Energy storage ems operation data management method and system and storage medium

Abstract

The embodiment of the application provides a kind of energy storage EMS operation data management method, system and storage medium, belong to data processing technical field.The method comprises: obtaining the multiple types of operation data generated in the operation process of energy storage EMS, and generating corresponding uniform data identifier;Unified indexing rule is established based on uniform data identifier, and the writing position of each operation data is determined to execute storage, while the corresponding index mapping record is generated;For the operation data of different time scales, the storage period and the cycle coverage strategy matching the generation period are established, and the historical operation data is executed to cover and update the corresponding index mapping record;The index mapping record of operation data is associated with the corresponding event data to establish the mapping relationship of running data reading positioning.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a method, system, and storage medium for managing operational data of an energy storage system (EMS). Background Technology

[0002] During the operation of an energy storage power station, the EMS (Energy Management System) typically needs to record various types of operational data over a long period, including the operating power of the PCS (Power Conversion System), charge and discharge curves, daily frozen data, monthly frozen data, and various operational events. This data is used to support operational status queries, historical behavior backtracking, and abnormal event analysis. Due to limitations in controller-side storage capacity, this operational data cannot be stored indefinitely. In engineering practice, historical data is typically processed through cyclic overwriting using methods such as partitioned storage, periodic archiving, or first-in-first-out (FIFO) to maintain data retention for a certain time span within limited storage space.

[0003] Furthermore, different types of operational data exhibit significant differences in generation cycle, time granularity, and writing frequency. For example, real-time operational data is typically generated by sampling at the second level, curve data is statistically formed at the minute level, while daily and monthly frozen data are generated based on daily and monthly cycles, respectively. Various operational events are recorded based on their trigger times. Existing data management methods often perform storage and overwriting processes based on data type or timestamp. When performing cyclical overwriting of historical data, there is a lack of a unified time-related organization mechanism between operational data at different time scales. This makes it difficult to accurately locate the corresponding operational status information for operational events with time-related relationships after some operational data has been overwritten, thus affecting the complete retrospective of historical operational behavior and the reliability of anomaly analysis results.

[0004] Therefore, there is an urgent need for a data management method that can uniformly organize operational data at different time scales under limited storage resources and maintain the temporal correlation between operational data and event data during the cyclical overlay of historical data, so as to improve the traceability and management consistency of energy storage EMS operational data. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and storage medium for managing operational data of an energy storage system (EMS), so as to at least solve the problem of mismatch between the time correlation between operational data and operational events during the cyclic coverage of historical operational data.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for managing operational data of an energy storage EMS. The method includes: acquiring multiple types of operational data generated during the operation of the energy storage EMS, and generating corresponding unified data identifiers based on the data type and time attributes of each operational data; establishing unified index rules based on the unified data identifiers, and determining the write location of each operational data according to the unified index rules to perform storage, while generating corresponding index mapping records; establishing a storage period and cyclic overlay strategy matching the generation period for operational data at different time scales, and performing overlay writing on historical operational data and updating the corresponding index mapping records based on the cyclic overlay strategy; performing read positioning on operational data based on the unified index rules, and establishing an association mapping relationship between the index mapping records of the operational data and the corresponding event data.

[0007] Optionally, the process involves acquiring various types of operational data generated during the operation of the energy storage EMS, and generating corresponding unified data identifiers based on the data type and time attributes of each operational data. This includes: extracting corresponding data type information and time attribute information from each operational data generated during the operation of the energy storage EMS; generating corresponding unified data identifiers based on the data type information and the time attribute information; and writing the unified data identifiers into the data description field of the corresponding operational data.

[0008] Optionally, establishing unified indexing rules based on the unified data identifier includes: parsing the data type information and time attribute information of the corresponding running data based on the unified data identifier, and determining the corresponding storage area identifier based on the data type information; determining the corresponding time slot identifier based on the time attribute information, and combining the storage area identifier and the time slot identifier to generate the index key of the corresponding running data.

[0009] Optionally, the write location of each running data is determined according to the unified indexing rules for storage, and a corresponding index mapping record is generated. This includes: generating a corresponding index key for the running data to be written based on the unified indexing rules; determining the corresponding write location in the unified storage space based on the index key, and writing the running data to the write location; after the writing is completed, generating an index mapping record corresponding to the index key, so that when overwriting historical running data is performed subsequently, the association between the running data and the corresponding event data is maintained based on the updated index mapping record.

