Fully immersion power charging method and system

Abstract

This invention relates to the field of power charging technology, and discloses a fully immersive power charging method and system, comprising: analyzing the charging characteristic information of the power device; matching the charging characteristic information with historical charging data in a preset charging strategy library to obtain charging matching data, and calculating the charging fit between the adapted charging strategy and the charging state parameters; analyzing the charging type of the power device, performing strategy retrieval on the adapted charging strategy to obtain the retrieved charging strategy, formulating a charging replenishment plan for the power device, analyzing the professional replenishment path in the charging replenishment plan, and generating charging control commands for the power device; locating the optimal charging path for the power device, detecting the real-time physical parameters of the charging medium of the power device, and calculating the energy conversion efficiency of the charging medium in the optimal charging path; and formulating a fully immersive charging scheme adapted to the power device. This invention can improve the efficiency of fully immersive power charging.

Description

Technical Field

[0001] This invention relates to a fully immersion power charging method and system, belonging to the field of power charging technology. Background Technology

[0002] Charging is crucial for the stable operation of devices. Currently, the charging field mainly employs technologies such as conventional contact charging, wireless inductive charging, and liquid-cooled assisted charging. Conventional contact charging achieves power transmission through a simple physical connection, but its charging interface is prone to wear and tear, posing safety hazards in humid or special environments. Furthermore, its charging efficiency is insufficient to meet the fast charging needs of high-power devices. Wireless inductive charging eliminates the constraints of cables and is convenient to use; however, it suffers from short transmission distances, low efficiency, and significant interference from foreign objects, making stable and efficient charging difficult. While liquid-cooled assisted charging can solve heat dissipation issues during charging and improve charging power to some extent, its complex and costly cooling system cannot comprehensively improve charging performance. Moreover, in situations with strict requirements for charging safety and spatial layout, existing charging technologies cannot meet the high-efficiency charging needs of devices in fully immersive environments. Therefore, a more efficient fully immersive power charging method is needed. Summary of the Invention

[0003] This invention provides a fully immersion power charging method and system, the main purpose of which is to improve the efficiency of fully immersion power charging.

[0004] To achieve the above objectives, the present invention provides a fully immersive power charging method, comprising:

[0005] Obtain the device parameter information of the power supply device to be charged, and analyze the charging characteristic information of the power supply device based on the device parameter information;

[0006] The charging feature information is matched with historical charging data in the preset charging strategy library to obtain charging matching data. The appropriate charging strategy in the charging matching data is queried, and the charging status parameters of the power device during the charging process are monitored in real time. The charging fit between the appropriate charging strategy and the charging status parameters is calculated.

[0007] Based on the charging compatibility, the charging type of the power device is analyzed. Based on the charging type, the adaptive charging strategy is retrieved to obtain the retrieved charging strategy, and the core charging operation points in the retrieved charging strategy are extracted.

[0008] Based on the core charging operation points, a charging replenishment plan for the power supply equipment is formulated, the professional replenishment paths in the charging replenishment plan are analyzed, and the charging replenishment criteria corresponding to the professional replenishment paths are queried. Based on the charging replenishment criteria, a charging control command for the power supply equipment is generated.

[0009] Based on the charging control command, the optimal charging path of the power supply device is located, the real-time physical parameters of the charging medium of the power supply device are detected, and the energy conversion efficiency of the charging medium in the optimal charging path is calculated based on the real-time physical parameters.

[0010] Record the charging waveform of the power supply device during the charging process, and combine the energy efficiency conversion rate and the charging waveform to formulate a fully immersive charging scheme adapted to the power supply device.

[0011] Optionally, the step of analyzing the charging characteristic information of the power supply device based on the device parameter information includes:

[0012] Identify the parameter tags corresponding to the device parameter information, and query the charging standard protocol and device identifier corresponding to the power supply device;

[0013] Analyze the tag coupling between the parameter tag and the charging standard protocol;

[0014] Based on the device identifier, analyze the device characteristics corresponding to the power supply device, and based on the device characteristics, analyze the tag logic relationship between the parameter tags;

[0015] By combining the tag coupling and the tag logical relationship, the charging characteristic information of the power supply device can be analyzed from the device parameter information.

[0016] Optionally, analyzing the tag coupling between the parameter tag and the charging standard protocol includes:

[0017] The parameter labels are extracted to obtain parameter label representations, and the label representation vectors corresponding to the parameter label representations are constructed.

[0018] The charging standard protocol is parsed to obtain the key terms of the protocol;

[0019] The key terms of the agreement are vectorized to obtain the key terms vector.

[0020] Combining the tag representation vector and the protocol key clause vector, the coupling coefficient between the parameter tag and the charging standard protocol is calculated using the following formula:

[0021] ;

[0022] Where A represents the coupling coefficient between the parameter tag and the charging standard protocol. This represents the label representation vector corresponding to the a-th label in the parameter labels. This represents the protocol key clause vector corresponding to the b-th protocol in the charging standard protocol, where a and b represent the parameter tag and the sequence number corresponding to the charging standard protocol, respectively, and m and n represent the number of parameter tags and the number of charging standard protocols, respectively.

[0023] Based on the coupling coefficient, the tag coupling between the parameter tag and the charging standard protocol is analyzed.

[0024] Optionally, calculating the charging fit between the adaptive charging strategy and the charging state parameters includes:

[0025] Query the charging timing sequence corresponding to the power supply device, and determine the charging transmission attribute corresponding to the power supply device based on the charging timing sequence.

[0026] Based on the charging transmission attributes, assign a state weight to each charging state in the charging state parameters.

[0027] Based on the adaptive charging strategy, determine the state setting value corresponding to each charging state in the charging state parameters;

[0028] By combining the charging state parameters, the state setting value, and the state weight, the charging fit between the adaptive charging strategy and the charging state parameters is calculated.

[0029] Optionally, the step of calculating the charging fit between the adaptive charging strategy and the charging state parameters by combining the charging state parameters, the state setting value, and the state weight includes:

[0030] ;

[0031] Where E represents the degree of fit between the adapted charging strategy and the charging state parameters. This represents the state weight of the d-th charging state in the charging state parameters. This represents the state setting value for the d-th charging state in the charging state parameters. This represents the actual measured value of the d-th charging state in the charging state parameters, where d represents the charging state sequence number of the charging state parameters, q represents the number of charging states in the charging state parameters, and q represents the number of charging states in the charging state parameters. This represents the maximum value of the d-th charging state among the actual measured values. This represents the minimum value of the d-th charging state in the actual measured values.

[0032] Optionally, analyzing the charging type of the power device based on the charging compatibility includes:

[0033] Based on the charging compatibility, the charging priority of the power supply device is determined;

[0034] Analyze the battery characteristics of the battery used in the power supply device and the current usage scenario of the device;

[0035] Extract the key features of the current device usage scenario and analyze the core charging needs corresponding to the key features of the scenario;

[0036] Based on the core charging requirements, the battery characteristics, and the charging priority, the charging type of the power supply device is determined.

