Battery stream data disorder processing method and system, electronic device and storage medium

Abstract

This application provides a method, system, electronic device, and storage medium for out-of-order processing of battery streaming data, relating to the field of data processing technology. The method includes: acquiring configuration information and detailed battery data of battery tenants, and binding the configuration information to the detailed battery data; grouping and sorting the detailed battery data into groups to construct a data buffer; acquiring data parameters from the data buffer, controlling the data outflow from the data buffer based on the data parameters and configuration information to obtain outflowing battery data; and performing monotonically increasing filtering on the outflowing battery data based on the data sampling time to obtain target streaming data. This application significantly improves the reliability and adaptability of cloud monitoring platforms by establishing an isolated and configurable streaming rearrangement mechanism for multi-tenant battery data, enabling the output of high-quality battery data sequences that are strictly time-ordered, non-redundant, and non-backtracking.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, electronic device, and storage medium for out-of-order processing of battery streaming data. Background Technology

[0002] Currently, to ensure the safe operation of power battery systems, battery management systems need to collect various battery operation data in real time. This data is typically transmitted to a cloud monitoring platform via mobile communication networks through terminal devices at a fixed sampling frequency. As a general-purpose monitoring system, this platform needs to have multi-tenant access capabilities to support data access from terminals from different equipment manufacturers, deployed in different regions, and using heterogeneous communication protocols. However, due to differences in network link conditions and inconsistencies in transmission paths, problems such as delays, timing errors, and duplicate transmissions are common during data upload.

[0003] Key early warning algorithms for analyzing thermal runaway and voltage consistency in power batteries typically require data acquisition from cloud monitoring platforms and are strictly dependent on the continuity and temporal accuracy of time-series data. Currently, the industry commonly uses a water level mechanism as the standard solution for handling out-of-order data. This mechanism periodically generates water level markers based on data timestamps, theoretically assuming that all data earlier than the marked time has arrived, thus triggering time window calculations. However, this mechanism inherently lacks dynamic optimization capabilities for buffer resource allocation and cannot guarantee the strictly monotonically increasing characteristics of the water level. These technical limitations result in poor out-of-order processing of battery streaming data, making it difficult to meet the requirement of strictly chronologically ordered data processing. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, this application provides a method, system, electronic device and storage medium for out-of-order processing of battery streaming data, which effectively solves the problem that the existing out-of-order processing methods for battery streaming data are not effective and make it difficult to achieve the data processing requirements of strictly arranging data in chronological order.

[0005] In a first aspect, this application provides a method for out-of-order processing of battery streaming data, the method comprising: Obtain the configuration information and battery details of the battery tenant, and bind the configuration information and battery details to the status. The battery detail data is grouped and sorted into queues to construct a data cache area; Obtain data parameters from the data buffer, and control the data outflow from the data buffer based on the data parameters and the configuration information to obtain battery outflow data; The battery outflow data is filtered monotonically increasing based on the data sampling time to obtain the target streaming data.

[0006] In an optional implementation, the configuration information includes the rearrangement waiting time and data reporting frequency, and the battery details data includes the battery serial number, battery device number, and battery operating parameters.

[0007] In an optional implementation, the battery detail data is grouped and sorted into queues to construct a data cache, including: Data is grouped according to the battery serial number and the battery device number to obtain multiple battery data groups; The latest battery operating parameters are stored in the priority queue of the corresponding battery data group; The battery operating parameters in the priority queue are sorted in ascending order according to the data sampling time to obtain the data buffer.

[0008] In an optional implementation, data parameters of the data buffer are obtained, and data outflow from the data buffer is controlled according to the data parameters and the configuration information to obtain battery outflow data, including: Obtain the queue length and battery operation parameter entry time of each priority queue in the data buffer; The optimal buffer depth is obtained by calculating based on the rearrangement waiting time and the data reporting frequency; If the queue length is greater than the optimal buffer depth, the data frame with the earliest battery operating parameter entry time is controlled to flow out.

[0009] In an optional implementation, controlling the data outflow of the data buffer based on the data parameters and the configuration information to obtain battery outflow data further includes: When a data frame enters the priority queue, a timer is started. If no new data frame enters within the reordering waiting time, the data frames in the priority queue are controlled to flow out in order.

