Method and device for enhancing communication signal of energy storage cabinet and medium

Abstract

The invention discloses a method and equipment for enhancing communication signals of an energy storage cabinet and a medium, and relates to the technical field of energy storage cabinet communication. Constructing an adaptive data compression model based on a Shannon algorithm and an adaptive algorithm; the adaptive data compression model is used for judging the probability distribution of each symbol group in the data stream and generating a coding strategy; when the data is sent, the adaptive data compression model generates a corresponding coding strategy according to a data stream obtained in real time, compresses the data stream and then sends the compressed data stream; when data are received, the compressed data are decompressed according to the received compressed data and the corresponding coding strategy; the method can ensure complete transmission of signals.

Description

Technical Field

[0001] The present invention relates to the field of energy storage cabinet communication technology, and in particular to a method, device and medium for enhancing energy storage cabinet communication signals. Background Art

[0002] With the rapid development and increasing maturity of energy storage technology, energy storage systems are no longer the single isolated systems of the past. Instead, they are equipped with communication modules that can transmit their internal data to other systems via Ethernet, direct communication lines, etc.

[0003] However, in actual applications, the energy storage system may face problems such as packet loss during communication between the energy storage system and other systems, or loss of connection between the energy storage system and other systems due to interruption of communication signals.

[0004] Traditional energy storage cabinet communication antennas are generally installed inside the energy storage cabinet. However, since the energy storage cabinet body is generally made of metal, the wireless signal emitted by the communication antenna will be interfered or partially absorbed by the energy storage cabinet body, resulting in communication signal loss between the energy storage cabinet and external devices.

[0005] The Chinese patent (CN118352767A) discloses an energy storage communication device, an energy storage system, and a communication control method. By simplifying the installation structure of the communication device, the energy storage communication device can be conveniently disassembled and assembled, and the energy storage communication device occupies a small installation space. When the communication function is needed, the antenna assembly can be controlled to rise and move away from the second port of the first accommodating cavity, thereby reducing the impact of high obstacles on the communication rate, improving the system data communication rate, and enhancing the data communication reliability; when the communication function is not used, the antenna assembly can be controlled to descend and move closer to the second port of the first accommodating cavity, so as to store the antenna assembly, avoid damage caused by long-term exposure of the antenna assembly, and improve the service life of the antenna assembly;

[0006] However, this system is still an improvement on the antenna structure, and cannot avoid the stability of signal transmission, so it will still cause packet loss between the energy storage system and the external system;

[0007] Therefore, we propose a method to ensure the complete transmission of the signal. Summary of the invention

[0008] In order to overcome the deficiencies in the background technology, the present invention discloses a method, a device and a medium for enhancing a communication signal of an energy storage cabinet.

[0009] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0010] A method for enhancing the communication signal of an energy storage cabinet, the specific steps are:

[0011] Using a communication antenna disposed on the outer wall of the energy storage cabinet to send and receive data streams;

[0012] An adaptive data compression model is constructed based on the Shannon algorithm and the adaptive algorithm; and the adaptive data compression model is used to determine the probability distribution of each symbol group in the data stream and generate a coding strategy;

[0013] When sending data, the adaptive data compression model generates a corresponding encoding strategy based on the real-time acquired data stream, and compresses the data stream before sending it;

[0014] When receiving data, the compressed data is decompressed according to the received compressed data and the corresponding encoding strategy.

[0015] Preferably, the adaptive algorithm adopts Huffman coding.

[0016] Preferably, the adaptive algorithm is used to calculate the probability distribution of each symbol group in the data stream and dynamically adjust the data compression strategy; and the Shannon algorithm is used to calculate the information entropy of the data stream based on the probability of each symbol group in the data stream.

[0017] Preferably, the information entropy calculation formula of the Shannon algorithm is:

[0018] H(X)=-∑ x p(x)log 2 p(x);

[0019] Where x is a symbol group, and x∈X, p(x) is the probability of symbol group x, and X is the data stream.