[0037] Optionally, extracting the core charging operation points from the retrieval charging strategy includes:

[0038] Analyze the strategy structure corresponding to the charging retrieval strategy, and based on the strategy structure, determine the key operation steps in the strategy;

[0039] Extract the charging parameters from the key operation steps, and identify the device charging stage and device status corresponding to the key operation steps;

[0040] By combining the charging stage and the state of the device, the charging parameters are adjusted for adaptability to obtain adaptive charging parameters;

[0041] Based on the adapted charging parameters, the core charging operation points in the retrieval charging strategy are extracted.

[0042] Optionally, generating the charging control command for the power supply device based on the charging replenishment criteria includes:

[0043] Analysis of the implementation specifications corresponding to the charging and replenishment criteria;

[0044] Based on the aforementioned criteria and implementation specifications, the charging execution level corresponding to the power supply equipment is determined;

[0045] Based on the charging execution level, the charging execution process corresponding to the power supply device is extracted;

[0046] Based on the charging execution process, a charging control framework corresponding to the power supply device is built.

[0047] Based on the charging control framework, charging control commands for the power supply device are generated.

[0048] Optionally, calculating the energy conversion efficiency of the charging medium in the optimal charging path based on the real-time physical parameters includes:

[0049] The real-time physical parameters are subjected to noise reduction processing to obtain noise-reduced physical parameters;

[0050] Based on the noise reduction material state parameters, the dielectric efficiency state tensor of the charging medium is constructed;

[0051] A charging thermodynamic simulation was performed on the energy efficiency state tensor of the medium to obtain the path energy distribution cloud map of the charging medium in the optimal charging path;

[0052] The energy distribution cloud map along the path is segmented into gradient fields to obtain an energy gradient partition map.

[0053] Identify the strong energy gradient intervals in the energy gradient partition map, and quantify the effective energy transport flux and polarization loss flux in the strong energy gradient intervals;

[0054] Based on the effective energy transfer flux and the polarization loss flux, the energy conversion efficiency of the charging medium in the optimal charging path is calculated.

[0055] To address the above problems, the present invention also provides a fully immersion power charging system, the system comprising:

[0056] The charging feature information analysis module is used to acquire the device parameter information of the power supply device to be charged, and analyze the charging feature information of the power supply device based on the device parameter information.

[0057] The charging compatibility calculation module is used to match the charging feature information with historical charging data in the preset charging strategy library to obtain charging matching data, query the appropriate charging strategy in the charging matching data, and monitor the charging status parameters of the power device in real time during the charging process, and calculate the charging compatibility between the appropriate charging strategy and the charging status parameters.

[0058] The core charging operation point extraction module is used to analyze the charging type of the power device based on the charging compatibility, perform strategy retrieval on the adapted charging strategy based on the charging type, obtain the retrieved charging strategy, and extract the core charging operation points in the retrieved charging strategy.

[0059] The charging control command generation module is used to formulate a charging replenishment plan for the power supply equipment based on the core charging operation points, analyze the professional replenishment paths in the charging replenishment plan, query the charging replenishment criteria corresponding to the professional replenishment paths, and generate charging control commands for the power supply equipment based on the charging replenishment criteria.

[0060] The energy efficiency conversion rate calculation module is used to locate the optimal charging path of the power supply device based on the charging control command, detect the real-time physical parameters of the charging medium of the power supply device, and calculate the energy efficiency conversion rate of the charging medium in the optimal charging path based on the real-time physical parameters.

[0061] The immersion charging scheme formulation module is used to record the charging waveform of the power supply device during the charging process, and formulate an immersion charging scheme adapted to the power supply device by combining the energy efficiency conversion rate and the charging waveform.

[0062] Compared to the problems described in the background art, this invention analyzes the charging characteristics of the power supply device based on the device parameter information. This transforms complex and numerous parameters into clear and targeted charging guidance information, laying an important foundation for subsequent processing. Furthermore, by matching the charging characteristic information with historical charging data in a preset charging strategy library to obtain charging matching data, this invention can deeply analyze the charging needs of the power supply device and query suitable charging strategies in the charging matching data. This greatly improves the accuracy and efficiency of charging management, providing a better and more practical charging service experience for the power supply device. Based on the charging compatibility, this invention analyzes the charging type of the power supply device, accurately grasping the actual state and needs of the power supply device during the charging process. This helps the charging management system provide highly adapted charging strategies for the power supply device, improving charging efficiency. To ensure equipment safety, extend battery life, and optimize the overall charging service experience, this invention further optimizes the charging process by formulating a charging replenishment plan for the power supply device based on the core charging operation points. This comprehensively meets the charging needs of the power supply device under different usage scenarios and performance requirements, effectively improving charging efficiency. Furthermore, by locating the optimal charging path for the power supply device based on the charging control commands, this invention improves charging efficiency, significantly shortens charging time, reduces energy loss during charging, effectively protects battery health, and reduces the risk of device malfunctions caused by charging. By combining the energy conversion efficiency and the charging waveform to formulate a fully immersive charging scheme adapted to the power supply device, this invention significantly improves charging efficiency, reduces energy loss during charging, and shortens charging time, thereby improving the efficiency of fully immersive power charging. Therefore, the fully immersive power charging method and system provided in this invention can improve the efficiency of fully immersive power charging. Attached Figure Description

[0063] Figure 1 This is a schematic flowchart of a fully immersive power charging method according to an embodiment of the present invention.

[0064] Figure 2This is a schematic diagram of a module for implementing the fully immersive power charging method according to an embodiment of the present invention.

[0065] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0066] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0067] This application provides a fully immersion power charging method. The executing entity of the fully immersion power charging method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the fully immersion power charging method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0068] Example 1:

[0069] Reference Figure 1 The diagram shown is a schematic flowchart of a fully immersion power charging method according to an embodiment of the present invention. In this embodiment, the fully immersion power charging method includes:

[0070] S1. Obtain the device parameter information of the power supply device to be charged, and analyze the charging characteristic information of the power supply device based on the device parameter information.

[0071] This invention analyzes the charging characteristics of power supply devices based on the device parameter information, transforming complex and numerous parameters into clear and targeted charging guidance information. This provides an important basis for subsequent processing. The power supply devices include various types of devices requiring external charging, ranging from portable electronic devices such as mobile phones and tablets to large electrical equipment such as electric vehicles and power tools. The device parameter information involves multiple aspects, such as rated voltage (indicating the voltage value required for normal operation), rated current (determining the maximum current allowed during charging), battery capacity (reflecting the amount of electrical energy the device can store), and charging interface type (determining the type of charging cable and charging device that is compatible). The charging characteristic information refers to a clear, systematic, and practical interpretation of the charging needs and characteristics of power supply devices obtained through the analysis and integration of device parameter information. It clarifies key charging requirements, such as charging power range, estimated charging time, and charging temperature limits.

[0072] As an embodiment of the present invention, the step of analyzing the charging characteristic information of the power supply device based on the device parameter information includes:

[0073] Identify the parameter tags corresponding to the device parameter information, and query the charging standard protocol and device identifier corresponding to the power supply device;

[0074] Analyze the tag coupling between the parameter tag and the charging standard protocol;

[0075] Based on the device identifier, analyze the device characteristics corresponding to the power supply device, and based on the device characteristics, analyze the tag logic relationship between the parameter tags;

[0076] By combining the tag coupling and the tag logical relationship, the charging characteristic information of the power supply device can be analyzed from the device parameter information.

