Intelligent charging method and system based on charging pile

Abstract

The invention relates to the technical field of charging management, and discloses an intelligent charging method and system based on a charging pile, and the method comprises the steps: collecting an identifier of a charging object connected with the charging pile, calling the historical charging data and safety evaluation information, obtaining a user charging instruction, and combining with a charging pile performance evaluation model, performing gradient analysis on the charging instruction to generate a test plan and a subsequent plan, performing testable charging according to the test plan, collecting test data to construct a test information flow, analyzing the test information flow to form a feedback factor matrix, obtaining guide information in combination with safety evaluation information, and substituting the guide information into the subsequent plan to realize final stable charging operation. According to the method, by introducing multi-dimensional data analysis and gradient control, a personalized and dynamic charging strategy is realized, the charging efficiency and safety are improved, the service life of the battery is prolonged, the risks of overcharging, overheating and the like are reduced, and the problem that in the prior art, a charging pile lacks individual difference adjustment of a charging object is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charge management, and particularly relates to an intelligent charging method and system based on a charging pile. BACKGROUND

[0002] With the popularity of electric vehicles (EV) and charging piles, charging piles have become an important infrastructure for electric vehicle charging. In order to improve the charging efficiency of charging piles, prolong the battery life and ensure the safety of the charging process, an intelligent charging method emerges as the times require. The traditional charging pile system generally relies on simple current and voltage control for charging, lacks intelligent adjustment for individual differences of charging objects (electric vehicle batteries), and is prone to low charging efficiency, long charging time and even damage to the battery. SUMMARY

[0003] The present application aims to provide an intelligent charging method and system based on a charging pile, which aims to solve the problem of lack of individual difference adjustment of charging objects in the prior art.

[0004] The present application is implemented in the following manner. In a first aspect, the present application provides an intelligent charging method based on a charging pile, comprising: identifying a charging object connected to the charging pile to retrieve historical charging data and safety evaluation information of the charging object; obtaining a charging instruction of the user for the charging object, performing mode analysis on the safety evaluation information, the historical charging data and the charging instruction according to a performance evaluation model of the charging pile, generating a test plan and a subsequent plan; performing a test charging operation on the charging object according to the test plan and collecting test information flow; analyzing the test information flow, combining the analysis result with the safety evaluation information to generate guidance information for subsequent charging; substituting the guidance information into the subsequent plan to perform a stable charging operation on the charging object.

[0005] In a second aspect, the present application provides an intelligent charging system based on a charging pile, which is used to implement the intelligent charging method based on a charging pile of any one of the first aspect, comprising: an information collection module for identifying a charging object connected to the charging pile to retrieve historical charging data and safety evaluation information of the charging object; a gradient analysis module for obtaining a charging instruction of the user for the charging object, performing mode analysis on the safety evaluation information, the historical charging data and the charging instruction according to a performance evaluation model of the charging pile, generating a test plan and a subsequent plan; A charging test module is configured to perform a test charging operation on the charging object according to the test plan and collect a test information stream; A test analysis module is configured to analyze the test information stream and generate guiding information for subsequent charging by combining the analysis result with the safety evaluation information. A stable charging module is configured to substitute the guiding information into the subsequent plan to perform a stable charging operation on the charging object.

[0006] The present application provides an intelligent charging method based on a charging pile, which has the following advantages: The present application calls historical charging data and safety evaluation information of a charging object connected to a charging pile by collecting an identifier of the charging object, acquires a user charging instruction, performs gradient analysis on the charging instruction by combining a charging pile performance evaluation model, generates a test plan and a subsequent plan, performs a test charging operation according to the test plan, collects test data to construct a test information stream, analyzes the test information stream to form a feedback factor matrix, and obtains guiding information by combining the safety evaluation information, and substitutes the guiding information into the subsequent plan to perform a final stable charging operation. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is a step schematic diagram of an intelligent charging method based on a charging pile provided by an embodiment of the present application; Figure 2 is a structural schematic diagram of an intelligent charging system based on a charging pile provided by an embodiment of the present application. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0009] The implementation of the present application is described in detail below in combination with specific embodiments.

[0010] Referring to Figure 1 , Figure 2 , a preferred embodiment of the present application is provided.

[0011] In a first aspect, the present application provides an intelligent charging method based on a charging pile, which includes: S1: identifying a charging object connected to a charging pile to call historical charging data and safety evaluation information of the charging object; S2: acquiring a user's charging instruction for a charging object, performing mode analysis on the safety evaluation information, the historical charging data, and the charging instruction according to a performance evaluation model of the charging pile, and generating a test plan and a subsequent plan; S3: performing a test charging operation on the charging object according to the test plan, and collecting a test information stream; S4: analyzing the test information stream, and generating guiding information for subsequent charging by combining the analysis result with the safety evaluation information; S5: substituting the guiding information into the subsequent plan to perform a stable charging operation on the charging object.

[0012] Specifically, in step S1 of the embodiment provided in the present application, the charging pile first performs connection state monitoring of a charging interface. This step is used to determine whether a charging object (such as an electric vehicle, a charging battery, etc.) has been successfully docked. The subsequent identifier collection operation is triggered by detecting the interface connection state. Once it is detected that the charging interface has been docked, the charging pile starts its wireless signal function (such as Wi-Fi, Bluetooth, etc.). This function ensures that the charging pile can establish a wireless communication connection with a smart terminal (such as a mobile phone, a tablet, etc.) of a user.

[0013] More specifically, the user establishes a connection with the charging pile through a charging service application on the smart terminal. The application has been pre-installed and configured, and can interact with the charging pile. The user selects or scans a charging object that is docked with the charging pile in the application, and collects an identifier. The identifier can be a unique identification code of the charging object, such as an RFID tag, a two-dimensional code, or other forms of unique identification marks. Through the charging service application downloaded by the smart terminal, the identifier of the charging object that is docked with the charging pile is obtained. Using the identifier, information positioning is performed from a platform that stores data of the charging service application to find detailed data records of the charging object.

[0014] More specifically, on the data platform, the historical charging data (such as charging duration, power, charging frequency, etc.) of the charging object is located according to the identifier. At the same time, the safety evaluation information (such as battery health status, temperature change, safety fault record, etc.) of the charging object is called to further evaluate the charging condition and safety of the charging object. Through the collection of the identifier, the charging pile can identify and obtain the historical charging data and safety evaluation information of each charging object, which enables the charging pile to provide personalized charging strategies according to the needs of different charging objects, and improves the accuracy of the charging service.

[0015] It can be understood that after obtaining the historical charging data and safety evaluation information of the charging object, potential battery problems (such as battery aging, battery temperature being too high, etc.) can be found in time, and combined with these data, the charging pile can take appropriate measures to avoid potential safety hazards during charging, improve charging safety, and use historical charging data for performance analysis to optimize current, voltage and charging time during charging, avoid invalid or excessive charging, improve charging efficiency, and prolong the service life of the charging object. Through the link of the intelligent terminal, real-time data interaction and monitoring can be realized between the charging pile and the user, and the user can view the charging progress, state and safety information in real time through the terminal such as mobile phone, thereby enhancing the user experience. Combined with historical data and safety evaluation information, the charging pile can automatically generate a suitable charging scheme through intelligent algorithm and adjust the charging strategy in real time. This intelligent decision-making process improves charging efficiency and safety and reduces the need for human intervention.

[0016] Specifically, in step S2 of the embodiments provided by the present application, the user sends a charging instruction to the charging pile through an intelligent terminal (such as a mobile phone or a tablet), which may contain multiple dimensions, such as charging time, charging capacity, budget, expected charging end time, etc. The user can also select a preset charging strategy, such as fast charging, standard charging or energy-saving charging, etc.

