Multi-mode communication energy storage converter cooperative control system and method

Abstract

The invention discloses a multi-mode communication energy storage converter cooperative control system and method, and relates to the technical field of energy storage converter cooperative control. The system comprises a signal processing module, a multi-mode communication optimization module, a multi-mode communication module, a data processing and analysis module, a control strategy module, an execution and feedback module and an interactive display module. The signal processing module collects physical signals, and characteristic parameters are extracted through preprocessing and dynamic threshold calibration; the multi-modal communication optimization module dynamically evaluates the link quality, distributes data priorities, selects an optimal communication mode and optimizes parameters; the multi-mode communication module dynamically switches a transmission path through a wired / wireless network; the data processing module analyzes the data to generate an equipment state report; the control strategy module generates an optimization instruction and issues the optimization instruction after verification; the execution module drives the power unit and feeds back data to form a closed loop. According to the invention, the problems of communication delay and low reliability of a traditional system are solved, and the multi-device cooperation efficiency and stability are improved.

Description

Technical Field

[0001] This document relates to the technical field of coordinated control of energy storage converters, and in particular to a coordinated control system and method for energy storage converters with multimodal communication. Background Art

[0002] The power storage converter (PCS) is a key device that connects the energy storage device to the power grid. It is mainly responsible for regulating the flow, frequency, and voltage of electric energy to achieve energy exchange between the energy storage system and the power grid. The coordinated control of the energy storage converter is an advanced control strategy that integrates multiple PCSs to achieve efficient operation and optimized management of the energy storage system. This control technology can automatically adjust the operating mode of each PCS according to grid demand, energy storage status, and power market signals to ensure the stability and economy of the power system. Coordinated control not only improves the response speed and regulation accuracy of the energy storage system, but also optimizes energy distribution and reduces operating costs, providing important technical support for the widespread access to renewable energy and the development of smart grids.

[0003] However, traditional energy storage converter systems mostly rely on a single communication mode, which is prone to signal interference, data packet loss and other problems in complex electromagnetic environments, resulting in control command delays or failures. In addition, there is a lack of a dynamic priority allocation mechanism, making it difficult to coordinate the efficient collaboration of multiple devices. At the same time, existing systems usually adopt fixed parameter configurations and rigid architectures. When faced with grid fluctuations or equipment expansion needs, there are defects such as response lags, which cannot meet the stringent requirements of new power systems for real-time regulation and flexible expansion. Summary of the Invention

[0004] The present invention provides a multi-modal communication energy storage converter coordinated control system and method, aiming to solve the above-mentioned problems.

[0005] According to an embodiment of the present invention, a multi-modal communication energy storage converter coordinated control system is provided, comprising:

[0006] Signal processing module, multimodal communication optimization module, multimodal communication module, data processing and analysis module, control strategy module, execution and feedback module, and interaction and display module;

[0007] The signal processing module is used to transmit the filtered characteristic signal to the data processing and analysis module, and the output end of the signal processing module is connected to the data input end of the data processing and analysis module through the transmission channel of the multimodal communication module;

[0008] The multimodal communication optimization module is used to dynamically evaluate the quality of communication links, assign data priorities, select the best communication mode and optimize communication parameters;

[0009] The multimodal communication module is used to transmit data through a hybrid wired and wireless network and dynamically switch the transmission path according to the optimization result;

[0010] The data processing and analysis module is used to store and analyze real-time and historical data and generate equipment status assessment reports;

[0011] The control strategy module is used to generate control instructions according to the power grid status and send them to the execution module through the multimodal communication module;

[0012] The execution and feedback module is used to execute control instructions and provide real-time feedback of operating status data to form a closed-loop control;

[0013] The interaction and display module is used to visualize system parameters, receive user instructions and provide a remote monitoring interface;

[0014] The multimodal communication optimization module includes: a link quality monitoring module, a data priority allocation module, a communication mode selection module and a parameter adaptive adjustment module.

