Intelligent control method and system of high-power charging module

Abstract

The invention relates to the technical field of charging control, and provides an intelligent control method and system for a high-power charging module, and the method comprises the steps: obtaining a charging state sequence of the charging module, carrying out the feature extraction and mode recognition processing of the charging state sequence, and obtaining a charging safety control parameter; performing hierarchical calculation on the charging safety control parameters to obtain a dynamic safety level, generating an overcharge protection signal and an overcurrent protection signal by using the dynamic safety level, and performing data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy; and generating a charging module control instruction according to the real-time protection strategy, and controlling the charging process of the charging module based on the charging module control instruction. Through hierarchical calculation of charging safety control parameters, a charging module control instruction is generated to control a charging process, and the problems of low charging efficiency and poor safety in a scene with a complex charging environment and dynamic demand change are solved.

Description

Technical Field

[0001] This application relates to the technical field of charging control, and particularly to an intelligent control method and system for high-power charging modules. Background Art

[0002] With the popularization of electric vehicles and intelligent devices, the market demand for efficient, safe and intelligent charging modules is increasing day by day. High-power charging technology has become an important support for electric transportation and portable devices. Especially in the era of fast charging, the optimization of technology is particularly crucial. However, the complexity of charging modules and the improvement of performance requirements also pose higher requirements for their control methods, including accuracy, dynamic adaptability and safety.

[0003] In related technical means, charging control methods are usually designed based on fixed-parameter current-voltage control (CC-CV mode). These methods adjust the charging voltage and current by presetting a series of control thresholds and optimizing the charging curve, so as to achieve the balance effect of charging speed and battery life. At the same time, some systems introduce overvoltage protection and overcurrent protection functions to avoid safety hazards caused by overcharging. These existing technologies can basically achieve the control and protection of the charging process.

[0004] For the above technical solutions, although stable and efficient charging control can be achieved through fixed-parameter control and simple protection mechanisms, in scenarios with complex charging environments and dynamic changes in requirements, the existing technologies have problems of insufficient regulation accuracy and poor adaptability. This limitation leads to problems such as decreased efficiency or safety hazards during the charging process. Summary of the Invention

[0005] In order to improve the problems of low charging efficiency and poor safety in scenarios with complex charging environments and dynamic changes in requirements, this application provides an intelligent control method and system for high-power charging modules.

[0006] The present invention provides an intelligent control method for a high-power charging module, including: obtaining a charging state sequence of the charging module, performing feature extraction and pattern recognition processing on the charging state sequence to obtain a charging state feature matrix; constructing a voltage mapping relationship and a current mapping relationship based on the charging state feature matrix, calculating the voltage mapping relationship and the current mapping relationship to obtain a charging regulation parameter; performing time series prediction analysis on the charging regulation parameter to obtain a predicted charging adjustment parameter and a predicted safety threshold, generating a charging control instruction based on the predicted charging adjustment parameter, using the charging control instruction to correct the predicted safety threshold, and generating a corresponding charging safety control parameter based on the corrected safety threshold; performing hierarchical calculation on the charging safety control parameter to obtain a dynamic safety level, generating an overcharge protection signal and an overcurrent protection signal using the dynamic safety level, performing data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy; generating a charging module control instruction according to the real-time protection strategy, and controlling the charging process of the charging module based on the charging module control instruction.

[0007] As a preferred solution, the step of obtaining a charging state sequence of the charging module, performing feature extraction and pattern recognition processing on the charging state sequence to obtain a charging state feature matrix includes: obtaining real-time voltage data, real-time current data and temperature data of the charging module, and constructing an initial charging state sequence based on the real-time voltage data, the real-time current data and the temperature data. Constructing a charging state hierarchical sequence based on the data distribution law of the initial charging state sequence, performing time series optimization on the charging state hierarchical sequence to obtain a charging state time series matrix; performing data dimensionality reduction analysis on the charging state time series matrix to obtain a set of charging key features, performing feature mapping calculation on the charging state time series matrix using the set of charging key features to obtain an initial state feature matrix; performing data correlation analysis on the initial state feature matrix to obtain a feature correlation parameter, and constructing a feature relationship topology graph using the feature correlation parameter; performing graph decomposition on the feature relationship topology graph to obtain a charging mode classification result and a feature classification weight, and updating the initial state feature matrix using the charging mode classification result and the feature classification weight to obtain a charging state feature matrix.

[0008] As a preferred solution, the steps of constructing a charging state hierarchical sequence based on the data distribution law of the initial charging state sequence and performing timing optimization on the charging state hierarchical sequence to obtain a charging state timing matrix include: obtaining historical data and real-time data of the initial charging state sequence, performing feature alignment on the historical data and the real-time data to obtain a data alignment sequence, dividing the data alignment sequence into time windows, and classifying and labeling the initial charging state sequence based on the time window division result to obtain a hierarchical charging state sequence; calculating data aggregation parameters based on the statistical distribution characteristics of the hierarchical charging state sequence, and using the data aggregation parameters to perform hierarchical aggregation on the hierarchical charging state sequence to obtain a charging state hierarchical sequence; sorting data using the timestamp information of the charging state hierarchical sequence, calculating charging state timing distribution parameters based on the data sorting result, and constructing a time series feature mapping relationship according to the charging state timing distribution parameters; performing time compensation calculation on the charging state hierarchical sequence according to the time series feature mapping relationship to obtain a time-compensated charging state sequence, and calculating a charging state correlation matrix based on the time-compensated charging state sequence; performing feature fusion calculation on the charging state correlation matrix, and performing timing optimization on the time-compensated charging state sequence based on the feature fusion calculation result to obtain a charging state timing matrix.

[0009] As a preferred solution, the steps of constructing a voltage mapping relationship and a current mapping relationship based on the charging state feature matrix and calculating charging regulation parameters by calculating the voltage mapping relationship and the current mapping relationship include: analyzing the voltage change characteristics in different charging stages using the charging state feature matrix to obtain a voltage characteristic curve and a current characteristic curve, and constructing a voltage mapping relationship and a current mapping relationship based on the voltage characteristic curve and the current characteristic curve; calculating charging stage regulation parameters using the voltage mapping relationship and the current mapping relationship to obtain charging regulation parameters.

