Freezing method for load record of intelligent flexible adjusting unit

Abstract

The invention discloses a freezing method for a load record of an intelligent flexible adjusting unit. According to the method, system configuration is analyzed and adjusted according to historical data, different scenes are rapidly adapted, and system adaptability and response efficiency are improved; the real-time current, voltage and electric energy quality are deeply analyzed, the load trend is predicted, and the adjustment accuracy and the intelligent decision-making capability are enhanced; recording intervals are automatically adjusted according to load changes, time periods and user characteristics, and fine control over data recording is achieved; non-critical data are intelligently identified and preferentially compressed, storage distribution is optimized, and the processing efficiency is improved; multi-format packaging is supported, and interoperability and data sharing convenience are improved; it is ensured that key data can be recorded outside an unconventional interval, response to abnormity and predictive maintenance is enhanced, and data integrity is ensured; through double verification, encryption and digital signature, the data security is ensured; efficient backup and disaster recovery are realized, data persistence and rapid recovery are ensured, and data security and service continuity are ensured.

Description

Technical Field

[0001] The invention relates to the field of power load management, and in particular to a method for freezing load records of an intelligent flexible regulation unit. Background Art

[0002] As the power system transforms towards intelligence and digitalization, new power load management has become a key link in promoting the development of energy Internet. As an important part of the power system, dedicated transformer users have complex and changeable load characteristics, covering multiple fields such as industry, commerce, and public facilities. This requires not only that the power system can accurately perceive the state of multiple loads in real time, but also that it must have the ability to efficiently adjust and interactively manage.

[0003] In the existing technology, load recording and management methods often lack flexibility and intelligence, and are difficult to adapt to the real-time and complexity of load changes. The data processing and storage efficiency is low, and they cannot meet the high standards of data security and persistence required by new power systems.

[0004] In response to the above problems, the present invention aims to provide an intelligent flexible regulation unit load recording method to achieve real-time monitoring and flexible management of dedicated transformer user loads, optimize storage resource utilization, ensure data integrity and security, and integrate cloud backup to cope with emergencies and improve the overall reliability and energy efficiency of the power system. Summary of the invention

[0005] The present invention provides a method for freezing load records of an intelligent flexible regulation unit, which can realize real-time monitoring and flexible management of special transformer user loads, optimize storage resource utilization, ensure data integrity and security, and integrate cloud backup to cope with emergencies, thereby improving the overall reliability and energy efficiency of the power system.

[0006] In order to achieve the above object, the present invention provides a method for freezing load records of an intelligent flexible regulation unit, the method comprising: Initialize the system, configure the software and hardware, set parameters, and complete joint debugging with the master station. Set dynamic recording interval adjustment thresholds and data processing parameters based on user needs or historical data; Collect load current, voltage, and power quality parameters in real time and conduct real-time data analysis; Dynamically adjust the recording interval according to the real-time analysis results, and automatically adjust the recording period based on the intelligent algorithm. When the load change exceeds the preset threshold, the recording period is shortened, and vice versa, to ensure the timeliness of data and efficient use of storage resources; It uses efficient algorithms to compress load data, manages storage space using a circular buffer, and automatically overwrites old data through a circular buffer to ensure redundant backup of new and critical data; Standardized data encapsulation: encapsulate the processed data into a unified format according to specific standards, using JSON or XML format for easy parsing, improving data interoperability and convenience; When an abnormality or predictive maintenance event is detected, the recording mechanism is triggered immediately without being constrained by regular intervals, ensuring the integrity of key events; Implement verification and encryption before and after data storage to ensure data integrity and security, and further strengthen data to prevent tampering through digital signature technology; Develop retention and backup strategies, redundantly store key data, integrate cloud synchronization mechanisms, implement remote data backup and disaster recovery, and ensure data persistence and high system reliability.

[0007] Preferably, the system initialization further includes: Based on an adaptive learning algorithm, the initial configuration parameters are dynamically optimized according to historical data.

[0008] Preferably, real-time data analysis is performed, specifically: Use machine learning algorithms to predict load change trends and improve the accuracy of dynamic adjustments.

[0009] Preferably, the recording interval is adjusted dynamically, specifically: Adopting multi-dimensional judgment logic, according to load change rate, time cycle and user load characteristics, the adjustment strategy is optimized to achieve more detailed interval control.

[0010] Preferably, an efficient algorithm is used to compress the load data, specifically: A multi-level compression algorithm is used to distinguish the importance of data and perform different degrees of compression, that is, important data fragments are intelligently identified and non-critical data is compressed first to optimize storage resource allocation.

