Battery cell data compression transmission and aggregation method, system, equipment and medium

Abstract

The invention provides a cell data compression transmission and aggregation method, system and device and a medium, and the method comprises the steps: obtaining cell monitoring data including a timestamp, voltage, temperature and current according to a preset sampling frequency, and carrying out the processing of the cell monitoring data through a differential compression technology, after generating battery cell differential data comprising a difference value of the battery cell monitoring data and differential reference data and a timestamp of the differential reference data, compressing the battery cell differential data by adopting Huffman coding to obtain corresponding compressed battery cell data, and sending the compressed battery cell data to a distributed storage node, and the distributed storage node carries out distributed aggregation processing according to the compressed cell data to obtain corresponding aggregation reduction data. According to the invention, the cell monitoring data is efficiently processed through the differential compression and distributed aggregation technology, so that the data storage cost can be reduced and the storage efficiency can be improved while the rapid transmission and synchronization of the high-frequency cell monitoring data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a method, system, device and medium for compressing, transmitting and aggregating battery cell data. Background Art

[0002] In order to effectively manage and protect the battery, the battery management system needs to frequently collect and transmit cell data to ensure efficient operation and safe management of the system. The real-time requirements for cell data collection are high, usually collected at a frequency of seconds, and the cell data needs to be transmitted to the station or cloud platform for distributed storage.

[0003] Existing data transmission methods usually transmit the high-frequency battery cell data in full and in real time. This transmission method not only causes large delays in high-frequency data transmission, but also requires high storage costs, and also causes the storage system network to be overloaded, affecting the real-time processing of the storage system. Therefore, it is urgent to provide a technical method that can efficiently compress and distribute the aggregation of battery cell data to reduce the amount of data transmission, improve the efficiency of battery cell data transmission and storage, and better meet the high-frequency data usage needs of the battery management system. Summary of the invention

[0004] The purpose of the present invention is to provide a method for compressing, transmitting and aggregating battery cell data, which can efficiently compress and transmit battery cell monitoring data through differential compression combined with Huffman coding technology, and reliably aggregate and restore compressed battery cell data using distributed aggregation technology, thereby effectively reducing the amount of high-frequency data transmission, ensuring rapid and synchronous transmission of high-frequency battery cell monitoring data, and reducing data storage costs and improving storage efficiency, so as to better meet the high-frequency data usage requirements of the battery management system.

[0005] In order to achieve the above objectives, it is necessary to provide a method, system, device and medium for compressing, transmitting and aggregating battery cell data in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for compressing, transmitting and aggregating battery cell data, the method comprising the following steps:

[0007] Obtaining battery cell monitoring data according to a preset sampling frequency; the battery cell monitoring data includes timestamp, voltage, temperature and current;

[0008] Processing the cell monitoring data by differential compression technology to generate corresponding cell differential data; the cell differential data includes a difference value between the cell monitoring data and differential reference data, and a timestamp of the differential reference data;

[0009] The cell differential data is compressed using Huffman coding to obtain corresponding compressed cell data;

[0010] The compressed cell data is sent to a distributed storage node, so that the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated restored data.

[0011] Furthermore, the step of processing the battery cell monitoring data by using the differential compression technology to generate corresponding battery cell differential data includes:

[0012] Acquire similar historical data of the battery cell monitoring data, and use the similar historical data as the differential reference data;

[0013] The differential reference data and the battery cell monitoring data are subjected to difference analysis to obtain the battery cell differential data.

[0014] Furthermore, the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated restored data, including:

[0015] Decompressing the compressed battery cell data to obtain corresponding decompressed battery cell data;

[0016] According to a preset hierarchical structure, the decompressed battery cell data is grouped to obtain corresponding hierarchical grouping data; the hierarchical grouping data includes battery cell hierarchical data, battery cluster hierarchical data and battery compartment hierarchical data;

[0017] According to the data type, each level of grouped data is divided into a corresponding plurality of level partitioned data;

[0018] In response to the aggregation query request, the partitioned data of each level is distributed to different task nodes for aggregation calculation to obtain the corresponding hierarchical partition aggregation results, and all hierarchical partition aggregation results of each hierarchical grouping data are reversely aggregated to obtain the corresponding hierarchical grouping original data;

[0019] All hierarchical grouping original data are aggregated according to the preset hierarchical structure to obtain the aggregated restored data.

