Method and system for calculating offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation

Abstract

The invention discloses an energy storage system equipment offline duration calculation method and system based on multi-source data fusion and cross-day compensation. The method comprises the steps of collecting energy storage system equipment offline data from a database, a real-time cache system and a historical log, and performing multi-source data fusion based on an equipment identifier to generate an offline data set; on the basis of the set, identifying a relative position relationship between an equipment offline time period and a statistical date-time boundary, calculating an effective offline time length and executing cross-day compensation; identifying the state of the equipment which does not generate the effective offline duration, distinguishing long-term non-login equipment and never-online equipment, and estimating the offline duration of the long-term non-login equipment; performing single-day duration upper limit constraint and abnormal marking processing on the compensated and estimated offline duration data; and persistently storing and outputting the verified offline duration data and details. According to the method, the problems of single data source and lack of cross-day statistics are solved, and accurate statistics and intelligent analysis of the off-line duration of the energy storage system equipment are realized.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method and system for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation. Background Technology

[0002] With the widespread application of energy storage systems in new energy power generation, power dispatch, and microgrids, the requirements for monitoring the status of energy storage system equipment are becoming increasingly stringent, particularly for battery management systems and their upper-level monitoring systems. Energy storage systems typically consist of multiple BMS units, each responsible for monitoring the operating status, charging and discharging status, and online status of its corresponding battery modules. Accurate statistical analysis of equipment offline time is crucial for ensuring safe system operation, optimizing maintenance plans, and evaluating power plant performance.

[0003] Currently, energy storage system status monitoring suffers from the following technical shortcomings: First, existing methods typically rely on a single data source, failing to integrate databases, real-time caches, and historical log information simultaneously, resulting in incomplete offline data. Second, for devices still offline across midnight, traditional statistical methods cannot accurately compensate for yesterday's offline duration, causing statistical bias. Third, existing technologies lack an effective mechanism for handling unlogged-in devices, often mistakenly including offline devices in offline statistics, and lack reasonable estimations for devices that have been offline for extended periods. Furthermore, existing methods lack a daily offline duration constraint mechanism, potentially leading to outliers interfering with analysis and decision-making. Finally, in scenarios involving multiple power plants and multiple time zones, existing technologies lack a unified time zone processing mechanism, making it difficult to achieve data comparability across power plants.

[0004] Therefore, how to achieve accurate statistics and intelligent analysis of the offline time of energy storage system equipment, and avoid problems such as inaccurate statistics, incomplete data and poor adaptability across scenarios, is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation, which can improve the statistical accuracy and efficiency of offline duration.

[0006] The first aspect of this invention provides a method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation, comprising: Offline data from energy storage system devices is collected from databases, real-time caching systems, and historical logs. Multi-source data is fused based on device identifiers to generate an offline data set. Based on the offline dataset, and targeting the time boundary of the preset statistical date, the relative positional relationship between the offline time period of the energy storage system equipment and the time boundary is identified. The effective offline duration of the energy storage system equipment within the statistical date is calculated, and cross-day duration compensation calculation is performed on the offline time period that crosses the time boundary to obtain the compensated offline duration data. Identify the status of energy storage system devices that have not generated effective offline time. Based on the login records in the real-time cache system and database, identify devices that have not logged in for a long time and devices that have never been online. Estimate the offline time of devices that have not logged in for a long time to obtain the estimated offline time data, and remove devices that have never been online. The daily duration limit constraint and anomaly marking are applied to the compensated offline duration data and the estimated offline duration data, and the offline duration data after reasonableness verification is output. The verified offline duration data and the start and end time details of each offline event are persistently stored.

[0007] Optionally, offline data from energy storage system devices can be collected from databases, real-time caching systems, and historical logs, including: Retrieve the device's historical offline and login times from the offline record table in the database; Obtain the device's last offline time and uncounted duration from the real-time caching system; Obtain the device's operating status and offline event records from historical logs.

[0008] Optionally, multi-source data can be fused based on device identifiers to generate an offline data set, including: Based on device identification, offline data collected from databases, real-time caching systems, and historical logs are aggregated to obtain multi-source offline data; Multi-source offline data is integrated according to energy storage system equipment to establish an offline data mapping table; Generate an offline data set based on the offline data mapping table.

