Cell-level ultrahigh frequency data acquisition method and system

Abstract

The invention discloses a cell-level ultrahigh-frequency data acquisition method and system, and the method comprises the steps: dividing continuous memory address data in an energy management system according to a preset segment number, obtaining a plurality of data address segments, carrying out the data collection of each data address segment through a multi-thread parallel collection method, obtaining collected data, and carrying out the collection of the collected data. The method comprises the following steps: acquiring data of each data address field, inputting the acquired data into a constructed multi-objective optimization model for calculation to obtain a calculation result, adjusting a data acquisition mode according to the calculation result, performing data acquisition on each data address field according to the adjusted acquisition mode to obtain final data, and aligning the final data to obtain aligned data. And performing format unification on the aligned data, performing compression processing to obtain compressed data, and storing the compressed data in a time sequence data table of a corresponding collector. According to the method, the data is collected through the multi-thread parallel method based on the multi-objective optimization algorithm, and the data collection speed is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a cell-level ultra-high frequency data acquisition method and system. Background Art

[0002] With the continuous development of energy management systems (EMS), data collection of power plant equipment has become an important part of ensuring the efficiency and safety of power plant operations. Traditional data collection methods usually have problems such as low collection efficiency and long time consumption, especially when faced with a large amount of battery cell data.

[0003] In the prior art, a single-threaded acquisition method is usually used to organize and collect the measurement point data provided by the EMS. However, the single-threaded acquisition method is prone to waste system resources when processing a large amount of measurement point data, and it is difficult to meet the needs of efficient and fast data acquisition. Especially in power stations, there are a large number of equipment, such as PCS-1, PCS-2, air conditioners, etc., as well as battery cell data divided by clusters. Traditional methods are difficult to achieve fast collection and processing of these data. Summary of the invention

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a cell-level ultra-high frequency data acquisition method and system, which collects data through a multi-threaded parallel method based on a multi-objective optimization algorithm, thereby improving the data acquisition speed.

[0005] A first aspect of an embodiment of the present invention provides a cell-level ultra-high frequency data acquisition method, the method comprising:

[0006] Dividing the continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data stored in the electrochemical energy storage system;

[0007] A multi-threaded parallel acquisition method is used to collect data from each data address segment to obtain the collected data, and the collected data is input into the constructed multi-objective optimization model for calculation to obtain the calculation result, and the data collection method is adjusted according to the calculation result. Data is collected from each data address segment according to the adjusted collection method to obtain the final data;

[0008] The final data is aligned to obtain aligned data, the aligned data is formatted and then compressed to obtain compressed data, and the compressed data is stored in a timing data table of the corresponding collector.

[0009] In a possible implementation of the first aspect, data is collected from each data address segment using a multi-threaded parallel collection method, including:

[0010] Multiple collectors are used to collect data in each data address segment, wherein one collector corresponds to one designated device and one time series database.

[0011] In a possible implementation of the first aspect, adjusting the data collection method according to the calculation result includes:

[0012] If the calculation result shows that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency;

[0013] If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

[0014] In a possible implementation manner of the first aspect, after storing the compressed data in the time series database of the corresponding collector, the method further includes:

[0015] The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector;

[0016] The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data is transmitted to the cloud via a long connection transmission method.

[0017] A second aspect of an embodiment of the present invention provides a cell-level ultra-high frequency data acquisition system, the system comprising:

[0018] Division module, acquisition module and storage module,

[0019] The division module is used to divide the continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data storage in the electrochemical energy storage system;

[0020] The acquisition module is used to collect data from each data address segment using a multi-threaded parallel acquisition method to obtain the collected data, and input the collected data into the constructed multi-objective optimization model for calculation to obtain the calculation result, adjust the data collection method according to the calculation result, and collect data from each data address segment according to the adjusted collection method to obtain the final data;

[0021] The storage module is used to align the final data to obtain the aligned data, unify the format of the aligned data and then compress it to obtain the compressed data, and store the compressed data in the timing data table of the corresponding collector.

[0022] In a possible implementation manner of the second aspect, data is collected from each data address segment using a multi-threaded parallel collection method, including:

[0023] Multiple collectors are used to collect data in each data address segment, wherein one collector corresponds to one designated device and one time series database.

