Distributed power supply integrated acquisition control system

Abstract

The invention discloses a distributed power supply integrated acquisition control system, which comprises a data acquisition module, a signal processing module, a communication coordination module and an intelligent analysis module, and is characterized in that the intelligent analysis module is coupled to the signal processing module, an optimization decision module, a control execution module and a user interaction module; the user interaction module is coupled to the intelligent analysis module and the control execution module and is used for receiving user input and displaying a system state; and the fault diagnosis module is coupled to the data acquisition module and the intelligent analysis module and is used for detecting equipment abnormity and giving an alarm. According to the invention, through an integrated design, key pain points in the background technology, such as signal distortion and decision-making safety disjunction, are systematically overcome; the core lies in creative expansion and synergistic effect of each module, an environment sensing unit of the data acquisition module dynamically compensates noise influence, an adaptive filtering engine of the signal processing module optimizes signal quality, and behavior prediction credibility calculation of the intelligent analysis module ensures model output precision.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control, and more specifically, relates to an integrated distributed power supply acquisition and control system. Background Art

[0002] Distributed power systems are now widely connected to the power grid. Their decentralization, environmental dependence, and operational complexity pose significant challenges to data collection and control. Traditional data collection systems often use independent modules for data processing, decision-making, and communication. Interaction between these modules is weak, and their adaptability to dynamic environments is insufficient. Optimization strategies often rely on static weights, ignoring real-time failure risks when making decisions. Diagnostic mechanisms are based solely on threshold triggers, making it difficult to integrate behavioral prediction and analysis. These systems frequently exhibit three major flaws in scenarios with variable lighting conditions, fluctuating loads, and aging equipment: poor signal interference immunity leads to inaccurate behavior recognition; scheduling strategies lack mechanisms for coordinated environmental and safety adjustments; and isolated fault warnings lead to delayed responses, ultimately reducing energy efficiency and system stability.

[0003] The core technical issues are as follows:

[0004] In dynamic environments, collected signals are susceptible to noise contamination. Existing filtering mechanisms cannot adapt to environmental changes, resulting in distortion of the purified signals. This increases the misjudgment rate of subsequent behavior prediction models, directly impacting the rationality and real-time performance of power distribution.

[0005] The functions of the decision-making module and the diagnosis module are separated, the fault risk cannot be dynamically integrated into the optimization strategy, and the diagnosis results do not reversely correct the prediction model, causing the system to ignore abnormal factors in the multi-objective trade-offs, and the scheduling instructions conflict with the equipment safety boundaries, increasing the risk of uncontrolled operation. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a distributed power supply integrated acquisition and control system. Through integrated design, it systematically overcomes the key pain points in the background technology: signal distortion and decision-making safety disconnection problems; the core lies in the creative expansion and synergy of each module - the environmental perception unit of the data acquisition module dynamically compensates for the influence of noise, the adaptive filtering engine of the signal processing module optimizes the signal quality, the behavior prediction credibility calculation of the intelligent analysis module ensures the accuracy of the model output, the knowledge base system of the fault diagnosis module quickly responds to risks, the multi-objective trade-off mechanism of the optimization decision module integrates fault factors to realize safety optimization strategy, and the communication coordination module seamlessly transmits data and enhances the feedback loop.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A distributed power supply integrated acquisition and control system, comprising:

[0009] a data acquisition module, a signal processing module, a communication coordination module, and an intelligent analysis module, wherein the intelligent analysis module is coupled to the signal processing module and is used to identify the dynamic behavior of the system based on the purification signal;

[0010] an optimization decision module, coupled to the intelligent analysis module, for generating an energy distribution optimization strategy; a control execution module, coupled to the optimization decision module, for executing the optimization strategy to adjust the power output; a user interaction module, coupled to the intelligent analysis module and the control execution module, for receiving user input and displaying system status;

[0011] a fault diagnosis module, coupled to the data acquisition module and the intelligent analysis module, for detecting equipment anomalies and issuing alarms;

[0012] The data acquisition module transmits the operating data to the signal processing module for denoising; the purified signal of the signal processing module is sent to the intelligent analysis module for behavior prediction, and the optimization decision module receives the prediction results and calculates the optimal scheduling plan; it is converted into action instructions by the control execution module and fed back to the communication coordination module to coordinate the synchronization of multiple devices; the user interaction module visualizes the status data and obtains adjustment parameters from the optimization decision module; the fault diagnosis module monitors the output of all modules, and triggers an alarm in the event of an abnormality and transmits it through the communication coordination module.

