Electromagnetic anti-interference method and system, electronic equipment and storage medium

Abstract

The invention provides an electromagnetic anti-interference method and system, electronic equipment and a storage medium, and relates to the technical field of electromagnetic anti-interference, electromagnetic interference modes can be recognized by collecting electromagnetic data and inputting the electromagnetic data into a pre-trained first recognition model, and shielding measures corresponding to the recognized electromagnetic interference modes can be taken, so that the anti-interference performance of the electronic equipment is improved. The adaptability to different interference modes is improved, so that the electromagnetic interference in the communication process is effectively reduced or eliminated, and the continuity and reliability of data transmission are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of electromagnetic anti-interference, and particularly to an electromagnetic anti-interference method, system, electronic device, and storage medium. Background Art

[0002] Electrochemical energy storage systems play an important role in modern energy storage due to their high efficiency and environmental friendliness. Electrochemical energy storage devices are widely used in scenarios such as power grid peak shaving and renewable energy grid connection due to their modularity and easy deployment advantages. However, the complex electromagnetic environment inside electrochemical energy storage devices poses a huge challenge to data communication, affecting system stability and communication accuracy.

[0003] Currently, fiber optic communication and wireless communication are mainly used for internal communication in electrochemical energy storage devices. However, fiber optic communication has complex wiring and high costs. Wireless communication is vulnerable to electromagnetic interference and has poor stability. In addition, the existing shielding and filtering technologies have limited effects and are difficult to cope with complex interference environments.

[0004] Therefore, there is an urgent need for a new electromagnetic anti-interference method. Summary of the Invention

[0005] In view of the above problems, embodiments of the present application provide an electromagnetic anti-interference method, system, electronic device, and storage medium to overcome or at least partially solve the above problems.

[0006] In a first aspect of the present application, an electromagnetic anti-interference method is provided. The method includes: Collect electromagnetic data inside an electrochemical energy storage device; Input the electromagnetic data into a pre-trained first recognition model to identify the electromagnetic interference pattern corresponding to the electromagnetic data; Determine an interference shielding measure corresponding to the identified electromagnetic interference pattern according to the identified electromagnetic interference pattern; When the electromagnetic interference pattern is a first type of electromagnetic interference pattern, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interference electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interference electromagnetic wave; When the electromagnetic interference pattern is a second type of electromagnetic interference pattern, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device; Wherein, the first recognition model is trained through a classification algorithm based on historical electromagnetic data inside the electrochemical energy storage device and labeled electromagnetic interference patterns as training samples.

[0007] Optionally, after determining the interference shielding measures corresponding to the electromagnetic interference pattern, the method further includes: Obtaining the device operation data of the electrochemical energy storage device; Inputting the device operation data and the electromagnetic data into a pre-trained second recognition model to identify the device fault information of the electrochemical energy storage device; The second recognition model is trained by a classification algorithm based on historical electromagnetic data and historical device operation data inside the electrochemical energy storage device as training samples.

[0008] Optionally, after determining the interference shielding measures corresponding to the electromagnetic interference pattern, the method further includes: Inputting the electromagnetic data and the electromagnetic interference pattern into a background analysis system to analyze the electromagnetic data and the electromagnetic interference pattern through the background analysis system to obtain a first analysis result; Optimizing the matching degree between the electromagnetic interference pattern and the interference shielding measures according to the first analysis result.

[0009] Optionally, after determining the interference shielding measures corresponding to the electromagnetic interference pattern, the method further includes: Obtaining the historical state data and the real-time state data of the electrochemical energy storage device; Inputting the historical state data and the real-time state data into a background analysis system to analyze the historical state data and the real-time state data through the background analysis system to obtain a second analysis result; Optimizing the first recognition model and the interference shielding measures respectively according to the second analysis result.

[0010] Optionally, the second type of electromagnetic interference pattern includes at least: narrowband interference, periodic interference, amplitude variation interference; the adjusting the signal parameters of the communication signals of each module inside the electrochemical energy storage device includes: In the case where the second type of interference pattern is narrowband interference, adjusting the transmission frequency of the communication signal of each module; In the case where the second type of interference pattern is periodic interference, adjusting the transmission timing of the communication signal of each module; In the case where the second type of interference pattern is amplitude variation interference, increasing the transmission power of the communication signal of each module.

[0011] Optionally, before inputting the electromagnetic data into a pre-trained first recognition model, the method further includes: Obtain the historical electromagnetic data and the labeled electromagnetic interference patterns inside the electrochemical energy storage device; Use the historical electromagnetic data and the labeled electromagnetic interference patterns as training samples, and train a first preset model using a classification algorithm to obtain a first prediction result; Determine a first objective function value based on the first prediction result and a first objective function; When the first objective function value meets the preset first prediction accuracy requirement, end the training and use the first preset model as the first recognition model; When the first objective function value does not meet the first prediction accuracy requirement, adjust the model parameters of the first preset model and continue iterative training on the adjusted first preset model.

[0012] Optionally, before inputting the device operation data and the electromagnetic data into a pre-trained second recognition model, the method further includes: Obtain the historical electromagnetic data and the historical device operation data of the electrochemical energy storage device; Use the historical electromagnetic data and the historical device operation data as training samples, and train a second preset model using a classification algorithm to obtain a second prediction result; Determine a second objective function value based on the second prediction result and a second objective function; When the second objective function value meets the preset second prediction accuracy requirement, end the training and use the second preset model as the second recognition model; When the second objective function value does not meet the second prediction accuracy requirement, adjust the model parameters of the second preset model and continue iterative training on the adjusted second preset model.

[0013] Optionally, the step of determining the target electromagnetic wave includes: Analyze the first type of electromagnetic interference pattern to obtain the frequency and phase of the interfering electromagnetic wave; Determine the frequency and phase of the target electromagnetic wave according to the frequency and phase of the target interfering electromagnetic wave.

[0014] Optionally, after identifying the device fault information of the electrochemical energy storage device, the method further includes: Generate an electromagnetic interference diagnosis analysis report according to the electromagnetic interference pattern; Generate a device fault diagnosis analysis report according to the device fault information; Combine the electromagnetic interference diagnosis analysis report and the device fault diagnosis analysis report to generate the target analysis report; Upload the analysis report to the user interface interaction system.

[0015] In a second aspect of the present application, an electromagnetic anti-interference system is provided. The system includes: A data acquisition module for acquiring electromagnetic data inside the electrochemical energy storage device; An identification module for inputting the electromagnetic data into a pre-trained first identification model to identify the electromagnetic interference pattern corresponding to the electromagnetic data; A determination module for determining an interference shielding measure corresponding to the electromagnetic interference pattern according to the identified electromagnetic interference pattern; In the case where the electromagnetic interference pattern is a first type of electromagnetic interference pattern, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interference electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interference electromagnetic wave; In the case where the electromagnetic interference pattern is a second type of electromagnetic interference pattern, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device; Wherein, the first identification model is trained by a classification algorithm based on historical electromagnetic data inside the electrochemical energy storage device and labeled electromagnetic interference patterns as training samples.

