Dynamic hybrid compression method, device and storage medium of bms timing data and vehicle

Abstract

The application discloses a kind of BMS timing data dynamic hybrid compression method, device and storage medium and vehicle, wherein, method includes: obtaining BMS timing data;Obtain compression combination strategy instruction, and compression combination strategy instruction is used to indicate the multi-layer compression combination strategy of BMS timing data;According to compression combination strategy instruction, BMS timing data is carried out multi-layer compression processing, and the BMS timing data after multi-layer compression processing is output. Therefore, based on compression combination strategy instruction, the multi-layer compression of BMS timing data is realized, so that, adapt to the change characteristics of BMS timing data under different working conditions, reach higher compression ratio and lower storage demand, alleviate the problem of big data computing resource shortage.

Description

Technical Field

[0001] This invention relates to the field of BMS time-series data storage technology, and in particular to a dynamic hybrid compression method for BMS time-series data, a computer-readable storage medium, a dynamic hybrid compression device for BMS time-series data, and a vehicle. Background Technology

[0002] In big data computing, the charging and discharging data (cell voltage, cell temperature, current, SOC, etc.) collected from BMS (Battery Management System) often span a long period of time. It is necessary to perform matrix transformation, storage and calculation on the time-series data throughout the entire charging / discharging cycle, which places high demands on compression methods.

[0003] However, the problem with the related technologies is that, on the one hand, compression algorithms usually use a single compression method, which cannot take into account all the changing characteristics of BMS time series data, resulting in low compression efficiency; on the other hand, although downsampling methods reduce the amount of data, they may miss key change information, affecting the accuracy of subsequent models. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the first objective of this invention is to propose a dynamic hybrid compression method for BMS time-series data, capable of adapting to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, and alleviating the problem of limited computing resources for big data.

[0005] A second objective of this invention is to provide a computer-readable storage medium.

[0006] The third objective of this invention is to provide a dynamic hybrid compression device for BMS time-series data.

[0007] The fourth objective of this invention is to provide a vehicle.

[0008] To achieve the above objectives, the first aspect of the present invention proposes a dynamic hybrid compression method for BMS time-series data, comprising: acquiring BMS time-series data; acquiring a compression combination strategy instruction, wherein the compression combination strategy instruction is used to indicate a multi-layer compression combination strategy for the BMS time-series data; performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction, and outputting the multi-layer compressed BMS time-series data.

[0009] According to an embodiment of the present invention, a dynamic hybrid compression method for BMS time-series data acquires BMS time-series data and obtains compression combination strategy instructions. Then, based on the compression combination strategy instructions, multi-layer compression processing is performed on the BMS time-series data, and the multi-layer compressed BMS time-series data is output. Thus, multi-layer compression of BMS time-series data is achieved based on compression combination strategy instructions, thereby adapting to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, and alleviating the problem of limited computing resources for big data.

[0010] Furthermore, the dynamic hybrid compression method for BMS time-series data according to the above embodiments of the present invention may also have the following embodiments: According to one embodiment of the present invention, the multi-layer compression combination strategy includes a compression combination strategy that sequentially performs Delta2 encoding, ZigTag encoding, and improved Simple8 / RLE encoding.

[0011] According to an embodiment of the present invention, the step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: compressing the data differences in the BMS time-series data through an encoder corresponding to Delta2 encoding.

[0012] According to an embodiment of the present invention, the step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: compressing the alternating positive and negative data in the BMS time-series data through an encoder corresponding to the ZigTag encoding.

[0013] According to an embodiment of the present invention, the step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: obtaining the data type of the data to be encoded in the BMS time-series data, wherein the data type includes irregularly changing integer data and consecutive identical integer data; and performing adaptive compression encoding on the data to be encoded in the BMS time-series data through the encoder corresponding to the improved Simple8 / RLE encoding according to the data type.