[0013] Preferably, the intelligent analysis module further integrates an analysis model based on a time series, and the time series analysis model is embedded in a behavior pattern recognition library to match the optimization strategy generated by the optimization decision module with historical operation data to form a behavior prediction map;

[0014] After the intelligent analysis module receives the purified signal from the signal processing module, the time series analysis model extracts pattern features and predicts future dynamic behavior trends. Its output graph is forwarded to the optimization decision module through the communication coordination module for reference in strategy generation. The user interaction module allows users to query the graph data to manually adjust the strategy threshold, and the fault diagnosis module monitors model deviations and issues alarm corrections.

[0015] A feedback iteration system is built between the analysis model and the optimization decision module. After the strategy is generated, the pattern recognition library is updated in real time to form a self-reinforcement mechanism.

[0016] Preferably, the optimization decision module further embeds a multi-objective trade-off mechanism, which is equipped with a strategic weight allocation scheme for integrating power efficiency, economy and environmental factors to generate a priority sequence based on the system dynamic behavior prediction results provided by the intelligent analysis module, and calculate the optimal allocation strategy;

[0017] After the optimization decision module receives input from the intelligent analysis module, the multi-objective trade-off mechanism automatically evaluates the energy supply and demand balance point and outputs an instruction sequence to the control execution module; at the same time, the communication coordination module transmits environmental perception data to the optimization decision module in real time, and the multi-objective trade-off mechanism dynamically adjusts the strategy weight distribution plan based on this to ensure that the strategy adapts to variable changes; the user interaction module allows users to set weight preferences and receive strategy feedback through the interface; the fault diagnosis module intervenes and issues an alarm when strategies conflict.

[0018] Preferably, the fault diagnosis module is further associated with a knowledge base system, which integrates historical fault patterns and case rule bases, and is used to infer the source of equipment abnormalities and generate diagnostic reports based on the operating data of the data acquisition module and the dynamic behavior prediction of the intelligent analysis module;

[0019] After the fault diagnosis module receives the input, the knowledge base system automatically matches the rule library to identify potential fault points and causes. Its report is forwarded to the user interaction module through the communication coordination module to display the alarm details; at the same time, the optimization decision module uses this report to adjust the strategy priority, and the signal processing module cooperates with the knowledge base to output the auxiliary filtering strategy.

[0020] Preferably, the optimization decision module further embeds a multi-objective optimization calculation engine, which executes the optimal allocation solution, expressed as:

[0021] ,in, To allocate the optimal solution, it is used to quantify the comprehensive effect of the allocation strategy. As the efficiency weight factor, the decision-making module receives the behavior prediction data of the intelligent analysis module through dynamic adjustment, strengthening the energy efficiency priority. is the efficiency utility value, is the environmental weight factor, is the environmental utility value, is the risk assessment scaling factor, is the failure risk index. The above formula integrates logarithmic transformation to avoid risk oversaturation, ensuring that the strategy remains globally optimal in scenarios of sudden failures or sudden load changes, and improving energy scheduling efficiency.

[0022] Preferably, the user interaction module further supports a remote access function that integrates a network interface element and a mobile terminal adaptation framework for receiving user input through an external smart device and remotely displaying system status and control responses;

[0023] After the user interaction module is coupled with the intelligent analysis module, optimization decision module and control execution module, the remote access function allows users to adjust parameters via wireless protocol and view the purification signal and behavior prediction visualization in real time.

[0024] Preferably, the intelligent analysis module is further configured with a dynamic behavior confidence calculation unit, which executes the behavior confidence calculation formula:

[0025] ,in, is the confidence value of behavior prediction, is the historical calibration coefficient, is the signal stability factor, is the time series correlation factor, is the environmental parameter variance, ϵ is the smoothing constant, and ρ is the adjustable weight exponent. The above formula calculates the behavioral confidence by integrating the core module data and dynamically adapts to changing operating conditions. The confidence value is fed back to the optimization decision module in real time as a basis for strategy formulation. The intelligent analysis module automatically adjusts the behavior pattern recognition library based on this value and synchronizes abnormal warnings to the fault diagnosis module via the communication coordination module.