[0010] In an optional implementation, the battery outflow data is monotonically filtered according to the data sampling time to obtain target streaming data, including: Obtain the first data sampling time of the current battery outflow data and the second data sampling time of the previous frame of battery outflow data; If the first data sampling time is less than or equal to the second data sampling time, then the current battery outflow data is discarded; If the first data sampling time is greater than the second data sampling time, then the current battery outflow data is taken as the target streaming data.

[0011] In an optional implementation, after binding the configuration information with the battery details data, the method further includes: When the configuration information is modified, the configuration information is updated in real time via a scheduled task.

[0012] Secondly, this application provides a battery streaming data out-of-order processing system, the system comprising: The data acquisition module is used to acquire the configuration information and battery detail data of the battery tenant, and to bind the configuration information and the battery detail data to a status. The data caching module is used to group and sort the battery detail data to construct a data cache area; The data triggering module is used to obtain data parameters from the data buffer, control the data outflow from the data buffer according to the data parameters and the configuration information, and obtain battery outflow data. The data filtering module is used to perform monotonically increasing filtering on the battery outflow data according to the data sampling time to obtain the target streaming data.

[0013] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery streaming data out-of-order processing method as described in any of the foregoing embodiments.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the battery streaming data out-of-order processing method as described in any of the foregoing embodiments.

[0015] The battery streaming data out-of-order processing method, system, electronic device, and storage medium provided in this application significantly improve the reliability and adaptability of the cloud monitoring platform by establishing an isolated and configurable streaming reordering mechanism for multi-tenant battery data. It avoids the problem of latency constraints between battery tenants, ensuring that high-latency battery tenants do not affect the real-time data of other battery tenants. By dynamically adapting to the data frequency and network characteristics of each tenant, it achieves on-demand allocation of buffer resources, preventing memory waste while ensuring the integrity of low-frequency data reordering. Simultaneously, through monotonically increasing checksums, it effectively filters duplicate and severely out-of-order data, outputting a strictly time-ordered, non-redundant, and backtrack-free high-quality battery data sequence, providing a stable and reliable data input foundation for subsequent battery safety alarm algorithms. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a first schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application; Figure 2 This is a second schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application; Figure 3 This is a third schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application; Figure 4 This is the fourth schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application; Figure 5 This is a schematic diagram of the battery streaming data out-of-order processing system provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0018] Explanation of key component symbols: 200. Battery streaming data out-of-order processing system; 210. Data acquisition module; 220. Data caching module; 230. Data triggering module; 240. Data filtering module; 300. Electronic device; 310. Processor; 320. Communication interface; 330. Memory; 340. Communication bus. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be further described clearly and completely below with reference to the accompanying drawings of the embodiments. It should be noted that the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0022] Currently, distributed stream processing frameworks commonly employ watermarking mechanisms as a standard solution for handling out-of-order time-series data. The core principle of this mechanism is to periodically generate watermark markers based on the timestamp attribute of data records, indicating that data before a certain time point should theoretically have all arrived in the system, thus triggering corresponding window calculation operations. However, this mechanism has several problems: First, due to differences in data sampling frequency and uneven network transmission latency among different tenants, a globally uniform watermark strategy can lead to the discarding of a large amount of data from high-latency tenants. Relaxing the watermark threshold to accommodate high-latency tenants would significantly reduce the real-time processing performance of the entire system. Second, traditional watermarking mechanisms typically configure fixed-size cache windows, lacking the ability to dynamically adjust based on the actual data throughput of tenants. This design flaw can lead to inefficient memory resource usage and poor time-series rearrangement of low-frequency data. Finally, when facing special cases such as data backtracking or duplicate reporting, a simple watermarking mechanism cannot ensure the strict temporal order of the output data stream. This temporal deviation may further trigger logical errors in downstream algorithms.