[0016] Optionally, the system further includes: A first acquisition sub-module for acquiring the device operation data of the electrochemical energy storage device; An identification sub-module for inputting the device operation data and the electromagnetic data into a pre-trained second identification model to identify the device fault information of the electrochemical energy storage device; The second identification model is trained by a classification algorithm based on historical electromagnetic data and historical device operation data inside the electrochemical energy storage device as training samples.

[0017] Optionally, the system further includes: A first analysis sub-module for inputting the electromagnetic data and the electromagnetic interference pattern into a background analysis system to analyze the electromagnetic data and the electromagnetic interference pattern through the background analysis system to obtain a first analysis result; A first optimization sub-module for optimizing the matching degree between the electromagnetic interference pattern and the interference shielding measure according to the first analysis result.

[0018] Optionally, the system further includes: A second acquisition sub-module for acquiring the historical state data and real-time state data of the electrochemical energy storage device; A second analysis sub-module, configured to input the historical status data and the real-time status data into a background analysis system, so as to analyze the historical status data and the real-time status data through the background analysis system to obtain a second analysis result; A second optimization sub-module, configured to optimize the first recognition model and the interference shielding measure respectively according to the second analysis result.

[0019] Optionally, the second type of electromagnetic interference mode includes at least: narrowband interference, periodic interference, and amplitude change interference; for adjusting the signal parameters of the communication signals of each module inside the electrochemical energy storage device, the system further includes: A first adjustment sub-module, configured to adjust the transmission frequency of the communication signal of each module when the second type of interference mode is narrowband interference; A second adjustment sub-module, configured to adjust the transmission timing of the communication signal of each module when the second type of interference mode is periodic interference; An increase sub-module, configured to increase the transmission power of the communication signal of each module when the second type of interference mode is amplitude change interference.

[0020] Optionally, the system further includes: A third acquisition sub-module, configured to acquire the historical electromagnetic data and the labeled electromagnetic interference mode inside the electrochemical energy storage device; A first model training sub-module, configured to use the historical electromagnetic data and the labeled electromagnetic interference mode as training samples, and perform model training on a first preset model by using a classification algorithm to obtain a first prediction result; A first determination sub-module, configured to determine a first objective function value based on the first prediction result and a first objective function; A second determination sub-module, configured to end the training and use the first preset model as the first recognition model when the first objective function value meets a preset first prediction accuracy requirement; A third adjustment sub-module, configured to adjust the model parameters of the first preset model and continue iterative training on the adjusted first preset model when the first objective function value does not meet the first prediction accuracy requirement.

[0021] Optionally, the system further includes: A fourth acquisition sub-module, configured to acquire the historical electromagnetic data and the historical device operation data of the electrochemical energy storage device; A second model training sub-module, configured to use the historical electromagnetic data and the historical device operation data as training samples, and perform model training on a second preset model by using a classification algorithm to obtain a second prediction result; A third determination sub-module, configured to determine a second objective function value based on the second prediction result and a second objective function; A fourth determination sub-module, configured to end the training and use the second preset model as the second recognition model when the second objective function value meets a preset second prediction accuracy requirement; A fourth adjustment sub-module, configured to adjust model parameters of the second preset model and continue iterative training on the adjusted second preset model when the second objective function value does not meet the second prediction accuracy requirement.

[0022] Optionally, the system further includes: A parsing sub-module, configured to parse the first type of electromagnetic interference pattern to obtain the frequency and phase of the interfering electromagnetic wave; A fifth determination sub-module, configured to determine the frequency and phase of the target electromagnetic wave according to the frequency and phase of the target interfering electromagnetic wave.

[0023] Optionally, the system further includes: A first generation sub-module, configured to generate an electromagnetic interference diagnosis and analysis report according to the electromagnetic interference pattern; A second generation sub-module, configured to generate a device fault diagnosis and analysis report according to the device fault information; A third generation sub-module, configured to combine the electromagnetic interference diagnosis and analysis report with the device fault diagnosis and analysis report to generate the target analysis report; An upload sub-module, configured to upload the analysis report to a user interface interaction system.

[0024] In a third aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the electromagnetic anti-interference method as described in the first aspect of the present application.

[0025] In a fourth aspect of the present application, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the electromagnetic anti-interference method as described in the first aspect of the present application is implemented.

[0026] Advantages of the present application: The present application provides an electromagnetic anti-interference method, and the method includes: First, collect electromagnetic data inside the electrochemical energy storage device; then input the electromagnetic data into a pre-trained first recognition model to identify the electromagnetic interference pattern corresponding to the electromagnetic data; and determine an interference shielding measure corresponding to the identified electromagnetic interference pattern according to the identified electromagnetic interference pattern; in the case where the electromagnetic interference pattern is a first type of electromagnetic interference pattern, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interfering electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interfering electromagnetic wave; in the case where the electromagnetic interference pattern is a second type of electromagnetic interference pattern, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device respectively.

[0027] By collecting electromagnetic data and inputting it into a pre-trained first recognition model, the present application can identify electromagnetic interference patterns and take shielding measures corresponding to the identified electromagnetic interference patterns, improve the adaptability to different interference patterns, thereby effectively reducing or eliminating electromagnetic interference in the communication process, and ensuring the continuity and reliability of data transmission. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a schematic diagram of the step flow of an electromagnetic anti-interference provided by an embodiment of the present application; Figure 2 It is a block diagram of the overall process of an electromagnetic anti-interference provided by an embodiment of the present application; Figure 3 It is a schematic diagram of a functional branch with a terminal shielding device as the core provided by an embodiment of the present application; Figure 4 It is a schematic diagram of a functional branch with a background analysis system as the core provided by an embodiment of the present application; Figure 5 It is a schematic diagram of an electromagnetic anti-interference system provided by an embodiment of the present application; Figure 6 It is a schematic diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0030] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be fully conveyed to those skilled in the art.

[0031] Based on the above problems, in the first aspect of the embodiments of the present application, an electromagnetic anti-interference method is provided, as Figure 1 shown, the method includes: Step S101, collect electromagnetic data inside the electrochemical energy storage device.

[0032] In this step, collecting the electromagnetic data inside the electrochemical energy storage device is the basic link of the entire electromagnetic anti-interference method. Its specific implementation includes the following content: arrange electromagnetic acquisition sensors at key positions of the electrochemical energy storage device, where the key positions of the electrochemical energy storage device include but are not limited to areas such as high-power modules, communication modules, and around power electronic devices. In practical applications, the electromagnetic acquisition sensors should have high sensitivity and wide-band coverage capabilities to capture interference signals in a complex electromagnetic environment. The collected electromagnetic data includes but is not limited to frequency, intensity, duration, waveform, energy distribution, etc., and can comprehensively reflect the complex electromagnetic environment inside the energy storage device.