[0010] As a preferred solution, the steps of performing time series prediction analysis on the charging regulation parameters to obtain predicted charging adjustment parameters and a predicted safety threshold, generating a charging control instruction based on the predicted charging adjustment parameters, using the charging control instruction to correct the predicted safety threshold, and generating corresponding charging safety control parameters based on the corrected safety threshold include: obtaining historical charging data of the charging regulation parameters, constructing a charging strategy time series based on the historical charging data, and performing trend decomposition on the charging strategy time series to obtain a charging trend parameter and a charging fluctuation parameter; using the charging trend parameter and the charging fluctuation parameter to perform prediction calculation on the charging strategy time series to obtain a charging prediction trend curve and a charging short-term fluctuation curve, and generating predicted charging adjustment parameters based on the charging prediction trend curve and the charging short-term fluctuation curve; calculating a charging safety range based on the predicted charging adjustment parameters to obtain an initial charging safety threshold and a dynamic charging safety threshold, and performing comprehensive calculation on the initial charging safety threshold and the dynamic charging safety threshold to obtain a predicted safety threshold; generating a charging control instruction using the predicted charging adjustment parameters, and correcting the predicted safety threshold based on the charging control instruction, and generating corresponding charging safety control parameters based on the corrected safety threshold.

[0011] As a preferred solution, the steps of performing hierarchical calculation on the charging safety control parameters to obtain a dynamic safety level, generating an overcharge protection signal and an overcurrent protection signal using the dynamic safety level, and performing data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy include: performing hierarchical calculation on the charging safety control parameters to obtain a safety level parameter and a charging anomaly determination parameter, and calculating a dynamic safety level based on the safety level parameter and the charging anomaly determination parameter; constructing a charging protection decision matrix based on the dynamic safety level, calculating an overcharge protection factor and an overcurrent protection factor according to the charging protection decision matrix, generating an overcharge protection signal using the overcharge protection factor, and generating an overcurrent protection signal using the overcurrent protection factor; constructing a safety control mapping relationship through the overcharge protection signal and the overcurrent protection signal, performing feature calculation on the safety control mapping relationship to obtain a safety control adjustment parameter, and calculating a charging protection strategy based on the safety control adjustment parameter to obtain a real-time protection strategy.

[0012] As a preferred solution, the step of generating a charging module control instruction according to the real-time protection policy and controlling the charging process of the charging module based on the charging module control instruction includes: generating charging safety control parameters according to the real-time protection policy, calculating charging protection adjustment parameters based on the charging safety control parameters, and generating an initial charging safety policy by using the charging protection adjustment parameters; using the initial charging safety policy to adjust the charging process of the charging module in real time to obtain charging dynamic control parameters and charging terminal protection parameters, and calculating a target charging safety policy based on the charging dynamic control parameters and the charging terminal protection parameters; generating a charging module control instruction by using the target charging safety policy, and controlling the charging process of the charging module based on the charging module control instruction.

[0013] The present application also provides an intelligent control system for a high-power charging module, including: an acquisition module, configured to acquire a charging state sequence of the charging module, perform feature extraction and pattern recognition processing on the charging state sequence to obtain a charging state feature matrix; an adjustment module, configured to construct a voltage mapping relationship and a current mapping relationship based on the charging state feature matrix, calculate the voltage mapping relationship and the current mapping relationship to obtain charging regulation parameters; an analysis module, configured to perform time series prediction analysis on the charging regulation parameters to obtain predicted charging adjustment parameters and a predicted safety threshold, generate a charging control instruction based on the predicted charging adjustment parameters, correct the predicted safety threshold by using the charging control instruction, and generate corresponding charging safety control parameters based on the corrected safety threshold; a calculation module, configured to perform hierarchical calculation on the charging safety control parameters to obtain a dynamic safety level, generate an overcharge protection signal and an overcurrent protection signal by using the dynamic safety level, and perform data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection policy; a control module, configured to generate a charging module control instruction according to the real-time protection policy, and control the charging process of the charging module based on the charging module control instruction.

[0014] Compared with the prior art, the present application has the following beneficial effects: fast charging efficiency and high safety. By performing feature extraction and pattern recognition on the charging state sequence of the charging module, a charging state feature matrix is generated; a voltage mapping relationship and a current mapping relationship are constructed by using the charging state feature matrix, so as to calculate charging regulation parameters and a predicted safety threshold, and a charging safety control parameter is formed by correcting the predicted safety threshold by generating a charging control instruction; by hierarchically calculating the charging safety control parameters, a dynamic safety level is generated to trigger an overcharge protection signal and an overcurrent protection signal, and a charging module control instruction is generated by means of a real-time protection policy to control the charging process, thereby improving the problems of low charging efficiency and poor safety in scenarios with complex charging environments and dynamically changing demands. Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] The structures, proportions, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical substantial significance. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0017] Figure 1 is a schematic flowchart of the intelligent control method for a high-power charging module provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of the structure of the intelligent control system for a high-power charging module provided by an embodiment of the present invention.

[0018] Explanation of reference numerals: 10. Intelligent control system for high-power charging module; 11. Acquisition module; 12. Adjustment module; 13. Analysis module; 14. Calculation module; 15. Control module. Detailed implementation manners

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0020] The flowchart shown in the drawings is only an example illustration, and does not necessarily include all the content and operations / steps, nor does it necessarily execute in the described order. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may change according to the actual situation.

[0021] It should also be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0022] It should also be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and through specific embodiments.

[0024] Embodiment 1: As Figure 1 shown, this application provides an intelligent control method for a high-power charging module, including step S100 to step S500.

[0025] Step S100: Obtain the charging status sequence of the charging module, perform feature extraction and pattern recognition processing on the charging status sequence, and obtain a charging status feature matrix.

[0026] In this step, by collecting real-time data during the operation of the charging module, a charging status sequence is formed. Specifically, the charging status sequence includes parameters such as voltage, current, and temperature. By using a multi-scale feature extraction algorithm to process each parameter in the charging status sequence, key features affecting the charging performance are extracted, and pattern recognition technology is used to classify and identify these features, thereby forming a charging status feature matrix.

[0027] For example, when a certain charging module operates in different temperature environments, the temperature parameter in the generated charging status sequence will be summarized into a specific pattern through the feature extraction algorithm, and combined with the voltage parameter to form a corresponding charging status feature matrix.

[0028] Step S200: Based on the charging status feature matrix, construct a voltage mapping relationship and a current mapping relationship, calculate the voltage mapping relationship and the current mapping relationship, and obtain a charging regulation parameter.

[0029] In this step, by analyzing the feature data in the charging status feature matrix, a voltage mapping relationship and a current mapping relationship are constructed in combination with the voltage change trend and current adjustment rule of the charging module. Specifically, multi-dimensional calculations are performed on the voltage mapping relationship and the current mapping relationship to generate a charging regulation parameter.

[0030] For example, at the initial stage of charging of a certain electric vehicle, the charging status feature matrix shows that the voltage change trend and current increase are non-linearly related. Based on the voltage mapping relationship and current mapping relationship constructed from this data, the charging regulation parameter obtained through calculation is dynamically adjusted in the later stage of charging to maximize the charging efficiency.