[0017] More specifically, the charging pile has a built-in performance evaluation model that can evaluate the performance during charging according to the state of the device (such as current load, health status, power output capability, etc.). The charging pile adjusts the power output, voltage control and other parameters according to the real-time running situation to ensure the charging efficiency and safety during charging. According to the identifier collected previously, the system will obtain the historical charging data (such as charging period, charging time, battery health status, charging frequency, etc.) of the charging object from the data platform. At the same time, the system retrieves the safety evaluation information of the charging object, including battery temperature, overcharging condition, whether there is a fault, abnormal situation in the charging history, etc.

[0018] More specifically, according to the charging instruction, the performance evaluation model, the safety evaluation information and the historical data, the multiple control parameters (such as voltage, current, charging power, etc.) during charging are optimized and calculated, and the analysis process considers multiple targets (such as charging time, charging capacity, cost, temperature, etc.), and ensures that the charging process is optimized in terms of charging efficiency under the premise of ensuring safety.

[0019] More specifically, the gradient analysis is mainly divided into two categories, the test plan is used to simulate the preliminary test stage in the charging process, the main purpose is to test the performance of various combinations of control parameters in the actual charging process. Through multiple simulations, adjust the charging power, current, temperature and other parameters to achieve a charging mode suitable for the charging object, the subsequent plan is the final charging execution mode determined after multiple adjustments. This mode will run stably during the charging process to ensure the realization of the charging goal while meeting the requirements of safety, performance and efficiency.

[0020] More specifically, according to the results of the test plan, the system generates a subsequent plan, which is the final charging parameter configuration (such as maximum power, voltage, current range, etc.) for actual charging operation. The subsequent plan not only optimizes the time and power control during charging, but also avoids potential safety hazards such as overcharging and overheating, ensuring efficient and safe charging. Once the subsequent plan is generated, the charging pile will execute the charging operation according to the gradient, that is, start the stable charging process. The charging pile will continuously monitor the state of the charging object during execution and adjust the charging parameters (such as current, voltage, power, etc.) in real time to maintain the safety and efficiency of charging.

[0021] It can be understood that through the analysis and execution of the user's charging instruction gradient generation, the charging pile can realize intelligent charging strategy, adjust the charging process according to different charging objects, charging needs and environmental conditions, which makes the charging not only a simple power input process, but also an intelligent decision-making process based on multi-factor optimization. Through the execution of gradient analysis, the charging pile can accurately calculate the best current, voltage and other parameter configurations in the charging process according to the historical data and performance evaluation model of the charging object. This optimization process can significantly improve charging efficiency, reduce invalid charging time and save energy. By combining the safety assessment information of the charging object with the performance model of the charging pile, the system can assess the safety risk in real time during the charging process. The test plan helps to discover potential safety hazards (such as high temperature and fast charging) in advance and adjust the strategy in time, thereby ensuring the safety of the charging process.

[0022] More specifically, user charging instructions (such as charging duration, cost control, etc.) will be personalized according to the characteristics of the charging object, and each charging object will receive the most suitable charging method based on its battery status, usage history, health status, and other information. This improves user experience and makes the charging process more flexible and convenient. The generation of subsequent plans is based on dynamic adjustment and real-time feedback mechanisms, which can monitor various parameter changes (such as battery temperature, voltage, current, etc.) in real time during the charging process. If any abnormalities occur, the system can immediately adjust to avoid safety problems or efficiency reduction during the charging process. Through optimized charging processes, problems such as overcharging and overheating are avoided, thereby extending the service life of the battery. In addition, the combination of safety assessment information and historical charging data also helps the charging pile better maintain battery health.

[0023] Specifically, in step S3 of the embodiments provided by the present application, after the test plan is generated and verified, the charging pile starts the test charging operation on the charging object according to the gradient. This process does not involve actual charging targets, but simulates the impact of different control parameters (such as voltage, current, power, etc.) on charging effects during the charging process. The test charging operation can be a short charging period (such as a few minutes), and its purpose is to collect response data of the charging object under various charging parameters.

[0024] More specifically, during the test charging operation, the charging pile collects test data of the charging object in real time. These parameters include but are not limited to: Voltage: battery voltage during charging, Current: current flowing into the battery during charging, Temperature: temperature data of the battery and charging interface, Battery Health: health status of the battery, such as charging efficiency, battery capacity loss, etc., Charging Power: energy transmitted to the battery per unit time, Charging Time: duration of the charging process. Through wireless communication (such as Wi-Fi, Bluetooth, etc.) between the charging pile and the intelligent terminal, these multi-dimensional data are transmitted to the data platform or local storage system in real time for further analysis and processing.

[0025] More specifically, the test information flow refers to the data stream composed of all charging parameters collected by the charging pile, including real-time data collected from the test charging operation, timestamps, charging pile status and other related performance evaluation data, which will be integrated and constructed into a complete information flow to form a detailed record describing the behavior of the charging object. The test information flow can be used for subsequent analysis to help identify potential problems in the charging process and optimize the charging scheme. The test information flow is usually saved in the form of structured data, which may include time series, sensor data points, event markers, etc.

[0026] More specifically, according to the test information flow, the charging pile system can analyze the parameter changes in real time during the charging process. If it finds that some charging strategies or charging parameters are not suitable for the current charging object (such as high battery temperature, large voltage fluctuation, etc.), the system can immediately adjust the parameters to optimize the charging operation. The test information flow can be further processed through data analysis and machine learning algorithms to automatically generate optimized charging strategies to ensure the best charging effect in the next formal charging operation.

[0027] More specifically, after the test charging operation is completed, the system will terminate the charging process and generate a detailed charging test report, including the trend of each parameter change during the charging process and the performance of the charging object. All test data, charging object test data and optimized strategies will be stored in the data platform for subsequent reference, analysis and prediction.

[0028] It can be understood that by performing test charging operation on the charging object, the reaction of the charging object under different charging conditions can be fully understood, which helps to adjust the charging parameters to achieve optimal charging efficiency. This process enables the charging pile to dynamically adjust the charging strategy according to the specific charging object to improve the overall efficiency of the charging process. During the test charging process, the charging pile can monitor and collect data in multiple dimensions in real time, and dynamically adjust the charging strategy to avoid potential charging safety problems or battery overload, which can greatly improve the safety and reliability of the charging process. By adjusting the charging strategy during the test charging operation, the charging pile can avoid battery overcharging or overheating, effectively prolonging the service life of the battery. Test charging operation helps to find the best temperature, power and current in the battery charging process, so that the battery can be charged under optimal conditions.

[0029] More specifically, the test charging operation can customize a personalized charging strategy for each charging object according to its real-time performance, as each charging object has different charging needs and characteristics, and the test information flow helps the charging pile to optimize in real time and design a suitable charging scheme for each charging object, thereby improving the charging experience and effect. The generation of the test information flow not only provides basic data support for the intelligent decision-making of the charging pile, but also provides rich historical data for subsequent data analysis, machine learning model training, etc. These data can be used to develop prediction models to improve the accuracy of future charging operations. Through the test charging operation and the collection of multi-dimensional parameters of the charging object, users can obtain more accurate state feedback and more stable charging effect during the charging process, and the user's charging experience is significantly improved. The charging process is more controllable and transparent, and the test information flow provides a comprehensive performance evaluation report for the charging object. These data not only help the charging pile to evaluate the effect of the current charging strategy, but also provide an important basis for the health management and maintenance strategy of the charging object.

[0030] Specifically, in step S4 of the embodiments provided by the present application, the test information flow is the test data collected in real time during the test charging operation. These data usually include voltage, current, temperature, battery health, charging power, etc. Information records the behavior of the charging object under different charging conditions.

[0031] More specifically, the test information flow can be divided into two forms: the overall form refers to the collection of all charging data, covering the entire test charging period, reflecting the overall trend of various parameters in the charging process; the segmented form divides the test data by time or charging stage, each segment representing a specific stage in the charging process (e.g. start-up stage, middle charging stage, end charging stage, etc.), for detailed analysis of the performance in different stages.