[0033] In some embodiments, after collecting the electromagnetic data inside the electrochemical energy storage device, these electromagnetic data need to be preprocessed, where the preprocessing includes denoising processing, normalization processing, etc., so as to improve the quality of the electromagnetic data.

[0034] Step S102, input the electromagnetic data into a pre-trained first recognition model, and recognize the electromagnetic interference pattern corresponding to the electromagnetic data.

[0035] In this step, by inputting the collected electromagnetic data into a pre-trained first recognition model, the accurate recognition of the electromagnetic interference pattern is completed. The specific implementation includes the following content: First, input the electromagnetic data collected and preprocessed in step S101 into the pre-trained first recognition model to ensure that the input data has high quality and consistency, so as to improve the recognition accuracy of the model.

[0036] Then, based on the input electromagnetic data, the first recognition model recognizes the electromagnetic interference pattern corresponding to the electromagnetic data.

[0037] Step S103: Determine the interference shielding measures corresponding to the identified electromagnetic interference pattern. When the electromagnetic interference pattern is a first - type electromagnetic interference pattern, the interference shielding measure is to determine and emit a target electromagnetic wave, where the frequency of the target electromagnetic wave is the same as that of the interfering electromagnetic wave, and the phase of the target electromagnetic wave is opposite to that of the interfering electromagnetic wave. When the electromagnetic interference pattern is a second - type electromagnetic interference pattern, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device. Among them, the first recognition model is trained through a classification algorithm based on the historical electromagnetic data inside the electrochemical energy storage device and the labeled electromagnetic interference patterns as training samples.

[0038] In step S103, based on the identified electromagnetic interference pattern, determine the matching interference shielding measures and implement corresponding strategies to effectively eliminate interference and ensure the stability of the communication system. The specific implementation includes the following: According to the recognition results of the first recognition model, take corresponding shielding measures for different types of electromagnetic interference, mainly including: For the recognition result of the first - type electromagnetic interference pattern: It can be a periodic or stable narrow - band electromagnetic interference signal. The corresponding shielding measure is to emit a target electromagnetic wave, whose frequency is the same as that of the interfering electromagnetic wave, but the phase is opposite, and use the principle of destructive interference to achieve interference cancellation.

[0039] For the recognition result of the second - type electromagnetic interference pattern: It can be a broadband or pulsed interference signal with large frequency and amplitude changes. The corresponding shielding measure is to adjust the parameters of the communication signals of each module inside the device, such as: dynamically adjusting the transmission power of the communication signal; changing the signal modulation method (such as switching to a coding method with stronger anti - interference ability); or selecting frequency - hopping communication in the frequency range with severe interference. Among them, the first recognition model is trained through a classification algorithm based on the historical electromagnetic data inside the electrochemical energy storage device and the labeled electromagnetic interference patterns as training samples.

[0040] In some embodiments, the implementation details of the first - type interference shielding measures specifically include: Through the first - type electromagnetic interference pattern output by the first recognition model, obtain the interference characteristics (such as frequency, intensity, phase, etc.) of the electromagnetic data in this pattern, and accurately determine the characteristics of the interfering electromagnetic wave.

[0041] Use a preset control algorithm to generate a target electromagnetic wave with the same frequency but opposite phase, and achieve mutual cancellation by superimposing the interfering electromagnetic wave to achieve the effect of shielding interference.

[0042] In some embodiments, the implementation details of the second - type interference shielding measures specifically include: By changing the transmission power, frequency, timing, modulation method, etc. of the communication signals of each module inside the electrochemical energy storage device, the key frequencies of interference or strong interference periods are avoided. For example: Dynamic frequency selection: Avoid interference frequencies and select relatively clean frequency bands for communication.

[0043] Time window adjustment: Perform data transmission during low-intensity interference periods.

[0044] Adaptive coding technology: Adopt more robust error correction coding and modulation methods, such as low-density parity-check codes or frequency hopping technology.

[0045] Inter-module cooperation: Adjust the synchronization of the communication signals of each module inside the energy storage device to ensure effective data transmission in the presence of interference.

[0046] This application can identify electromagnetic interference patterns and take shielding measures corresponding to the identified electromagnetic interference patterns by collecting electromagnetic data and inputting it into a pre-trained first recognition model, improving the adaptability to different interference patterns, thereby effectively reducing or eliminating electromagnetic interference during the communication process and ensuring the continuity and reliability of data transmission.

[0047] In some embodiments, after determining the interference shielding measures corresponding to the electromagnetic interference pattern, the method further includes: Obtain the device operation data of the electrochemical energy storage device; Input the device operation data and the electromagnetic data into a pre-trained second recognition model to identify the device fault information of the electrochemical energy storage device; The second recognition model is trained through a classification algorithm based on historical electromagnetic data and historical device operation data inside the electrochemical energy storage device as training samples.

[0048] In this embodiment, the operation data is obtained from sensors inside the electrochemical energy storage device, including but not limited to voltage, current, temperature, charge and discharge state, operation power, module synchronization state, etc. Among them, the device operation data needs to be time-synchronized with the collected electromagnetic data for correlation analysis and pattern recognition. In some cases, environmental data (such as temperature, humidity, vibration, etc.) inside the electrochemical energy storage device can be fused as a supplement to identify device anomalies caused by environmental factors.

[0049] Then, the obtained device operation data and the electromagnetic data in step S101 are input into a pre-trained second recognition model together. In practical applications, before inputting these data into the second recognition model, normalization processing is required to ensure that the numerical ranges of these data are consistent.

[0050] Finally, based on the device operation data and electromagnetic data, the second recognition model identifies the device fault information of the electro-chemical energy storage device, where the second recognition model is trained by a classification algorithm using the historical electromagnetic data and historical device operation data inside the electro-chemical energy storage device as training samples.

[0051] In some embodiments, the second recognition model outputs device fault information, including: Fault type: such as too high temperature of the battery module, insulation fault of the PCS module, communication module fault, etc.; Fault location: locate the fault source of a specific module or device; Fault cause: speculate the fault cause based on the characteristics of the device operation data and electromagnetic data (for example, high-frequency interference accompanied by current fluctuations may point to an inverter problem).

[0052] In some embodiments, by identifying the abnormal association between the electromagnetic signal and the device operation state, it is further determined whether the fault is affected by the environment or is related to abnormal device operation parameters.

[0053] For example: Battery module fault detection: When a certain battery module operates abnormally due to too high temperature, the device operation data (such as temperature, charge and discharge current) and the electromagnetic signal (specific frequency interference) are jointly input into the second recognition model to identify that the fault source is the failure of the temperature control module and issue a warning.

[0054] Insulation fault of the PCS module: When it is detected that the frequency of the electromagnetic interference signal deviates and is accompanied by bus voltage fluctuation, the second recognition model can identify and locate the insulation fault of the PCS module, indicating that on-site inspection and repair are required.