[0011] Preferably, the standardized data encapsulation is specifically: Standardized data format generation supports multi-standard compatibility, including industry standard DL / T 645 protocol and international standard IEC 61850, and can automatically switch to different formats according to the requirements of the master station.

[0012] Preferably, the recording mechanism is specifically: Adopt a priority sorting mechanism to ensure that high-priority incidents are recorded and handled first.

[0013] Preferably, data integrity and security are ensured, specifically: Using double verification technology, primary verification ensures data integrity and advanced verification enhances security.

[0014] Preferably, a retention and backup strategy is formulated, specifically: Including automated data lifecycle management, regular review and optimization of storage resources.

[0015] Preferably, the cloud synchronization mechanism is integrated, specifically: Based on intelligent synchronization strategy, the synchronization speed is dynamically adjusted according to network conditions to ensure data transmission efficiency and system stability.

[0016] The embodiment of the present application discloses a method for freezing the load record of an intelligent flexible regulation unit. The method dynamically optimizes parameters through an adaptive learning algorithm, and adjusts the initial configuration according to historical data analysis, so that the system can quickly adapt to specific scenarios when it starts, improves the accuracy and response speed of load monitoring, and lays the foundation for subsequent intelligent adjustment; uses machine learning algorithms to conduct in-depth analysis of current, voltage and power quality parameters collected in real time, and predicts load change trends, which not only improves the accuracy of dynamic adjustment, but also provides strong data support for intelligent decision-making, and improves the intelligence level of load management; through multi-dimensional logical judgment, the intelligent algorithm automatically adjusts the recording cycle, and optimizes the strategy according to the load change rate, time period and user load characteristics, so as to achieve fine control of data recording, which not only ensures the timely capture of key data, but also reasonably utilizes storage resources; adopts a multi-level compression strategy, intelligently identifies and preferentially compresses non-critical data, optimizes storage allocation, and ensures the key data Redundant backup makes effective use of limited storage space and improves data processing efficiency; it automatically adapts to a variety of standard data encapsulation formats to ensure data interoperability and ease of parsing, facilitates data sharing and interaction between different systems, and enhances data value; it ensures priority recording in special events and can capture key data even outside of abnormal recording intervals, enhancing the system's responsiveness to abnormal or predictive maintenance and improving data integrity; through double verification and encryption processing combined with digital signature technology, a strict data security protection network is built to ensure data integrity and tamper-proof capabilities during storage and transmission, enhancing the credibility of the system; through automated data lifecycle management and cloud synchronization mechanisms, efficient data storage and backup are achieved, ensuring data persistence and the system's rapid recovery capabilities in the face of disasters, and improving the overall reliability and resilience of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 The present invention is a flow chart of a method for freezing load records of an intelligent flexible regulation unit. DETAILED DESCRIPTION

[0018] As described in the background technology, in the existing technology, load recording and management methods often lack flexibility and intelligence, are difficult to adapt to the real-time and complexity of load changes, have low data processing and storage efficiency, and cannot meet the high standards of new power systems for data security and persistence.

[0019] In order to solve the above problems, the embodiment of the present application proposes a method for freezing the load record of an intelligent flexible regulation unit. The solution can realize real-time monitoring and flexible management of the load of dedicated transformer users, optimize the utilization of storage resources, ensure the integrity and security of data, and integrate cloud backup to cope with emergencies and improve the overall reliability and energy efficiency of the power system.

[0020] like Figure 1 As shown, it is a flow chart of the freezing method of the load record of the intelligent flexible regulation unit. Before introducing the specific scheme, the concepts involved below are introduced: JSON (JavaScript Object Notation, a lightweight data exchange format): It is easy for people to read and write, and easy for machines to parse and generate. JSON is based on JavaScript's object literal syntax, but is independent of any language and is commonly used for data transmission between servers and browsers and between servers. JSON uses key-value pairs to represent data, supports strings, numbers, Boolean values, arrays, objects, null, and objects, etc., and is one of the most commonly used data exchange formats in modern network applications.

[0021] XML (Extensible Markup Language): used to store and transmit data, it defines a set of standard ways to mark structured data. XML tags are used to enclose data, and the content is described by tag names and attributes. The structure and relationship of data can be customized to make the data self-descriptive. XML is designed to be independent of software and hardware, and is widely used for data exchange, configuration file storage, configuration information description, etc. It provides a standardized cross-platform data expression and exchange method.