[0024] In a possible implementation of the second aspect, adjusting the data collection method according to the calculation result includes:

[0025] If the calculation result shows that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency;

[0026] If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

[0027] In a possible implementation manner of the second aspect, after storing the compressed data in the time series database of the corresponding collector, the method further includes:

[0028] The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector;

[0029] The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data is transmitted to the cloud via a long connection transmission method.

[0030] A third aspect of an embodiment of the present invention provides a computer device, including:

[0031] Memory for storing computer programs;

[0032] A processor is used to implement the cell-level ultra-high frequency data acquisition method of the first aspect when executing the computer program.

[0033] A fourth aspect of an embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the cell-level ultra-high frequency data acquisition method of the first aspect are implemented.

[0034] The cell-level ultra-high frequency data acquisition method provided by the embodiment of the present invention divides the continuous memory address data in the energy management system according to the preset number of segments to obtain multiple data address segments, wherein the preset number of segments is determined by the data storage capacity in the electrochemical energy storage system, and uses a multi-threaded parallel acquisition method to collect data from each data address segment to obtain the collected data, and inputs the collected data into the constructed multi-objective optimization model for calculation to obtain the calculation result, adjusts the data acquisition method according to the calculation result, collects data from each data address segment according to the adjusted acquisition method to obtain the final data, aligns the final data to obtain the aligned data, unifies the format of the aligned data and compresses it to obtain the compressed data, and stores the compressed data in the timing data table of the corresponding collector. Through the above method, data is collected through a multi-threaded parallel method based on a multi-objective optimization algorithm, thereby improving the data collection speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 : A flow chart of a collection method for an embodiment of a cell-level ultra-high frequency data collection method provided by the present invention;

[0036] Figure 2 : A system block diagram of an embodiment of the cell-level ultra-high frequency data acquisition method provided by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Please refer to Figure 1 , is a flow chart of an embodiment of a cell-level ultra-high frequency data acquisition method provided by an embodiment of the present invention, including steps S101 to S103, each of which is specifically as follows:

[0039] S101. Divide continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data stored in the electrochemical energy storage system.

[0040] In this embodiment, the measuring point data provided by the EMS is sorted and data addresses are assigned. The specific method is as follows: the continuous memory address data is divided into multiple segments. The number of segments depends on the specific storage resources and the storage capacity of each collection point data. Each segment contains a maximum of 125 test data collection points. One collection point refers to the data information for a battery module. As much single collection of measuring point data as possible is achieved. In this way, data blocks can be efficiently managed in the memory to ensure that each collection operation can process as much measuring point data as possible, thereby reducing the overhead of multiple collections.

[0041] It can also collect as much data from measuring points as possible in a single time, thereby reducing the number of collections and shortening the overall collection time. Specifically, by optimizing the data segmentation strategy, the continuity and integrity of each segment of data in the memory are ensured, which improves the efficiency of data reading and writing.

[0042] S102. Use a multi-threaded parallel collection method to collect data from each data address segment to obtain collected data, and input the collected data into the constructed multi-objective optimization model for calculation to obtain calculation results, adjust the data collection method according to the calculation results, and collect data from each data address segment according to the adjusted collection method to obtain final data.

[0043] In this embodiment, multi-threaded parallel acquisition is used to reduce the time spent on the acquisition of multiple measurement point data segments. Through multi-threading technology, the sorted data address segments are allocated to multiple threads in the collector, and multiple collectors can be used in parallel to process multiple data segments at the same time, significantly improving the speed of data acquisition and effectively utilizing system resources.

[0044] Implement thread pool management in the collector, input the collected data into the designed multi-objective optimization algorithm for calculation, obtain the calculation result, adjust the data collection method according to the calculation result, collect data for each data address segment according to the adjusted collection method, and obtain the final data.

[0045] In some embodiments, the step S102 of "collecting data from each data address segment using a multi-threaded parallel collection method" includes but is not limited to the following steps:

[0046] Multiple collectors are used to collect data in each data address segment, wherein one collector corresponds to one designated device and one time series database.

[0047] In this embodiment, multiple collectors are used to collect data in each data address segment, wherein one collector corresponds to one designated device and one time series database.