[0055] In this application, not only can electromagnetic interference be shielded, but also device faults can be accurately diagnosed, improving the operation and maintenance efficiency, reducing the failure rate, and providing an important guarantee for the safe and stable operation of the electro-chemical energy storage device.

[0056] In one embodiment, after determining the interference shielding measure corresponding to the electromagnetic interference pattern, the method further includes: Input the collected electromagnetic data and the identified electromagnetic interference pattern into the background analysis system, so as to analyze the electromagnetic data and the electromagnetic interference pattern through the background analysis system to obtain a first analysis result; According to the first analysis result, optimize the matching degree between the electromagnetic interference pattern and the interference shielding measure.

[0057] In this embodiment, to further optimize the matching degree between the electromagnetic interference pattern and the interference shielding measure, the background analysis system deeply analyzes the collected electromagnetic data and the identified interference pattern, and optimizes the shielding strategy based on the analysis result.

[0058] Specifically, the collected electromagnetic data and the electromagnetic interference patterns identified by the first recognition model are transmitted to the background analysis system. After receiving the data, the background analysis system performs standardization and denoising processing on it to ensure data quality and lay a foundation for subsequent analysis. The background analysis system analyzes the characteristics of the electromagnetic interference patterns, such as spectral distribution, waveform characteristics, and the source of interference. Combining the electromagnetic data with the actual application scenario, it identifies the causes and propagation paths of the interference signals. At the same time, it evaluates the effectiveness of the implemented interference shielding measures, for example, quantifies whether the shielding measures significantly reduce the communication error rate or eliminate the interference in a specific frequency band, and outputs the first analysis result, including the matching evaluation of the interference pattern and the shielding measure and the potential optimization direction (such as the target electromagnetic wave frequency deviation or insufficient power). According to the first analysis result, the shielding strategy can be dynamically optimized to improve the matching degree between the electromagnetic interference pattern and the interference shielding measure, thereby further enhancing the accuracy and effectiveness of electromagnetic shielding and achieving more efficient interference elimination for complex and changing electromagnetic environments.

[0059] Through the optimization of the matching degree, this embodiment significantly improves the stability and anti-interference ability of communication, and forms an iterative optimization mechanism by combining real-time feedback and historical data, laying a foundation for the long-term adaptation of the electrochemical energy storage device to complex electromagnetic environments.

[0060] In one embodiment, after determining the interference shielding measure corresponding to the electromagnetic interference pattern, the method further includes: Obtaining the historical state data and real-time state data of the electrochemical energy storage device; Inputting the historical state data and the real-time state data into the background analysis system to analyze the historical state data and the real-time state data through the background analysis system to obtain a second analysis result; Optimizing the first recognition model and the interference shielding measure respectively according to the second analysis result.

[0061] In this embodiment, to further improve the accuracy of electromagnetic interference pattern recognition and the effectiveness of the shielding measure, after determining the shielding measure corresponding to the electromagnetic interference pattern, the method further includes the following steps: First, obtain the historical state data and real-time state data of the electrochemical energy storage device and input these data into the background analysis system. The background analysis system deeply analyzes the historical state data and the real-time state data. Through data standardization, denoising, and feature extraction, it comprehensively evaluates the operation trend of the device and the changes in the current electromagnetic environment, and outputs the second analysis result. This result includes the analysis of potential causes of abnormal device states, the correlation between the electromagnetic interference pattern and the actual device state, and the adaptability evaluation of the existing shielding measures in the current scenario.

[0062] According to the second analysis result, the first recognition model and the interference shielding measures are optimized respectively. For the first recognition model, the classification algorithm parameters of the model are optimized by using historical and real-time data, and the interference pattern feature library is updated to adapt to the dynamic changes of the interference environment; the ability to recognize electromagnetic interference patterns is improved by online learning or retraining the model. For the interference shielding measures, the parameters of the shielding strategy are dynamically adjusted according to the second analysis result, such as optimizing the frequency, phase and transmission power of the target electromagnetic wave, so as to more accurately eliminate specific electromagnetic interference. Through this embodiment, the two-way optimization of the interference pattern recognition and the shielding strategy is realized, and the operation stability and anti-interference ability of the electrochemical energy storage device in a complex and changeable electromagnetic environment are significantly improved.

[0063] In one embodiment, the second type of electromagnetic interference pattern at least includes: narrowband interference, periodic interference, amplitude change interference; the adjustment of the signal parameters of the communication signals of each module inside the electrochemical energy storage device respectively includes: In the case where the second type of interference pattern is narrowband interference, the transmission frequency of the communication signal of each module is adjusted; In the case where the second type of interference pattern is periodic interference, the transmission timing of the communication signal of each module is adjusted; In the case where the second type of interference pattern is amplitude change interference, the transmission power of the communication signal of each module is increased.

[0064] In this embodiment, for the second type of electromagnetic interference pattern, the method effectively copes with different types of interference by adjusting the signal parameters of the communication signals of each module inside the electrochemical energy storage device. The second type of electromagnetic interference pattern at least includes narrowband interference, periodic interference and amplitude change interference, and the following specific adjustment measures are taken for different interference types: Measures to cope with narrowband interference: When narrowband interference is detected (that is, the interference is concentrated in a specific frequency range), by dynamically adjusting the transmission frequency of the communication signals of each module, the communication frequency is switched to a frequency band with less interference, avoiding the frequency range of the interference signal, so as to ensure the reliable transmission of the signal.

[0065] Measures to cope with periodic interference: For periodic interference (that is, the interference signal repeats at fixed time intervals), by analyzing the periodic characteristics of the interference, the transmission timing of the communication signals of each module is adjusted, so that the signal transmission avoids the interference peak period and data transmission is carried out within the time window with lower interference intensity, thereby reducing the interference impact.

[0066] Measures to address amplitude variation interference: For amplitude variation interference (i.e., the intensity of the interference signal varies with time), by increasing the transmission power of the communication signals of each module, the signal intensity is increased to enhance the signal's anti-interference ability, ensuring that the receiving end can correctly decode the transmitted data and maintaining communication stability even when the interference intensity is high.

[0067] Through the above signal parameter adjustment measures for different interference types, the system can optimize the communication strategies of each module in real time according to the characteristics of the interference mode, effectively improving the communication stability and anti-interference ability of the electrochemical energy storage device in a complex electromagnetic environment.

[0068] In some embodiments, the second type of electromagnetic interference mode further includes: broadband noise interference, pulsed interference, local signal interference, etc.

[0069] Measures to address broadband noise interference: By transmitting target electromagnetic waves with the same frequency but opposite phase as the interference waveform, the interference is actively cancelled using the principle of destructive interference. This method is particularly suitable for environments with a wide spectrum range and randomly varying interference source intensity, and can significantly reduce the impact of background noise on communication signals, thereby improving the signal transmission stability of the system.