[0026] Preferably, the communication coordination module further has an adaptive path optimization function, which is embedded in a network topology scanning device and redundant switching logic, and is used to automatically detect network congestion and optimize transmission paths when transmitting control instructions and data between distributed devices.

[0027] Preferably, the data acquisition module is further configured with an environment sensing unit, which is embedded with a temperature and humidity sensing element and a light intensity detection device, and is used to synchronously capture the physical environment parameters of the distributed power supply equipment to form a comprehensive operation data environment package;

[0028] The output of the environmental perception unit is directly coupled to the signal processing module, which performs filtering processing on the environmental parameters and operating data together, and establishes a real-time data feedback loop with the environmental perception unit through the communication coordination module. It adaptively adjusts the collection frequency and data packet format according to environmental changes to avoid signal drift, ensures that the operating data is closely integrated with the environmental status, and forms a more stable purification signal input to the intelligent analysis module.

[0029] The technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a distributed power supply integrated acquisition and control system with the following effects:

[0030] The intelligent analysis module integrates the time series analysis model and the behavior pattern recognition library to perform historical data matching and dynamic trend prediction on the purification signal. The behavior confidence calculation unit integrates signal stability, environmental parameters and historical calibration factors through an original formula to comprehensively evaluate the reliability of the prediction results.

[0031] The prediction graph is shared in real time with the optimization decision module and the fault diagnosis module through the communication coordination module, forming a cross-validation mechanism. The user interaction module supports manual intervention threshold adjustment, further enhancing the model's adaptability.

[0032] The multi-objective trade-off mechanism of the optimization decision module integrates energy efficiency, environmental sustainability and fault risk factors. The original calculation formula dynamically adjusts the weight distribution to ensure that the risk index from the fault diagnosis module is forcibly integrated when the strategy is generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is the architecture diagram of the distributed power supply integrated acquisition and control system of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] The present invention provides Figure 1 The distributed power supply integrated acquisition and control system shown in the figure systematically overcomes the key pain points in the background technology through integrated design: signal distortion and disconnection of decision-making security.

[0036] The core lies in the creative expansion and synergy of each module - the environmental perception unit of the data acquisition module dynamically compensates for the impact of noise, the adaptive filtering engine of the signal processing module optimizes signal quality, the behavior prediction credibility calculation of the intelligent analysis module ensures the accuracy of the model output, the knowledge base system of the fault diagnosis module quickly responds to risks, the multi-objective trade-off mechanism of the optimization decision-making module integrates fault factors to achieve safety optimization strategy, and the communication coordination module seamlessly transmits data and enhances the feedback loop.

[0037] The interaction of these innovative elements significantly improves the overall robustness of the system and dynamically adapts to changing environmental load challenges.

[0038] Operationally, the entire process from signal acquisition to execution is seamlessly transitioned, with decision-making and diagnosis working in tandem to reduce uncontrollable risks. Performance-wise, this ensures long-term stable operation, reduces maintenance costs, and optimizes energy efficiency. The overall beneficial effects are reflected in: precise data acquisition as a foundation, reliable behavior prediction as a core, multi-objective safety decision-making as a guide, and efficient fault diagnosis as a guarantee, forming a sustainable, adaptive distributed power control system that consistently maintains efficiency, safety, and stability in dynamic operating scenarios.

[0039] The architecture includes:

[0040] The data acquisition module is used to collect the operating data of the distributed power supply equipment in real time; the data acquisition module is further configured with an environmental perception unit, which is embedded with temperature and humidity sensing elements and light intensity detection devices to synchronously capture the physical environmental parameters of the distributed power supply equipment to form a comprehensive operating data environment package.

[0041] The output of the environmental perception unit is directly coupled to the signal processing module. The signal processing module performs filtering processing on the environmental parameters and operating data together, and establishes a real-time data feedback loop with the environmental perception unit through the communication coordination module. It adaptively adjusts the collection frequency and data packet format according to environmental changes to avoid signal drift and ensure that the operating data is closely integrated with the environmental status to form a more stable purification signal input to the intelligent analysis module.