[0032] More specifically, time series analysis is performed on the entire test information flow to view the overall trend of various charging parameters (such as voltage, current, temperature, etc.) in the charging process, identify key points in the charging process (such as battery temperature reaching a safety threshold, voltage fluctuation, etc.), and extract key performance characteristics and behavior patterns through statistical methods (such as mean, standard deviation, maximum and minimum values, etc.).

[0033] More specifically, the test information flow is segmented according to different stages of the charging process (e.g. start-up stage, stable charging stage, end stage, etc.), and the parameter changes in each stage are analyzed. The parameter fluctuations and stability in each stage can reflect potential problems in the charging process, such as instability and power fluctuations during charging. Through segmented analysis, feedback factors for each stage can be obtained to optimize the performance of the charging strategy in each stage.

[0034] More specifically, the test feedback factor matrix (FFM) is a matrix derived from the analysis of test information flow, which contains the response behavior of various charging parameters (such as voltage, current, temperature, etc.) at different time periods or charging stages. These response behaviors can reflect the performance changes during the charging process. Each row of the matrix corresponds to a charging stage or time period, and each column represents a charging parameter (such as voltage, current, etc.). Each element in the matrix represents the feedback value or change amplitude under specific stage and specific parameter. Through the data in the matrix, key features of each stage can be extracted, such as temperature fluctuation range, voltage stability, current change rate, etc. These features help to evaluate the performance of various parameters during the charging process and provide a basis for further adjustment.

[0035] More specifically, the safety evaluation information includes the battery temperature of the charging object, the battery health status, whether the battery has overcharged, overheated, and other abnormal situations. The charging pile combines these information with the test feedback factor matrix to ensure that the charging operation is within a safe range. After generating the test feedback factor matrix, comprehensive evaluation needs to be conducted in combination with the safety evaluation information to determine whether there are safety hazards. For example, if the temperature feedback value in the test feedback factor matrix is abnormal (such as high temperature), and the safety evaluation information also indicates that the battery temperature is too high, the charging parameters need to be adjusted or the charging needs to be suspended. If some parameters (such as current, voltage, etc.) in the test feedback factor matrix conflict with the safety evaluation information (for example, the battery temperature is too high and the current is too large), the system will adjust the parameters according to the safety evaluation information, such as reducing the charging current, adjusting the charging power, etc., to ensure the safety of the charging process.

[0036] More specifically, the guidance information refers to the optimized and adjusted charging parameters under the comprehensive reference of the test feedback factor matrix and the safety evaluation information. These parameters ensure the stability, safety, and efficiency of various indicators during the charging process. Through the analysis of the overall and segmented form of the test feedback factor matrix, the optimal charging parameter configuration (such as current, voltage, battery temperature control range, etc.) for each stage is obtained. According to the safety evaluation information, unsafe factors in the test feedback factor matrix are screened and corrected, such as adjusting the charging power when the temperature is too high, increasing the current output when the battery voltage is too low, etc. Combined with the optimized parameter adjustment, the guidance information is generated. These parameters will be applied in the actual charging process to ensure that the charging operation is both efficient and safe.

[0037] It is understandable that by analyzing the overall and segmented test information flow, each stage of the charging process and its parameter changes can be accurately identified, thereby optimizing the charging strategy for each stage. The generation of guidance information ensures optimal performance at each stage of the charging process. By combining safety assessment information with the test feedback factor matrix, the system can monitor potential safety risks during the charging process in real time and adjust charging parameters in a timely manner. For example, it can avoid situations such as excessive battery temperature and excessive current, thereby effectively preventing safety hazards.

[0038] More specifically, based on the historical data and real-time feedback of the charging objects, personalized charging parameters can be provided for different charging objects (such as different battery types, battery health conditions, etc.). This flexibility enables the charging process to adapt to various charging needs and battery characteristics. Through optimized guidance information, the charging pile can improve the efficiency of the charging process, shorten the charging time, and maximize the battery's charging capacity while ensuring safety. The combination of test feedback factor matrix and safety assessment information provides intelligent decision-making support for the charging pile system, enabling charging operations to be dynamically adjusted according to real-time data, reducing human intervention and realizing automated and efficient charging management.

[0039] Specifically, in step S5 of the embodiment provided by the present invention, as mentioned above, these are the charging parameters optimized after comprehensive analysis of the test feedback factor matrix and the safety assessment information. These parameters include current, voltage, charging power, etc., which are used to ensure the stability and safety of the charging process at all stages. In the actual charging process, the subsequent plan refers to the control strategy of the charging system that dynamically adjusts according to the guidance information, and executes the gradient to convert the guidance information into actual charging operations. Through real-time monitoring and adjustment of the system, it is ensured that the current, voltage and other parameters during the charging process do not exceed the safety range, and the charging process is smooth and efficient.

[0040] More specifically, based on the test feedback factor matrix and safety assessment information, the optimal charging parameters for each stage, such as current, voltage, power, etc., are obtained, the guidance information is substituted into the control algorithm of the charging system, and the corresponding execution gradient is calculated. The execution gradient usually involves a gradient descent algorithm or a feedback control strategy based on a predictive model. This process mainly ensures that the battery gradually stabilizes in each charging stage and the parameter adjustment range is not too large to prevent the battery or charging system from overreacting or becoming unstable. The generation of subsequent plans not only needs to consider the values ​​of the gradient parameters, but also needs to smooth the rate of parameter change to avoid mutations or fluctuations. During the charging process, the charging pile will gradually adjust the output power, current and voltage according to the subsequent plans to ensure that the charging operation is continuous and stable.

[0041] The present invention provides an intelligent charging method based on a charging pile, which has the following beneficial effects: The present application calls historical charging data and safety assessment information of the charging object connected with the charging pile by collecting the identifier of the charging object, obtains user charging instructions, and combines a charging pile performance evaluation model to perform gradient analysis on the charging instructions to generate test plans and subsequent plans, performs test charging according to the test plans, collects test data to construct test information flow, analyzes the test information flow to form a feedback factor matrix, and obtains guidance information in combination with the safety assessment information to realize the final stable charging operation by substituting the subsequent plans. The method realizes personalized and dynamic charging strategies by introducing multi-dimensional data analysis and gradient control, improves charging efficiency and safety, prolongs battery life, reduces the risk of overcharging, overheating, and other risks, and solves the problem of lack of individual difference adjustment of charging objects in the prior art.

[0042] Preferably, the step of collecting the identifier of the charging object connected with the charging pile to retrieve the historical charging data and safety assessment information of the charging object from the data platform according to the identifier comprises: S11: Real-time detection of the connection state of the charging interface of the charging pile, when the charging interface is in the docking state, the wireless signal linkage function of the charging pile is started to enable the user's smart terminal to link with the charging pile through the designated charging service application; S12: Collecting the identifier of the charging object connected with the charging pile through the charging service application downloaded by the smart terminal to obtain the identifier of the charging object; S13: Locating the data platform storing the charging service application data according to the identifier to obtain the application data record of the charging object in the data platform; S14: Retrieving the data record of the charging object accepting the charging service of the charging pile belonging to the charging service application in the past time from the application data record as the historical charging data, and retrieving the safety assessment information of the charging object from the application data record.

[0043] Specifically, the charging pile monitors the connection state of the charging interface in real time through the built-in sensor and communication module. When the charging interface is correctly connected with the charging object, the charging pile detects that the connection state is in the "docking" state. This real-time monitoring mechanism ensures that the charging pile can timely identify the connection state of the charging object and prepare for subsequent charging operation and data exchange. If the charging interface is not connected, invalid operation or unnecessary data exchange is avoided.

[0044] More specifically, after the charging interface is successfully docked, the charging pile will automatically enable the wireless signal linking function. At this time, the charging pile provides Wi-Fi, Bluetooth, or other wireless communication methods for smart terminals (such as smartphones, tablets, etc.). The wireless signal linking function between the charging pile and the smart terminal is to realize data intercommunication, ensure that the identifier of the charging object can be successfully collected, and enable real-time communication with the charging pile through wireless signals. The opening of this function provides a data transmission channel, which helps subsequent data access and historical record retrieval.