[0014] According to one embodiment of the present invention, the encoder corresponding to the improved Simple8 / RLE encoding includes a Simple8b selector and an RLE selector. The step of adaptively compressing the data to be encoded in the BMS time-series data using the encoder corresponding to the improved Simple8 / RLE encoding, based on the data type, includes: if the data type of the data to be encoded is irregularly changing integer data, then the Simple8b selector is used to perform Simple8b compression on the data to be encoded; if the data type of the data to be encoded is consecutive identical integer data, then the RLE selector is used to perform RLE compression on the data to be encoded.

[0015] According to an embodiment of the present invention, the method further includes: performing multi-layer decompression processing on the BMS timing data after multi-layer compression processing according to the compression combination strategy instruction, and outputting the BMS timing data after multi-layer decompression processing.

[0016] To achieve the above objectives, a computer-readable storage medium is provided in the second aspect of the present invention, which stores a dynamic hybrid compression program for BMS timing data. When the dynamic hybrid compression program for BMS timing data is executed by a processor, it implements the dynamic hybrid compression method for BMS timing data described in the embodiments of the present invention.

[0017] According to embodiments of the present invention, a computer-readable storage medium can adapt to the changing characteristics of BMS time-series data under different operating conditions by executing a dynamic hybrid compression program for BMS time-series data stored thereon, thereby achieving a higher compression ratio and lower storage requirements and alleviating the problem of tight big data computing resources.

[0018] To achieve the above objectives, a dynamic hybrid compression apparatus for BMS time-series data, as proposed in a third aspect embodiment of the present invention, includes: a first acquisition module for acquiring BMS time-series data; a second acquisition module for acquiring a compression combination strategy instruction, wherein the compression combination strategy instruction is used to indicate a multi-layer compression combination strategy for the BMS time-series data; and a data processing module for performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction, and outputting the multi-layer compressed BMS time-series data.

[0019] According to an embodiment of the present invention, a dynamic hybrid compression device for BMS time-series data implements multi-layer compression of BMS time-series data based on compression combination strategy instructions. This adapts to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, thus alleviating the problem of tight big data computing resources.

[0020] To achieve the above objectives, the vehicle proposed in the fourth aspect of the present invention includes the dynamic hybrid compression device for BMS time-series data described in the above embodiments of the present invention.

[0021] According to the vehicle of the present invention, the aforementioned dynamic hybrid compression device for BMS time-series data can adapt to the changing characteristics of BMS time-series data under different operating conditions, achieve a higher compression rate and lower storage requirements, and alleviate the problem of tight big data computing resources.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a dynamic hybrid compression method for BMS time-series data according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating a dynamic hybrid compression method for BMS time-series data according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating a dynamic hybrid compression method for BMS time-series data according to another embodiment of the present invention; Figure 4 This is a flowchart illustrating a dynamic hybrid compression method for BMS time-series data according to yet another embodiment of the present invention; Figure 5 This is a block diagram of a dynamic hybrid compression device for BMS time-series data according to an embodiment of the present invention; Figure 6 This is a block diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] The following describes, with reference to the accompanying drawings, a dynamic hybrid compression method for BMS time-series data, a computer-readable storage medium, a dynamic hybrid compression apparatus for BMS time-series data, and a vehicle according to embodiments of the present invention.

[0026] Figure 1 This is a flowchart illustrating a dynamic hybrid compression method for BMS time-series data according to an embodiment of the present invention.

[0027] Specifically, in some embodiments of the present invention, such as Figure 1As shown, the dynamic hybrid compression method for BMS time-series data includes: S101, obtain BMS timing data.

[0028] It is understandable that BMS time-series data will exhibit different characteristics under different operating conditions. For example, the voltage during charging and discharging will increase or decrease slowly, the current during charging and discharging will fluctuate alternately, and the static data will remain basically unchanged. Therefore, in this embodiment of the present invention, a dynamic hybrid compression method for BMS time-series data is proposed. Based on the dynamic changes of the time-series data, the optimal encoding strategy is automatically switched to improve the compression ratio and reduce storage requirements.

[0029] Optionally, in some embodiments of the present invention, the BMS timing data may include a multi-dimensional sensor sequence with timestamps, such as voltage, current, temperature, and SOC / SOH, collected by the battery management system at a fixed frequency, for real-time safety monitoring, status estimation, and fault early warning.