[0070] Measures to address pulsed interference: In the presence of short-term high-intensity pulsed interference, robust error correction coding and modulation techniques are adopted, such as low-density parity-check codes (LDPC) or Turbo codes. These techniques improve the decoding success rate of signals under pulsed interference by increasing the error correction ability, and at the same time, in combination with modulation schemes (such as quadrature amplitude modulation or frequency hopping modulation with stronger anti-interference ability), enhance the anti-interference performance of signal transmission.

[0071] Measures to address local signal interference: For local signal interference within a specific spatial range, multi-path transmission technology is adopted, that is, signals are sent using multiple communication paths simultaneously. Even if one path is severely interfered with, the other paths can still maintain the continuity and reliability of communication. This strategy disperses the interference risk through spatial diversity and is suitable for application scenarios in multi-interference source or signal occlusion environments.

[0072] In some embodiments, before inputting the electromagnetic data into a pre-trained first recognition model, the method further includes: Obtaining the historical electromagnetic data and the labeled electromagnetic interference modes inside the electrochemical energy storage device; Using the historical electromagnetic data and the labeled electromagnetic interference modes as training samples, and training a first preset model using a classification algorithm to obtain a first prediction result; Based on the first prediction result and a first objective function, determining a first objective function value; When the first objective function value meets the preset first prediction accuracy requirement, end the training and use the first preset model as the first recognition model; When the first objective function value does not meet the first prediction accuracy requirement, adjust the model parameters of the first preset model and continue iterative training on the adjusted first preset model.

[0073] In this embodiment, a training process of the first recognition model is provided, which specifically includes the following steps: Obtain historical electromagnetic data and labeled electromagnetic interference patterns: Collect historical electromagnetic data of the electrochemical energy storage device under different operating conditions, and combine the electromagnetic interference patterns (such as narrowband interference, periodic interference, etc.) labeled manually or by an existing system to form high-quality training samples.

[0074] Train the first preset model: Use the historical electromagnetic data and the labeled electromagnetic interference patterns as training samples, and train the first preset model using a classification algorithm (such as support vector machine, random forest or neural network) to generate a first prediction result.

[0075] Calculate the first objective function value: By comparing the first prediction result with the true interference pattern label, calculate the first objective function value based on a loss function (such as cross-entropy loss or mean square error) to quantify the accuracy of the model prediction.

[0076] Determine whether the prediction accuracy requirement is met: If the first objective function value meets the preset first prediction accuracy requirement (for example, the accuracy rate exceeds 95%), end the training and use the first preset model as the first recognition model for subsequent identification of actual interference patterns; if the first objective function value does not meet the accuracy requirement, adjust the model parameters (such as learning rate, regularization coefficient or number of network layers, etc.) according to the error analysis results during the model training process, and continue iterative training on the adjusted model.

[0077] Iteratively train until convergence: In each iteration, continuously optimize the model parameters and improve the model's classification ability for complex interference patterns until the objective function value reaches the preset prediction accuracy requirement.

[0078] The first recognition model training method provided in this embodiment not only ensures the high accuracy and robustness of the model, but also lays a solid foundation for the efficient operation of the electrochemical energy storage device in a complex electromagnetic environment through an adaptive iterative optimization mechanism.

[0079] In one embodiment, before inputting the device operation data and the electromagnetic data into a pre-trained second recognition model, the method further includes: Obtain the historical electromagnetic data and the historical device operation data of the electrochemical energy storage device; Using the historical electromagnetic data and the historical device operation data as training samples, a classification algorithm is used to train the second preset model to obtain a second prediction result; Based on the second prediction result and the second objective function, determine the second objective function value; When the second objective function value meets the preset second prediction accuracy requirement, end the training and use the second preset model as the second recognition model; When the second objective function value does not meet the second prediction accuracy requirement, adjust the model parameters of the second preset model and continue iterative training on the adjusted second preset model.

[0080] In this embodiment, training steps of the second recognition model are provided to improve the accuracy and reliability of device fault recognition. Specifically, the following steps are included: Obtain historical data: Collect historical electromagnetic data (such as frequency, intensity, waveform characteristics, etc.) and historical device operation data (such as voltage, current, temperature, charge and discharge status, etc.) of the electrochemical energy storage device to form training samples covering multi-dimensional features.

[0081] Train the second preset model: Input the historical electromagnetic data and the historical device operation data into the second preset model as training samples, and use a classification algorithm (such as support vector machine, random forest or deep learning model) to train the model to generate a second prediction result for preliminarily judging the operation status or fault information of the device.

[0082] Calculate the second objective function value: Based on the comparison between the second prediction result and the true fault annotation value, calculate the second objective function value through a loss function (such as mean square error, cross-entropy loss, etc.) to quantify the accuracy and effect of the model prediction.

[0083] Judge the prediction accuracy: If the second objective function value meets the preset second prediction accuracy requirement (such as the accuracy rate reaches 95%), end the training and use the second preset model as the final second recognition model for actual device fault diagnosis. If the second objective function value does not meet the accuracy requirement, analyze the reasons for the insufficient model performance, adjust the model parameters (such as learning rate, regularization coefficient, number of hidden layers, etc.), and perform further iterative training on the adjusted model.

[0084] Iterative optimization: In each iteration, combined with real-time feedback and the diversity of training samples, continuously optimize the model parameters to improve the classification ability of the model for complex fault patterns. The model is trained until the objective function value reaches the preset accuracy requirement.

[0085] Through the above steps, the second recognition model can effectively learn the complex relationship between historical electromagnetic data and equipment operation data, accurately identify potential fault modes of the equipment, and provide reliable support for the real-time monitoring and intelligent diagnosis of the equipment operation status. This model has good generalization ability and can adapt to the fault diagnosis requirements of electrochemical energy storage equipment under complex working conditions.

[0086] In one embodiment, the step of determining the target electromagnetic wave includes: Analyze the first type of electromagnetic interference pattern to obtain the frequency and phase of the interfering electromagnetic wave; Determine the frequency and phase of the target electromagnetic wave according to the frequency and phase of the target interfering electromagnetic wave.

[0087] In this embodiment, first, the electromagnetic interference pattern is analyzed by the first recognition model to extract the key characteristic parameters of the interfering electromagnetic wave, including its frequency and phase. This step accurately captures the characteristic values of the interference signal through the time-domain and frequency-domain characteristic analysis of the interference signal, providing a basis for the design of the subsequent target electromagnetic wave.

[0088] According to the frequency and phase of the interfering electromagnetic wave obtained by analysis, design the characteristics of the target electromagnetic wave so that its frequency is the same as that of the interfering electromagnetic wave and its phase is opposite to that of the interfering electromagnetic wave. Through the principle of destructive interference, the target electromagnetic wave can cancel out with the interfering electromagnetic wave after superposition, thus effectively eliminating the interference.

[0089] In this embodiment, by analyzing the electromagnetic interference pattern and designing a matching target electromagnetic wave, the accurate cancellation of the interference signal is achieved, effectively improving the communication stability of the system. In addition, the analysis of the electromagnetic interference pattern and the determination of the target electromagnetic wave parameters are both based on real-time data, which can dynamically adapt to the complex and changeable electromagnetic environment, enhancing the flexibility of the system. Moreover, by accurately designing the target electromagnetic wave, the waste of resources caused by blindly increasing the shielding range or intensity is avoided, and the operation efficiency of the anti-interference system is optimized.

