A data management method and system for an industrial energy storage system

Abstract

The present invention relates to the technical field of data management, and particularly to a data management method and system for an industrial energy storage system. The method includes the following steps: performing real-time energy storage data collection on industrial energy storage units to generate corrected energy storage monitoring data; performing distributed redundant transmission on the corrected energy storage monitoring data, and performing upload data difference analysis to obtain monitoring upload difference data; performing difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to generate monitoring shard feature data; performing dynamic cache synchronization processing according to the monitoring shard feature data to obtain monitoring cache status data; performing comprehensive analysis of the management status on the monitoring shard feature data through the monitoring cache status data to generate a comprehensive data management index. The present invention realizes the efficient, reliable and secure upload management optimization of a large amount of industrial energy storage monitoring data through distributed intelligent caching, and effectively solves the problems of inconsistent data upload and even data loss.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a data management method and system for an industrial energy storage system. Background Art

[0002] With the rapid development of renewable energy and the advancement of industrial intelligence, industrial energy storage systems play an increasingly important role in the power system. Especially in the integration of renewable energy and grid stabilization, they can not only balance power supply and demand but also improve the reliability of the power system. With the popularization of distributed energy resources (such as solar panels and wind turbines), energy storage systems usually consist of multiple energy storage nodes distributed in different geographical locations. These nodes can be batteries, supercapacitors, or other energy storage devices. Each energy storage node needs to monitor and manage a large amount of data in real time. Distributed energy storage systems need to have characteristics such as energy storage nodes being dispersed in multiple locations and data collection and processing requiring high real-time performance. However, traditional data management methods for industrial energy storage systems usually rely on centralized storage management. However, the collected data is distributed among multiple nodes, and in cases such as network latency, node failures, or partitions, this centralized management is prone to problems such as data inconsistency, storage delays, and even data loss. Summary of the Invention

[0003] Based on this, the present invention provides a data management method and system for an industrial energy storage system to solve at least one of the above technical problems.

[0004] To achieve the above object, a data management method for an industrial energy storage system includes the following steps:

[0005] Step S1: Perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; collect real-time energy storage data according to the intelligent perception network topology data to obtain time-series energy storage monitoring data; correct abnormal data according to the time-series energy storage monitoring data to generate corrected energy storage monitoring data;

[0006] Step S2: Optimize distributed storage nodes according to the intelligent perception network topology data to obtain a distributed data storage network; perform distributed redundant transmission on the corrected energy storage monitoring data using the distributed data storage network to obtain redundant monitoring upload data;

[0007] Step S3: Process upload vouchers for the redundant monitoring upload data to generate upload voucher data; perform upload data difference analysis according to the upload voucher data to obtain monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain energy storage unit monitoring data;

[0008] Step S4: Perform dynamic data sharding processing based on the energy storage unit monitoring data to generate monitored shard feature data; perform dynamic cache synchronization processing based on the monitored shard feature data to generate cache management log data;

[0009] Step S5: Evaluate the cache status based on the cache management log data to obtain monitored cache status data;

[0010] Step S6: Conduct comprehensive analysis of the management status of the monitored shard feature data through the monitored cache status data to generate a comprehensive data management index.

[0011] The present invention performs edge computing topology processing on industrial energy storage units, establishes an efficient network structure, enables each energy storage unit to achieve fast data interaction and processing, and improves the real-time response ability and intelligent level of the system. Real-time energy storage data is collected according to the intelligent perception network topology data to ensure that the system can continuously monitor the operating status of each energy storage unit and obtain timely and accurate monitoring data. Abnormal data correction is performed according to the sequential energy storage monitoring data, which can eliminate noise and errors in the data collection process and ensure that subsequent analysis is based on reliable data sources. The optimization of distributed storage nodes according to the intelligent perception network topology data effectively improves the reliability and access efficiency of data storage, forming a distributed data storage network. The distributed redundant transmission strategy using this network further enhances data security. Even if some nodes fail, data can be uploaded through redundant monitoring to ensure data integrity. Through upload certificate processing and upload data difference analysis, not only the security of data upload is strengthened, but also differences or conflicts generated during data transmission are effectively identified and resolved, ensuring the consistency and accuracy of the final energy storage unit monitoring data. The large-scale monitoring data is segmented into smaller, easier-to-manage and process segments, improving data access efficiency and flexibility. The dynamic cache synchronization processing ensures the high efficiency of data access, and the formed cache management log data helps monitor and optimize the use of data caches. The cache status evaluation based on the cache management log data can provide the system with the health status and performance indicators of the cache, helping to identify potential failure risks in a timely manner. Through comprehensive consideration of the monitored cache status data, the comprehensive analysis of the management status of the monitored shard feature data generates a comprehensive data management index, which is a highly generalized index reflecting the efficiency and health status of the entire data management process. This index can not only be used as the basis for optimizing and adjusting data management, but also help decision-makers make more accurate management and scheduling decisions. Therefore, the data management method of an industrial energy storage system of the present invention collects industrial energy storage monitoring data in real time for energy storage units through edge computing, and ensures the consistency and integrity of data in the event of network failures or node failures by optimizing distributed storage nodes and implementing redundant transmission, thereby reducing the risk of data loss. Through dynamic data sharding and intelligent cache management, resource allocation is optimized according to the data access pattern, further reducing the upload storage delay and achieving efficient and intelligent data upload management.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Deploy intelligent monitoring sensors for industrial energy storage units to obtain monitoring node distribution data;

[0014] Step S12: Configure the wireless network based on the monitoring node distribution data, and perform edge computing topology processing to generate intelligent sensing network topology data, where the intelligent sensing network topology data includes energy storage monitoring nodes and distributed computing nodes;

[0015] Step S13: Set data acquisition parameters for the energy storage monitoring nodes based on the intelligent sensing network topology data, and perform real-time industrial energy storage data stream acquisition to obtain initial energy storage unit monitoring data;

[0016] Step S14: Use the distributed computing nodes to annotate timestamps for the initial energy storage unit monitoring data to obtain time-series energy storage monitoring data;

[0017] Step S15: Perform data redundancy filtering based on the time-series energy storage monitoring data to obtain refined energy storage monitoring data;

[0018] Step S16: Detect outliers in the refined energy storage monitoring data to generate abnormal data points; perform data resampling based on the abnormal data points to obtain resampled monitoring data;

[0019] Step S17: Based on the abnormal data points, use the resampled monitoring data to correct the abnormal data in the refined energy storage monitoring data to generate corrected energy storage monitoring data.

[0020] Through the deployment of intelligent monitoring sensors for industrial energy storage units, the present invention ensures that the states of each energy storage unit can be comprehensively covered and real-time monitored, enhancing the monitoring ability of the system and the breadth of data acquisition. Configuring the wireless network according to the distribution data of the monitoring nodes can achieve efficient data transmission, optimize the network structure, and reduce the data transmission delay. Through the data acquisition parameter setting guided by the intelligent sensing network topology data, the data acquisition process becomes more accurate and efficient, and the acquisition of real-time industrial energy storage data stream ensures the timeliness of the data. Using the distributed computing nodes to annotate timestamps for the initial energy storage unit monitoring data ensures the time-series characteristics of the data. Performing data redundancy filtering based on the time-series energy storage monitoring data can remove duplicate or invalid data points, effectively reducing the data volume and improving the data processing efficiency. Detecting outliers in the monitoring data through outlier detection, and subsequent data resampling operations supplement or correct these abnormal data, and the generated resampled monitoring data is more accurate and continuous, improving the data quality. Using the abnormal data points and the resampled monitoring data to correct the refined energy storage monitoring data ensures the accuracy and integrity of the corrected energy storage monitoring data. It is particularly crucial to exclude the error data caused by equipment failures or environmental interferences, enhancing the robustness of the entire data link.

[0021] Preferably, step S2 includes the following steps:

[0022] Step S21: Perform geospatial analysis based on the intelligent perception network topology data to obtain spatial location distribution data;

[0023] Step S22: Evaluate the wireless signal strength of the industrial energy storage unit through the spatial location distribution data, and optimize the communication link of the intelligent perception network topology data to generate a perception node communication network;

[0024] Step S23: Connect distributed storage nodes according to the perception node communication network to obtain a distributed data storage network;

[0025] Step S24: Use the distributed data storage network to select upload nodes for the corrected energy storage monitoring data and perform parallel task decomposition to obtain multi-path upload instruction data;

[0026] Step S25: Perform redundant replication on the corrected energy storage monitoring data to obtain redundant monitoring data packets;

[0027] Step S26: Perform distributed redundant transmission on the redundant monitoring data packets through the multi-path upload instruction data to obtain redundant monitoring upload data.

[0028] Through the geospatial analysis performed by the intelligent perception network topology data of the present invention, the physical location layout of each monitoring node can be clearly displayed. Evaluating the wireless signal strength of the industrial energy storage unit using the spatial location distribution data and optimizing the communication link of the intelligent perception network topology data can effectively improve the stability and efficiency of data transmission. Connecting distributed storage nodes based on the optimized perception node communication network to form a distributed data storage network, this architecture can disperse the storage pressure, improve the access speed and security of data, and at the same time enhance the scalability and fault tolerance of the system. Using the distributed data storage network for upload node selection and parallel task decomposition not only optimizes the data upload path, realizes the efficient parallel processing of data upload, and greatly speeds up the data processing speed. By performing redundant replication on the corrected energy storage monitoring data, the reliability and persistence of the data are significantly enhanced. Even in the case of partial data transmission failure, the complete recovery of the data can be ensured, improving the robustness of the system. Effectively distributing the redundant monitoring data packets to multiple storage nodes not only ensures the instant backup of the data, but also increases the success rate of data transmission through multi-path transmission.