[0015] According to an embodiment of the present invention, a method for coordinated control of an energy storage converter with multimodal communication is provided, comprising:

[0016] S1, signal acquisition and processing: physical signals are collected through a multi-channel sensor array, and characteristic parameters are extracted through ADC conversion, Kalman filtering and dynamic threshold calibration;

[0017] S2, communication optimization decision: Dynamically select the communication method and adjust the parameters based on the link quality score and data priority;

[0018] S3. Data transmission and processing: The characteristic signal is transmitted to the data processing and analysis module through the multimodal communication module to generate an equipment status assessment report;

[0019] S4, control strategy generation: Based on real-time data and historical experience, the optimal control instructions are calculated through a multi-objective optimization algorithm;

[0020] S5, command execution and feedback: Send control commands to the execution module, drive the power unit to operate, and feedback the operation data to the verification interface in real time;

[0021] S6. Human-computer interaction and closed-loop optimization: Display the system status through a visual interface, receive user input and dynamically adjust the control strategy to form a closed-loop control loop.

[0022] The innovative design of the multimodal communication optimization module employed in this embodiment significantly improves the system's operational efficiency and reliability. Specifically, the link quality monitoring and dynamic adjustment mechanism automatically selects the optimal communication path in complex electromagnetic environments by real-time evaluating key metrics such as signal strength and bit error rate, avoiding data loss or delays caused by single-channel failures. In the event of a sudden grid voltage change, the system prioritizes the transmission of urgent control commands via 5G high-speed channels while backing up critical data via wired channels, ensuring end-to-end transmission latency of control commands is less than 50 milliseconds. Data prioritization and adaptive parameter optimization intelligently identify the urgency of different data types, such as control commands, status monitoring, and log information, and dynamically allocates transmission resources, increasing the transmission success rate of critical control commands to over 99.99%. This multimodal collaborative mechanism not only addresses the bandwidth bottleneck of traditional single-mode communication but also significantly improves the coordinated response speed of multiple converters. Millisecond-level synchronous control can be achieved in scenarios such as grid frequency regulation and power balancing, improving overall system energy efficiency by 15%-20%.

[0023] The intelligent decision-making capability of the multimodal communication optimization module provides a solid guarantee for the long-term stable operation of the energy storage system. The communication mode decision model constructed by the reinforcement learning algorithm can autonomously adapt to changes in the communication environment (such as factory electromagnetic interference, extreme weather, etc.), and can still maintain effective communication by dynamically adjusting the transmission power and modulation mode in scenarios with 30% signal attenuation. Parameter adaptive adjustment and multi-channel redundancy design realize the fault self-healing capability of the communication system. When an abnormality occurs in a certain channel, the system can switch to the backup channel within 10 milliseconds and simultaneously optimize the data compression rate and transmission frequency to ensure that critical data is not lost and control is not interrupted. This highly intelligent communication architecture not only reduces the complexity of operation and maintenance, but also provides scalability support for the access of large-scale energy storage clusters in the future. It realizes the coordinated control of thousands of converters through distributed communication strategies, significantly improving the grid-connected adaptability and grid support capabilities of new energy power stations, and providing key technical support for the construction of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 Schematic diagram of a multimodal communication energy storage converter collaborative control system according to an embodiment of the present invention;

[0026] Figure 2Schematic diagram of a multimodal communication optimization module according to an embodiment of the present invention;

[0027] Figure 3 This is a flow chart of a collaborative control method for energy storage converters with multimodal communication according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0029] System Example

[0030] According to an embodiment of the present invention, a multi-modal communication energy storage converter coordinated control system is provided. Figure 1 Schematic diagram of a multi-modal communication energy storage converter cooperative control system according to an embodiment of the present invention. Figure 1 As shown, the multimodal communication energy storage converter cooperative control system of the embodiment of the present invention specifically includes:

[0031] Signal processing module 10, multimodal communication optimization module 11, multimodal communication module 12, data processing and analysis module 13, control strategy module 14, execution and feedback module 15 and interaction and display module 16;

[0032] The signal processing module 10 is used to transmit the filtered characteristic signal to the data processing and analysis module. The output end of the signal processing module 10 is connected to the data input end of the data processing and analysis module 13 through the transmission channel of the multimodal communication module 12. The signal processing module 10 of the embodiment of the present invention is composed of a signal acquisition unit, a preprocessing unit and a feature extraction unit. The front end is equipped with a multi-channel sensor array, which can collect physical signals such as voltage / current waveforms, temperature, vibration, etc., and use Kalman filtering to suppress noise after ADC conversion. The core processor is equipped with an FPGA+DSP heterogeneous architecture, and performs harmonic analysis (FFT), transient feature recognition (wavelet transform) and other technologies in real time. It is specially integrated with a dynamic threshold calibration algorithm, which can automatically adjust the signal detection sensitivity according to the environmental noise to ensure that the capture accuracy of key characteristic parameters (such as voltage flicker and frequency deviation) reaches ±0.05%.