[0042] Meanwhile, the fault diagnosis module continuously monitors abnormal fluctuations in the environmental sensing unit. When fluctuations exceed a threshold, it automatically triggers an alarm and notifies the user interaction module to visualize environmental risks. This integrated environmental data significantly improves data collection accuracy, enhances the system's dynamic adaptability to climate change, and makes the integrated operational data more reliable, thereby improving the accuracy of subsequent behavioral predictions.

[0043] The signal processing module is coupled to the data acquisition module and is used to perform filtering processing on the operating data and output a purified signal. The signal processing module further integrates an adaptive filtering engine, which includes a time-domain noise reduction circuit and a frequency-domain conversion device. The adaptive filtering engine is used to dynamically switch the filtering strategy to optimize the quality of the purified signal based on the system dynamic behavior type identified by the intelligent analysis module.

[0044] After receiving operational data from the data acquisition module, the signal processing module's adaptive filtering engine first identifies noise source characteristics based on behavior type. It then applies a multi-stage smoothing filtering algorithm to reduce harmonic distortion, outputting a refined, purified signal that is transmitted to the intelligent analysis module. Simultaneously, the communication coordination module shares this behavior type data with the signal processing module's adaptive filtering engine in real time, forming a closed-loop correction mechanism. The user interaction module displays filtering dynamics through a display interface, allowing users to manually intervene in strategy switching. The fault diagnosis module detects filter status anomalies and issues audible and visual alarms.

[0045] This adaptive filtering process significantly reduces the risk of interference, improves signal processing efficiency, and ensures that the purified signal maintains high stability and continuity under complex working conditions.

[0046] The intelligent analysis module is coupled to the signal processing module and is used to identify the dynamic behavior of the system based on the purification signal. The intelligent analysis module further integrates the time series-based analysis model, which is embedded in the behavior pattern recognition library to match the optimization strategy generated by the optimization decision module with the historical operation data to form a behavior prediction map.

[0047] After the intelligent analysis module receives the purified signal from the signal processing module, the time series analysis model extracts pattern features and predicts future dynamic behavior trends. Its output graph is forwarded to the optimization decision module through the communication coordination module for reference in strategy generation. At the same time, the user interaction module supports users to query graph data in order to manually adjust the strategy threshold, and the fault diagnosis module monitors model deviations and issues alarm corrections.

[0048] A feedback iteration system is built between the analysis model and the optimization decision-making module. After the strategy is generated, the pattern recognition library is updated in real time to form a self-reinforcement mechanism. This time series analysis significantly improves the recognition depth and long-term stability of the system's dynamic behavior, reduces the misjudgment rate through cyclic iteration, and thus enhances the overall system's intelligent decision-making robustness and prediction accuracy.

[0049] The intelligent analysis module is further configured with a dynamic behavior confidence calculation unit, which executes the behavior confidence calculation formula:

[0050] ,in, is the confidence value of behavior prediction, is the historical calibration coefficient, is the signal stability factor, is the time series correlation factor, is the environmental parameter variance, ϵ is the smoothing constant, and ρ is the adjustable weight index. The above formula calculates the behavior confidence by integrating the core module data and dynamically adapts to changing operating conditions. The confidence value is fed back to the optimization decision module in real time as a basis for strategy formulation. The intelligent analysis module automatically adjusts the behavior pattern recognition library based on this, and synchronizes abnormal warnings to the fault diagnosis module through the communication coordination module.

[0051] The optimization decision module, coupled to the intelligent analysis module, generates an energy distribution optimization strategy. This module further incorporates a multi-objective trade-off mechanism equipped with a strategy weighting scheme. This multi-objective trade-off mechanism integrates energy efficiency, economic efficiency, and environmental factors to generate a priority sequence and calculate the optimal distribution strategy based on the system dynamic behavior prediction results provided by the intelligent analysis module. After receiving input from the intelligent analysis module, the multi-objective trade-off mechanism automatically evaluates the energy supply and demand balance point and outputs a command sequence to the control execution module.