[0023] Example 1 This application provides a method for out-of-order processing of battery streaming data, which effectively solves the problem that existing methods for out-of-order processing of battery streaming data are ineffective and make it difficult to achieve the data processing requirement of strictly arranging data in chronological order. Figure 1 This is a first schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application, as shown below. Figure 1 As shown, the method includes the following steps: S100: Obtain the configuration information and battery details of the battery tenant, and bind the configuration information and battery details to the status.

[0024] In this embodiment, the KeyedProcessFunction operator, a low-level function used for key partitioning in the distributed stream processing framework Apache Flink, is employed to implement out-of-order processing of battery streaming data.

[0025] Optionally, a broadcast stream can be set up for each battery tenant. The broadcast stream includes the configuration information of the corresponding battery tenant. The configuration information includes, but is not limited to, the reordering wait time and the data reporting frequency. The reordering wait time can be determined based on the device network latency characteristics and service real-time requirements of the battery tenant.

[0026] Battery detail data can be obtained through the cloud monitoring platform corresponding to each battery tenant. This battery detail data includes, but is not limited to, battery serial number, battery device number, and battery operating parameters, including, but not limited to, voltage, current, temperature, and data sampling time. The battery detail data stream is connected to the broadcast stream via a connect operation to ensure that configuration information is included in the battery detail data stream, thereby completing the state binding between the configuration information and the battery detail data stream.

[0027] As a further implementation of this application's embodiments, when the configuration information is modified, the configuration information is updated in real time via a scheduled task. That is, when a battery tenant modifies the configuration on the business side, the operator updates the configuration snapshot in memory in real time via a scheduled task, ensuring that the reordering strategy takes effect within seconds without stopping the task. The specific steps for updating the configuration information in real time via a scheduled task include the following: In the custom broadcast stream, the loop function while(true) and the thread sleep function Tread.sleep(interval) are used to periodically call the interface of the battery tenant to obtain the configuration information of multiple battery tenants, and the configuration object TenantConfig of a single tenant is used as the element to flow downstream.

[0028] After connecting the battery detail data stream and the broadcast stream, the broadcast connection stream processing function `processBroadcastElement` is overridden in the `KeyedProcessFunction` operator to process the broadcast data, and the abstract class function `processElement` is overridden to process the battery detail data.

[0029] After the processBroadcastElement function obtains TenantConfig, it saves it to the operator's state MapState. MapState uses the battery device number as the key and TenantConfig as the value. If the value exists, it is updated.

[0030] The `processElement` function retrieves the battery device number from a single battery detail data entry, obtains the corresponding `TenantConfig` from `MapState`, extracts the rearrangement wait time and data reporting frequency from the configuration information, and saves them as fields in the battery detail data. At this point, the battery detail data updates the configuration information and flows downstream to be used to build a data cache.

[0031] Based on this, by dynamically loading multi-tenant configuration information, the latest configuration information of each battery tenant can be obtained and applied in real time. Battery tenants can independently adjust their policies according to their own device network conditions and monitoring needs, without the need for technical personnel intervention or task restart. Configuration information updates are accurately applied to the data stream of the corresponding battery tenant, avoiding global policy conflicts and ensuring the isolation of processing logic between different tenants.

[0032] S200: Group and sort the battery detail data into queues to build a data cache.

[0033] Figure 2 This is a second schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application, as shown below. Figure 2 As shown, building a data cache includes the following steps: S210. Group the data according to the battery serial number and battery device number to obtain multiple battery data groups.

[0034] In this embodiment, since multiple battery devices of the same model may exist under the same tenant, and the acquisition terminal of a single battery may be replaced during its life cycle, resulting in a change of battery device number, the grouping operation uses the battery serial number and battery device number as a composite key. This ensures that each battery entity has a logically isolated state storage space, achieving fine-grained physical entity-level isolation. Each battery data group can dynamically allocate cache space according to its actual sampling rhythm and network latency, ensuring the accuracy of high-frequency data rearrangement while preventing low-frequency data from lingering and wasting resources.

[0035] S220. Store the latest battery operating parameters into the priority queue of the corresponding battery data group.

[0036] In this embodiment, each battery data group is assigned a priority queue, and the latest arriving battery operating parameters are stored in the priority queue of the corresponding battery data group.

[0037] S230. Sort the battery operating parameters in the priority queue in ascending order according to the data sampling time to obtain the data buffer area.