[0020] Preferably, the data stream compression step is:

[0021] Collect the initial data stream and calculate the probability distribution of the symbol group, then use Shannon's formula to calculate the initial information entropy as a preliminary compression indicator of the data;

[0022] Formulate an initial coding strategy based on the probability distribution of each symbol group in the initial data stream;

[0023] Update the probability distribution according to the changes in the data stream;

[0024] At each stage of the data stream, the information entropy is recalculated according to the latest probability distribution, and the encoding strategy is adjusted based on the updated information entropy and probability distribution;

[0025] The initial data is compressed using the initial encoding strategy, and the subsequent data is compressed using the adjusted encoding strategy.

[0026] Preferably, the coding strategy is adjusted by reconstructing the Huffman tree to ensure that a symbol group with a high probability of occurrence corresponds to a shorter code, and a symbol group with a low probability of occurrence corresponds to a longer code.

[0027] A device for enhancing the communication signal of an energy storage cabinet comprises a base, a conical cover and a data processing unit. The base is mounted on the outer wall of the energy storage cabinet, the conical cover is mounted on the base, and a communication antenna is arranged inside the conical cover. The data processing unit is used to receive and send data streams, and compress and decompress the data streams.

[0028] Preferably, the data processing unit includes a data transceiver module, a data processing module, a data compression module and a data decompression module, wherein the data transceiver module is electrically connected to the data processing module, and the data processing module is electrically connected to the data compression module and the data decompression module respectively.

[0029] Preferably, the data processing module uses the Shannon formula to increase the transmission rate C of the communication antenna, and the Shannon formula is specifically:

[0030]

[0031] Where B is the channel bandwidth, S is the signal power, and N is the noise power.

[0032] A computer-readable storage medium for enhancing communication signals of an energy storage cabinet. The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for enhancing communication of an energy storage cabinet is implemented.

[0033] Due to the adoption of the above-mentioned technical solution, the present invention has the following beneficial effects:

[0034] The present invention discloses a method, device and medium for enhancing the communication signal of an energy storage cabinet. By arranging a communication antenna outside the energy storage cabinet, the instability of signal reception and transmission is greatly reduced. In addition, an adaptive data compression model is used to optimize signal reception and transmission. Therefore, the energy storage cabinet can maintain efficient and stable communication even when objective environmental factors cannot meet the optimal communication requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a method flow of the present invention;

[0036] Figure 2 To enhance the structure of the energy storage cabinet communication signal equipment;

[0037] Figure 3 A schematic diagram of a data processing unit.

[0038] In the figure: 1. base; 2. conical cover; 3. energy storage cabinet. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. In the description, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right" and the like indicating directions or positional relationships, they are only corresponding to the drawings of the present invention, for the convenience of describing the present invention, and do not indicate or imply that the devices or components referred to must have a specific direction:

[0040] Embodiment 1 is:

[0041] Combined with Figure 1 The method for enhancing the communication signal of the energy storage cabinet comprises the following specific steps:

[0042] Using a communication antenna disposed on the outer wall of the energy storage cabinet 3 to send and receive data streams;

[0043] Among them, in this setting mode, since the communication antenna is no longer limited by the shielding effect of the metal shell of the energy storage cabinet 3, the signal can directly interact with the external communication environment, reducing obstacles on the signal transmission path, thereby improving signal quality, reducing signal loss, and enhancing signal strength; and making signal transmission more stable and less susceptible to changes in the internal environment of the energy storage cabinet 3, such as temperature fluctuations, electromagnetic interference and other factors; in addition, the external placement of the communication antenna makes maintenance and upgrading more convenient, and the antenna and related components can be inspected and repaired without opening the entire energy storage cabinet 3.