[0009] Optionally, the effective offline duration of the device within the statistical date is calculated, and cross-day duration compensation calculation is performed on offline time periods that cross time boundaries to obtain compensated offline duration data, including: Get the start and end times of the statistical dates; Calculate the effective offline time within the statistical period based on the last offline time of the energy storage system equipment and the current login time. For offline time periods that have not yet been written to the database, the corresponding offline duration is obtained through the real-time caching system and compensated and accumulated to obtain the compensated offline duration data.

[0010] Optionally, identify devices that have not logged in for a long time and devices that have never been online, including: If the real-time cache system contains the last offline time of an energy storage system device, and there is no login record in the database that is later than the last offline time, the device is determined to be a device that has not logged in for a long time. If the last offline time of the energy storage system device is not found in the real-time cache system, and the database login records do not contain any login records for the energy storage system device, it is determined to be a device that has never been online.

[0011] Optionally, the method further includes: Based on the time zone information of the power station to which the energy storage system equipment belongs, the offline time and login time of the energy storage system equipment are uniformly converted to the standard time zone; Under the standard time zone, the offline time of energy storage system equipment in different power stations is uniformly summarized and statistically analyzed.

[0012] Optionally, the offline duration data can be persistently stored, including: Write the cumulative offline time of each energy storage system device into the total time mapping table; Write the start and end time details of each offline event into the offline details database table in batches.

[0013] The second aspect of this invention provides a system for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation, comprising: The data fusion unit is used to collect offline data from energy storage system devices in databases, real-time caching systems and historical logs, and perform multi-source data fusion based on device identifiers to generate an offline data set. The compensation calculation unit is used to identify the relative positional relationship between the offline time period of the energy storage system equipment and the time boundary of the preset statistical date based on the offline data set, calculate the effective offline duration of the energy storage system equipment within the statistical date, and perform cross-day duration compensation calculation for offline time periods that cross the time boundary to obtain the compensated offline duration data. The status identification unit is used to identify the status of energy storage system devices that have not generated effective offline time. Based on the login records in the real-time cache system and database, it identifies devices that have not logged in for a long time and devices that have never been online. It estimates the offline time of devices that have not logged in for a long time to obtain the estimated offline time data, and removes devices that have never been online. The data verification unit is used to perform daily duration limit constraints and anomaly marking on the compensated offline duration data and the estimated offline duration data, and output the offline duration data after reasonableness verification. The data storage unit is used to persistently store the verified offline duration data and the start and end time details of each offline event.

[0014] A third aspect of the present invention provides an electronic device, comprising: One or more processors; A memory on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation as described above.

[0015] The fourth aspect of the present invention provides a computer storage medium for storing a program, which, when executed, is used to implement the method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation as described in any of the preceding claims.

[0016] This invention discloses a method and system for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation. The method includes: collecting offline data from energy storage system equipment from a database, a real-time caching system, and historical logs; generating an offline data set through multi-source data fusion based on equipment identifiers; based on this set, identifying the relative positional relationship between the offline time period and the time boundary of a preset statistical date, calculating the effective offline duration, and performing cross-day compensation calculation; identifying the status of equipment that has not generated effective offline duration, confirming equipment that has not logged in for a long time and equipment that has never been online, and estimating the offline duration of equipment that has not logged in for a long time; applying daily duration upper limit constraints and anomaly marking to the compensated and estimated offline duration data; and finally, persistently storing the verified offline duration data and details. This invention solves the problems of single data source, missing cross-day statistics, and improper handling of unlogged-in equipment in existing technologies, achieving accurate statistics and intelligent analysis of the offline duration of energy storage system equipment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation, provided in an embodiment of the present invention; Figure 2 A schematic diagram of the structure of an energy storage system device offline duration calculation system based on multi-source data fusion and cross-day compensation provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a method and system for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation, which can improve the statistical accuracy and efficiency of offline duration.

[0020] See Figure 1 This figure is a flowchart illustrating a method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and inter-day compensation, according to an embodiment of the present invention. The method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and inter-day compensation, as provided in this embodiment of the present invention, can be implemented, for example, through the following steps S101-S105.

[0021] S101: Collect offline data from energy storage system devices in the database, real-time caching system and historical logs, and perform multi-source data fusion based on device identifiers to generate an offline data set.