[0029] Preferably, step S3 includes the following steps:

[0030] Step S31: Analyze the monitoring characteristics of the redundant monitoring upload data to generate key energy storage monitoring characteristic data; perform upload voucher processing according to the key energy storage monitoring characteristic data to generate upload voucher data;

[0031] Step S32: Process the redundant monitoring upload data with the uploaded voucher data for voucher identification to obtain the identified monitoring upload data;

[0032] Step S33: Analyze the redundant upload data through a distributed data storage network for multi-node storage network analysis to generate a multi-node storage network;

[0033] Step S34: Use the multi-node storage network to construct a consensus network for the identified monitoring upload data to generate stored consensus network data; perform upload data difference analysis based on the stored consensus network data to obtain the monitored upload difference data;

[0034] Step S35: Evaluate the node performance of the stored consensus network data to generate consensus node performance data; recommend confidence nodes for the stored consensus network data through the consensus node performance data to obtain confidence node data;

[0035] Step S36: Based on the consensus node performance data, use the confidence node data to perform a confidence difference weighted judgment on the monitored upload difference data and perform difference conflict processing to generate upload difference conflict processing data;

[0036] Step S37: Based on the confidence node data, update the data consistency of the identified monitoring upload data through the upload difference conflict processing data to obtain the energy storage unit monitoring data.

[0037] The present invention conducts monitoring feature analysis on redundant monitoring uploaded data, which can extract the main features and trends of the operation of energy storage devices. According to the generated key energy storage monitoring feature data, upload voucher processing is carried out. Through the generation of upload vouchers, the integrity and security of the data upload process are ensured, the source and verification basis of the data are provided, and the credibility of data management is enhanced. Through the voucher identification processing of redundant monitoring uploaded data using upload voucher data, this processing ensures that all uploaded data is attached with corresponding voucher identifiers, which helps with subsequent tracking and management. The multi-node storage network analysis using a distributed data storage network optimizes the layout of data storage, and the generated multi-node storage network enhances the distributed storage capacity and access efficiency of the data. By constructing a consensus network, the consistency and integrity of the data are ensured, and the risk of information loss caused by node failure or data conflict is reduced. The node performance of the stored consensus network data is evaluated to generate consensus node performance data. This evaluation process can identify the performance advantages and disadvantages of each storage node, ensuring that the system can still maintain good performance under high load conditions. Through the consensus node performance data, confidence nodes are recommended for the stored consensus network data, ensuring that reliable nodes for data storage and processing are preferentially used. After weighted judgment, data is processed for difference conflicts to ensure that data conflicts can be effectively resolved in the case of multiple data sources. Based on the confidence node data, the data consistency of the identified monitoring uploaded data is updated by uploading the difference conflict processing data. This update process ensures that all monitoring data undergoes strict verification and processing, and the finally generated energy storage unit monitoring data has high credibility and effectiveness.

[0038] Preferably, step S31 includes the following steps:

[0039] Step S311: Identify the data type of the redundant monitoring uploaded data to obtain monitoring type identification data;

[0040] Step S312: Based on a preset monitoring type statistical rule, perform feature statistics on the redundant monitoring uploaded data using the monitoring type identification data to generate energy storage feature statistical data;

[0041] Step S313: Conduct feature correlation analysis according to the energy storage feature statistical data, and extract key monitoring features to generate key energy storage monitoring feature data;

[0042] Step S314: Use a preset hash algorithm to gradually read the monitoring features of the key energy storage monitoring feature data and calculate a fixed hash value to generate initial hash value data;

[0043] Step S315: Perform rolling update processing on the initial hash value data to obtain key monitoring hash data; attach meta-information according to the key monitoring hash data to obtain monitoring voucher metadata;

[0044] Step S316: Perform credential serialization processing on the monitoring credential metadata through a preset upload credential template to generate upload credential data.

[0045] The present invention can clearly distinguish different types of data content by identifying the data type of redundant monitoring uploaded data, so that different types of data can be analyzed and processed in a targeted manner. The redundant monitoring uploaded data is subjected to feature statistics through monitoring type identification data, and the feature performance under different monitoring types is quantified, such as the mean, standard deviation, extreme value, rate of change and other indicators of the feature. Feature correlation analysis is performed based on energy storage feature statistical data, and key monitoring feature extraction is performed. This analysis can reveal the relationship between different features and identify the key features that have the greatest impact on system performance. The initial hash value data generated by the step-by-step reading and fixed hash value calculation of the key energy storage monitoring feature data using a preset hash algorithm provides a unique digital fingerprint for the data, enhancing the uniqueness and non-tamperability of the data. The initial hash value data is subjected to rolling update processing to ensure that the hash value can reflect the latest monitoring data status, capture data changes in a timely manner, and enhance the dynamic adaptability of data management. The voucher serialization processing of the monitoring voucher metadata by the preset upload voucher template ensures the consistency and compatibility of data transmission between different systems, simplifies the complexity of data exchange, and improves the standardization and automation of data management.

[0046] Preferably, step S34 includes the following steps:

[0047] Step S341: using a multi-node storage network to perform node timing credential exchange on the identification monitoring uploaded data to obtain inter-node credential exchange data;

[0048] Step S342: performing hash collision calculation based on the credential exchange data between nodes to obtain preliminary credential similarity data;

[0049] Step S343: Based on a preset similarity threshold, the storage node threshold of the multi-node storage network is screened through preliminary credential similarity data to obtain consensus node list data;

[0050] Step S344: construct and store consensus network data according to the consensus node list data;

[0051] Step S345: Using the storage consensus network data to store node redundant data for the identification monitoring upload data, to obtain consensus node redundant mapping data;

[0052] Step S346: performing data comparison between nodes based on the consensus node redundant mapping data to generate preliminary comparison difference data;

[0053] Step S347: Perform metadata difference analysis based on the preliminary comparison difference data, and perform serialization processing to generate monitored upload difference data.

[0054] The present invention uses the multi-node storage network to perform node timing certificate exchange on the identity monitored upload data, ensuring the secure synchronization and verification of data among various nodes. Hash collision calculation is performed on the certificate exchange data among nodes to obtain preliminary certificate similarity data. This calculation process can identify the similarity between different node certificates, helping to discover existing data redundancy or inconsistency phenomena. By setting a threshold, only nodes that meet the similarity requirements are included in the consensus node list. The storage consensus network data constructed based on the consensus node list data ensures the correct replication and synchronization of data among multiple nodes, enhancing the data reliability and fault tolerance of the system. By storing redundant data in multiple consensus nodes, the security and reliability of the data are improved. The consensus node redundancy mapping data ensures that when a certain node fails or makes an error, the system can still quickly recover the data, reducing the risk of data loss. Based on the consensus node redundancy mapping data, data comparison among nodes can identify the data differences between storage nodes, helping to discover potential data inconsistency problems and improving the accuracy of data management. The metadata difference analysis performed based on the preliminary comparison difference data and the serialization processing finally obtain the monitored upload difference data, ensuring that all differences are effectively identified and recorded.

[0055] Preferably, step S4 includes the following steps:

[0056] Step S41: Perform dynamic data sharding processing on the energy storage unit monitoring data to generate monitored shard feature data;

[0057] Step S42: Calculate the monitoring update rate based on the monitored shard feature data to generate shard update frequency data;

[0058] Step S43: Evaluate the cache requirements for the energy storage monitoring shard data through the shard update frequency data to generate shard cache requirement data;

[0059] Step S44: Based on a preset update rate threshold, divide the data update status of the shard cache requirement data. When the shard cache requirement data is higher than or equal to the preset update rate threshold, mark the monitored shard feature data as high-frequency monitored shard data; when the shard cache requirement data is lower than the preset update rate threshold, mark the monitored shard feature data as low-frequency monitored shard data;

[0060] Step S45: Calculate the average update interval for the high-frequency monitored shard data to generate average update interval data; perform real-time update window caching processing based on the average update interval data to obtain the high-frequency shard caching strategy;

[0061] Step S46: Estimate the cache capacity requirement for the low-frequency monitored shard data to generate cache capacity requirement data; obtain the available memory resource data; perform secure cache processing on the cache capacity requirement data through the available memory resource data to obtain low-frequency shard cache data;

[0062] Step S47: Perform dynamic cache policy processing according to the high-frequency shard cache policy and the low-frequency shard cache data to generate dynamic cache policy data;

[0063] Step S48: Perform cache synchronization management processing on the monitored shard feature data through the dynamic cache policy data to generate cache management log data.