[0052] At the same time, the communication coordination module transmits environmental perception data to the optimization decision-making module in real time. The multi-objective trade-off mechanism dynamically adjusts the strategy weight distribution plan based on this to ensure that the strategy adapts to variable changes; the user interaction module allows users to set weight preferences and receive strategy feedback through the interface; the fault diagnosis module intervenes and issues an alarm when strategies conflict.

[0053] This mechanism achieves flexible optimization through dynamic weight allocation, improves the practical universality of the strategy and the overall scheduling efficiency of the system, and meets the demand for coordinated energy distribution in changing scenarios.

[0054] The optimization decision module further embeds a multi-objective optimization calculation engine, which executes the optimal allocation solution, expressed as:

[0055] ,in, To allocate the optimal solution, it is used to quantify the comprehensive effect of the allocation strategy. As the efficiency weight factor, the decision-making module receives the behavior prediction data of the intelligent analysis module through dynamic adjustment, strengthening the energy efficiency priority. is the efficiency utility value, is the environmental weight factor, is the environmental utility value, is the risk assessment scaling factor, is the failure risk index. The above formula integrates logarithmic transformation to avoid risk oversaturation, ensuring that the strategy remains globally optimal in scenarios of sudden failures or sudden load changes, and improving energy scheduling efficiency.

[0056] A control execution module is coupled to the optimization decision module and is used to execute the optimization strategy to regulate the power output; the control execution module further includes a safety protection subunit, which is equipped with overload interception logic and an emergency braking mechanism, and is used to monitor the safety threshold of the output instruction and adjust the action instruction in real time when executing the optimization strategy generated by the optimization decision module.

[0057] After the control execution module receives the strategy from the optimization decision module, the safety protection subunit automatically verifies the equipment operating status, integrates the alarm data of the fault diagnosis module to determine the risk of exceeding the limit, and executes instructions within the threshold; if the risk of exceeding the limit is detected, the safety protection subunit intervenes and switches to execute the emergency plan, coordinating the synchronous load reduction of multiple devices through the communication coordination module.

[0058] The user interaction module displays security status and provides alarm log queries, which the optimization decision module automatically optimizes subsequent strategies based on. This safety protection mechanism significantly reduces operational risks, enhances system control stability and anti-interference capabilities, and ensures uninterrupted, compliant, and efficient regulation of distributed power output.

[0059] The user interaction module is coupled to the intelligent analysis module and the control execution module, and is used to receive user input and display the system status. The user interaction module further supports a remote access function that integrates a network interface element and a mobile terminal adaptation framework to receive user input through an external smart device and remotely display the system status and control response.

[0060] After the user interaction module is coupled with the intelligent analysis module, the optimization decision module, and the control execution module, the remote access function allows users to adjust parameters via wireless protocols and view real-time visualizations of purification signals and behavior predictions. At the same time, the communication coordination module transmits this data to the signal processing module to assist in filter calibration, and the fault diagnosis module remotely triggers an alarm to alert the user.

[0061] This function makes the entire system status controllable and adjustable through remote interaction, enhances the convenience of user intervention and system transparency, and significantly improves the response speed of distributed power management and the universality of user experience.

[0062] The fault diagnosis module, coupled to the data acquisition module and the intelligent analysis module, detects equipment anomalies and issues alerts. The fault diagnosis module is further linked to a knowledge base system that integrates historical failure patterns and case-based rule libraries. This system is used to infer the source of equipment anomalies and generate diagnostic reports based on operational data from the data acquisition module and dynamic behavior predictions from the intelligent analysis module. After receiving input from the fault diagnosis module, the knowledge base system automatically matches the rule library to identify potential failure points and causes. This report is then forwarded via the communication coordination module to the user interaction module, which displays the alert details.

[0063] At the same time, the optimization decision module uses this report to adjust the strategy priority, and the signal processing module cooperates with the knowledge base to output auxiliary filtering strategies; the reasoning results of the fault diagnosis module continuously update the knowledge base system for self-optimization.

[0064] This knowledge base system improves diagnostic accuracy and response speed, reduces false alarms through rule deduction, and ensures system reliability and maintainability.