[0077] Wherein, the parameter tag is the name identifier corresponding to the device parameter information, used to classify and refer to the parameter; the charging standard protocol and the device identifier are the charging specification and identification criteria corresponding to the power supply device, respectively; the tag coupling is the degree of interdependence and mutual influence between the parameter tag and the charging standard protocol; the device characteristics are the inherent attributes and functional features of the power supply device; and the tag logical relationship is the inherent and rule-compliant interrelationship between the parameter tags.

[0078] Furthermore, the parameter tags corresponding to the device parameter information can be identified using a tag recognition tool, which is compiled using a programming language; the charging standard protocol and device identification corresponding to the power supply device can be queried through the device manual.

[0079] The system identifies the functional description content in the device identifier and analyzes the device characteristics corresponding to the power supply device based on the functional description content. Based on the device characteristics, it analyzes the tag logic relationship between the parameter tags, such as using a semantic analysis model to sort out the associations between tags, including implied, causal, and parallel relationships, and explores the inherent connections between parameter tags in describing the device's charging needs, status, and performance, laying a solid foundation for subsequent accurate analysis of charging feature information. Combining the tag coupling and the tag logic relationship, it analyzes the charging feature information of the power supply device from the device parameter information. For example, by constructing a tag relationship graph, it quantifies and analyzes the association strength of different tags, and derives charging feature information such as the adaptation range of charging voltage and current, the optimal charging time, and the temperature control requirements during the charging process based on the tag logic.

[0080] Furthermore, as an optional embodiment of the present invention, analyzing the tag coupling between the parameter tag and the charging standard protocol includes:

[0081] The parameter labels are extracted to obtain parameter label representations, and the label representation vectors corresponding to the parameter label representations are constructed.

[0082] The charging standard protocol is parsed to obtain the key terms of the protocol;

[0083] The key terms of the agreement are vectorized to obtain the key terms vector.

[0084] Combining the tag representation vector and the protocol key clause vector, the coupling coefficient between the parameter tag and the charging standard protocol is calculated using the following formula:

[0085] ;

[0086] Where A represents the coupling coefficient between the parameter tag and the charging standard protocol. This represents the label representation vector corresponding to the a-th label in the parameter labels. This represents the protocol key clause vector corresponding to the b-th protocol in the charging standard protocol, where a and b represent the parameter tag and the sequence number corresponding to the charging standard protocol, respectively, and m and n represent the number of parameter tags and the number of charging standard protocols, respectively.

[0087] Based on the coupling coefficient, the tag coupling between the parameter tag and the charging standard protocol is analyzed.

[0088] Wherein, the parameter label representation is a numerical feature description of the parameter label; the label representation vector is a multi-dimensional feature vector corresponding to the parameter label representation; the key terms of the protocol are the technical indicators and operating specifications defined in the charging standard protocol; and the key terms vector of the protocol is the quantized encoding vector of the key terms of the protocol.

[0089] Furthermore, the parameter labels can be extracted using feature engineering methods to obtain parameter label representations; a label representation vector corresponding to the parameter label representation can be constructed using a vector space model; the charging standard protocol can be parsed using natural language processing techniques to obtain key protocol clauses; the key protocol clauses can be vectorized using word embedding algorithms to obtain key protocol clause vectors; based on the coupling coefficient, the label coupling between the parameter labels and the charging standard protocol can be analyzed using a correlation analysis algorithm. For example, the higher the coupling coefficient, the stronger the correlation between the two in the label dimension, and the higher the label coupling; conversely, the lower the coupling coefficient, the lower the label coupling.

[0090] S2. Match the charging feature information with historical charging data in the preset charging strategy library to obtain charging matching data, query the appropriate charging strategy in the charging matching data, and monitor the charging status parameters of the power device in real time during the charging process, and calculate the charging fit between the appropriate charging strategy and the charging status parameters.

[0091] This invention obtains charging matching data by matching charging feature information with historical charging data in a preset charging strategy library. This allows for in-depth analysis of the charging needs of power supply devices and querying the appropriate charging strategies in the charging matching data, greatly improving the accuracy and efficiency of charging management and providing power supply devices with a better charging service experience that better meets actual needs.

[0092] The preset charging strategy library refers to a systematically organized and constructed database that stores a large amount of knowledge and data related to the charging field. This includes standard charging curves for different types of power devices, optimal charging strategies for various charging scenarios, such as the applicable conditions and operating specifications for fast charging, slow charging, constant voltage charging, and constant current charging. The charging matching data refers to historical charging data extracted from the matching relationship set that is closely related to the current charging characteristics. This data contains key information from similar past charging cases. The adapted charging strategy is the charging method in the charging matching data. Furthermore, the charging characteristics can be matched with the historical charging data in the preset charging strategy library using a matching algorithm, such as a fuzzy matching algorithm. The adapted charging strategy can be obtained by querying the strategy information recorded in the charging matching data.

[0093] This invention calculates the charging fit between the adapted charging strategy and the charging state parameters to determine the rationality of the charging strategy, promptly detect charging anomalies, and flexibly adjust the charging strategy based on the charging fit. This provides power devices with more tailored and personalized charging services, greatly improving the effectiveness of charging services and device safety. The charging state parameters refer to a series of quantitative indicators that reflect the current charging state characteristics and performance of the power device during charging. These parameters can be measured from multiple dimensions, including but not limited to charging current, charging voltage, battery temperature, remaining charging time, and charging progress. The charging fit is a quantitative indicator used to measure the degree of fit between the adapted charging strategy and the charging state parameters. A larger value indicates a better fit, meaning the current charging strategy better matches the actual state and needs of the power device during charging; a smaller value indicates a worse fit. Furthermore, the charging state parameters of the power device are acquired in real time through sensors and a data acquisition system.

[0094] As an embodiment of the present invention, calculating the charging fit between the adaptive charging strategy and the charging state parameters includes:

[0095] Query the charging timing sequence corresponding to the power supply device, and determine the charging transmission attribute corresponding to the power supply device based on the charging timing sequence.

[0096] Based on the charging transmission attributes, assign a state weight to each charging state in the charging state parameters.

[0097] Based on the adaptive charging strategy, determine the state setting value corresponding to each charging state in the charging state parameters;

[0098] By combining the charging state parameters, the state setting value, and the state weight, the charging fit between the adaptive charging strategy and the charging state parameters is calculated.

[0099] Wherein, the charging timing sequence is the division of the charging stages corresponding to the power supply device; the charging transmission attribute is the electrical transmission-specific property corresponding to the power supply device; the state weight is the evaluation weight coefficient corresponding to each charging state in the charging state parameters; and the state setting value is the parameter target value corresponding to the charging state parameters determined based on the adaptive charging strategy.