[0064] The present invention performs dynamic data sharding processing on the energy storage unit monitoring data, divides the large-scale monitoring data into smaller, manageable and processable data segments, and improves the data access efficiency. Organize this information into a structured format, for example, the ID of each shard, data range, feature statistics, etc. Calculate the monitoring update rate according to the monitored shard feature data, which can identify the update frequency of each data segment, thereby reflecting the importance and dynamic changes of the data. Evaluate the cache requirements for the energy storage monitoring shard data through the shard update frequency data to determine the cache resources required for each data segment, ensuring that the data with high-frequency updates can be preferentially cached. Based on the preset update rate threshold, the data update status of the shard cache requirement data is divided, and the monitored shard feature data is divided into high-frequency and low-frequency categories. This classification strategy is conducive to adopting differential management for data with different update characteristics and improving the utilization efficiency of cache resources. The calculation of the average update interval and the real-time update window cache processing for the high-frequency monitored shard data can ensure a fast response and timely update of high-dynamic data, reduce data latency, and improve the system response speed. The estimation of the cache capacity requirement for the low-frequency monitored shard data, combined with the secure cache processing of the available memory resource data, not only ensures the effective caching of relatively static data but also avoids excessive occupation of memory resources, achieving reasonable resource allocation. Perform dynamic cache policy processing according to the high-frequency shard cache policy and the low-frequency shard cache data. This policy comprehensively considers the characteristics of high-frequency and low-frequency data to ensure that the system can flexibly adjust the allocation of cache resources and optimize the data access efficiency. Perform cache synchronization management processing on the monitored shard feature data through the dynamic cache policy data to ensure that all collected industrial energy storage data can be synchronized in a timely manner and achieve data cache management, avoiding errors caused by data asynchronization.

[0065] Preferably, step S5 includes the following steps:

[0066] Step S51: Perform data update monitoring according to the cache management log data to generate a data update status report;

[0067] Step S52: Perform an update notification broadcast process on the distributed data storage network based on the data update status report, and extract a cache status summary to generate cache status summary data;

[0068] Step S53: Perform cache consistency evaluation based on the cache status summary data to generate cache consistency evaluation data; identify inconsistent nodes from the cache consistency evaluation data to obtain inconsistent node list data;

[0069] Step S54: Repair the node caches according to the inconsistent node list data, and re-evaluate the cache status to obtain monitored cache status data.

[0070] The present invention monitors data updates based on cache management log data to ensure real-time tracking of the data update status and provide transparency regarding the data update process. The generated data update status report provides important information for system administrators to help timely identify the success or failure of data updates and ensure that the data always remains up-to-date. Perform an update notification broadcast process on the distributed data storage network based on the data update status report, and extract a cache status summary to generate cache status summary data. This broadcast process can quickly disseminate data update information to all relevant nodes, ensuring that all parts of the system can timely obtain the latest data update status. Performing cache consistency evaluation based on the cache status summary data can identify the consistency status of the cached data in the system, ensure data synchronization between different nodes, and help determine whether further data repair or update operations are required. By repairing the cached data of these nodes, the data consistency of the system can be effectively restored, avoiding incorrect decisions or operations caused by data inconsistency. Subsequently, the re-evaluation of the cache status and the obtained monitored cache status data not only verify the effect of the repair operation but also provide the latest information for continuous monitoring and optimization of the cache management strategy.

[0071] Preferably, step S6 includes the following steps:

[0072] Step S61: Obtain historical energy storage data access records; identify the access pattern of the monitored shard feature data through the historical energy storage data access records to generate a data access pattern;

[0073] Step S62: Estimate the access probability of the monitored shard feature data based on the data access pattern to generate shard access probability data; construct a predictive shard data index according to the shard access probability data;

[0074] Step S63: Perform a shard data query simulation according to the predictive shard data index, and locate the target data shard to obtain shard query feature data;

[0075] Step S64: Perform dynamic access path evaluation based on the sharded query feature data to generate access path evaluation data; perform data prefetch efficiency evaluation based on the sharded query feature data to generate data prefetch evaluation data;

[0076] Step S65: Evaluate the caching effect on the sharded query feature data by monitoring the cache status data to generate cache effect evaluation data;

[0077] Step S66: Conduct a comprehensive analysis of the management status based on the access path evaluation data, data prefetch evaluation data, and cache effect evaluation data to generate a comprehensive data management index.

[0078] The present invention can identify the access patterns of the monitored sharded feature data through the historical energy storage data access records, which can reveal the user's data access habits at different time periods and help identify the frequently accessed data segments. Based on the data access patterns, the access probabilities of the monitored sharded feature data are estimated, quantifying the likelihood of each data segment being accessed. The predictive sharded data index constructed accordingly can anticipate the data access requirements in advance, accelerate the data retrieval process, and reduce the query latency. The simulation of sharded data query based on the predictive sharded data index provides a rehearsal for actual data access, ensuring that the system can identify the data to be accessed in advance, improving the timeliness and accuracy of data access. The dynamic access path evaluation performed through the sharded query feature data helps identify the optimal data access path and reduce the data transmission time. Meanwhile, the data prefetch evaluation data generated by the data prefetch efficiency evaluation can guide the system to preload the accessed data in advance, further enhancing the data processing efficiency and user experience. The cache effect evaluation of the sharded query feature data using the monitored cache status data evaluates the effectiveness of the current caching strategy, provides feedback information for adjusting the cache configuration and optimizing the cache hit rate, and ensures the efficient utilization of cache resources. The comprehensive analysis of the management status based on the access path evaluation data, data prefetch evaluation data, and cache effect evaluation data reflects the overall health status and efficiency of data management.

[0079] Preferably, the present invention further provides a data management system for an industrial energy storage system, which executes the data management method for the industrial energy storage system as described above. The data management system for the industrial energy storage system includes:

[0080] A data real-time acquisition module, configured to perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; perform real-time energy storage data acquisition based on the intelligent perception network topology data to obtain time-series energy storage monitoring data; perform abnormal data correction on the time-series energy storage monitoring data to generate corrected energy storage monitoring data;

[0081] Distributed redundant transmission module, which is used to optimize distributed storage nodes according to intelligent perception network topology data to obtain a distributed data storage network; and perform distributed redundant transmission on the corrected energy storage monitoring data by using the distributed data storage network to obtain redundant monitoring upload data;

[0082] Data consistency maintenance module, which is used to process upload vouchers for the redundant monitoring upload data to generate upload voucher data; perform upload data difference analysis based on the upload voucher data to obtain monitoring upload difference data; and perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain energy storage unit monitoring data;

[0083] Dynamic data caching module, which is used to perform dynamic data sharding processing according to the energy storage unit monitoring data to generate monitoring sharding feature data; and perform dynamic cache synchronization processing based on the monitoring sharding feature data to generate cache management log data;

[0084] Cache status analysis module, which is used to evaluate the cache status according to the cache management log data to obtain monitoring cache status data;

[0085] Comprehensive data management evaluation module, which is used to perform comprehensive analysis of the management status on the monitoring sharding feature data through the monitoring cache status data to generate a comprehensive data management index. Description of the Drawings

[0086] Figure 1 It is a schematic step - flow diagram of a data management method for an industrial energy storage system according to the present invention;

[0087] Figure 2 is Figure 1 a detailed implementation step - flow diagram of step S2 in

[0088] Figure 3 is Figure 1 a detailed implementation step - flow diagram of step S5 in

[0089] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0090] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0091] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0092] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0093] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a data management method for an industrial energy storage system, including the following steps:

[0094] Step S1: Perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; collect real-time energy storage data according to the intelligent perception network topology data to obtain time-series energy storage monitoring data; correct abnormal data according to the time-series energy storage monitoring data to generate corrected energy storage monitoring data;

[0095] Step S2: Optimize the distributed storage nodes according to the intelligent perception network topology data to obtain a distributed data storage network; use the distributed data storage network to perform distributed redundant transmission on the corrected energy storage monitoring data to obtain redundant monitoring upload data;

[0096] Step S3: Process the upload vouchers for the redundant monitoring upload data to generate upload voucher data; perform upload data difference analysis according to the upload voucher data to obtain monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain energy storage unit monitoring data;

[0097] Step S4: Perform dynamic data sharding processing according to the energy storage unit monitoring data to generate monitoring sharding feature data; perform dynamic cache synchronization processing according to the monitoring sharding feature data to generate cache management log data;

[0098] Step S5: Evaluate the cache status according to the cache management log data to obtain monitoring cache status data;

[0099] Step S6: Perform a comprehensive analysis of the management status of the monitored shard feature data by monitoring the cache status data to generate a comprehensive data management index.

[0100] In the embodiment of the present invention, with reference to Figure 1 As described, it is a schematic diagram of the step flow of the data management method of the industrial energy storage system of the present invention. In this embodiment, the data management method of the industrial energy storage system includes the following steps:

[0101] Step S1: Perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; collect real-time energy storage data according to the intelligent perception network topology data to obtain time-series energy storage monitoring data; perform abnormal data correction on the time-series energy storage monitoring data to generate corrected energy storage monitoring data;

[0102] In the embodiment of the present invention, an edge computing gateway supporting the Modbus TCP / IP protocol is used to connect to each industrial energy storage unit. For example, by analyzing the network connection information of the industrial energy storage unit, such as IP address, port number, and network protocol, etc., a network topology diagram including all energy storage units and edge computing nodes is constructed. According to the generated topology data, a data collection program is deployed on the edge computing gateway. The program uses the MQTT protocol to collect the real-time monitoring data of key parameters such as voltage, current, temperature, and SOC of each energy storage unit in real time according to a preset sampling frequency (for example, voltage and current are collected once per second, and temperature is collected once per minute). Correct the time-series energy storage monitoring data. For example, an abnormal data rejection method based on the 3σ criterion is adopted, the sliding window size is set to 10 minutes, and the average value and standard deviation of the data within each window are calculated. The data that deviates from the average value by more than 3 times the standard deviation is determined as abnormal data and rejected, and the linear interpolation method or data resampling method is used to fill the rejected data points, and finally the corrected energy storage monitoring data is generated.