[0090] In one embodiment, after identifying the equipment fault information of the electrochemical energy storage equipment, the method further includes: Generate an electromagnetic interference diagnosis analysis report according to the electromagnetic interference pattern; Generate an equipment fault diagnosis analysis report according to the equipment fault information; Combine the electromagnetic interference diagnosis analysis report with the equipment fault diagnosis analysis report to generate the target analysis report; Upload the analysis report to the user interface interaction system.

[0091] In this embodiment, after identifying the device fault information of the electrochemical energy storage device, comprehensive fault and interference information is provided to the user by generating and integrating diagnostic analysis reports, which specifically includes the following steps: According to the identified electromagnetic interference pattern, analyze the characteristics of the interference signal (such as frequency, amplitude, duration, etc.) and its impact on the device operation to form a detailed electromagnetic interference diagnostic analysis report. The report content includes: interference type (such as narrowband interference, periodic interference, etc.); interference intensity and spectral characteristics; possible interference sources; specific impacts of interference on communication and device performance; implemented shielding measures and their effectiveness evaluation.

[0092] Based on the identified device fault information, analyze the operation data of the device (such as voltage, current, temperature) and its abnormal characteristics to form a device fault diagnostic analysis report. The report content includes: Fault type (such as over-temperature, insulation fault, bus under-voltage, etc.); fault location (such as specific module or subsystem); fault cause analysis (such as caused by electromagnetic interference or abnormal device operation); severity of the fault and possible impact range; recommended maintenance measures and priorities.

[0093] Then, integrate the electromagnetic interference diagnostic analysis report with the device fault diagnostic analysis report, and combine the possible impact of electromagnetic interference on device faults to generate a comprehensive target analysis report. The report content includes: correlation analysis between interference patterns and device faults; comprehensive evaluation of the overall health status of the electrochemical energy storage device; put forward specific operation and maintenance suggestions, including anti-interference optimization measures and device maintenance plans.

[0094] Finally, upload the generated target analysis report to the user interface interaction system for the user to view and operate. The content displayed on the user interface includes: core summary of the analysis report; distributed visualization charts of interference and fault status; recommended operation and maintenance plan and fault priority handling list; comparative analysis of historical data and current diagnostic results.

[0095] In this embodiment, by integrating the electromagnetic interference pattern and device fault information, a comprehensive target analysis report is generated to provide users with one-stop device health assessment. In addition, through the user interface interaction system, the diagnostic results and maintenance suggestions are presented in real time to improve the user's operation and maintenance efficiency. Moreover, by combining the interference pattern and device fault information, it helps users quickly locate the root cause of the problem and optimize fault handling and anti-interference measures.

[0096] In some embodiments, the electrochemical energy storage device can be managed and operated through the user interface interaction system, and the specific implementation process is as follows: User Login: Maintenance personnel log in to the operation interface through authentication. After the user interface interaction system verifies the user's identity, it provides personalized permission settings to ensure that only authorized users can access and operate the system.

[0097] Status Monitoring of User Interface Interaction System: After logging in, maintenance personnel can view the electromagnetic environment status inside the electrochemical energy storage device in real time, including the intensity, frequency, and change trend of electromagnetic interference. At the same time, it can also monitor the device operation status, such as parameters like voltage, current, and temperature, to comprehensively grasp the real-time health status of the device.

[0098] Remote Control: Maintenance personnel can remotely start or adjust the interference shielding module. For example, after detecting interference at a specific frequency, the user interface interaction system can prompt maintenance personnel to adjust the shielding frequency or enhance the shielding intensity to quickly respond to interference and ensure communication stability.

[0099] Fault Diagnosis: The user interface interaction system provides a detailed fault diagnosis report. Maintenance personnel can view the analysis results of abnormal electromagnetic wave signals and device operation data, including the fault type, fault location, and possible causes, and take targeted maintenance measures in combination with the suggestions of the user interface interaction system.

[0100] Maintenance Operations: According to the fault diagnosis results, maintenance personnel can perform remote maintenance operations through the interface, such as restarting the device, adjusting operation parameters, or arranging on-site maintenance tasks. At the same time, operation logs can be recorded in the user interface interaction system for traceability and optimization of subsequent maintenance strategies.

[0101] Historical Data Analysis: The user interface interaction system also provides a historical data analysis function. Maintenance personnel can view historical electromagnetic data and device operation data, analyze the correlation between electromagnetic interference patterns and device failures, optimize future maintenance plans and preventive maintenance strategies, and further improve the operation and maintenance efficiency.

[0102] Alarm and Notification: The user interface interaction system supports setting alarm thresholds, such as the intensity or frequency range of electromagnetic interference. When it detects that the interference exceeds the preset threshold, the user interface interaction system will automatically send alarm notifications to maintenance personnel through the interface, text message, or email to ensure timely response.

[0103] In one embodiment, an overall flowchart of electromagnetic anti-interference is provided, as Figure 2 shown, and the specific steps of this process are as follows: Electrochemical Energy Storage Device Electromagnetic Environment: Taking the electrochemical energy storage device electromagnetic environment as the background, start the acquisition and analysis work of electromagnetic data.

[0104] Data Acquisition: Obtain the original data in the electromagnetic environment through the terminal decoupling device.

[0105] Data Processing Unit: The collected data enters the data processing unit for preliminary screening and preprocessing, laying a foundation for subsequent analysis.

[0106] Precise Extraction: Machine learning algorithms are used to identify electromagnetic interference patterns in the processed data; based on the identification results, targeted interference shielding measures are generated; subsequently, these interference shielding measures are executed to achieve the purpose of suppressing or dealing with specific electromagnetic interference.

[0107] Reverse Electromagnetic Emission Suppression: According to the formulated interference shielding measures, reverse electromagnetic emission suppression is carried out to further optimize the electromagnetic environment.

[0108] Data and Status Transmission: After the suppression work is completed, the results of interference suppression and related status data are transmitted.

[0109] Backend Analysis System: Receives and stores the transmitted data for in-depth analysis; applies the target learning model to optimize the model performance, providing a reliable policy basis for subsequent use.

[0110] User Interaction Interface: Users view the analysis results through the interaction interface and operate the system as needed.

[0111] Operation Feedback: The user's operations are input into the backend analysis system as feedback to further adjust the model behavior.

[0112] Data Storage and Report Generation: Finally, the data generated throughout the process is archived, and a detailed report is generated for users to reference and make decisions.

[0113] In one embodiment, there is also provided a Figure 3 schematic diagram of functional branches with a terminal shielding device as the core as shown, mainly including the following modules and corresponding functions: Data Acquisition: The data acquisition work is completed through an electromagnetic interference detector to capture electromagnetic interference signals.