[0031] Step S300: Conduct time series prediction analysis on the charging control parameters to obtain predicted charging adjustment parameters and a predicted safety threshold. Generate a charging control instruction based on the predicted charging adjustment parameters, use the charging control instruction to correct the predicted safety threshold, and generate corresponding charging safety control parameters based on the corrected safety threshold.

[0032] In this step, by applying a time series prediction model, analyze the charging control parameters to predict future charging adjustment parameters and safety thresholds. Specifically, the predicted charging adjustment parameters are dynamically updated according to the voltage mapping relationship and current mapping relationship, while the predicted safety threshold is corrected based on the environmental parameters and device status of the charging module, and finally charging safety control parameters are generated.

[0033] For example, when the external temperature of a certain charging module suddenly rises during operation, its predicted safety threshold will be updated in real time to reflect the environmental change, and the charging control instruction will also adjust the operating parameters of the charging module simultaneously to adapt to the new environment.

[0034] Step S400: Perform hierarchical calculation on the charging safety control parameters to obtain a dynamic safety level. Generate overcharge protection signals and overcurrent protection signals using the dynamic safety level, and perform data mapping calculation on the overcharge protection signals and overcurrent protection signals to obtain a real-time protection strategy.

[0035] In this step, by performing hierarchical analysis on the charging safety control parameters, divide the dynamic safety level according to its safety level. Specifically, the dynamic safety level is used to generate overcharge protection signals and overcurrent protection signals, and further analyze and transform the protection signals through data mapping technology to form a real-time protection strategy to ensure the safety of the charging process.

[0036] For example, when the dynamic safety level reaches a high-risk state, the real-time protection strategy will quickly trigger the overcurrent protection signal and correct the operating parameters of the charging module to prevent damage.

[0037] Step S500: Generate a charging module control instruction according to the real-time protection strategy, and control the charging process of the charging module based on the charging module control instruction.

[0038] In this step, generate a control instruction for the charging module through the real-time protection strategy to adjust the operating parameters during the charging process. Specifically, this instruction combines the charging control parameters and the dynamic safety level of the charging module to ensure the dual goals of charging efficiency and safety.

[0039] For example, during the charging process, the control instruction generated by the real-time protection strategy enables the charging module to perform dynamic adjustment under high-power operation, avoiding overcharging and improving the stability of the charging process.

[0040] In this embodiment, by obtaining the charging status sequence of the charging module, feature extraction and pattern recognition processing are performed on the charging status sequence to generate a charging status feature matrix; then, a voltage mapping relationship and a current mapping relationship are constructed based on the charging status feature matrix, and charging regulation parameters are generated through the calculation of the voltage mapping relationship and the current mapping relationship; subsequently, time series prediction analysis is carried out on the charging regulation parameters to obtain predicted charging adjustment parameters and a predicted safety threshold, and a charging control instruction is generated based on the predicted charging adjustment parameters. At the same time, the predicted safety threshold is corrected by using the charging control instruction, and corresponding charging safety control parameters are generated based on the corrected safety threshold; hierarchical calculation is performed on the charging safety control parameters to generate a dynamic safety level, and overcharge protection signals and overcurrent protection signals are generated according to the dynamic safety level; finally, through data mapping calculation on the overcharge protection signals and the overcurrent protection signals, a real-time protection strategy is generated, and a charging module control instruction is generated based on the real-time protection strategy to precisely control the charging process of the charging module.

[0041] Through the above workflow, the technical solution realizes high-precision dynamic regulation of the charging module, ensuring that the efficiency and safety of the charging process are significantly improved. The feature extraction and pattern recognition of the charging status sequence improve the system's perception ability, enabling the charging regulation parameters to adapt to the dynamic changes of the charging environment; the time series analysis of the predicted charging adjustment parameters and the predicted safety threshold enhances the system's foresight and intelligence; the generation of the real-time protection strategy and the dynamic safety level further ensure the stability and safety of the charging module, thus effectively solving the problems of insufficient regulation accuracy and poor adaptability in the prior art.

[0042] Embodiment 2: In step S100, real-time voltage data, real-time current data, and temperature data of the charging module are obtained, and an initial charging status sequence is constructed based on the real-time voltage data, real-time current data, and temperature data.

[0043] By collecting the real-time voltage data, real-time current data, and temperature data during the operation of the charging module, the data acquisition module monitors the working status of the charging module in real time and forms an initial charging status sequence. Specifically, the real-time voltage data reflects the voltage change trend of the charging module during the charging process, the real-time current data monitors the dynamic fluctuations of the current, and the temperature data is the monitoring result of the ambient temperature and the internal temperature of the module. These data are integrated in units of timestamps to construct an initial charging status sequence to ensure comprehensive data support for the charging process.

[0044] For example, when the charging module performs fast charging, the real-time voltage data collected shows that the module voltage changes in a periodic oscillation state, while the real-time current data shows a relatively stable linear growth trend, and the temperature data records the stability of the external environment temperature and the dynamic changes of the internal temperature of the module. By integrating these data into the initial charging state sequence, the current working state of the charging module and the external environmental conditions can be accurately reflected, providing basic support for subsequent data processing and optimization.

[0045] Based on the data distribution law of the initial charging state sequence, a hierarchical charging state sequence is constructed, and the hierarchical charging state sequence is optimized in time series to obtain a charging state time series matrix.

[0046] By analyzing the distribution laws of the historical data and real-time data of the initial charging state sequence, and combining the method of hierarchical processing, the data features of each item are extracted hierarchically to construct a hierarchical charging state sequence. Specifically, the sequence is hierarchically optimized using the long-term trend of historical data and the short-term dynamic changes of real-time data, and a time series processing algorithm is introduced to uniformly adjust the time information in the hierarchical charging state sequence to form a charging state time series matrix, providing a standardized data input for subsequent optimization calculations.

[0047] For example, analyzing the historical data of the initial charging state sequence reveals that the voltage change has a daily periodic fluctuation, while the real-time data shows a large dynamic change trend of the instantaneous voltage of the charging module. By classifying the voltage data and other monitoring data hierarchically and optimizing the time series in combination with timestamps, a charging state time series matrix is formed, providing a clear structured input for data analysis.

[0048] Perform data dimensionality reduction analysis on the charging state time series matrix to obtain a set of key charging features, and use the set of key charging features to perform feature mapping calculation on the charging state time series matrix to obtain an initial state feature matrix.

