Direct current power supply system control device and method

Abstract

The invention relates to the technical field of power electronics and energy management. The invention provides a DC power supply system control device and method. The method comprises the following steps: acquiring voltage and current data of a DC power supply system in real time; the collected voltage and current data are preprocessed, the preprocessed data are analyzed through a data analysis algorithm, and the operation state and potential faults of the direct-current power supply system are identified; based on a data analysis result, dynamically adjusting a preset control strategy and algorithm to obtain a control instruction of the current direct-current power supply system; and sending the control instruction of the current direct-current power supply system to an execution mechanism, and performing control operation on the direct-current power supply system by the execution mechanism. The problems that in the prior art, a direct-current power supply system is low in management efficiency, slow in response, difficult to accurately recognize and process abnormal conditions, high in maintenance cost and high in safety risk are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronics and energy management, and in particular to a direct current power supply system control device and method. Background Art

[0002] With the rapid development of power electronics technology and the increasing demand for energy management, DC power systems play a vital role in many fields, such as data centers, electric vehicle charging stations, communication base stations and industrial automation. These systems require efficient, stable and reliable power supply to ensure the normal operation of equipment and extend its service life. However, in practical applications, DC power systems face many challenges, including voltage fluctuations, current overloads, overheating and potential hardware failures. If these problems are not discovered and handled in time, they may lead to system performance degradation and even cause serious safety accidents.

[0003] Traditional DC power system management mainly relies on manual inspections and regular maintenance, which is not only inefficient, but also difficult to achieve real-time monitoring and rapid response to system status. In addition, with the increasing complexity of DC power systems, it is difficult to accurately identify and handle various abnormal situations based on experience alone, which increases the maintenance cost of DC power systems and poses a high safety risk. Summary of the invention

[0004] The purpose of the present invention is to provide a DC power supply system control method, aiming to solve the problems of low management efficiency, slow response, difficulty in accurately identifying and handling abnormal situations, increased maintenance costs and high safety risks of DC power supply systems in the prior art.

[0005] The present invention is achieved through the following technical solutions: A DC power supply system control method comprises the steps of: Real-time acquisition of voltage and current data of DC power supply system; Preprocess the collected voltage and current data, and analyze the preprocessed data through data analysis algorithms to identify the operating status and potential faults of the DC power supply system; Based on the results of data analysis, dynamically adjust the preset control strategy and algorithm to obtain the control instructions of the current DC power supply system; The control instructions of the current DC power supply system are sent to the actuator, and the actuator controls the DC power supply system.

[0006] Optionally, the specific process of real-time acquisition of voltage and current data of the system is: A sensor module is installed at a key position of the DC power supply system; the sensor module communicates with the data acquisition module via wired or wireless means; the data acquisition module obtains voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements.

[0007] Optionally, the specific process of preprocessing the collected voltage and current data is: The collected voltage and current data are filtered; the filtered data are formatted; the formatted data are verified to check the integrity and rationality of the data to obtain a preprocessed data set; the preprocessed data set is stored in a designated data buffer or database.

[0008] Optionally, the specific process of analyzing the preprocessed data by using a data analysis algorithm to identify the operating status and potential faults of the DC power supply system is: Perform trend analysis and outlier detection on the pre-processed voltage and current data through statistical analysis methods, identify mutation points or abnormal fluctuations in the data, and mark the corresponding abnormalities; Use machine learning algorithms to train and learn the preprocessed data sets and establish a system status assessment model; Based on the real-time data of the DC power system, the operating status of the DC power system is predicted through the system status assessment model, and the potential faults of the DC power system are preliminarily classified; Combined with the expert system or rule base, further verify and refine the prediction results of the system status assessment model; Generate an operating status report and potential fault warning information of the DC power supply system based on the analysis results.

[0009] Optionally, the specific process of using a machine learning algorithm to train and learn the preprocessed data set to establish a system status assessment model is as follows: Construct an initial assessment model of system status through unsupervised learning algorithms; Perform feature extraction on the preprocessed data set to extract key features reflecting the operating status of the DC power supply system and obtain a key feature data set; The key feature data set is input into the system status initial assessment model, and the clustering structure or abnormal points in the data are automatically discovered according to the intrinsic characteristics of the key feature data set to obtain the system status assessment model.