[0048] The data of multiple devices (such as PCS-1, PCS-2, air conditioners, etc.) in the power station and the cells divided by clusters are collected, where the cell data includes information such as voltage, current, and temperature of the cell in the current state. The methods are as follows: First, assign a specified device to each collector. This ensures that each collector focuses on data collection of a specific device and avoids interference between data of different devices. Second, multiple collection instances and multi-threaded parallel collection are implemented to shorten the time spent on data collection. By setting up multiple collection instances, the parallelism and efficiency of data collection can be further improved.

[0049] In some embodiments, "adjusting the data collection method according to the calculation result" in step S102 includes but is not limited to the following steps:

[0050] If the calculation result shows that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency;

[0051] If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

[0052] In this embodiment, the collected data is analyzed and optimized in real time. According to the changing trend and characteristics of the data, the frequency and strategy of data collection are dynamically adjusted using a multi-objective optimization algorithm. For battery data with fast amplitude changes, fast frequency collection is maintained to ensure the correctness of the data. For battery data with slow amplitude changes, the performance of the collection equipment can be guaranteed by appropriately reducing the frequency to ensure the overall collection efficiency. Through adaptive means, it is ensured that the best collection efficiency is always maintained under different workloads and data changes.

[0053] It should be noted that the multi-objective optimization algorithm uses the existing collected battery cell data and the corresponding performance efficiency of the equipment as training data to obtain different results under different input conditions. The equipment working efficiency, the total amount of information collected per unit time, and the interpolation accuracy are used as training targets to optimize the final multi-objective optimization algorithm.

[0054] S103, aligning the final data to obtain aligned data, unifying the format of the aligned data and then compressing it to obtain compressed data, and storing the compressed data in a time series data table of the corresponding collector.

[0055] In this embodiment, the collected data needs to ensure uniformity of step length, that is, the time interval between each data is the same. Due to the different collection frequencies, an interpolation algorithm is used to align different data to ensure uniformity of step length.

[0056] The standard device format of data depends on the specific device and data format. CSV files are a common way to store tabular data. Since the directly collected battery data formats vary, converting them to a standard device format will help with subsequent compression, storage, and other operations.

[0057] Compress the converted data in the standard data format. Select a historical data point as the benchmark through the intelligent differential algorithm, compare the subsequent new data with the historical data, store only the difference value after comparison, and compress the difference value twice for storage, and finally achieve a compression ratio of 20:1. Data compression can significantly reduce the storage space and transmission volume of data and improve the efficiency of data processing.

[0058] In some embodiments, the step S103 "after storing the compressed data in the time series database of the corresponding collector" includes but is not limited to the following steps:

[0059] The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector;

[0060] The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data is transmitted to the cloud via a long connection transmission method.

[0061] In this embodiment, the compressed data is stored in the time series database of the current collector. The compressed data not only takes up less storage space, but also speeds up the data writing speed and improves the overall system performance. Since each designated device is stored in a separate time series data table, each collector stores a different table. In this way, it can ensure that the data of different devices are stored independently in the database to avoid data confusion and conflict.

[0062] When data is aggregated, the data of each device will not affect each other, avoiding the time consumed by complex data merging logic in the data aggregation process. Through an independent data table structure, the data aggregation process can be simplified and the efficiency and accuracy of data processing can be improved.

[0063] The merged binary logs of the time series database are then transmitted to the cloud through a persistent connection. Compared with other transmission protocols, the adoption of persistent connection transmission reduces the overhead of establishing and closing connections, thereby improving the efficiency of network transmission. Persistent connections can maintain a stable communication link, reduce the time overhead of establishing and removing connections, and improve the continuity and stability of data transmission.

[0064] After receiving the binlog, the cloud will store the device data persistently. The cloud system will parse and write the received incremental data to ensure the integrity and persistence of the data, thereby achieving efficient management and long-term preservation of the data.

[0065] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0066] In some embodiments, Figure 2 As shown, it shows a block diagram of a cell-level ultra-high frequency data acquisition system 200 provided in an embodiment of the present application, including: a division module 201, a collection module 202 and a storage module 203, wherein:

[0067] The division module 201 is used to divide the continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data storage in the electrochemical energy storage system;

[0068] The acquisition module 202 is used to collect data from each data address segment using a multi-threaded parallel acquisition method to obtain collected data, and input the collected data into the constructed multi-objective optimization model for calculation to obtain calculation results, adjust the data acquisition method according to the calculation results, and collect data from each data address segment according to the adjusted acquisition method to obtain final data;

[0069] The storage module 203 is used to align the final data to obtain aligned data, unify the format of the aligned data and then perform compression processing to obtain compressed data, and store the compressed data in the timing data table of the corresponding collector.