[0033] The multimodal communication optimization module 11 is used to dynamically evaluate the quality of the communication link, assign data priority, select the optimal communication method and optimize the communication parameters; the decision output end of the multimodal communication optimization module is directly connected to the control interface of the multimodal communication module, and by dynamically adjusting the communication parameters and transmission path, it ensures that the operation status report generated by the data processing and analysis module can be fed back to the input end of the control strategy module in real time; the multimodal communication optimization module 11 includes: a link quality monitoring module, a data priority assignment module, a communication method selection module and a parameter adaptive adjustment module. Figure 2 FIG. 4 is a schematic diagram of a multimodal communication optimization module according to an embodiment of the present invention.

[0034] The link quality monitoring module includes: a signal receiver, a signal processor and a quality indicator database; the signal receiver is used to receive communication signals; the signal processor is used to analyze the received signals and extract quality indicators; the quality indicator database: stores the link quality indicators monitored in real time.

[0035] The Link Quality Index (LQI) formula is:

[0036] LQI=w1·RSSI+w2·(1-BER)+w3·(1-PER);

[0037] Among them: RSSI is the received signal strength indicator; BER is the bit error rate; PER is the packet error rate; w1, w2, and w3 are weight coefficients used to adjust the importance of each indicator.

[0038] The data priority allocation module includes: a data classifier, a priority rule library and a priority calculator. The data classifier is used to classify data according to data type and content; the priority rule library is used to store data priority allocation rules; the priority calculator calculates the priority of the data packet according to the rules;

[0039] The calculation formula for data priority (DP) is:

[0040] DP = f(urgency, data size, reliability requirement);

[0041] Where: urgency represents the urgency of data processing. Data size represents the size of the data packet. Reliability requirement represents the reliability requirement for data transmission. f is a mapping function that maps data characteristics to priority.

[0042] The communication mode selection module includes: a communication mode decider and a decision strategy library. The communication mode decider selects a communication mode based on link quality score and data priority; the decision strategy library is used to store communication mode selection strategies under different situations.

[0043] The calculation formula for communication mode selection is:

[0044] Comm_Mode=argmax m∈M Q(s,m);

[0045] Where: M is the set of communication modes. Q(s,m) is the action-value function of selecting communication mode m in state s.

[0046] The parameter adaptive adjustment module specifically includes: a parameter adjuster, a parameter optimization algorithm and a parameter database. The parameter adjuster adjusts the communication parameters according to the communication effect; the parameter optimization algorithm is used to find the optimal parameter configuration; and the parameter database is used to store current and historical communication parameter configurations.

[0047] The formula for parameter adjustment is:

[0048] θ * =argmin θ L(y,f(x;θ));

[0049] Where: θ is the set of communication parameters; L is the loss function, which measures the difference between the actual communication effect y and the expected effect f(x;θ); x is the input feature, and θ* is the optimal parameter configuration.

[0050] The multimodal communication module 12 is used to transmit data via a hybrid wired and wireless network and dynamically switch transmission paths based on optimization results. The multimodal communication module 12 of this embodiment of the present invention is internally configured with both wired and wireless dual communication networks: the wired portion utilizes industrial fieldbus technology to establish stable, reliable, low-latency connections between devices; the wireless portion supports fifth-generation mobile communications and dedicated Internet of Things protocols, adapting to data transmission requirements in complex environments. The communication management unit incorporates a built-in intelligent selection algorithm that evaluates the signal quality of each channel in real time and dynamically switches to the optimal transmission path. Priority strategies are set for different data types, with control instructions preferentially transmitted via high-speed channels, while monitoring data is transmitted in batches via high-capacity channels, achieving optimal allocation of bandwidth resources.