[0010] Optionally, the initial system state assessment model is constructed by using a Gaussian mixture model. Suppose the preprocessed key feature dataset is , Indicates The feature vector of the data point, ; Then the expression of the initial evaluation model of the system state is as follows (1):

[0011] in, Indicates the number of Gaussian components; Indicates The weights of the Gaussian components; Indicates The probability density function of the Gaussian components; represents the mean vector; represents the covariance matrix; Calculate the posterior probability that each data point belongs to each Gaussian component ;in, Represents a latent variable, used to indicate a data point Is it the Gaussian components; According to the posterior probability of each data point, the anomaly score of each data point is calculated. The calculation expression of the anomaly score is shown in the following formula (2):

[0012] in, Indicates Anomaly score for each data point; set threshold ,if , then mark the data points are outliers.

[0013] Optionally, the specific process of dynamically adjusting the preset control strategy and algorithm based on the result of data analysis to obtain the control instruction of the current DC power supply system is: Evaluate the health and performance of the current system based on the DC power system operation status report and potential fault warning information output by the data analysis algorithm; Compare the evaluation results with the preset control strategies and algorithm library to identify the applicable control strategies and algorithms; Dynamically adjust the parameters of the selected control strategy and algorithm according to the current operating status of the system and the type of potential faults to optimize the performance of the DC power supply system; Based on the adjusted control strategy and algorithm, the control instructions of the current DC power supply system are generated.

[0014] Optionally, when dynamically adjusting the parameters of the selected control strategy and algorithm, the voltage and current data collected in real time are fed back to the system state evaluation model, and iterative adjustments are made with a preset step size, and the adjustment effect is evaluated in real time; if it is found that the system state is significantly improved or the fault is effectively controlled during the adjustment process, the current adjustment plan is maintained; if the system state is not significantly improved or the fault tends to intensify, the operating state of the DC power supply system is re-evaluated, and the control strategy and algorithm parameters are adjusted according to the new evaluation results until the optimal control effect is achieved.

[0015] Optionally, the warning information is divided into warning levels according to the severity and urgency of the potential fault; an emergency response plan library is preset to store emergency response plans for different fault types and warning levels; when a potential fault is identified, a corresponding warning signal is sent to relevant personnel or systems according to the warning level, and the relevant personnel or systems select and execute corresponding emergency response plans based on the warning signal.

[0016] The present invention also provides a DC power supply system control device, which is used to implement the DC power supply system control method, comprising: Sensor module, used to monitor and collect voltage and current data of the system in real time; A data acquisition module is used to obtain voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements, and transmit the data to the preprocessing module; A preprocessing module is used to preprocess the collected voltage and current data to obtain a preprocessed data set, and store the preprocessed data set in a designated data buffer area or database; An analysis module, used to analyze the pre-processed data through a data analysis algorithm to identify the operating status and potential faults of the DC power supply system; A strategy adjustment module is used to dynamically adjust the preset control strategy and algorithm based on the results of data analysis to obtain the control instructions of the current DC power supply system; An execution module, used for receiving control instructions sent by the strategy adjustment module and performing control operations on the DC power supply system; The early warning module is used to send corresponding early warning signals to relevant personnel or systems according to the early warning level and trigger the corresponding emergency response plan.

[0017] The technical solution of the present invention has at least the following advantages and beneficial effects: Real-time monitoring and rapid response: By collecting the voltage and current data of the DC power supply system in real time and immediately performing preprocessing and data analysis, the operating status and potential faults of the system can be quickly identified. Compared with traditional manual inspections and regular maintenance methods, the real-time and response speed of monitoring are greatly improved, which helps to promptly discover and deal with problems, prevent faults from expanding, and ensure the stable operation of the system.

[0018] Intelligent management: Using data analysis algorithms to analyze pre-processed data can more accurately determine the actual situation of the DC power supply system based on data-driven decisions. This not only improves the level of intelligent management, but also reduces misoperation and maintenance costs caused by inaccurate experience judgments.

[0019] Dynamically adjust control strategies: Based on the results of data analysis, the preset control strategies and algorithms can be dynamically adjusted to generate the most suitable control instructions at the moment, so that the system can respond to various complex situations more flexibly and improve the adaptability and stability of the system.

[0020] Reduce safety risks: Through real-time monitoring and intelligent management, problems such as voltage fluctuations, current overloads, overheating, and potential hardware failures in the DC power supply system can be discovered and handled in a timely manner, thereby effectively avoiding safety accidents that may be caused by these problems and reducing the safety risks of the system.