[0030] S102, obtain the compression combination strategy instruction. The compression combination strategy instruction is used to indicate the multi-layer compression combination strategy of BMS time series data.

[0031] It is understood that, in this embodiment of the present invention, the multi-layer compression combination strategy (e.g., the encoding form to be used, etc.) is indicated by the compression combination strategy instruction during the compression of BMS time-series data.

[0032] Specifically, in the above embodiments of the present invention, the BMS time-series data supports streaming input and output, wherein the first stored data of the BMS time-series data represents the multi-layer compression combination strategy adopted for this data compression, as shown in Table 1 below: Table 1: Configuration Table of Multi-layer Compression Combination Strategy

[0033] As shown in Table 1, if the first stored data is 2111, it means that a multi-layer compression combination of "Delta2 encoding + ZigTag encoding + Simple8 / RLE encoding" is used to achieve multi-layer compression processing of BMS time-series data. Alternatively, if the first stored data is 0111, it means that a multi-layer compression combination of "ZigTag encoding + Simple8 / RLE encoding" is used to achieve multi-layer compression processing of BMS time-series data, and so on. This will not be elaborated further here.

[0034] S103, according to the compression combination strategy instruction, performs multi-layer compression processing on the BMS timing data and outputs the multi-layer compressed BMS timing data.

[0035] Specifically, in this embodiment of the invention, by setting compression combination strategy instructions, the multi-layer compression combination strategy adopted in the BMS time-series data compression process is dynamically adjusted. Different compression combination strategy instructions correspond to different multi-layer compression combination strategies. Then, the BMS time-series data is subjected to multi-layer compression processing through dynamically combined multi-layer compression combination strategies, and the multi-layer compressed BMS time-series data is output to adapt to the changing characteristics of BMS time-series data under different operating conditions, thereby achieving a higher compression ratio and lower storage requirements.

[0036] Furthermore, in some embodiments of the present invention, the multi-layer compression combination strategy includes a compression combination strategy that sequentially performs Delta2 encoding, ZigTag encoding, and improved Simple8 / RLE encoding.

[0037] It is understood that in this embodiment of the present invention, the compression of BMS timing data (e.g., cell voltage, cell temperature, current, soc, etc.) is designed in three layers (i.e., Delta2 encoding, ZigTag encoding and improved Simple8 / RLE encoding are performed sequentially) to adapt to the changing characteristics of BMS timing data under different operating conditions, thereby achieving a higher compression ratio and lower storage requirements.

[0038] Furthermore, in some embodiments of the present invention, multi-layer compression processing is performed on BMS timing data according to compression combination strategy instructions, including: compressing the data differences in BMS timing data through the encoder corresponding to Delta2 encoding.

[0039] It is understandable that, since BMS timing data changes relatively stably in most cases, for example, the individual voltage and SOC usually remain unchanged or increase / decrease steadily, the current remains unchanged during the resting process, and the temperature remains unchanged in a stable environment, in this embodiment of the present invention, the data difference in BMS timing data is compressed by the encoder corresponding to Delta2 encoding, so as to significantly compress the data changes in most time periods.

[0040] Furthermore, in some embodiments of the present invention, multi-layer compression processing is performed on the BMS timing data according to the compression combination strategy instruction, including: compressing the alternating positive and negative data in the BMS timing data through the encoder corresponding to the ZigTag encoding.

[0041] It is understood that in this embodiment of the present invention, the alternating positive and negative data in the BMS timing data is compressed by the encoder corresponding to the ZigTag encoding, so as to convert the negative numbers after compression caused by data fluctuations, as well as some data that cannot be compressed well in the alternating process (such as current during charging, driving behavior, temperature jumps, etc.), and unify them into positive integers.

[0042] Furthermore, in some embodiments of the present invention, such as Figure 2 As shown, according to the compression combination strategy instructions, multi-layer compression processing is performed on the BMS time-series data, including: S201, obtain the data type of the data to be encoded in the BMS time series data. The data types include irregularly changing integer data and consecutive identical integer data.