[0100] Furthermore, the charging sequence of the power supply device can be queried through the built-in charging information recording module or charging management system. Based on the charging sequence, the charging transmission attribute of the power supply device is determined. For example, in the fast charging stage, the charging transmission attribute is usually characterized by high current input and large charging power, which enables electrical energy to be quickly transferred to the power supply device in a short time, significantly shortening the charging time. Based on the charging transmission attribute, the state weight corresponding to each charging state in the charging state parameter is assigned. If the charging transmission attribute indicates that it is in the fast charging stage, in order to balance the rapid replenishment of power and the long-term health of the battery, the charging state that can ensure that the battery does not overheat and maintains a reasonable voltage and current is given a higher weight. For example, the charging state that can adjust the current in real time to prevent the battery from being damaged by heat due to high current is set to 0.8; the charging state that may aggravate battery wear, such as the state of continuous high current and unstable voltage, is set to 0.2. In the slow charging stage, the state that helps the battery to charge evenly and extend its life, such as the low current charging state with small fluctuations and stability, is given a high weight. The state values ​​recorded in the adaptive charging strategy are retrieved, and each state value is used as the state setting value corresponding to the charging state parameter.

[0101] Furthermore, as an optional embodiment of the present invention, the step of calculating the charging fit between the adaptive charging strategy and the charging state parameters by combining the charging state parameters, the state setting value, and the state weight includes:

[0102] ;

[0103] Where E represents the degree of fit between the adapted charging strategy and the charging state parameters. This represents the state weight of the d-th charging state in the charging state parameters. This represents the state setting value for the d-th charging state in the charging state parameters. This represents the actual measured value of the d-th charging state in the charging state parameters, where d represents the charging state sequence number of the charging state parameters, q represents the number of charging states in the charging state parameters, and q represents the number of charging states in the charging state parameters. This represents the maximum value of the d-th charging state among the actual measured values. This represents the minimum value of the d-th charging state in the actual measured values.

[0104] S3. Based on the charging compatibility, analyze the charging type of the power supply device, and based on the charging type, perform strategy retrieval on the adapted charging strategy to obtain the retrieved charging strategy, and extract the core charging operation points in the retrieved charging strategy.

[0105] Based on the charging compatibility, this invention analyzes the charging type of the power supply device, enabling precise understanding of the actual state and needs of the power supply device during the charging process. This helps the charging management system provide highly adaptable charging strategies for the power supply device, improving charging efficiency, ensuring device safety, extending battery life, and optimizing the overall charging service experience. The charging type refers to the classification of the current charging needs of the power supply device after comprehensively considering information such as charging compatibility, power supply device type, battery characteristics, and charging scenario, such as emergency fast charging, balanced maintenance, and regular slow charging.

[0106] As an embodiment of the present invention, the step of analyzing the charging type of the power supply device based on the charging compatibility includes:

[0107] Based on the charging compatibility, the charging priority of the power supply device is determined;

[0108] Analyze the battery characteristics of the battery used in the power supply device and the current usage scenario of the device;

[0109] Extract the key features of the current device usage scenario and analyze the core charging needs corresponding to the key features of the scenario;

[0110] Based on the core charging requirements, the battery characteristics, and the charging priority, the charging type of the power supply device is determined.

[0111] The charging priority is determined based on the range of charging compatibility, reflecting the urgency of the power device's current charging needs. If the charging compatibility is in the high compatibility range, it means the current charging strategy is well-suited and the urgency of charging is relatively low. Conversely, if the charging compatibility is in the low compatibility range, it indicates that the charging strategy needs adjustment and the urgency of charging is high. The current device usage scenario refers to the usage environment of the power device. The key features of the charging scenario are a set of information reflecting the characteristics of the charging environment and device usage status in the current device usage scenario, including whether the charging location is portable, whether the power device is in use, and whether the remaining power can meet short-term needs. The core charging demand refers to the charging requirements extracted from the key features of the charging scenario that play a decisive role in determining the charging type. For example, if the power device is outdoors and has extremely low power, and urgently needs to restore a certain amount of power in a short time, the core charging demand is emergency fast charging.

[0112] Furthermore, the charging compatibility is divided into corresponding intervals using an equidistant partitioning method or a clustering algorithm based on machine learning, and the charging priority of the power supply device is determined based on the compatibility intervals.

[0113] By integrating machine learning algorithms and big data analytics, performance data of batteries across different charge-discharge cycles, as well as environmental parameters and operational load data in device usage scenarios, can be collected. Clustering algorithms and association rule mining techniques can be used to accurately characterize battery features, identify device usage scenario patterns, and analyze the battery characteristics of the power supply device and the current device usage scenario. Key features of the current device usage scenario can be extracted using feature extraction models, such as convolutional neural networks or recurrent neural networks, from charging device feedback data and user operation information. The core charging needs reflected by these key features can be analyzed using semantic understanding algorithms, such as natural language processing, analyzing charging scenario description text to uncover core charging needs. Based on these core charging needs, battery characteristics, and charging priorities, multi-dimensional decision-making algorithms, such as combining analytic hierarchy process (AHP) and fuzzy logic reasoning, can determine the charging type of the power supply device. For example, when the core charging need is rapid power replenishment, the battery tolerates high-power charging, and the charging priority is high, AHP can be used to determine the weights of each factor, and then fuzzy logic reasoning can be used to comprehensively evaluate the charging scenario, thereby determining that the device is suitable for high-power fast charging.

[0114] Based on the charging type, this invention performs strategy retrieval on the adapted charging strategy, which helps the charging management system quickly obtain strategies that meet the charging needs of power devices, avoids blindly screening among a large number of charging strategies, improves the accuracy and timeliness of charging decisions, and provides scientific guidance for subsequent charging operations, thereby enhancing user satisfaction with charging services.

[0115] The adaptive charging strategy library refers to a pre-built and stored collection of charging strategies for different power devices and charging scenarios. It covers voltage and current control schemes for different charging stages, protection strategies to deal with abnormal situations such as battery overheating and overcharging, and optimization strategies to improve charging efficiency and extend battery life. The retrieved charging strategy refers to a charging strategy that is highly matched to the current charging type of the power device and is selected from the adaptive charging strategy library. It includes a series of specific charging operation steps and parameter settings.

[0116] Optionally, policy retrieval for the adapted charging strategy can be achieved through policy indexing technology, such as using a hash table-based index structure or an inverted index to quickly retrieve the adapted charging strategy library and thus locate the relevant charging strategy.

[0117] This invention extracts the core charging operation points from the retrieved charging strategy to help charging devices accurately execute charging tasks, avoid invalid or erroneous operations, improve the stability and safety of the charging process, and provide users with clear and concise charging guidance. The core charging operation points refer to the key instructions and parameters that are further refined based on the retrieved charging strategy and can directly guide the charging device to perform effective charging. For example, in a fast charging strategy, the core charging operation points may be "initially charging with a constant current of 2A, and when the battery voltage reaches 4.2V, switching to constant voltage charging mode and maintaining the voltage at 4.2V".

[0118] As an embodiment of the present invention, the extraction of core charging operation points in the retrieval charging strategy includes:

[0119] Analyze the strategy structure corresponding to the charging retrieval strategy, and based on the strategy structure, determine the key operation steps in the strategy;

[0120] Extract the charging parameters from the key operation steps, and identify the device charging stage and device status corresponding to the key operation steps;

[0121] By combining the charging stage and the state of the device, the charging parameters are adjusted for adaptability to obtain adaptive charging parameters;

[0122] Based on the adapted charging parameters, the core charging operation points in the retrieval charging strategy are extracted.

