Multi-modal secure storage method and system for electrical energy data

Abstract

This invention relates to the field of electrical energy data processing technology, specifically including a multi-mode secure storage method and system for electrical energy data. The method includes: constructing a multi-region chained storage structure based on basic configuration parameters; optimizing data consistency during the energy accumulation process through a dynamic path selection mechanism; and constructing an electricity-storage-time coupling model to manage abnormal events during data storage. This solves the technical problem that single storage areas or simple backup structures lack multi-region collaborative settings, making them susceptible to inconsistencies in accumulated data due to power outages, voltage drops, and other anomalies, thus failing to meet the high-precision and high-reliability storage requirements of smart grids. By constructing a multi-region chained storage structure and a dynamic path selection mechanism, when the energy meter stores abnormal energy, the meter is operating normally and can retain the original data for analyzing the cause of the problem. This improves the flexibility and adaptability of the electrical energy data storage structure, achieving effective control over the entire energy accumulation process.

Description

Technical Field

[0001] This invention relates to the field of electrical energy data processing technology, specifically to a method and system for secure storage of electrical energy data in multiple modes. Background Technology

[0002] With the comprehensive advancement of smart grid construction, electrical energy data, as the core foundational data for grid dispatching, electricity billing, electricity consumption monitoring, and energy management, is directly related to the stable operation of the power system, fair transactions in the electricity market, and the rights and interests of users due to the security, integrity, and consistency of its storage. Smart meters, as key terminals for collecting electrical energy data, are widely distributed in various electricity consumption scenarios and need to continuously collect and store massive amounts of time-series electrical energy data.

[0003] The complex power grid operating environment, frequent power fluctuations, and potential data transmission interference place higher demands on the reliability of electrical energy data storage. Under normal circumstances, absolute safety is required during the storage and transmission of electrical energy. When the electricity meter experiences abnormal conditions, a recovery and alarm mechanism is needed. However, existing storage solutions mostly adopt a single storage area or a simple backup structure, without building a flexible multi-area collaborative storage architecture. They cannot dynamically switch storage paths based on the data write status, and are prone to data loss when the main storage area is abnormal. In addition, the accumulation of electrical energy is susceptible to abnormalities such as power outages and voltage drops. Without an effective coupling and control mechanism between electrical energy data and storage status and time dimensions, it is difficult to ensure the consistency of accumulated data.

[0004] In summary, existing technologies suffer from the technical problems of lacking multi-area collaborative settings due to single storage areas or simple backup structures. They are also prone to inconsistencies in accumulated data due to abnormalities such as power outages and voltage drops, making it difficult to meet the high-precision and high-reliability storage requirements of smart grids. Summary of the Invention

[0005] This application provides a multi-mode secure storage method and system for electrical energy data, aiming to solve the technical problems in the existing single storage area or simple backup structure that lack multi-area collaborative settings, are prone to inconsistencies in accumulated data due to abnormalities such as power outages and voltage drops, and are difficult to meet the high-precision and high-reliability storage requirements of smart grids.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: In a first aspect, this application provides a multi-mode secure storage method for electrical energy data, wherein the method includes: obtaining basic configuration parameters of a smart meter with electrical energy metering function; configuring the address mapping relationship and capacity allocation strategy of the main storage area and the backup storage area based on the basic configuration parameters, and constructing a multi-region chained storage structure; integrating an abnormal data isolation area into the multi-region chained storage structure, and determining whether to switch to the backup storage area / abnormal data isolation area in real time through a dynamic path selection mechanism; simultaneously, optimizing data consistency during the electrical energy accumulation process, and constructing an electricity-storage-time coupling model to manage abnormal events during the data storage process.

[0007] Preferably, before writing electrical energy data into the main storage area, a data verification unit is integrated to repair data bit flips caused by power supply interference through a bit flip fault tolerance repair mechanism.

[0008] Preferably, after the main storage area is written, the verification data block is read back in real time; based on the verification result, the address allocation and metadata update of the backup storage area are triggered by decoding the storage status flag bit.

[0009] Preferably, the system obtains a storage anomaly score; when the storage anomaly score exceeds a preset anomaly threshold, it triggers an anomaly isolation command; using the anomaly isolation command, it writes the verified abnormal electrical energy data into the anomaly data isolation area and freezes the write permissions of the main storage area.

[0010] Preferably, an abnormal event log buffer is integrated below the RAM power accumulation area, and a watchdog timer and voltage monitoring unit are deployed to provide real-time feedback on the power accumulation operation status to the storage control logic; when a power failure or voltage drop exceeds the preset safety range, an emergency save process is triggered and the current power data is written to the non-volatile memory.