[0049] By using the principal component analysis (PCA) and factor analysis (FA) methods to perform data dimensionality reduction processing on the charging state time series matrix, the features with high correlations are merged, and the key features that can significantly affect the charging performance are screened out to obtain a set of key charging features. At the same time, the remaining information is extracted as a set of charging auxiliary features. Specifically, the multi-dimensional features of the charging state time series matrix are standardized, the importance of each feature is evaluated using a statistical model, and a set of key charging features is established based on the dimensionality reduction results. Then, based on the set of key charging features, a mapping rule is constructed to compress the high-dimensional space features of the time series matrix into low-dimensional structured features, and finally an initial state feature matrix is obtained, providing an efficient feature expression form for subsequent calculations.

[0050] For example, the charging state time series matrix contains multiple features such as voltage, current, temperature, and charging time. Through dimensionality reduction analysis, it is found that the voltage fluctuation amplitude and current change rate are the main influencing factors of charging performance. These two parameters are used as the key charging feature set, while temperature changes and charging time are classified as the auxiliary charging feature set. The key feature set is used to perform mapping calculations on the time series matrix to form an initial state feature matrix containing important features of voltage and current.

[0051] Based on the initial state feature matrix, data correlation analysis is carried out to obtain feature correlation parameters, and the feature correlation parameters are used to construct a feature relationship topology graph.

[0052] By performing statistical correlation and causal relationship analysis on each feature in the initial state feature matrix, the correlation parameters between features are calculated, including Pearson correlation coefficient and partial correlation coefficient, etc., so as to quantify the dependence relationship between charging features. Specifically, bivariate and multivariate interaction analysis is carried out for key features such as voltage and current and auxiliary features such as temperature to obtain the correlation parameters describing the interaction strength of features. Based on these correlation parameters, a graph theory method is used to construct a feature relationship topology graph to intuitively represent the complex connections between each charging feature.

[0053] For example, by analyzing the initial state feature matrix, it is found that voltage and current show a high positive correlation, and at the same time, temperature has an indirect effect on the change of voltage. According to the calculation results, a feature relationship topology graph is constructed, where the nodes represent features and the weights of the edges represent the association strength. Through this graph, the internal connections of each parameter in the charging state can be clearly understood.

[0054] The feature relationship topology graph is decomposed to obtain the charging mode classification result and feature classification weight. The initial state feature matrix is updated using the charging mode classification result and feature classification weight to obtain the charging state feature matrix.

[0055] By decomposing the feature relationship topology graph using spectral clustering algorithm and modular analysis method, the charging modes and feature classification rules hidden in the topological structure are mined. Specifically, the feature relationship topology graph is divided into different subgraphs, each subgraph represents a charging mode, and the feature classification weight is calculated according to the weight information of the subgraph nodes, and the core feature categories affecting charging efficiency and safety are refined. Then, the initial state feature matrix is rearranged and updated using the charging mode classification result and feature classification weight to form the charging state feature matrix, providing high-quality data support for subsequent charging control parameters.

[0056] For example, through spectral decomposition analysis, three typical charging modes are identified: fast charging mode, stable charging mode, and safety protection mode. At the same time, the characteristic weights of voltage and current in each mode are calculated, and the initial state characteristic matrix is updated using these results to obtain a more accurate charging state characteristic matrix for dynamically regulating the charging process.

[0057] Among them, the steps of constructing a hierarchical charging state sequence based on the data distribution law of the initial charging state sequence and performing temporal optimization on the hierarchical charging state sequence to obtain a charging state temporal matrix include: obtaining the historical data and real-time data of the initial charging state sequence, performing feature alignment on the historical data and real-time data to obtain a data alignment sequence, dividing the data alignment sequence into time windows, and classifying and labeling the initial charging state sequence based on the time window division result to obtain a hierarchical charging state sequence.

[0058] Through a comprehensive analysis of the initial charging state sequence, the historical data and real-time data are integrated onto a unified time axis to achieve feature alignment. Specifically, the dynamic time warping (DTW) algorithm is used to align the historical data and real-time data to ensure the synchronization and consistency of the two data sets on the time axis, generating a data alignment sequence. Subsequently, the sliding window technique is applied to divide the data alignment sequence into multiple time windows, and the feature data within each time window is classified and labeled according to its category and change trend, thereby obtaining a hierarchical charging state sequence to ensure the hierarchical and temporal nature of the charging state features.

[0059] For example, the historical data shows that the voltage has an obvious linear growth trend at the initial stage of charging, while the real-time data shows that the current has slight fluctuations in the same stage. Through the feature alignment process, the voltage and current are synchronized onto a unified time axis, and this time range is divided into multiple windows, such as a 5-minute time window, and finally a hierarchical charging state sequence labeled with the "initial charging" state is generated.

[0060] Calculate the data aggregation parameters based on the statistical distribution characteristics of the hierarchical charging state sequence, and use the data aggregation parameters to perform hierarchical aggregation on the hierarchical charging state sequence to obtain a charging state hierarchical sequence.

[0061] By analyzing the statistical distribution characteristics of each feature in the hierarchical charging state sequence, such as mean, variance, peak value, etc., parameters describing the degree of data aggregation are extracted, such as the hierarchical center point based on clustering analysis and the inter-layer correlation coefficient. Specifically, the clustering algorithm is used for hierarchical classification of the hierarchical charging state sequence, and the sequence features of different charging states are dynamically grouped according to the data aggregation parameters to generate a hierarchical data structure and form a charging state hierarchical sequence to improve the efficiency and reliability of data processing.

[0062] For example, in the hierarchical charging state sequence, the cluster analysis of voltage data shows that there are three typical levels: low voltage, medium voltage, and high voltage. By calculating the distribution center points and correlation degrees of these levels, a hierarchical sequence of charging states with high resolution is successfully generated, providing a solid foundation for subsequent analysis and optimization.

[0063] Sort the data using the timestamp information of the hierarchical charging state sequence, and calculate the timing distribution parameters of the charging state based on the sorted data results. Construct a time series feature mapping relationship according to the timing distribution parameters of the charging state.

[0064] By sorting each record entry in the hierarchical charging state sequence from earliest to latest timestamp, the data shows a natural timing. Then, through statistical analysis of the sorted data, the timing distribution parameters of the charging state are generated, including the mean value, change rate, and peak time of the time interval, etc. Specifically, combined with the characteristic change trend during the operation of the charging module, a time series feature mapping relationship diagram is drawn according to the timing distribution parameters, intuitively reflecting the correlation strength and change law between the characteristics of different charging stages.

[0065] For example, the voltage and current data in a certain time period show obvious symmetry after sorting. Using the timing distribution parameters, it is found that the charging state reaches its peak at 20 minutes. By constructing the time series feature mapping relationship, it clearly shows the effect of current growth on voltage fluctuation, facilitating subsequent strategy adjustment.

