network operation status.

[0028] This invention provides a network data adaptive acquisition system based on error detection, used to implement the aforementioned network data adaptive acquisition method based on error detection, comprising: a network data acquisition module, an error detection module, and a frequency dynamic adjustment module, wherein: The network data acquisition module is used to periodically acquire power distribution network data based on the current acquisition frequency; The error detection module is used to perform hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data, and to obtain error detection results; The frequency dynamic adjustment module is used to perform frequency adjustment actions based on the error detection results when a network error is determined to exist: it performs error statistics on the distribution network data based on a preset sliding time window and the error detection results to obtain the number of errors within the preset sliding time window; it obtains the error severity of the distribution network data based on the number of errors and a preset anomaly threshold; when the error severity determines that a preset frequency adjustment condition is met, it adjusts the current acquisition frequency according to preset frequency adjustment parameters to obtain a target acquisition frequency; and it updates the current acquisition frequency to the target acquisition frequency so that the network data acquisition module acquires the current distribution network data based on the target acquisition frequency.

[0029] This invention provides an adaptive network data acquisition system based on error detection. The system comprises a network data acquisition module, an error detection module, and a frequency dynamic adjustment module. Each module performs core actions in the acquisition method, such as data acquisition, hierarchical error detection, error judgment, and frequency adjustment. The modular functional division clarifies the boundaries of each part of the system, facilitating independent development, maintenance, and upgrades of each module. At the same time, the modules work together to execute each step of the acquisition method accurately and orderly, ensuring the stable and efficient implementation of the error detection-based adaptive network data acquisition method. The collaborative operation between modules also reduces the connection loss between functional parts and improves the overall operating efficiency of the system.

[0030] Furthermore, the preset multi-error detection algorithm includes a parity check algorithm, a cyclic redundancy check algorithm, and a Hamming code check algorithm; the error detection module is used to perform hierarchical error detection based on the preset multi-error detection algorithm and the distribution network data, and obtain error detection results, including: Single-bit error detection is performed based on the aforementioned power distribution network data and parity check algorithm to obtain parity detection results; Based on the power distribution network data and the cyclic redundancy check algorithm, sudden error detection is performed to obtain the sudden error detection results. Error location detection and correction detection are performed based on the aforementioned power distribution network data and Hamming code verification algorithm to obtain error location detection results; Error detection results are generated based on the odd / even detection results, sudden error detection results, and error location detection results.

[0031] Furthermore, the frequency dynamic adjustment module is also used to, after performing hierarchical error detection based on the preset multi-error detection algorithm and the distribution network data to obtain the error detection result: when it is determined based on the error detection result that there is no network error, return to the process of periodically acquiring distribution network data based on the current acquisition frequency.

[0032] Furthermore, the frequency dynamic adjustment module is used to obtain the error severity of the distribution network data based on the number of errors and a preset anomaly threshold, including: The preset abnormal threshold includes a first abnormal preset threshold and a second abnormal preset threshold, wherein the first abnormal preset threshold is less than the second abnormal preset threshold; When the number of errors is less than the first preset threshold for anomalies, the error severity of the power distribution network data is determined to be no anomaly. When the number of errors is not less than the first preset threshold for anomalies and the number of errors is less than the second preset threshold for anomalies, the severity of the error in the distribution network data is determined to be a minor anomaly. When the number of errors is not less than the second preset threshold for anomalies, the severity of the errors in the power distribution network data is determined to be a serious anomaly.

[0033] Furthermore, the frequency dynamic adjustment module is also used to, after obtaining the error severity of the distribution network data based on the number of errors and a preset anomaly threshold: When the error severity is no abnormality, it is determined that the preset frequency adjustment conditions are not met based on the error severity. At this time, the process returns to the point of periodically acquiring distribution network data based on the current acquisition frequency.

[0034] Furthermore, the frequency dynamic adjustment module is used to adjust the current acquisition frequency according to preset frequency adjustment parameters to obtain the target acquisition frequency when the error severity is determined to meet the preset frequency adjustment conditions, including: The preset frequency adjustment parameters include preset mild abnormality adjustment parameters; When the severity of the error is mild, it is determined that the preset first frequency adjustment condition is met in the preset frequency adjustment conditions. At this time, the current acquisition frequency is dynamically adjusted proportionally based on the preset mild error adjustment parameters to obtain the target acquisition frequency.

[0035] Furthermore, the frequency dynamic adjustment module is used to adjust the current acquisition frequency according to preset frequency adjustment parameters to obtain the target acquisition frequency when the error severity is determined to meet the preset frequency adjustment conditions, including: The preset frequency adjustment parameters include preset severe anomaly adjustment parameters; When the severity of the error is severe abnormal, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met. At this time, the current acquisition frequency is dynamically proportionally adjusted based on the preset severe abnormality adjustment parameter to obtain the target acquisition frequency.

[0036] Furthermore, the frequency dynamic adjustment module is used to update the current acquisition frequency to the target acquisition frequency so as to acquire the current distribution network data based on the target acquisition frequency, including: The current acquisition frequency is progressively updated based on the target acquisition frequency and a preset progressive method until the current acquisition frequency is updated to the target acquisition frequency. This enables the network data acquisition module to obtain current distribution network data based on the target acquisition frequency, thereby achieving adaptive acquisition of network data.

[0037] Furthermore, before the network data acquisition module enables the network data acquisition module to acquire the current distribution network data based on the target acquisition frequency and achieve adaptive acquisition of network data, the frequency dynamic adjustment module is also used for: When the target acquisition frequency is greater than the preset maximum acquisition frequency protection threshold, the preset maximum acquisition frequency protection threshold is used as the target acquisition frequency. When the target acquisition frequency is less than the preset minimum acquisition frequency protection threshold, the preset minimum acquisition frequency protection threshold is used as the target acquisition frequency.

[0038] Furthermore, it also includes an error alarm module for: Based on the error detection results, the error type and the corresponding terminal in the distribution network data are obtained, and the error type is transmitted to the corresponding terminal for error alarm.

[0039] Furthermore, it also includes a visualization module for: Based on the error detection results, obtain the error network data from the distribution network data; The error network data and error detection results are visualized to obtain visualized error monitoring results, and the visualized error monitoring results are transmitted to the visualized monitoring platform.

[0040] Another embodiment of the present invention provides a terminal device, including: 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 steps of the network data adaptive acquisition method based on error detection of the present invention.

[0041] Another embodiment of the present invention provides a computer-readable storage medium item, including: 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 network data adaptive acquisition method based on error detection of the present invention.