[0011] Preferably, the storage strategy is dynamically adjusted according to the power accumulation operation status. When abnormal fluctuations in the accumulation process or a decrease in voltage stability are detected, a data write-to-disk strategy is configured. The data write-to-disk frequency is increased through the data write-to-disk strategy, and a dual-write redundancy mechanism is enabled.

[0012] Preferably, based on historical operational data in the abnormal event log buffer, the power increment validity threshold and storage trigger interval are dynamically optimized through local abnormal pattern learning.

[0013] Preferably, local abnormal events are uploaded to the electricity information collection master station for global aggregation and analysis; after global aggregation and analysis, updated validity thresholds and storage strategies are sent to the smart meter.

[0014] Preferably, a lightweight learning agent is deployed based on a smart meter that supports edge computing; the local abnormal events are anonymized on the lightweight learning agent.

[0015] A second aspect of this application provides a multi-mode secure storage system for electrical energy data, wherein the system includes: a basic configuration parameter acquisition module for acquiring basic configuration parameters of a smart meter with electrical energy metering function; a multi-region chained storage structure construction module for configuring the address mapping relationship and capacity allocation strategy of the main storage area and the backup storage area based on the basic configuration parameters, and constructing a multi-region chained storage structure; a real-time judgment module for integrating an abnormal data isolation area in the multi-region chained storage structure, and determining whether to switch to the backup storage area / abnormal data isolation area in real time through a dynamic path selection mechanism; and an abnormal event management module for simultaneously optimizing data consistency during the electrical energy accumulation process and constructing an electricity-storage-time coupling model to manage abnormal events during the data storage process.

[0016] In summary, one or more technical solutions provided in this application, by constructing a multi-regional chained storage structure and a dynamic path selection mechanism, can improve the flexibility and adaptability of the energy storage structure when the energy stored in the energy meter is abnormal, while the meter is operating normally and can save the original data for analyzing the cause of the problem. This achieves the technical effect of effectively controlling the entire process of energy accumulation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This application provides a flowchart illustrating a multi-mode secure storage method for electrical energy data.

[0019] Figure 2 This application provides a schematic diagram of the structure of a multi-mode secure storage system for electrical energy data.

[0020] Figure labeling: Module 11 for obtaining basic configuration parameters, Module 12 for constructing a multi-regional linked storage structure, Module 13 for real-time judgment, and Module 14 for abnormal event management. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides a multi-mode secure storage method for electrical energy data, the method comprising: Obtain the basic configuration parameters of a smart meter with electricity metering function; based on the basic configuration parameters, configure the address mapping relationship and capacity allocation strategy between the main storage area and the backup storage area, and construct a multi-area chained storage structure.

[0023] It is important to understand that to prevent the loss of electrical energy data during storage, dual backups are typically used to store the data on the medium. This ensures accurate data storage because electricity meters are susceptible to interference from the power supply system during operation, which can lead to anomalies during energy storage. When such anomalies occur, the meter needs to have self-recovery capabilities, and to ensure the traceability of the original data, the abnormally stored data cannot be overwritten. In this case, new storage space needs to be created to store new electrical energy to ensure the meter's safe and correct operation. To prevent errors caused by storing electrical energy data at the same time as interference, fixed-time storage and fixed-quantity storage are used. Fixed-time storage means storing electrical energy after a certain unit of time has elapsed, while fixed-quantity storage means storing electrical energy after a unit of electricity has elapsed. This method avoids storage anomalies caused by interference.

[0024] When an anomaly occurs in any of the stored or timed electrical energy, a new storage space is created to restore the stored or timed electrical energy. If an anomaly occurs during the storage process, it will overwrite the original anomaly, resulting in an anomaly in the electrical energy data. At the same time, the original abnormal electrical energy data will not be retained for subsequent analysis.

[0025] In one embodiment, the basic configuration parameters refer to the hardware and software parameters preset by the smart meter at the factory, including storage medium type, data transmission rate, power supply stability parameters, communication protocol, and storage capacity, reflecting the basic performance and functional characteristics of the smart meter; the address mapping relationship refers to the logical mapping of the addresses of the main storage area and the backup storage area on the physical storage medium so that data can be accurately written and read; the capacity allocation strategy refers to the dynamic allocation of the storage capacity of the main storage area and the backup storage area according to the configuration parameters of the smart meter and the actual usage requirements; the multi-area chained storage structure connects storage areas with different functions such as the main storage area, backup storage area, and abnormal data isolation area through a chain structure to realize flexible data storage and management.