[0045] More specifically, the user scans or reads the identifier (such as a two-dimensional code, NFC tag, RFID, etc.) of the charging object through the charging service application downloaded on the smart terminal. This identifier contains the unique identity information of the charging object. By collecting the identifier through the charging service application, the uniqueness of each charging object in the charging system can be ensured. This identifier serves as the key for subsequent data query and retrieval and is the basis for distinguishing different charging objects.

[0046] More specifically, according to the collected identifier, the charging service application transmits the identifier to the data platform. In the platform, the identifier is used for positioning query, pointing to the application data record of the corresponding charging object in the platform. By positioning the specific charging object data through the identifier, the charging service application can accurately obtain the data record related to the charging object from the platform. This process can reduce the probability of incorrect queries and improve data retrieval efficiency.

[0047] More specifically, from the application data record in the data platform, the historical charging data of the charging object is obtained. These data include charging duration, charging power, charging start and end time, battery state, charging current, voltage, and other information, which record the detailed information of the charging object receiving charging services in the past period of time. The retrieval of historical charging data provides a basis for behavior analysis of the charging object for charging services. The system can evaluate the usage and charging habits of the charging object based on these data to further optimize future charging processes.

[0048] More specifically, in addition to historical charging data, the safety evaluation information of the charging object will also be retrieved. This information may include battery health status, battery temperature monitoring, battery capacity degradation, whether the battery has faults or hidden dangers, etc. The retrieval of safety evaluation information can help the charging pile to perform safety monitoring during the charging process. According to the battery health status, the charging pile can dynamically adjust the charging power, current, and other parameters to ensure the safety of the battery during the charging process and avoid overcharging, overheating, and other problems.

[0049] More specifically, the charging pile adjusts the charging process according to historical data and safety evaluation information. If the battery health of the charging object is poor or there is a safety hazard, the system will automatically reduce the charging power or current to ensure that the charging process does not damage the battery. This data-driven dynamic adjustment can improve the intelligence level of charging and enhance the safety of the charging process. By adjusting the charging behavior according to historical charging data and safety evaluation information, the charging pile can minimize battery damage and improve battery life.

[0050] It can be understood that the system can automatically adjust the charging strategy according to the historical charging data and safety evaluation information of the charging object. This intelligent management effectively improves the charging efficiency and ensures the health and safety of the battery. By retrieving the safety evaluation information of the charging object, the charging pile can respond to potential battery risks in real time, avoid overcharging and overheating, and protect the safety of the charging object. The historical charging data of each charging object can be used as a reference for future charging decisions. According to the charging habits and battery conditions of different charging objects, the system can provide personalized charging solutions to optimize charging time and improve charging effectiveness.

[0051] More specifically, users and charging operators can query the historical charging records and safety evaluation information of the charging object at any time. The charging process is more transparent and traceable, which not only enhances user trust but also facilitates the maintenance and management of charging equipment. After retrieving historical data and safety evaluation information, the charging pile can more efficiently perform charging scheduling, avoid overcharging the battery, reduce charging time and energy waste, and ensure that the charging process does not unnecessarily burden the battery. The seamless integration between the charging service application and the data platform allows the historical data of the charging object, charging service records, and safety evaluation information to be quickly obtained and integrated, which not only improves charging efficiency but also provides platform operators with more accurate user behavior and equipment condition data, further promoting the development of the intelligent charging ecosystem.

[0052] Preferably, the step of obtaining the charging instruction of the user for the charging object, and retrieving the performance evaluation model of the charging pile to analyze the execution gradient of the charging instruction according to the performance evaluation model, the safety evaluation information, and the historical charging data to generate the test plan and the subsequent plan comprises: S21: Obtain the charging instruction of the user for the charging object, and perform triple-dimensional information analysis on the charging instruction to generate expected target information composed of expected time dimension, expected power dimension, and expected cost dimension; S22: Perform multi-objective analysis on the expected target information by the performance evaluation model of the charging pile, the safety evaluation information, and the historical charging data to generate charging constraint information; S23: Based on the charging constraint information, a security boundary exploration is performed, and various forms of trial adjustment and feasibility analysis are performed on the charging constraint information based on the security boundary, to generate a plurality of charging preparation strategies within the security boundary, to jointly construct a subsequent plan; S24: The test scheme requirement analysis is performed on each charging preparation strategy in the subsequent plan to generate a test plan.

[0053] Specifically, the system receives charging instructions issued by the user, which usually include charging time, charging capacity, and expected cost. After obtaining the instructions, the system analyzes the charging instructions in three dimensions: expected time dimension: the time range or duration that the user hopes to complete the charging, expected capacity dimension: the specific capacity that the user expects to charge, and expected cost dimension: the charging cost that the user hopes to pay. This step of analysis refines the charging demand into quantifiable target information, providing clear input for subsequent execution gradient analysis.

[0054] More specifically, based on the expected target information obtained from the charging instructions, combined with the performance evaluation model of the charging pile, safety evaluation information, and historical charging data, the system will perform multi-objective analysis, which means that the system will consider the performance of the charging pile (such as maximum output power, charging efficiency), battery health status, historical charging habits, etc., and perform a comprehensive optimization analysis. This multi-objective analysis ensures that the charging process can meet the user's demand to the greatest extent, while avoiding the risk of overloading or unsafe risks to the charging pile and the battery. Through comprehensive analysis, a more accurate charging constraint condition can be provided for the charging process.

[0055] More specifically, based on the results of multi-objective analysis, the system will generate a charging constraint information scheme, which includes: charging power limit, maximum charging current, battery temperature range, etc. to ensure that the charging process meets all safety requirements when executing user instructions. By generating reasonable charging constraints, it can be ensured that the charging process will not exceed the safety range of the battery or the charging pile. The generation of this constraint is the basis for ensuring safe and efficient charging.

[0056] More specifically, based on the generated charging constraints, the system performs security boundary exploration, which involves trying different charging schemes, power adjustments, etc. while meeting the constraints, and analyzing the feasibility of these schemes. Trial adjustment within the security boundary includes: charging time analysis under different power modes, the influence of battery temperature, charging current, etc. on the charging process. Exploring the security boundary helps to ensure that the charging process can meet the user's demand and not exceed the safety limit of the battery or device. In addition, trial adjustment and feasibility analysis can help to find the best charging strategy, further improving the efficiency and safety of charging.

[0057] More specifically, through security boundary exploration, the system can generate several charging preparation strategies, which represent the adjustment schemes of different stages (such as the initial, middle, and late stages of charging) to cope with different charging demands and battery states. The purpose of generating charging preparation strategies is to ensure that the charging process can adjust the charging method according to different stages of the battery state, so that the charging process can be carried out in a stable and safe state, avoiding unexpected risks during the charging process.

[0058] More specifically, the system iteratively simulates each charging preparation strategy to evaluate indicators such as stability, efficiency, and safety of the charging process. These simulations are usually simulated through charging models to evaluate the performance of each strategy in actual charging processes. Through the analysis of test plan requirements, potential problems can be discovered and corrected in a timely manner, and charging strategies can be optimized. In subsequent plans, the system can obtain multiple execution plans to help determine the most suitable charging strategy. This simulation process provides a scientific basis for subsequent actual charging operations.

[0059] More specifically, after multiple rounds of testing and simulation, the system will generate a test plan. This gradient represents the execution strategy under various conditions during the charging process, including optimization strategies for different time periods, charging power, and current adjustment. The test plan can help evaluate the actual execution effect of the charging instruction under different conditions. By generating a test plan, the optimal charging scheme can be obtained to help optimize charging time, cost, and battery life, etc., ensuring the overall optimization of the charging operation.