[0051] The data processing and analysis module 13 is used to store and analyze real-time and historical data and generate equipment status assessment reports. The data processing and analysis module 13 of the embodiment of the present invention includes: a data storage unit, a streaming computing engine, a batch computing cluster and an intelligent analysis unit, wherein the data storage unit adopts a distributed architecture to store real-time data and historical data in layers; the streaming computing engine performs instantaneous analysis on the real-time incoming data and automatically triggers the early warning mechanism; the batch computing cluster is responsible for in-depth mining of massive historical data and generating equipment health assessment reports; the intelligent analysis unit is equipped with a machine learning model, which can predict key indicators such as battery degradation trends and power component life in advance by learning the operating rules of the equipment, providing data support for maintenance decisions.

[0052] The control strategy module 14 is used to generate control instructions based on the grid status and transmit them to the execution module via the multimodal communication module. The output instructions of the control strategy module 14 are transmitted to the control terminal of the execution and feedback module via the priority channel of the multimodal communication module. While driving the power unit, the status acquisition terminal of the execution module transmits real-time operating data back to the verification interface of the data processing and analysis module via the monitoring channel of the communication module, forming a closed-loop control circuit. The control strategy module 14 of this embodiment of the present invention includes a library of multiple preset operating mode algorithms and can automatically switch operating modes based on the grid status. When the grid is stable, the economic dispatch mode is used to optimize energy consumption, and when the voltage fluctuates, the voltage stabilization compensation mode is switched. The strategy generation system combines real-time data and historical experience to dynamically calculate the optimal control parameters through a multi-objective optimization algorithm. The simulation verification unit establishes a virtual test environment, and all control instructions are issued and executed after simulation verification to ensure system security.

[0053] The execution and feedback module 15 is used to execute control instructions and provide real-time feedback of operating status data, forming a closed-loop control. The execution and feedback module 15 of the embodiment of the present invention includes: a power conversion unit, a drive circuit, and a state feedback system. The power conversion unit uses the latest semiconductor devices to achieve efficient bidirectional conversion of electric energy. The drive circuit is equipped with multiple protection mechanisms, which can monitor current and voltage changes in real time and automatically trigger circuit breaker protection when an abnormality is detected. The state feedback system collects equipment operating data through a high-speed sampling loop, compares and analyzes the execution results with the expected targets, and transmits error information back to the control system in real time, forming a dynamic adjustment closed loop. All hardware units adopt a redundant design, and key components are equipped with dual backup to ensure continuous operation of the system.

[0054] The interaction and display module 16, the data interaction end of the interaction and display module 16 is bidirectionally connected to the external interface of the data processing and analysis module to realize the retrieval and visualization of historical data, and its control instruction input end is connected to the human-machine interface of the control strategy module through a secure channel to complete the closed-loop transmission of parameter settings and mode switching instructions; the visualization interface of the interaction and display module 16 of the embodiment of the present invention adopts three-dimensional modeling technology to stereoscopically display the equipment layout and energy flow status. The main control screen displays the core parameters of the system in real time and supports multi-dimensional data visualization, including trend curves, topology coloring, dynamic warning and other functions. The remote access platform provides a standardized data interface, and the system status can be viewed at any time through the web and mobile terminals. The auxiliary decision module has a built-in expert knowledge base, which automatically generates processing suggestions for abnormal situations, and supports operation record tracing and automatic report generation.

[0055] By adopting the embodiments of the present invention, the following beneficial effects are achieved:

[0056] Communication efficiency optimization: The optimal communication path is dynamically selected based on the link quality score (LQI). 5G high-speed channels prioritize the transmission of urgent commands (end-to-end latency <50ms), and wired channels simultaneously back up critical data, solving the bandwidth bottleneck of traditional single-mode communications.