[0026] Optionally, after obtaining the basic configuration parameters of the smart meter, the address mapping relationship and capacity allocation strategy of the main storage area and the backup storage area can be dynamically configured according to the basic configuration parameters. Specifically, if the configuration parameters of the smart meter show that its storage demand is large and the data writing frequency is high, the capacity allocation of the main storage area will be increased accordingly, and the address mapping will be optimized to improve the data writing and reading efficiency. After constructing a multi-region chained storage structure, when the main storage area is abnormal, it can quickly switch to the backup storage area through the chained structure to ensure the continuous storage of data.

[0027] Specifically, anomalies in the main storage area include write errors and insufficient storage space. Through a dynamic path selection mechanism, the reliability of data storage is enhanced, and the flexibility and adaptability of the storage structure are improved. Furthermore, during power grid operation, if a smart meter experiences a write anomaly in the main storage area due to voltage fluctuations, it immediately switches to the backup storage area to avoid data loss. At the same time, the abnormal data is written to the abnormal data isolation area for subsequent analysis, thereby achieving effective control over the entire process of energy accumulation.

[0028] An abnormal data isolation area is integrated into the multi-region chained storage structure. Through a dynamic path selection mechanism, the write status of the main storage area is judged in real time and it is decided whether to switch to the backup storage area / abnormal data isolation area. At the same time, the data consistency during the power accumulation process is optimized, and an electricity-storage-time coupling model is constructed to manage abnormal events in the data storage process.

[0029] In one embodiment, the abnormal data isolation zone is an independent area in a multi-region chained storage structure specifically used to store abnormal data. Its purpose is to isolate potentially problematic data from normal data, preventing abnormal data from interfering with the entire storage system. The dynamic path selection mechanism is a real-time decision-making mechanism that can dynamically select data storage paths based on the write status of the main storage area, including switching to the backup storage area or the abnormal data isolation zone. The dynamic path selection mechanism relies on real-time monitoring and intelligent judgment to ensure the security and reliability of data storage. The power-storage-time coupling model refers to a model that comprehensively considers power energy data, storage status, and time dimensions. By performing correlation analysis on data from these three dimensions, it achieves comprehensive control over the data storage process and proactive management of abnormal events.

[0030] Optionally, after integrating an abnormal data isolation area into the multi-region chained storage structure, the write status of the main storage area is monitored in real time through a dynamic path selection mechanism. Furthermore, when the main storage area detects voltage fluctuations or data verification errors while writing data, the dynamic path selection mechanism will quickly determine the type and severity of the anomaly. If the anomaly is temporary, such as a slight voltage fluctuation, it will temporarily switch to the backup storage area to ensure data continuity. If the anomaly is severe, such as a data verification failure that cannot be repaired, the abnormal data will be written to the abnormal data isolation area, and the write permissions of the main storage area will be frozen to prevent the abnormal data from further affecting the system.

[0031] Meanwhile, to optimize data consistency during the energy accumulation process, an energy-storage-time coupling model is constructed. This model monitors the energy accumulation process, storage status, and changes in the time dimension in real time to manage abnormal events during data storage. Specifically, during grid operation, if a voltage drop or abnormal data fluctuation is detected during energy accumulation, the energy-storage-time coupling model immediately triggers an emergency save process, writing the current energy data to non-volatile memory to ensure data consistency and integrity. In addition, the energy-storage-time coupling model dynamically adjusts storage strategies based on the frequency and type of abnormal events, such as increasing the data write frequency to disk and enabling a dual-write redundancy mechanism, thereby effectively improving the reliability and security of data storage. Through this multi-dimensional coupling management, various abnormal situations can be proactively addressed, ensuring high-precision and high-reliability storage of energy data.

[0032] Furthermore, the method described in this application includes: Before writing electrical energy data into the main storage area, an integrated data verification unit is used to repair data bit flips caused by power supply interference through a bit flip fault-tolerant repair mechanism.

[0033] In one embodiment, a data verification unit refers to a module used to detect and verify the integrity and accuracy of data. It is usually implemented through a cyclic redundancy check algorithm. Its function is to detect possible errors in the data before the data is written to the storage area. A bit flip fault tolerance repair mechanism is a technology used to repair data bit errors caused by power interference or other external factors. The bit flip fault tolerance repair mechanism detects abnormal changes in data bits and repairs them according to preset rules or algorithms, thereby ensuring the correctness of the data.

[0034] Optionally, before writing the electrical energy data to the main storage area, an integrated data verification unit can perform real-time verification of the data. For example, the electrical energy data collected by the smart meter is a 32-bit value. The data verification unit will calculate the verification value of the data using the CRC (Cyclic Redundancy Check) verification algorithm. If a bit flips due to power interference during data transmission, the data verification unit can detect this error through the verification algorithm.