[0042] This invention provides an adaptive network data acquisition method and system based on error detection, forming a complete adaptive acquisition system for distribution network data based on error detection. First, it utilizes parity checking, cyclic redundancy check, and Hamming code verification algorithms to perform hierarchical error detection, achieving comprehensive and accurate detection of various errors in distribution network data, providing a reliable error data foundation for all subsequent operations. Next, it uses a sliding time window to count the number of errors and combines this with two levels of preset anomaly thresholds to scientifically classify the severity of errors, making the triggering determination of frequency adjustment more objective and targeted, freeing it from subjectivity. It adopts a logic of triggering adjustment only when erroneous network data is present, and simplifying the process when no errors occur. Simultaneously, it configures dedicated parameters for minor and severe anomalies to dynamically adjust the proportional frequency, achieving dynamic adaptive changes in the acquisition frequency. This reduces network bandwidth issues caused by data acquisition when the distribution network is stable. The method minimizes bandwidth and computational resource consumption, avoids redundant data collection, and can precisely increase the collection frequency when the network is abnormal, ensuring that critical abnormal data is completely captured. This solves the resource waste and missed abnormal data problems caused by fixed-frequency collection. At the same time, it pushes error types to corresponding terminals for accurate alarms and visualizes error data and detection results before uploading them to the monitoring platform, realizing accurate alarms and intuitive monitoring of network errors. This significantly improves the response efficiency and global awareness of distribution network operation and maintenance. The modular and systematic design makes the implementation of the entire adaptive collection method more stable and efficient. The coordinated cooperation of various functional links not only comprehensively improves the intelligence and efficiency of distribution network data collection, but also enhances the accuracy of network error detection, alarms, and fault handling. It effectively solves various drawbacks of the traditional fixed-frequency collection mode and meets the refined and intelligent perception needs of distribution network communication network operation monitoring and data collection. Attached Figure Description

[0043] 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.

[0044] Figure 1 This is a schematic diagram of a network data adaptive acquisition method based on error detection provided in this embodiment; Figure 2 This embodiment provides a schematic diagram of a network data adaptive acquisition method and system architecture based on error detection. Figure 3 This is a schematic diagram of the collaborative architecture of the multi-error detection algorithm provided in this embodiment. Detailed Implementation

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0050] 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).

[0051] 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.

[0052] Example 1: This embodiment provides a network data adaptive acquisition method based on error detection, such as... Figure 1 As shown, it includes: S1. Periodically acquire power distribution network data based on the current acquisition frequency; S2. Perform hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data to obtain error detection results; S3. When a network error is determined to exist based on the error detection result, a frequency adjustment action is performed according to the error detection result: Based on a preset sliding time window and the error detection results, error statistics are performed on the distribution network data to obtain the number of errors within the preset sliding time window; The severity of errors in the power distribution network data is obtained based on the number of errors and a preset anomaly threshold. When the error severity is determined to meet the preset frequency adjustment conditions, the current acquisition frequency is adjusted according to the preset frequency adjustment parameters to obtain the target acquisition frequency; The current acquisition frequency is updated to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency.

[0053] In the above scheme, firstly, distribution network data is acquired periodically based on the current acquisition frequency to ensure the continuity of distribution network data acquisition. Then, layered error detection is carried out based on a preset multi-error detection algorithm to achieve multi-dimensional detection of network errors and ensure the comprehensiveness of error detection. Furthermore, frequency adjustment is triggered only when a network error is detected, avoiding meaningless frequency adjustment operations and useless operating conditions. Error statistics are performed on the error detection results through a preset sliding time window to concisely and effectively quantify the occurrence of errors within a preset time period. Combined with a preset abnormal threshold, the severity of errors is obtained, providing a clear basis for frequency adjustment. When the frequency adjustment conditions are met, the current acquisition frequency is adjusted to obtain the target acquisition frequency. The acquisition frequency is then updated to carry out subsequent data acquisition, so that the acquisition frequency can dynamically and adaptively change with the network error status of the distribution network. This abandons the fixed frequency acquisition mode and realizes adaptive acquisition of network data, making the data acquisition behavior compatible with the actual operating state of the distribution network.

[0054] In practical implementation, the distribution communication network operates in a complex environment, requiring the collection and monitoring of network data to promptly detect and address network errors. In the field of distribution communication network operation monitoring and data acquisition, existing systems typically collect and analyze network data using a fixed period or frequency. However, in most cases, the data from the distribution communication network is normal, and fixed-frequency collection wastes network bandwidth and computing resources. Furthermore, when sudden network errors occur, fixed frequencies may miss the detection of erroneous data. Therefore, it is necessary to dynamically adjust the frequency of distribution communication network data acquisition based on actual conditions, achieving adaptive adjustment of the data acquisition frequency according to error type, error weight, and other factors.

[0055] In the specific implementation process, when periodically acquiring distribution network data based on the current acquisition frequency, the distribution network data acquired in this embodiment includes the acquisition of network transmission data units and their verification fields, i.e., network data packets (Ethernet frames, IP packets, transport layer segments, distribution communication protocol messages, etc.). The acquired data is used to perform subsequent parity checks, CRC cyclic redundancy checks, and Hamming code detection to obtain the error types and error frequencies that occur during data transmission, and to dynamically adjust the acquisition frequency of subsequent network data based on the error detection results.

[0056] Optionally, the preset multi-error detection algorithm includes a parity check algorithm, a cyclic redundancy check algorithm, and a Hamming code check algorithm; step S2 includes: Single-bit error detection is performed based on the aforementioned power distribution network data and parity check algorithm to obtain parity detection results; Based on the power distribution network data and the cyclic redundancy check algorithm, sudden error detection is performed to obtain the sudden error detection results. Error location detection and correction detection are performed based on the aforementioned power distribution network data and Hamming code verification algorithm to obtain error location detection results; Error detection results are generated based on the odd / even detection results, sudden error detection results, and error location detection results.

[0057] In the above scheme, this embodiment uses parity check algorithm, cyclic redundancy check algorithm, and Hamming code check algorithm as preset multi-error detection algorithms to carry out single-bit error detection, burst error detection, error location detection, and correction detection respectively. The three types of algorithms perform their respective functions to achieve accurate identification of different types of network errors. Then, the detection results of each algorithm are integrated to form the final error detection result, so that the error detection result fully covers all types of error in the data transmission process of the distribution network, and realizes error location and corrective detection, thereby improving the accuracy and completeness of hierarchical error detection.