[0022] DL / T 645 (DL / T 64545, Multi-function Energy Meter Communication Protocol): is a communication protocol standard for China's power industry, mainly used for data transmission and communication of energy meters (electricity meters). DL / T is the code for "Electricity Industry Standard", where "T" stands for "Recommended" and " / " indicates "Electricity Industry". DL / T645 specifies the format, commands, data structure, timing number and process of data exchange between energy meters and master stations (such as data acquisition systems or management systems), ensuring that energy meter products from different manufacturers are compatible with the system and facilitating data collection and processing.

[0023] IEC 61850 (IEC 61850:2003 - Communication networks and systems for power utility automation): This standard was developed by the International Electrotechnical Commission (IEC) and is a comprehensive standard framework for power system automation communication networks and systems, with a particular focus on communication requirements in substation automation systems. The core purpose of the IEC 61850 standard is to unify and standardize the communication interfaces in substation automation systems to ensure that equipment from different manufacturers can be seamlessly integrated and interoperable. It defines an object-oriented communication model that uses a layered architecture to describe substation equipment, functions, and data, making information exchange efficient and consistent. The standard covers communication protocols from the physical layer to the application layer, including data encoding, message format, service model, logical device model, and data objects, ensuring the integrity and reliability of information transmission from sensors to control centers.

[0024] Specifically, the method comprises the following steps: Step S101, initialize the system, configure the software and hardware, set parameters, and complete joint debugging with the master station, and set the dynamic recording interval adjustment threshold and data processing parameters based on user needs or historical data.

[0025] As mentioned above, this step is the starting point of the load record freezing method of the entire intelligent regulation unit, which involves the comprehensive configuration and initialization settings of the system. It includes the basic configuration of hardware components (such as processors, memory, storage devices, communication modules, etc.), as well as the construction of the software environment. The system also needs to complete communication coordination with the main control station to ensure that data can be uploaded smoothly and instructions are issued correctly. On this basis, according to the specific needs of the user or the results of historical data analysis in the past, the dynamic adjustment threshold of the recording interval is set, which means that the system can automatically adjust the frequency of data recording according to the intensity of load changes to optimize resource utilization. At the same time, the data processing parameters are personalized, such as data compression level, encryption strength, etc., to meet the efficiency and security requirements of data processing in different scenarios.

[0026] For example, in a smart grid project, the intelligent regulation unit needs to record load data once a minute during peak power consumption periods, and once every half an hour during off-peak periods to save storage resources. During initialization, engineers will set a dynamic recording interval adjustment threshold based on this demand. For example, when the load changes by more than 5%, it is considered a high change rate, and the recording interval is shortened to 1 minute; below this value, the load change is considered to be gentle, and the recording interval is extended to half an hour. At the same time, according to historical data analysis, it is found that the load fluctuation is small at night, so the default setting is low-frequency recording mode, and high-frequency during the day, so as to achieve a balance between resources and real-time performance.

[0027] It should be noted that in order to further enhance the adaptability and flexibility of the system, an adaptive module of the machine learning algorithm can be added during the initialization phase, so that the system can automatically learn the grid load pattern at the initial stage of operation, and automatically generate the optimal recording interval adjustment strategy and data processing parameters based on real-time load behavior to reduce human intervention. For example, the system can analyze the load change pattern within a week and automatically adjust the recording interval to adapt to the different load characteristics of weekdays and weekends, thereby improving the pertinence and efficiency of data recording. In addition, the introduction of edge computing capabilities allows the intelligent adjustment unit to have preliminary data preprocessing capabilities and reduce the burden on the master station, all of which fall within the scope of protection of this application.

[0028] Step S102: collect load current, voltage, and power quality parameters in real time, and perform real-time data analysis.

[0029] As mentioned above, this step involves using advanced sensor technology and communication means to comprehensively monitor the real-time status of the power system. Load current and voltage are the basic parameters for the operation of the power system, which directly reflect the load condition and power transmission status of the system; power quality parameters, such as harmonic content, voltage fluctuation, frequency deviation, etc., are key indicators for evaluating the quality of power supply and system stability. The real-time data analysis part includes instant data collection, preliminary processing (such as filtering, correction), anomaly detection and preliminary diagnosis, with the aim of quickly identifying any potential problems in the system and providing a data basis for subsequent decision support.

[0030] For example, smart meters are installed at key power access points. They continuously monitor current and voltage changes on the production line at a frequency of milliseconds, and record power quality parameters, such as three-phase imbalance or voltage sag. Once an abnormal increase in current is detected, it may mean that a certain device is overloaded. The system immediately identifies this abnormality through real-time analysis and triggers an early warning signal to the maintenance team, prompting them to check the relevant equipment to avoid downtime accidents caused by overload.