[0060] It can be understood that through multi-objective analysis and constraint solving, the system can more accurately control the charging process, avoid resource waste, and improve charging efficiency. Different execution gradients provide optimization schemes for different charging scenarios, helping to select the best charging strategy under different battery conditions and charging demands. The charging pile always remains within the safety boundary during execution, effectively preventing problems such as overcharging, overheating, or battery damage. Through security boundary exploration and experimental adjustment, the system ensures that the charging process meets the safety requirements of the battery and equipment, reducing potential risks. Through the analysis of user demand expectation information, the system can provide personalized charging services according to the needs of different users. Whether it is time, cost, or power demand, the execution strategy can be adjusted according to specific circumstances to improve user experience.

[0061] More specifically, through iterative simulation and generation of charging preparation strategies, the system can analyze and optimize different stages of the charging process, ensuring the stability and feasibility of the entire charging process, whether in the initial charging stage or the final stage before charging is completed. The system can make corresponding adjustments to ensure the smooth completion of charging. Through the generation of test plans and optimization of charging strategies, the system can optimize the charging process while protecting the battery, avoiding overcharging or fast charging, and maximizing the service life of the battery. Precise execution of gradients and personalized services can improve charging efficiency, reduce charging time, and provide users with a reliable and safe charging experience.

[0062] Preferably, the step of generating charging constraint information by multi-objective analysis of the expected target information through the performance evaluation model of the charging pile, the safety evaluation information, and the historical charging data comprises: S221: retrieve the performance evaluation model of the charging pile to analyze the charging efficiency curve and the heat dissipation performance curve of the charging pile; S222: analyze the change trend of the charging object in the historical charging data through the time sequence correlation algorithm to obtain the aging coefficient, efficiency acceptance, and temperature rise mode of the charging object at the current time as charging reference elements; S223: based on the charging reference elements and the safety evaluation information, cut off the specified interval of the charging efficiency curve, and perform paragraph decomposition and time limit assignment on the cut-off charging efficiency curve to obtain a plurality of charging efficiency paragraphs with safety charging time limits; S224: construct a charging object constraint axis according to each charging efficiency paragraph with a safety charging time limit, construct a charging pile constraint axis according to the heat dissipation performance curve, and jointly construct a charging constraint space by combining the charging object constraint axis and the charging pile constraint axis; S225: substitute the expected target information into the charging constraint space to perform multi-objective analysis of the expected target information in the charging constraint space through a multi-objective optimization algorithm to generate charging constraint information.

[0063] Specifically, the system first retrieves the performance evaluation model of the charging pile, which includes the charging efficiency curve and the heat dissipation performance curve of the charging pile. The charging efficiency curve describes the efficiency performance of the charging pile at different charging power or time periods, and the heat dissipation performance curve reflects the relationship between the heat generated by the charging pile during charging and its heat dissipation capacity. By obtaining the performance evaluation model of the charging pile, the system can understand the working efficiency and heat dissipation of the charging pile under different working conditions, providing key data support for subsequent charging constraint information. The heat dissipation performance curve is particularly important because it relates to whether the charging pile will overheat during charging, affecting its safety and reliability.

[0064] More specifically, the historical charging data is analyzed using a time correlation algorithm, focusing on the changing trends of the charging object, including: aging coefficient: the aging condition experienced by the charging object (such as the battery) over time, efficiency acceptance: the charging efficiency acceptance ability of the battery or charging object under different charging power, temperature rise pattern: the temperature change pattern of the battery or charging object during the charging process, which may affect the safety and efficiency of the charging process. The time correlation algorithm can accurately assess the state of the charging object at the current time, especially its aging degree, acceptance of charging efficiency and temperature rise pattern, which will become an important reference factor in the charging process, helping to determine the most appropriate charging strategy.

[0065] More specifically, combining charging reference factors (such as aging coefficient, efficiency acceptance, temperature rise pattern) with safety assessment information, the system analyzes the charging efficiency curve of the charging pile, according to the current state of the charging object and the safety assessment, intercepts the specific interval of the charging efficiency curve, excludes the part that is not suitable for the current condition, and performs paragraph decomposition on the intercepted charging efficiency curve, sets appropriate time limits for each paragraph, to ensure that the charging process is carried out within the safety range, this processing ensures that the charging process can be dynamically adjusted according to the actual situation of the charging object, avoiding overload or overcharging, optimizing the efficiency of the charging process, and protecting the safety of the equipment and battery.

[0066] More specifically, according to the charging state of the charging object (such as aging coefficient, efficiency acceptance, temperature rise pattern, etc.), a charging object constraint axis is constructed, which describes the charging intensity, time, temperature and other conditions that the charging object can withstand, according to the heat dissipation capacity, charging power and other performance indicators of the charging pile, a charging pile constraint axis is constructed, which describes the stable working state that the charging pile can maintain under different load conditions, the charging object constraint axis and the charging pile constraint axis are the core of the charging constraint information, combined with the two constraint axes, a safe and efficient charging process can be theoretically constructed.

[0067] More specifically, the charging object constraint axis and the charging pile constraint axis are combined to generate a charging constraint space, which is a multi-dimensional space containing all the safety and performance limit conditions that need to be followed in the charging process, the construction of the charging constraint space integrates all possible charging process conditions and constraints into a unified framework, helping to further analyze and optimize various factors in the charging process.

[0068] More specifically, the user-provided expected target information (including expected time, expected power, and expected cost) is substituted into the charging constraint space, and a multi-objective optimization algorithm is used for analysis. This optimization algorithm considers multiple objectives such as charging time, power, and cost, ensuring that the charging process meets user needs while also meeting various safety and performance requirements in the charging constraint space. Through multi-objective optimization, the system can simultaneously optimize charging time, power, and cost, thereby providing the user with an optimal charging strategy that considers efficiency and safety comprehensively. This step ensures the rationality of the charging process, maximizes user demand, and guarantees the safety and efficiency of the charging process.

[0069] More specifically, after the above multi-objective optimization, the system generates charging constraint information that meets the charging instructions. This solution not only meets user needs in terms of charging time, power, and cost, but also ensures that the charging process is within the safety constraints of the charging pile and the battery. The generated charging constraint information is an optimal solution that balances multiple objectives and strictly adheres to safety and performance constraints. This not only improves charging efficiency, but also guarantees the safety and stability of the charging process, ultimately improving the user's charging experience.

[0070] It can be understood that by retrieving the performance evaluation model of the charging pile and historical charging data, the system can more accurately control the charging process, ensuring that the charging power, time, and temperature are within a safe range, thereby avoiding equipment failure or battery damage. By reasonably intercepting and segmenting the charging efficiency curve, the system can maximize charging efficiency while ensuring safety, reducing charging time, and optimizing all constraints in the charging process based on the actual conditions of the aging degree, temperature change, and charging efficiency of the charging object, ensuring the safety of the battery and the charging pile. Through the combination of the charging object constraint axis and the charging pile constraint axis, problems such as overloading and overheating are avoided.

[0071] More specifically, by substituting the user's charging requirements (time, power, and cost) into the charging constraint space and performing multi-objective optimization, the system can provide personalized charging solutions that meet the needs of different users. By considering charging efficiency, safety, and user demand comprehensively, the system can provide a more intelligent and personalized charging experience, optimize battery life, and improve the efficiency of the charging pile, ultimately improving user satisfaction and the long-term reliability of the device.

[0072] Preferably, the step of analyzing the test plan requirements of each charging preparation strategy in the subsequent plan includes: S241: Analyzing the charging object adaptability of each charging preparation strategy in the subsequent plan to obtain adaptability parameters corresponding to each charging preparation strategy for various expected condition characteristics of the charging object; S242: dividing the test priorities of various expected condition features of the charging object according to the adaptability parameters, to assign test priorities to various expected condition features; S243: configuring initial test strategies for various expected condition features, and calculating the differential test effects of various expected condition features with respect to the initial test strategies; S244: evaluating the differential test effects with the test priorities of various expected condition features as supervision conditions, and iteratively optimizing the initial test strategies based on the evaluation results to obtain a test plan meeting preset standards.