[0044] An adaptive data compression model is constructed based on the Shannon algorithm and the adaptive algorithm; and the adaptive data compression model is used to determine the probability distribution of each symbol group in the data stream and generate a coding strategy;

[0045] In particular, the adaptive algorithm adopts Huffman coding; and the adaptive algorithm is used to calculate the probability distribution of each symbol group in the data stream and dynamically adjust the data compression strategy; and the Shannon algorithm is used to calculate the information entropy of the data stream according to the probability of each symbol group in the data stream;

[0046] As required, a symbol refers to an element that appears in a data stream, such as a letter in a text, a number, or another type of identifier;

[0047] When sending data, the adaptive data compression model generates a corresponding encoding strategy based on the real-time acquired data stream, and compresses the data stream before sending it;

[0048] When receiving data, decompressing the compressed data according to the received compressed data and the corresponding encoding strategy;

[0049] Among them, using the adaptive data compression model to compress data streams has the following advantages:

[0050] 1. Save bandwidth: By compressing the data stream, the amount of data required for transmission can be reduced, thereby reducing the demand for communication bandwidth. This is especially important in bandwidth-constrained communication environments, and available bandwidth resources can be used more efficiently.

[0051] 2. Reduce transmission time: The amount of compressed data is smaller, and the transmission time is shortened accordingly. Especially in long-distance or high-latency communication links, compression can significantly reduce the time required for data transmission.

[0052] 3. Reduce energy consumption: Compressing data transmission can reduce the workload of the communication module and thus reduce energy consumption; this is especially beneficial for scenarios that rely on battery power or have energy-saving requirements.

[0053] 4. Enhanced anti-interference ability: Adaptive algorithms are usually accompanied by error detection and correction mechanisms, which help improve the reliability of data transmission and ensure data integrity even in the presence of noise or interference

[0054] Using the adaptive data compression model to decompress the received compressed data has the following advantages:

[0055] 1. Restore original data: The receiving end uses the same adaptive data compression model to decompress the compressed data to restore the original data, ensuring the integrity and accuracy of the data.

[0056] 2. Simplified storage management: Compressed data takes up less storage space, which can more effectively manage storage capacity for devices with limited storage resources.

[0057] 3. Improve processing efficiency: The decompressed data is easier to process, which reduces the complexity of subsequent data processing and improves the processing efficiency of the overall system.

[0058] In summary, the adaptive data compression model has the following advantages:

[0059] 1. Dynamic adjustment: Combining the Shannon algorithm with the adaptive algorithm, the adaptive data compression model can dynamically adjust the compression strategy according to the actual probability distribution of the data stream to ensure that the compression efficiency is always at a high level.

[0060] 2. Strong adaptability: The model can maintain efficient data transmission under different communication conditions. Even when the signal-to-noise ratio decreases, it can compensate for the noise impact by increasing the signal power or applying a more effective coding scheme, thereby ensuring the quality of data transmission.

[0061] Embodiment 2 is:

[0062] Based on Example 1, the Shannon algorithm is further limited, and the information entropy calculation formula of the Shannon algorithm is:

[0063] H(X)=-∑ x p(x)log 2 p(x);

[0064] Where x∈X, p(x) is the probability of symbol group x, and X is the data stream;

[0065] Information entropy H(X) is mainly used to measure the uncertainty of data stream X.

[0066] The adaptive algorithm adopts Huffman coding, and the algorithm has the following advantages:

[0067] 1. Lossless Compression: Huffman coding is a lossless data compression technology, which means that the original data can be fully restored after decoding without any information loss. This is very useful for application scenarios that need to ensure data integrity and accuracy.

[0068] 2. Adaptability: In adaptive Huffman coding, as the data stream changes, the algorithm dynamically updates the probability distribution of the symbol group and adjusts the coding strategy based on the latest distribution. This adaptability enables the coding to better match the actual data characteristics, thereby improving compression efficiency.

[0069] 3. Real-time adjustment: During data stream transmission, adaptive Huffman coding allows the encoding rules to be adjusted in real time according to the actual probability distribution of the data stream, thus ensuring that efficient compression effects can be maintained when data characteristics change.