[0021] Improve system efficiency and reliability: It can significantly improve the operating efficiency of the DC power supply system, reduce downtime caused by faults, and improve the reliability and stability of the system. It is of great significance to ensure the normal operation and extend the service life of equipment in data centers, electric vehicle charging stations, communication base stations and industrial automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a flow chart of a DC power supply system control method according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of a DC power supply system control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following is a specific implementation method in conjunction with the drawings.

[0024] Reference Figure 1 , a DC power system control method, comprising the steps of: Step 1: Collect voltage and current data of the DC power supply system in real time.

[0025] In some embodiments, the specific process of real-time acquisition of voltage and current data of the DC power supply system is: A sensor module is installed at a key position of the DC power supply system; the sensor module communicates with the data acquisition module via wired or wireless means; the data acquisition module obtains voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements.

[0026] In some embodiments, key positions such as input terminals, output terminals, main load connection points, etc., can accurately reflect the overall operating status and possible abnormal conditions of the DC power supply system. Select high-precision and fast-response sensors to ensure the accuracy and real-time nature of data acquisition. For example, a Hall effect sensor can be used to measure current, and a voltage divider circuit can be used with a high-precision ADC to measure voltage. The sensor module can communicate with the data acquisition module via wired (such as RS-485, CAN bus, etc.) or wireless (such as Wi-Fi, LoRa, Zigbee, etc.). The wireless method is suitable for scenarios where wiring is not easy or flexible deployment is required. The data acquisition module obtains the voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals (such as every second, every millisecond, etc.) or according to system requirements (such as system state changes, abnormality detection, etc.). In order to quickly process and analyze data, the collected data can be stored in a data cache. The data cache can be a part of the memory or a dedicated storage device. For data that is stored for a long time and subsequently analyzed, it can be stored in a database. In order to prevent data loss or damage, data needs to be backed up regularly. Backups can be stored on local storage devices or on remote servers.

[0027] Step 2: pre-process the collected voltage and current data, and analyze the pre-processed data through a data analysis algorithm to identify the operating status and potential faults of the DC power supply system.

[0028] In some embodiments, the specific process of preprocessing the collected voltage and current data is as follows: The collected voltage and current data are filtered to remove noise and interference and improve the accuracy of the data; the filtered data are formatted to meet the requirements of the subsequent analysis algorithm; the formatted data are verified to check the integrity and rationality of the data to obtain a preprocessed data set; the preprocessed data set is stored in a specified data buffer or database.

[0029] In some embodiments, the filtering algorithm can be implemented by programming, or the collected data can be processed directly using a hardware filter, and a low-pass filter can be used for filtering. According to the requirements of the data analysis algorithm, the data is converted into a specified data type, data range, and resolution. For example, the voltage and current data are converted into floating-point types, and the data units are unified. In the preprocessing module, the data format conversion can be implemented by programming. Check the integrity and rationality of the data to ensure the validity of the data. Use data verification algorithms, such as checksum, parity check, etc., to verify the collected data. At the same time, according to the physical characteristics and operating rules of the system, set a reasonable data range threshold, and mark or remove the data that exceeds the range.

[0030] In some embodiments, the specific process of analyzing the pre-processed data by a data analysis algorithm to identify the operating status and potential faults of the DC power supply system is as follows: The pre-processed voltage and current data are subjected to trend analysis and outlier detection through statistical analysis methods, mutation points or abnormal fluctuations in the data are identified, and corresponding anomalies are marked; the pre-processed data set is trained and learned using a machine learning algorithm to establish a system status assessment model; based on the real-time data of the DC power supply system, the operating status of the DC power supply system is predicted through the system status assessment model, and the potential faults of the DC power supply system are preliminarily classified; in combination with an expert system or rule base, the prediction results of the system status assessment model are further verified and refined; based on the analysis results, an operating status report of the DC power supply system and potential fault warning information are generated.

[0031] In some embodiments, statistical analysis methods, such as time series analysis, moving average, etc., can be used to perform trend analysis on the preprocessed voltage and current data, which helps to identify long-term trends, seasonal changes, or periodic fluctuations in the data; outliers in the data can be detected by setting thresholds or using statistical methods (such as Z-score, IQR, etc.). Outliers indicate sudden changes in the operating state of the DC power supply system or potential failures.

[0032] In some embodiments, the specific process of using a machine learning algorithm to train and learn the preprocessed data set to establish a system status assessment model is as follows: An initial evaluation model of the system state is constructed through an unsupervised learning algorithm; the unsupervised learning algorithm may be a Gaussian mixture model GMM, a clustering algorithm, and the like.