[0035] Once a bit flip error is detected, the bit flip fault tolerance and repair mechanism will be activated immediately. The mechanism will repair the erroneous bit according to preset rules or algorithms. For example, by using redundant coding technology, each data bit has a redundant backup. The repair mechanism can correct the erroneous bit by comparing the redundant data. Through the bit flip fault tolerance and repair mechanism, data bit flips caused by power interference can be repaired before the data is written to the main storage area, thereby ensuring that the data written to the main storage area is accurate and error-free. This significantly improves the reliability and accuracy of electrical energy data storage and provides a high-quality data foundation for subsequent multi-area chained storage and abnormal event management.

[0036] Furthermore, this application provides a method for real-time determination of the write status of the primary storage area and decision on whether to switch to the backup storage area / abnormal data isolation area, the method comprising: After the main storage area is written, the verification data block is read back in real time; based on the verification result, the address allocation and metadata update of the backup storage area are triggered by decoding the storage status flag.

[0037] In one embodiment, real-time readback of the verification data block refers to immediately reading a specific data block from the main storage area for verification after the data is written. This data block typically contains key information used to verify the integrity and accuracy of the written data. Storage status flags are binary bits used to mark the storage status. By decoding these flags, the current status of the main storage area can be understood, such as whether the write was successful or whether there are any errors. Metadata update refers to updating the data used to describe the data in the storage system, such as updating the storage location, timestamp, and verification status of the data block.

[0038] Optionally, after the data is written to the main storage area, a real-time readback operation is immediately performed to read a predefined verification data block. By comparing the verification data blocks before and after the write, it is quickly determined whether the data in the main storage area is complete and accurate. If the verification result shows that the data is abnormal, the specific type of abnormality will be determined by decoding the storage status flag. For example, flag 0b00 indicates that the data is normal, 0b01 indicates that the data bit is flipped, and 0b10 indicates that the storage space is insufficient.

[0039] Based on the decoding results, address allocation and metadata updates for the backup storage area are triggered. Furthermore, when the flag is 0b01, a new address is allocated to the backup storage area, and the metadata, including the storage location of data blocks, timestamps, and verification status, is updated. By reading back the verification and decoding storage status flags in real time, address allocation and metadata updates for the backup storage area can be completed even when an anomaly occurs in the primary storage area, ensuring highly reliable data storage. This also provides accurate data for dynamic path selection and abnormal data isolation, further enhancing the robustness and reliability of the entire storage system.

[0040] Furthermore, this application provides a method for decoding and storing a status flag bit based on the verification result, the method comprising: Obtain the storage anomaly score. When the storage anomaly score exceeds a preset anomaly threshold, trigger an anomaly isolation command. Use the anomaly isolation command to write the power energy data that is verified to be abnormal into the anomaly data isolation area and freeze the write permissions of the main storage area.

[0041] In one embodiment, the storage anomaly score is an indicator used by the system to quantify and record anomalies that occur during storage. By accumulating the severity or frequency of each anomaly, a score is formed to assess the overall anomaly status of the storage system. The preset anomaly threshold is a pre-set critical value. When the storage anomaly score exceeds the preset anomaly threshold, it indicates that the system anomaly has reached a certain level and measures need to be taken. The anomaly isolation command is used to trigger the system to transfer abnormal data to the abnormal data isolation area and perform protection operations on the main storage area, such as freezing write permissions.

[0042] Optionally, when monitoring the write status of the main storage area, a storage anomaly score will be obtained in real time. Specifically, each time a data verification failure or write error is detected, a certain score will be given according to the severity of the anomaly. When the accumulated storage anomaly score exceeds the preset anomaly threshold, an anomaly isolation command will be triggered. According to the anomaly isolation command, the power energy data with verification anomalies will be written to the anomaly data isolation area, and the write permissions of the main storage area will be frozen. This can effectively prevent abnormal data from further interfering with the normal operation of the main storage area and ensure the stability of the storage system and the reliability of the data.

[0043] Specifically, during power grid operation, a smart meter experienced frequent data verification errors in its main storage area due to external interference. By accumulating abnormal points, the system quickly isolated the abnormal data and froze write permissions to the main storage area when the accumulated value exceeded a threshold, preventing the spread of abnormal data and providing support for subsequent troubleshooting and data recovery. By setting stored abnormal points and thresholds, the system responded promptly in the early stages of anomalies, improving the abnormal data isolation rate, significantly reducing interference from abnormal data to the main storage area, and ensuring the high reliability and integrity of electrical energy data storage.