[0070] 4. Simple and easy to implement: Huffman coding algorithm is relatively simple, easy to implement, and highly efficient in execution. This makes it very suitable for both hardware and software implementations, especially in resource-constrained environments.

[0071] 5. Fast decoding speed: The characteristics of the Huffman tree structure make the decoding process very fast, because the decoding operation can be completed by simply searching down along the Huffman tree.

[0072] Embodiment 3 is:

[0073] On the basis of Example 2, the compression step of the signal enhancement model is further limited, and the compression step of the data stream is:

[0074] Collect the initial data stream and calculate the probability distribution of the symbol group, then use Shannon's formula to calculate the initial information entropy as a preliminary compression indicator of the data;

[0075] Formulate an initial coding strategy based on the probability distribution of each symbol group in the initial data stream;

[0076] Update the probability distribution according to the changes in the data stream;

[0077] The updated probability distribution is p i (t), where t is the time step or current state of the data stream;

[0078] At each stage of the data stream, the information entropy is recalculated according to the latest probability distribution, and the encoding strategy is adjusted based on the updated information entropy and probability distribution;

[0079] The updated information entropy H(t) can be calculated as:

[0080] H(t)=-∑ i p i (t)log 2 p i (t);

[0081] In addition, the coding strategy is adjusted in the following steps: reconstructing the Huffman tree to ensure that a symbol group with a high probability of occurrence corresponds to a shorter code, and a symbol group with a low probability of occurrence corresponds to a longer code;

[0082] The initial data is compressed using the initial encoding strategy, and the subsequent data is compressed using the adjusted encoding strategy.

[0083] In a specific embodiment:

[0084] First, assume that the initial probability of symbol group A in the data stream is 0.4 and the probability of symbol group B is 0.6. The initial information entropy is:

[0085] H(X)=-(0.4log 2 0.4+0.6log 2 0.6);

[0086] Then, as the data stream changes, the probabilities are updated to PA=0.5, PB=0.5.

[0087] The updated information entropy is:

[0088] H(X)=-(0.5log 2 0.5+0.5log 2 0.5) = 1 bit / symbol

[0089] In this way, the adaptive algorithm can continuously adjust according to the actual situation of the data and optimize the data compression efficiency. The design idea of ​​this algorithm is to dynamically adapt to the characteristics of the data to achieve higher compression ratios and lower redundancy.

[0090] A device for enhancing the communication signal of an energy storage cabinet, combined with an attached Figure 2 and attached Figure 3, including an antenna bracket and a data processing unit, the antenna bracket includes a base 1 and a conical cover 2, wherein the base 1 is installed on the outer wall of the energy storage cabinet 3, the conical cover 2 is installed on the base 1, and the conical cover 2 is used to set a communication antenna; the data processing unit is used to receive and send data streams, and compress and decompress the data streams;

[0091] The base 1 and the energy storage cabinet are fixedly installed to provide stable support conditions for the conical cover 2, while the conical cover 2 and the base 1 can be detachably installed, and the purpose of quickly replacing the communication antenna can be achieved by disassembling the conical cover 2; in addition, the shape of the conical cover 2 also determines that the conical cover 2 body has little impact on the communication of the communication antenna;

[0092] In addition, placing the communication antenna outside the energy storage cabinet greatly reduces the instability of signal reception and transmission.

[0093] In addition, the data processing unit includes a data transceiver module, a data processing module, a data compression module and a data decompression module, wherein the data transceiver module is electrically connected to the data processing module, and the data processing module is electrically connected to the data compression module and the data decompression module respectively;

[0094] Among them, the data transceiver module is responsible for communicating with the outside world, receiving and sending data; it includes antenna design optimization to ensure signal strength and quality.

[0095] In particular, the Shannon formula is used to increase the transmission rate C of the communication antenna, and the Shannon formula is specifically:

[0096]

[0097] Where B is the channel bandwidth, S is the signal power, and N is the noise power;

[0098] Specifically: increasing the signal power S can increase the channel capacity. If the signal power tends to infinity, the channel capacity also tends to infinity.