[0020] Furthermore, the method further comprises:

[0021] The cell data change rate and the corresponding sampling frequency threshold range are obtained, and the preset sampling frequency is dynamically adjusted according to the cell data change rate and the sampling frequency threshold range to obtain the latest sampling frequency.

[0022] Further, the latest sampling frequency is expressed as:

[0023]

[0024] Where, F represents the latest sampling frequency; F min and F max They represent the lower and upper limits of the sampling frequency threshold range respectively; R represents the rate of change of the battery cell data; T low and T high They respectively represent the lower and upper limits of the cell data change rate threshold range.

[0025] Furthermore, the step of acquiring the sampling frequency threshold range includes:

[0026] The cell data change rate is input into a pre-built sampling frequency range prediction model to perform frequency range prediction, and a corresponding sampling frequency threshold range is obtained.

[0027] Furthermore, the steps of constructing the sampling frequency range prediction model include:

[0028] Get the historical battery cell data change rate time series data of the preset duration;

[0029] By a preset marking method, the cell data change rate at each historical moment in the historical cell data change rate time series data is marked with a corresponding ideal sampling frequency range to obtain the corresponding ideal sampling frequency range time series data; the ideal sampling frequency range time series data includes an ideal sampling frequency upper limit time series data and an ideal sampling frequency lower limit time series data;

[0030] According to the historical battery cell data change rate time series data and the ideal sampling frequency range time series data, the long short-term memory network model is trained to obtain the sampling frequency range prediction model.

[0031] In a second aspect, an embodiment of the present invention provides a system for compressing, transmitting and aggregating battery cell data, the system comprising:

[0032] A data acquisition module, used to obtain battery cell monitoring data according to a preset sampling frequency; the battery cell monitoring data includes timestamp, voltage, temperature and current;

[0033] A differential calculation module, used for processing the battery cell monitoring data by differential compression technology to generate corresponding battery cell differential data; the battery cell differential data includes a difference value between the battery cell monitoring data and differential reference data, and a timestamp of the differential reference data;

[0034] A data compression module, used for compressing the battery cell differential data by using Huffman coding to obtain corresponding compressed battery cell data;

[0035] The aggregation and restoration module is used to send the compressed battery cell data to the distributed storage nodes, so that the distributed storage nodes perform distributed aggregation processing according to the compressed battery cell data to obtain corresponding aggregation and restoration data.

[0036] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0038] The present application provides a method, system, device and medium for compressing, transmitting and aggregating battery cell data. The method can realize obtaining battery cell monitoring data including timestamp, voltage, temperature and current according to a preset sampling frequency, processing the battery cell monitoring data by differential compression technology, generating corresponding battery cell differential data including the difference value between the battery cell monitoring data and the differential reference data, and the timestamp of the differential reference data, and then compressing the battery cell differential data by Huffman coding to obtain corresponding compressed battery cell data, and sending the compressed battery cell data to a distributed storage node so that the distributed storage node performs distributed aggregation processing according to the compressed battery cell data to obtain a technical solution for the corresponding aggregated restored data. Compared with the prior art, the method for compressing, transmitting and aggregating battery cell data can efficiently compress and transmit battery cell monitoring data by differential compression combined with Huffman coding technology, and reliably aggregate and restore compressed battery cell data by distributed aggregation technology, effectively reducing the amount of high-frequency data storage, ensuring the rapid transmission and synchronization of high-frequency battery cell monitoring data, while also reducing data storage costs and improving storage efficiency, so as to better meet the high-frequency data usage requirements of the battery management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic flow chart of a method for compressing, transmitting and aggregating cell data in an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a method for acquiring battery cell differential data in an embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of a preset hierarchical structure for grouping cell data in an embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of data partitioning results of grouping data at each level in an embodiment of the present invention;

[0043] Figure 5 is another schematic flow chart of the method for compressing, transmitting and aggregating cell data in an embodiment of the present invention;

[0044] Figure 6 is a schematic structural diagram of a system for compressing, transmitting and aggregating cell data in an embodiment of the present invention;

[0045] Figure 7 is another structural schematic diagram of the system for compressing, transmitting and aggregating cell data in an embodiment of the present invention;

[0046] Figure 8 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and beneficial effects of the present application clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. 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.