[0058] In the specific implementation process, various different error detection algorithms can be configured to perform error detection on network data to obtain error detection data such as the types of errors that occur during data transmission, and obtain error detection results. Multiple error detection algorithms are preset to support configurable error detection algorithms. In this embodiment, parity check, CRC check and Hamming code check are supported by default. At the same time, new check algorithms can be manually configured according to the needs of the scenario.

[0059] Specifically, parity checking is used for rapid detection of single-bit errors in distribution network data. Its main function is to perceive whether bit-level errors occur during data transmission with extremely low computational complexity, providing a fast, coarse-grained error detection capability for network operation status. In this embodiment, parity checking, as a high-frequency, lightweight error detection method, is used to quickly determine whether there are any abnormal signs in the current network data, and serves as a preliminary basis for whether further fine-tuning or increasing the acquisition frequency is needed. The input is the binary bit sequence of the network data units contained in the acquired distribution network data, and the output parity detection result is a verification result flag, indicating whether the verification passed or failed. When the verification result is passed, it indicates that no single-bit error was detected in the detected data unit under the current verification rules; when the verification result is failed, it indicates that at least one bit error exists in the data unit. In this process, parity checking cannot locate the error position, nor can it detect errors where an even number of bits occur simultaneously. Its conclusion is mainly used for rapid anomaly detection, rather than precise error correction, to achieve single-bit error detection.

[0060] Cyclic Redundancy Check (CRC) algorithm is used for high-reliability detection of burst errors and multi-bit errors in network data. Its main function is to identify consecutive bit errors caused by link interference, equipment malfunctions, etc., and it is one of the most widely used error detection methods in network communication. In this embodiment, the CRC algorithm is used to further verify data with abnormal trends detected by parity check, thereby improving the accuracy of error detection and assisting in judging the severity and persistence of network errors. The input is the bit sequence of network data units contained in the collected distribution network data, and the output burst error detection result is the result of verification and comparison based on the recalculated CRC check value, used to indicate whether the verification is consistent or inconsistent. When the verification results are consistent, it indicates that no burst or multi-bit errors were detected under the current verification model; when the verification results are inconsistent, it indicates that a burst or multi-bit error occurred during data transmission. In this process, CRC can detect multiple error types with a high probability, but it does not have the ability to locate and correct errors. Its conclusion is used to judge the network link quality and whether there is continuous interference, thus realizing burst error detection.

[0061] Hamming code verification algorithms are used to detect and locate correctable bit errors in network data. Their main function is to determine whether an error is within a correctable range while detecting it, thus providing a more refined basis for network data reliability assessment. In this embodiment, the Hamming code verification algorithm is used to deeply analyze the detected error data to distinguish between minor disturbances and severe anomalies, providing a more discriminative decision-making basis for frequency adjustment. The input consists of network data blocks contained in the collected distribution network data and redundant check bits inserted according to Hamming code rules. The output includes a check result vector, error location indication information, and error correctability judgment results. When the check result is a zero vector, it indicates that no bit error was detected; when the check result indicates a single error location, it indicates the existence of a correctable single-bit error; when the check result cannot locate a unique error location, it indicates the existence of an uncorrectable multi-bit error. In this process, Hamming codes provide error location and correctability judgment, and the conclusions are used to assess the severity of the error and serve as an important basis for entering high-frequency acquisition and key monitoring modes, thus realizing error location detection and correction detection.

[0062] In practical applications, the parity check, cyclic redundancy check, and Hamming code used in this embodiment are respectively used for rapid anomaly detection, sudden error detection, and error location and correctability judgment. The three work together to form a hierarchical error detection mechanism, providing a reliable basis for adaptive adjustment of network data acquisition frequency.

[0063] Optionally, after step S2, the method further includes: when it is determined that there is no network error based on the error detection result, returning to the process of periodically acquiring distribution network data based on the current acquisition frequency.

[0064] In the above scheme, after the hierarchical error detection step, if it is determined that there is no network error, it directly returns to the step of periodically acquiring distribution network data based on the current acquisition frequency. There is no need to perform a series of operations such as error statistics, error severity determination, and frequency adjustment. This simplifies the method execution process in the absence of network errors, reduces unnecessary calculation and operation steps in the system, reduces the system's computing resource consumption, and keeps the current acquisition frequency unchanged. This avoids unnecessary adjustment of the acquisition frequency in the stable and error-free state of the distribution network, ensuring the stability and continuity of data acquisition in a stable state.

[0065] In the specific implementation process, error assessment and decision-making are performed on network errors based on error detection results. A comprehensive assessment of network errors is conducted based on error type, frequency, and severity to determine whether data acquisition frequency adjustment is necessary. If adjustment is required, frequency adjustment actions are executed. If no adjustment is needed, network data errors in the distribution network data are directly stored, visualized, alarmed, and analyzed based on the error detection results. The results of parity checks, CRC checks, and Hamming code checks are statistically analyzed according to verification methods, stored in a database, and visualized on the monitoring platform. Simultaneously, error types are pushed to designated terminals for alarm purposes. Based on the statistical error verification results, the network condition during the specified time period is analyzed, and future network conditions are predicted based on the analysis results.

[0066] Optionally, obtaining the error severity of the distribution network data based on the number of errors and a preset anomaly threshold includes: The preset abnormal threshold includes a first abnormal preset threshold and a second abnormal preset threshold, wherein the first abnormal preset threshold is less than the second abnormal preset threshold; When the number of errors is less than the first preset threshold for anomalies, the error severity of the power distribution network data is determined to be no anomaly. When the number of errors is not less than the first preset threshold for anomalies and the number of errors is less than the second preset threshold for anomalies, the severity of the error in the distribution network data is determined to be a minor anomaly. When the number of errors is not less than the second preset threshold for anomalies, the severity of the errors in the power distribution network data is determined to be a serious anomaly.

[0067] In the above solution, based on obtaining the error severity by the number of errors and the preset anomaly threshold, the preset anomaly threshold is divided into a first anomaly preset threshold and a second anomaly preset threshold, and it is set that the first anomaly preset threshold is less than the second anomaly preset threshold. According to the comparison results of the number of errors with the two types of thresholds, the error severity is divided into three levels: no anomaly, mild anomaly, and severe anomaly, converting the abstract error state of the distribution network into specific grade indicators that can be quantified and clearly determined, making the determination criteria for error severity clearer and more unified, avoiding subjectivity in the determination process, providing a specific and executable grading basis for the subsequent determination of whether it meets the preset frequency regulation conditions, and making the triggering determination of frequency regulation more targeted.