[0043] It is understood that in this embodiment of the present invention, during the compression process of BMS time-series data, the data type of the BMS time-series data will be determined. If a large number of consecutive identical data are found, the data type of the data to be encoded will be determined as consecutive identical integer data. Conversely, if the data is found to be changing, the data type of the data to be encoded will be determined as irregularly changing integer data.

[0044] S202, based on the data type, adaptively compresses and encodes the data to be encoded in the BMS time-series data by improving the encoder corresponding to Simple8 / RLE encoding.

[0045] It is understood that in this embodiment of the present invention, when the BMS time-series data is a positive integer sequence that remains unchanged or changes regularly for most of the time and changes irregularly for a small part of the time, in order to further compress the storage by bit storage while being compatible with storing the irregularly changing parts, an improved encoder corresponding to Simple8 / RLE encoding that can adapt to the two data types and automatically switch compression strategies is introduced. This encoder can achieve both Simple8 encoding and RLE encoding of the data to be encoded, thus performing adaptive compression encoding on the data to be encoded.

[0046] Furthermore, in some embodiments of the present invention, the encoder corresponding to the improved Simple8 / RLE encoding includes a Simple8b selector and an RLE selector.

[0047] It should be noted that the improved Simple8 / RLE encoding is a hybrid compression algorithm based on Simple8b encoding and Run-Length Encoding (RLE). The original Simple8b is a 64-bit algorithm that maintains a lookup table as shown in Table 2 below to compress multiple integer data into a 64-bit long integer. The first 4 bits represent the selector, which is used to mark how many bits each value uses, and the last 60 bits are used to store the data.

[0048] Table 2: Configuration Parameters for Data Compression Algorithms

[0049] As shown in Table 2, the encoded integer represents the size of the compressible dataset, and each integer bit represents how many bits are allocated to represent each integer. For example, to compress 8 data points, selector 8 is selected, and each data point is represented by 7 bits. However, if the value of a data point exceeds the range of 7 bits, then selector 9 is used, which can only compress the first 7 data points, each represented by 8 bits, and so on. The first batch of uncompressed data is compressed into a new 64-bit long integer. Therefore, the simple8b algorithm has a better compression effect on small integers and is well-suited to the characteristics of BMS time-series data.

[0050] Furthermore, in this embodiment of the present invention, the usage of the selector is improved based on simple8b, as shown in Table 3 below. Among them, 0-14 follow the usage of simple8b selectors 1-15 respectively, while selector 15 is modified to use RLE for encoding. The first 32 bits of the 60 bits store the original value (the value of BMS timing data generally does not exceed 32 bits, basically covering the precision range of all timing data storage), and the last 28 bits indicate that a maximum of 28 consecutive identical values ​​can be stored.

[0051] Table 3: Configuration Parameters of the Improved Data Compression Algorithm

[0052] Specifically, in the above embodiments of the present invention, selectors 0-14 follow the rules of the traditional Simple8b and are used to compress small integers that change irregularly, and selector 15 (introducing RLE run-length encoding): when the algorithm detects that the data are consecutively identical, it automatically switches to selector 15.

[0053] Furthermore, in some embodiments of the present invention, such as Figure 3 As shown, based on the data type, adaptive compression encoding is performed on the data to be encoded in the BMS time series data by improving the encoder corresponding to Simple8 / RLE encoding, including: S301, if the data type of the data to be encoded is irregularly changing integer data, then the Simple8b selector is used to perform Simple8b compression on the data to be encoded.

[0054] S302, if the data type of the data to be encoded is consecutive identical integer data, then the RLE selector and RLE compression of the data to be encoded are used.