[0038] In this embodiment, the priority queue uses the data sampling time in the battery details data as the sorting weight to ensure that the battery operating parameters inside the priority queue are always in ascending order, thereby initially correcting the disorder caused by network transmission at the operator layer.

[0039] Based on this, the data cache buffer is constructed independently on a per-physical-cell basis, fundamentally achieving logical isolation of data processing. Data from different cells does not interfere with each other, avoiding the problem of overall sorting failure due to a single device malfunction. The on-demand allocation of cache space significantly improves the utilization efficiency of system resources and enhances the stability of data processing in a multi-tenant environment.

[0040] S300: Obtain data parameters from the data buffer, control the data outflow from the data buffer based on the data parameters and configuration information, and obtain battery outflow data.

[0041] In this embodiment of the application, in order to balance sorting accuracy and alarm real-time performance, a dual-trigger logic can be used to control the data outflow. Figure 3 This is a third schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application, as shown below. Figure 3 As shown, controlling the outflow of data specifically includes the following steps: S310. Obtain the queue length of each priority queue in the data buffer and the entry time of battery operating parameters.

[0042] Understandably, the queue length of each priority queue can be obtained in real time based on the number of data frames in the queue, and the entry time of battery operating parameters is recorded when the data frame enters the priority queue.

[0043] S320: Calculate the optimal buffer depth based on the rearrangement waiting time and data reporting frequency.

[0044] In this embodiment, the formula for calculating the optimal cache depth is as follows:

[0045] In the above formula, L Indicates the optimal cache depth. waitTime This indicates the waiting time for reordering. dataFrequency This indicates the frequency of data reporting.

[0046] S330. If the queue length is greater than the optimal buffer depth, control the data frame with the earliest battery operating parameter entry time to flow out.

[0047] In this embodiment of the application, when the queue length is greater than the optimal buffer depth L At that time, the earliest data frame that automatically controls the battery operating parameters will be output.

[0048] Based on this, by dynamically calculating the optimal buffer depth for each battery tenant, a deeper queue is allocated to battery tenant devices that report frequently to ensure sorting accuracy, while a very shallow queue is allocated to battery tenant devices that report infrequently to save resources. This adaptive mechanism avoids the risks of storage bloat and memory overflow caused by blind caching, and can support concurrent monitoring of a larger number of battery entities under the same resource configuration, achieving fine-grained configuration of state resources and optimizing system throughput and stability.

[0049] S340. When a data frame enters the priority queue, a timer is started. If no new data frame enters within the reordering waiting time, the data frames in the priority queue are controlled to flow out in order.

[0050] Furthermore, if data does not continuously flow in and the queue size does not reach the outflow threshold, a timer-based fallback mechanism can be used to prevent data from waiting indefinitely and thus failing to flow into downstream computation. Specifically, when a new data frame enters the priority queue, a timer is automatically calculated and registered. The timer's trigger time is the current time plus the reordering wait time. If no new data frame enters the priority queue within the reordering wait time, the remaining data frames in the priority queue are forcibly cleared and sent in order after the timer's trigger time expires, thereby preventing data from being stuck in the operator indefinitely due to battery outages.

[0051] Based on this, a timer is independently registered for each battery entity. Even if a battery tenant experiences a severe power outage, it only affects its own private priority queue. The timers of other battery tenants will still trigger and output on time after the preset delay. This ensures real-time processing of battery health data, eliminates the backflow effect of delays between battery tenants, and improves the determinism of real-time monitoring.

[0052] S400: The battery outflow data is monotonically filtered according to the data sampling time to obtain the target streaming data.

[0053] Figure 4 This is the fourth schematic diagram of the battery streaming data out-of-order processing method provided in the embodiments of this application, as shown below. Figure 4 As shown, the data monotonically increasing filtering process specifically includes the following steps: S410: Obtain the first data sampling time of the current battery outflow data and the second data sampling time of the previous frame of battery outflow data.

[0054] In this embodiment of the application, the current battery outflow data is the data in the real-time outflow data buffer, while the battery outflow data of the previous frame is the data that was successfully sent to the downstream algorithm in the previous frame, i.e. the target streaming data. Based on the battery detail data, the corresponding first data sampling time and second data sampling time can be obtained.