[0031] It should be noted that the introduction of deep learning models to conduct more detailed analysis of real-time collected data, such as predicting load trends through neural networks, scheduling resources in advance, or using anomaly detection algorithms to automatically identify complex failure modes; combining historical data and real-time data, using advanced analysis algorithms to predict equipment failures, shifting from passive response to active prevention, reducing unplanned downtime, and improving production efficiency, all fall within the scope of protection of this application.

[0032] Step S103, dynamically adjust the recording interval according to the real-time analysis results, and automatically adjust the recording period based on the intelligent algorithm. When the load change amplitude exceeds the preset threshold, the recording period is shortened, otherwise it is extended, to ensure the timeliness of the data and the efficient use of storage resources.

[0033] As mentioned above, this step is based on the results of real-time data analysis, using intelligent algorithms to dynamically adjust the interval time for recording data collection, so as to achieve a balance between data real-time performance and efficient use of storage resources. The algorithm automatically adjusts the recording cycle by monitoring the magnitude of load changes: when the load fluctuates significantly, indicating that the system is in a highly dynamic operating state, the recording interval is shortened to ensure that every subtle change is captured and the data is kept detailed; conversely, when the load tends to be stable and the system runs smoothly, the recording interval is extended to reduce unnecessary frequent recording, save storage space, and achieve reasonable allocation of resources.

[0034] For example, the intelligent regulation unit is responsible for monitoring power output. When the generator output power fluctuates significantly, the intelligent algorithm identifies that the load change exceeds the preset threshold and immediately and automatically shortens the recording interval from the original 10 minutes to every 10 seconds to capture the rapid fluctuations in power output. When the generator power output fluctuates slightly, the algorithm determines that the load change is lower than the threshold, so it automatically extends the recording interval to every 5 minutes, reducing storage space consumption while maintaining accurate tracking of the overall output trend.

[0035] It should be noted that the adaptive learning algorithm optimization, that is, the algorithm can self-learn and optimize according to historical data, automatically adjust the threshold to adapt to the changes in the impact of external factors such as seasons and weather on the load, so as to make the recording interval adjustment more accurate; the event trigger mechanism, that is, when a specific event (such as power grid failure, extreme weather) occurs, regardless of whether the load change reaches the threshold, high-frequency recording is automatically enabled to collect data during key events and enhance the system's fault analysis and response capabilities; multi-dimensional considerations, that is, the algorithm considers more dimensions, such as weather forecast data, historical load curves, holiday patterns, etc., comprehensively analyzes and predicts load changes, further refines the recording strategy, and improves the efficiency and practicality of recording, all of which belong to the scope of protection of this application.

[0036] Step S104: Use an efficient algorithm to compress the load data, use a circular buffer to manage the storage space, and automatically overwrite the old data through the circular buffer to ensure redundant backup of new data and key data.

[0037] As mentioned above, this step focuses on solving the problems of effective management of storage space and redundant data backup. First, by adopting efficient data compression algorithms, such as LZ77 or Huffman coding, the original load data is compressed to significantly reduce the storage space it occupies. Secondly, the circular buffer technology is introduced, which is a strategy for dynamically managing storage space. When the storage space is close to saturation, the earliest recorded data is automatically overwritten to ensure that the latest data is always saved. At the same time, the system will identify and retain redundant backups of key data, ensuring the security of important information even when the data is overwritten, thereby improving the system's data recovery capability and reliability.

[0038] For example, a smart grid monitoring system generates a large amount of load data every day. The system uses the LZ77 algorithm to compress the data. The original data that originally occupied 1GB is compressed to only 50MB, which greatly saves storage space. The circular buffer is set to store 10 days of data, and the data of the oldest day is automatically overwritten every day. However, during the circulation process, the system intelligently identifies certain data such as overload event records as critical data and retains redundant backups in another area. In this way, even if the data on the 31st day is overwritten due to the cycle, the critical data of the overload event is still safely backed up, ensuring that the system can trace back and analyze critical events.

[0039] It should be noted that the use of adaptive compression level adjustment, that is, dynamically adjusting the compression ratio according to the importance of data or storage pressure, using a lower compression ratio for critical data to reduce information loss, and a high compression ratio for non-critical data to save space; intelligent data tiered storage: combined with cloud storage, frequently accessed hot data is stored in cache, and seldom accessed cold data is moved to low-cost cloud storage, improving data access efficiency while reducing costs; adopting a dynamic redundancy strategy, that is, dynamically adjusting the redundant backup strategy based on the importance of data and the real-time status of storage space, such as only backing up extremely important critical data when storage is tight; adopting a data aging mechanism, that is, introducing a data aging strategy to automatically mark and delete old data that is no longer needed, such as outdated prediction model training data, to continuously release storage space, all of which fall within the scope of protection of this application.