[0123] The strategy structure refers to the organizational form and logical architecture of the retrieval charging strategy, including the strategy's process sequence and conditional judgment branches. For example, some charging strategies select different charging paths based on battery temperature. The key operation steps refer to the operational links in the retrieval charging strategy that play a decisive role in achieving the charging goal, such as switching charging modes and handling abnormal situations. The charging parameters refer to the quantitative indicators such as voltage, current, and time involved in the key operation steps, which directly affect the charging effect. The adaptability adjustment refers to optimizing the charging parameters according to the real-time status of the power supply equipment and the charging stage to ensure the safety and efficiency of the charging process. For example, when the battery temperature is too high, the charging current is appropriately reduced.

[0124] Furthermore, the analysis of the strategy structure corresponding to the retrieved charging strategy can be achieved through process mining tools, such as using ProM or Disco to perform process analysis on the charging strategy, thereby determining the strategy structure; the determination of key operation steps in the strategy can be achieved through a rule engine, such as filtering key operation steps from the strategy process based on preset key operation identification rules; the extraction of charging parameters involved in the key operation steps can be achieved through data parsing algorithms, such as using regular expressions or JSON parsing technology to extract charging parameters from the strategy text; the adaptation adjustment of charging parameters can be achieved through feedback control algorithms, such as dynamically adjusting charging parameters based on real-time monitoring data of the power supply equipment; the extraction of core charging operation points in the retrieved charging strategy can be achieved through text summarization algorithms, such as using LexRank or SumBasic to generate summaries of the charging strategy text, thereby determining the core charging operation points.

[0125] S4. Based on the core charging operation points, formulate a charging replenishment plan for the power supply equipment, analyze the professional replenishment paths in the charging replenishment plan, query the charging replenishment criteria corresponding to the professional replenishment paths, and generate charging control commands for the power supply equipment based on the charging replenishment criteria.

[0126] This invention, by formulating a charging replenishment plan for the power supply device based on the core charging operation points, can significantly optimize the charging process, fully meet the charging needs of the power supply device under different usage scenarios and performance requirements, and effectively improve charging efficiency. The charging replenishment plan is a systematic and targeted charging strategy designed based on the real-time status of the power supply device, charging needs, and the impact of various charging operations on battery performance and charging progress. Furthermore, the specific planning steps for formulating the charging replenishment plan for the power supply device based on the core charging operation points are as follows: using professional data analysis tools, from multiple dimensions... The core charging operation parameters are analyzed in depth, including the time sequence relationship between different parameters and power demand characteristics. Machine learning algorithms are used to learn from the past charging data and usage scenario data of the power equipment to build a charging characteristic model of the equipment. Based on the analysis results of the operation points and the charging characteristic model of the equipment, multiple charging planning schemes adapted to the current state of the power equipment are generated. Subsequently, a multi-objective optimization algorithm is used to comprehensively consider multiple dimensions of objectives such as charging efficiency, battery health loss, and equipment usage needs to screen and optimize the charging planning schemes, so as to form a scientific, reasonable and feasible charging replenishment plan for the power equipment.

[0127] This invention analyzes the specialized charging routes in the charging replenishment plan and queries the corresponding charging replenishment criteria for the specialized routes. On the one hand, it helps the charging management system to detect potential problems in the charging routes in a timely manner and make optimizations and adjustments. On the other hand, by referring to the charging replenishment criteria, it can ensure the safety and standardization of the charging process, improve the reliability of the charging service, and help power equipment successfully complete its charging goals.

[0128] The professional replenishment path refers to a series of orderly charging operation steps and strategies planned by the charging management system for power equipment to achieve charging replenishment planning. For example, for mobile phone charging, the professional replenishment path may first perform constant current charging, and then switch to constant voltage charging when the battery voltage reaches a certain value until it is fully charged. The charging replenishment criteria are a series of standards and specifications formulated in the charging field to ensure charging safety, efficiency, and standardize charging behavior. These standards cover battery safety standards, such as regulations to prevent overcharging and over-discharging; charging efficiency optimization requirements, such as methods to improve charging power utilization; and equipment compatibility specifications to ensure that charging equipment and power equipment are compatible. Optionally, the analysis of the professional replenishment path in the charging replenishment plan can be achieved through path analysis tools, such as using Petri nets or fault tree analysis, to evaluate the charging replenishment plan and obtain the professional replenishment path. The query of the charging replenishment criteria corresponding to the professional replenishment path can be achieved through a knowledge database, such as using an SQL database or a NoSQL database, to perform a correlation query on the professional replenishment path and its criteria, thereby determining the charging replenishment criteria.

[0129] Based on the aforementioned charging replenishment criteria, this invention generates charging control commands for the power supply device, enabling more standardized and precise charging operations. These precise control commands effectively guide the charging device to execute operations, quickly responding to the power supply device's charging needs and improving charging stability. The charging control commands are generated according to the charging control framework and are used to guide the charging device to perform specific operations during the charging process. For example, if the battery temperature is detected to be too high during charging, the charging control command will require the charging device to reduce the charging power to ensure battery safety.

[0130] As an embodiment of the present invention, generating charging control commands for the power supply device based on the charging replenishment criteria includes:

[0131] Analysis of the implementation specifications corresponding to the charging and replenishment criteria;

[0132] Based on the aforementioned criteria and implementation specifications, the charging execution level corresponding to the power supply equipment is determined;

[0133] Based on the charging execution level, the charging execution process corresponding to the power supply device is extracted;

[0134] Based on the charging execution process, a charging control framework corresponding to the power supply device is built.

[0135] Based on the charging control framework, charging control commands for the power supply device are generated.

[0136] The aforementioned guidelines refer to the specific operational requirements and guiding principles of charging and replenishment guidelines in practical applications. For example, in guidelines for preventing battery overcharging, the guidelines would clearly stipulate that when the battery capacity reaches 95%, the charging current should be reduced, and charging should be stopped when it reaches 98%. The charging execution level is a classification of the depth and complexity of charging operations based on the guidelines and the specific circumstances of the power supply equipment. For example, for ordinary lithium battery equipment, the charging execution level is relatively low, mainly focusing on the conventional charging process and safety protection; while for high-capacity, high-value energy storage equipment, the charging execution level will be higher, requiring more stringent charging monitoring and refined charging control. The charging execution process is a series of ordered charging operation steps designed for a specific charging execution level, covering all stages from the start to the end of charging. It clarifies the tasks and sequence that the charging equipment needs to complete at each stage. For example, in the charging execution process of an electric vehicle, the battery must first be tested, and then a suitable charging mode is selected based on the battery status and the power grid conditions. The charging control framework is built based on the charging execution process and is used to guide the overall architecture and mode of interaction between the charging equipment and the power supply equipment. For example, the charging control framework clarifies that during the charging process, the charging equipment needs to monitor the power, voltage, temperature and other parameters of the power supply equipment in real time, and adjust the charging strategy according to the monitoring results.