[0010] Optionally, a storage period and cyclic coverage strategy matching the generation period of the runtime data at different time scales are established, including: setting corresponding storage periods for runtime data at different time scales based on the generation period of the runtime data; constructing a cyclic coverage strategy according to the unified indexing rules; wherein, the cyclic coverage strategy is used to determine the coverable range of historical runtime data based on the time attributes of the runtime data during the data writing process, so that runtime data with time correlation maintains a consistent data positioning relationship during the cyclic coverage process.

[0011] Optionally, performing overwrite writing on historical running data and updating the corresponding index mapping records based on the cyclic overwrite strategy includes: upon receiving running data to be written, invoking the cyclic overwrite strategy to determine a set of historical running data that has a preset time association with the running data to be written, and designating the set of historical running data as objects to be overwritten; determining a set of write positions for the objects to be overwritten in a unified storage space according to the unified indexing rules, and writing the running data to be written into the set of write positions to complete the overwrite writing; after completing the overwrite writing, updating the index mapping records corresponding to the set of write positions.

[0012] Optionally, the process of reading and locating runtime data based on the unified indexing rules, and establishing an association mapping relationship between the index mapping records of the runtime data and the corresponding event data, includes: obtaining the event time attribute of the event data and generating an event index key; determining a set of runtime data index keys that have a preset time association relationship with the event data based on the event time attribute by calling the unified indexing rules, and establishing an association mapping record between the event index key and the set of runtime data index keys; and upon receiving a read request, generating an index key based on the unified indexing rules and querying the corresponding index mapping record to locate and read the target runtime data.

[0013] Optionally, the method further includes: after performing an overwrite write on the running data, performing a consistency check on the updated index mapping record based on the unified index rule; and when a mismatch is detected in the associated mapping record corresponding to event data that has a time correlation with the running data, reconstructing the associated mapping record according to the unified index rule.

[0014] A second aspect of the present invention provides an energy storage EMS operation data management system, the system comprising: a data acquisition unit, configured to acquire multiple types of operation data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attributes of each operation data; a rule construction unit, configured to establish unified index rules based on the unified data identifiers, and determine the write location of each operation data according to the unified index rules to perform storage, while generating corresponding index mapping records; a processing unit, configured to establish storage cycles and cyclic overlay strategies matching the generation cycles of operation data at different time scales, and perform overlay writing on historical operation data and update the corresponding index mapping records based on the cyclic overlay strategy; and a storage unit, configured to perform read positioning on operation data based on the unified index rules, and establish an association mapping relationship between the index mapping records of the operation data and the corresponding event data.

[0015] On the other hand, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described energy storage EMS operation data management method.

[0016] Through the above technical solution, the present invention generates a unified data identifier for the various types of operational data generated during the operation of the energy storage EMS, and establishes a unified index rule based on the unified data identifier, so that operational data at different time scales have a unified time correlation and positioning basis during storage. On this basis, a matching storage cycle and cyclic overwrite strategy are established according to the operational data generation cycle, and the corresponding index mapping record is updated synchronously when historical operational data is overwritten. At the same time, an association mapping relationship is established between the index mapping record of the operational data and the corresponding event data, thereby maintaining the temporal correlation consistency between operational data and operational events under the condition of limited storage resources, and improving the completeness and accuracy of operational status query and historical behavior backtracking.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of the steps of an energy storage EMS operation data management method provided in one embodiment of the present invention;

[0020] Figure 2This is a detailed flowchart of step S10 of the energy storage EMS operation data management method provided in one embodiment of the present invention;

[0021] Figure 3 This is a detailed flowchart of step S30 of the energy storage EMS operation data management method provided in one embodiment of the present invention;

[0022] Figure 4 This is a curve comparing the retention rate of operational data and operational events provided by one embodiment of the present invention;

[0023] Figure 5 This is a system structure diagram of an energy storage EMS operation data management system provided in one embodiment of the present invention;

[0024] Figure 6 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0026] like Figure 1 As shown, this invention provides a method for managing operational data of an energy storage system (EMS), the method comprising:

[0027] Step S10: Obtain various types of operational data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attributes of each operational data.