[0103] Step S2: Optimize the distributed storage nodes according to the intelligent perception network topology data to obtain a distributed data storage network; use the distributed data storage network to perform distributed redundant transmission on the corrected energy storage monitoring data to obtain redundant monitoring upload data;

[0104] In the embodiments of the present invention, according to the intelligent perception network topology data, especially the geographical location information of the energy storage unit contained therein, the distributed storage system etcd is optimized for nodes by using the distributed consensus algorithm Raft. For example, three servers closer to the energy storage unit are selected as the leader nodes and follower nodes of the etcd cluster, and load balancing allocation is performed according to the network bandwidth and storage space of the nodes, and finally a highly available and low-latency distributed data storage network is obtained. The distributed message queue Kafka is used to perform distributed redundant transmission on the corrected energy storage monitoring data. For example, the corrected energy storage monitoring data is sent to three different partitions of Kafka respectively, and each partition corresponds to a different server node. By using the message replication mechanism of Kafka, even if one of the nodes fails, the data will not be lost, and finally redundant monitoring upload data is obtained to ensure the reliability of data transmission.

[0105] Step S3: Process the redundant monitoring upload data to generate upload credential data; perform upload data difference analysis according to the upload credential data to obtain monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain the energy storage unit monitoring data;

[0106] In the embodiments of the present invention, hash calculation is performed on the redundant monitoring upload data. For example, the SHA256 algorithm is used to generate a unique hash value as the upload credential. The hash value, the corresponding data block ID, and the timestamp are stored in the distributed cache Redis to form the upload credential data. The data comparison algorithm (such as: string comparison algorithm or hash algorithm) is used to perform upload data difference analysis on the upload credential data to obtain the monitoring upload difference data. For example, compare the hash values corresponding to the same data block ID in the three partitions to analyze whether there are differences. If the hash values are exactly the same, it means that the data has no differences; if there are inconsistent hash values, it means that the data has differences, and the data block IDs of the differences are recorded to form the monitoring upload difference data. The data merging algorithm (such as: three-way merge algorithm or voting algorithm) is used to perform difference conflict processing on the redundant monitoring upload data to obtain the final energy storage unit monitoring data. For example, use the three-way merge algorithm to compare the data on the three storage nodes and determine the final correct data according to the voting results.

[0107] Step S4: Perform dynamic data sharding processing according to the energy storage unit monitoring data to generate monitoring sharding feature data; perform dynamic cache synchronization processing according to the monitoring sharding feature data to generate cache management log data;

[0108] In the embodiments of the present invention, dynamic data sharding is performed according to the characteristics of the monitoring data of the energy storage unit, such as the time range, data type, and the affiliated energy storage unit, etc. For example, the monitoring data of the energy storage unit for one day can be sharded by hour, and each shard contains the voltage, current, temperature, SOC, etc. data of all energy storage units within that hour. A unique ID is generated for each shard, and information such as the time range, data type, and data volume of the shard is stored in the monitoring shard feature data. According to the monitoring shard feature data, using the consistent hashing algorithm, the data shards are evenly distributed to different cache server nodes. For example, the hash value corresponding to each shard ID is calculated using the consistent hashing algorithm, and the shard data is stored on the corresponding cache server node according to the hash value. At the same time, information such as the storage location, time, and update times of each shard data is recorded to generate cache management log data.

[0109] Step S5: Evaluate the cache status according to the cache management log data to obtain the monitored cache status data;

[0110] In the embodiments of the present invention, according to information such as the load status of the cache server node, the access frequency of the data shard, and the data update time, a machine learning algorithm is used to evaluate the cache status. For example, a cache status evaluation model can be constructed using a logistic regression algorithm to predict the access probability of each data shard in the next period of time, and the cache data is preheated or eliminated according to the prediction results. Finally, the monitored cache status data is generated, including information such as the predicted access probability, cache status, and storage location of each data shard.

[0111] Step S6: Perform a comprehensive analysis of the management status of the monitoring shard feature data through the monitored cache status data to generate a comprehensive data management index.

[0112] In the embodiments of the present invention, information such as the predicted access probability, cache status, and storage location of each data shard can be visualized as a chart, and the data shards are classified and displayed according to different metrics. For example, the shards with a high access probability are marked in red, and the shards with a low access probability are marked in green. Statistical analysis is performed on the monitoring data, and a comprehensive data management index is generated according to the analysis results. For example, the comprehensive data management index can be calculated according to metrics such as the cache hit rate, data update frequency, and data volume, and used as an important indicator to measure the data management efficiency and performance of the industrial energy storage system. For example, for data shards with a high access probability, they can be cached on cache server nodes with higher performance, or the number of their replicas can be increased to improve data access efficiency; for data shards with a low access probability, they can be eliminated from the cache, or migrated to a storage system with lower storage costs to reduce storage costs, thereby realizing the intelligent management of industrial energy storage data.

[0113] Preferably, step S1 includes the following steps:

[0114] Step S11: Deploy intelligent monitoring sensors for the industrial energy storage unit to obtain monitoring node distribution data;

[0115] Step S12: Configure the wireless network according to the monitoring node distribution data, and perform edge computing topology processing to generate intelligent perception network topology data, where the intelligent perception network topology data includes energy storage monitoring nodes and distributed computing nodes;

[0116] Step S13: Set data acquisition parameters for the energy storage monitoring nodes based on the intelligent perception network topology data, and perform real-time industrial energy storage data stream acquisition to obtain initial energy storage unit monitoring data;

[0117] Step S14: Use the distributed computing nodes to label time stamps for the initial energy storage unit monitoring data to obtain time-series energy storage monitoring data;

[0118] Step S15: Perform data redundancy filtering according to the time-series energy storage monitoring data to obtain refined energy storage monitoring data;

[0119] Step S16: Detect outliers for the refined energy storage monitoring data to generate abnormal data points; perform data resampling according to the abnormal data points to obtain resampled monitoring data;

[0120] Step S17: Based on the abnormal data points, correct the abnormal data in the refined energy storage monitoring data through the resampled monitoring data to generate corrected energy storage monitoring data.

[0121] In the embodiments of the present invention, according to the specific situation of the industrial energy storage unit, appropriate intelligent monitoring sensors are selected, such as battery voltage sensors, current sensors, temperature sensors, capacity sensors, etc. Intelligent monitoring sensors are deployed at different positions of the industrial energy storage unit, and information such as the installation location, type, and model of each sensor is recorded to form monitoring node distribution data. According to the monitoring node distribution data, an appropriate wireless communication protocol is selected, such as ZigBee, WiFi, LoRa, etc., to configure the sensor network and establish a communication connection between the sensor nodes and the gateway. For example, according to the connection relationship between the sensors and the edge computing nodes, a network topology diagram including all energy storage monitoring nodes and distributed computing nodes is constructed. Using the configuration tool provided by the gateway device, data acquisition parameters are set for each sensor node. For example, the sampling frequency of the temperature sensor is set to 1 second, the sampling frequency of the voltage sensor is set to 0.5 second, and the sampling frequency of the current sensor is set to 2 seconds, etc. After the parameter setting is completed, the sensor nodes start to collect real-time data at the set frequency and upload the collected data to the gateway through the wireless network, and finally converge to form the initial energy storage unit monitoring data. Using a high-precision clock service, such as the NTP service, an accurate timestamp is added to each piece of data to record the data collection time. Data redundancy filtering is performed on the time-series energy storage monitoring data. For example, a sliding window algorithm or a time window algorithm is used to remove duplicate data points or overly dense data points. For example, an outlier detection method based on the 3σ criterion can be adopted to calculate the average value and standard deviation of each data point and the data within a certain period before and after it. If the data point deviates from the average value by more than 3 times the standard deviation, then this data point is considered an abnormal data point. For the detected abnormal data points, data resampling can be performed according to the actual situation. For example, the sampling frequency can be reduced, or a more stable and accurate sensor can be used for data collection, and the resampled data is used as the resampled monitoring data. The abnormal data points in the refined energy storage monitoring data are corrected using the resampled monitoring data. For example, linear interpolation can be used to perform interpolation calculations on the abnormal data points according to the values of the normal data points before and after the abnormal data points, and the interpolation result is used as the corrected data.

[0122] As an example of the present invention, refer to Figure 2 shown in Figure 1 the detailed implementation step flow diagram of step S2 in

[0123] Step S21: Perform geospatial analysis based on the intelligent perception network topology data to obtain spatial location distribution data;

[0124] In the embodiments of the present invention, the geographical location information of all nodes, such as latitude and longitude coordinates, is extracted from the intelligent perception network topology data. Then, using geographical information system (GIS) tools, such as ArcGIS or QGIS, the node location information is visualized and spatial analysis is performed. For example, spatial clustering algorithms can be used to identify areas with dense nodes and areas with sparse nodes, or spatial interpolation algorithms can be used to estimate the signal strength in areas where nodes are not deployed. Finally, information such as the node distribution characteristics, node density, and signal coverage obtained from the analysis is summarized to form spatial location distribution data.