[0022] In this embodiment of the invention, the historical offline time and login time of the device are obtained from the offline record table of the database; the last offline time and uncounted duration of the device are obtained from the real-time caching system; and the operating status and offline event records of the device are obtained from the historical logs. Based on the device identifier, the offline data collected from the database, the real-time caching system, and the historical logs are aggregated to obtain multi-source offline data; the multi-source offline data is integrated according to the energy storage system devices to establish an offline data mapping table; and an offline data set is generated based on the offline data mapping table.

[0023] Specifically, offline time, login time, device identifier, power station identifier, and time zone information are collected from offline record tables in databases, real-time caching systems (such as Redis), and historical logs. Data is categorized and integrated using device identifiers to form a unified offline data mapping table, ensuring complete coverage of offline data for each device. This eliminates the limitations of a single data source, achieving high-precision fusion of multi-source data and providing a reliable foundation for subsequent cross-day compensation.

[0024] In this embodiment of the invention, accurate offline duration statistics are achieved by prioritizing multi-source data (cache, database, historical logs). Real-time cache is given priority, the database serves as a stable baseline, and historical logs are used as a supplementary reference. To address time differences or data conflicts between the cache and the database, a "latest record priority + data compensation" strategy is adopted. First, it is determined whether the cached data has been overwritten by the database; if not, the offline duration is added according to cross-day compensation logic. Simultaneously, a fallback rule is applied to devices that have not logged in for a long time or have never been online, and anomaly logs are recorded to ensure that the offline statistical results are complete, reasonable, and traceable.

[0025] S102: Based on the offline data set, for the time boundary of the preset statistical date, identify the relative positional relationship between the offline time period of the energy storage system equipment and the time boundary, calculate the effective offline duration of the energy storage system equipment within the statistical date, and perform cross-day duration compensation calculation for the offline time period that crosses the time boundary to obtain the compensated offline duration data.

[0026] In this embodiment of the invention, the start and end times of the statistical date are obtained; the effective offline duration within the statistical date is calculated based on the last offline time of the energy storage system equipment and the current login time; for offline periods that have not yet been written to the database, the corresponding offline duration is obtained through the real-time caching system and compensated and accumulated to obtain the compensated offline duration data.

[0027] Specifically, cross-day time slicing is performed to obtain the start and end times of the target statistical date (e.g., 00:00:00~23:59:59), and offline time is divided by date. Then, offline duration is calculated, combining device offline time and login time to accurately calculate the effective offline duration of the previous day, while handling the case where offline start dates span across days. Next, the database and cache work together to compensate for offline records that have not yet been written to the database by obtaining the uncounted duration from the real-time cache. This solves the problem of missing cross-day statistics in traditional methods, achieves accurate compensation for device offline duration, and improves statistical reliability.

[0028] This invention addresses the issue of cross-day offline statistics for devices in energy storage systems by proposing a cross-day time slicing and compensation method based on login logs and caching. First, start and end times (e.g., 00:00:00~23:59:59) are generated based on the target statistical date and uniformly converted to the power station's standard time zone to limit the statistical range of yesterday's effective offline time. Each time a device logs in, the previous offline time is retrieved from the cache (e.g., Redis), and both the previous offline time and the current login time are recorded in the login log table to persist the offline time. A fallback rule is applied to devices that have not logged in or have never been online. The previous offline time, current login time, and statistical date boundaries are compared, categorizing them into four types: For cross-day offline events that resume today, yesterday's effective offline time equals the difference between the end and start of today; for events entirely within yesterday's period, yesterday's effective offline time equals the difference between this login and the previous offline time; for events partially offline yesterday that resume today, yesterday's effective offline time equals the difference between the end of today and the previous offline time; and for cross-day offline events that resume yesterday, yesterday's effective offline time equals the difference between this login and the start of today. Offline durations not yet recorded in the database are compensated for through caching, with the total offline duration constrained to no more than 24 hours. For example: if the last offline time was 23:50 on October 16, 2025, and the current login was 00:20 on October 17, 2025, then the effective offline time yesterday was 9 minutes; if the last offline time was 10:00 on October 16, 2025, and the current login was 18:00 on October 16, 2025, then the effective offline time yesterday was 8 hours; if the last offline time was 22:00 on October 15, 2025, and the current login was 02:00 on October 16, 2025, then the effective offline time yesterday was 2 hours. This method ensures accurate statistics of the effective offline time yesterday for cross-day offline events. Combined with caching compensation and unified processing across multiple time zones, it guarantees data integrity and accuracy, providing a reliable foundation for operational analysis and decision-making.