[0065] The fault diagnosis module is further associated with an abnormality severity evaluation unit, which performs a composite fault index calculation formula as follows:

[0066] ,in, is the fault severity index, is the absolute deviation of the data, is the integral of the signal change rate, is the behavioral deviation component, α is the data deviation weight coefficient, β is the signal trend weight coefficient, and γ is the behavioral deviation weight coefficient. The above formula integrates real-time data changes, cumulative signal changes, and behavioral prediction deviations to comprehensively assess fault risks. The index output is broadcast to all modules through the communication coordination module. The fault diagnosis module generates a report based on this for display in the user interaction module and triggers the optimization decision module to modify the weight strategy.

[0067] A communication coordination module, coupled to each module, is used to transmit control instructions and data between distributed devices; the communication coordination module further has an adaptive path optimization function, which is embedded in the network topology scanning device and redundant switching logic to automatically detect network congestion and optimize the transmission path when transmitting control instructions and data between distributed devices.

[0068] After the communication coordination module is coupled to each module, the adaptive path optimization function analyzes transmission load and delay in real time and reduces conflicts through path redirection. At the same time, its output is associated with the data acquisition module to adjust the acquisition frequency and feedback is fed back to the signal processing module to assist in purifying signal synchronization. The fault diagnosis module monitors communication anomalies and triggers path switching alarms. The user interaction module displays a communication status diagram.

[0069] This feature significantly improves data transmission efficiency and reliability, ensures seamless synchronization of command coordination in a multi-device environment, and enhances the overall robustness and dynamic collaboration capabilities of the system.

[0070] Each module interacts with each other through a preset bus architecture: the data acquisition module transmits operating data to the signal processing module for denoising; the purified signal from the signal processing module is sent to the intelligent analysis module for behavior prediction; the optimization decision module receives the prediction results and calculates the optimal scheduling plan;

[0071] The control execution module converts the data into action instructions and feeds them back to the communication coordination module to coordinate the synchronization of multiple devices; the user interaction module visualizes the status data and obtains adjustment parameters from the optimization decision module; the fault diagnosis module monitors the output of all modules and triggers an alarm in the event of an abnormality, which is transmitted through the communication coordination module.

[0072] In summary, the present invention has the following effects:

[0073] The signal processing accuracy is significantly enhanced, and environmental noise interference is effectively suppressed. The system captures temperature, humidity, and light physical parameters in real time through the built-in environmental perception unit in the data acquisition module, and links it with the signal processing module through a feedback loop.

[0074] The adaptive filtering engine dynamically adjusts its strategy, switching noise reduction modes based on different system dynamics, significantly reducing harmonic distortion and signal drift. This purified signal quality can be easily applied to the behavior prediction model of the intelligent analysis module, avoiding misjudgments caused by inaccurate collected data.

[0075] At the same time, the fault diagnosis module monitors the filter status and quickly warns of abnormal changes, forming a closed-loop correction mechanism. This fundamentally solves the static failure problem of traditional system filtering mechanisms, ensuring that the signal remains highly stable under variable climate or load interference, providing a reliable input foundation for subsequent behavior recognition and decision-making, and improving the data integrity and real-time responsiveness of the energy system.

[0076] The reliability and accuracy of behavior prediction have been greatly improved. The intelligent analysis module adapted to complex operating scenarios integrates the time series analysis model and the behavior pattern recognition library to perform historical data matching and dynamic trend prediction on the purification signal. The behavior confidence calculation unit integrates signal stability, environmental parameters and historical calibration factors through original formulas to comprehensively evaluate the reliability of the prediction results.

[0077] The prediction graph is shared in real time with the optimization decision module and fault diagnosis module via the communication coordination module, forming a cross-validation mechanism. The user interaction module supports manual intervention threshold adjustment, further enhancing the model's adaptability.

[0078] This effect overcomes the defect of increased misjudgment rate caused by signal distortion, significantly optimizes long-term prediction accuracy, and enables the system to efficiently identify dynamic behaviors under conditions of equipment aging and unstable light mutations, laying a solid and reliable analytical support for power distribution strategies.

[0079] The robustness of optimization decisions is enhanced to achieve multi-objective safety collaborative optimization. The multi-objective trade-off mechanism of the optimization decision module integrates energy efficiency, environmental sustainability and fault risk factors, dynamically adjusts the weight distribution, and ensures that the risk index from the fault diagnosis module is forcibly integrated when generating strategies.