[0066] Perform time compensation calculation on the hierarchical charging state sequence according to the time series feature mapping relationship to obtain a time-compensated charging state sequence, and calculate the charging state correlation matrix based on the time-compensated charging state sequence.

[0067] Perform time compensation calculation on the time drift or missing data in the time series feature mapping relationship to correct the inconsistent information in the hierarchical charging state sequence. Specifically, an interpolation algorithm is used to estimate the interval void points in the time series data, and an extrapolation method is used to adjust the abnormal points, thereby generating a time-compensated charging state sequence. In addition, by calculating the charging state correlation matrix based on the time-compensated charging state sequence, the mutual influence and connection between different state characteristics are revealed, providing a reference basis for further optimization.

[0068] For example, when there is a sampling missing in the current data of a charging process, the interpolation method is used to compensate for the missing value. By calculating the correlation matrix, it is found that the current fluctuation has a significant impact on voltage regulation, thereby further improving the description information of the charging state.

[0069] Perform feature fusion calculation on the charging state correlation matrix, and perform timing optimization on the time-compensated charging state sequence based on the feature fusion calculation results to obtain a charging state timing matrix.

[0070] By performing feature fusion calculations on the data in the charging state correlation matrix, integrating multi-dimensional features and removing redundant information, the overall expression ability of the charging data is improved. Specifically, by using the weighted average method and the principal component analysis method to perform dimensionality reduction processing on the features in the correlation matrix, the time-compensated charging state sequence is optimized into a charging state time series matrix with high precision and strong continuity.

[0071] For example, it is found in the charging state correlation matrix that there is a strong correlation between voltage fluctuations and temperature changes. By fusing the correlation data of these two features and mapping the optimization results to the charging state time series matrix, a foundation is laid for further intelligent charging regulation.

[0072] In step S200, the voltage change characteristics in different charging stages are analyzed using the charging state feature matrix to obtain the voltage characteristic curve and the current characteristic curve, and the voltage mapping relationship and the current mapping relationship are constructed based on the voltage characteristic curve and the current characteristic curve.

[0073] Each variable of the voltage characteristic curve and the current characteristic curve is fitted through non-linear regression analysis to generate a charging state adjustment factor to correct the voltage or current deviation caused by environmental changes, and at the same time calculate the error compensation parameter to improve the fitting accuracy and system reliability. Specifically, the voltage mapping relationship is adjusted according to the fitting model to make it closer to the actual voltage characteristics; at the same time, the current mapping relationship is optimized to reduce the fluctuation problem that occurs during high-power charging, thereby forming an accurate mapping model.

[0074] For example, in a certain actual test, the increase in the external temperature causes the voltage mapping relationship to deviate from the design value. It is corrected by the adjustment factor, and at the same time the measurement error is compensated. The finally generated voltage mapping relationship can accurately reflect the voltage change law under different temperature environments and ensure the stability of the charging process.

[0075] Calculate the charging stage regulation parameters using the voltage mapping relationship and the current mapping relationship to obtain the charging regulation parameters.

[0076] By integrating the voltage mapping relationship and the current mapping relationship into a unified calculation framework, the voltage and current during the charging process are dynamically regulated in stages. Specifically, calculate the initial charging regulation parameters to determine the optimal voltage and current values when starting charging, and introduce charging stage adjustment parameters to adapt to the changing requirements during the subsequent charging process, thereby optimizing the entire charging curve.

[0077] For example, in the initial stage of charging, the initial control parameters are set to a relatively high current to quickly increase the battery capacity. In the middle and later stages, the current is dynamically reduced by adjusting the parameters in stages to delay battery aging and avoid overheating. These control parameters can ensure the efficiency and safety of the entire charging process.

[0078] In step S300, historical charging data of the charging control parameters is obtained, a charging strategy time series is constructed based on the historical charging data, and the charging strategy time series is decomposed by trend to obtain charging trend parameters and charging fluctuation parameters.

[0079] Through a structured analysis of the historical charging data of the charging control parameters, each data feature is integrated into a charging strategy time series in chronological order. Specifically, the sliding window method is used to extract the features representing the long-term stable trend in the charging data, and the short-term fluctuation situation is refined, so as to decompose the charging trend parameters and charging fluctuation parameters. The charging trend parameters reflect the overall change law of the charging efficiency, while the charging fluctuation parameters reveal the minor instabilities in the charging process, providing basic data for the subsequent prediction model.

[0080] For example, by analyzing the historical charging data of a certain device, it is found that the long-term trend of the voltage shows a slow increase, while the short-term fluctuation frequency of the current is relatively high. Based on this, the generated trend parameters represent the change of voltage stability, and the fluctuation parameters quantify the dynamic impact of the current, providing a reliable basis for further predicting the charging strategy.

[0081] The charging strategy time series is predicted and calculated using the charging trend parameters and charging fluctuation parameters to obtain a charging prediction trend curve and a charging short-term fluctuation curve, and prediction charging adjustment parameters are generated based on the charging prediction trend curve and the charging short-term fluctuation curve.

[0082] By applying time series prediction algorithms, such as the ARIMA model and the LSTM neural network, the trend parameters and fluctuation parameters in the charging strategy time series are modeled and calculated to generate a charging prediction trend curve and a charging short-term fluctuation curve. These curves provide a basis for adjusting the charging strategy by predicting the future charging environment and performance. Specifically, the prediction trend curve depicts the development direction of long-term stability, while the short-term fluctuation curve reveals the local non-uniformity in the charging process. Prediction charging adjustment parameters are generated based on the combined data of the two to achieve precise optimization of the charging strategy.

[0083] For example, in a fast charging scenario, the prediction model shows that the future voltage trend curve will gradually approach the saturation value, while the short-term fluctuation curve indicates the random fluctuation of the current in the later stage of charging. Combining this information, the generated prediction charging adjustment parameters will optimize the voltage rise speed strategy and appropriately limit the current fluctuation amplitude.

[0084] Based on the predicted charging adjustment parameters, calculate the charging safety range to obtain the initial charging safety threshold and the dynamic charging safety threshold, and comprehensively calculate the initial charging safety threshold and the dynamic charging safety threshold to obtain the predicted safety threshold.

[0085] By dynamically analyzing the predicted charging adjustment parameters and comprehensively considering the safety range under different charging conditions, gradually improve the initial charging safety threshold and the dynamic charging safety threshold to the predicted safety threshold. Specifically, the initial charging safety threshold provides a fixed safety baseline for the charging process, while the dynamic charging safety threshold reflects the impact of external environmental changes on safety through real-time data updates. The comprehensive calculation process integrates the advantages of both, laying the foundation for the stability and flexibility of the predicted safety threshold.