[0057] Intelligent resource allocation: Through the data priority (DP) mapping model, it intelligently identifies control instructions, monitors the urgency of data, and dynamically allocates transmission resources. The key instruction transmission success rate is ≥ 99.99%;

[0058] Fault self-healing capability: Adopting multi-channel redundancy design and parameter adaptive adjustment mechanism, it switches to the backup channel within 10ms in case of an abnormality, optimizes the compression ratio and transmission frequency, and ensures zero data loss and control continuity;

[0059] Large-scale collaborative control: A communication decision-making model based on reinforcement learning supports stable communication in scenarios with 30% signal attenuation. A distributed strategy enables millisecond-level synchronization of thousands of converters (increasing grid frequency response speed by 20%), improving overall system energy efficiency by 15%-20%.

[0060] Scalability and applicability: The intelligent architecture reduces operation and maintenance complexity, adapts to complex electromagnetic environments and grid fluctuation scenarios, and provides key technical support for the grid connection of new energy power stations and the construction of new power systems.

[0061] Method Example

[0062] According to an embodiment of the present invention, a method for cooperative control of an energy storage converter with multi-modal communication is provided. Figure 3 This is a flow chart of a method for cooperative control of energy storage converters with multimodal communication according to an embodiment of the present invention. Figure 3 As shown, the multimodal communication energy storage converter coordinated control method of the embodiment of the present invention specifically includes:

[0063] S1, signal acquisition and processing: physical signals are collected through a multi-channel sensor array, and characteristic parameters are extracted through ADC conversion, Kalman filtering and dynamic threshold calibration;

[0064] S2, communication optimization decision: Dynamically select the communication method and adjust the parameters based on the link quality score and data priority;

[0065] S3. Data transmission and processing: The characteristic signal is transmitted to the data processing and analysis module through the multimodal communication module to generate an equipment status assessment report;

[0066] S4, control strategy generation: Based on real-time data and historical experience, the optimal control instructions are calculated through a multi-objective optimization algorithm;

[0067] S5, command execution and feedback: Send control commands to the execution module, drive the power unit to operate, and feedback the operation data to the verification interface in real time;

[0068] S6. Human-computer interaction and closed-loop optimization: Display the system status through a visual interface, receive user input and dynamically adjust the control strategy to form a closed-loop control loop.

[0069] The multimodal communication energy storage converter coordinated control method of the embodiment of the present invention is a method embodiment corresponding one-to-one to the above-mentioned system embodiment. Please refer to the content of the above-mentioned system embodiment for the specific implementation method, which will not be repeated here.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-modal communication energy storage converter cooperative control system, characterized in that include: Signal processing module, multimodal communication optimization module, multimodal communication module, data processing and analysis module, control strategy module, execution and feedback module, and interaction and display module; The signal processing module is used to transmit the filtered characteristic signal to the data processing and analysis module, and the output end of the signal processing module is connected to the data input end of the data processing and analysis module through the transmission channel of the multimodal communication module; The multimodal communication optimization module is used to dynamically evaluate the quality of communication links, assign data priorities, select the optimal communication mode and optimize communication parameters; The multimodal communication module is used to transmit data through a hybrid wired and wireless network and dynamically switch the transmission path according to the optimization result; The data processing and analysis module is used to store and analyze real-time and historical data and generate equipment status assessment reports; The control strategy module is used to generate control instructions according to the power grid status and send them to the execution module through the multimodal communication module; The execution and feedback module is used to execute control instructions and provide real-time feedback of operating status data to form a closed-loop control; The interaction and display module is used to visualize system parameters, receive user instructions and provide a remote monitoring interface; The multimodal communication optimization module includes: a link quality monitoring module, a data priority allocation module, a communication mode selection module and a parameter adaptive adjustment module.

2. The system according to claim 1, wherein: The signal processing module specifically includes: a signal acquisition unit, a preprocessing unit and a feature extraction unit; The signal acquisition unit is configured with a multi-channel sensor array for acquiring physical signals; The preprocessing unit is used to preprocess the collected physical signals; The feature extraction unit is used to perform filtering processing on the pre-processed physical signal. The feature extraction unit integrates a dynamic threshold calibration algorithm to automatically adjust the signal detection sensitivity according to the environmental noise.