[0080] The communication coordination module transmits environmental data and behavior prediction results in real time, guiding the dynamic priority output of the strategy to the control execution module; the safety protection subunit monitors the execution threshold and collaborates with the fault diagnosis module to intervene in emergency adjustments to avoid conflicts between scheduling instructions and equipment safety boundaries.

[0081] This effect solves the problem of ignoring abnormal risks caused by the fragmentation of decision-making functions, provides a highly flexible trade-off solution, improves the strategy's global optimization capabilities and safety control level in scenarios with sudden changes in grid load or equipment failures, and enhances system resource utilization efficiency and operational stability.

[0082] Fault diagnosis efficiency is improved and error rate is reduced, and system maintenance convenience is significantly enhanced. The knowledge base system of the fault diagnosis module associates historical fault modes and case rule base, combines the original abnormality severity assessment formula with the fault prediction cross-validation formula, integrates data deviation, signal integration and behavior prediction deviation dimensions, comprehensively infers the abnormal source and generates a diagnosis report.

[0083] A cross-validation mechanism integrates the confidence value and system stability factor of the intelligent analysis module to ensure reliable diagnostic results. The knowledge base continuously updates itself, reducing false positives and accelerating alert response. The user interaction module remotely displays diagnostic details, and the optimization decision module adjusts strategies based on these reports, creating a closed-loop feedback loop for improvement.

[0084] This effect significantly optimizes the isolation problem of traditional diagnostic mechanisms, provides efficient and accurate fault warnings in equipment access or aging scenarios, reduces invalid alarms and response delays, and enhances system maintenance feasibility and overall reliability.

[0085] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed power supply integrated acquisition and control system, characterized in that: include: a data acquisition module, a signal processing module, a communication coordination module, and an intelligent analysis module, wherein the intelligent analysis module is coupled to the signal processing module and is used to identify the dynamic behavior of the system based on the purification signal; An optimization decision module, coupled to the intelligent analysis module, is used to generate an optimization strategy for power distribution; a control execution module, coupled to the optimization decision module, for executing the optimization strategy to adjust the power output; a user interaction module, coupled to the intelligent analysis module and the control execution module, for receiving user input and displaying system status; a fault diagnosis module, coupled to the data acquisition module and the intelligent analysis module, for detecting equipment anomalies and issuing alarms; The data acquisition module transmits the operating data to the signal processing module for denoising. The purified signal from the signal processing module is sent to the intelligent analysis module for behavior prediction. The optimization decision module receives the prediction results and calculates the optimal scheduling plan. The control execution module converts them into action instructions and feeds them back to the communication coordination module to coordinate the synchronization of multiple devices. The user interaction module visualizes the status data and obtains adjustment parameters from the optimization decision module; the fault diagnosis module monitors the output of all modules and triggers alarms when abnormalities occur, which are transmitted through the communication coordination module.

2. A distributed power supply integrated acquisition and control system according to claim 1, characterized in that: The intelligent analysis module further integrates an analysis model based on a time series, which is embedded in a behavior pattern recognition library to match the optimization strategy generated by the optimization decision module with historical operation data to form a behavior prediction map; After the intelligent analysis module receives the purified signal from the signal processing module, the time series analysis model extracts pattern features and predicts future dynamic behavior trends. Its output graph is forwarded to the optimization decision module through the communication coordination module for reference in strategy generation. The user interaction module allows users to query the graph data to manually adjust the strategy threshold, and the fault diagnosis module monitors model deviations and issues alarm corrections. A feedback iteration system is built between the analysis model and the optimization decision module. After the strategy is generated, the pattern recognition library is updated in real time to form a self-reinforcement mechanism.