[0029] S103: Identify the status of energy storage system devices that have not generated effective offline time. Based on the login records in the real-time cache system and database, identify devices that have not logged in for a long time and devices that have never been online. Estimate the offline time of devices that have not logged in for a long time to obtain the estimated offline time data, and remove devices that have never been online.

[0030] In this embodiment of the invention, when the last offline time of an energy storage system device exists in the real-time cache system and there is no login record in the database login record that is later than the last offline time, it is determined to be a device that has not logged in for a long time; when the last offline time of an energy storage system device does not exist in the real-time cache system and there is no login record in the database login record that includes any login record for the energy storage system device, it is determined to be a device that has never been online.

[0031] Specifically, the system determines whether a device has been inactive for an extended period or has never been online. For devices that have been inactive for a long time but have previously been online: offline time is calculated according to custom rules (e.g., from the end time of the previous day); for devices that have never been online: they are considered "inactive" and their offline time is not calculated. This avoids mistakenly including devices that have never been online in offline statistics, while supplementing reasonable offline data for devices that have been inactive for a long time, thus improving the accuracy and completeness of offline statistics results.

[0032] In the device offline statistics method of this invention, "long-term inactivity" and "never online" are determined jointly by cache and login logs. The specific logic is as follows: Long-term inactivity: When the last offline time (offlineTime) of a device exists in the Redis cache and there are no login records in the database login logs for a time greater than that offline time, the device is considered to have been inactive for a long time. If the device was previously online but no new login records have been generated since the last offline time, cross-day offline compensation statistics can be performed based on the last offline time in Redis.

[0033] Never Online: If the Redis cache does not contain the device's last offline time and the database login logs do not contain any login records for that device, the device is considered never online. Since the device has not yet performed any online actions, there is no valid offline data available for calculating cross-day offline duration, therefore no offline compensation is applied.

[0034] Based on the above logic, device states can be divided into three categories: Never online: Redis has no offline time and the database has no login records; Long period without login: Redis has been offline for a period of time and the database has no login records after that offline time; Normal online status: There are records in the database whose login time is later than the Redis offline time.

[0035] This determination method ensures the accuracy of cross-day offline compensation and can reasonably distinguish between the device's first online status and its long-term inactivity.

[0036] S104: Perform daily duration limit constraints and anomaly marking on the compensated offline duration data and the estimated offline duration data, and output the offline duration data after reasonableness verification.

[0037] In this embodiment of the invention, the total offline time is compared with the maximum number of seconds per day (24×60×60 seconds), and any excess is counted as one day. Abnormal offline records are marked and logs are generated for maintenance and operation troubleshooting. This ensures the reasonableness of the offline time data and prevents outliers from affecting analysis, early warning, and maintenance decisions. To ensure the reasonableness of daily statistics and avoid abnormal data or device outages causing offline time to exceed one day, thus affecting statistical results and analytical accuracy, the daily offline time is set to a maximum of 24 hours.

[0038] Abnormal offline status is flagged and handled by a scheduled task that periodically scans device login logs and the last offline time to identify abnormal offline behavior, such as exceeding a set threshold for cumulative offline activity in a single day, too many offline occurrences in a single day, or offline activity over multiple consecutive days. Records meeting these criteria are flagged as abnormal and written to an anomaly log table, which is displayed on the operations and maintenance page for easy real-time troubleshooting and analysis. The daily offline duration is still constrained by a maximum of 24 hours to ensure data integrity.

[0039] In one implementation of this invention, the offline time and login time of the energy storage system equipment are uniformly converted to the standard time zone based on the time zone information of the power station to which the energy storage system equipment belongs; under the standard time zone, the offline time of the energy storage system equipment of different power stations is uniformly summarized and statistically analyzed.

[0040] Specifically, the system obtains the offset based on the time zone of the power station and converts the offline and login times of the equipment to the standard time zone. Under the standard time zone, the offline duration of equipment at each power station is calculated and summarized, achieving data comparability across power stations. This resolves the data inconsistency issue across multiple power stations and time zones, supporting system-level analysis and comparison.

[0041] The power station time zone offset is used to unify the device's offline and login times to the standard time zone. For example, if the power station is in UTC+8, local time 2025-10-17 01:30:00 corresponds to standard time UTC 2025-10-16 17:30:00. The standard time zone is used consistently when calculating offline duration to ensure data comparability across multiple power stations and time zones. If the power station's time zone information changes, the new time zone is applied to calculate the data for that day and subsequent days, but historical offline records are not modified.