[0068] In the specific implementation process, this embodiment uses a preset sliding time window combined with the error detection results to perform error statistics on the distribution network network data, and calculates the number of errors N within a preset sliding time window. Two thresholds are preset according to the specific application scenario: the first anomaly preset threshold Th1 and the second anomaly preset threshold Th2, which are used to judge the severity of the error: when N < Th1, it is determined that the error severity of the distribution network network data is no anomaly; when Th1 ≤ N < Th2, it is determined that the error severity of the distribution network network data is a mild anomaly; when Th2 ≤ N, it is determined that the error severity of the distribution network network data is a severe anomaly (or severe error), thereby realizing dynamically adjusting the acquisition frequency according to the severity of the error.

[0069] Optionally, after obtaining the error severity of the distribution network network data based on the number of errors and the preset anomaly threshold, it further includes: When the error severity is no anomaly, it is determined that it does not meet the preset frequency regulation conditions based on the error severity, and at this time, it returns to periodically obtain the distribution network network data based on the current acquisition frequency.

[0070] In the above solution, when it is determined that the error severity is no anomaly, it is directly determined that it does not meet the preset frequency regulation conditions and returns to the step of periodically obtaining the distribution network network data based on the current acquisition frequency, further simplifying the method execution process in the case of no network error, avoiding the ineffective operation of the system, and at the same time maintaining the current acquisition frequency without adjustment. In the case of a stable and error-free distribution network, it avoids unnecessary frequency adjustment operations, reduces resource waste, and at the same time ensures the continuity and stability of the distribution network network data acquisition in the stable state.

[0071] Optionally, when it is determined that it meets the preset frequency regulation conditions based on the error severity, adjusting the current acquisition frequency according to the preset frequency adjustment parameter to obtain the target acquisition frequency includes: The preset frequency adjustment parameter includes a preset mild anomaly adjustment parameter; When the severity of the error is mild, it is determined that the preset first frequency adjustment condition is met in the preset frequency adjustment conditions. At this time, the current acquisition frequency is dynamically adjusted proportionally based on the preset mild error adjustment parameters to obtain the target acquisition frequency.

[0072] In the above scheme, for minor anomaly error states, a preset minor anomaly adjustment parameter is used as the basis for frequency adjustment. It is determined that the preset first frequency adjustment condition is met, and the current acquisition frequency is dynamically proportionally adjusted based on this parameter. This allows the acquisition frequency adjustment to accurately adapt to the minor anomaly network error state of the distribution network, avoiding drastic changes in the acquisition frequency caused by over-adjustment. At the same time, the dynamic proportional adjustment method makes the frequency adjustment more flexible and can adapt to different minor anomaly application scenarios according to the preset parameters. This ensures the acquisition of distribution network data under minor anomaly conditions, saving network and computing resources while maintaining basic error detection accuracy.

[0073] Optionally, when the error severity determines that a preset frequency adjustment condition is met, adjusting the current acquisition frequency according to preset frequency adjustment parameters to obtain the target acquisition frequency includes: The preset frequency adjustment parameters include preset severe anomaly adjustment parameters; When the severity of the error is severe abnormal, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met. At this time, the current acquisition frequency is dynamically proportionally adjusted based on the preset severe abnormality adjustment parameter to obtain the target acquisition frequency.

[0074] In the above scheme, for severe abnormal error states, a preset severe abnormality adjustment parameter is used as the basis for frequency adjustment. It is determined that the preset second frequency adjustment condition is met, and the current acquisition frequency is dynamically proportionally adjusted based on this parameter. This allows the acquisition frequency adjustment to accurately match the severe abnormal network error state of the distribution network. By adjusting the ratio, the data acquisition frequency can be quickly increased, ensuring that the data acquisition frequency can be increased in the case of high network error incidence, accurately capturing more network error data, and providing sufficient and effective data sources for the location and handling of distribution network faults. At the same time, the dynamic proportional adjustment method can adapt to different severe abnormality scenarios, making the frequency adjustment more targeted.

[0075] In the specific implementation process, when the error severity of the distribution network data is determined to be no abnormality, it is determined based on the error severity that the preset frequency adjustment condition is not met. At this time, the acquisition frequency remains unchanged, that is, the target acquisition frequency f is set to be equal to the current acquisition frequency f0, i.e., f = f0. The specific implementation process of dynamic proportional adjustment includes: when the error severity of the distribution network data is determined to be slightly abnormal, it is determined to meet the preset first frequency adjustment condition in the preset frequency adjustment condition. At this time, it is set to... (The preset mild anomaly adjustment parameter α < 1, which can be dynamically configured); when the error severity of the distribution network data is determined to be a severe error, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met, and at this time, let (The preset severe abnormality adjustment parameter β>1 can be dynamically configured), where f0 represents the current acquisition frequency. Before starting, the system will set a basic acquisition frequency as the current acquisition frequency.

[0076] In the specific implementation process, if the error severity of the distribution network data is determined to be no abnormality, then based on the error severity, it is determined that the preset frequency adjustment conditions are not met. At this time, the following process can be directly performed: Based on the error detection results, the network data errors in the distribution network data are stored, visualized, alarmed, and analyzed. The results of parity check, CRC check, and Hamming code check are statistically analyzed according to the verification method, stored in the database, and visualized on the monitoring platform; at the same time, the error type is pushed to the designated terminal for alarm. Based on the statistical error verification results, the network condition during this period is analyzed, and the future network condition is predicted based on the analysis results.

[0077] Optionally, updating the current acquisition frequency to the target acquisition frequency to obtain current distribution network data based on the target acquisition frequency includes: The current acquisition frequency is progressively updated based on the target acquisition frequency and a preset progressive method until the current acquisition frequency is updated to the target acquisition frequency. The current power distribution network data is acquired based on the target acquisition frequency to achieve adaptive acquisition of network data.

[0078] In the above scheme, the current acquisition frequency is updated gradually based on the target acquisition frequency and a preset gradual method, rather than jumping directly to the target acquisition frequency. This avoids drastic oscillations in the acquisition frequency, ensures the stability of the data acquisition frequency adjustment in the distribution network, prevents instability in the acquisition system due to sudden frequency changes, and allows the acquisition frequency to gradually adapt to the actual network error state of the distribution network. This makes the frequency adjustment more in line with the dynamic changes in the network, further improving the rationality of adaptive acquisition of network data and the stability of system operation.

[0079] In practice, the frequency of data acquisition is updated gradually to avoid oscillations. For example, assuming the current acquisition frequency f0 of the distribution network data is 1 time / second, after error detection and evaluation, the network is determined to be in a severely abnormal state, and the target acquisition frequency f is calculated to be 5 times / second, the preset gradual method is to increase the acquisition frequency by a fixed step size of 1 time / second each time, and the time interval between each step adjustment is 10 seconds.