[0125] Step S22: Evaluate the wireless signal strength of the industrial energy storage unit based on the spatial location distribution data, and optimize the communication links of the intelligent perception network topology data to generate a perception node communication network;

[0126] In the embodiments of the present invention, the signal strength from each sensor node to the gateway node can be calculated according to the node distance and the signal propagation model, and whether the signal quality meets the data transmission requirements can be judged according to a preset threshold. According to the signal strength evaluation results and the network topology data, the communication links are optimized. For example, for nodes with weak signal strength, they can be connected to a gateway node that is closer, or the signal can be enhanced by adding relay nodes. The routing strategy can also be dynamically adjusted according to the network load situation to select the optimal communication path to reduce data transmission delay and packet loss rate. Finally, a perception node communication network containing information such as the optimized node connection relationship, communication path, and signal strength is generated.

[0127] Step S23: Connect the distributed storage nodes according to the perception node communication network to obtain a distributed data storage network;

[0128] In the embodiments of the present invention, multiple gateway nodes that are geographically close can be connected to the same data center, and the data is stored on the distributed storage nodes in the data center. At the same time, to improve data reliability and access efficiency, data replicas can be stored in multiple data centers, and the distribution and quantity of data replicas can be dynamically adjusted according to the network conditions and node load conditions. Finally, a highly available, high-performance, and scalable distributed data storage network is constructed.

[0129] Step S24: Select upload nodes for the corrected energy storage monitoring data using the distributed data storage network, and perform parallel task decomposition to obtain multi-path upload instruction data;

[0130] In the embodiments of the present invention, a distributed data storage network is utilized to select an upload node for the corrected energy storage monitoring data according to a load balancing algorithm (e.g., round-robin algorithm or least connections algorithm). For example, a suitable storage node is selected for data upload according to factors such as the remaining storage space and network bandwidth of the storage node. For example, the nearest data center can be selected according to the geographical location to which the data belongs, and different storage nodes can be selected according to the data type and importance. According to the upload node selection result, a task scheduling tool (e.g., Apache Airflow or Luigi) is used to decompose the data upload task into parallel tasks, generating multi-path upload instruction data. For example, the corrected energy storage monitoring data is divided into multiple data packets, and corresponding upload nodes and upload paths are assigned to each data packet according to the upload node selection result.

[0131] Step S25: Perform redundant replication on the corrected energy storage monitoring data to obtain redundant monitoring data packets;

[0132] In the embodiments of the present invention, before data upload, redundant replication is performed on the corrected energy storage monitoring data to generate multiple copies. For example, a three-copy strategy can be adopted to generate three identical data copies. At the same time, to ensure the integrity of data transmission, check information, such as CRC checksum or MD5 checksum, can be added to each data packet for the receiving party to perform data verification to ensure that the data has not been corrupted during transmission. Finally, the data copies and check information are encapsulated into redundant monitoring data packets for preparation for upload. For example, for a 100MB data block, three identical copies can be generated, and information such as CRC checksum and data block number is added to each copy, finally forming three independent redundant monitoring data packets.

[0133] Step S26: Perform distributed redundant transmission on the redundant monitoring data packets through the multi-path upload instruction data to obtain redundant monitoring upload data.

[0134] In the embodiments of the present invention, the redundant monitoring data packets are uploaded to different storage nodes through different network paths. For example, the three data copies can be uploaded to three different data centers respectively, or to three different nodes in the same data center. Distributed redundant transmission can effectively improve the reliability and efficiency of data transmission. Even if one of the network paths fails or a certain storage node malfunctions, the data can still be successfully uploaded through other paths and nodes, avoiding data loss. Finally, after all data copies are successfully uploaded, redundant monitoring upload data is obtained.

[0135] Preferably, step S3 includes the following steps:

[0136] Step S31: Analyze the monitoring features of the redundant monitoring upload data to generate key energy storage monitoring feature data; process the upload vouchers based on the key energy storage monitoring feature data to generate upload voucher data;

[0137] Step S32: Process the redundant monitoring upload data with the upload voucher data for voucher identification to obtain the identified monitoring upload data;

[0138] Step S33: Analyze the multi-node storage network of the redundant upload data through the distributed data storage network to generate a multi-node storage network;

[0139] Step S34: Use the multi-node storage network to construct a consensus network for the identified monitoring upload data to generate storage consensus network data; analyze the differences in the upload data based on the storage consensus network data to obtain the monitoring upload difference data;

[0140] Step S35: Evaluate the node performance of the storage consensus network data to generate consensus node performance data; recommend confidence nodes for the storage consensus network data through the consensus node performance data to obtain confidence node data;

[0141] Step S36: Based on the consensus node performance data, use the confidence node data to make a weighted judgment on the confidence differences of the monitoring upload difference data and perform difference conflict processing to generate upload difference conflict processing data;

[0142] Step S37: Based on the confidence node data, update the data consistency of the identified monitoring upload data through the upload difference conflict processing data to obtain the energy storage unit monitoring data.

[0143] In the embodiments of the present invention, a data analysis tool (such as Pandas or Scikit-learn) is used to analyze the monitoring features of redundant monitoring upload data to generate key energy storage monitoring feature data. For example, analyze the change trends, outliers, fluctuation ranges and other features of key parameters such as battery voltage, current, temperature, capacity, etc., and extract key index data. Hash calculation is performed on the extracted key energy storage monitoring feature data to generate a unique hash value as the upload voucher, and the hash value is bound to information such as the corresponding data block ID and timestamp to generate upload voucher data. For example, the SHA256 algorithm can be used to perform hash calculation on the key feature data to generate a 256-bit hash value as the unique voucher for this data block. The upload voucher data is added to the corresponding redundant monitoring upload data to identify the data source and integrity. For example, the upload voucher data can be used as the header information of the data packet or stored in the metadata of the data packet. A network monitoring tool (such as Nagios or Zabbix) is used to perform multi-node storage network analysis on the distributed data storage network to generate multi-node storage network data. According to the upload voucher information in the identified monitoring upload data, a consensus network is constructed on the multi-node storage network using a distributed consensus algorithm, such as Raft or Paxos, to ensure that all nodes reach a consensus on the authenticity and consistency of the data, and generate storage consensus network data. Compare the identified monitoring upload data stored on different nodes to analyze whether there are differences. For example, compare the upload voucher data corresponding to the same data block ID. If the hash values are exactly the same, it means the data has no differences; if there are inconsistent hash values, it means the data has differences during the upload process, and monitoring upload difference data is formed. According to the information such as the number of times a node participates in the consensus, response time, and data transmission volume recorded in the storage consensus network data, evaluate the node performance to generate consensus node performance data. For example, nodes can be scored according to indicators such as contribution degree, stability, and reliability. According to the consensus node performance data, use a voting mechanism or a scoring mechanism to recommend confidence nodes for the storage consensus network data to obtain confidence node data. For example, score the nodes according to their performance indicators and select high-confidence nodes according to the scoring results. According to the confidence node data, perform a confidence difference weighted judgment on the monitoring upload difference data. For example, different weights can be assigned to the data uploaded by different nodes according to the scores or reputation values of the confidence nodes. The higher the confidence of the node, the higher the weight of the data it uploads. Then, according to the weighted data, use a preset conflict resolution strategy, such as the "majority rule" or "time priority" strategy, to process the conflicting data. For example, if among three nodes, the data of two nodes is the same, then the data of these two nodes shall prevail; if the data of all three nodes is inconsistent, then the data with the latest timestamp shall prevail, and upload difference conflict processing data is generated.Update the identification monitoring upload data according to the final data version recorded in the data for handling upload differences and conflicts, ensuring that the data stored on all nodes is consistent. For example, the final data version can be synchronized to all nodes, or the old version data can be deleted, leaving only the final version data.

[0144] Preferably, step S31 includes the following steps:

[0145] Step S311: Identify the data type of the redundant monitoring upload data to obtain the monitoring type identification data;

[0146] Step S312: Based on the preset monitoring type statistical rules, perform feature statistics on the redundant monitoring upload data through the monitoring type identification data to generate energy storage feature statistical data;

[0147] Step S313: Conduct feature correlation analysis based on the energy storage feature statistical data and extract key monitoring features to generate key energy storage monitoring feature data;

[0148] Step S314: Use the preset hash algorithm to gradually read the monitoring features of the key energy storage monitoring feature data and calculate the fixed hash value to generate the initial hash value data;

[0149] Step S315: Perform rolling update processing on the initial hash value data to obtain the key monitoring hash data; attach meta-information according to the key monitoring hash data to obtain the monitoring voucher metadata;

[0150] Step S316: Perform voucher serialization processing on the monitoring voucher metadata through the preset upload voucher template to generate upload voucher data.

[0151] In the embodiments of the present invention, information such as the data structure, data tags, and data content of the analyzed redundant monitoring upload data is analyzed to identify the type of each piece of data, such as voltage data, current data, temperature data, etc. Methods such as regular expression matching and dictionary matching can be used for data type identification. Preset statistical rules are applied for feature statistics. For example, for voltage data, features such as maximum value, minimum value, average value, and standard deviation can be statistically analyzed; for current data, features such as charging current, discharging current, and average current can be statistically analyzed; for temperature data, features such as highest temperature, lowest temperature, and average temperature can be statistically analyzed. Using correlation analysis methods, such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc., the correlation between different monitoring features is analyzed to identify redundant features and key features. For example, if the maximum value and minimum value of voltage data are highly correlated, then one of the features can be selected as the key feature. A secure hash algorithm, such as SHA256 or MD5, is selected to perform hash calculation on the key energy storage monitoring feature data. To improve the calculation efficiency, a step-by-step reading method can be adopted, where only a part of the feature data is read each time for hash calculation, and the calculation result is iteratively operated with the previous hash value to finally generate a hash value with a fixed length as the initial hash value data. As new monitoring data arrives, the initial hash value data is updated in a rolling manner to ensure that the hash value can reflect the latest data changes. For example, a sliding window method can be used. Each time an update is made, the hash value of the latest data block is added to the calculation, and the hash value of the oldest data block is removed, so as to realize the rolling update of the hash value and obtain the key monitoring hash data. The monitoring voucher metadata is voucher serialized through a preset upload voucher template using a data serialization tool (such as JSON or XML) to generate upload voucher data.