[0042] S105: Persistently store the verified offline duration data and the start and end time details of each offline event.

[0043] In this embodiment of the invention, the cumulative offline time of each device is written into an offline time mapping table. Offline time details are then written in batches into an offline details table, providing a data foundation for subsequent analysis, reporting, and visualization. This supports data traceability and analyzability, meeting the needs of operation and maintenance management and performance optimization.

[0044] In one implementation of this invention, complete log recording and monitoring are performed on each stage of the processing. The stored data can be accessed by visualization systems (such as Grafana and ECharts) to achieve trend analysis of device offline time, anomaly warnings, and report generation. Visualization and decision support capabilities are provided to enable intelligent monitoring and analysis of device offline status.

[0045] Through the above technical solution, the present invention has the following beneficial effects compared with the prior art: 1. High accuracy in offline duration statistics: Through the fusion of multi-source data from databases, real-time caches and historical logs, the offline information of each device is fully covered; the cross-day offline compensation mechanism further makes up for the statistical omissions of traditional methods, making yesterday's offline duration accurate and reliable, and reducing the error of cross-day and long-term inactive devices by about 60% to 80%.

[0046] 2. The offline statistics for unlogged devices are reasonable. By using the fallback strategy for unlogged devices, the offline time of devices that have not logged in for a long time but have been online can be reasonably estimated to avoid statistical gaps. At the same time, devices that have never been online are excluded to ensure that the statistical results are true and valid, and to improve the data integrity and analysis accuracy.

[0047] 3. It is highly adaptable to multi-power station and multi-time zone scenarios. Based on a unified time zone conversion and cross-power station aggregation mechanism, it enables the comparability of offline data from different power station equipment; it supports the deployment of large-scale energy storage systems, improves system-level operation and maintenance and analysis capabilities, and increases computing efficiency by about 75%.

[0048] 4. High data reliability and anomaly control capabilities, with daily offline time constraints and outlier marking mechanisms to effectively prevent abnormal data from affecting statistics and analysis; provides traceable offline detailed data to support operation and maintenance troubleshooting and performance evaluation.

[0049] 5. Intelligent analysis and visualization support: offline total duration and detailed data can be directly written to the database and connected to the visualization system to realize trend analysis, anomaly warning and report generation; improve data transparency and visualization effect, and make it easier for maintenance personnel to quickly grasp the equipment status.

[0050] 6. The system has low implementation cost, strong scalability, modular design, and highly decoupled interfaces for data acquisition, fusion, compensation, persistence, and visualization; it supports rapid expansion to new power plants, new equipment, and different data sources without modifying the core algorithm, thus reducing implementation and maintenance costs.

[0051] Based on the methods provided in the above embodiments, this invention also provides an offline duration calculation system for energy storage system equipment based on multi-source data fusion and cross-day compensation. The following describes the offline duration calculation system for energy storage system equipment based on multi-source data fusion and cross-day compensation in conjunction with the accompanying drawings.

[0052] See Figure 2 The figure is a schematic diagram of the structure of an energy storage system device offline time calculation system based on multi-source data fusion and cross-day compensation provided in an embodiment of the present invention.

[0053] The offline duration calculation system 200 for energy storage system equipment based on multi-source data fusion and cross-day compensation provided in this embodiment of the invention includes: a data fusion unit 201, a compensation calculation unit 202, a status identification unit 203, a data verification unit 204, and a data storage unit 205.

[0054] The data fusion unit 201 is used to collect offline data from the database, real-time caching system and historical logs of energy storage system devices, and perform multi-source data fusion based on device identifiers to generate an offline data set. The compensation calculation unit 202 is used to identify the relative positional relationship between the offline time period of the energy storage system equipment and the time boundary of the preset statistical date based on the offline data set, calculate the effective offline duration of the energy storage system equipment within the statistical date, and perform cross-day duration compensation calculation for the offline time period that crosses the time boundary to obtain the compensated offline duration data. The status identification unit 203 is used to identify the status of energy storage system devices that have not generated effective offline time. Based on the login records in the real-time cache system and database, it identifies devices that have not logged in for a long time and devices that have never been online. It estimates the offline time of devices that have not logged in for a long time to obtain estimated offline time data, and removes devices that have never been online. The data verification unit 204 is used to perform daily duration limit constraints and anomaly marking on the compensated offline duration data and the estimated offline duration data, and output the offline duration data after reasonableness verification. The data storage unit 205 is used to persistently store the verified offline duration data and the start and end time details of each offline event.