[0040] Step S105: Standardize data encapsulation, encapsulate the processed data into a unified format according to specific standards, and use JSON or XML format to facilitate parsing, thereby improving data interoperability and convenience.

[0041] As mentioned above, this step involves reorganizing and packaging the compressed and processed data according to predefined industry standards to facilitate communication and understanding between different systems. Using JSON or XML as the packaging format has good structure, self-explanatory and wide support, so that data does not need additional conversion when it is transferred between different software and platforms, improving interoperability and convenience of data processing. Standardized packaging ensures the consistency and standardization of data, so that it can be quickly understood and processed whether it is internal system analysis or external data sharing.

[0042] It should be noted that in addition to JSON and XML formats, support for other formats such as CSV, Protobuf, etc. is added to adapt to the preferences and performance requirements of different systems or specific needs; data encryption is adopted, that is, a data encryption step is added during the encapsulation process, and algorithms such as AES and RSA are used to ensure the security of data during transmission; compression embedding is adopted, that is, the data before encapsulation can be first compressed in binary form, such as gzip, and then embedded in JSON or XML, to further reduce the transmission volume and improve efficiency, all of which belong to the protection scope of this application.

[0043] Step S106: When an abnormality or predictive maintenance event is detected, the recording mechanism is triggered immediately without being constrained by regular intervals, thereby ensuring the integrity of key events.

[0044] As mentioned above, this step is designed to ensure that the system immediately starts recording when it detects any abnormal conditions or predictive maintenance needs, without being restricted by the regular data recording interval. This means that even when the system is in a low-frequency recording cycle, such as during the night when the load is stable, once a critical event such as overload, voltage anomaly, current sudden change frequency, or potential equipment maintenance needs are predicted based on historical data analysis, the system will automatically trigger high-frequency recording, recording the data at these critical moments with a resolution of seconds or even milliseconds, ensuring the complete recording of these critical events, providing a detailed basis for fault analysis, safety assessment and preventive maintenance.

[0045] For example, the intelligent monitoring system records the operating data of the equipment every 15 minutes under normal conditions. When the system predicts that a certain parameter of the equipment is about to exceed the warning threshold based on historical data and other related parameter analysis, although it is still in the low-frequency recording cycle, the system immediately triggers the high-frequency recording mechanism and starts recording each key parameter every second. Finally, it accurately records the abnormal increase of the parameter to the alarm and notifies the maintenance team in time, avoiding possible failures.

[0046] It should be noted that the integrated intelligent early warning system, that is, the deep integration with the early warning system, automatically pushes early warning information to maintenance personnel according to the type of abnormal event when the record is triggered, so as to speed up the response; the adaptive recording depth is adopted, that is, the recording depth is automatically adjusted according to the severity of the abnormality, and more relevant parameters such as frequency and waveform are recorded in serious events for in-depth analysis; the event sequence analysis is adopted, that is, combined with machine learning, to analyze the data sequence before and after the event, identify the precursors of abnormal events, optimize the prediction model, and improve the prediction accuracy, all of which belong to the protection scope of this application.

[0047] Step S107: Verification and encryption are performed before and after data storage to ensure data integrity and security, and digital signature technology is used to further strengthen the data to prevent tampering.

[0048] As mentioned above, this step emphasizes two key aspects of data processing, namely security and integrity. Checksum and encryption are implemented before and after data storage to ensure that the data is intact and secure during transmission and storage. Checksums usually use algorithms such as CRC or MD5, SHA, etc., which calculate the data summary and compare the source data with the checksum to verify whether the data has been accidentally changed. Encryption processing, such as AES, RSA and other algorithms, encrypts the data, making it difficult to interpret even if the data is intercepted, thereby ensuring data security. Digital signatures use asymmetric encryption technology to encrypt the data summary with a private key to form a signature, and the public key verifies the signature to confirm that the data has not been tampered with, further strengthening the anti-counterfeiting protection of the data.

[0049] For example, before a record package containing critical load data is stored on the hard disk, the system first calculates the CRC checksum of the data and attaches it to the end of the data package. Then, the AES algorithm is used to encrypt the data package to ensure that even if the data is stolen during transmission or storage, it cannot be directly read. Finally, the system uses the private key to digitally sign the encrypted data summary and save it together. When the data is taken out for use, the system will verify the checksum, decrypt the data, and then verify the digital signature with the public key to ensure the integrity and authenticity of the data. The entire process effectively prevents the risk of data tampering or leakage at every link.