[0152] Preferably, step S34 includes the following steps:

[0153] Step S341: Use a multi-node storage network to perform node timing voucher exchange on the identified monitoring upload data to obtain inter-node voucher exchange data;

[0154] Step S342: Perform hash collision calculation based on the inter-node voucher exchange data to obtain preliminary voucher similarity data;

[0155] Step S343: Based on a preset similarity threshold, perform storage node threshold screening on the multi-node storage network through the preliminary voucher similarity data to obtain a consensus node list data;

[0156] Step S344: Construct storage consensus network data according to the consensus node list data;

[0157] Step S345: Use the stored consensus network data to perform node redundant data storage on the identity monitoring upload data to obtain consensus node redundant mapping data;

[0158] Step S346: Based on the consensus node redundant mapping data, perform data comparison between nodes to generate preliminary comparison difference data;

[0159] Step S347: According to the preliminary comparison difference data, perform metadata difference analysis and serialization processing to generate monitoring upload difference data.

[0160] In the embodiments of the present invention, a fixed time interval is set, for example, every 5 minutes, so that each node in the multi-node storage network exchanges the upload vouchers for identifying and monitoring the uploaded data stored by each other. Each node packs the data block ID it stores and the corresponding upload voucher and sends them to other nodes, while receiving data packets from other nodes. Finally, each node collects the data block IDs and upload voucher information of all nodes, and uses a hash algorithm (such as: SHA-256 or MD5) to perform hash collision calculation on the voucher exchange data between nodes to obtain preliminary voucher similarity data. For example, each node performs a hash calculation on the received voucher data and compares it with the voucher data stored by itself, and calculates the proportion of the same hash value as the preliminary voucher similarity. A similarity threshold is preset in advance, for example, 80%. According to the preliminary voucher similarity data, the data blocks with a consensus degree higher than the threshold are screened out, and the nodes storing these data blocks are added to the consensus node list. For example, if among 10 nodes, 9 nodes have the same upload voucher for a data block, then the consensus degree of this data block is 90%, exceeding the preset threshold of 80%, so these 9 nodes are added to the consensus node list. According to the data in the consensus node list, a new network topology structure is constructed, called the storage consensus network. The storage consensus network only contains consensus nodes, and the connection relationship between nodes is adjusted according to the storage situation of data blocks. For example, if two consensus nodes store the same data block, a connection is established between these two nodes. The identifying and monitoring uploaded data is stored in the storage consensus network, and it is ensured that each data block is stored on at least two different consensus nodes to achieve redundant data storage. Record on which consensus nodes each data block is stored to generate consensus node redundancy mapping data. According to the consensus node redundancy mapping data, find the consensus nodes that store the same data block, and perform a byte-by-byte comparison on the data stored on these nodes. Record the byte positions where the data is inconsistent, the original data values, node information, etc. to generate preliminary comparison difference data. Analyze the preliminary comparison difference data to identify the reasons for the data differences, such as data transmission errors, data writing errors, malicious data tampering, etc. Extract information such as the positions, types, and reasons of the difference data, and combine it with metadata such as the data block ID and timestamp for serialization processing, such as converting it to the JSON format, to generate the final monitoring upload difference data.

[0161] Preferably, step S4 includes the following steps:

[0162] Step S41: Perform dynamic data sharding processing on the energy storage unit monitoring data to generate monitoring shard feature data;

[0163] Step S42: Calculate the monitoring update rate according to the monitoring shard feature data to generate shard update frequency data;

[0164] Step S43: Evaluate the caching requirements of the energy storage monitoring shard data based on the sharding update frequency data to generate shard caching requirement data;

[0165] Step S44: Divide the data update status of the shard caching requirement data based on a preset update rate threshold. When the shard caching requirement data is higher than or equal to the preset update rate threshold, mark the monitoring shard feature data as high-frequency monitoring shard data; when the shard caching requirement data is lower than the preset update rate threshold, mark the monitoring shard feature data as low-frequency monitoring shard data;

[0166] Step S45: Calculate the average update interval for the high-frequency monitoring shard data to generate average update interval data; perform real-time update window caching processing based on the average update interval data to obtain the high-frequency shard caching policy;

[0167] Step S46: Estimate the caching capacity requirements for the low-frequency monitoring shard data to generate caching capacity requirement data; obtain the available memory resource data; perform secure caching processing on the caching capacity requirement data using the available memory resource data to obtain the low-frequency shard caching data;

[0168] Step S47: Perform dynamic caching policy processing based on the high-frequency shard caching policy and the low-frequency shard caching data to generate dynamic caching policy data;

[0169] Step S48: Perform caching synchronization management processing on the monitoring shard feature data using the dynamic caching policy data to generate caching management log data.

[0170] In the embodiments of the present invention, dynamic data sharding is performed according to the characteristics of the monitoring data of the energy storage unit, such as time, data type, the affiliated energy storage unit, etc. For example, the monitoring data of the energy storage unit for one day can be sharded by hour. Each shard contains data such as voltage, current, temperature, SOC, etc. of all energy storage units within that hour, and a unique ID is generated for each shard, recording information such as the time range, data type, and data volume of the shard. Analyze the time range and data volume of each shard in the monitoring shard feature data, and calculate the average data update rate of each shard, such as the amount of newly added data per second. For example, the number of data points within each shard can be counted and divided by the time length of the shard (in seconds) to obtain the average update rate of the shard. According to the shard update frequency data, combined with a preset caching strategy, such as the LRU (Least Recently Used) algorithm or the FIFO (First In First Out) algorithm, evaluate the caching requirements of each shard. For example, according to the update frequency of the shard and the preset cache size, calculate how long the data of this shard can be stored in the cache, or how much data needs to be cached to meet the access requirements within a certain period of time. Preset an update rate threshold, such as 100 pieces / second. According to the shard update frequency data and the threshold, divide the monitoring shard feature data into high-frequency monitoring shard data and low-frequency monitoring shard data. For example, if the update frequency of a shard is 200 pieces / second, higher than the preset threshold, it is marked as high-frequency monitoring shard data; if the update frequency of a shard is 50 pieces / second, lower than the preset threshold, it is marked as low-frequency monitoring shard data. For the high-frequency monitoring shard data, calculate the average update interval according to its update frequency, such as the time interval between two data points. According to the average update interval, set a real-time update window, such as 10 seconds, and only cache the data within this time window, and eliminate the old data. For example, if the average update interval of a shard is 0.1 second, a 10-second real-time update window can be set to only cache the data within the update window. For the low-frequency monitoring shard data, estimate the required cache capacity according to its data volume and the preset cache time. For example, if the data volume of a shard is 1GB and the preset cache time is 1 hour, the required cache capacity is 1GB. Obtain the available memory resource data of the current system, such as the size of free memory, the size of allocable memory, etc. Perform secure caching processing on the low-frequency monitoring shard data according to the available memory resource data and the cache capacity requirement data. For example, a cache upper limit can be set, such as 50% of the total memory, and only cache the data that does not exceed the upper limit. If the cache capacity requirement exceeds the upper limit, some data can be selectively cached, such as only caching the latest data, or caching the data with a higher degree of importance. Combine the high-frequency shard caching strategy and the low-frequency shard caching data to generate the final dynamic caching strategy data, which includes information such as the size of the real-time update window of the high-frequency shard, the cache capacity upper limit of the low-frequency shard, and the cache eviction algorithm.Perform cache synchronization management on the monitored shard feature data according to the dynamic cache policy data. For example, for high-frequency monitored shard data, cache it according to the real-time update window; for low-frequency monitored shard data, cache it according to the cache capacity limit and the eviction algorithm. Record the log information of the cache operation, such as cache time, cache data ID, cache result, etc., and generate cache management log data.

[0171] As an example of the present invention, refer to Figure 3 shown in Figure 1 the detailed implementation step flow diagram of step S5 in

[0172] Step S51: Perform data update monitoring based on the cache management log data to generate a data update status report;

[0173] In the embodiment of the present invention, by analyzing indicators such as cache hit rate, cache eviction rate, and data update frequency, it is identified which data is being frequently updated and which data has not been updated for a long time. For example, a time window can be set, such as 5 minutes, to count indicators such as the number of updates, cache hit times, and cache eviction times of each data shard within this time window. If the number of updates of a data shard exceeds a preset threshold, it is considered that this shard is in an active state and the cache needs to be updated in a timely manner; if a data shard has not been updated for a long time, such as more than 1 hour, it can be considered to evict it from the cache to free up cache space.