[0080] In this embodiment, the specific execution process of progressively updating the current acquisition frequency based on the target acquisition frequency and a preset progressive method is as follows: In the initial stage, the acquisition system acquires distribution network data at the current acquisition frequency f0 = 1 time / second. After the first frequency update is triggered, the frequency is increased to 2 times / second in steps of 1 time / second, and this frequency is maintained for 10 seconds. After 10 seconds, the acquisition frequency is increased from 2 times / second to 3 times / second in steps of the preset step, and this frequency is maintained for 10 seconds. After another 10 seconds, the acquisition frequency is increased from 3 times / second to 4 times / second, and this frequency is maintained for 10 seconds. Finally, the acquisition frequency is increased from 4 times / second to the target acquisition frequency of 5 times / second in steps, completing the progressive frequency update. Subsequently, the current distribution network data is acquired at the updated target acquisition frequency of 5 times / second to achieve adaptive acquisition of network data, until subsequent error detection results determine a change in network status and trigger a new round of frequency adjustment.

[0081] If the network is determined to be slightly abnormal, the current sampling frequency f0 is 5 times / second, the target sampling frequency ftarget is 2 times / second, the preset gradual method is to reduce the sampling frequency in a fixed step of 1 time / second each time, with an adjustment time interval of 10 seconds. Then, using the same gradual logic, the frequency is gradually reduced from 5 times / second to 4 times / second, 3 times / second, and finally to 2 times / second, and then data is collected at the target frequency.

[0082] Optionally, before updating the current acquisition frequency to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency, the method further includes: When the target acquisition frequency is greater than the preset maximum acquisition frequency protection threshold, the preset maximum acquisition frequency protection threshold is used as the target acquisition frequency. When the target acquisition frequency is less than the preset minimum acquisition frequency protection threshold, the preset minimum acquisition frequency protection threshold is used as the target acquisition frequency.

[0083] In the above scheme, the target acquisition frequency is limited by setting maximum and minimum acquisition frequency protection thresholds. When the target acquisition frequency exceeds the threshold range, the corresponding protection threshold is used as the target acquisition frequency for subsequent updates. This avoids excessive consumption of network bandwidth and computing resources due to excessively high target acquisition frequencies, while preventing the problem of missed network error data and insufficient accuracy in anomaly capture due to excessively low target acquisition frequencies. It sets reasonable adjustment boundaries for the acquisition frequency, ensuring that the dynamic adjustment of the acquisition frequency is always within a controllable range, taking into account both resource utilization efficiency and the need for anomaly data capture, further optimizing the effect of adaptive network data acquisition, and ensuring the effectiveness of distribution network monitoring.

[0084] Optional, also includes: Based on the error detection results, the error type and the corresponding terminal in the distribution network data are obtained, and the error type is transmitted to the corresponding terminal for error alarm.

[0085] In the above solution, the error type and corresponding terminal of the distribution network data are extracted from the error detection results, and the error type is transmitted to the corresponding terminal for error alarm. This enables distribution network operation and maintenance personnel to know the terminal location and specific error type of the error in a timely manner, realizing the precision and targeting of network error alarms, avoiding indiscriminate global alarms, improving the response efficiency of operation and maintenance personnel to distribution network errors, and facilitating the rapid location of the terminal where the error occurred, thus saving time for subsequent network fault handling.

[0086] Optional, also includes: Based on the error detection results, obtain the error network data from the distribution network data; The error network data and error detection results are visualized to obtain visualized error monitoring results, and the visualized error monitoring results are transmitted to the visualized monitoring platform.

[0087] In the above solution, by visualizing the error network data and error detection results and transmitting them to the visualization monitoring platform, the abstract error data and detection results are transformed into an intuitive visualization form. This allows maintenance personnel to clearly and intuitively grasp the network error situation of the distribution network without having to perform tedious analysis of the original error data, thus improving the efficiency of network error status perception. At the same time, transmitting the visualized error monitoring results to the monitoring platform enables centralized management and display of error information, facilitating maintenance personnel to conduct global network error monitoring and analysis of the distribution network.

[0088] In the specific implementation process, network data errors in the distribution network data are stored, visualized, alarmed, and analyzed based on the error detection results. The results of parity checks, CRC checks, and Hamming code checks are statistically analyzed according to the verification methods and stored in a database. The erroneous network data and error detection results are visualized to obtain visualized error monitoring results for display on the monitoring platform. Simultaneously, the error type is pushed to the designated terminal (i.e., the terminal corresponding to the error) for alarm purposes. Furthermore, based on the statistical error detection results, the network condition during the specified time period can be analyzed, and future network conditions can be predicted based on the analysis results.

[0089] This embodiment provides an adaptive network data acquisition method based on error detection. By periodically acquiring distribution network data and employing parity check, cyclic redundancy check, and Hamming code verification algorithms for hierarchical error detection, it achieves comprehensive and accurate identification of various network errors in the distribution network, providing reliable error data support for subsequent frequency regulation. The frequency regulation process is triggered only when a network error is detected; otherwise, it directly returns to the acquisition step, significantly reducing invalid system operations and lowering computing resource consumption. By using a sliding time window to count the number of errors and combining dual preset anomaly thresholds, the severity of errors is divided into three levels: no anomaly, minor anomaly, and severe anomaly. This provides a quantitative and unified standard for error judgment, offering a clear and targeted triggering basis for frequency regulation. Dedicated adjustment parameters are configured for different anomaly levels to dynamically adjust the frequency proportionally. A gradual approach is used to update the acquisition frequency, and maximum and minimum acquisition frequency protection thresholds are set. This avoids drastic oscillations in the acquisition frequency, ensuring the smoothness of frequency adjustment and system stability. It also defines reasonable adjustment boundaries for the acquisition frequency, preventing excessive consumption of network and computing resources due to excessively high frequencies and missed acquisition of critical anomaly data due to excessively low frequencies. Meanwhile, the method pushes error types to the corresponding terminals for precise alarms and visualizes the error data and detection results before transmitting them to the monitoring platform. This enables maintenance personnel to quickly locate the error and intuitively understand the network error situation, improving the response efficiency of network error handling. Overall, the method achieves dynamic adaptive adjustment of the data acquisition frequency of the distribution network according to the network error state, completely abandoning the traditional fixed-frequency acquisition mode. It breaks down the barriers between error detection results and acquisition decisions, achieving deep integration of error detection and network monitoring. When the network is stable, it reduces redundant data acquisition and saves various resources; when the network is abnormal, it increases the acquisition frequency and accurately captures error data. This effectively solves the problems of low resource utilization efficiency and insufficient anomaly capture accuracy in traditional acquisition methods, significantly improving the timeliness and accuracy of anomaly detection and fault location, and meeting the actual needs of distribution communication networks for refined and intelligent perception of network operation status.