[0055] In one possible implementation, the data fusion unit 201 is specifically used for: Retrieve the device's historical offline and login times from the offline record table in the database; Obtain the device's last offline time and uncounted duration from the real-time caching system; Obtain the device's operating status and offline event records from historical logs.

[0056] In one possible implementation, the data fusion unit 201 is specifically used for: Based on device identification, offline data collected from databases, real-time caching systems, and historical logs are aggregated to obtain multi-source offline data; Multi-source offline data is integrated according to energy storage system equipment to establish an offline data mapping table; Generate an offline data set based on the offline data mapping table.

[0057] In one possible implementation, the compensation calculation unit 202 is specifically used for: Get the start and end times of the statistical dates; Calculate the effective offline time within the statistical period based on the last offline time of the energy storage system equipment and the current login time. For offline time periods that have not yet been written to the database, the corresponding offline duration is obtained through the real-time caching system and compensated and accumulated to obtain the compensated offline duration data.

[0058] In one possible implementation, the state recognition unit 203 has the following functions: If the real-time cache system contains the last offline time of an energy storage system device, and there is no login record in the database that is later than the last offline time, the device is determined to be a device that has not logged in for a long time. If the last offline time of the energy storage system device is not found in the real-time cache system, and the database login records do not contain any login records for the energy storage system device, it is determined to be a device that has never been online.

[0059] One possible implementation also includes a time zone unification unit, specifically used for: Based on the time zone information of the power station to which the energy storage system equipment belongs, the offline time and login time of the energy storage system equipment are uniformly converted to the standard time zone; Under the standard time zone, the offline time of energy storage system equipment in different power stations is uniformly summarized and statistically analyzed.

[0060] In one possible implementation, the data storage unit 205 is specifically used for: Write the cumulative offline time of each energy storage system device into the total time mapping table; Write the start and end time details of each offline event into the offline details database table in batches.

[0061] Since System 200 is the system corresponding to the method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation provided in the above method embodiments, the specific implementation of each unit of System 200 is based on the same concept as that in the above method embodiments. Therefore, for the specific implementation of each unit of System 200, please refer to the description of the method for calculating the offline duration of energy storage system equipment based on multi-source data fusion and cross-day compensation in the above method embodiments, which will not be repeated here.

[0062] This invention also provides an electronic device, the device comprising: a processor and a memory; The memory is used to store instructions; The processor is used to execute the instructions in the memory to perform the offline duration calculation method for energy storage system equipment based on multi-source data fusion and cross-day compensation mentioned in the above embodiments.

[0063] It should be noted that the hardware structure of the electronic devices provided in the embodiments of the present invention can be as follows. Figure 3 The structure shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0064] Please see Figure 3 As shown, device 300 includes: a processor 310, a communication interface 320, and a memory 330. The number of processors 310 in device 300 can be one or more. Figure 3 Taking a processor as an example, in this embodiment of the invention, the processor 310, communication interface 320, and memory 330 can be connected via a bus system or other means. Figure 3 Taking the connection between China and Israel via bus system 340 as an example.

[0065] Processor 310 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP. Processor 310 may further include hardware chips. These hardware chips may be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), generic array logic (GAL), or any combination thereof.

[0066] The memory 330 may include volatile memory, such as random-access memory (RAM); the memory 330 may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 330 may also include a combination of the above types of memory.

[0067] Optionally, the memory 330 stores an operating system and programs, executable modules, or data structures, or subsets thereof, or extended sets thereof. The programs may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic services and handling hardware-based tasks. The processor 310 can read the programs in the memory 330 to implement the offline duration calculation method for energy storage system equipment based on multi-source data fusion and inter-day compensation provided in this embodiment of the invention.

[0068] The bus system 340 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus system 340 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0069] This invention also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the offline duration calculation method for energy storage system equipment based on multi-source data fusion and cross-day compensation mentioned in the above embodiments.

[0070] This invention also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the offline duration calculation method for energy storage system equipment based on multi-source data fusion and cross-day compensation mentioned in the above embodiments.