[0073] Specifically, each charging preparation strategy in the subsequent plan is analyzed, focusing on the adaptability analysis of different equipment condition features (such as battery state, temperature, aging degree, etc.) of the charging object. The result of the analysis is to determine the adaptability parameters of each equipment condition feature corresponding to different charging preparation strategies. This step ensures that the preparation strategy of each stable stage can be targeted to optimize and adjust, improving the overall adaptability and accuracy of the charging process through in-depth analysis of different states of the charging object.

[0074] More specifically, according to the adaptability parameters obtained in the previous step, various expected condition features of the charging object are divided into different test priorities. The division of test priorities takes into account the importance and influence of each equipment condition feature on the test results. This division ensures the rational allocation of test resources, so that the most critical and influential equipment features are tested first, thereby improving the efficiency and effectiveness of the test and avoiding the waste of resources for low-priority tests.

[0075] More specifically, according to the test priorities, initial test strategies are configured for each expected condition feature. These strategies aim to evaluate the performance and adaptability of different equipment condition features under various charging preparation strategies through preliminary testing. The configuration of initial test strategies provides a test benchmark that can quickly evaluate the performance of each equipment feature in actual application, providing a basis for subsequent optimization and iteration.

[0076] More specifically, after performing the initial test, the differential test effects of various expected condition features with respect to the initial test strategies are calculated. These effects reflect the applicability of the initial test strategies under different charging object equipment conditions. By calculating the differential test effects, the system can quantify the adaptability of each expected condition feature to the initial strategy, which helps to identify which equipment features and test strategies have a large gap, and thus reveals the shortcomings of the test strategies.

[0077] More specifically, the system evaluates the differential test effect under the supervision of the test priority of various expected condition characteristics. By considering the importance of each device condition characteristic and weighting it according to its priority, the system ensures that test resources are prioritized for the most critical parts. Through weighted evaluation, the system can adjust the evaluation process according to the priority of different device condition characteristics, ensuring that the most important differences receive more attention, further improving the effectiveness and accuracy of the test.

[0078] More specifically, according to the results of the differential test effect evaluation, the system iteratively optimizes the initial test strategy. This optimization process adjusts and improves the test strategy to better adapt to various device condition characteristics of the charging object and improve its performance in each stable stage. Through iterative optimization, the system can gradually improve the test strategy to meet the needs of different device characteristics, ensuring that the test is stable and effective at each stage. The final test plan generated meets the preset standards and can efficiently and accurately perform the test.

[0079] More specifically, after the above analysis, division, configuration, testing, and evaluation, a test plan that meets the preset standards is finally generated. The gradient ensures the stability, accuracy, and efficiency of the test process, while meeting the adaptability needs of the charging object in different states. The generated test plan optimizes the test strategy, allowing various device condition characteristics during charging to be accurately evaluated and adjusted, thereby ensuring the quality of the test and the effectiveness of the final execution.

[0080] It can be understood that through adaptive analysis and priority division, the system can more targetedly perform the test, avoiding unnecessary testing and improving efficiency. Meanwhile, the test strategy is iteratively optimized to accurately meet the needs of different device conditions of the charging object, improving test precision. The division of test priority ensures that the most critical device condition characteristics are tested first, thereby reasonably allocating test resources and ensuring the efficiency and stability of the test process. Through evaluation and iterative optimization of the differential test effect, the test strategy can dynamically adjust according to the feedback of device condition characteristics, ensuring that the test process can be flexibly adjusted as the charging object changes, avoiding rigid and inadaptability in the strategy.

[0081] More specifically, the generated test plan can be stably executed under the framework that meets the preset standards, ensuring the safety and performance standards of the charging process, reducing potential device damage or efficiency loss. The system can automatically adjust the strategy according to the changes in the aging, temperature, and state of different devices through analysis and testing of the device condition characteristics of the charging object, having strong self-adaptability and being able to cope with complex and variable charging environments.

[0082] Preferably, the step of performing a test charging operation on the charging object according to the test plan and continuously collecting test data of the charging object to construct a test information flow comprises: S31: configuring electrical parameters of the charging object according to the test plan to perform a test charging operation on the charging object; wherein the electrical parameters include a charging voltage parameter and a charging current parameter; S32: continuously recording the electrical parameters and collecting battery state parameters of the charging object, and combining the electrical parameters and the battery state parameters as test data; wherein the battery state parameters include a battery temperature parameter and a battery capacity parameter; S33: time-series combining test data of each time node to obtain a test information flow.

[0083] Specifically, according to the previously generated test plan, the electrical parameters of the charging object are set, mainly including the charging voltage parameter and the charging current parameter. This process ensures that the charging process meets the predetermined test standards and gradients. This step ensures that the electrical conditions of the charging process meet the test requirements, provides accurate parameter support for subsequent charging tests, and ensures that the test execution process can simulate the predetermined charging phase, thereby obtaining real charging data.

[0084] More specifically, during the charging process, the electrical parameters such as charging voltage and charging current are continuously recorded, and the battery state parameters, especially the battery temperature and battery capacity, are synchronously collected. These battery state parameters reflect the key factors in the charging process. Continuous recording of electrical parameters and collection of battery state parameters can monitor the changes of the charging process in real time, providing comprehensive data support for subsequent test data analysis and optimization. This data collection can ensure the safety and stability of the charging process.

[0085] More specifically, the collected electrical parameters (such as charging voltage and charging current) and battery state parameters (such as battery temperature and battery capacity) are combined to form test data. This way, each variable in the charging process and their relationships can be more comprehensively described. Combining electrical parameters and battery state parameters can provide a more detailed and comprehensive view of charging data, reflecting the interaction between different factors in the charging process, which facilitates the analysis and prediction of charging performance and potential problems.

[0086] More specifically, the test data at each time node is time-series combined in chronological order according to the time sequence of the charging process, which can reflect the changes and trends in the charging process, so that the test data presents a coherent time series structure, and the time-series combination can help the system analyze the dynamic changes in the charging process, identify the rules, abnormalities or potential problems in the charging process, and at the same time, the combination of time-series data also facilitates trend analysis and prediction of different situations that may occur in the charging process.

[0087] More specifically, through time-series combination, a test information stream is finally obtained, which contains various parameter information in the charging process. These information streams can provide data support for subsequent charging strategy optimization, fault warning and performance improvement. The generated test information stream is a multi-dimensional data representation of the charging process, which can provide support for performance evaluation, data analysis and optimization adjustment of the charging system. Through these information streams, the equipment state can be monitored in real time during the charging process, and intelligent adjustment can be made to improve charging efficiency and safety.

[0088] It can be understood that by continuously recording electrical parameters and battery state parameters, the system can monitor various key indicators (such as voltage, current, temperature, power, etc.) of the charging process in real time. This real-time monitoring helps to discover problems in the charging process in a timely manner, avoids the risk of overcharging, overheating and other factors that may cause equipment damage, and combines electrical parameters and battery state parameters to form test data, making the analysis of the charging process more comprehensive and enabling a deeper understanding of the relationship between electrical characteristics and battery conditions in the charging process, thereby optimizing charging strategies and equipment design.

[0089] More specifically, through time-series combination, the system can generate time-series data in the charging process, which not only helps to identify the change pattern in the charging process, but also predicts the future charging state, helps to discover potential problems or optimize the charging strategy in advance. The test information stream provides a rich source of data for subsequent charging strategy optimization, fault diagnosis and performance evaluation, and these data support can help develop more accurate charging management systems to improve efficiency, safety and reliability in the charging process. The generated test information stream can provide strong data support for performance evaluation and tuning of the charging system. Through in-depth analysis of these data, the electrical parameters and equipment state management strategies in the charging process can be continuously adjusted and optimized to improve overall charging efficiency and extend battery life.