[0114] Data Processing: The collected data is processed using a data processing unit to prepare for subsequent analysis.

[0115] Feature Extraction: Extract the interference features in the data for further identification and analysis.

[0116] Pattern Recognition: Machine learning algorithms are used to perform pattern recognition on the extracted features to determine the type and characteristics of the interference.

[0117] Interference Shielding: Based on the identification results, interference shielding actions are executed to reduce or eliminate the interference impact.

[0118] Data Transmission: The processed data and results are transmitted to other devices or systems through a communication module.

[0119] In one embodiment, there is also provided a schematic diagram of function branches with a background analysis system as the core as shown in Figure 4 which mainly includes the following modules and corresponding functions: Data reception: Receive data sent by the terminal shielding device.

[0120] Analysis and optimization: Deeply analyze the received data through a data analysis engine.

[0121] Self-learning: Apply a target learning model to optimize the model performance and provide a reliable policy basis for subsequent use.

[0122] User interaction: The user views the analysis results through an interaction interface and operates the system as needed.

[0123] Report generation: Archive the data generated in the whole process and generate a detailed report for the user to refer to and make decisions.

[0124] This application provides an electromagnetic anti-interference method. First, collect electromagnetic data inside the electrochemical energy storage device; then input the electromagnetic data into a pre-trained first recognition model to identify the electromagnetic interference pattern corresponding to the electromagnetic data; and determine the interference shielding measure corresponding to the identified electromagnetic interference pattern according to the identified electromagnetic interference pattern. When the electromagnetic interference pattern is a first type of electromagnetic interference pattern, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interference electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interference electromagnetic wave; when the electromagnetic interference pattern is a second type of electromagnetic interference pattern, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device. By collecting electromagnetic data and inputting it into a pre-trained first recognition model, it is possible to identify the electromagnetic interference pattern and take corresponding shielding measures for the identified electromagnetic interference pattern, improve the adaptability to different interference patterns, and thus effectively reduce or eliminate electromagnetic interference in the communication process and ensure the continuity and reliability of data transmission.

[0125] Based on the same inventive concept, in the second aspect of this application, there is provided an electromagnetic anti-interference system as shown in Figure 5 which includes: A data acquisition module 201 for collecting electromagnetic data inside the electrochemical energy storage device; An identification module 202 for inputting the electromagnetic data into a pre-trained first recognition model to identify the electromagnetic interference pattern corresponding to the electromagnetic data; A determination module 203 for determining the interference shielding measure corresponding to the electromagnetic interference pattern according to the identified electromagnetic interference pattern; When the electromagnetic interference mode is the first type of electromagnetic interference mode, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interference electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interference electromagnetic wave; When the electromagnetic interference mode is the second type of electromagnetic interference mode, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device; Wherein, the first recognition model is trained through a classification algorithm based on historical electromagnetic data inside the electrochemical energy storage device and labeled electromagnetic interference modes as training samples.

[0126] Optionally, the system further includes: A first acquisition sub-module, configured to acquire the device operation data of the electrochemical energy storage device; An identification sub-module, configured to input the device operation data and the electromagnetic data into a pre-trained second recognition model to identify the device fault information of the electrochemical energy storage device; The second recognition model is trained through a classification algorithm based on historical electromagnetic data and historical device operation data inside the electrochemical energy storage device as training samples.

[0127] Optionally, the system further includes: A first analysis sub-module, configured to input the electromagnetic data and the electromagnetic interference mode into a background analysis system to analyze the electromagnetic data and the electromagnetic interference mode through the background analysis system to obtain a first analysis result; A first optimization sub-module, configured to optimize the matching degree between the electromagnetic interference mode and the interference shielding measure according to the first analysis result.

[0128] Optionally, the system further includes: A second acquisition sub-module, configured to acquire the historical state data and the real-time state data of the electrochemical energy storage device; A second analysis sub-module, configured to input the historical state data and the real-time state data into a background analysis system to analyze the historical state data and the real-time state data through the background analysis system to obtain a second analysis result; A second optimization sub-module, configured to optimize the first recognition model and the interference shielding measure respectively according to the second analysis result.

[0129] Optionally, the second type of electromagnetic interference mode at least includes: narrowband interference, periodic interference, and amplitude variation interference; for adjusting the signal parameters of the communication signals of each module inside the electro-chemical energy storage device, the system further includes: A first adjustment sub-module, configured to adjust the transmission frequency of the communication signal of each module when the second type of interference mode is narrowband interference; A second adjustment sub-module, configured to adjust the transmission timing of the communication signal of each module when the second type of interference mode is periodic interference; An increase sub-module, configured to increase the transmission power of the communication signal of each module when the second type of interference mode is amplitude variation interference.

[0130] Optionally, the system further includes: A third acquisition sub-module, configured to acquire the historical electromagnetic data and the labeled electromagnetic interference modes inside the electro-chemical energy storage device; A first model training sub-module, configured to use the historical electromagnetic data and the labeled electromagnetic interference modes as training samples, and perform model training on a first preset model by using a classification algorithm to obtain a first prediction result; A first determination sub-module, configured to determine a first objective function value based on the first prediction result and a first objective function; A second determination sub-module, configured to end the training and use the first preset model as the first recognition model when the first objective function value meets a preset first prediction accuracy requirement; A third adjustment sub-module, configured to adjust the model parameters of the first preset model and continue iterative training on the adjusted first preset model when the first objective function value does not meet the first prediction accuracy requirement.

[0131] Optionally, the system further includes: A fourth acquisition sub-module, configured to acquire the historical electromagnetic data and the historical device operation data of the electro-chemical energy storage device; A second model training sub-module, configured to use the historical electromagnetic data and the historical device operation data as training samples, and perform model training on a second preset model by using a classification algorithm to obtain a second prediction result; A third determination sub-module, configured to determine a second objective function value based on the second prediction result and a second objective function; A fourth determination sub-module, configured to end the training and use the second preset model as the second recognition model when the second objective function value meets a preset second prediction accuracy requirement; A fourth adjustment sub-module, configured to adjust the model parameters of the second preset model when the second objective function value does not meet the second prediction accuracy requirement, and continue to perform iterative training on the adjusted second preset model.

[0132] Optionally, the system further includes: A parsing sub-module, configured to parse the first type of electromagnetic interference pattern to obtain the frequency and phase of the interfering electromagnetic wave; A fifth determination sub-module, configured to determine the frequency and phase of the target electromagnetic wave according to the frequency and phase of the target interfering electromagnetic wave.

[0133] Optionally, the system further includes: A first generation sub-module, configured to generate an electromagnetic interference diagnosis and analysis report according to the electromagnetic interference pattern; A second generation sub-module, configured to generate a device fault diagnosis and analysis report according to the device fault information; A third generation sub-module, configured to combine the electromagnetic interference diagnosis and analysis report with the device fault diagnosis and analysis report to generate the target analysis report; An upload sub-module, configured to upload the analysis report to the user interface interaction system.