[0028] Specifically, during the operation of an energy storage system (EMS), the system continuously generates various types of operational data, including real-time operational data, curve data, and various event data. Because different types of operational data differ in their generation cycles and time granularity, directly storing data based on its source can easily lead to inconsistencies in the location criteria used during subsequent indexing and retrieval. Therefore, the system extracts corresponding data type information and time attribute information from the operational data and generates a unified data identifier based on this information. This ensures that operational data at different time scales have a unified identification basis before entering the storage space, providing a consistent data organization foundation for subsequent index generation and data writing location. Specifically, for example... Figure 2 Step S10 includes the following steps:

[0029] Step S101: Extract the corresponding data type information and time attribute information from the various operating data generated during the operation of the energy storage EMS.

[0030] Specifically, during the operation of an energy storage system (EMS), the operational data generated by the system includes at least real-time operational data, curve data, daily frozen data, monthly frozen data, and event data. Different types of operational data differ in their generation frequency and statistical period. For the aforementioned operational data, corresponding data type information and time attribute information are extracted. The data type information characterizes the category to which the operational data belongs, and the time attribute information characterizes the generation time and statistical period of the operational data.

[0031] For example, the sampling and generation time can be extracted from real-time running data, and the start and end times of the daily statistical period can be extracted from daily frozen data. The extraction methods for the data type information and time attribute information are not limited to field reading, timestamp parsing, or period attribution determination.

[0032] Step S102: Generate a corresponding unified data identifier based on the data type information and the time attribute information.

[0033] Specifically, after obtaining the data type information and time attribute information, a corresponding unified data identifier can be generated for the running data based on a preset data identifier generation rule. This unified data identifier serves as a unified location basis for the running data during subsequent storage and indexing. For example, the data type code representing the category of the running data can be combined with the time attribute code representing the generation time to generate a corresponding unified data identifier, or the data type information and time attribute information can be mapped to a unified identifier space to form a corresponding data identifier value.

[0034] The method of generating the unified data identifier is not limited to encoding combination, field mapping or rule matching, etc. As long as it enables data running at different time scales to have a unified identification basis before entering the storage space, it falls within the scope of protection claimed in this application.

[0035] Step S103: Write the unified data identifier into the data description field of the corresponding running data.

[0036] Specifically, after generating the unified data identifier, it can be written into the data description field of the corresponding running data, so that the running data has a recognizable unified identifier during subsequent index generation and storage location processes. For example, a data description field can be preset in the data structure corresponding to the running data, and the unified data identifier can be written into the data description field as the basis for subsequent index key generation and writing position determination.

[0037] The data description field can be set in the data header area or the additional metadata area of ​​the running data. Its specific storage form is not limited. As long as the association record relationship between the unified data identifier and the corresponding running data can be realized, it should be considered to fall within the protection scope of this application.

[0038] Step S20: Establish unified index rules based on the unified data identifier, determine the write location of each running data according to the unified index rules to perform storage, and generate corresponding index mapping records.

[0039] Specifically, establishing unified indexing rules based on the unified data identifier includes: parsing the data type information and time attribute information of the corresponding running data based on the unified data identifier, and determining the corresponding storage area identifier based on the data type information; determining the corresponding time slot identifier based on the time attribute information, and combining the storage area identifier and the time slot identifier to generate the index key of the corresponding running data.

[0040] In this embodiment of the invention, establishing a unified index rule based on the unified data identifier includes: parsing the data type information and time attribute information of the corresponding running data based on the unified data identifier, and determining the corresponding storage area identifier based on the data type information; determining the corresponding time slot identifier based on the time attribute information, and combining the storage area identifier and the time slot identifier to generate an index key for the corresponding running data.

[0041] In this embodiment of the invention, when runtime data enters the storage space, its data type information and time attribute information are parsed based on the unified data identifier, and the organization method of the runtime data in the storage space is determined accordingly. For example, for data type information in the identifier that represents real-time runtime data, the corresponding runtime data is assigned to the real-time data storage area, while for daily frozen data or monthly frozen data, they can be assigned to the data storage area of ​​the corresponding statistical period, respectively.

[0042] Regarding time attribute information, the corresponding time slot can be determined based on the generation time or the statistical period to which the running data belongs. For example, curve data generated within a certain time period can be mapped to the same time slot range, thereby forming a data organization structure with time correlation in the storage space.

[0043] After determining the storage area identifier and time slot identifier, the two are combined to generate an index key for the corresponding running data, and the running data is uniformly located based on this index key. For example, daily frozen data generated within the same daily statistical period and running event data triggered within the same period are mapped to the same time slot through the time attribute, and their data location relationship is kept consistent in the storage space after the index key is generated.