[0055] The following describes the specific dynamic switching process of Simple8 / RLE encoding in an embodiment of the present invention, using a specific example: When reading and processing the data buffer, the characteristics of the data before and after the buffer are monitored in real time, and the following judgment logic is executed: Identify change characteristics (the conditions that trigger selectors 0-14, i.e., the data type of the data to be encoded is irregularly changing integer data): If the newly read data in the cache is different from the previous data (no consecutive identical data is triggered), it means that the data belongs to the "change part". At this time, the traditional Simple8b rule is adopted to evaluate the maximum number of binary bits required for the irregular short sequence, and the optimal selector in selectors 0-14 is automatically selected for dense packing and storage.

[0056] Identify consecutive identical features (the condition that triggers selector 15, i.e., the data type of the data to be encoded is consecutive identical integer data): compare whether the data currently read in the cache is the same as the previous data. If it is found that the data starts to repeat consecutively, it will enter the run-length encoding (RLE) state and start counting.

[0057] Perform RLE packing (using selector 15): When the new data read is no longer the same (i.e. it has changed and is no longer continuous), or when the number of consecutive identical data reaches the upper limit threshold of the storage number (the capacity limit of the last 28 bits), settlement will be determined immediately. Selector 15 will be used to pack this batch of consecutive identical data into a 64-bit structure (4-bit selector 15 + 32-bit original value + 28-bit consecutive count) and write it to storage.

[0058] Furthermore, in some embodiments of the present invention, the method further includes: performing multi-layer decompression processing on the BMS timing data after multi-layer compression processing according to the compression combination strategy instruction, and outputting the BMS timing data after multi-layer decompression processing.

[0059] It is understood that, in this embodiment of the present invention, based on the compression combination strategy instruction, the BMS time series data after multi-layer compression can also be decompressed in multiple layers, and the BMS time series data after multi-layer decompression can be output to restore the BMS time series data for subsequent data analysis, etc.

[0060] The decompression process of the dynamic hybrid compression method for BMS time-series data according to a specific embodiment of the present invention will be described below: Taking a multi-layer compression combination strategy as an example, which sequentially performs Delta2 encoding, ZigTag encoding, and improved Simple8 / RLE encoding, as shown below... Figure 4 As shown, perform the following steps: S1, when encoding is required, read the combination strategy of the first stored data, and create Delta2, ZigTag, and Simple8 / RLE encoders respectively according to the strategy, and specify the encoding order of the encoders: Delta2→ZigTag→Simple8 / RLE.

[0061] S2, for the input BMS timing data, perform integer conversion (multiply by the corresponding factor according to the required precision, for example, if one bit of precision is required, then SOC 88.3 multiplied by one bit becomes 883).

[0062] S3 inputs data according to the timing sequence, and then encodes and stores it sequentially through Delta2→ZigTag→Simple8 / RLE.

[0063] Optionally, the input data can be replaced with timing data other than BMS timing data, but the data type cannot exceed 32 bits.

[0064] S4, when decoding is required, reads the combination strategy of the first stored data, and creates Delta2, ZigTag, and Simple8 / RLE decoders according to the strategy, and specifies the decoding order of the encoder: Simple8 / RLE→Delta2→ZigTag.

[0065] S5 stores data according to the timing sequence, and then decodes it sequentially through Delta2→ZigTag→Simple8 / RLE.

[0066] S6, if there is an integer conversion flag that is not 0, then the decoded data is restored to its original precision in sequence.

[0067] It should be noted that the dynamic hybrid compression method for BMS time-series data in this embodiment of the invention is generally applicable to BMS time-series data, with a compression rate of 10-20 times or more. Moreover, the compression and decompression process supports streaming input and output. In big data computing scenarios where time-series data is converted into matrices, it does not occupy too much cache, greatly reducing CPU and memory usage and reducing the occurrence of GC in programs such as Spark and Flink.

[0068] In summary, the dynamic hybrid compression method for BMS time-series data according to embodiments of the present invention acquires BMS time-series data and compression combination strategy instructions. Then, based on the compression combination strategy instructions, multi-layer compression processing is performed on the BMS time-series data, and the multi-layer compressed BMS time-series data is output. Thus, multi-layer compression of BMS time-series data is achieved based on compression combination strategy instructions, thereby adapting to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, and alleviating the problem of limited computing resources for big data.