[0070] In some embodiments, data is collected from each data address segment using a multi-threaded parallel collection method, including:

[0071] Multiple collectors are used to collect data in each data address segment, wherein one collector corresponds to one designated device and one time series database.

[0072] In some embodiments, adjusting the data collection method according to the calculation result includes:

[0073] If the calculation result shows that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency;

[0074] If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

[0075] In some embodiments, after storing the compressed data in the time series database of the corresponding collector, the method further includes:

[0076] The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector;

[0077] The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data is transmitted to the cloud via a long connection transmission method.

[0078] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiments, and will not be repeated here.

[0079] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the above steps are implemented when the computer program is executed by a processor; the computer-readable storage medium provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0080] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0081] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cell-level ultra-high frequency data acquisition method, characterized in that: include: Dividing the continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data stored in the electrochemical energy storage system; Using a multi-threaded parallel acquisition method to collect data from each of the data address segments to obtain collected data, and inputting the collected data into the constructed multi-objective optimization model for calculation to obtain calculation results, adjusting the data acquisition method according to the calculation results, and collecting data from each of the data address segments according to the adjusted acquisition method to obtain final data; The final data is aligned to obtain aligned data, the aligned data is compressed after formatting is unified to obtain compressed data, and the compressed data is stored in a timing data table of a corresponding collector.

2. The cell-level ultra-high frequency data acquisition method according to claim 1, characterized in that: The method of collecting data from each data address segment using a multi-threaded parallel collection method includes: The data in each of the data address segments is collected using a plurality of collectors, wherein one collector corresponds to one designated device and one time series database.

3. The cell-level ultra-high frequency data acquisition method according to claim 1, characterized in that: The adjusting the data collection method according to the calculation result includes: If the calculation result is that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency; If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

4. The cell-level ultra-high frequency data acquisition method according to claim 1, characterized in that: After storing the compressed data in the time series database of the corresponding collector, the method further includes: The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector; The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data are transmitted to the cloud via a long connection transmission method.

5. A cell-level ultra-high frequency data acquisition system, characterized in that: Including division module, acquisition module and storage module, The division module is used to divide the continuous memory address data in the energy management system according to a preset number of segments to obtain a plurality of data address segments, wherein the preset number of segments is determined by the amount of data storage in the electrochemical energy storage system; The acquisition module is used to collect data from each of the data address segments using a multi-threaded parallel acquisition method to obtain collected data, and input the collected data into the constructed multi-objective optimization model for calculation to obtain calculation results, adjust the data acquisition method according to the calculation results, and collect data from each of the data address segments according to the adjusted acquisition method to obtain final data; The storage module is used to align the final data to obtain aligned data, unify the format of the aligned data and then perform compression processing to obtain compressed data, and store the compressed data in the timing data table of the corresponding collector.

6. The cell-level ultra-high frequency data acquisition system according to claim 5, characterized in that: The method of collecting data from each data address segment using a multi-threaded parallel collection method includes: The data in each of the data address segments is collected using a plurality of collectors, wherein one collector corresponds to one designated device and one time series database.

7. The cell-level ultra-high frequency data acquisition system according to claim 5, characterized in that: The adjusting the data collection method according to the calculation result includes: If the calculation result is that the amplitude change of the collected data is greater than the preset change value, the frequency of data collection is increased to a first preset frequency; If the calculation result is that the amplitude change of the collected data is less than the preset change value, the frequency of data collection is reduced to a second preset frequency.

8. The cell-level ultra-high frequency data acquisition system according to claim 5, characterized in that: After storing the compressed data in the time series database of the corresponding collector, the method further includes: The compressed data is stored in the time series data table of the corresponding collector to obtain the stored data of each collector; The stored data of each collector are aggregated to obtain aggregated data, and the aggregated data are transmitted to the cloud via a long connection transmission method.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the cell-level ultra-high frequency data acquisition method as described in any one of claims 1 to 4 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the cell-level ultra-high frequency data acquisition method as described in any one of claims 1 to 4 are implemented.

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