[0050] It should be noted that the adoption of a dynamic encryption strategy, that is, dynamically adjusting the encryption strength and algorithm according to the sensitivity of the data, using stronger encryption for highly sensitive data to balance security and performance; the adoption of a layered encryption strategy, that is, implementing multiple layers of encryption, such as using TLS / SSL encryption in the data transmission layer and AES in the storage layer, to achieve end-to-end comprehensive protection; the adoption of a real-time verification strategy, that is, real-time verification during data transmission, and immediate retransmission once an error is found, all fall within the scope of protection of this application.

[0051] Step S108: Formulate retention and backup strategies, redundantly store key data, integrate cloud synchronization mechanisms, implement remote data backup and disaster recovery, and ensure data persistence and high system reliability.

[0052] As mentioned above, this step focuses on ensuring the long-term security of data and high reliability of the system. Through a carefully designed data retention and backup strategy, critical data is not only redundantly stored locally to prevent single point failures, but also integrated with cloud synchronization mechanisms to achieve remote backup of data. This includes automated backup plans, data version control, disaster recovery plans, etc. to ensure data persistence. Even in the face of hardware failures, natural disasters or human errors, the system can quickly recover to the most recent state to reduce the risk of business interruption.

[0053] For example, the data retention and backup strategy stipulates that key load data is backed up to the local disk array every day, and fully backed up once a week, and all key data is synchronized to the cloud through the cloud storage service. When a power outage occurs due to special circumstances, if the backup data in the local redundant hard disk array is not affected, the system can quickly restore the business data of the recent period; if some hardware is damaged and the local data recovery is not complete, the remote backup in the cloud can achieve complete data recovery to reduce the risk of business interruption.

[0054] It should be noted that the combination of hot backup and cold backup, that is, using hot backup to quickly restore key business and cold backup to store long-term archived data, balancing cost and recovery speed; adopting intelligent backup strategies, that is, dynamically adjusting the backup frequency and strategy according to the importance and change frequency of data to reduce redundant storage costs; adopting data lifecycle management, that is, automatically managing the storage level of data, such as migrating hot and cold data to lower-cost storage media, and optimizing resource allocation, all fall within the scope of protection of this application.

[0055] Preferably, the system initialization further includes dynamically optimizing initial configuration parameters according to historical data based on an adaptive learning algorithm.

[0056] As mentioned above, the adaptive learning algorithm is used to dynamically optimize the initial configuration parameters. When the system is first deployed or restarted each time, the system can automatically adjust the hardware and software configuration and parameter settings according to the collected historical data to achieve the optimal working state. For example, if the historical data analysis shows that the load has a significant growth trend in a specific time period, the system will automatically increase the initial frequency of data recording or reserve more storage resources to ensure the accuracy and timeliness of data recording during the expected high-load period. This adaptability not only improves the flexibility and response speed of the system, but also reduces the need for manual intervention and enhances the intelligence level of the system.

[0057] Preferably, real-time data analysis is performed, specifically by predicting load change trends through machine learning algorithms to improve the accuracy of dynamic adjustment.

[0058] As mentioned above, machine learning algorithms are introduced to predict load change trends, thereby improving the accuracy of dynamically adjusting recording intervals. This involves using historical load data to train models so that they can identify factors such as load patterns, seasonal changes, and the impact of special events, and then quickly identify precursors to load changes in real-time data streams. For example, through a deep learning network, the system can predict the arrival of peak electricity consumption hours in advance, thereby shortening the recording interval before the actual load surges to ensure that every subtle change is captured, which is crucial for load scheduling, fault warning, and energy efficiency optimization of the power grid.

[0059] Preferably, the recording interval is adjusted dynamically, specifically by adopting a multi-dimensional judgment logic, optimizing the adjustment strategy according to the load change rate, time period and user load characteristics, and achieving more detailed interval control.

[0060] As mentioned above, the strategy of adopting multi-dimensional judgment logic not only considers the load change rate, but also integrates the time cycle (such as the load difference between weekdays and weekends, daytime and nighttime) and the specific load characteristics of users (such as the production cycle of industrial users, the business hours of commercial users, etc.). By comprehensively evaluating these factors, the system can more accurately decide when to shorten or extend the recording interval. For example, during the stable load period, the recording interval can be appropriately extended to save storage resources, while during the period of frequent load fluctuations, the recording interval can be shortened to capture more details, achieving the best balance between resource utilization and data accuracy.