[0048] The method for compressing, transmitting and aggregating battery cell data provided by the present invention can be understood as a method for compressing, transmitting and aggregating battery cell data in seconds, which is based on the fact that the transmission of existing high-frequency battery cell monitoring data will lead to excessive network load, large transmission delay, low storage efficiency and high cost, and it is difficult to effectively meet the application defects of the high-frequency data usage requirements of the battery management system. The following embodiment will explain in detail a method for compressing, transmitting and aggregating battery cell data of a battery cell in the present invention in detail.

[0049] In one embodiment, Figure 1 As shown, a method for compressing, transmitting and aggregating cell data is provided, comprising the following steps:

[0050] S11. Obtain battery cell monitoring data according to the preset sampling frequency; wherein the preset sampling frequency can be understood as the time frequency that is determined in advance based on historical experience and application requirements to provide basic analysis data for the battery management system or other data platform; the corresponding battery cell monitoring data can be understood as the real-time monitoring data of the battery cell obtained by the data acquisition terminal according to the deployed relevant sensors and microcontrollers according to the preset sampling frequency (usually in seconds), which needs to be transmitted to the battery management system or other data platform through the network for analysis and use. Specifically, the battery cell monitoring data includes timestamp, voltage, temperature, current, etc., that is, the data acquisition terminal can collect the relevant monitoring data of each battery cell on demand according to the preset sampling frequency and generate corresponding second-level data samples.

[0051] S12. Process the cell monitoring data by differential compression technology to generate corresponding cell differential data; the cell differential data includes the difference value between the cell monitoring data and the differential reference data, and the timestamp of the differential reference data; wherein, the differential compression technology can be understood as an adaptive data compression method that extracts the difference value between the real-time collected data and the corresponding most similar data as the transmission data, which can effectively reduce the high-frequency data transmission volume; the differential reference data in this embodiment can be understood as pre-selected historical cell monitoring data with a high similarity to the cell monitoring data collected at the current moment.

[0052] Specifically, the step of processing the battery cell monitoring data by using the differential compression technology to generate corresponding battery cell differential data includes:

[0053] Obtain similar historical data of the battery cell monitoring data, and use the similar historical data as the differential reference data; wherein, the method for obtaining similar historical data can be understood as obtaining historical battery cell monitoring data with the highest similarity to the current battery cell monitoring data based on a preset similarity analysis method, and the specific similarity analysis method can adopt any one of the existing similarity measurement methods such as Euclidean distance, Pearson correlation coefficient or cosine similarity, which is not specifically limited here.

[0054] The differential benchmark data and the cell monitoring data are subjected to difference analysis to obtain the cell differential data; wherein the difference analysis can be understood as respectively calculating the difference between each data in the cell monitoring data and the differential benchmark data, such as Figure 2 As shown, assuming that the differential reference data of the battery cell monitoring data L(t) collected at time t is L(0), the battery cell differential data DL(t,0) can be obtained through difference analysis. The specific calculation process will not be described in detail here.

[0055] This embodiment selects the historical battery cell monitoring data with the highest similarity as the differential compression of the battery cell monitoring data at the current moment, which can minimize the size of the differential data, thereby reducing the pressure of data transmission and improving data transmission efficiency and storage efficiency; at the same time, by setting the battery cell differential data to include the differential reference data timestamp, it can provide effective guarantee for the reliable aggregation and restoration of subsequent battery cell monitoring data.

[0056] S13. Use Huffman coding to compress the battery cell differential data to obtain corresponding compressed battery cell data; wherein, the specific method for obtaining the compressed battery cell data can refer to the related existing technology implementation of data coding compression based on Huffman coding, which will not be described in detail here.