[0071] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for calculating the off-line duration of a storage system device based on multi-source data fusion and cross-day compensation, characterized in that, The method comprises: Collecting offline data of energy storage system equipment from a database, a real-time cache system and historical logs, performing multi-source data fusion based on equipment identification, and generating an offline data set; Based on the offline data set, for the time boundary of the preset statistical date, the relative position relationship between the offline time period of the energy storage system equipment and the time boundary is identified, the effective offline duration of the energy storage system equipment in the statistical date is calculated, and the offline time period across the time boundary is compensated for the cross-day duration calculation to obtain the compensated offline duration data; Identify the state of the energy storage system equipment that does not generate effective offline duration, confirm the long-term offline equipment and never online equipment based on the login records in the real-time cache system and the database, estimate the offline duration of the long-term offline equipment to obtain the estimated offline duration data, and exclude the never online equipment; The compensated offline duration data and the estimated offline duration data are subjected to single-day duration upper limit constraint and abnormality marking processing, and offline duration data that has passed rationality verification is output; The verified offline duration data and the start and end time details of each offline event are persistently stored.

2. The method of claim 1, wherein, The collection of offline data of energy storage system equipment from a database, a real-time cache system and historical logs comprises: Obtain the historical offline time and login time of the equipment from the offline record table of the database; Obtain the last offline time and unstatistical duration of the equipment from the real-time cache system; Obtain the running state and offline event record of the equipment from the historical logs.

3. The method of claim 1, wherein, The multi-source data fusion based on equipment identification to generate an offline data set comprises: Based on the equipment identification, the offline data collected from the database, the real-time cache system and the historical logs are collected to obtain multi-source offline data; Integrate the multi-source offline data by energy storage system equipment to establish an offline data mapping table; Generate the offline data set based on the offline data mapping table.

4. The method of claim 1, wherein, The calculation of the effective offline duration of the equipment in the statistical date, and the compensation calculation of the offline time period across the time boundary to obtain the compensated offline duration data, comprises: Obtain the start time and end time of the statistical date; According to the last offline time and the login time of the energy storage system equipment, the effective offline duration in the statistical date is calculated; For the offline time period that has not been written into the database, the corresponding offline duration is obtained through the real-time cache system and is compensated and accumulated to obtain the compensated offline duration data.

5. The method of claim 1, wherein, The confirmation of long-term offline equipment and never online equipment comprises: When there is last offline time of the energy storage system equipment in the real-time cache system, and there is no login record later than the last offline time in the database login record, it is determined as long-term offline equipment; When there is no last offline time of the energy storage system equipment in the real-time cache system, and there is no login record of the energy storage system equipment in the database login record, it is determined as never online equipment.

6. The method of claim 1, wherein, The method further comprises: According to the time zone information of the power station to which the energy storage system device belongs, the offline time and the login time of the energy storage system device are uniformly converted to a standard time zone; In the standard time zone, the offline time lengths of the energy storage system devices of different power stations are uniformly summarized and statistically analyzed.

7. The method of claim 1, wherein, The persistent storage of the offline time length data includes: writing the cumulative offline time length of each energy storage system device into a total time length mapping table; writing the start and end time details of each offline event into an offline detail database table in batches.

8. A multi-source data fusion and cross-day compensation based energy storage system equipment offline duration calculation system, characterized in that, The system includes: a data fusion unit configured to collect offline data of energy storage system devices from a database, a real-time cache system, and historical logs, perform multi-source data fusion based on device identification, and generate an offline data set; a compensation calculation unit configured to, based on the offline data set, identify the relative positional relationship between the offline time period of an energy storage system device and the time boundary of a preset statistical date, calculate the effective offline time length of the energy storage system device in the statistical date, and perform cross-day time length compensation calculation on the offline time period that crosses the time boundary to obtain compensated offline time length data; a state identification unit configured to identify the state of an energy storage system device that does not generate effective offline time length, confirm long-term offline devices and never online devices based on login records in the real-time cache system and the database, estimate the offline time length of the long-term offline devices to obtain estimated offline time length data, and exclude the never online devices; a data verification unit configured to perform single-day time length upper limit constraint and abnormality marking processing on the compensated offline time length data and the estimated offline time length data, and output offline time length data that has passed rationality verification; a data storage unit configured to persistently store the verified offline time length data and the start and end time details of each offline event.

9. An electronic device, comprising: The device includes a processor and a memory; The memory is configured to store instructions; The processor is configured to execute the instructions in the memory and perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The instructions, when executed on a computer, cause the computer to perform the method of any one of claims 1-7.

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