[0055] S420. If the first data sampling time is less than or equal to the second data sampling time, then the current battery outflow data is discarded.

[0056] Understandably, if the first data sampling time is less than the second data sampling time, it indicates that the current battery outflow data belongs to expired, out-of-order, or retrospective data after the processed time period, and is therefore discarded and logged. If the first data sampling time is equal to the second data sampling time, it indicates a duplicate message, which can also be directly discarded and logged. The log record can include parameters such as the battery device number, battery serial number, original sampling time and reception time of the discarded data, and reason for discarding. The log can mark the number of discarded data, thus providing a traceable record in case of insufficient data volume.

[0057] S430. If the first data sampling time is longer than the second data sampling time, then the current battery outflow data will be used as the target streaming data.

[0058] In this embodiment of the application, if the first data sampling time is greater than the second data sampling time, it indicates that the current battery outflow data has passed the verification and can be collected and output to the downstream alarm algorithm for application. Therefore, the current battery outflow data can be used as target streaming data.

[0059] This application embodiment effectively filters out duplicate and severely out-of-order data through monotonically increasing verification, outputting a high-quality battery data sequence that is strictly time-ordered, non-redundant, and non-backtracking, providing a stable and reliable data input foundation for subsequent battery safety alarm and other algorithms.

[0060] Example 2 Based on the same technical concept as Embodiment 1 above, this application provides a battery streaming data out-of-order processing system. Figure 5 This is a schematic diagram of the battery streaming data out-of-order processing system provided in an embodiment of this application, as shown below. Figure 5 As shown, the battery streaming data out-of-order processing system 200 includes: The data acquisition module 210 is used to acquire the configuration information and battery detail data of the battery tenant, and bind the configuration information and battery detail data to the status.

[0061] The data caching module 220 is used to group and sort the battery detail data to build a data cache area.

[0062] The data triggering module 230 is used to obtain data parameters from the data buffer, control the data outflow from the data buffer according to the data parameters and configuration information, and obtain battery outflow data.

[0063] The data filtering module 240 is used to perform monotonically increasing filtering on the battery outflow data according to the data sampling time to obtain the target streaming data.

[0064] The battery streaming data out-of-order processing system provided in this application embodiment, under the premise of ensuring that the data of different battery tenants do not interfere with each other, reorganizes the out-of-order, duplicate and delayed original battery sampling data into a high-quality sequence that is strictly arranged according to the sampling time, without duplication and monotonically increasing, thereby providing the most reliable basic data source for subsequent battery safety early warning.

[0065] It is understood that the implementation method of the battery streaming data out-of-order processing method described in Embodiment 1 above is also applicable to this embodiment and can achieve the same technical effect, so it will not be described again here.

[0066] Example 3 Based on the same concept, this application also provides an electronic device. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 6 As shown, the electronic device 300 may include a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute the steps of the battery streaming data out-of-order processing method as described in the above embodiments. For example, this includes: S100: Obtain the configuration information and battery detail data of the battery tenant, and bind the configuration information and battery detail data to the status. S200: Group and sort the battery detail data into queues, and build a data cache area; S300: Obtain data parameters from the data buffer, control the data outflow from the data buffer based on the data parameters and configuration information, and obtain battery outflow data; S400: The battery outflow data is monotonically filtered according to the data sampling time to obtain the target streaming data.

[0067] The processor 310 can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0068] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The memory 330 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] Example 4 Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program containing at least one piece of code executable by a master control device to control the master control device to implement the steps of the battery streaming data out-of-order processing method as described in the above embodiments. For example, it includes: S100: Obtain the configuration information and battery detail data of the battery tenant, and bind the configuration information and battery detail data to the status. S200: Group and sort the battery detail data into queues, and build a data cache area; S300: Obtain data parameters from the data buffer, control the data outflow from the data buffer based on the data parameters and configuration information, and obtain battery outflow data; S400: The battery outflow data is monotonically filtered according to the data sampling time to obtain the target streaming data.