[0057] S14. Send the compressed cell data to a distributed storage node, so that the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated and restored data; wherein the distributed storage node can be understood as the data storage system corresponding to each energy storage station, or other platform nodes with distributed storage functions, which are not specifically limited here. The distributed storage node can aggregate and restore the received compressed cell data based on pre-set distributed aggregation conditions for subsequent display and analysis.

[0058] Specifically, the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated restored data, including:

[0059] The compressed cell data is decompressed to obtain corresponding decompressed cell data; wherein, the acquisition of the decompressed cell data can be understood as data obtained by decoding the compressed cell data based on the Huffman encoding technology for compressing the cell differential data as mentioned above. The specific decoding process can also be implemented by referring to the relevant existing technology and will not be described in detail here.

[0060] According to the preset hierarchical structure, the decompressed battery cell data is grouped to obtain corresponding hierarchical grouping data; wherein the preset hierarchical structure can be understood as a data management level set to facilitate better management and analysis of the battery cell monitoring data of the energy storage station, such as Figure 3 As shown, the preset hierarchical structure includes, from bottom to top, a battery cell layer, a battery cluster layer for managing the battery cell layer, and a battery compartment layer for managing the battery cluster layer. The acquired data can be divided into a three-level structure through the distributed computing framework Spark; correspondingly, the hierarchical grouping data includes battery cell level data, battery cluster level data, and battery compartment level data. The specific number of battery cells corresponding to each battery cluster management and the number of battery clusters corresponding to each battery compartment can be set according to actual application requirements and are not specifically limited here.

[0061] According to the data type, each level grouping data is divided into a plurality of corresponding level partitioning data; wherein the level partitioning data can be understood as the various types of battery cell monitoring data in each level grouping data; in practical applications, the data partitioning method of each level grouping data is the same, such as Figure 4 As shown, RDD (Resilient Distributed Dataset) can be used to partition and store data at each level according to the same conditions (according to different data types such as voltage, current, temperature, etc.).

[0062] In response to the aggregate query request, each level partition data is distributed to different task nodes for aggregate calculation to obtain the corresponding level partition aggregation results, and all level partition aggregation results of each level group data are reversely aggregated to obtain the corresponding level group original data; wherein, the aggregate query request can be understood as an original data aggregation and restoration request including the aggregate query time window, and different aggregate query time windows are used to calculate different time series original data. In actual applications, the aggregate query time window and the corresponding sliding interval can be set according to actual application requirements, for example, one window per hour, sliding every 30 minutes, and different task nodes calculate the partition data aggregation and restoration results of different time windows according to the aggregate query request. Although the aggregation and restoration results of different time windows belong to the same group, the results are ordered in time. It is only necessary to sort and merge the calculation results in time series to obtain the required level group original data.

[0063] All hierarchical grouped original data are aggregated according to the preset hierarchical structure to obtain the aggregated restored data; wherein, the aggregation operation can be implemented based on the "join" operation in the spark framework, which will not be described in detail here.

[0064] In this embodiment, the battery cell monitoring data is grouped according to a hierarchical structure, then stored in partitions according to data types, and aggregated and restored according to time aggregation requests. While improving storage efficiency and reducing storage costs, it can also improve computing efficiency by processing aggregation tasks in parallel, supporting fast real-time query and analysis.

[0065] The embodiment of the present application proposes a technical solution of acquiring battery cell monitoring data including timestamp, voltage, temperature and current according to a preset sampling frequency, processing the battery cell monitoring data through differential compression technology, generating corresponding battery cell differential data including the difference value between the battery cell monitoring data and differential reference data, and the timestamp of the differential reference data, compressing the battery cell differential data through Huffman coding to obtain corresponding compressed battery cell data, and sending the compressed battery cell data to a distributed storage node so that the distributed storage node performs distributed aggregation processing according to the compressed battery cell data to obtain corresponding aggregated restored data. The innovative design of efficiently compressing and transmitting the battery cell monitoring data through differential compression combined with Huffman coding technology and reliably aggregated and restored the compressed battery cell data through distributed aggregation technology effectively reduces the amount of high-frequency data storage, ensures the rapid transmission and synchronization of high-frequency battery cell monitoring data, and can also reduce data storage costs and improve storage efficiency, so as to better meet the high-frequency data usage requirements of the battery management system.

