Data processing method and device of a charger

Abstract

The application provides a data processing method and device of a charger, relates to the technical field of data processing, and obtains charger collection data by collecting data of the charger through a preset data collection device, performs collection frequency analysis and adjustment on the basis of the charger collection data, obtains frequency analysis and adjustment data, and further obtains collection update data; performs preliminary processing and analysis on the collection update data, obtains collection preliminary processing data, performs processing data transmission analysis on the basis of the collection preliminary processing data, obtains processing transmission analysis data, performs charger data trend analysis on the basis of the processing transmission analysis data, obtains charger state trend analysis data, performs charger health state analysis on the basis of the charger state trend analysis data, obtains health state analysis data, performs data recognition and storage analysis on the basis of the health state analysis data, and obtains charger data processing information. The application realizes integrated data collection, processing, analysis and storage.

Description

Technical Field

[0001] This invention proposes a data processing method and apparatus for a charger, relating to the field of data processing technology, specifically to the field of data processing technology for chargers. Background Technology

[0002] Existing chargers mostly rely on fixed-frequency data collection and simple thresholds to determine status and trigger protection. This results in drawbacks such as difficulty adapting to dynamic changes, poor anti-interference capabilities, low data processing efficiency, and a lack of trend analysis and health status traceability. Specifically, traditional systems cannot flexibly adjust the collection frequency according to changes in parameters such as voltage, current, and temperature. They are prone to missing key information during periods of drastic data fluctuations or generating redundant sampling during stable phases. Furthermore, the judgment method is simplistic, making it difficult to accurately distinguish between electromagnetic interference and genuine continuous fluctuations, leading to a high false alarm rate and poor protection. In addition, the raw data lacks systematic cleaning, clustering, and feature extraction mechanisms, and status judgments lack trend-based basis, making it difficult to accurately identify health status and potential faults. Moreover, they lack structured historical data management and traceability capabilities, hindering data-driven optimization and intelligent analysis. Summary of the Invention

[0003] This invention provides a data processing method and apparatus for a charger to solve the above-mentioned problems: This invention proposes a data processing method and apparatus for a charger, the method comprising: S1. Data is collected from the charger through a preset data acquisition device to obtain the charger's collected data. The collected frequency is analyzed and adjusted based on the charger's collected data to obtain frequency analysis and adjustment data, and then the collected update data is obtained. S2. Perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data. Based on the preliminary processed data, perform processing data transmission analysis to obtain processing data transmission analysis data. S3. Perform charger data trend analysis based on the processed transmission analysis data to obtain charger status trend analysis data. Perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. S4. Based on the health status analysis data, perform data identification, storage and analysis to obtain charger data processing information.

[0004] Further, S1 includes: A sensor array is built using a pre-set data acquisition device, and the sensor array is bidirectionally connected to the charger and charging equipment respectively. The charger collects charging data by using a sensor array, thus obtaining the charger's collected data. Data fluctuation analysis is performed on the data collected from the charger to obtain charging fluctuation analysis data; Based on the charging fluctuation analysis data, frequency regulation analysis is performed to obtain frequency regulation analysis data. Data is collected and updated based on frequency adjustment data to obtain the updated data.

[0005] Furthermore, the step of performing data fluctuation analysis on the charger-collected data to obtain charging fluctuation analysis data includes: The data range is divided based on the data collected by the charger to obtain the baseline range data and the fluctuation range data. Calculate the average value of the baseline range data to obtain standard data; Calculate the difference between each fluctuation range data and the standard data to obtain the fluctuation difference data; Based on the fluctuation difference data, fluctuations are labeled for each fluctuation node to obtain fluctuation node labeling data; Time-annotated fluctuation node data is obtained by performing time-annotated fluctuation node data. The fluctuation difference data of the time fluctuation node labeled data is compared with the preset fluctuation difference threshold to obtain the fluctuation comparison result; Based on the fluctuation comparison results, anomaly detection is performed on the time fluctuation node labeled data to obtain anomaly detection nodes; Obtain the interval between adjacent abnormal judgment nodes and the number of normal judgment nodes; The number of nodes that are judged to be normal at intervals is compared with the preset threshold for nodes that are judged to be normal at intervals to obtain the interval comparison result; Data fluctuations are determined based on the interval comparison results, and charging fluctuation analysis data is obtained.