[0044] The combination of index keys is not limited to specific forms such as field concatenation or encoding mapping. As long as it can reflect the category characteristics of the running data and its temporal attribution, and is used in the subsequent running data writing, locating, and reading query process, it falls within the protection scope of this application. Through the above method, running data at different time scales can form a unified temporal association structure during the storage organization stage, providing basic support for consistency maintenance in the subsequent historical data cyclic overlay process.

[0045] Furthermore, the write location of each running data is determined according to the unified indexing rules for storage, and a corresponding index mapping record is generated. This includes: generating a corresponding index key for the running data to be written based on the unified indexing rules; determining the corresponding write location in the unified storage space based on the index key, and writing the running data to the write location; after the writing is completed, generating an index mapping record corresponding to the index key, so that when overwriting historical running data is performed subsequently, the association between the running data and the corresponding event data is maintained based on the updated index mapping record.

[0046] In this embodiment of the invention, for runtime data to be written, the system can invoke unified indexing rules to parse its unified data identifier and generate a corresponding index key accordingly. The index key is used to characterize the logical location information of the runtime data in the unified storage space. In practical applications, runtime data generated in different time periods can generate corresponding index keys based on their unified data identifiers and be mapped to the corresponding write location in the unified storage space through the index keys. For example, curve data generated within a certain statistical period and runtime event data triggered within the same period can be mapped to a range of storage locations with time correlation after generating index keys, thereby forming an organizational structure with time correlation characteristics at the physical storage level.

[0047] After determining the write location based on the index key, the running data to be written is stored in the corresponding write location, and an index mapping record corresponding to the index key is generated after the write is completed. The index mapping record is used to record the correspondence between the index key and the actual storage location of the running data, and can be stored in a preset index area for subsequent retrieval. During subsequent operation, when the system performs cyclic overwrite of historical running data, the original running data may be replaced by new running data. In this case, by updating the corresponding index mapping record, the index mapping record is made to point to the running data storage location after the overwrite, thereby maintaining the stability of the mapping relationship between the index key and the running data.

[0048] Furthermore, given that a mapping relationship has been established between runtime data and corresponding event data, synchronous updates to the index mapping records ensure that even after historical runtime data is overwritten, event data with a temporal relationship can still be located in the overwritten runtime data through the mapping records. This prevents the relationship between runtime data and event data from becoming invalid due to historical data replacement. Under limited storage resources, this method maintains the continuity of runtime data location relationships while performing cyclic overwrites, providing a stable data foundation for subsequent runtime status queries and historical behavior backtracking.

[0049] Step S30: Establish a storage period and cyclic overwrite strategy that matches the generation period for the running data at different time scales, and perform overwrite writing on the historical running data and update the corresponding index mapping records based on the cyclic overwrite strategy.

[0050] Specifically, during the operation of an energy storage system (EMS), operational data at different time scales exhibit differences in generation cycles and retention requirements. A matching storage cycle is established based on the generation cycle of the operational data, and a cyclical overwrite strategy is constructed accordingly. This ensures that various types of operational data are retained and updated in an orderly manner according to their time attributes within a limited storage space. When writing historical operational data, the corresponding index mapping records are updated synchronously, allowing newly written data to inherit the original time-related positioning relationships. This maintains the consistency between operational data and operational events while completing data replacement, providing continuous data support for subsequent operational status queries and historical backtracking. Specifically, such as... Figure 3 Step S30 includes the following steps:

[0051] Step S301: Based on the generation cycle of the running data, set corresponding storage cycles for running data at different time scales.

[0052] Specifically, during the operation of an energy storage system (EMS), different types of operational data differ in their generation frequency and statistical granularity. For example, real-time operational data is typically generated by sampling at the second level, curve data is statistically formed at the minute level, while daily and monthly frozen data are generated based on daily and monthly statistical cycles, respectively. For these operational data at different time scales, a corresponding storage period can be set based on their generation cycle to determine the retention time of various types of operational data in a unified storage space.

[0053] For example, a shorter storage period can be set for real-time running data to ensure the sustainable use of storage space, while a relatively longer storage period can be set for daily or monthly frozen data to meet the needs of historical running analysis.

[0054] The method of setting the storage period is not limited to fixed period configuration or period interval division, as long as it can reflect the correspondence between the frequency of running data generation and retention requirements, and is used for the construction of subsequent cyclic coverage strategies, it falls within the protection scope of this application.