[0066] In addition, considering that data changes vary in different situations in actual applications, if data is collected uniformly at a fixed sampling frequency, it is very easy to waste resources when data changes are small or the data accuracy is difficult to meet the requirements when data changes are large. In order to effectively balance resource usage and data accuracy, this embodiment preferably uses monitoring data change rate to dynamically adjust the data collection frequency to achieve more refined data differential compression, reduce compression errors, and improve data accuracy. Specifically, Figure 5 As shown, the method also includes:

[0067] S15, obtaining the cell data change rate and the corresponding sampling frequency threshold range, and dynamically adjusting the preset sampling frequency according to the cell data change rate and the sampling frequency threshold range to obtain the latest sampling frequency; wherein, the acquisition of the cell data change rate can be understood as analyzing the currently acquired cell monitoring data with the cell monitoring data collected in the previous cycle; for example, for the voltage V in the cell monitoring data at time t+1 t+1 In terms of the rate of change, it can be expressed as:

[0068]

[0069] Where Δt is the time interval between two sampling points; V t+1 and V t Respectively represent the voltage values ​​at time t+1 and time t; R t+1 Indicates the voltage change rate corresponding to time t+1;

[0070] It should be noted that in actual applications, the battery cell monitoring data includes many different types of data. In order to ensure the reliability of all data acquisition frequency settings, this embodiment preferably obtains the change rates of various types of data based on the above-mentioned data change rate calculation method, and uses the maximum value of the change rates of various types of data as the change rate value of the overall battery cell monitoring data, and then uses this as the basis for subsequent dynamic adjustment of the data sampling frequency.

[0071] The sampling frequency threshold range in this embodiment includes a sampling frequency upper limit value and a sampling frequency lower limit value. In order to ensure the reliability and efficiency of obtaining the sampling frequency range, this embodiment preferably uses a machine learning model for predictive analysis. Specifically, the steps of obtaining the sampling frequency threshold range include:

[0072] The cell data change rate is input into a pre-constructed sampling frequency range prediction model to perform frequency range prediction, and obtain a corresponding sampling frequency threshold range; wherein, in principle, the sampling frequency range prediction model can adopt any network model that can realize the corresponding prediction function, but in order to ensure the accuracy of the prediction analysis, this embodiment preferably adopts a prediction model constructed based on a long short-term memory network model; specifically, the steps of constructing the sampling frequency range prediction model include:

[0073] Obtain historical cell data change rate time series data of a preset duration; wherein, the preset duration can be selected according to actual application requirements and is not specifically limited here; the cell data change rate data at each historical moment in the corresponding historical cell data change rate time series data can refer to the above-mentioned method for obtaining the cell data change rate, which will not be repeated here.

[0074] By using a preset labeling method, the cell data change rate at each historical moment in the historical cell data change rate time series data is labeled with the corresponding ideal sampling frequency range, and the corresponding ideal sampling frequency range time series data is obtained; wherein, the sampling frequency upper limit value and the sampling frequency lower limit value in the ideal sampling frequency range corresponding to the cell data change rate at each historical moment can be obtained based on expert knowledge or actual experimental data, and are not specifically limited here. The corresponding ideal sampling frequency range time series data obtained includes the ideal sampling frequency upper limit time series data and the ideal sampling frequency lower limit time series data;

[0075] The long short-term memory network model is trained according to the historical battery cell data change rate time series data and the ideal sampling frequency range time series data to obtain the sampling frequency range prediction model; wherein, the acquisition process of the sampling frequency range prediction model can be understood as a process of using the historical battery cell data change rate time series data as the input data of the model, and using the corresponding ideal sampling frequency range time series data as the output data of the model, and performing parameter update training on the long short-term memory network model. The specific training process can be implemented by referring to the relevant training technology of the long short-term memory network model.