[0090] Preferably, the test information stream is analyzed in whole and segmented forms to generate a test feedback factor matrix, and the step of obtaining guidance information by comprehensively referencing the test feedback factor matrix and the safety evaluation information includes: S41: Analyze the data stability and correlation stability of the electrical parameters and battery state parameters in the test information stream in an overall form, to preliminarily identify abnormal data in the test information stream based on the analysis result; S42: Segment the test information stream according to the preliminary identification result of abnormal data and the time stage corresponding to each part of the test information stream, to divide the test information stream into several test information paragraphs in a segmented form; S43: Identify the nature of the abnormal data in each test information paragraph, and analyze the paragraph proportion of each test information paragraph, to evaluate the consistency of the overall test effect of the test information stream based on the data nature and paragraph proportion of the abnormal data possessed by each test information paragraph, to generate an overall test consistency index of the test information stream; S44: Based on the overall test consistency index, analyze the test information stream in time sequence units and device condition feedback, to disassemble the test information stream into several information units arranged according to time sequence, and analyze and generate a feedback pointing information matrix of the corresponding charging object device condition for each information unit; S45: Information fusion is performed on the feedback pointing information matrix of each information unit to construct a test feedback factor matrix; S46: The subsequent plan contains several pre-deployed charging preparation strategies, and the adaptive analysis is performed on each charging preparation strategy contained in the subsequent plan according to the safety evaluation information and the test feedback factor matrix, to determine the most adaptive charging preparation strategy, and convert to generate corresponding guidance information.

[0091] Specifically, the data stability and correlation stability of the electrical parameters and battery state parameters in the test information stream are analyzed, and the abnormal data is preliminarily identified to determine whether the data is stable or abnormal, which can identify the abnormal data in the test information stream, ensure the reliability of subsequent analysis, ensure that the test data is stable at the global level, and avoid the influence of abnormal data on the entire analysis process.

[0092] More specifically, the information stream is segmented according to the time stage of the test information stream and the existence relationship of abnormal data, the test information stream is divided into multiple test sections, each section corresponds to a specific time period in which abnormal data appears, which facilitates analysis, effectively cuts the complex information stream into multiple small sections for analysis, provides phased analysis capability, and enhances the possibility of local anomaly detection and accurate processing.

[0093] More specifically, the nature of the abnormal data in each test information paragraph is identified, the specific problem type of the data is analyzed, the proportion of abnormal data in each test paragraph is analyzed, and through the analysis of the nature of the abnormal data in each paragraph, the root cause of the data problem can be accurately identified, which helps to solve the problem more targetedly. Paragraph proportion analysis can help evaluate the impact of different paragraphs on the overall test consistency, thereby providing a quantitative basis for the overall test consistency index.

[0094] More specifically, based on the abnormal data nature and paragraph proportion of each test information paragraph, the overall test consistency is evaluated, and the generated overall test consistency index can comprehensively reflect the stability of the test information flow, providing a basis for subsequent guidance information adjustment.

[0095] More specifically, the test information flow is disassembled into time-sequential units, the feedback relationship between each information unit and the status of the device is analyzed, and for each information unit, a feedback pointing information matrix related to the charging device status is generated. Time sequence unit disassembly helps to accurately analyze each time period of the test information flow and effectively monitor the device status. By generating a feedback pointing information matrix, the impact of each information unit on the device status can be described in detail, helping to monitor the device status in real time.

[0096] More specifically, the feedback pointing information matrices of all information units are fused to construct a test feedback factor matrix, and the generated test feedback factor matrix provides a multi-dimensional and global feedback information, which can comprehensively reflect the impact of the test information flow on the device status, and provide accurate input for subsequent guidance information optimization.

[0097] More specifically, pre-deployed charging preparation strategies are used, and based on the safety evaluation information and the test feedback factor matrix, each charging preparation strategy is adaptively analyzed, the most adaptive charging preparation strategy is selected, and the corresponding guidance information is converted and generated. This step ensures that the most suitable charging preparation strategy is selected based on real-time feedback information, which can dynamically adjust the gradient parameters to optimize the stability of the system. Through adaptive analysis and selection of appropriate strategies, the safety and stability of the device during the test process are improved.

[0098] It can be understood that through systematic analysis and abnormal data identification of the test information flow, the stability of data quality is ensured, and abnormal data is prevented from affecting subsequent analysis results. Through segmented processing and paragraph analysis, local problems in the information flow can be deeply mined to provide accurate basis for overall evaluation. Through time sequence unit disassembly and feedback information matrix generation, the device status change can be accurately fed back to improve the monitoring efficiency. Finally, through the guidance information optimization strategy, the stability and adaptability of the system are enhanced, ensuring that the device can maintain the best operating state at different stages.

[0099] Preferably, the step of substituting the guidance information into the subsequent plan to perform stable charging operation on the charging object comprises: S51: The subsequent plan contains a plurality of pre-deployed charging preparation strategies, and the guidance information is substituted into the subsequent plan to select a corresponding charging preparation strategy in the subsequent plan. S52: According to the charging preparation strategy, the electrical parameters of the charging object are configured to perform stable charging operation on the charging object.

[0100] S53: The subsequent plan contains a plurality of pre-deployed charging preparation strategies, which are designed to cope with different charging states and external conditions.

[0101] S54: The guidance information calculated in advance is substituted into the subsequent plan to select a corresponding charging preparation strategy.

[0102] Specifically, the substitution of the guidance information is based on the adaptability analysis results of each charging preparation strategy according to the aforementioned test feedback factor matrix and safety evaluation information. During this process, the guidance information provides appropriate adjustment parameters for the system, ensuring the matching of the strategy. By substituting the calculated guidance information, it is ensured that the strategy of each charging stage is dynamically adjusted according to the actual state of the charging object and external conditions, which makes the charging process flexible according to the needs of different stages, achieves the optimal charging effect, and the substituted guidance information can accurately guide the system to select the appropriate charging preparation strategy, ensuring the correctness and adaptability of the strategy and improving the charging stability.

[0103] More specifically, in the subsequent plan, based on the substituted guidance information, the system selects the charging preparation strategy that best meets the current charging demand. Each preparation strategy represents a specific charging stage scheme, aiming to ensure the safety, efficiency and stability of the equipment during the charging process. By combining the guidance information with the pre-set stage strategy, the system can automatically select the most appropriate charging strategy, which avoids the complexity of manual setting and selection of the strategy, improves the automation level of the charging operation, and ensures that the optimal charging strategy is adopted under different charging conditions, avoiding unnecessary charging risks.

[0104] More specifically, according to the selected charging preparation strategy, the charging object is configured with electrical parameters including voltage, current, power and other key parameters, and the purpose of configuration is to ensure the stability of charging in different charging stages. During the configuration process, the system adjusts the electrical parameters to adapt to the needs of the charging object according to the requirements of each stage. At this time, the setting of electrical parameters not only considers the predetermined maximum and minimum values, but also considers real-time variables such as battery state, temperature, load, etc. The accurate configuration of electrical parameters ensures that the current, voltage and other values of the battery or device during the charging process do not exceed the safety range. The risk of overcharging or overdischarging is avoided. Through dynamic configuration of electrical parameters, the system can respond to any problems that may occur during the charging process in real time, such as changes in battery temperature, current fluctuations, etc., to ensure the stability of the charging process. Each step of the configuration process combines safety evaluation and real-time data feedback to ensure that the charging operation is always within a safe range, effectively preventing device damage or battery aging.

[0105] More specifically, based on the configured electrical parameters, stable charging operation is performed. The charging operation continues until the charging is completed or the preset end condition (such as charging completion, abnormal battery temperature, etc.) is reached. The charging operation not only ensures the stability of each stage, but also optimizes the charging time and charging effect, avoiding battery loss caused by overcharging and efficiency loss caused by slow charging. The implementation of stable charging operation ensures smooth transition of charging current and voltage during the charging process, avoiding the risk of sudden current shock or voltage fluctuation of the battery. Through stable and optimized charging methods, the battery is prevented from running under conditions of excessive voltage, excessive current or excessive heat, prolonging the service life of the battery and the device.