[0055] Step S302: Construct a cyclic coverage strategy based on the unified index rules.

[0056] Specifically, the cyclic coverage strategy is used to determine the coverage range of historical running data based on the time attribute of the running data during the data writing process, so that running data with time correlation can maintain a consistent data positioning relationship during the cyclic coverage process.

[0057] In this embodiment of the invention, after setting the storage period for various types of operational data, a cyclic overlay strategy is constructed based on unified index rules to limit the replacement range of historical operational data during subsequent data writing. After the operational data generates a unified data identifier and participates in the index key generation, its time attribute information has formed a corresponding mapping relationship in the index structure. Based on this time attribute information, the range of historical operational data with the same statistical period or time attribution relationship is determined and used as the overlayable objects.

[0058] For example, daily frozen data generated within the same daily statistical period can be used to determine the historical data of the same period as the coverage area after the corresponding storage period is reached, based on its time attribute, so as to maintain the consistency of the positioning relationship of the running data within the same time period when performing data replacement.

[0059] By employing the above methods, operational data at different time scales maintain a unified temporal correlation structure during the cyclical coverage process, providing a stable data foundation for subsequent correlation and positioning between operational data and event data.

[0060] Step S303: Upon receiving the running data to be written, the cyclic overwrite strategy is invoked to determine the set of historical running data that has a preset time correlation with the running data to be written, and the set of historical running data is designated as the object to be overwritten.

[0061] Specifically, during the operation of the energy storage EMS, when new operational data is generated and ready to be written to the unified storage space, the time attribute information contained in the unified data identifier is matched according to the cyclic overlay strategy to determine the range of historical operational data that has a preset time correlation with the operational data to be written in terms of statistical period or generation time.

[0062] The range of historical operational data may include a set of operational data generated within the same statistical period, such as historical curve data within the same time interval as the currently generated curve data, or historical daily frozen data with the same daily statistical period as the currently generated daily frozen data.

[0063] By invoking the cyclic overwrite strategy, the historical running data set with time correlation can be filtered out from the unified storage space and identified as the objects to be overwritten, so that the corresponding data can be replaced in the subsequent writing process.

[0064] It should be noted that the method for determining the preset time association relationship is not limited to time interval matching, period attribution determination or unified index key parsing, etc. As long as it can reflect the correspondence between the running data to be written and the historical running data in the time dimension and be used to determine the coverage writing range, it falls within the protection scope of this application.

[0065] Step S304: Determine the set of write locations of the object to be overwritten in the unified storage space according to the unified indexing rules, and write the running data to be written into the set of write locations to complete the overwrite.

[0066] Specifically, after identifying the historical running data set as the objects to be covered, the index keys corresponding to the objects to be covered are parsed based on unified indexing rules, and the actual storage location set of these objects in the unified storage space is determined accordingly. Since the objects to be covered have already been located according to the unified indexing rules during the storage phase, their corresponding storage locations can be identified through index mapping records, thereby obtaining a set of write locations that have a time-related relationship with the running data to be written.

[0067] In the actual writing process, the running data to be written can be directly written to the set of writing locations to replace and update historical running data. For example, when new curve data generated within a certain statistical period reaches the writing stage, the storage location occupied by the historical curve data of the same period can be determined according to the unified indexing rules, and the newly generated data can be written to the corresponding location to complete the overwrite.

[0068] Step S305: After completing the overwrite, update the index mapping record corresponding to the set of write positions.

[0069] Specifically, after the runtime data to be written is overwritten, the index mapping relationship corresponding to the original historical runtime data will change. To ensure the continuity of the runtime data location relationship under the unified indexing rules, the index mapping records corresponding to the set of write positions can be updated. During the update process, the corresponding index key can be regenerated based on the unified data identifier of the newly written runtime data, and the mapping relationship between the index key and the set of write positions can be written into a preset index area to replace the index mapping records corresponding to the original historical runtime data.

[0070] For example, after the running data within a certain statistical period is overwritten by new running data, the corresponding index mapping record is updated so that the unified index rule can point to the running data after the overwrite during subsequent reading and positioning processes, thereby maintaining the locatability of the running data under the condition of cyclic overwriting.

[0071] Step S40: Based on the unified indexing rules, perform reading and locating of the running data, and establish an association mapping relationship between the index mapping record of the running data and the corresponding event data.