[0069] Based on the dynamic hybrid compression method for BMS time-series data in the foregoing embodiments of the present invention, the present invention also proposes a computer-readable storage medium storing a dynamic hybrid compression program for BMS time-series data. When the dynamic hybrid compression program for BMS time-series data is executed by a processor, it implements the dynamic hybrid compression method for BMS time-series data in the foregoing embodiments of the present invention.

[0070] It should be understood that the specific implementation of the computer-readable storage medium in the embodiments of the present invention can be found in the specific implementation of the dynamic hybrid compression method for BMS time-series data described in the foregoing embodiments of the present invention. To reduce redundancy, it will not be described again here.

[0071] In summary, the computer-readable storage medium according to embodiments of the present invention, by executing a dynamic hybrid compression program for BMS time-series data stored thereon, can adapt to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, thus alleviating the problem of strained big data computing resources.

[0072] Figure 5 This is a block diagram of a dynamic hybrid compression device for BMS time-series data according to an embodiment of the present invention.

[0073] Specifically, in some embodiments of the present invention, such as Figure 5 As shown, the dynamic hybrid compression device 100 for BMS time-series data includes: a first acquisition module 10, a second acquisition module 20, and a data processing module 30.

[0074] The first acquisition module 10 is used to acquire BMS timing data; the second acquisition module 20 is used to acquire compression combination strategy instructions, which are used to indicate the multi-layer compression combination strategy of BMS timing data; the data processing module 30 is used to perform multi-layer compression processing on BMS timing data according to the compression combination strategy instructions, and output the multi-layer compressed BMS timing data.

[0075] Furthermore, in some embodiments of the present invention, the multi-layer compression combination strategy includes a compression combination strategy that sequentially performs Delta2 encoding, ZigTag encoding, and improved Simple8 / RLE encoding.

[0076] Furthermore, in some embodiments of the present invention, the data processing module 30 is also used to compress the data difference in the BMS time series data by using the encoder corresponding to the Delta2 encoding.

[0077] Furthermore, in some embodiments of the present invention, the data processing module 30 is also used to compress the alternating positive and negative data in the BMS time series data by using the encoder corresponding to the ZigTag encoding.

[0078] Furthermore, in some embodiments of the present invention, the data processing module 30 is also used to obtain the data type of the data to be encoded in the BMS time series data, the data type including irregularly changing integer data and consecutive identical integer data; and according to the data type, to perform adaptive compression encoding on the data to be encoded in the BMS time series data by improving the encoder corresponding to Simple8 / RLE encoding.

[0079] Furthermore, in some embodiments of the present invention, the encoder corresponding to the improved Simple8 / RLE encoding includes a Simple8b selector and an RLE selector. The data processing module 30 is also used to: if the data type of the data to be encoded is irregularly changing integer data, then use the Simple8b selector to perform Simple8b compression on the data to be encoded; if the data type of the data to be encoded is consecutive identical integer data, then use the RLE selector and RLE compression on the data to be encoded.

[0080] Furthermore, in some embodiments of the present invention, the data processing module 30 is also used to perform multi-layer decompression processing on the BMS time series data after multi-layer compression processing according to the compression combination strategy instruction, and output the BMS time series data after multi-layer decompression processing.

[0081] It should be understood that the specific implementation of the dynamic hybrid compression device 100 for BMS time-series data in this embodiment of the invention corresponds one-to-one with the specific implementation of the dynamic hybrid compression method for BMS time-series data in the foregoing embodiments of the invention. To reduce redundancy, it will not be described again here.

[0082] In summary, the dynamic hybrid compression device for BMS time-series data according to embodiments of the present invention achieves multi-layer compression of BMS time-series data based on compression combination strategy instructions, thereby adapting to the changing characteristics of BMS time-series data under different operating conditions, achieving higher compression ratios and lower storage requirements, and alleviating the problem of tight big data computing resources.

[0083] Figure 6 This is a block diagram of a vehicle according to an embodiment of the present invention.