[0090] Example 2: This embodiment provides a network data adaptive acquisition system based on error detection, used to implement the aforementioned network data adaptive acquisition method based on error detection, including: a network data acquisition module, an error detection module, and a frequency dynamic adjustment module, wherein: The network data acquisition module is used to periodically acquire power distribution network data based on the current acquisition frequency; The error detection module is used to perform hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data, and to obtain error detection results; The frequency dynamic adjustment module is used to perform frequency adjustment actions based on the error detection results when a network error is determined to exist: it performs error statistics on the distribution network data based on a preset sliding time window and the error detection results to obtain the number of errors within the preset sliding time window; it obtains the error severity of the distribution network data based on the number of errors and a preset anomaly threshold; when the error severity determines that a preset frequency adjustment condition is met, it adjusts the current acquisition frequency according to preset frequency adjustment parameters to obtain a target acquisition frequency; and it updates the current acquisition frequency to the target acquisition frequency so that the network data acquisition module acquires the current distribution network data based on the target acquisition frequency.

[0091] This embodiment provides a network data adaptive acquisition system based on error detection. The system consists of a network data acquisition module, an error detection module, and a frequency dynamic adjustment module. Each module performs core actions in the acquisition method, such as data acquisition, hierarchical error detection, error judgment, and frequency adjustment. The modular functional division clarifies the boundaries of each part of the system, facilitating independent development, maintenance, and upgrades of each module. At the same time, the modules work together to execute each step of the acquisition method accurately and orderly, ensuring that the network data adaptive acquisition method based on error detection can be stably and efficiently implemented. The collaborative operation between modules also reduces the connection loss between functional parts and improves the overall operating efficiency of the system.

[0092] Optionally, the preset multi-error detection algorithm includes a parity check algorithm, a cyclic redundancy check algorithm, and a Hamming code check algorithm; The error detection module is used to perform hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data, and to obtain error detection results, including: Single-bit error detection is performed based on the aforementioned power distribution network data and parity check algorithm to obtain parity detection results; Based on the power distribution network data and the cyclic redundancy check algorithm, sudden error detection is performed to obtain the sudden error detection results. Error location detection and correction detection are performed based on the aforementioned power distribution network data and Hamming code verification algorithm to obtain error location detection results; Error detection results are generated based on the odd / even detection results, sudden error detection results, and error location detection results.

[0093] Optionally, the frequency dynamic adjustment module is further configured to, after performing hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data to obtain error detection results: when it is determined based on the error detection results that there are no network errors, return to the process of periodically acquiring distribution network data based on the current acquisition frequency.

[0094] Optionally, the frequency dynamic adjustment module is used to obtain the error severity of the distribution network data based on the number of errors and a preset anomaly threshold, including: The preset abnormal threshold includes a first abnormal preset threshold and a second abnormal preset threshold, wherein the first abnormal preset threshold is less than the second abnormal preset threshold; When the number of errors is less than the first preset threshold for anomalies, the error severity of the power distribution network data is determined to be no anomaly. When the number of errors is not less than the first preset threshold for anomalies and the number of errors is less than the second preset threshold for anomalies, the severity of the error in the distribution network data is determined to be a minor anomaly. When the number of errors is not less than the second preset threshold for anomalies, the severity of the errors in the power distribution network data is determined to be a serious anomaly.

[0095] Optionally, the frequency dynamic adjustment module is further configured to, after obtaining the error severity of the distribution network data based on the number of errors and a preset anomaly threshold: When the error severity is no abnormality, it is determined that the preset frequency adjustment conditions are not met based on the error severity. At this time, the process returns to the point of periodically acquiring distribution network data based on the current acquisition frequency.

[0096] Optionally, the frequency dynamic adjustment module is used to adjust the current acquisition frequency according to preset frequency adjustment parameters to obtain the target acquisition frequency when the error severity is determined to meet the preset frequency adjustment conditions. This includes: The preset frequency adjustment parameters include preset mild abnormality adjustment parameters; When the severity of the error is mild, it is determined that the preset first frequency adjustment condition is met in the preset frequency adjustment conditions. At this time, the current acquisition frequency is dynamically adjusted proportionally based on the preset mild error adjustment parameters to obtain the target acquisition frequency.

[0097] Optionally, the frequency dynamic adjustment module is used to adjust the current acquisition frequency according to preset frequency adjustment parameters to obtain the target acquisition frequency when the error severity is determined to meet the preset frequency adjustment conditions. This includes: The preset frequency adjustment parameters include preset severe anomaly adjustment parameters; When the severity of the error is severe abnormal, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met. At this time, the current acquisition frequency is dynamically proportionally adjusted based on the preset severe abnormality adjustment parameter to obtain the target acquisition frequency.

[0098] Optionally, the frequency dynamic adjustment module is used to update the current acquisition frequency to the target acquisition frequency so as to obtain the current distribution network data based on the target acquisition frequency, including: The current acquisition frequency is progressively updated based on the target acquisition frequency and a preset progressive method until the current acquisition frequency is updated to the target acquisition frequency. This enables the network data acquisition module to obtain current distribution network data based on the target acquisition frequency, thereby achieving adaptive acquisition of network data.

[0099] Optionally, before enabling the network data acquisition module to acquire current distribution network data based on the target acquisition frequency and achieve adaptive acquisition of network data, the frequency dynamic adjustment module is further configured to: When the target acquisition frequency is greater than the preset maximum acquisition frequency protection threshold, the preset maximum acquisition frequency protection threshold is used as the target acquisition frequency. When the target acquisition frequency is less than the preset minimum acquisition frequency protection threshold, the preset minimum acquisition frequency protection threshold is used as the target acquisition frequency.

[0100] Optionally, an error alarm module is also included, for: Based on the error detection results, the error type and the corresponding terminal in the distribution network data are obtained, and the error type is transmitted to the corresponding terminal for error alarm.

[0101] Optionally, a visualization module is also included for: Based on the error detection results, obtain the error network data from the distribution network data; The error network data and error detection results are visualized to obtain visualized error monitoring results, and the visualized error monitoring results are transmitted to the visualized monitoring platform.