[0072] Specifically, the event time attribute of the event data is obtained and an event index key is generated; based on the event time attribute, the unified indexing rule is invoked to determine a set of running data index keys that have a preset time association with the event data, and an association mapping record is established between the event index key and the set of running data index keys; when a read request is received, an index key is generated based on the unified indexing rule and the corresponding index mapping record is queried to locate and read the target running data.

[0073] In this embodiment of the invention, during the operation of an energy storage EMS, various operational events are typically triggered along with changes in operational status. To support subsequent operational status queries and historical behavior backtracking, the time attributes of the event data can be extracted, and corresponding event index keys can be generated based on the event time attributes. The event index keys are used to characterize the location information of the event data in the time dimension and can participate in the data association process under unified indexing rules.

[0074] In practical applications, a set of running data index keys that have a preset time association with the event in the statistical period or time interval can be determined by calling a unified index rule based on the event occurrence time. For example, for a protection event triggered within a certain time period, the set of running data index keys generated in the same period can be located by matching the time attribute, and an association mapping record can be established between the event index key and the set of running data index keys to form an association relationship between running events and running data.

[0075] Upon receiving a read request, a corresponding index key can be generated based on unified indexing rules, and relevant index mapping records can be queried to locate and read runtime data that has a time-related relationship with the target event. This method ensures that runtime events maintain their association with corresponding runtime status information even after historical runtime data is cyclically overwritten, providing stable data support for subsequent runtime status analysis and historical behavior backtracking.

[0076] Preferably, the method further includes: after performing an overwrite write on the running data, performing a consistency check on the updated index mapping record based on the unified index rule; and when a mismatch is detected in the associated mapping record corresponding to event data that has a time correlation with the running data, reconstructing the associated mapping record according to the unified index rule.

[0077] In this embodiment of the invention, after the running data has been overwritten and the corresponding index mapping record has been updated, a consistency check can be performed on the updated index mapping record to confirm that the positioning relationship of the running data under the unified index rules has not changed abnormally.

[0078] In actual operation, due to the replacement of historical operational data, some original associated mapping records may still point to the overwritten data locations, causing event data with time-related relationships to fail to accurately locate the corresponding operational status information during subsequent queries. Therefore, based on unified indexing rules, the relevant associated mapping records are validated. When a mismatch is detected between the operational data index key corresponding to the event data and the current index mapping record, the set of operational data index keys with time-related relationships is re-determined according to the unified indexing rules, and the associated mapping records are reconstructed, enabling the event data to be re-associated with the overwritten operational data.

[0079] By using the above methods, the consistency between running data and running events is maintained after historical data replacement, providing continuous data support for subsequent running status queries and historical behavior backtracking.

[0080] In another possible implementation, after the running data generates a unified data identifier and participates in the index key generation, cycle boundary marker information is set for the running data within the statistical cycle boundary range. When executing the cyclical overlay strategy, historical running data with the cycle boundary marker information undergoes delayed overlay processing. For example, for curve data generated within the end period of a statistical cycle on a certain day, if it has a temporal correlation with a running event triggered on that day, the replacement of this part of the running data can be temporarily suspended during subsequent overlay processes. This ensures that the event data with a temporal correlation can still be located to the running status information within the complete cycle during statistical analysis.

[0081] Once the preset retention conditions are met, the data is included in subsequent coverage according to unified indexing rules. This method ensures that operational data at the boundary of the statistical period has independent retention capabilities during cyclical coverage, thereby avoiding distortion of operational event analysis results due to cross-period data replacement and improving data consistency in the energy storage EMS during execution cycle statistics and anomaly backtracking.

[0082] In another possible implementation, after the running data generates a unified data identifier, the corresponding time window range is determined based on its time attribute, and when executing the cyclic coverage strategy, a temporary lock mark is set for the running data that is within the time window and has a relationship with the recorded event data.

[0083] For example, a protection event triggered within a certain time period may have associated real-time operational data and curve data spanning multiple sampling periods. When subsequent overwrite operations are performed, historical operational data within the aforementioned time window can be identified according to unified indexing rules, and overwrite processing can be temporarily suspended for a preset locking duration to ensure that operational status information for the entire time period can be obtained during event analysis. After the locking duration expires, the data is then included in the cyclic overwrite range.

[0084] By employing the above methods, operational data that is temporally correlated with critical events can be retained in stages during the coverage process, thereby improving the data continuity and accuracy of energy storage EMS when performing anomaly backtracking and operational behavior analysis.