[0137] Furthermore, the parsing of the execution specifications corresponding to the charging replenishment criteria can be achieved through text parsing tools, such as regular expressions or semantic analysis tools, to parse the charging replenishment criteria and obtain the execution specifications; the determination of the charging execution level corresponding to the power supply equipment can be achieved through a hierarchical evaluation model, such as using cluster analysis or decision tree models to evaluate and classify the charging needs and characteristics of the power supply equipment, thereby determining the charging execution level; the extraction of the charging execution process corresponding to the power supply equipment can be achieved through process mining tools, such as using ProM or Disco to mine and extract the charging execution process; the construction of the charging control framework corresponding to the power supply equipment can be achieved through framework design tools, such as using Visio or OmniGraffle to visually design the charging control framework, thereby building the charging control framework; the generation of the charging control instructions for the power supply equipment can be achieved through instruction generation algorithms, such as using state machine algorithms or rule engines to generate corresponding charging control instructions based on the state of the charging equipment.

[0138] S5. Based on the charging control command, locate the optimal charging path of the power supply device, detect the real-time physical parameters of the charging medium of the power supply device, and calculate the energy conversion efficiency of the charging medium in the optimal charging path based on the real-time physical parameters.

[0139] This invention, by locating the optimal charging path of the power supply device based on the charging control command, can improve charging efficiency, significantly shorten charging time, reduce energy loss during charging, effectively protect battery health, and reduce the risk of device failure caused by charging. The optimal charging path is a charging path and operation procedure that maximizes power transmission efficiency and optimizes transmission stability while meeting the power supply device's charging needs, and conforms to device safety standards and the actual load of the power grid. The charging medium is the power supply device's [specific component / function], and the real-time physical parameters are physical and chemical state indicators of the charging medium that affect power transmission and charging effect during the charging process, such as temperature, humidity, and conductivity. Furthermore, the specific steps for locating the optimal charging path of the power supply device based on the charging control command are as follows: First, the charging control command is parsed to extract key power transmission parameters such as charging current, voltage, and power. Next, based on the power supply equipment type, battery capacity, remaining power, and the number and characteristics of charging interfaces, multiple feasible charging path models are constructed. Then, through a multi-objective evaluation system, power transmission efficiency, stability, charging time, and equipment heat generation are considered. Multi-objective optimization algorithms such as genetic algorithms and particle swarm optimization are used to screen and rank the charging path models. Then, the ranking results are dynamically adjusted based on external factors such as real-time grid load and peak / valley electricity prices. Finally, the optimal charging path that best suits the current state of the power supply equipment and achieves efficient and stable charging is determined. Real-time physical parameters of the charging medium can be detected by deploying temperature sensors, humidity sensors, and electrochemical sensors at key locations on the charging medium.

[0140] This invention calculates the energy efficiency conversion rate of the charging medium in the optimal charging path based on the real-time physical parameters, thereby understanding the energy utilization efficiency of the charging process and promptly detecting energy consumption anomalies in the charging process. This lays an important foundation for subsequently developing a fully immersive charging scheme adapted to the power supply equipment. The energy efficiency conversion rate represents a quantitative indicator of the energy utilization efficiency of the real-time physical parameters in the optimal charging path, that is, the proportion of the effective electrical energy actually stored by the power supply equipment during the charging process to the total electrical energy input to the power supply equipment. It is used to intuitively reflect the degree of effective utilization of electrical energy by the charging system under specific real-time physical parameters and the optimal charging path.

[0141] As an embodiment of the present invention, calculating the energy conversion efficiency of the charging medium in the optimal charging path based on the real-time physical parameters includes:

[0142] The real-time physical parameters are subjected to noise reduction processing to obtain noise-reduced physical parameters;

[0143] Based on the noise reduction material state parameters, the dielectric efficiency state tensor of the charging medium is constructed;

[0144] A charging thermodynamic simulation was performed on the energy efficiency state tensor of the medium to obtain the path energy distribution cloud map of the charging medium in the optimal charging path;

[0145] The energy distribution cloud map along the path is segmented into gradient fields to obtain an energy gradient partition map.

[0146] Identify the strong energy gradient intervals in the energy gradient partition map, and quantify the effective energy transport flux and polarization loss flux in the strong energy gradient intervals;

[0147] Based on the effective energy transfer flux and the polarization loss flux, the energy conversion efficiency of the charging medium in the optimal charging path is calculated.

[0148] The denoised state parameters are obtained by denoising the real-time state parameters. The dielectric energy efficiency state tensor is a quantity constructed based on the denoised state parameters to describe the energy efficiency state of the charging dielectric. The path energy distribution cloud map is a graph reflecting the energy distribution of the charging dielectric in the optimal charging path, obtained by performing a charging thermodynamic simulation on the dielectric energy efficiency state tensor. The energy gradient partition map is a partitioned graph obtained by dividing the path energy distribution cloud map into gradient fields. The strong energy gradient interval is the region with a strong energy gradient in the energy gradient partition map. The effective energy transfer flux and the polarization loss flux are physical quantities reflecting the energy transfer and loss in the strong energy gradient interval.

[0149] Furthermore, the real-time physical parameters can be denoised using a moving average filtering algorithm to obtain denoised physical parameters. Based on these denoised physical parameters, tensor analysis theory and multiphysics coupling modeling methods are used to construct the dielectric energy efficiency state tensor of the charging medium. Using professional finite element analysis software, boundary conditions and parameters matching the charging thermodynamic process are set to perform a charging thermodynamic simulation on the dielectric energy efficiency state tensor, obtaining a path energy distribution cloud map of the charging medium in the optimal charging path. Digital image processing techniques such as edge detection and threshold segmentation are used to segment the path energy distribution cloud map into gradient fields, obtaining an energy gradient partition map. Finally, a pattern recognition algorithm is applied based on the energy gradient threshold range... The system identifies strong energy gradient intervals in the energy gradient partition map. Virtual sensors are set up within these intervals, and transmission line theory and electromagnetic loss models are used to quantify the effective energy transfer flux and polarization loss flux. Based on the effective energy transfer flux and polarization loss flux, the energy efficiency conversion rate of the charging medium in the optimal charging path is calculated. The calculation steps are as follows: A preliminary energy efficiency conversion value is calculated using the ratio of the energy efficiency conversion rate to the sum of the effective energy transfer flux and the polarization loss flux. This value is then compared with historical data under different operating conditions of the charging system in multiple dimensions. A linear regression algorithm is used to correct data deviations, thereby obtaining an accurate energy efficiency conversion rate that conforms to actual operating conditions.

[0150] S6. Record the charging waveform of the power supply device during the charging process, and combine the energy efficiency conversion rate and the charging waveform to formulate a fully immersive charging scheme adapted to the power supply device.