[0033] Feature extraction is performed on the preprocessed data set to extract key features reflecting the operating status of the DC power supply system and obtain a key feature data set; the key features include voltage fluctuation range, current peak value, system temperature and other features.

[0034] The key feature data set is input into the system status initial assessment model, and the clustering structure or abnormal points in the data are automatically discovered according to the intrinsic characteristics of the key feature data set to obtain the system status assessment model.

[0035] In some embodiments, the initial evaluation model of the system state is constructed by a Gaussian mixture model, and the preprocessed key feature data set is assumed to be , Indicates The feature vector of the data point, ; Then the expression of the initial evaluation model of the system state is as follows (1):

[0036] in, Indicates the number of Gaussian components; Indicates The weights of the Gaussian components; Indicates The probability density function of the Gaussian components; represents the mean vector; represents the covariance matrix; Calculate the posterior probability that each data point belongs to each Gaussian component ;in, Represents a latent variable, used to indicate a data point Is it the Gaussian components; According to the posterior probability of each data point, the anomaly score of each data point is calculated. The calculation expression of the anomaly score is shown in the following formula (2):

[0037] in, Indicates Anomaly score for each data point; set threshold ,if , then mark the data points are outliers.

[0038] In some embodiments, the system status initial assessment model can be constructed by support vector machine (SVM), random forest or neural network. For example, the system status initial assessment model is constructed by using SVM: Add labels to the preprocessed data set. These labels represent different operating states of the system, such as normal, minor fault, major fault, etc. The label data can be obtained through expert judgment, historical records or experiments. With the goal of identifying the operating state and potential faults of the DC power supply system, build an initial system state assessment model based on SVM. Select the optimal kernel function and its parameters through methods such as cross-validation. Tune the parameters through methods such as grid search, random search or Bayesian optimization. Use the preprocessed data set and the selected SVM type, kernel function and parameters to train the initial system state assessment model. During the training process, the initial system state assessment model will learn how to map data points in the feature space to different categories or regression values. After training the model, use cross-validation (such as k-fold cross-validation) to evaluate the performance of the model. Select appropriate performance indicators to evaluate the model according to the nature of the problem. For identifying the operating state of the DC power supply system, the model can be evaluated using accuracy, recall or F1 score. For identifying potential faults of the DC power supply, the mean square error (MSE) or determination coefficient (R²) can be used for evaluation. The system status assessment model obtained after evaluation is applied to the DC power system data collected in real time to evaluate the current status of the DC power system and predict potential faults.

[0039] Step 3: Based on the results of data analysis, dynamically adjust the preset control strategy and algorithm to obtain the control instructions of the current DC power supply system.

[0040] In some embodiments, based on the results of data analysis, the specific process of dynamically adjusting the preset control strategy and algorithm to obtain the control instructions of the current DC power supply system is: Based on the DC power system operation status report and potential fault warning information output by the data analysis algorithm, the health status and performance of the current system are evaluated. Based on the voltage and current data and their trend analysis, outlier detection, and machine learning model prediction results in the DC power system operation status report, the health status and performance of the current system are evaluated, including but not limited to quantitative evaluation of key indicators such as system stability, efficiency, and load capacity.

[0041] Compare the evaluation results with the preset control strategy and algorithm library to identify the applicable control strategy and algorithm; a comprehensive control strategy and algorithm library needs to be built. This library should contain a variety of control strategies and algorithms for different operating conditions and potential faults that the DC power supply system may encounter. These strategies and algorithms may include but are not limited to: Basic control strategies: such as voltage regulation, current limiting, etc.; Advanced control algorithms: such as fuzzy control, neural network control, genetic algorithm optimization, etc.; Fault handling algorithms: such as fault detection and isolation, fault recovery strategies, etc.

[0042] Each strategy and algorithm should have clear applicable conditions and expected effects, and be accompanied by detailed instructions and parameter setting guidelines. During the data analysis phase, the generated DC power system operating status report and potential fault warning information are classified and coded. This helps to accurately match the evaluation results with the entries in the control strategy and algorithm library. The classification and coding of the evaluation results are compared and matched with the entries in the control strategy and algorithm library. The comparison and matching process can be achieved through the following steps: Preliminary screening: Screen out possible applicable control strategies and algorithms based on the operating status classification; Fine matching: Based on the potential fault code, further screen out the control strategy and algorithm that best matches the current fault condition; Parameter matching: For the selected strategies and algorithms, adjust their parameter settings according to the specific parameters in the evaluation results (such as voltage fluctuation range, current peak, etc.) to better adapt to the current system status.