[0086] For example, in the case of rapid temperature changes, the initial threshold provides a basic temperature tolerance range, while the dynamic threshold further adjusts the adaptation strategy through real-time measurement, enabling the generated predicted safety threshold to fully adapt to the current environmental conditions, thus ensuring the safety of the charging process.

[0087] Generate a charging control instruction using the predicted charging adjustment parameters, and correct the predicted safety threshold based on the charging control instruction, and generate corresponding charging safety control parameters based on the corrected safety threshold.

[0088] By inputting the predicted charging adjustment parameters into the charging control system, generate real-time charging control instructions to gradually adjust various safety thresholds during the charging process to form accurate charging safety control parameters. Specifically, the charging control instruction corrects the predicted safety threshold according to real-time requirements to adapt to the changing charging environment and improve charging efficiency and safety. The final safety control parameters integrate the latest charging status characteristics, providing an important basis for the implementation of subsequent protection strategies.

[0089] For example, in the high-power charging mode, the real-time generated charging control instruction fine-tunes the predicted safety threshold to adapt to the suddenly increased load of the device, thus forming stable and reliable charging safety control parameters, effectively avoiding potential overheating or overcurrent problems.

[0090] In step S400, perform hierarchical calculation on the charging safety control parameters to obtain the safety level parameters and the charging anomaly determination parameters, and calculate the dynamic safety level based on the safety level parameters and the charging anomaly determination parameters.

[0091] By performing multi-dimensional analysis on the charging safety control parameters, combining the operating status of the charging module with external environmental parameters, the safety level parameters and charging anomaly determination parameters are gradually classified and calculated. Specifically, the safety level parameters are used to indicate the safety status of the charging module at different stages, including low-risk, medium-risk, and high-risk levels; the charging anomaly determination parameters detect abnormal situations during the charging process in real time through threshold determination technology, such as current overload or voltage drift. The comprehensive calculation of the two generates a dynamic safety level, which intuitively reflects the safety status of the current charging process.

[0092] For example, in the initial stage of the charging process, the safety level parameters indicate a relatively low risk, but when the current rises rapidly, the charging anomaly determination parameters prompt an overcurrent problem. The finally calculated dynamic safety level indicates that the charging module has entered a medium-risk state, and protective measures need to be taken in a timely manner.

[0093] Based on the dynamic safety level, a charging protection decision matrix is constructed. The overcharge protection factor and overcurrent protection factor are calculated according to the charging protection decision matrix. The overcharge protection signal is generated using the overcharge protection factor, and the overcurrent protection signal is generated using the overcurrent protection factor.

[0094] By utilizing the dynamic safety level, it is used as a key basis to construct a charging protection decision matrix, clarifying the protection measures for different charging risk levels. Specifically, the overcharge protection factor and overcurrent protection factor are calculated according to the rules in the decision matrix. The overcharge protection factor evaluates the impact of excessive voltage on charging safety, while the overcurrent protection factor analyzes the potential risk of current overload. These factors generate protection signals through real-time monitoring, triggering the overcharge protection signal and overcurrent protection signal respectively, providing an important guarantee for the safety of the charging process.

[0095] For example, when the charging protection decision matrix detects that the voltage exceeds the preset threshold, the overcharge protection factor is activated, generating an overcharge protection signal to slow down the voltage rise speed in a timely manner to avoid overcharging problems; similarly, when the current experiences an instantaneous overload situation, the overcurrent protection factor generates an overcurrent protection signal to limit the current flow to protect the safety of the charging module and the battery.

[0096] A safety control mapping relationship is constructed through the overcharge protection signal and overcurrent protection signal. Feature calculations are performed on the safety control mapping relationship to obtain safety control adjustment parameters. Based on the safety control adjustment parameters, a charging protection strategy is calculated to obtain a real-time protection strategy.

[0097] By combining the overcharge protection signal and the overcurrent protection signal, a safety control mapping relationship of the charging module is established to clarify the association between each protection signal and the operating parameters of the charging module. Specifically, feature calculation technology is used to extract the core information in the safety control mapping relationship, generate safety control adjustment parameters, and accordingly formulate a charging protection strategy to achieve real-time protection of the charging module. The real-time protection strategy effectively improves the safety and stability of the charging process by dynamically analyzing the protection signals.

[0098] For example, in a certain environment, when the charging module triggers the overcurrent protection signal due to a rapid increase in the external temperature, adjustment parameters are generated through the constructed safety control mapping relationship to optimize the current curve of the module, and at the same time, the voltage change rate is adjusted, and finally a real-time protection strategy is formed, thereby effectively avoiding the overcurrent risk and improving the stability of the module.

[0099] In step S500, charging safety control parameters are generated according to the real-time protection strategy, charging protection adjustment parameters are calculated based on the charging safety control parameters, and an initial charging safety strategy is generated using the charging protection adjustment parameters.

[0100] By applying the real-time protection strategy to the control system of the charging module, charging safety control parameters are generated, and based on these parameters, the charging protection adjustment parameters are further calculated. Specifically, the charging protection adjustment parameters are used to correct the key characteristic values of the safety strategy to generate an initial charging safety strategy, ensuring that the protection requirements of the charging module in each stage are met. The initial charging safety strategy integrates the state characteristics of the charging module and provides precise guidance for subsequent control.

[0101] For example, after the charging module enters the high-power mode, the real-time protection strategy provides a safe range of the charging voltage. The safety control parameters further optimize this range through a feedback mechanism. After adjusting the parameters, an initial charging safety strategy is generated to achieve comprehensive protection for high-power charging.

[0102] The charging process of the charging module is adjusted in real time using the initial charging safety strategy to obtain charging dynamic control parameters and charging terminal protection parameters, and a target charging safety strategy is calculated based on the charging dynamic control parameters and the charging terminal protection parameters.

[0103] By applying the initial charging safety strategy in real time, the operating parameters of the charging module are refined and adjusted to generate charging dynamic control parameters and charging terminal protection parameters. Specifically, the dynamic control parameters optimize the voltage and current curves of the module in real time, while the terminal protection parameters ensure the safety of the module at the end of charging. Combining the two, a target charging safety strategy for the entire charging process is calculated. The target charging safety strategy is continuously optimized through a feedback mechanism to ensure the stable operation of the charging process.

[0104] For example, in the later stage of the charging module, the amplitude of current fluctuation increases, and the dynamic control parameters are adjusted to smooth the current, while the terminal protection parameter correction module corrects the voltage peak value when charging stops, and the ultimate target safety policy realizes the coordinated protection of the entire charging process.

[0105] Generate charging module control instructions using the target charging safety policy, and control the charging process of the charging module based on the charging module control instructions.