3. The distributed power supply integrated acquisition and control system according to claim 1, characterized in that: The optimization decision module further embeds a multi-objective trade-off mechanism equipped with a strategic weight allocation scheme for generating a priority sequence based on the system dynamic behavior prediction results provided by the intelligent analysis module, integrating power efficiency, economy and environmental factors, and calculating the optimal allocation strategy; After the optimization decision module receives input from the intelligent analysis module, the multi-objective trade-off mechanism automatically evaluates the energy supply and demand balance point and outputs an instruction sequence to the control execution module; at the same time, the communication coordination module transmits environmental perception data to the optimization decision module in real time, and the multi-objective trade-off mechanism dynamically adjusts the strategy weight distribution plan based on this to ensure that the strategy adapts to variable changes; the user interaction module allows users to set weight preferences and receive strategy feedback through the interface; the fault diagnosis module intervenes and issues an alarm when strategies conflict.

4. The distributed power supply integrated acquisition and control system according to claim 1, characterized in that: The fault diagnosis module is further linked to a knowledge base system that integrates historical fault patterns and case rule bases to infer the source of equipment anomalies and generate diagnostic reports based on the operating data of the data acquisition module and the dynamic behavior prediction of the intelligent analysis module; After the fault diagnosis module receives the input, the knowledge base system automatically matches the rule library to identify potential fault points and causes. Its report is forwarded to the user interaction module through the communication coordination module to display the alarm details; at the same time, the optimization decision module uses this report to adjust the strategy priority, and the signal processing module cooperates with the knowledge base to output the auxiliary filtering strategy.

5. The distributed power supply integrated acquisition and control system according to claim 3, characterized in that: The optimization decision module further embeds a multi-objective optimization calculation engine, which executes the optimal allocation solution, expressed as: ,in, To allocate the optimal solution, it is used to quantify the comprehensive effect of the allocation strategy. As the efficiency weight factor, the decision-making module receives the behavior prediction data of the intelligent analysis module through dynamic adjustment, strengthening the energy efficiency priority. is the efficiency utility value, is the environmental weight factor, is the environmental utility value, is the risk assessment scaling factor, is the failure risk index. The above formula integrates logarithmic transformation to avoid risk oversaturation, ensuring that the strategy remains globally optimal in scenarios of sudden failures or sudden load changes, and improving energy scheduling efficiency.

6. The distributed power supply integrated acquisition and control system according to claim 1, characterized in that: The user interaction module further supports a remote access function that integrates a network interface element and a mobile terminal adaptation framework for receiving user input through an external smart device and remotely displaying system status and control responses; After the user interaction module is coupled with the intelligent analysis module, optimization decision module and control execution module, the remote access function allows users to adjust parameters via wireless protocol and view the purification signal and behavior prediction visualization in real time.

7. The distributed power supply integrated acquisition and control system according to claim 2, characterized in that: The intelligent analysis module is further configured with a dynamic behavior confidence calculation unit, which executes the behavior confidence calculation formula: ,in, is the confidence value of behavior prediction, is the historical calibration coefficient, is the signal stability factor, is the time series correlation factor, is the environmental parameter variance, ϵ is the smoothing constant, and ρ is the adjustable weight exponent. The above formula calculates the behavioral confidence by integrating the core module data and dynamically adapts to changing operating conditions. The confidence value is fed back to the optimization decision module in real time as a basis for strategy formulation. The intelligent analysis module automatically adjusts the behavior pattern recognition library based on this value and synchronizes abnormal warnings to the fault diagnosis module via the communication coordination module.

8. The distributed power supply integrated acquisition and control system according to claim 1, characterized in that: The communication coordination module further has an adaptive path optimization function, which is embedded in the network topology scanning device and redundant switching logic, and is used to automatically detect network congestion and optimize the transmission path when transmitting control instructions and data between distributed devices.

9. The distributed power integrated acquisition and control system according to claim 1, characterized in that: The data acquisition module is further configured with an environment sensing unit, which is embedded with a temperature and humidity sensing element and a light intensity detection device, and is used to synchronously capture the physical environment parameters of the distributed power supply equipment to form a comprehensive operation data environment package; The output of the environmental perception unit is directly coupled to the signal processing module, which performs filtering processing on the environmental parameters and operating data together, and establishes a real-time data feedback loop with the environmental perception unit through the communication coordination module. It adaptively adjusts the collection frequency and data packet format according to environmental changes to avoid signal drift, ensures that the operating data is closely integrated with the environmental status, and forms a more stable purification signal input to the intelligent analysis module.

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