[0102] In the field of power distribution communication network operation monitoring and data acquisition, existing systems typically use fixed periods or fixed frequencies to collect and analyze network data. While this fixed-frequency acquisition method has advantages such as simplicity and low deployment costs, it has significant shortcomings in practical applications. Maintaining high-frequency acquisition for extended periods not only consumes substantial network bandwidth and system computing resources but also puts continuous pressure on backend storage systems and analysis platforms, resulting in low resource utilization efficiency. When network interference, attacks, or equipment failures occur, the probability of data errors increases significantly. If monitoring continues at the original lower acquisition frequency, critical abnormal data may be missed. Network errors exhibit distinct phased and regional characteristics. The fixed-frequency acquisition mechanism cannot fully utilize error detection results to guide acquisition behavior, nor can it adaptively adjust when network conditions change. This leads to significant resource waste when the network is stable and a lack of sufficient monitoring accuracy when the network is abnormal, making it difficult to meet the actual needs for refined and intelligent perception of network operation status.

[0103] This embodiment provides a method and system for adaptive network data acquisition based on error detection. The acquisition frequency can be dynamically adjusted according to actual conditions. When the network condition is good and errors are few, the acquisition frequency is reduced to minimize the consumption of network and computing resources. When the network condition deteriorates, the acquisition frequency is increased to avoid misinterpreting critical network error data. Simultaneously, a configurable error detection algorithm is set. Based on the actual conditions of the region and network, detection and statistical algorithms suitable for the site conditions can be manually configured to calculate algorithms that better suit the power distribution network environment. This makes the adjustment of the data acquisition frequency more practical, improving the timeliness and accuracy of anomaly detection and fault location.

[0104] Specifically, this embodiment establishes an adaptive acquisition mechanism driven by error detection, abandoning the traditional fixed-frequency data acquisition mode. It routinely performs error detection operations such as parity checking, cyclic redundancy check, and Hamming code checking on distribution network communication data to perceive the error occurrence status during network data transmission in real time. Based on key information such as the detected error type and frequency, the system dynamically adjusts the data acquisition frequency. When the network data transmission status is stable and the error occurrence rate is below a preset threshold, the system automatically lowers the acquisition frequency to reduce redundant data acquisition. When an increase in the network error occurrence frequency or a change in the error type is detected, the system automatically raises the acquisition frequency and switches to a refined monitoring mode, thereby achieving adaptive matching between the acquisition strategy and the actual network operating status. Simultaneously, this embodiment adds an error perception module to the network data acquisition system, standardizing and mapping various error detection results to generate a unified error perception index. This index serves as the core decision-making basis for adjusting the acquisition frequency, completely breaking down the technical barriers between the underlying error detection mechanism and the upper-level network data acquisition decision-making, thus overcoming the technical barriers. This embodiment addresses the disconnect between error detection results and data acquisition strategy decisions, achieving deep integration and coordinated control between error detection and network monitoring. Furthermore, it dynamically adjusts the acquisition frequency based on the actual network operating status. During stable network operation, it proactively reduces the data acquisition frequency, significantly decreasing the scale of invalid data collection and effectively reducing the occupancy of network bandwidth, computing resources, and storage resources. During periods of network anomalies, it promptly increases the data acquisition frequency to ensure comprehensive and complete capture of critical network anomaly data. This allows for dynamic optimization and on-demand adjustment of network monitoring accuracy without increasing the overall system load, significantly improving the overall resource utilization efficiency of the network monitoring system. Simultaneously, during periods of high network error incidence, this embodiment automatically increases the data acquisition granularity, recording the entire evolution process of network errors completely. This allows for more accurate characterization of the time boundaries and development trends of network anomalies, providing high-quality, high-value data source support for subsequent network fault analysis, network risk assessment, and proactive network security defense decisions, effectively improving the timeliness and accuracy of network anomaly detection and fault location. In summary, this embodiment addresses the technical pain points of existing network data acquisition and monitoring systems, such as rigid and inflexible acquisition strategies, disconnect between error detection results and acquisition decision-making processes, low system resource utilization efficiency, and insufficient accuracy in network anomaly capture. It proposes targeted technical solutions to ultimately achieve a new adaptive, efficient, and refined network data acquisition and monitoring model that adapts to dynamic changes in network operating status.

[0105] Example 3: This embodiment provides a network data adaptive acquisition method and system architecture based on error detection, such as... Figure 2As shown, it includes a network data acquisition module, an error detection module, a frequency dynamic adjustment module, and a network data monitoring and analysis system. The network data monitoring and analysis system includes an error alarm module and a visualization module. The frequency dynamic adjustment module includes an error assessment and decision unit and a frequency adaptive control unit. The network data acquisition module is used to periodically acquire power distribution network data from the power distribution communication network based on the current acquisition frequency and transmit it to the error detection module; The error detection module is used to perform hierarchical error detection based on preset multi-error detection algorithms and distribution network data, and obtain error detection results; when an error is determined based on the error detection results, the error assessment and decision unit is triggered to perform error assessment and decision. The error assessment decision unit is used to perform frequency adjustment actions based on the error detection results when a network error is determined to exist: it performs error statistics on the distribution network data based on a preset sliding time window and the error detection results to obtain the number of errors within the preset sliding time window; it obtains the error severity of the distribution network data based on the number of errors and a preset abnormality threshold; it determines whether the acquisition frequency needs to be adjusted based on the error severity, and if so, it triggers the frequency adaptive control unit to perform frequency adaptive adjustment, including frequency increase, frequency decrease, and frequency maintenance. The frequency adaptive control unit is used to adjust the current acquisition frequency according to the preset frequency adjustment parameters when the error severity is determined to meet the preset frequency adjustment conditions, and obtain the target acquisition frequency; and update the current acquisition frequency to the target acquisition frequency so that the network data acquisition module can obtain the current distribution network data based on the target acquisition frequency; The error alarm module is used to obtain the error type and the corresponding terminal in the distribution network data based on the error detection result, and transmit the error type to the corresponding terminal to perform an error alarm.

[0106] The visualization module is used to store, visualize, alarm, and analyze network data errors in the distribution network data based on error detection results. It statistically analyzes the results of parity checks, CRC checks, and Hamming code checks according to verification methods and stores them in a database. It also visualizes the erroneous network data and error detection results to obtain visualized error monitoring results for display on the monitoring platform. Based on the statistical error detection results, it analyzes the network condition during the specified time period and predicts future network conditions based on the analysis results.

[0107] The error detection module in this embodiment adopts the following... Figure 3The multi-error detection algorithm collaborative architecture shown in the figure performs single-bit error detection, burst error detection, and error location detection on the original distribution network data through the parity check unit, CRC check unit, and Hamming code check unit, respectively, to obtain the parity detection result, burst error detection result, and error location detection result. Then, the error type analysis, intensity analysis, and confidence analysis are performed by the error fusion analysis unit of the parity detection result, burst error detection result, and error location detection result, and the final required error detection result is output.