[0084] Specifically, in some embodiments of the present invention, such as Figure 6 As shown, the vehicle 1000 includes the dynamic hybrid compression device 100 for BMS time-series data described in the above embodiment of the present invention.

[0085] It should be understood that the specific implementation of the vehicle 1000 in the embodiments of the present invention can refer to the specific implementation of the dynamic hybrid compression method of BMS time series data in the foregoing embodiments of the present invention. In order to reduce redundancy, it will not be described again here.

[0086] In summary, the vehicle according to the embodiments of the present invention, using the aforementioned dynamic hybrid compression device for BMS time-series data, can adapt to the changing characteristics of BMS time-series data under different operating conditions, achieving a higher compression ratio and lower storage requirements, thus alleviating the problem of tight big data computing resources.

[0087] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, for example, by optically scanning the paper or other medium, then editing, interpreting or otherwise processing it as necessary to obtain the program electronically, and then storing it in a computer memory.

[0088] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0090] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0091] 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 at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0092] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0093] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0094] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A dynamic hybrid compression method for BMS time-series data, characterized in that, The method includes: Obtain BMS timing data; Obtain a compression combination strategy instruction, which is used to indicate the multi-layer compression combination strategy of the BMS time series data; According to the compression combination strategy instruction, the BMS timing data is subjected to multi-layer compression processing, and the multi-layer compressed BMS timing data is output.

2. The dynamic hybrid compression method for BMS time-series data according to claim 1, characterized in that, The multi-layer compression combination strategy includes a compression combination strategy that sequentially performs Delta2 encoding, ZigTag encoding, and improved Simple8 / RLE encoding.

3. The dynamic hybrid compression method for BMS time-series data according to claim 2, characterized in that, The step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: The data differences in the BMS time series data are compressed using the encoder corresponding to Delta2 encoding.

4. The dynamic hybrid compression method for BMS time-series data according to claim 2, characterized in that, The step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: The alternating positive and negative data in the BMS time series data are compressed using the encoder corresponding to the ZigTag encoding.

5. The dynamic hybrid compression method for BMS time-series data according to claim 2, characterized in that, The step of performing multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction includes: Obtain the data type of the data to be encoded in the BMS time series data, wherein the data type includes irregularly changing integer data and consecutive identical integer data; Based on the data type, the encoder corresponding to the improved Simple8 / RLE encoding is used to adaptively compress and encode the data to be encoded in the BMS time series data.

6. The dynamic hybrid compression method for BMS time-series data according to claim 5, characterized in that, The encoder corresponding to the improved Simple8 / RLE encoding includes a Simple8b selector and an RLE selector. The step of adaptively compressing and encoding the data to be encoded in the BMS time-series data using the encoder corresponding to the improved Simple8 / RLE encoding, based on the data type, includes: If the data type of the data to be encoded is irregularly changing integer data, then the Simple8b selector is used to perform Simple8b compression on the data to be encoded. If the data type of the data to be encoded is consecutive identical integer data, then the RLE selector is used and the data to be encoded is RLE compressed.

7. The dynamic hybrid compression method for BMS time-series data according to any one of claims 1-6, characterized in that, The method further includes: According to the compression combination strategy instruction, the BMS timing data after multi-layer compression is subjected to multi-layer decompression processing, and the BMS timing data after multi-layer decompression processing is output.

8. A computer-readable storage medium, characterized in that, It stores a dynamic hybrid compression program for BMS timing data, which, when executed by the processor, implements the dynamic hybrid compression method for BMS timing data as described in any one of claims 1-7.

9. A dynamic hybrid compression device for BMS time-series data, characterized in that, The device includes: The first acquisition module is used to acquire BMS timing data; The second acquisition module is used to acquire compression combination strategy instructions, which are used to indicate the multi-layer compression combination strategy of the BMS time series data. The data processing module is used to perform multi-layer compression processing on the BMS time-series data according to the compression combination strategy instruction, and output the multi-layer compressed BMS time-series data.

10. A vehicle, characterized in that, The vehicle includes a dynamic hybrid compression device for BMS time-series data as described in claim 9.

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