[0076] The embodiment of the present application obtains the cell monitoring data including the timestamp, voltage, temperature and current according to the preset sampling frequency, processes the cell monitoring data by differential compression technology, generates the corresponding cell differential data including the difference value between the cell monitoring data and the differential reference data, and the timestamp of the differential reference data, and then compresses the cell differential data by Huffman coding to obtain the corresponding compressed cell data, and sends the compressed cell data to the distributed storage node, so that the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain the corresponding aggregated restored data, and obtains the cell data change rate and the corresponding sampling frequency threshold range, and dynamically adjusts the preset sampling frequency according to the cell data change rate and the sampling frequency threshold range to obtain the latest sampling frequency solution, which can not only effectively reduce the high-frequency data transmission volume, ensure the rapid transmission and synchronization of high-frequency cell monitoring data, but also reduce the data storage cost and improve the storage efficiency, and can also dynamically adjust the data sampling frequency according to the adaptive data change scenario, avoid resource waste, and effectively ensure the accuracy of the collected data, better meet the high-frequency data usage requirements of the battery management system and provide reliable data support for subsequent analysis and research.

[0077] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, 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.

[0078] In one embodiment, Figure 6 As shown, a compression transmission and aggregation system for battery cell data is provided, the system comprising:

[0079] The data acquisition module 1 is used to obtain the battery monitoring data according to the preset sampling frequency; the battery monitoring data includes timestamp, voltage, temperature and current;

[0080] A differential calculation module 2, used for processing the battery cell monitoring data by differential compression technology to generate corresponding battery cell differential data; the battery cell differential data includes a difference value between the battery cell monitoring data and the differential reference data, and a timestamp of the differential reference data;

[0081] A data compression module 3 is used to compress the battery cell differential data using Huffman coding to obtain corresponding compressed battery cell data;

[0082] The aggregation and restoration module 4 is used to send the compressed cell data to the distributed storage nodes, so that the distributed storage nodes perform distributed aggregation processing according to the compressed cell data to obtain corresponding aggregation and restoration data.

[0083] In one embodiment, Figure 7 As shown, a compression transmission and aggregation system for battery cell data is provided, and the system also includes:

[0084] The frequency adjustment module 5 is used to obtain the cell data change rate and the corresponding sampling frequency threshold range, and dynamically adjust the preset sampling frequency according to the cell data change rate and the sampling frequency threshold range to obtain the latest sampling frequency.

[0085] For the specific definition of a compression transmission and aggregation system for battery cell data, please refer to the definition of a compression transmission and aggregation method for battery cell data mentioned above. The corresponding technical effects can also be obtained equivalently, which will not be repeated here. Each module in the above-mentioned compression transmission and aggregation system for battery cell data can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] Figure 8 FIG. 1 shows an internal structure diagram of a computer device in an embodiment, and the computer device may specifically be a terminal or a server. Figure 8As shown, the computer device includes a processor, a memory, a network interface, a display, a camera and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for compressing, transmitting and aggregating battery cell data is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0087] It can be understood by those skilled in the art that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the computer device to which the present application scheme is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0088] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the computer program.

[0089] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0090] In summary, the compression transmission and aggregation method, system, device and medium of battery cell data provided by the embodiments of the present invention realize the compression transmission and aggregation method of battery cell data, which realizes obtaining battery cell monitoring data including timestamp, voltage, temperature and current according to a preset sampling frequency, processing the battery cell monitoring data by differential compression technology, generating corresponding battery cell differential data including the difference value between the battery cell monitoring data and the differential reference data, and the timestamp of the differential reference data, and then compressing the battery cell differential data by Huffman coding to obtain corresponding compressed battery cell data, and sending the compressed battery cell data to distributed storage nodes so that the distributed storage nodes perform distributed aggregation processing according to the compressed battery cell data to obtain corresponding aggregated restored data. The method uses differential compression combined with Huffman coding technology to efficiently compress and transmit battery cell monitoring data, and uses distributed aggregation technology to reliably aggregate and restore compressed battery cell data, effectively reducing the amount of high-frequency data storage, ensuring the rapid transmission and synchronization of high-frequency battery cell monitoring data, while also reducing data storage costs and improving storage efficiency, so as to better meet the high-frequency data usage requirements of the battery management system.