[0061] Preferably, an efficient algorithm is used to compress the load data, specifically a multi-level compression algorithm is used to distinguish the importance of data and perform different degrees of compression, that is, important data fragments are intelligently identified and non-critical data is compressed preferentially to optimize storage resource allocation.

[0062] As mentioned above, the strategy of compressing load data using a multi-level compression algorithm is designed to implement differentiated compression according to the importance of different data. The system first identifies key data fragments, such as abnormal event records, load peaks, etc., and compresses them lightly or not to preserve details, while adopting a high compression ratio strategy for regular data or low-importance data to save storage space. This intelligent compression method not only optimizes the allocation of storage resources, but also ensures that the integrity and availability of key information are not affected after the data is decompressed, providing a high-quality foundation for subsequent data analysis and decision-making.

[0063] Preferably, standardized data encapsulation, specifically standardized data format generation supports multi-standard compatibility, including industry standard DL / T 645 protocol and international standard IEC 61850, and can automatically switch to different formats for output according to the requirements of the master station.

[0064] As mentioned above, the system can generate data formats that comply with a variety of industry standards (such as DL / T 645 Power User Power Information Collection System Communication Protocol) and international standards (such as IEC 61850 Substation Communication Network and System). In addition, the system has the ability to automatically switch output formats according to the master station instructions, which means that both domestic and international master station systems can be seamlessly connected, improving the versatility and convenience of data interaction. This design simplifies the data integration process, enhances the interoperability of the system, and provides a solid foundation for cross-platform data sharing and analysis.

[0065] Preferably, the recording mechanism, specifically a priority sorting mechanism, is used to ensure that high priority events are recorded and processed first.

[0066] As mentioned above, a priority sorting mechanism is used to ensure that the system records and processes high-priority events first when resources are limited. For example, in the power system, emergency events such as sudden load changes and voltage drops are given the highest priority to ensure that these data can be recorded and reported immediately, even when the system is busy or resources are tight. This mechanism is of great significance for rapid response to faults and prevention of accidents, and helps to improve the stability and safety of the power system.

[0067] Preferably, data integrity and security are ensured, specifically by adopting double verification technology, primary verification to ensure data integrity, and advanced verification to enhance security.

[0068] As mentioned above, the reliability and security of data are ensured by implementing double verification technology. Primary verification usually refers to data integrity verification, such as CRC cyclic redundancy check or hash check, which is used to verify whether the data is accidentally damaged during storage or transmission. Advanced verification involves encryption verification, such as using encryption algorithms such as AES to encrypt data, and using digital signature technology to confirm the legitimacy and integrity of the data source to prevent malicious tampering. The double verification mechanism builds a solid security line for data, ensuring the accuracy of data even in complex and changing network environments.

[0069] Preferably, a retention and backup strategy is developed, specifically including automated data lifecycle management, regular review and optimization of storage resources.

[0070] As mentioned above, based on the concept of automated data lifecycle management, the system can automatically review the stored data regularly and perform data archiving, migration or deletion operations based on factors such as the timeliness and importance of the data to optimize the utilization efficiency of storage resources. For example, older, less frequently accessed routine data may be migrated to low-cost long-term storage devices, while critical data is kept in high-performance storage for quick access. This dynamic management strategy reduces unnecessary storage overhead while ensuring the immediate availability of critical data.

[0071] Preferably, a cloud synchronization mechanism is integrated, specifically based on an intelligent synchronization strategy, to dynamically adjust the synchronization speed according to network conditions to ensure data transmission efficiency and system stability.

[0072] As mentioned above, based on the cloud synchronization mechanism, the system can dynamically adjust the data synchronization speed according to the current network conditions. When the bandwidth is sufficient, the synchronization rate is accelerated to complete the backup task as soon as possible, and when the network is congested or resources are limited, the synchronization speed is automatically reduced to avoid affecting other key businesses. This adaptive synchronization strategy ensures the efficiency of data transmission and the overall stability of the system, especially in the data backup and disaster recovery process in the cloud environment, it can flexibly respond to various network conditions and ensure data consistency and availability.