3. The method according to claim 1, characterized in that The link quality monitoring module specifically includes: a signal receiver, a signal processor, and a quality indicator database. The signal receiver is used to receive communication signals. The signal processor is used to analyze the received communication signals and extract quality indicators. The quality indicator database is used to store link quality indicators monitored in real time. The link quality scoring formula is: LQI=w1·RSSI+w2·(1-BER)+w3·(1-PER); Among them: RSSI is the received signal strength indicator; BER is the bit error rate; PER is the packet error rate; w1, w2, and w3 are weight coefficients used to adjust the importance of each indicator.

4. The method according to claim 1, wherein The data priority allocation module includes a data classifier, a priority rule library, and a priority calculator. The data classifier is used to classify data according to data type and content, the priority rule library is used to store data priority allocation rules; the priority calculator is used to calculate the priority of the data packet according to the priority allocation rules; The calculation formula for data priority DP is: DP = f(urgency, data size, reliability requirement); Wherein: urgency indicates the urgency of data processing; data size indicates the size of the data packet; reliability requirement indicates the reliability requirement of data transmission; f is a mapping function that maps data characteristics to priority.

5. The method according to claim 1, wherein The communication mode selection module includes a communication mode decision maker and a decision strategy library. The communication mode decision maker selects a communication mode based on link quality score and data priority. The decision strategy library is used to store communication mode selection strategies under different circumstances. The calculation formula for communication mode selection is: Comm_Mode=argmax m∈M Q(s,m); Where: M is the set of communication modes; Q(s,m) is the action-value function of selecting communication mode m in state s.

6. The method according to claim 1, characterized in that The parameter adaptive adjustment module includes: a parameter adjuster, a parameter optimization algorithm, and a parameter database. The parameter adjuster adjusts the communication parameters according to the communication effect; the parameter optimization algorithm is used to find the optimal parameter configuration; and the parameter database is used to store current and historical communication parameter configurations. The formula for parameter adjustment is: i * =argmin θ L(y,f(x;θ)); Where: θ is the set of communication parameters; L is the loss function, which measures the difference between the actual communication effect y and the expected effect f(x;θ); x is the input feature, and θ* is the optimal parameter configuration.

7. The method according to claim 1, characterized in that: The multimodal communication module includes: Wired communication unit: uses industrial fieldbus technology to establish low-latency, highly reliable connections between devices; Wireless communication unit: supports 5G and Internet of Things protocols and adapts to complex electromagnetic environments; The communication management unit has a built-in intelligent selection algorithm to evaluate the quality of each channel in real time, dynamically switch the transmission path, and set the control instructions to be transmitted through the high-speed channel first.

8. The method according to claim 1, characterized in that The data processing and analysis module includes: Distributed data storage unit: hierarchical storage of real-time data and historical data; Streaming computing engine: Instantly analyze real-time data and trigger alerts; Batch computing cluster: mining historical data to generate equipment health reports; Intelligent analysis unit: predicts battery degradation and component life through machine learning models.

9. The method according to claim 1, characterized in that The control strategy module includes: Multi-mode algorithm library: Enable economic dispatch mode when the grid is stable, and switch to voltage stabilization compensation mode when the voltage fluctuates; Strategy generation system: combines real-time data with historical experience to dynamically calculate optimal control parameters through a multi-objective optimization algorithm; Simulation verification unit: Build a virtual test environment to verify the safety of control instructions before issuing them for execution.

10. A multimodal communication energy storage converter cooperative control method applied to the multimodal communication energy storage converter cooperative control system according to any one of claims 1 to 9, characterized in that: S1, signal acquisition and processing: physical signals are collected through a multi-channel sensor array, and characteristic parameters are extracted through ADC conversion, Kalman filtering and dynamic threshold calibration; S2, communication optimization decision: Dynamically select the communication method and adjust the parameters based on the link quality score and data priority; S3. Data transmission and processing: The characteristic signal is transmitted to the data processing and analysis module through the multimodal communication module to generate an equipment status assessment report; S4, control strategy generation: Based on real-time data and historical experience, the optimal control instructions are calculated through a multi-objective optimization algorithm; S5, command execution and feedback: Send control commands to the execution module, drive the power unit to operate, and feedback the operation data to the verification interface in real time; S6. Human-computer interaction and closed-loop optimization: Display the system status through a visual interface, receive user input and dynamically adjust the control strategy to form a closed-loop control loop.

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