[0106] Generate control instructions specifically for the charging module through the target charging safety policy to guide the real-time management of the charging process. Specifically, the charging module control instructions combine the comprehensive adjustment strategies of voltage, current, and temperature to dynamically control the operation of the module, ensuring that the charging performance and safety reach the optimal effect.

[0107] For example, in the fast charging mode, the charging module control instructions synchronously optimize the voltage rise speed and current stability, achieving an improvement in charging efficiency, while avoiding overheating or overloading problems, and ensuring an efficient and safe charging process.

[0108] In this embodiment, through in-depth analysis of the real-time data and historical data of the charging module, an initial charging state sequence is constructed, and a hierarchical sequence is generated based on its distribution law. Subsequently, the hierarchical sequence is optimized in time series to form a charging state time series matrix, effectively integrating multi-dimensional charging characteristics. The dimensionality reduction analysis of the time series matrix further extracts key and auxiliary characteristics, combines the characteristic correlation parameters to construct a characteristic relationship topology graph, decomposes it to generate the charging mode classification result and characteristic weights, thereby updating and optimizing the charging state characteristic matrix. Based on the optimized characteristic matrix, the voltage and current characteristics of different charging stages are fitted and calculated to generate charging regulation parameters. Through the time series prediction model, the historical data of the charging regulation parameters is further analyzed to generate prediction adjustment parameters and prediction safety thresholds, and charging safety control parameters are generated based on the safety thresholds to provide support for subsequent protection strategies. In the hierarchical calculation of the safety control parameters, a dynamic safety level is introduced to generate a charging protection decision matrix, and the protection factor is calculated based on the matrix and overcharge and overcurrent protection signals are generated. Finally, based on the protection signals, a mapping relationship is constructed, safety control adjustment parameters are generated, and a real-time protection strategy is designed to optimize the operating state of the charging module in real time through the feedback mechanism. The entire embodiment adopts a multi-level characteristic analysis and dynamic regulation strategy, significantly improving the efficiency, intelligence, and safety of the charging process, and comprehensively solving the problems of insufficient adaptability and regulation accuracy in the prior art.

[0109] Embodiment 3: Such as Figure 2As shown in the figure, the present application also provides an intelligent control system 10 for a high-power charging module, including an acquisition module 11, an adjustment module 12, an analysis module 13, a calculation module 14, and a control module 15.

[0110] The acquisition module 11 is mainly used to acquire the charging status sequence of the charging module, perform feature extraction and pattern recognition processing on the charging status sequence, and obtain the charging status feature matrix.

[0111] The adjustment module 12 is mainly used to construct a voltage mapping relationship and a current mapping relationship based on the charging status feature matrix, calculate the voltage mapping relationship and the current mapping relationship, and obtain the charging regulation parameters.

[0112] The analysis module 13 is mainly used to perform time series prediction analysis on the charging regulation parameters, obtain the predicted charging adjustment parameters and the predicted safety threshold, generate a charging control instruction based on the predicted charging adjustment parameters, correct the predicted safety threshold using the charging control instruction, and generate corresponding charging safety control parameters based on the corrected safety threshold.

[0113] The calculation module 14 is mainly used to perform hierarchical calculation on the charging safety control parameters, obtain the dynamic safety level, generate an overcharge protection signal and an overcurrent protection signal using the dynamic safety level, and perform data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain the real-time protection strategy.

[0114] The control module 15 is mainly used to generate a charging module control instruction according to the real-time protection strategy, and control the charging process of the charging module based on the charging module control instruction.

[0115] In this embodiment, the acquisition module 11 monitors and collects the charging status sequence of the charging module in real time. By combining feature extraction and pattern recognition technologies, a charging status feature matrix is generated to provide data support for subsequent operations. The adjustment module 12 uses the charging status feature matrix to dynamically construct voltage mapping relationships and current mapping relationships, and generates charging regulation parameters through high-precision calculations to achieve efficient and intelligent charging regulation. The analysis module 13 deeply analyzes the charging regulation parameters through a time series prediction model, generates predicted charging adjustment parameters and predicted safety thresholds, and generates charging control instructions based on these data. After correcting the predicted safety thresholds, charging safety control parameters are further obtained to ensure the safety of the charging process. The calculation module 14 performs hierarchical calculations on the charging safety control parameters to generate a dynamic safety level, which is used to trigger overcharge protection signals and overcurrent protection signals, and generates a real-time protection strategy through mapping calculations of the protection signals, so as to quickly respond to emerging risks. The control module 15 generates charging module control instructions based on the real-time protection strategy and dynamically adjusts the working state of the charging module to ensure the safety and stability of the entire charging process. Through the collaborative work of the five major modules of acquisition, adjustment, analysis, calculation, and control, the entire system solves the deficiencies of existing charging modules in terms of dynamic adaptability, regulation accuracy, and safety guarantee, and significantly improves the efficiency and intelligence level of the charging system.

[0116] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the foregoing Embodiment 1 and will not be elaborated herein.

[0117] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have technical essence. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0118] The above is the case. The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent control method for a high-power charging module, characterized in that Including: Obtain the charging status sequence of the charging module, perform feature extraction and pattern recognition processing on the charging status sequence to obtain a charging status feature matrix; Based on the charging status feature matrix, construct a voltage mapping relationship and a current mapping relationship, calculate the voltage mapping relationship and the current mapping relationship to obtain a charging regulation parameter; Perform time series prediction analysis on the charging regulation parameter to obtain a predicted charging adjustment parameter and a predicted safety threshold, generate a charging control instruction based on the predicted charging adjustment parameter, use the charging control instruction to correct the predicted safety threshold, and generate a corresponding charging safety control parameter based on the corrected safety threshold; Perform hierarchical calculation on the charging safety control parameter to obtain a dynamic safety level, generate an overcharge protection signal and an overcurrent protection signal using the dynamic safety level, perform data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy; Generate a charging module control instruction according to the real-time protection strategy, and control the charging process of the charging module based on the charging module control instruction.

2. The intelligent control method of the high-power charging module according to claim 1, characterized in that The step of obtaining the charging status sequence of the charging module, performing feature extraction and pattern recognition processing on the charging status sequence to obtain a charging status feature matrix includes: Obtain the real-time voltage data, real-time current data and temperature data of the charging module, and construct an initial charging status sequence based on the real-time voltage data, the real-time current data and the temperature data; Construct a hierarchical charging status sequence based on the data distribution law of the initial charging status sequence, perform time series optimization on the hierarchical charging status sequence to obtain a charging status time series matrix; Perform data dimensionality reduction analysis on the charging status time series matrix to obtain a set of key charging features, and perform feature mapping calculation on the charging status time series matrix using the set of key charging features to obtain an initial status feature matrix; Perform data correlation analysis based on the initial status feature matrix to obtain a feature correlation parameter, and construct a feature relationship topology graph using the feature correlation parameter; Perform graph decomposition on the feature relationship topology graph to obtain a charging mode classification result and a feature classification weight, and update the initial status feature matrix using the charging mode classification result and the feature classification weight to obtain a charging status feature matrix.