[0043] According to the current operating status and potential fault types of the system, the parameters of the selected control strategy and algorithm are dynamically adjusted to optimize the performance of the DC power supply system; according to the adjusted control strategy and algorithm, the control instructions of the current DC power supply system are generated.

[0044] In some embodiments, when dynamically adjusting the parameters of the selected control strategy and algorithm, the voltage and current data collected in real time are fed back to the system state evaluation model, iterative adjustment is performed with a preset step size, and the adjustment effect is evaluated in real time; if it is found that the system state is significantly improved or the fault is effectively controlled during the adjustment process, the current adjustment plan is maintained; if the system state is not significantly improved or the fault tends to intensify, the operating state of the DC power supply system is re-evaluated, and the control strategy and algorithm parameters are adjusted according to the new evaluation results until the optimal control effect is achieved.

[0045] Step 4: Send the control instructions of the current DC power supply system to the actuator, which controls the DC power supply system. After completing the dynamic adjustment of the preset control strategy and algorithm, the specific control instructions of the current DC power supply system are generated. The control instructions may include adjusting the output voltage, current limit, system protection settings, load balancing strategy, etc. After receiving the control instructions, the actuator parses the instructions and performs corresponding control operations according to their contents. The actuator includes power regulators, circuit breakers, load switches, etc., which are used to directly adjust the operating parameters of the DC power supply system. After executing the control operation, the actuator can obtain the real-time status information of the system through the sensor module or other monitoring means. This information is collected and sent to the data analysis process again to verify the validity of the control instructions and provide a basis for subsequent control adjustments.

[0046] In some embodiments, the warning information is divided into warning levels according to the severity and urgency of the potential fault; an emergency response plan library is preset to store emergency response plans for different fault types and warning levels; when a potential fault is identified, a corresponding warning signal is sent to relevant personnel or systems according to the warning level, and the relevant personnel or systems select and execute the corresponding emergency response plan based on the warning signal.

[0047] Based on the same inventive concept, corresponding to any of the above embodiments, refer to Figure 2 The present invention also provides a DC power supply system control device for implementing a DC power supply system control method, comprising: Sensor module, used to monitor and collect voltage and current data of the system in real time; A data acquisition module is used to obtain voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements, and transmit the data to the preprocessing module; A preprocessing module is used to preprocess the collected voltage and current data to obtain a preprocessed data set, and store the preprocessed data set in a designated data buffer area or database; An analysis module, used to analyze the pre-processed data through a data analysis algorithm to identify the operating status and potential faults of the DC power supply system; A strategy adjustment module is used to dynamically adjust the preset control strategy and algorithm based on the results of data analysis to obtain the control instructions of the current DC power supply system; An execution module, used for receiving control instructions sent by the strategy adjustment module and performing control operations on the DC power supply system; The early warning module is used to send corresponding early warning signals to relevant personnel or systems according to the early warning level and trigger the corresponding emergency response plan.

Claims

1. A DC power supply system control method, characterized in that: Includes steps: Real-time acquisition of voltage and current data of DC power supply system; Preprocess the collected voltage and current data, and analyze the preprocessed data through data analysis algorithms to identify the operating status and potential faults of the DC power supply system; Based on the results of data analysis, dynamically adjust the preset control strategy and algorithm to obtain the control instructions of the current DC power supply system; The control instructions of the current DC power supply system are sent to the actuator, and the actuator controls the DC power supply system.

2. The DC power supply system control method according to claim 1, characterized in that: The specific process of real-time acquisition of voltage and current data of the DC power supply system is as follows: A sensor module is installed at a key position of the DC power supply system; the sensor module communicates with the data acquisition module via wired or wireless means; the data acquisition module obtains voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements.

3. The DC power supply system control method according to claim 1, characterized in that: The specific process of preprocessing the collected voltage and current data is as follows: Filter the collected voltage and current data; format the filtered data; Perform data verification on the formatted data to check the integrity and rationality of the data and obtain the preprocessed data set; Store the preprocessed data set in a specified data buffer or database.