[0151] This invention, by combining the energy conversion efficiency and the charging waveform, formulates a fully immersion charging scheme adapted to the power supply equipment. This significantly improves charging efficiency, reduces energy loss during charging, and shortens charging time, thereby enhancing the high efficiency of fully immersion power supply charging. The charging waveform is a curve representing the change of charging current or voltage over time during charging, reflecting the dynamic characteristics of the charging process. The fully immersion charging scheme is adapted to the power supply equipment, completely immersing it in a coolant with good electrical insulation and heat dissipation properties. By adjusting the charging waveform and charging parameters, a systematic strategy for efficient and safe charging is achieved. Furthermore, high-precision current and voltage sensors can be used to collect data in real time at a certain sampling frequency. The analog signals are then converted into digital signals using a data acquisition card and stored in a database to record the data. The charging waveform of the power supply device during the charging process is described. Based on the energy efficiency conversion rate and the charging waveform, a fully immersive charging scheme adapted to the power supply device is formulated. The formulation steps are as follows: The collected energy efficiency conversion rate and charging waveform are classified and organized. Using a clustering analysis algorithm, different energy efficiency and waveform combination types are identified, clarifying the charging performance of the power supply device under each combination. Based on the classification results, a charging effect evaluation model is established, using charging time, heat generation, and battery life loss as evaluation indicators to quantitatively analyze the impact of different combinations on the power supply device and obtain a comprehensive evaluation value for each combination. Based on the evaluation value, a multi-objective optimization algorithm is used, considering factors such as charging efficiency, safety, and device lifespan, to select the optimal energy efficiency conversion rate and charging waveform matching scheme. The matching parameters for fully immersive charging, such as coolant flow rate and temperature, are determined to form a fully immersive charging scheme adapted to the power supply device.

[0152] Compared to the problems described in the background art, this invention analyzes the charging characteristics of the power supply device based on the device parameter information. This transforms complex and numerous parameters into clear and targeted charging guidance information, laying an important foundation for subsequent processing. Furthermore, by matching the charging characteristic information with historical charging data in a preset charging strategy library to obtain charging matching data, this invention can deeply analyze the charging needs of the power supply device and query suitable charging strategies in the charging matching data. This greatly improves the accuracy and efficiency of charging management, providing a better and more practical charging service experience for the power supply device. Based on the charging compatibility, this invention analyzes the charging type of the power supply device, accurately grasping the actual state and needs of the power supply device during the charging process. This helps the charging management system provide highly adapted charging strategies for the power supply device, improving charging efficiency. To ensure equipment safety, extend battery life, and optimize the overall charging service experience, this invention further optimizes the charging process by formulating a charging replenishment plan for the power supply device based on the core charging operation points. This comprehensively meets the charging needs of the power supply device under different usage scenarios and performance requirements, effectively improving charging efficiency. Furthermore, by locating the optimal charging path for the power supply device based on the charging control commands, this invention improves charging efficiency, significantly shortens charging time, reduces energy loss during charging, effectively protects battery health, and reduces the risk of device malfunctions caused by charging. By combining the energy conversion efficiency and the charging waveform to formulate a fully immersive charging scheme adapted to the power supply device, this invention significantly improves charging efficiency, reduces energy loss during charging, and shortens charging time, thereby improving the efficiency of fully immersive power charging. Therefore, the fully immersive power charging method and system provided in this invention can improve the efficiency of fully immersive power charging.

[0153] Example 2:

[0154] like Figure 2 The diagram shown is a functional block diagram of a fully immersive power charging system according to the present invention.

[0155] The fully immersion power charging system 200 described in this invention can be installed in electronic devices. Depending on the functions implemented, the fully immersion power charging system may include a charging characteristic information analysis module 201, a charging compatibility calculation module 202, a core charging operation point extraction module 203, a charging control command generation module 204, an energy efficiency conversion rate calculation module 205, and a fully immersion charging scheme formulation module 206. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0156] In this embodiment of the invention, the functions of each module / unit are as follows:

[0157] The charging feature information analysis module 201 is used to acquire the device parameter information of the power supply device to be charged, and analyze the charging feature information of the power supply device based on the device parameter information.

[0158] The charging compatibility calculation module 202 is used to match the charging feature information with historical charging data in the preset charging strategy library to obtain charging matching data, query the appropriate charging strategy in the charging matching data, monitor the charging status parameters of the power device in real time during the charging process, and calculate the charging compatibility between the appropriate charging strategy and the charging status parameters.

[0159] The core charging operation point extraction module 203 is used to analyze the charging type of the power device based on the charging compatibility, perform strategy retrieval on the adapted charging strategy based on the charging type, obtain the retrieved charging strategy, and extract the core charging operation points in the retrieved charging strategy.

[0160] The charging control command generation module 204 is used to formulate a charging replenishment plan for the power supply equipment based on the core charging operation points, analyze the professional replenishment path in the charging replenishment plan, query the charging replenishment criteria corresponding to the professional replenishment path, and generate a charging control command for the power supply equipment based on the charging replenishment criteria.

[0161] The energy efficiency conversion rate calculation module 205 is used to locate the optimal charging path of the power supply device based on the charging control command, detect the real-time physical parameters of the charging medium of the power supply device, and calculate the energy efficiency conversion rate of the charging medium in the optimal charging path based on the real-time physical parameters.

[0162] The fully immersive charging scheme formulation module 206 is used to record the charging waveform of the power supply device during the charging process, and formulate a fully immersive charging scheme adapted to the power supply device by combining the energy efficiency conversion rate and the charging waveform.

[0163] In detail, the modules in the fully immersive power charging system 200 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The same technical means are used as the fully immersive power charging method described in the article, and it can produce the same technical effect, so it will not be repeated here.