[0044] Furthermore, this application provides a method for optimizing data consistency during the energy accumulation process, constructing an energy-storage-time coupling model to manage abnormal events during data storage, the method comprising: An abnormal event log buffer is integrated below the RAM power accumulation area, and a watchdog timer and voltage monitoring unit are deployed to provide real-time feedback on the power accumulation operation status to the storage control logic. When a power failure or voltage drop exceeds the preset safety range, an emergency save process is triggered and the current power data is written to the non-volatile memory.

[0045] In one embodiment, the abnormal event log buffer is a temporary storage area used to record abnormal events during the power accumulation process. It is usually located in RAM and is used for quick recording and reading of abnormal information. The watchdog timer is a hardware or software mechanism used to monitor the system's operating status and trigger corresponding protection measures when a system abnormality or fault is detected. The voltage monitoring unit is a module used to monitor the power supply voltage in real time and can detect whether the voltage is within a safe range. The emergency save process refers to a protection mechanism that is automatically activated when a system abnormality is detected, saving the current critical data to non-volatile memory to prevent data loss. System abnormalities include power failure and voltage drop.

[0046] Optionally, after integrating an abnormal event log buffer below the RAM power accumulation area, the system deploys a watchdog timer and a voltage monitoring unit. The watchdog timer periodically checks the operating status of the power accumulation area to ensure its normal operation. At the same time, the voltage monitoring unit monitors the power supply voltage in real time. When the voltage is lower or higher than the preset safety range, it immediately feeds back an abnormal signal to the storage control logic.

[0047] Specifically, when the voltage monitoring unit detects that the voltage drop exceeds the preset safety range, it will immediately trigger the emergency save process. At this time, the data in the current power accumulation area is written to the non-volatile memory to ensure that critical data will not be lost in the event of power failure or voltage abnormality. The data in the current power accumulation area includes key information such as the accumulated power value and timestamp.

[0048] Furthermore, the abnormal event log buffer records detailed information about each abnormal event, including the time of occurrence, voltage value, and anomaly type. For example, by analyzing log data, areas with frequent voltage fluctuations in the power grid can be identified, allowing for targeted optimization of grid configuration or adjustment of smart meter storage strategies. By integrating the abnormal event log buffer with the emergency save process, data retention success rates can be improved in the event of voltage anomalies, significantly enhancing the reliability and security of energy data storage. Simultaneously, by providing real-time feedback on the energy accumulation and operating status, storage strategies can be dynamically adjusted, further improving the flexibility and adaptability of data storage.

[0049] Furthermore, the method described in this application also includes: The storage strategy is dynamically adjusted based on the power accumulation operation status. When abnormal fluctuations in the accumulation process or a decrease in voltage stability are detected, a data write-to-disk strategy is configured. The data write-to-disk frequency is increased through the data write-to-disk strategy, and a dual-write redundancy mechanism is enabled.

[0050] In one embodiment, the power accumulation operation status refers to the real-time performance of various parameters during the power accumulation process, including accumulation speed, data fluctuation amplitude, and voltage stability, reflecting whether the power accumulation process is normal or not; the data write-to-disk strategy refers to the system's strategy for dynamically adjusting the data writing to the storage medium based on the current operating status, including write frequency and write timing, where the storage medium refers to hard disk or flash memory; the dual-write redundancy mechanism refers to ensuring data reliability and integrity by simultaneously writing data to two different storage areas, so that even if one storage area fails, the other area can still provide complete data.

[0051] Optionally, by monitoring the power accumulation operation status, the data fluctuations and voltage stability during the accumulation process can be analyzed in real time. For example, when abnormal fluctuations in the power accumulation rate or a decrease in voltage stability are detected, the data write-to-disk strategy can be automatically adjusted. Specifically, the data write-to-disk frequency can be increased, and data can be written from the temporary storage area to the non-volatile memory more frequently. At the same time, a dual-write redundancy mechanism can be enabled, writing data to both the main storage area and the backup storage area simultaneously. The dual-write redundancy mechanism not only improves the reliability of data storage, but also provides dual protection for data recovery and anomaly analysis.

[0052] During power grid operation, if a voltage drop occurs momentarily in a certain area due to a lightning strike, the voltage monitoring unit of the smart meter will immediately detect this anomaly and trigger an adjustment to the data write-to-disk strategy. The current accumulated power data is quickly written to the main storage area and the backup storage area. Even if the main storage area fails due to voltage issues, the backup storage area can still provide complete data, ensuring that no data is lost. By dynamically adjusting the data write-to-disk strategy and enabling a dual-write redundancy mechanism, the system can quickly respond and protect critical data in abnormal situations, providing strong support for the stable operation of the smart grid.