4. The DC power supply system control method according to claim 1, characterized in that: The specific process of analyzing the pre-processed data by the data analysis algorithm to identify the operating status and potential faults of the DC power supply system is as follows: Perform trend analysis and outlier detection on the pre-processed voltage and current data through statistical analysis methods, identify mutation points or abnormal fluctuations in the data, and mark the corresponding abnormalities; Use machine learning algorithms to train and learn the preprocessed data sets and establish a system status assessment model; Based on the real-time data of the DC power system, the operating status of the DC power system is predicted through the system status assessment model, and the potential faults of the DC power system are preliminarily classified; Combined with the expert system or rule base, further verify and refine the prediction results of the system status assessment model; Generate an operating status report and potential fault warning information of the DC power supply system based on the analysis results.

5. The DC power supply system control method according to claim 4, characterized in that: The specific process of using the machine learning algorithm to train and learn the preprocessed data set and establish the system status assessment model is as follows: Construct an initial assessment model of system status through unsupervised learning algorithms; Perform feature extraction on the preprocessed data set to extract key features reflecting the operating status of the DC power supply system and obtain a key feature data set; The key feature data set is input into the system status initial assessment model, and the clustering structure or abnormal points in the data are automatically discovered according to the intrinsic characteristics of the key feature data set to obtain the system status assessment model.

6. The DC power supply system control method according to claim 5, characterized in that: The initial evaluation model of the system state is constructed by using the Gaussian mixture model. Assume that the preprocessed key feature data set is , Indicates The feature vector of the data point, ; Then the expression of the initial evaluation model of the system state is as follows (1): in, Indicates the number of Gaussian components; Indicates The weights of the Gaussian components; Indicates The probability density function of the Gaussian components; represents the mean vector; represents the covariance matrix; Calculate the posterior probability that each data point belongs to each Gaussian component ;in, Represents a latent variable, used to indicate a data point Is it the Gaussian components; According to the posterior probability of each data point, the anomaly score of each data point is calculated. The calculation expression of the anomaly score is shown in the following formula (2): in, Indicates Anomaly score for each data point; set threshold ,if , then mark the data points are outliers.

7. The DC power supply system control method according to claim 4, characterized in that: The specific process of dynamically adjusting the preset control strategy and algorithm based on the result of data analysis to obtain the control instructions of the current DC power supply system is as follows: Evaluate the health and performance of the current system based on the DC power system operation status report and potential fault warning information output by the data analysis algorithm; Compare the evaluation results with the preset control strategies and algorithm library to identify the applicable control strategies and algorithms; Dynamically adjust the parameters of the selected control strategy and algorithm according to the current operating status of the system and the type of potential faults to optimize the performance of the DC power supply system; Based on the adjusted control strategy and algorithm, the control instructions of the current DC power supply system are generated.

8. The DC power supply system control method according to claim 7, characterized in that: When dynamically adjusting the parameters of the selected control strategy and algorithm, the voltage and current data collected in real time are fed back to the system state evaluation model, and iterative adjustments are made with a preset step size, and the adjustment effect is evaluated in real time; If the system status is found to be significantly improved or the fault is effectively controlled during the adjustment process, the current adjustment plan will be maintained; If the system status does not improve significantly or the fault tends to worsen, the operating status of the DC power supply system will be re-evaluated, and the control strategy and algorithm parameters will be adjusted according to the new evaluation results until the optimal control effect is achieved.

9. The DC power supply system control method according to claim 1, characterized in that: According to the severity and urgency of potential faults, the warning information is divided into warning levels; A preset emergency response plan library stores emergency response plans for different fault types and warning levels. When a potential fault is identified, a corresponding warning signal is sent to relevant personnel or systems according to the warning level. The relevant personnel or systems select and execute the corresponding emergency response plan based on the warning signal.

10. A DC power system control device, used to implement the DC power system control method according to any one of claims 1 to 9, characterized in that: include: Sensor module, used to monitor and collect voltage and current data of the system in real time; A data acquisition module is used to obtain voltage and current data of the DC power supply system in real time through the sensor module at preset time intervals or in a manner triggered according to system requirements, and transmit the data to the preprocessing module; A preprocessing module is used to preprocess the collected voltage and current data to obtain a preprocessed data set, and store the preprocessed data set in a designated data buffer area or database; An analysis module, used to analyze the pre-processed data through a data analysis algorithm to identify the operating status and potential faults of the DC power supply system; A strategy adjustment module is used to dynamically adjust the preset control strategy and algorithm based on the results of data analysis to obtain the control instructions of the current DC power supply system; An execution module, used for receiving control instructions sent by the strategy adjustment module and performing control operations on the DC power supply system; The early warning module is used to send corresponding early warning signals to relevant personnel or systems according to the early warning level and trigger the corresponding emergency response plan.

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