3. The intelligent control method of the high-power charging module according to claim 2, wherein, The step of constructing a hierarchical charging status sequence based on the data distribution law of the initial charging status sequence, performing time series optimization on the hierarchical charging status sequence to obtain a charging status time series matrix includes: Obtain the historical data and real-time data of the initial charging status sequence, perform feature alignment on the historical data and the real-time data to obtain a data alignment sequence, perform time window division on the data alignment sequence, and classify and label the initial charging status sequence based on the time window division result to obtain a hierarchical charging status sequence; Calculate a data aggregation parameter based on the statistical distribution characteristics of the hierarchical charging status sequence, and perform hierarchical aggregation on the hierarchical charging status sequence using the data aggregation parameter to obtain a charging status hierarchical sequence; Sort the data using the timestamp information of the charging state hierarchical sequence, calculate the charging state timing distribution parameters based on the result of the data sorting, and construct a time series feature mapping relationship according to the charging state timing distribution parameters; Perform time compensation calculation on the charging state hierarchical sequence according to the time series feature mapping relationship to obtain a time-compensated charging state sequence, and calculate a charging state correlation matrix based on the time-compensated charging state sequence; Perform feature fusion calculation on the charging state correlation matrix, and perform timing optimization on the time-compensated charging state sequence based on the result of the feature fusion calculation to obtain a charging state timing matrix.

4. The intelligent control method of the high-power charging module according to claim 1, characterized in that, The step of constructing a voltage mapping relationship and a current mapping relationship based on the charging state feature matrix, and calculating charging regulation parameters for the voltage mapping relationship and the current mapping relationship includes: Analyze the voltage change characteristics in different charging stages using the charging state feature matrix to obtain a voltage characteristic curve and a current characteristic curve, and construct a voltage mapping relationship and a current mapping relationship based on the voltage characteristic curve and the current characteristic curve; Calculate charging stage regulation parameters using the voltage mapping relationship and the current mapping relationship to obtain charging regulation parameters.

5. The intelligent control method of the high-power charging module according to claim 1, characterized in that The step of performing time series prediction analysis on the charging regulation parameters to obtain predicted charging adjustment parameters and a predicted safety threshold, generating a charging control instruction based on the predicted charging adjustment parameters, correcting the predicted safety threshold using the charging control instruction, and generating corresponding charging safety control parameters based on the corrected safety threshold includes: Obtain the historical charging data of the charging regulation parameters, construct a charging strategy time series based on the historical charging data, and perform trend decomposition on the charging strategy time series to obtain a charging trend parameter and a charging fluctuation parameter; Perform prediction calculation on the charging strategy time series using the charging trend parameter and the charging fluctuation parameter to obtain a charging prediction trend curve and a charging short-term fluctuation curve, and generate predicted charging adjustment parameters based on the charging prediction trend curve and the charging short-term fluctuation curve; Calculate a charging safety range based on the predicted charging adjustment parameters to obtain an initial charging safety threshold and a dynamic charging safety threshold, and perform comprehensive calculation on the initial charging safety threshold and the dynamic charging safety threshold to obtain a predicted safety threshold; Generate a charging control instruction using the predicted charging adjustment parameters, correct the predicted safety threshold based on the charging control instruction, and generate corresponding charging safety control parameters based on the corrected safety threshold.

6. The intelligent control method of the high-power charging module according to claim 1, characterized in that The step of performing hierarchical calculation on the charging safety control parameters to obtain a dynamic safety level, generating an overcharge protection signal and an overcurrent protection signal using the dynamic safety level, and performing data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy includes: Perform hierarchical calculation on the charging safety control parameters to obtain a safety level parameter and a charging anomaly determination parameter, and calculate a dynamic safety level based on the safety level parameter and the charging anomaly determination parameter; Construct a charging protection decision matrix based on the dynamic security level, calculate an overcharge protection factor and an overcurrent protection factor according to the charging protection decision matrix, generate an overcharge protection signal using the overcharge protection factor, and generate an overcurrent protection signal using the overcurrent protection factor; Construct a safety control mapping relationship through the overcharge protection signal and the overcurrent protection signal, perform feature calculation on the safety control mapping relationship to obtain a safety control adjustment parameter, calculate a charging protection strategy based on the safety control adjustment parameter, and obtain a real-time protection strategy.

7. The intelligent control method of the high-power charging module according to claim 1, characterized in that, The step of generating a charging module control instruction according to the real-time protection strategy and controlling the charging process of the charging module based on the charging module control instruction includes: Generate a charging safety control parameter according to the real-time protection strategy, calculate a charging protection adjustment parameter based on the charging safety control parameter, and generate an initial charging safety strategy using the charging protection adjustment parameter; Use the initial charging safety strategy to perform real-time adjustment on the charging process of the charging module to obtain a charging dynamic control parameter and a charging terminal protection parameter, and calculate a target charging safety strategy based on the charging dynamic control parameter and the charging terminal protection parameter; Generate a charging module control instruction using the target charging safety strategy, and control the charging process of the charging module based on the charging module control instruction.

8. An intelligent control system for a high-power charging module, characterized in that, It includes: An acquisition module, configured to acquire a charging status sequence of a charging module, perform feature extraction and pattern recognition processing on the charging status sequence to obtain a charging status feature matrix; An adjustment module, configured to construct a voltage mapping relationship and a current mapping relationship based on the charging status feature matrix, perform calculations on the voltage mapping relationship and the current mapping relationship to obtain a charging regulation parameter; An analysis module, configured to perform time series prediction analysis on the charging regulation parameter to obtain a predicted charging adjustment parameter and a predicted safety threshold, generate a charging control instruction based on the predicted charging adjustment parameter, correct the predicted safety threshold using the charging control instruction, and generate a corresponding charging safety control parameter based on the corrected safety threshold; A calculation module, configured to perform hierarchical calculation on the charging safety control parameter to obtain a dynamic security level, generate an overcharge protection signal and an overcurrent protection signal using the dynamic security level, perform data mapping calculation on the overcharge protection signal and the overcurrent protection signal to obtain a real-time protection strategy; A control module, configured to generate a charging module control instruction according to the real-time protection strategy, and control the charging process of the charging module based on the charging module control instruction.

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