[0099] Reducing the noise power N can increase the channel capacity. If the noise power approaches 0, the channel capacity approaches infinity.

[0100] Increasing the channel bandwidth B can increase the channel capacity, but it cannot increase the channel capacity indefinitely. When the channel bandwidth B tends to infinity, the limit value of the channel capacity is

[0101] In addition, the module may include a radio frequency (RF) front end for signal amplification, filtering, and probability transformation. In addition, it may also include a signal modulation / demodulation circuit responsible for converting a baseband signal into an RF signal suitable for transmission or converting a received RF signal into a baseband signal.

[0102] The data processing module mainly processes the raw signal from the data transceiver module and prepares the data to be sent. It may perform signal preprocessing such as filtering, demodulation, error correction coding, etc. In addition, it is also responsible for interacting with the data compression / decompression module to ensure that the data can be compressed and decompressed correctly.

[0103] In addition, the data processing module may contain a microprocessor or an application-specific integrated circuit (ASIC) to execute the signal processing algorithm. The module also needs memory resources to temporarily store the data being processed and may be equipped with a cache to speed up processing.

[0104] The data compression module is used to compress data using an adaptive algorithm to reduce the amount of data for more efficient transmission. The adaptive algorithm may be based on Huffman coding or other adaptive coding methods, dynamically adjusting the coding strategy according to the characteristics of the real-time data stream.

[0105] In addition, the module may contain a dedicated hardware accelerator to speed up the execution of the adaptive algorithm, or be implemented in software and run on a microprocessor or graphics processing unit (GPU). It needs to access the data provided by the data processing module and transmit the compressed results to the data transceiver module.

[0106] The data decompression module is used to receive the compressed data from the data transceiver module and decompress it using the same algorithm as the compression module to restore the original data, ensure data integrity, and prepare to pass the data to other systems or storage devices.

[0107] In addition, similar to the data compression module, the data decompression module may also contain hardware accelerators or software implementations. It also requires memory resources to cache the data being decompressed, and must be able to work seamlessly with the data processing module to ensure the correct processing of the decompressed data.

[0108] In addition, when designing each module, it is generally ensured that the interface definition between modules is clear to facilitate integration and coordination. The communication protocol between modules should be standardized to simplify system design and maintenance.

[0109] In addition, error detection and correction mechanisms are added to ensure that data is not damaged during the compression / decompression process. During the communication process, CRC checksum or other error detection methods can be used to verify the integrity of the data.

[0110] Considering the security of data transmission, an encryption step can be added before data compression or after data decompression to ensure the confidentiality of data during transmission.

[0111] And the design takes into account future expandability, such as supporting higher data transmission rates or more communication protocols. By using a modular design, new functions can be easily added without affecting the existing system.

[0112] Finally, consider power management when designing, especially for wireless communication modules, optimizing power consumption is critical to extending the life of the device. Low power operation can be achieved by dynamically adjusting the working mode.

[0113] According to the needs, the data processing module uses the Shannon formula to increase the transmission rate C of the communication antenna, and the Shannon formula is specifically:

[0114]

[0115] Where B is the channel bandwidth, S is the signal power, and N is the noise power.

[0116] A computer-readable storage medium for enhancing communication signals of an energy storage cabinet, wherein the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for enhancing communication of the energy storage cabinet 3 is implemented;

[0117] The computer-readable storage medium stores computer program instructions, which, when executed by the processor, can implement the enhanced energy storage cabinet 3 communication method in the first aspect embodiment or the enhanced energy storage cabinet 3 communication method in the second aspect embodiment, and can achieve the same technical effect. To avoid repetition, it is not repeated here. Among them, the above-mentioned computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a read-only memory (Read-Only Memory, referred to as ROM), a random access memory (RandomAccess Memory, referred to as RAM), a disk or an optical disk, etc., which is not limited here.