[0091] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0092] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for compressing, transmitting and aggregating battery cell data, characterized in that: The method comprises the following steps: Obtaining battery cell monitoring data according to a preset sampling frequency; the battery cell monitoring data includes timestamp, voltage, temperature and current; Processing the cell monitoring data by differential compression technology to generate corresponding cell differential data; the cell differential data includes a difference value between the cell monitoring data and differential reference data, and a timestamp of the differential reference data; The cell differential data is compressed using Huffman coding to obtain corresponding compressed cell data; The compressed cell data is sent to a distributed storage node, so that the distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated restored data.

2. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 1, characterized in that: The step of processing the battery cell monitoring data by using the differential compression technology to generate corresponding battery cell differential data includes: Acquire similar historical data of the battery cell monitoring data, and use the similar historical data as the differential reference data; The differential reference data and the battery cell monitoring data are subjected to difference analysis to obtain the battery cell differential data.

3. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 1, characterized in that: The distributed storage node performs distributed aggregation processing according to the compressed cell data to obtain corresponding aggregated restored data, comprising: Decompressing the compressed battery cell data to obtain corresponding decompressed battery cell data; According to a preset hierarchical structure, the decompressed battery cell data is grouped to obtain corresponding hierarchical grouping data; the hierarchical grouping data includes battery cell hierarchical data, battery cluster hierarchical data and battery compartment hierarchical data; According to the data type, each level of grouped data is divided into a corresponding plurality of level partitioned data; In response to the aggregation query request, the partitioned data of each level is distributed to different task nodes for aggregation calculation to obtain the corresponding hierarchical partition aggregation results, and all hierarchical partition aggregation results of each hierarchical grouping data are reversely aggregated to obtain the corresponding hierarchical grouping original data; All hierarchical grouping original data are aggregated according to the preset hierarchical structure to obtain the aggregated restored data.

4. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 1, characterized in that: The method further comprises: The cell data change rate and the corresponding sampling frequency threshold range are obtained, and the preset sampling frequency is dynamically adjusted according to the cell data change rate and the sampling frequency threshold range to obtain the latest sampling frequency.

5. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 4, characterized in that: The latest sampling frequency is expressed as: Where, F represents the latest sampling frequency; F min and F max They represent the lower and upper limits of the sampling frequency threshold range respectively; R represents the rate of change of the battery cell data; T low and T high They respectively represent the lower and upper limits of the cell data change rate threshold range.

6. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 4, characterized in that: The step of obtaining the sampling frequency threshold range includes: The cell data change rate is input into a pre-built sampling frequency range prediction model to perform frequency range prediction, and a corresponding sampling frequency threshold range is obtained.

7. A method for compressing, transmitting and aggregating battery cell data as claimed in claim 6, characterized in that: The steps of constructing the sampling frequency range prediction model include: Get the historical battery cell data change rate time series data of the preset duration; By a preset marking method, the cell data change rate at each historical moment in the historical cell data change rate time series data is marked with a corresponding ideal sampling frequency range to obtain the corresponding ideal sampling frequency range time series data; the ideal sampling frequency range time series data includes an ideal sampling frequency upper limit time series data and an ideal sampling frequency lower limit time series data; According to the historical battery cell data change rate time series data and the ideal sampling frequency range time series data, the long short-term memory network model is trained to obtain the sampling frequency range prediction model.

8. A compression transmission and aggregation system for battery cell data, characterized in that: The system comprises: A data acquisition module, used to obtain battery cell monitoring data according to a preset sampling frequency; the battery cell monitoring data includes timestamp, voltage, temperature and current; A differential calculation module, used for processing the battery cell monitoring data by differential compression technology to generate corresponding battery cell differential data; the battery cell differential data includes a difference value between the battery cell monitoring data and differential reference data, and a timestamp of the differential reference data; A data compression module, used for compressing the battery cell differential data by using Huffman coding to obtain corresponding compressed battery cell data; The aggregation and restoration module is used to send the compressed battery cell data to the distributed storage nodes, so that the distributed storage nodes perform distributed aggregation processing according to the compressed battery cell data to obtain corresponding aggregation and restoration data.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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