[0053] Furthermore, the method described in this application also includes: Based on historical operational data from the abnormal event log buffer, the system dynamically optimizes the power increment validity threshold and storage trigger interval through local abnormal pattern learning.

[0054] In one embodiment, the historical operational data of the abnormal event log buffer refers to the data set stored in the abnormal event log buffer that records past abnormal events and their related parameters, reflecting the abnormal patterns and frequencies of the system under different conditions; local abnormal pattern learning refers to the system using locally stored abnormal event log data to automatically identify and summarize the patterns and rules of abnormal events through machine learning or statistical analysis methods; the power increment validity threshold is a threshold used by the system to determine whether power data is valid. When the power increment exceeds this threshold, the data is considered valid and a storage operation is triggered; the storage trigger interval is the time interval at which the system automatically writes data to the storage medium, which can be dynamically adjusted according to the results of abnormal pattern learning.

[0055] Optionally, by analyzing historical operational data in the abnormal event log buffer, a local abnormal pattern learning algorithm can be used to dynamically optimize the validity threshold of power increment and the storage trigger interval. For example, if abnormal events such as voltage drops and data verification failures that occurred in the past month are recorded, statistical analysis will show that in the case of voltage drops, the validity threshold of power increment needs to be reduced to capture data changes more sensitively. At the same time, it was found that abnormal events mostly occur during peak electricity consumption periods at night, so the storage trigger interval will be shortened.

[0056] Through this dynamic optimization, parameters can be flexibly adjusted according to actual operating conditions, improving the efficiency and reliability of data storage. Specifically, in the operation of the power grid, if the power grid in a certain area experiences frequent voltage fluctuations during a specific period, the power increment validity threshold and storage trigger interval can be automatically adjusted through local anomaly mode learning to ensure that critical data can be saved more promptly during these periods and reduce the risk of data loss.

[0057] Furthermore, this application provides a method for dynamically optimizing the power increment validity threshold and storage trigger interval through local anomaly pattern learning, the method further comprising: Local abnormal events are uploaded to the electricity information collection master station for global aggregation and analysis; after global aggregation and analysis, updated validity thresholds and storage policies are sent to the smart meters.

[0058] In one embodiment, the electricity consumption information collection master station serves as a central server for centralized management and analysis of electricity consumption information. It is responsible for receiving data from various smart meters and performing unified processing and analysis. Global aggregation analysis refers to summarizing and analyzing abnormal event data from different smart meters to identify global abnormal patterns and trends. The updated validity threshold and storage strategy refer to the parameters optimized and adjusted based on the results of global aggregation analysis. These parameters will be distributed to each smart meter to guide its subsequent operation.

[0059] Optionally, after local abnormal events are uploaded to the electricity information collection master station, the master station will collect abnormal event data from multiple smart meters and perform global aggregation analysis. For example, the master station will analyze the frequency, type, and related parameters of abnormal events in different regions and time periods to identify potential power grid operation problems or abnormal patterns. Through this global analysis, some problems that have not been detected in some local areas can be discovered, such as frequent voltage fluctuations or data verification errors in a specific area.

[0060] After the global aggregation analysis is completed, the electricity information acquisition master station generates updated validity thresholds and storage strategies based on the analysis results, and distributes these updated parameters to each smart meter. These updated parameters help the smart meters better adapt to the actual operating conditions of the power grid, improving the reliability and accuracy of data storage. By uploading local abnormal events to the master station for global aggregation analysis, problems in power grid operation can be identified and resolved at a macro level, optimizing the operating parameters of smart meters. After global aggregation analysis and parameter updates, the data storage success rate of smart meters under abnormal power grid conditions is further improved, and the timeliness and accuracy of data storage are significantly enhanced.

[0061] Furthermore, this application provides a method for distributing updated validity thresholds and storage policies to the smart meter, the method further including: A lightweight learning agent is deployed based on a smart meter that supports edge computing; the local abnormal events are anonymized on the lightweight learning agent.

[0062] In one embodiment, a smart meter supporting edge computing refers to a smart meter with edge computing capabilities, capable of performing data processing and analysis locally without uploading all data to the cloud or a central server; a lightweight learning agent is a lightweight machine learning or data analysis module deployed locally on the smart meter, used to perform data processing, analysis, and learning tasks on the edge device; and data anonymization refers to processing data to remove or hide sensitive information, ensuring the privacy and security of data during transmission and sharing.