[0118] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the enhanced energy storage cabinet 3 communication method in the first aspect embodiment or the enhanced energy storage cabinet communication method in the second aspect embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0119] It should be clear that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. For the transmitting device embodiment, the receiving device embodiment, the system embodiment, the computer-readable storage medium embodiment, and the computer program product embodiment, the relevant parts can refer to the description part of the method embodiment. The present application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application. In addition, for the sake of brevity, a detailed description of the known technology is omitted here.

[0120] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0121] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention, and any figure marks in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for enhancing the communication signal of an energy storage cabinet, characterized in that: Specific steps for: Using a communication antenna arranged on the outer wall of the energy storage cabinet (3) to send and receive data streams; An adaptive data compression model is constructed based on the Shannon algorithm and the adaptive algorithm; and the adaptive data compression model is used to determine the probability distribution of each symbol group in the data stream and generate a coding strategy; When sending data, the adaptive data compression model generates a corresponding encoding strategy based on the real-time acquired data stream, and compresses the data stream before sending it; When receiving data, the compressed data is decompressed according to the received compressed data and the corresponding encoding strategy.

2. The method for enhancing the communication signal of the energy storage cabinet according to claim 1, characterized in that: The adaptive algorithm adopts Huffman coding.

3. The method for enhancing the communication signal of the energy storage cabinet according to claim 2, characterized in that: The information entropy calculation formula of the Shannon algorithm is: H(X)=-∑ x p(x)log2p(x); Where x is a symbol group, and x∈X, p(x) is the probability of symbol group x, and X is the data stream.

4. The method for enhancing the communication signal of the energy storage cabinet according to claim 2, characterized in that: The adaptive algorithm is used to calculate the probability distribution of each symbol group in the data stream and dynamically adjust the data compression strategy; while the Shannon algorithm is used to calculate the information entropy of the data stream based on the probability of each symbol group in the data stream.

5. The method for enhancing the communication signal of the energy storage cabinet according to claim 4, characterized in that: The compression steps of the data stream are: Collect the initial data stream and calculate the probability distribution of the symbol group, then use Shannon's formula to calculate the initial information entropy as a preliminary compression indicator of the data; Formulate an initial coding strategy based on the probability distribution of each symbol group in the initial data stream; Update the probability distribution according to the changes in the data stream; At each stage of the data stream, the information entropy is recalculated according to the latest probability distribution, and the encoding strategy is adjusted based on the updated information entropy and probability distribution; The initial data is compressed using the initial encoding strategy, and the subsequent data is compressed using the adjusted encoding strategy.

6. The method for enhancing the communication signal of the energy storage cabinet according to claim 5, characterized in that: The adjustment process of the coding strategy is: reconstructing the Huffman tree to ensure that the symbol group with high probability of occurrence corresponds to a shorter code, and the symbol group with low probability of occurrence corresponds to a longer code.

7. A device for enhancing the communication signal of an energy storage cabinet, characterized in that: The invention comprises a base (1), a conical cover (2) and a data processing unit, wherein the base (1) is mounted on the outer wall surface of an energy storage cabinet (3), the conical cover (2) is mounted on the base (1), and a communication antenna is arranged inside the conical cover (2); the data processing unit is used to receive and send data streams, and to compress and decompress the data streams.

8. The device for enhancing the communication signal of the energy storage cabinet according to claim 7, characterized in that: The data processing unit includes a data transceiver module, a data processing module, a data compression module and a data decompression module, wherein the data transceiver module is electrically connected to the data processing module, and the data processing module is electrically connected to the data compression module and the data decompression module respectively.

9. The device for enhancing the communication signal of the energy storage cabinet according to claim 8, characterized in that: The data processing module uses the Shannon formula to increase the transmission rate C of the communication antenna, and the Shannon formula is specifically: Where B is the channel bandwidth, S is the signal power, and N is the noise power.

10. A computer-readable storage medium for enhancing communication signals of an energy storage cabinet, characterized in that: The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the enhanced energy storage cabinet (3) communication method.

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