[0106] It can be understood that through the substitution of guidance information and the selection of stage strategy, each stage of the charging process can be scientifically and reasonably managed and optimized. The automatic adjustment and real-time feedback control during the process make the charging process self-adaptive to environmental changes, improving the charging efficiency and safety. Precise configuration of electrical parameters in each stage maximizes the safety of the charging process and avoids damage to the device or risk during charging. Stable charging operation not only ensures short-term charging effect, but also prolongs the service life of the battery and the device by reducing potential damage to the battery during charging.

[0107] Referring to Figure 2 The second aspect of the present application provides an intelligent charging system based on a charging pile for implementing the intelligent charging method based on a charging pile according to any one of the first aspect, comprising: An information collection module is configured to identify a charging object connected to the charging pile to retrieve historical charging data and safety evaluation information of the charging object; A gradient analysis module is configured to acquire a charging instruction of a user for a charging object, perform mode analysis on the safety evaluation information, the historical charging data, and the charging instruction according to a performance evaluation model of the charging pile, and generate a test plan and a subsequent plan; A charging test module is configured to perform a test charging operation on the charging object according to the test plan, and collect a test information stream; A test analysis module is configured to analyze the test information stream, and generate guiding information for subsequent charging by combining the analysis result with the safety evaluation information; A stable charging module is configured to substitute the guiding information into the subsequent plan, so as to perform a stable charging operation on the charging object.

[0108] In the embodiment, the specific implementation of each module in the system embodiment is described above in the method embodiment, and will not be described here.

[0109] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent charging method based on a charging pile, characterized in that: include: Identify the charging object connected to the charging pile to retrieve the historical charging data and safety assessment information of the charging object; Obtaining a user's charging instruction for a charging object, analyzing the execution mode of the safety assessment information, the historical charging data, and the charging instruction according to a performance evaluation model of the charging pile, and generating a test plan and a subsequent plan; Performing a test charging operation on the charging object according to the test plan and collecting test information flow; Parsing the test information flow, and combining the parsing results with the safety assessment information to generate guidance information for subsequent charging; The guidance information is substituted into a subsequent plan to perform a stable charging operation on the charging object.

2. The intelligent charging method based on a charging pile according to claim 1, characterized in that: The steps of identifying a charging object connected to a charging pile to retrieve historical charging data and safety assessment information of the charging object include: When the charging port of the charging pile is in docking state, the wireless signal link function is turned on to allow the user's smart terminal to connect with the wireless signal; The identifier of the charging object is obtained through the smart terminal, and historical charging data and safety assessment information are retrieved from the data platform based on the identifier.

3. The intelligent charging method based on a charging pile according to claim 1, characterized in that: The steps of obtaining a user's charging instruction for a charging object, analyzing the execution mode of the safety assessment information, the historical charging data, and the charging instruction according to a performance evaluation model of the charging pile, and generating a test plan and a subsequent plan include: Obtaining a user's charging instruction for a charging object, and parsing the charging instruction to generate expected target information; performing constraint analysis on the safety assessment information, the historical charging data, and the expected target information through a performance evaluation model of the charging pile to generate charging constraint information; Exploring a safety boundary based on the charging constraint information, and performing various forms of experimental adjustments and feasibility analysis on the charging constraint information based on the safety boundary to generate a subsequent plan consisting of multiple charging preparation strategies; A test plan requirement analysis is performed on each charging preparation strategy in the subsequent plan to generate a test plan.

4. The intelligent charging method based on a charging pile according to claim 3, characterized in that: The step of performing constraint analysis on the safety assessment information, the historical charging data, and the expected target information through a performance evaluation model of the charging pile to generate charging constraint information includes: Analyze the performance evaluation model of the charging pile to obtain the charging efficiency curve and heat dissipation performance curve of the charging pile; Analyze the changing trends of charging objects based on historical charging data to obtain charging reference factors; The charging efficiency curve is parsed based on the charging reference factor and the safety assessment information to construct a charging object constraint axis according to the parsing result. Constructing a charging pile constraint axis based on the heat dissipation performance curve, and combining it with the charging object constraint axis to jointly construct a charging constraint space; The expected target information is substituted into the charging constraint space, and a multi-objective analysis of the expected target information is performed on the charging constraint space using a multi-objective optimization algorithm to generate charging constraint information.

5. The intelligent charging method based on a charging pile according to claim 3, characterized in that: The steps of analyzing the test plan requirements for each charging preparation strategy in the subsequent plan to generate a test plan include: Analyzing the adaptability of each charging preparation strategy in the subsequent plan to the charging object to obtain adaptability parameters of each charging preparation strategy corresponding to various expected status characteristics of the charging object; Dividing the test priorities of various expected condition characteristics according to the adaptability parameters to assign test priorities to various expected condition characteristics; configuring an initial test strategy for each type of expected condition feature, and calculating the differential test effects of each type of expected condition feature relative to the initial test strategy; The test priorities of various expected condition characteristics are used as supervision conditions to evaluate the differential test effects, and the initial test strategy is iteratively optimized based on the evaluation results to obtain a test plan that meets the preset standards.

6. The intelligent charging method based on a charging pile according to claim 1, characterized in that: The steps of performing a test charging operation on the charging object according to the test plan and collecting the test information flow include: configuring electrical parameters of a charging object according to the test plan to perform a test charging operation on the charging object; The electrical parameters are continuously recorded and the battery status parameters of the charging object are collected as test data; the test data of each time node are combined in time sequence to obtain a test information flow.

7. The intelligent charging method based on a charging pile according to claim 6, characterized in that: The steps of parsing the test information flow and combining the parsing result with the safety assessment information to generate subsequent charging guidance information include: Performing preliminary identification of abnormal data on the test information flow, and segmenting the test information flow according to the preliminary identification result to form a plurality of segmented test information segments; Identify the nature of abnormal data in each test information paragraph and analyze the paragraph ratio of each test information paragraph. Use the nature identification results and paragraph ratio to evaluate the consistency of the overall test effect of the test information flow to obtain an overall test consistency index. Decomposing the test information flow into a number of information units arranged in chronological order based on the overall test consistency index, and generating a corresponding feedback-directed information matrix of the charging target device status for each information unit; Perform information fusion on each feedback pointing information matrix to construct a test feedback factor matrix; The adaptability of each charging preparation strategy included in the subsequent plan is analyzed according to the safety assessment information and the test feedback factor matrix to determine the most adaptable charging preparation strategy, and convert it into corresponding guidance information.

8. The intelligent charging method based on a charging pile according to claim 1, characterized in that: Substituting the guidance information into a subsequent plan to perform a stable charging operation on the charging object includes: Substituting the guidance information into a subsequent plan to select a corresponding charging preparation strategy in the subsequent plan; The electrical parameters of the charging object are configured according to the charging preparation strategy to perform a stable charging operation on the charging object.

9. An intelligent charging system based on a charging pile, characterized in that: A smart charging method based on a charging pile for implementing any one of claims 1 to 8, comprising: An information collection module is used to identify the charging object connected to the charging pile to retrieve the historical charging data and safety assessment information of the charging object; A gradient parsing module is used to obtain the user's charging instructions for the charging object, analyze the execution mode of the safety assessment information, the historical charging data and the charging instructions according to the performance evaluation model of the charging pile, and generate a test plan and a subsequent plan; A charging test module, configured to perform a test charging operation on the charging object according to the test plan and collect test information flow; A test analysis module, configured to parse the test information flow and combine the analysis results with the safety assessment information to generate guidance information for subsequent charging; The stable charging module is used to substitute the guidance information into the subsequent plan to perform a stable charging operation on the charging object.

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