[0108] Example 4: Based on the above embodiment of the network data adaptive acquisition method based on error detection, 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 a network data adaptive acquisition method based on error detection according to any embodiment of the present invention.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 network data adaptive acquisition method based on error detection as described in any of the above-described method embodiments of the present invention.

[0113] 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.

[0114] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A network data adaptive acquisition method based on error detection, characterized in that, include: Data from the power distribution network is acquired periodically based on the current acquisition frequency. Layered error detection is performed based on a preset multi-error detection algorithm and the distribution network data to obtain error detection results; When a network error is determined to exist based on the error detection result, a frequency adjustment action is performed according to the error detection result: Based on a preset sliding time window and the error detection results, error statistics are performed on the distribution network data to obtain the number of errors within the preset sliding time window; The severity of errors in the power distribution network data is obtained based on the number of errors and a preset anomaly threshold. When the error severity is determined to meet the preset frequency adjustment conditions, the current acquisition frequency is adjusted according to the preset frequency adjustment parameters to obtain the target acquisition frequency; The current acquisition frequency is updated to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency.

2. The network data adaptive acquisition method based on error detection as described in claim 1, characterized in that, The preset multi-error detection algorithm includes parity check algorithm, cyclic redundancy check algorithm and Hamming code check algorithm; The step of performing hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data to obtain error detection results includes: Single-bit error detection is performed based on the aforementioned power distribution network data and parity check algorithm to obtain parity detection results; Based on the power distribution network data and the cyclic redundancy check algorithm, sudden error detection is performed to obtain the sudden error detection results. Error location detection and correction detection are performed based on the aforementioned power distribution network data and Hamming code verification algorithm to obtain error location detection results; Error detection results are generated based on the odd / even detection results, sudden error detection results, and error location detection results.

3. The network data adaptive acquisition method based on error detection as described in claim 1, characterized in that, After performing hierarchical error detection based on the preset multi-error detection algorithm and the distribution network data to obtain the error detection result, the method further includes: when it is determined based on the error detection result that there is no network error, returning to the process of periodically acquiring distribution network data based on the current acquisition frequency.

4. The network data adaptive acquisition method based on error detection as described in claim 1, characterized in that, The method of obtaining the error severity of the distribution network data based on the number of errors and a preset anomaly threshold includes: The preset abnormal threshold includes a first abnormal preset threshold and a second abnormal preset threshold, wherein the first abnormal preset threshold is less than the second abnormal preset threshold; When the number of errors is less than the first preset threshold for anomalies, the error severity of the power distribution network data is determined to be no anomaly. When the number of errors is not less than the first preset threshold for anomalies and the number of errors is less than the second preset threshold for anomalies, the severity of the error in the distribution network data is determined to be a minor anomaly. When the number of errors is not less than the second preset threshold for anomalies, the severity of the errors in the power distribution network data is determined to be a serious anomaly.

5. The network data adaptive acquisition method based on error detection as described in claim 4, characterized in that, After obtaining the error severity of the distribution network data based on the number of errors and the preset anomaly threshold, the method further includes: When the error severity is no abnormality, it is determined that the preset frequency adjustment conditions are not met based on the error severity. At this time, the process returns to the point of periodically acquiring distribution network data based on the current acquisition frequency.

6. The network data adaptive acquisition method based on error detection as described in claim 4, characterized in that, When the error severity determines that a preset frequency adjustment condition is met, the current acquisition frequency is adjusted according to preset frequency adjustment parameters to obtain a target acquisition frequency, including: The preset frequency adjustment parameters include preset mild abnormality adjustment parameters; When the severity of the error is mild, it is determined that the preset first frequency adjustment condition is met in the preset frequency adjustment conditions. At this time, the current acquisition frequency is dynamically adjusted proportionally based on the preset mild error adjustment parameters to obtain the target acquisition frequency.

7. The network data adaptive acquisition method based on error detection as described in claim 4, characterized in that, When the error severity determines that a preset frequency adjustment condition is met, the current acquisition frequency is adjusted according to preset frequency adjustment parameters to obtain a target acquisition frequency, including: The preset frequency adjustment parameters include preset severe anomaly adjustment parameters; When the severity of the error is severe abnormal, it is determined that the preset second frequency adjustment condition in the preset frequency adjustment conditions is met. At this time, the current acquisition frequency is dynamically proportionally adjusted based on the preset severe abnormality adjustment parameter to obtain the target acquisition frequency.

8. The network data adaptive acquisition method based on error detection as described in claim 1, characterized in that, The step of updating the current acquisition frequency to the target acquisition frequency, so as to obtain the current distribution network data based on the target acquisition frequency, includes: The current acquisition frequency is progressively updated based on the target acquisition frequency and a preset progressive method until the current acquisition frequency is updated to the target acquisition frequency. The current power distribution network data is acquired based on the target acquisition frequency to achieve adaptive acquisition of network data.

9. The network data adaptive acquisition method based on error detection as described in claim 1, characterized in that, Before updating the current acquisition frequency to the target acquisition frequency to obtain the current distribution network data based on the target acquisition frequency, the method further includes: When the target acquisition frequency is greater than the preset maximum acquisition frequency protection threshold, the preset maximum acquisition frequency protection threshold is used as the target acquisition frequency. When the target acquisition frequency is less than the preset minimum acquisition frequency protection threshold, the preset minimum acquisition frequency protection threshold is used as the target acquisition frequency.

10. A network data adaptive acquisition system based on error detection, characterized in that, include: The module includes a network data acquisition module, an error detection module, and a frequency dynamic adjustment module, among which: The network data acquisition module is used to periodically acquire power distribution network data based on the current acquisition frequency; The error detection module is used to perform hierarchical error detection based on a preset multi-error detection algorithm and the distribution network data, and to obtain error detection results; The frequency dynamic adjustment module is used to perform frequency adjustment actions based on the error detection results when a network error is determined to exist: it performs error statistics on the distribution network data based on a preset sliding time window and the error detection results to obtain the number of errors within the preset sliding time window; it obtains the error severity of the distribution network data based on the number of errors and a preset anomaly threshold; when the error severity determines that a preset frequency adjustment condition is met, it adjusts the current acquisition frequency according to preset frequency adjustment parameters to obtain a target acquisition frequency; and it updates the current acquisition frequency to the target acquisition frequency so that the network data acquisition module acquires the current distribution network data based on the target acquisition frequency.

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