[0071] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0072] The computer program may be stored, in whole or in part, on a computer-readable storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0073] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0074] In summary, the battery streaming data out-of-order processing method, system, electronic device, and storage medium provided in this application significantly improve the reliability and adaptability of the cloud monitoring platform by establishing an isolated and configurable streaming rearrangement mechanism for multi-tenant battery data. It avoids the problem of latency constraints between battery tenants, ensuring that high-latency battery tenants do not affect the real-time performance of other battery tenants' data. By dynamically adapting to the data frequency and network characteristics of each tenant, it achieves on-demand allocation of buffer resources, preventing memory waste while ensuring the integrity of low-frequency data rearrangement. Simultaneously, through monotonically increasing checksums, it effectively filters duplicate and severely out-of-order data, outputting a strictly time-ordered, non-redundant, and backtrack-free high-quality battery data sequence, providing a stable and reliable data input foundation for subsequent battery safety alarm algorithms.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for processing out-of-order battery streaming data, characterized in that, The method includes: Obtain the configuration information and battery details of the battery tenant, and bind the configuration information and battery details to the status. The battery detail data is grouped and sorted into queues to construct a data cache area; Obtain data parameters from the data buffer, and control the data outflow from the data buffer based on the data parameters and the configuration information to obtain battery outflow data; The battery outflow data is filtered monotonically increasing based on the data sampling time to obtain the target streaming data.

2. The battery streaming data out-of-order processing method according to claim 1, characterized in that, The configuration information includes the reordering waiting time and data reporting frequency, and the battery details include the battery serial number, battery device number, and battery operating parameters.

3. The battery streaming data out-of-order processing method according to claim 2, characterized in that, The battery detail data is grouped and sorted into queues to construct a data cache, including: Data is grouped according to the battery serial number and the battery device number to obtain multiple battery data groups; The latest battery operating parameters are stored in the priority queue of the corresponding battery data group; The battery operating parameters in the priority queue are sorted in ascending order according to the data sampling time to obtain the data buffer.

4. The battery streaming data out-of-order processing method according to claim 3, characterized in that, Obtain data parameters from the data buffer, control the data outflow from the data buffer based on the data parameters and the configuration information, and obtain battery outflow data, including: Obtain the queue length and battery operation parameter entry time of each priority queue in the data buffer; The optimal buffer depth is obtained by calculating based on the rearrangement waiting time and the data reporting frequency; If the queue length is greater than the optimal buffer depth, the data frame with the earliest battery operating parameter entry time is controlled to flow out.

5. The battery streaming data out-of-order processing method according to claim 4, characterized in that, Controlling the data outflow from the data buffer based on the data parameters and configuration information to obtain battery outflow data, and further including: When a data frame enters the priority queue, a timer is started. If no new data frame enters within the reordering waiting time, the data frames in the priority queue are controlled to flow out in order.

6. The battery streaming data out-of-order processing method according to claim 1, characterized in that, The battery outflow data is monotonically filtered based on the data sampling time to obtain target streaming data, including: Obtain the first data sampling time of the current battery outflow data and the second data sampling time of the previous frame of battery outflow data; If the first data sampling time is less than or equal to the second data sampling time, then the current battery outflow data is discarded; If the first data sampling time is greater than the second data sampling time, then the current battery outflow data is taken as the target streaming data.

7. The battery streaming data out-of-order processing method according to claim 1, characterized in that, After binding the configuration information with the battery details data, the method further includes: When the configuration information is modified, the configuration information is updated in real time via a scheduled task.

8. A battery-based streaming data out-of-order processing system, characterized in that, The system includes: The data acquisition module is used to acquire the configuration information and battery detail data of the battery tenant, and to bind the configuration information and the battery detail data to a status. The data caching module is used to group and sort the battery detail data to construct a data cache area; The data triggering module is used to obtain data parameters from the data buffer, control the data outflow from the data buffer according to the data parameters and the configuration information, and obtain battery outflow data. The data filtering module is used to perform monotonically increasing filtering on the battery outflow data according to the data sampling time to obtain the target streaming data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the battery streaming data out-of-order processing method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the battery streaming data out-of-order processing method as described in any one of claims 1-7.

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