[0085] In one specific implementation, the method of this application is applied to the EMS operation data management process of a 10MWh independent energy storage power station. This energy storage power station is equipped with a PCS device with a rated power of 2MW. The EMS system collects real-time operating power data of the PCS at a 1s sampling period and generates curve data at a 5-minute period. Simultaneously, it generates daily and monthly frozen data according to daily and monthly statistical periods, respectively. During continuous operation of the power station, the EMS also records various operating events triggered by the system, including power limit events, SOC (State of Charge) protection events, and PCS descent events.

[0086] During operation, EMS extracts the corresponding data type and time attribute information from the various types of operational data, and generates a unified data identifier based on this information. This unified data identifier is then written into the data description field of the operational data. Subsequently, a unified indexing rule is established based on the unified data identifier. The corresponding storage area identifier is determined according to the data type information of the operational data, and the corresponding time slot identifier is determined according to the time attribute information. An index key is generated by combining the storage area identifier and the time slot identifier to determine the write location of each piece of operational data in the unified storage space and perform storage. Simultaneously, a corresponding index mapping record is generated.

[0087] For data operating at different time scales, this embodiment sets a 3-day storage period for real-time data, a 7-day storage period for curve data, and a 30-day storage period for daily frozen data. During operation, when the corresponding time slot in the unified storage space reaches the storage period limit, a cyclic overwrite strategy is invoked to determine the set of historical data with the same statistical period as the data to be written, and this set is used as the object to be overwritten to perform overwrite writing, while updating the corresponding index mapping record.

[0088] To verify the ability of the proposed method to maintain the temporal correlation between operational data and operational events during the cyclical coverage of historical operational data, a comparative test was conducted between the existing EMS data management method and the proposed method under the condition of continuous 30-day operation of the power plant. A total of 1246 operational events were recorded during the test, and the correlation retention rate between operational events and corresponding operational data within each operational cycle was calculated. The test results are as follows: Figure 4 As shown, in the early stage of operation, the difference in event association retention rate between the two data management methods is small. However, as the number of historical operation data cycles increases, the association retention rate between operation events and corresponding operation data under the existing EMS data management method gradually decreases, dropping to about 62% after 30 days of operation. After adopting the method of this application, the event association retention rate remains above 94% under the same operating conditions.

[0089] As can be seen from the above embodiments, under the condition of limited storage resources, the method of this application can maintain the consistency of the temporal correlation between operating data and operating events during the process of overwriting historical operating data, thereby providing continuous and complete data support for querying the operating status of energy storage power stations and tracing back abnormal events.

[0090] like Figure 5As shown, this invention provides an energy storage EMS operation data management system. The system includes: a data acquisition unit, used to acquire various types of operation data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attributes of each operation data; a rule construction unit, used to establish unified index rules based on the unified data identifiers, and determine the write location of each operation data according to the unified index rules to perform storage, while generating corresponding index mapping records; a processing unit, used to establish storage cycles and cyclic overlay strategies matching the generation cycles of operation data at different time scales, and perform overlay writing on historical operation data and update the corresponding index mapping records based on the cyclic overlay strategy; and a storage unit, used to perform read positioning on operation data based on the unified index rules, and establish an association mapping relationship between the index mapping records of the operation data and the corresponding event data.

[0091] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described energy storage EMS operation data management method.

[0092] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor A01, a network interface A02, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements a data management method for energy storage EMS operation.

[0093] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0094] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0095] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for managing operational data of an energy storage EMS, characterized in that, The method includes: Acquire various types of operational data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attributes of each operational data. A unified index rule is established based on the unified data identifier, and the write location of each running data is determined according to the unified index rule to perform storage, while generating a corresponding index mapping record. For runtime data at different time scales, a storage period and cyclic overwrite strategy matching its generation cycle are established. Based on the cyclic overwrite strategy, historical runtime data is overwritten and the corresponding index mapping records are updated. Periodic boundary marker information is set for the running data within the statistical period boundary range, and when the cyclic coverage strategy is executed, delayed coverage processing is performed on the historical running data with the periodic boundary marker information. After generating a unified data identifier for the running data, the corresponding time window range is determined based on its time attribute. When executing the cyclic coverage strategy, a temporary lock mark is set for the running data that is within the time window and has a correlation with the recorded event data. Based on the unified indexing rules, the running data is read and located, and an association mapping relationship is established between the index mapping records of the running data and the corresponding event data, including: After overwriting the running data, the updated index mapping records are subjected to consistency verification based on the unified indexing rules. When a mismatch is detected in the associated mapping records corresponding to event data that has a time relationship with the running data, the set of running data index keys with a time relationship is re-determined according to the unified indexing rules, and the associated mapping records are reconstructed so that the event data can be re-associated with the running data after the overwrite.