[0073] Compared with the existing technology, this method uses an adaptive learning algorithm to dynamically optimize parameters, and adjusts the initial configuration according to historical data analysis, so that the system can quickly adapt to specific scenarios when it starts, improves the accuracy and response speed of load monitoring, and lays the foundation for subsequent intelligent adjustment; uses machine learning algorithms to conduct in-depth analysis of current, voltage and power quality parameters collected in real time, and predicts load change trends, which not only improves the accuracy of dynamic adjustment, but also provides strong data support for intelligent decision-making and improves the intelligence level of load management; through multi-dimensional logical judgment, the intelligent algorithm automatically adjusts the recording cycle, and optimizes the strategy according to the load change rate, time period and user load characteristics, realizing fine control of data recording, ensuring the timely capture of key data, and rationally utilizing storage resources; adopts a multi-level compression strategy to intelligently identify and prioritize the compression of non-critical data, optimize storage allocation, and ensure the Redundant backup of key data effectively utilizes limited storage space and improves data processing efficiency; it automatically adapts to a variety of standard data encapsulation formats to ensure data interoperability and ease of parsing, facilitates data sharing and interaction between different systems, and enhances data value; it ensures priority recording in special events and can capture key data even outside of abnormal recording intervals, enhancing the system's responsiveness to abnormal or predictive maintenance and improving data integrity; through double verification and encryption processing combined with digital signature technology, a strict data security protection network is built to ensure data integrity and tamper-proof capabilities during storage and transmission, enhancing the credibility of the system; through automated data lifecycle management and cloud synchronization mechanisms, efficient data storage and backup are achieved, ensuring data persistence and the system's rapid recovery capabilities in the face of disasters, and improving the overall reliability and resilience of the system.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0075] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0076] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0077] The above serial numbers of the present invention are only for description and do not represent the advantages or disadvantages of the implementation scenarios.

[0078] The above disclosure is only a few specific implementation scenarios of the present invention, but the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for freezing load records of an intelligent flexible regulation unit, characterized in that: include: Initialize the system, configure the software and hardware, set parameters, and complete joint debugging with the master station. Set dynamic recording interval adjustment thresholds and data processing parameters based on user needs or historical data; Collect load current, voltage, and power quality parameters in real time and conduct real-time data analysis; Dynamically adjust the recording interval according to the real-time analysis results, and automatically adjust the recording period based on the intelligent algorithm. When the load change exceeds the preset threshold, the recording period is shortened, and vice versa, to ensure the timeliness of data and efficient use of storage resources; It uses efficient algorithms to compress load data, manages storage space using a circular buffer, and automatically overwrites old data through a circular buffer to ensure redundant backup of new and critical data; Standardized data encapsulation: encapsulate the processed data into a unified format according to specific standards, using JSON or XML format for easy parsing, improving data interoperability and convenience; When an abnormality or predictive maintenance event is detected, the recording mechanism is triggered immediately without being constrained by regular intervals, ensuring the integrity of key events; Implement verification and encryption before and after data storage to ensure data integrity and security, and further strengthen data to prevent tampering through digital signature technology; Develop retention and backup strategies, redundantly store key data, integrate cloud synchronization mechanisms, implement remote data backup and disaster recovery, and ensure data persistence and high system reliability.

2. The method according to claim 1, wherein the system initialization further comprises: Based on an adaptive learning algorithm, the initial configuration parameters are dynamically optimized according to historical data.

3. According to the method of claim 1, the real-time data analysis is specifically: Use machine learning algorithms to predict load change trends and improve the accuracy of dynamic adjustments.

4. According to the method of claim 3, the dynamically adjusting the recording interval comprises: Adopting multi-dimensional judgment logic, according to load change rate, time cycle and user load characteristics, the adjustment strategy is optimized to achieve more detailed interval control.

5. According to the method of claim 1, the method of compressing the load data using an efficient algorithm is specifically: A multi-level compression algorithm is used to distinguish the importance of data and perform different degrees of compression, that is, important data fragments are intelligently identified and non-critical data is compressed first to optimize storage resource allocation.

6. According to the method of claim 1, the standardized data encapsulation is specifically: Standardized data format generation supports multi-standard compatibility, including industry standard DL / T 645 protocol and international standard IEC61850, and can automatically switch to different formats for output according to the requirements of the master station.

7. The method according to claim 1, wherein the recording mechanism is specifically: Adopt a priority sorting mechanism to ensure that high-priority incidents are recorded and handled first.

8. The method according to claim 1, wherein ensuring data integrity and security comprises: Using double verification technology, primary verification ensures data integrity and advanced verification enhances security.

9. According to the method of claim 1, the formulation of retention and backup strategy is specifically: Including automated data lifecycle management, regular review and optimization of storage resources.

10. The method according to claim 9, wherein the integrated cloud synchronization mechanism comprises: Based on intelligent synchronization strategy, the synchronization speed is dynamically adjusted according to network conditions to ensure data transmission efficiency and system stability.

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