[0134] Based on the same inventive concept, in the third aspect of the present application, there is provided an electronic device 100 as shown in Figure 6 including a memory 110, a processor 120, and a computer program stored on the memory 110, and the processor 120 executes the computer program to implement the electromagnetic anti-interference method as described in the first aspect of the present application.

[0135] Based on the same inventive concept, in the fourth aspect of the present application, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the electromagnetic anti-interference method as described in the first aspect of the present application.

[0136] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts among the embodiments can be referred to each other.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0138] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0139] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0141] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0142] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or terminal device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

[0143] The above provides a detailed introduction to an electromagnetic anti-interference method, system, electronic device and storage medium. In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An electromagnetic anti-interference method, characterized in that: The method comprises: Collect electromagnetic data inside electrochemical energy storage devices; Inputting the electromagnetic data into a pre-trained first recognition model to identify an electromagnetic interference pattern corresponding to the electromagnetic data; Determining interference shielding measures corresponding to the electromagnetic interference pattern according to the identified electromagnetic interference pattern; In the case where the electromagnetic interference mode is a first type electromagnetic interference mode, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interfering electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interfering electromagnetic wave; When the electromagnetic interference mode is the second type of electromagnetic interference mode, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device; The first recognition model is obtained by training through a classification algorithm based on historical electromagnetic data and labeled electromagnetic interference patterns inside the electrochemical energy storage device as training samples.

2. The electromagnetic anti-interference method according to claim 1, characterized in that: After determining the interference shielding measures corresponding to the electromagnetic interference mode, the method further includes: Acquiring equipment operation data of the electrochemical energy storage device; Inputting the device operation data and the electromagnetic data into a pre-trained second recognition model to identify device fault information of the electrochemical energy storage device; The second recognition model is obtained by training with a classification algorithm based on historical electromagnetic data and historical equipment operation data inside the electrochemical energy storage device as training samples.

3. The electromagnetic anti-interference method according to claim 1, characterized in that: After determining the interference shielding measures corresponding to the electromagnetic interference mode, the method further includes: Inputting the electromagnetic data and the electromagnetic interference pattern into a background analysis system, so as to analyze the electromagnetic data and the electromagnetic interference pattern through the background analysis system to obtain a first analysis result; According to the first analysis result, the matching degree between the electromagnetic interference pattern and the interference shielding measure is optimized.

4. The electromagnetic anti-interference method according to claim 1, characterized in that: After determining the interference shielding measures corresponding to the electromagnetic interference mode, the method further includes: Acquiring historical status data and real-time status data of the electrochemical energy storage device; Inputting the historical status data and the real-time status data into a background analysis system, so as to analyze the historical status data and the real-time status data through the background analysis system to obtain a second analysis result; According to the second analysis result, the first identification model and the interference shielding measure are optimized respectively.

5. The electromagnetic anti-interference method according to claim 1, characterized in that: The second type of electromagnetic interference mode includes at least: narrowband interference, periodic interference, and amplitude variation interference; the signal parameters of the communication signals of each module inside the electrochemical energy storage device are adjusted, including: When the second type of interference mode is narrowband interference, adjusting the transmission frequency of the communication signal of each module; When the second type of interference pattern is periodic interference, adjusting the transmission timing of the communication signals of each of the modules; When the second type of interference mode is amplitude variation interference, the transmission power of the communication signal of each module is increased.

6. The electromagnetic anti-interference method according to claim 1, characterized in that: Before inputting the electromagnetic data into a pre-trained first recognition model, the method further comprises: Acquiring the historical electromagnetic data and the marked electromagnetic interference pattern inside the electrochemical energy storage device; The historical electromagnetic data and the marked electromagnetic interference pattern are used as training samples, and a classification algorithm is used to perform model training on a first preset model to obtain a first prediction result; Determining a first objective function value based on the first prediction result and the first objective function; When the first objective function value satisfies a preset first prediction accuracy requirement, the training is terminated, and the first preset model is used as the first recognition model; When the first objective function value does not meet the first prediction accuracy requirement, the model parameters of the first preset model are adjusted, and the iterative training of the adjusted first preset model is continued.

7. The electromagnetic anti-interference method according to claim 2, characterized in that: Before inputting the device operation data and the electromagnetic data into a pre-trained second recognition model, the method further includes: Acquiring the historical electromagnetic data and the historical device operation data of the electrochemical energy storage device; The historical electromagnetic data and the historical equipment operation data are used as training samples, and a classification algorithm is used to perform model training on a second preset model to obtain a second prediction result; Determining a second objective function value based on the second prediction result and the second objective function; When the second objective function value satisfies the preset second prediction accuracy requirement, the training is terminated, and the second preset model is used as the second recognition model; When the second objective function value does not meet the second prediction accuracy requirement, the model parameters of the second preset model are adjusted, and the iterative training of the adjusted second preset model is continued.

8. The electromagnetic interference prevention method according to claim 1, characterized in that: The step of determining the target electromagnetic wave comprises: Analyze the first type of electromagnetic interference mode to obtain the frequency and phase of the interfering electromagnetic wave; The frequency and phase of the target electromagnetic wave are determined according to the frequency and phase of the target interfering electromagnetic wave.

9. The electromagnetic interference prevention method according to claim 2, characterized in that: After identifying the device fault information of the electrochemical energy storage device, the method further includes: Generating an electromagnetic interference diagnosis and analysis report according to the electromagnetic interference mode; Generate a device fault diagnosis and analysis report based on the device fault information; Combining the electromagnetic interference diagnosis analysis report with the equipment fault diagnosis analysis report to generate the target analysis report; The analysis report is uploaded to the user interface interaction system.

10. An electromagnetic anti-interference system, characterized in that: The system comprises: A data acquisition module, used to collect electromagnetic data inside the electrochemical energy storage device; A recognition module, used for inputting the electromagnetic data into a pre-trained first recognition model to identify an electromagnetic interference pattern corresponding to the electromagnetic data; A determination module, configured to determine, according to the identified electromagnetic interference pattern, interference shielding measures corresponding to the electromagnetic interference pattern; In the case where the electromagnetic interference mode is a first type electromagnetic interference mode, the interference shielding measure is to determine and emit a target electromagnetic wave, the frequency of the target electromagnetic wave is the same as the frequency of the interfering electromagnetic wave, and the phase of the target electromagnetic wave is opposite to the phase of the interfering electromagnetic wave; When the electromagnetic interference mode is the second type of electromagnetic interference mode, the interference shielding measure is to adjust the signal parameters of the communication signals of each module inside the electrochemical energy storage device; The first recognition model is obtained by training through a classification algorithm based on historical electromagnetic data and labeled electromagnetic interference patterns inside the electrochemical energy storage device as training samples.

11. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the electromagnetic interference resistance method according to any one of claims 1 to 9.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the electromagnetic interference prevention method according to any one of claims 1 to 9 is implemented.

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