2. The energy storage EMS operation data management method according to claim 1, characterized in that, Acquire various types of operational data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attribute of each operational data, including: Extract the corresponding data type information and time attribute information from the various operational data generated during the operation of the energy storage EMS; A corresponding unified data identifier is generated based on the data type information and the time attribute information; Write the unified data identifier into the data description field of the corresponding running data.

3. The energy storage EMS operation data management method according to claim 1, characterized in that, Establish unified indexing rules based on the unified data identifier, including: Based on the unified data identifier, the data type information and time attribute information of the corresponding running data are parsed, and the corresponding storage area identifier is determined according to the data type information; Based on the time attribute information, the corresponding time slot identifier is determined, and the storage area identifier is combined with the time slot identifier to generate the index key of the corresponding running data.

4. The energy storage EMS operation data management method according to claim 3, characterized in that, Based on the unified indexing rules, the write location of each piece of running data is determined to perform storage, and corresponding index mapping records are generated, including: Generate corresponding index keys for the running data to be written based on the unified indexing rules; Based on the index key, determine the corresponding write position in the unified storage space, and write the running data to the write position; After the write is completed, an index mapping record corresponding to the index key is generated so that when the historical running data is overwritten in a subsequent process, the association between the running data and the corresponding event data is maintained based on the updated index mapping record.

5. The energy storage EMS operation data management method according to claim 1, characterized in that, Establish storage period and cyclic overlay strategies that match the generation cycle of operational data at different time scales, including: Based on the generation cycle of the operational data, corresponding storage cycles are set for operational data at different time scales; A cyclic coverage strategy is constructed based on the unified indexing rules; wherein, The cyclic coverage strategy is used to determine the coverage range of historical running data based on the time attribute of the running data during the data writing process, so that the running data with time correlation maintains a consistent data positioning relationship during the cyclic coverage process.

6. The energy storage EMS operation data management method according to claim 5, characterized in that, Based on the aforementioned cyclic overwrite strategy, historical running data is overwritten and the corresponding index mapping records are updated, including: Upon receiving the running data to be written, the cyclic overwrite strategy is invoked to determine the set of historical running data that has a preset time association with the running data to be written, and the set of historical running data is designated as the object to be overwritten; The set of write locations of the object to be overwritten in the unified storage space is determined according to the unified indexing rules, and the running data to be written is written to the set of write locations to complete the overwrite; After completing the overwrite, update the index mapping record corresponding to the set of write positions.

7. The energy storage EMS operation data management method according to claim 1, characterized in that, Based on the unified indexing rules, the running data is read and located, and an association mapping relationship is established between the index mapping records of the running data and the corresponding event data, including: Obtain the event time attribute of the event data and generate an event index key; Based on the event time attribute, the unified indexing rule is invoked to determine the set of running data index keys that have a preset time association relationship with the event data, and an association mapping record is established between the event index keys and the set of running data index keys; Upon receiving a read request, an index key is generated based on the unified indexing rules, and the corresponding index mapping record is queried to locate and read the target running data.

8. A data management system for energy storage EMS operation, characterized in that, The system is used to execute the energy storage EMS operation data management method according to any one of claims 1-7, and the system includes: The acquisition unit is used to acquire various types of operational data generated during the operation of the energy storage EMS, and generate corresponding unified data identifiers based on the data type and time attributes of each operational data. The rule building unit is used to establish unified index rules based on the unified data identifier, determine the write location of each running data according to the unified index rules to perform storage, and generate corresponding index mapping records. The processing unit is used to establish a storage cycle and cyclic overwrite strategy that matches the generation cycle of the running data at different time scales, and to perform overwrite writing on the historical running data and update the corresponding index mapping record based on the cyclic overwrite strategy. The storage unit is used to read and locate the running data based on the unified indexing rules, and to establish an association mapping relationship between the index mapping record of the running data and the corresponding event data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the energy storage EMS operation data management method as described in any one of claims 1-7.

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