[0063] Optionally, after deploying a lightweight learning agent on a smart meter that supports edge computing, the agent can perform preliminary processing and analysis of local abnormal event data. For example, after detecting an abnormal event, the smart meter sends relevant data to the lightweight learning agent to identify abnormal patterns and extract key features. After completing the preliminary analysis, the local abnormal event data undergoes anonymization. Furthermore, since the abnormal event data contains sensitive information such as user electricity consumption patterns and specific timestamps, the anonymization process replaces this information with anonymous identifiers or obfuscates it. By deploying a lightweight learning agent on edge devices, smart meters can complete preliminary data processing and analysis locally, reducing the amount of data that needs to be uploaded to the main station, lowering data transmission costs and latency, and significantly improving data privacy protection to prevent the leakage of sensitive electricity consumption behavior.

[0064] In summary, the beneficial effects of the embodiments of this application are: This application employs a multi-mode secure storage method and system for electrical energy data. By acquiring the basic configuration parameters of a smart meter with electricity metering function, and based on these parameters, configuring the address mapping relationship and capacity allocation strategy between the main storage area and the backup storage area, a multi-region chained storage structure is constructed. An abnormal data isolation area is integrated into this multi-region chained storage structure. Through a dynamic path selection mechanism, the write status of the main storage area is judged in real time, and a decision is made on whether to switch to the backup storage area / abnormal data isolation area. Simultaneously, data consistency during the electricity accumulation process is optimized, and an electricity-storage-time coupling model is constructed to manage abnormal events during data storage. This application provides a multi-mode secure storage method and system for electrical energy data. By constructing a multi-region chained storage structure and a dynamic path selection mechanism, when the electricity meter stores abnormal electricity, the meter is operating normally and can retain the original data for analyzing the cause of the problem. This improves the flexibility and adaptability of the electrical energy data storage structure and achieves effective control over the entire electricity accumulation process.

[0065] Example 2, based on the same inventive concept as the multi-mode secure storage method for electrical energy data in the foregoing examples, such as... Figure 2 As shown, this application provides a multi-mode secure storage system for electrical energy data, the system comprising: The basic configuration parameter acquisition module 11 is used to acquire the basic configuration parameters of a smart meter with electricity metering function.

[0066] The multi-region chained storage structure construction module 12 is used to configure the address mapping relationship and capacity allocation strategy between the main storage area and the backup storage area based on the basic configuration parameters, and to construct a multi-region chained storage structure.

[0067] The real-time judgment module 13 is used to integrate an abnormal data isolation area in the multi-region chained storage structure, and to judge the write status of the main storage area in real time and decide whether to switch to the backup storage area / abnormal data isolation area through a dynamic path selection mechanism.

[0068] The abnormal event control module 14 is used to simultaneously optimize data consistency during the power accumulation process and construct an electricity-storage-time coupling model to control abnormal events during the data storage process.

[0069] Furthermore, the multi-region linked storage structure construction module 12 is also used to perform the following method: Before writing electrical energy data into the main storage area, an integrated data verification unit is used to repair data bit flips caused by power supply interference through a bit flip fault-tolerant repair mechanism.

[0070] Furthermore, the real-time judgment module 13 is used to perform the following method: After the main storage area is written, the verification data block is read back in real time; based on the verification result, the address allocation and metadata update of the backup storage area are triggered by decoding the storage status flag.

[0071] Furthermore, the real-time judgment module 13 is also used to perform the following method: Obtain the storage anomaly score. When the storage anomaly score exceeds a preset anomaly threshold, trigger an anomaly isolation command. Use the anomaly isolation command to write the power energy data that is verified to be abnormal into the anomaly data isolation area and freeze the write permissions of the main storage area.

[0072] Furthermore, the abnormal event control module 14 is used to execute the following method: An abnormal event log buffer is integrated below the RAM power accumulation area, and a watchdog timer and voltage monitoring unit are deployed to provide real-time feedback on the power accumulation operation status to the storage control logic. When a power failure or voltage drop exceeds the preset safety range, an emergency save process is triggered and the current power data is written to the non-volatile memory.

[0073] Furthermore, the abnormal event control module 14 is used to execute the following method: The storage strategy is dynamically adjusted based on the power accumulation operation status. When abnormal fluctuations in the accumulation process or a decrease in voltage stability are detected, a data write-to-disk strategy is configured. The data write-to-disk frequency is increased through the data write-to-disk strategy, and a dual-write redundancy mechanism is enabled.

[0074] Furthermore, the abnormal event control module 14 is used to execute the following method: Based on historical operational data from the abnormal event log buffer, the system dynamically optimizes the power increment validity threshold and storage trigger interval through local abnormal pattern learning.