[0164] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A fully immersion power charging method, characterized in that, The method includes: S1. Obtain the device parameter information of the power supply device to be charged, and analyze the charging characteristic information of the power supply device based on the device parameter information; S2. Match the charging feature information with historical charging data in the preset charging strategy library to obtain charging matching data, query the appropriate charging strategy in the charging matching data, and monitor the charging status parameters of the power device in real time during the charging process, and calculate the charging fit between the appropriate charging strategy and the charging status parameters. S3. Based on the charging compatibility, analyze the charging type of the power supply device, and based on the charging type, perform strategy retrieval on the adaptive charging strategy to obtain the retrieved charging strategy, and extract the core charging operation points in the retrieved charging strategy. S4. Based on the core charging operation points, formulate a charging replenishment plan for the power supply equipment, analyze the professional replenishment paths in the charging replenishment plan, query the charging replenishment criteria corresponding to the professional replenishment paths, and generate charging control commands for the power supply equipment based on the charging replenishment criteria. S5. Based on the charging control command, locate the optimal charging path of the power supply device, detect the real-time physical parameters of the charging medium of the power supply device, and calculate the energy conversion efficiency of the charging medium in the optimal charging path based on the real-time physical parameters. S6. Record the charging waveform of the power supply device during the charging process, and combine the energy efficiency conversion rate and the charging waveform to formulate a fully immersive charging scheme adapted to the power supply device; In step S1, analyzing the charging characteristic information of the power supply device based on the device parameter information includes: Identify the parameter tags corresponding to the device parameter information, and query the charging standard protocol and device identifier corresponding to the power supply device; Analyze the tag coupling between the parameter tag and the charging standard protocol; Based on the device identifier, analyze the device characteristics corresponding to the power supply device, and based on the device characteristics, analyze the tag logic relationship between the parameter tags; By combining the tag coupling and the tag logical relationship, the charging characteristic information of the power supply device can be analyzed from the device parameter information; In step S2, calculating the charging fit between the adaptive charging strategy and the charging state parameters includes: Query the charging timing sequence corresponding to the power supply device, and determine the charging transmission attribute corresponding to the power supply device based on the charging timing sequence. Based on the charging transmission attributes, assign a state weight to each charging state in the charging state parameters. Based on the adaptive charging strategy, determine the state setting value corresponding to each charging state in the charging state parameters; By combining the charging state parameters, the state setting value, and the state weight, the charging fit between the adaptive charging strategy and the charging state parameters is calculated. The step of calculating the charging fit between the adaptive charging strategy and the charging state parameters by combining the charging state parameters, the state setting value, and the state weight includes: ； Where E represents the degree of fit between the adapted charging strategy and the charging state parameters. This represents the state weight of the d-th charging state in the charging state parameters. This represents the state setting value for the d-th charging state in the charging state parameters. This represents the actual measured value of the d-th charging state in the charging state parameters, where d represents the charging state sequence number of the charging state parameters, and q represents the number of charging states in the charging state parameters. This represents the maximum value of the d-th charging state among the actual measured values. This represents the minimum value of the d-th charging state in the actual measured values. In step S5, based on the charging control command, the optimal charging path for the power supply device is located. The specific steps are as follows: First, the charging control command is parsed to extract key power transmission parameters such as charging current, voltage, and power. Next, based on the power supply device type, battery capacity, remaining power, and the number and characteristics of charging interfaces, multiple feasible charging path models are constructed. Then, through a multi-objective evaluation system, power transmission efficiency, stability, charging time, and device heat generation are considered. A multi-objective optimization algorithm is used to screen and rank each charging path model. Then, the ranking results are dynamically adjusted based on real-time grid load and peak / valley electricity prices. Finally, the optimal charging path that best suits the current state of the power supply device and achieves efficient and stable charging is determined. In step S5, calculating the energy conversion efficiency of the charging medium in the optimal charging path based on the real-time physical parameters includes: The real-time physical parameters are subjected to noise reduction processing to obtain noise-reduced physical parameters; Based on the noise reduction material state parameters, the dielectric efficiency state tensor of the charging medium is constructed; A charging thermodynamic simulation was performed on the energy efficiency state tensor of the medium to obtain the path energy distribution cloud map of the charging medium in the optimal charging path; The energy distribution cloud map along the path is segmented into gradient fields to obtain an energy gradient partition map. Identify the strong energy gradient intervals in the energy gradient partition map, and quantify the effective energy transport flux and polarization loss flux in the strong energy gradient intervals; Based on the effective energy transfer flux and the polarization loss flux, the energy conversion efficiency of the charging medium in the optimal charging path is calculated. In step S6, combining the energy conversion efficiency and the charging waveform, a fully immersive charging scheme adapted to the power supply device is formulated. The formulation steps are as follows: the collected energy conversion efficiency and charging waveform are classified and organized. Using a clustering analysis algorithm, different energy efficiency and waveform combination types are identified, and the charging performance of the power supply device under each combination is clarified. Based on the classification results, a charging effect evaluation model is established, using charging time, heat generation, and battery life loss as evaluation indicators to quantitatively analyze the impact of different combinations on the power supply device and obtain a comprehensive evaluation value for each combination. Based on the evaluation value, a multi-objective optimization algorithm is used, considering charging efficiency, safety, and device life factors, to select the optimal energy conversion efficiency and charging waveform matching scheme and determine the supporting parameters for fully immersive charging, thus forming a fully immersive charging scheme adapted to the power supply device.

2. The fully immersive power charging method as described in claim 1, characterized in that, The analysis of the tag coupling between the parameter tag and the charging standard protocol includes: The parameter labels are extracted to obtain parameter label representations, and the label representation vectors corresponding to the parameter label representations are constructed. The charging standard protocol is parsed to obtain the key terms of the protocol; The key terms of the agreement are vectorized to obtain the key terms vector. Combining the tag representation vector and the protocol key clause vector, the coupling coefficient between the parameter tag and the charging standard protocol is calculated using the following formula: ； Where A represents the coupling coefficient between the parameter tag and the charging standard protocol. This represents the label representation vector corresponding to the a-th label in the parameter labels. This represents the protocol key clause vector corresponding to the b-th protocol in the charging standard protocol, where a and b represent the parameter tag and the sequence number corresponding to the charging standard protocol, respectively, and m and n represent the number of parameter tags and the number of charging standard protocols, respectively. Based on the coupling coefficient, the tag coupling between the parameter tag and the charging standard protocol is analyzed.

3. The fully immersive power charging method as described in claim 1, characterized in that, The analysis of the charging type of the power supply device based on the charging compatibility includes: Based on the charging compatibility, the charging priority of the power supply device is determined; Analyze the battery characteristics of the battery used in the power supply device and the current usage scenario of the device; Extract the key features of the current device usage scenario and analyze the core charging needs corresponding to the key features of the scenario; Based on the core charging requirements, the battery characteristics, and the charging priority, the charging type of the power supply device is determined.

4. The fully immersive power charging method as described in claim 1, characterized in that, The extraction of core charging operation points from the retrieval charging strategy includes: Analyze the strategy structure corresponding to the charging retrieval strategy, and based on the strategy structure, determine the key operation steps in the strategy; Extract the charging parameters from the key operation steps, and identify the device charging stage and device status corresponding to the key operation steps; By combining the charging stage and the state of the device, the charging parameters are adjusted for adaptability to obtain adaptive charging parameters; Based on the adapted charging parameters, the core charging operation points in the retrieval charging strategy are extracted.

5. The fully immersive power charging method as described in claim 1, characterized in that, The step of generating charging control commands for the power supply device based on the charging replenishment criteria includes: Analysis of the implementation specifications corresponding to the charging and replenishment criteria; Based on the aforementioned criteria and implementation specifications, the charging execution level corresponding to the power supply equipment is determined; Based on the charging execution level, the charging execution process corresponding to the power supply device is extracted; Based on the charging execution process, a charging control framework corresponding to the power supply device is built. Based on the charging control framework, charging control commands for the power supply device are generated.

6. A fully immersion power charging system for implementing the fully immersion power charging method of claim 1, characterized in that, The system includes: The charging feature information analysis module is used to acquire the device parameter information of the power supply device to be charged, and analyze the charging feature information of the power supply device based on the device parameter information. The charging compatibility calculation module is used to match the charging feature information with historical charging data in the preset charging strategy library to obtain charging matching data, query the appropriate charging strategy in the charging matching data, and monitor the charging status parameters of the power device in real time during the charging process, and calculate the charging compatibility between the appropriate charging strategy and the charging status parameters. The core charging operation point extraction module is used to analyze the charging type of the power device based on the charging compatibility, perform strategy retrieval on the adapted charging strategy based on the charging type, obtain the retrieved charging strategy, and extract the core charging operation points in the retrieved charging strategy. The charging control command generation module is used to formulate a charging replenishment plan for the power supply equipment based on the core charging operation points, analyze the professional replenishment paths in the charging replenishment plan, query the charging replenishment criteria corresponding to the professional replenishment paths, and generate charging control commands for the power supply equipment based on the charging replenishment criteria. The energy efficiency conversion rate calculation module is used to locate the optimal charging path of the power supply device based on the charging control command, detect the real-time physical parameters of the charging medium of the power supply device, and calculate the energy efficiency conversion rate of the charging medium in the optimal charging path based on the real-time physical parameters. The immersion charging scheme formulation module is used to record the charging waveform of the power supply device during the charging process, and formulate a immersion charging scheme adapted to the power supply device by combining the energy efficiency conversion rate and the charging waveform.

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