[0174] Step S52: Perform update notification broadcast processing on the distributed data storage network based on the data update status report, and extract a cache status summary to generate cache status summary data;

[0175] In the embodiment of the present invention, according to the data update status report, the data shards that need to update the cache are identified, and update notifications are broadcast to all nodes in the distributed data storage network. The publish / subscribe mode can be used to send update notifications to all nodes subscribed to this data shard. At the same time, key cache status information is extracted from the data update status report, such as the latest update time, cache status, cache node information, etc. of each data shard, to generate cache status summary data.

[0176] Step S53: Perform cache consistency evaluation according to the cache status summary data to generate cache consistency evaluation data; identify inconsistent nodes from the cache consistency evaluation data to obtain inconsistent node list data;

[0177] In an embodiment of the present invention, after all nodes receive the cache status summary data, they compare the data shard information in their own cache with the summary data to evaluate the consistency of the cache. For example, the latest update time of the data shards can be compared. If the update time of the local cache is earlier than the update time in the summary data, it means that the local cache is expired and needs to be updated. A cache consistency evaluation is performed based on the cache status summary data to generate cache consistency evaluation data. For example, the contents of the same data shards on different storage nodes are compared to determine whether the cached data is consistent. The cache consistency evaluation data is analyzed to identify nodes with inconsistent caches, such as nodes with expired cache data or inconsistent data versions, and these nodes are added to the inconsistent node list data.

[0178] Step S54: repair the node cache according to the inconsistent node list data, and re-evaluate the cache status to obtain monitoring cache status data.

[0179] In an embodiment of the present invention, for nodes in the inconsistent node list data, cache repair is performed according to the specific inconsistency situation. For example, if the data cached by the node has expired, the latest data is obtained from the data source or other cache nodes for updating; if the data versions are inconsistent, according to the preset conflict resolution strategy, for example, the system compares the cache data timestamp of the inconsistent node with the latest timestamp in the cache status summary data. If the timestamp of the node cache data is earlier than the summary data, the node cache data is determined to be expired, and the system initiates a data request to the data source or other cache nodes with the latest data, obtains the latest version of the data shard, and replaces the expired cache data with the new data. After the cache repair is completed, the cache status evaluation is performed again to ensure that the cache data of all nodes has been updated to the latest version and is consistent. The final cache status evaluation results are recorded to generate monitoring cache status data.

[0180] Preferably, step S6 comprises the following steps:

[0181] Step S61: Obtain historical energy storage data access records; perform access pattern recognition on monitoring slice feature data through historical energy storage data access records to generate data access patterns;

[0182] Step S62: Estimating the access probability of the monitored shard feature data based on the data access pattern to generate shard access probability data; constructing a predictive shard data index based on the shard access probability data;

[0183] Step S63: performing a shard data query simulation according to the predictive shard data index, and locating the target data shard to obtain shard query feature data;

[0184] Step S64: Perform dynamic access path evaluation based on the sharded query feature data to generate access path evaluation data; perform data prefetch efficiency evaluation based on the sharded query feature data to generate data prefetch evaluation data;

[0185] Step S65: Evaluate the caching effect of the sharded query feature data by monitoring the cache status data to generate caching effect evaluation data;

[0186] Step S66: Conduct a comprehensive analysis of the management status based on the access path evaluation data, data prefetch evaluation data, and caching effect evaluation data to generate a comprehensive data management index.

[0187] In the embodiments of the present invention, historical energy storage data access records are obtained from a database or a log file, for example, recording which users or application programs accessed which data shards at what time. Data mining techniques, such as association rule mining or sequential pattern mining, can be used to analyze the historical access records to identify data access patterns. According to the identified data access patterns, combined with context information such as the current time, user identity, and application scenario, the access probability of the monitored shard feature data is estimated to predict the likelihood of each shard being accessed in a future period. For example, machine learning algorithms, such as logistic regression, support vector machines, etc., can be used to build a prediction model to predict the access probability of each shard based on historical access patterns and current context information. According to the shard access probability data, a predictive shard data index is constructed. For example, shards with high access probability are placed at the front of the index, and shards with low access probability are placed at the back of the index to find the data required by the user more quickly. Simulate a user submitting a data query request, such as querying the voltage data of a certain energy storage unit within a certain time period. According to the predictive shard data index, quickly locate the candidate shards containing the target data. Record the feature information of the query request, such as the query time range, data type, data source, etc., and the information of the located candidate shards to generate shard query feature data. According to the shard query feature data, evaluate different data access paths, such as reading data from the cache, reading data from the local disk, reading data from a remote node, etc., and calculate metrics such as the access latency and network bandwidth consumption of each path. Store the evaluation results in the access path evaluation data. According to the shard query feature data and data access patterns, evaluate the efficiency of data prefetching. For example, the data hit rate of prefetching can be calculated, that is, what proportion of the actually accessed data has been prefetched into the cache. Store the evaluation results in the data prefetch evaluation data. According to the monitored cache status data, such as the cache hit rate and cache capacity utilization rate, evaluate the effect of the current cache policy. For example, the cache hit rates of different data shards can be analyzed to identify shards with poor cache effects, and the cache policy can be adjusted according to the analysis results. Comprehensively analyze the access path evaluation data, data prefetch evaluation data, and cache effect evaluation data to evaluate the overall performance of the data management system, such as data access latency, data throughput, resource utilization, etc. Weights can be set according to different metrics, and a comprehensive data management index can be calculated to reflect the overall health status and efficiency of data management, realizing the effective management and security guarantee of the monitoring data of industrial energy storage units.

[0188] Preferably, the present invention also provides a data management system for an industrial energy storage system, which executes the data management method for an industrial energy storage system as described above. The data management system for an industrial energy storage system includes:

[0189] The data real-time acquisition module is used to perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; perform real-time energy storage data acquisition according to the intelligent perception network topology data to obtain time-series energy storage monitoring data; perform abnormal data correction according to the time-series energy storage monitoring data to generate corrected energy storage monitoring data;

[0190] The distributed redundant transmission module is used to optimize distributed storage nodes according to the intelligent perception network topology data to obtain a distributed data storage network; perform distributed redundant transmission on the corrected energy storage monitoring data using the distributed data storage network to obtain redundant monitoring upload data;

[0191] The data consistency maintenance module is used to process upload vouchers for the redundant monitoring upload data to generate upload voucher data; perform upload data difference analysis according to the upload voucher data to obtain monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain energy storage unit monitoring data;

[0192] The dynamic data caching module is used to perform dynamic data sharding processing on the energy storage unit monitoring data to generate monitoring shard feature data; perform dynamic cache synchronization processing according to the monitoring shard feature data to generate cache management log data;

[0193] The cache status analysis module is used to evaluate the cache status according to the cache management log data to obtain monitoring cache status data;

[0194] The comprehensive data management evaluation module is used to perform comprehensive analysis of the management status on the monitoring shard feature data through the monitoring cache status data to generate a comprehensive data management index.

[0195] The beneficial effects of this application are as follows. By deploying intelligent monitoring sensors in each energy storage unit and using wireless networks and edge computing nodes, a distributed sensing network is constructed to achieve decentralized acquisition and preprocessing of data at the source. The edge computing nodes add timestamps in real time and perform redundant filtering, anomaly detection, and correction on the data, ensuring the timeliness, conciseness, and accuracy of the data, and reducing the pressure on subsequent data transmission and processing. Using geospatial analysis technology, the wireless signal strength and communication links are optimized, and multiple gateway nodes in close geographical proximity are connected to the same data center to achieve near-source data storage and reduce network latency. At the same time, a data sharding and multi-copy storage strategy is adopted to disperse the data across multiple nodes, so that even if a single node fails, it will not affect the integrity and availability of the data. A unique hash value is generated as a voucher for each data block before uploading, and through voucher exchange and collision calculation between nodes, situations of data inconsistency are identified. At the same time, a consensus algorithm is used to ensure that all nodes reach an agreement on the final version of the data, effectively avoiding data conflicts and errors. According to the data update frequency and access pattern, the data shards are divided into high-frequency and low-frequency categories, and real-time update window caching and capacity-demand estimation caching strategies are respectively adopted to improve the cache hit rate and reduce data access latency. At the same time, by analyzing historical data access records, future data access patterns are predicted, and a predictive sharded data index is constructed to further optimize data query efficiency. By monitoring the data update status in real time, situations of cache inconsistency are promptly detected, and according to the type of inconsistency and the preset conflict resolution strategy, the node caches are repaired to avoid errors caused by data asynchronization. This flexible cache management strategy improves the system's response speed and realizes the efficient, reliable, and secure upload management of massive industrial energy storage monitoring data.

[0196] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0197] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data management method for an industrial energy storage system, characterized in that: The following steps are involved: Step S1: Perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; perform real-time energy storage data collection based on the intelligent perception network topology data to obtain time-series energy storage monitoring data; Correct abnormal data according to the time-series energy storage monitoring data to generate corrected energy storage monitoring data; Step S2: Optimizing distributed storage nodes according to the intelligent sensing network topology data to obtain a distributed data storage network; using the distributed data storage network to perform distributed redundant transmission on the corrected energy storage monitoring data to obtain redundant monitoring upload data; Step S3: Perform upload voucher processing on the redundant monitoring upload data to generate upload voucher data; perform upload data difference analysis based on the upload voucher data to obtain monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain energy storage unit monitoring data; Step S4: Perform dynamic data slicing processing according to the energy storage unit monitoring data to generate monitoring slicing feature data; Perform dynamic cache synchronization processing based on the monitoring shard feature data to generate cache management log data; Step S5: performing cache status evaluation according to the cache management log data to obtain monitoring cache status data; Step S6: Perform a comprehensive management status analysis on the monitored slice feature data by monitoring the cache status data to generate a comprehensive data management index.

2. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: deploy intelligent monitoring sensors on industrial energy storage units to obtain monitoring node distribution data; Step S12: Perform wireless network configuration according to the monitoring node distribution data, and perform edge computing topology processing to generate intelligent perception network topology data, wherein the intelligent perception network topology data includes energy storage monitoring nodes and distributed computing nodes; Step S13: Setting data collection parameters for energy storage monitoring nodes based on intelligent sensing network topology data, and collecting real-time industrial energy storage data streams to obtain initial energy storage unit monitoring data; Step S14: using the distributed computing nodes to timestamp the initial energy storage unit monitoring data to obtain time-series energy storage monitoring data; Step S15: performing data redundancy filtering according to the time series energy storage monitoring data to obtain simplified energy storage monitoring data; Step S16: performing outlier detection on the simplified energy storage monitoring data to generate abnormal data points; performing data resampling according to the abnormal data points to obtain resampled monitoring data; Step S17: Based on the abnormal data points, the simplified energy storage monitoring data is corrected by resampling the monitoring data to generate corrected energy storage monitoring data.

3. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Performing geographic spatial analysis based on the intelligent sensing network topology data to obtain spatial location distribution data; Step S22: evaluating the wireless signal strength of the industrial energy storage unit through the spatial location distribution data, optimizing the communication link of the intelligent sensing network topology data, and generating a sensing node communication network; Step S23: Connecting distributed storage nodes according to the sensing node communication network to obtain a distributed data storage network; Step S24: using the distributed data storage network to select an upload node for the corrected energy storage monitoring data, and performing parallel task decomposition to obtain multiple upload instruction data; Step S25: redundantly copy the corrected energy storage monitoring data to obtain a redundant monitoring data packet; Step S26: performing distributed redundant transmission on redundant monitoring data packets through multiple upload instruction data to obtain redundant monitoring upload data.

4. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing monitoring characteristic analysis on the redundant monitoring upload data to generate key energy storage monitoring characteristic data; performing upload voucher processing according to the key energy storage monitoring characteristic data to generate upload voucher data; Step S32: performing credential identification processing on the redundant monitoring upload data by uploading the credential data to obtain the identified monitoring upload data; Step S33: Perform multi-node storage network analysis on the redundant uploaded data through the distributed data storage network to generate a multi-node storage network; Step S34: using a multi-node storage network to construct a consensus network for the identification monitoring upload data, generating storage consensus network data; performing upload data difference analysis based on the storage consensus network data, obtaining monitoring upload difference data; Step S35: Perform node performance evaluation on the storage consensus network data to generate consensus node performance data; perform confidence node recommendation on the storage consensus network data based on the consensus node performance data to obtain confidence node data; Step S36: Based on the consensus node performance data, the confidence node data is used to perform a confidence difference weighted judgment on the monitored uploaded difference data, and difference conflict processing is performed to generate uploaded difference conflict processing data; Step S37: Based on the confidence node data, the identification monitoring uploaded data is updated for data consistency by uploading the difference conflict processing data to obtain the energy storage unit monitoring data.

5. The data management method for an industrial energy storage system according to claim 4, characterized in that: Step S31 includes the following steps: Step S311: Identify the data type of the redundant monitoring uploaded data to obtain monitoring type identification data; Step S312: performing feature statistics on the redundant monitoring uploaded data through the monitoring type identification data based on the preset monitoring type statistical rules to generate energy storage feature statistical data; Step S313: performing feature correlation analysis based on energy storage feature statistical data, and extracting key monitoring features to generate key energy storage monitoring feature data; Step S314: using a preset hash algorithm to gradually read the key energy storage monitoring characteristic data, and performing fixed hash value calculation to generate initial hash value data; Step S315: rolling update processing is performed on the initial hash value data to obtain key monitoring hash data; metadata is added according to the key monitoring hash data to obtain monitoring credential metadata; Step S316: Perform credential serialization processing on the monitoring credential metadata through a preset upload credential template to generate upload credential data.

6. The data management method for an industrial energy storage system according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: using a multi-node storage network to perform node timing credential exchange on the identification monitoring uploaded data to obtain inter-node credential exchange data; Step S342: performing hash collision calculation based on the credential exchange data between nodes to obtain preliminary credential similarity data; Step S343: Based on a preset similarity threshold, the storage node threshold of the multi-node storage network is screened through preliminary credential similarity data to obtain consensus node list data; Step S344: construct and store consensus network data according to the consensus node list data; Step S345: Using the storage consensus network data to store node redundant data for the identification monitoring upload data, to obtain consensus node redundant mapping data; Step S346: performing data comparison between nodes based on the consensus node redundant mapping data to generate preliminary comparison difference data; Step S347: Perform metadata difference analysis based on the preliminary comparison difference data, and perform serialization processing to generate monitoring upload difference data.

7. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Perform dynamic data slicing processing on the monitoring data of the energy storage unit to generate monitoring slicing feature data; Step S42: Calculate the monitoring update rate according to the monitoring slice feature data to generate slice update frequency data; Step S43: Evaluate the cache demand of the energy storage monitoring slice data by slice update frequency data to generate slice cache demand data; Step S44: dividing the data update status of the shard cache demand data based on the preset update rate threshold, when the shard cache demand data is higher than or equal to the preset update rate threshold, marking the monitoring shard feature data as high-frequency monitoring shard data; when the shard cache demand data is lower than the preset update rate threshold, marking the monitoring shard feature data as low-frequency monitoring shard data; Step S45: Calculate the average update interval of the high-frequency monitoring slice data to generate average update interval data; perform real-time update window cache processing according to the average update interval data to obtain a high-frequency slice cache strategy; Step S46: estimating the cache capacity requirement of the low-frequency monitoring shard data to generate cache capacity requirement data; obtaining available memory resource data; performing secure caching processing on the cache capacity requirement data through the available memory resource data to obtain low-frequency shard cache data; Step S47: Perform dynamic cache strategy processing according to the high-frequency shard cache strategy and the low-frequency shard cache data to generate dynamic cache strategy data; Step S48: Perform cache synchronization management processing on the monitoring slice feature data through dynamic cache strategy data to generate cache management log data.

8. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Perform data update monitoring according to the cache management log data and generate a data update status report; Step S52: performing update notification broadcast processing on the distributed data storage network based on the data update status report, and extracting the cache status summary to generate cache status summary data; Step S53: performing cache consistency evaluation according to the cache status summary data to generate cache consistency evaluation data; performing inconsistent node identification on the cache consistency evaluation data to obtain inconsistent node list data; Step S54: repair the node cache according to the inconsistent node list data, and re-evaluate the cache status to obtain monitoring cache status data.

9. The data management method for an industrial energy storage system according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Obtain historical energy storage data access records; perform access pattern recognition on monitoring slice feature data through historical energy storage data access records to generate data access patterns; Step S62: Estimating the access probability of the monitored shard feature data based on the data access pattern to generate shard access probability data; constructing a predictive shard data index based on the shard access probability data; Step S63: performing a shard data query simulation according to the predictive shard data index, and locating the target data shard to obtain shard query feature data; Step S64: Perform dynamic access path evaluation according to the shard query feature data to generate access path evaluation data; perform data prefetch efficiency evaluation according to the shard query feature data to generate data prefetch evaluation data; Step S65: performing cache effect evaluation on the shard query feature data by monitoring the cache status data to generate cache effect evaluation data; Step S66: Perform a comprehensive analysis of the management status based on the access path evaluation data, the data pre-fetch evaluation data, and the cache effect evaluation data to generate a comprehensive data management index.

10. A data management system for an industrial energy storage system, characterized in that: For executing the data management method of the industrial energy storage system according to claim 1, the data management system of the industrial energy storage system comprises: The real-time data acquisition module is used to perform edge computing topology processing on the industrial energy storage unit to generate intelligent perception network topology data; perform real-time energy storage data acquisition based on the intelligent perception network topology data to obtain time-series energy storage monitoring data; perform abnormal data correction based on the time-series energy storage monitoring data to generate corrected energy storage monitoring data; A distributed redundant transmission module is used to optimize distributed storage nodes according to the intelligent perception network topology data to obtain a distributed data storage network; and to perform distributed redundant transmission of the corrected energy storage monitoring data using the distributed data storage network to obtain redundant monitoring upload data; The data consistency maintenance module is used to process the upload voucher of the redundant monitoring upload data to generate the upload voucher data; perform upload data difference analysis according to the upload voucher data to obtain the monitoring upload difference data; perform difference conflict processing on the redundant monitoring upload data through the monitoring upload difference data to obtain the energy storage unit monitoring data; A dynamic data cache module is used to perform dynamic data sharding processing according to the monitoring data of the energy storage unit to generate monitoring shard feature data; perform dynamic cache synchronization processing according to the monitoring shard feature data to generate cache management log data; The cache status analysis module is used to evaluate the cache status according to the cache management log data to obtain the monitoring cache status data; The comprehensive data management evaluation module is used to conduct a comprehensive analysis of the management status of the monitoring shard feature data by monitoring the cache status data and generate a comprehensive data management index.

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