[0075] Furthermore, the abnormal event control module 14 is used to execute the following method: Local abnormal events are uploaded to the electricity information collection master station for global aggregation and analysis; after global aggregation and analysis, updated validity thresholds and storage policies are sent to the smart meters.

[0076] Furthermore, the abnormal event control module 14 is used to execute the following method: A lightweight learning agent is deployed based on a smart meter that supports edge computing; the local abnormal events are anonymized on the lightweight learning agent.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0078] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0079] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for secure storage of electrical energy data using multiple methods, characterized in that, The method includes: Obtain the basic configuration parameters of a smart meter with electricity metering function; Based on the aforementioned basic configuration parameters, configure the address mapping relationship and capacity allocation strategy between the primary storage area and the backup storage area to construct a multi-region chained storage structure. An abnormal data isolation area is integrated into the multi-region chained storage structure. Through a dynamic path selection mechanism, the write status of the main storage area is judged in real time and it is decided whether to switch to the backup storage area / abnormal data isolation area. Meanwhile, data consistency is optimized during the energy accumulation process, and an energy-storage-time coupling model is constructed to manage abnormal events during the data storage process.

2. The method as described in claim 1, characterized in that, The method includes: Before writing electrical energy data into the main storage area, an integrated data verification unit is used to repair data bit flips caused by power supply interference through a bit flip fault-tolerant repair mechanism.

3. The method as described in claim 2, characterized in that, The method for real-time determination of the write status of the primary storage area and decision on whether to switch to the backup storage area / abnormal data isolation area includes: After the write operation to the main storage area is completed, the verification data block is read back in real time. Based on the verification results, the address allocation and metadata update of the backup storage area are triggered by decoding the storage status flag.

4. The method as described in claim 3, characterized in that, Based on the verification result, the method includes decoding the storage status flag bit: Obtain the storage anomaly score; when the storage anomaly score exceeds a preset anomaly threshold, trigger an anomaly isolation command. Using the aforementioned anomaly isolation instruction, the power energy data that indicates an anomaly is written to the anomaly data isolation area, and the write permissions of the main storage area are frozen.

5. The method as described in claim 1, characterized in that, To optimize data consistency during the energy accumulation process, a coupled energy-storage-time model is constructed to manage abnormal events during data storage. The method includes: An abnormal event log buffer is integrated below the RAM power accumulation area, and a watchdog timer and voltage monitoring unit are deployed to provide real-time feedback of the power accumulation operation status to the storage control logic. When a power outage or voltage drop exceeding a preset safety range is detected, an emergency save process is triggered, and the current power data is written to a non-volatile memory.

6. The method as described in claim 5, characterized in that, The method further includes: The storage strategy is dynamically adjusted according to the power accumulation operation status, and a data persistence strategy is configured when abnormal fluctuations in the accumulation process or a decrease in voltage stability are detected. The data write-to-disk strategy is used to increase the frequency of data write-to-disk and enable a dual-write redundancy mechanism.

7. The method as described in claim 6, characterized in that, The method further includes: Based on historical operational data from the abnormal event log buffer, the system dynamically optimizes the power increment validity threshold and storage trigger interval through local abnormal pattern learning.

8. The method as described in claim 7, characterized in that, The method further includes: dynamically optimizing the power increment validity threshold and storage trigger interval through local anomaly pattern learning. Local abnormal events are uploaded to the electricity consumption information collection main station for global aggregation and analysis; After global aggregation analysis, the updated validity threshold and storage strategy are sent to the smart meter.

9. The method as described in claim 8, characterized in that, The method further includes sending the updated validity threshold and storage policy to the smart meter. Deploy a lightweight learning agent based on smart meters that support edge computing; On the lightweight learning agent, the local abnormal events are anonymized.

10. A multi-mode secure storage system for electrical energy data, characterized in that, For implementing the multi-mode secure storage method for electrical energy data according to any one of claims 1-9, the system comprises: The basic configuration parameter acquisition module is used to acquire the basic configuration parameters of smart meters with electricity metering function. A multi-region chained storage structure construction module is used to configure the address mapping relationship and capacity allocation strategy between the main storage area and the backup storage area based on the basic configuration parameters, and to construct a multi-region chained storage structure. The real-time judgment module is used to integrate an abnormal data isolation area in the multi-region chained storage structure. Through a dynamic path selection mechanism, it judges the write status of the main storage area in real time and decides whether to switch to the backup storage area / abnormal data isolation area. The abnormal event management module is used to simultaneously optimize data consistency during the power accumulation process and construct an electricity-storage-time coupled model to manage abnormal events during the data storage process.

Images