[0006] Furthermore, the step of collecting frequency adjustment analysis based on charging fluctuation analysis data to obtain frequency adjustment analysis data includes: Determine the preset sampling frequency reference value, and adjust the preset sampling frequency reference value according to the charging fluctuation analysis data to obtain the sampling frequency adjustment data; Based on the collected frequency adjustment data, feedback data of charging fluctuation analysis data is obtained to acquire fluctuation feedback data; The frequency adjustment data is updated and adjusted based on the fluctuation feedback data to obtain frequency analysis and adjustment data; Based on frequency analysis and adjustment data, data collection and updates are performed to obtain the collected and updated data.

[0007] Further, S2 includes: The collected and updated data is clustered by state to obtain state clustering data. Data is divided into abnormal and normal data points based on state clustering. By combining adjacent abnormal data points and normal data points, a state transition combination is obtained; The state transition combinations are preliminarily processed and analyzed to obtain the collected preliminary processing and analysis data. Based on the collected and pre-processed data, data transmission sequence analysis is performed to obtain transmission sequence analysis data. Based on the transmission sequence analysis data, the data is collected, preliminarily processed, and analyzed during transmission to obtain processed transmission analysis data.

[0008] Furthermore, the preliminary processing and analysis of the state transition combinations to obtain the collected preliminary processing and analysis data includes: The data point information of the state transition combination is compared with the preset combination data point range to obtain the combination comparison result; Based on the results of the combination comparison, the effectiveness of the combination is determined, and information on the effectiveness of the combination is obtained. The state transition combinations are filtered based on the combination validity determination information to obtain valid transition combinations; The effective transformation combinations are sorted according to the data acquisition time sequence to obtain the preliminary processed and analyzed data.

[0009] Further, the step of performing data transmission sequence analysis based on the collected preliminary processing and analysis data to obtain transmission sequence analysis data includes: The collected, pre-processed, and analyzed data is processed to extract data types, data quality levels, and time-series timestamps to obtain preliminary extracted data. An initial transmission sequence is constructed from the initially extracted data based on the time sequence timestamps and the data quality level priority. The initial transmission sequence is preprocessed to obtain a preprocessed transmission sequence; The transmission sequence length is determined based on the preprocessed transmission sequence, and then the transmission fragmentation rules are determined. The long sequence is fragmented into segments with fixed frame lengths, and each segment is assigned a unique identifier and checksum, thereby obtaining transmission sequence analysis data.

[0010] Further, S3 includes: Data feature extraction is performed on the processed transmission analysis data to obtain transmission feature extraction data; Based on the extracted transmission feature data and the preset acquisition time sequence information, the transmission feature trend information is determined; Determine abnormal trend points and normal trend points based on transmission characteristic trend information; By combining adjacent abnormal trend points with adjacent normal trend points, a trend reversal combination is obtained; Calculate the trend data difference of the trend transformation combination to obtain charger status trend analysis data; The charger status trend analysis data is compared with the preset trend analysis threshold to obtain the status trend comparison results. The health status of the charger is determined based on the comparison results of the status trends, and health status analysis data is obtained.

[0011] Further, S4 includes: Based on health status analysis data, normal and abnormal health information are determined. By associating and binding features between normal and abnormal health information, normal health feature information and abnormal health feature information can be obtained; Multiple normal health characteristics are stored normally to obtain normal identification and storage data; Multiple abnormal health characteristic information are stored abnormally to obtain abnormal identification and storage data; The normal identification and storage data and the abnormal identification and storage data are associated and stored together to obtain the charger data processing information.

[0012] Further, the device includes: The data acquisition and monitoring module is used to acquire data from the charger through a preset data acquisition device, obtain the charger's acquired data, analyze and adjust the acquisition frequency based on the charger's acquired data, obtain frequency analysis and adjustment data, and then obtain the acquired and updated data. The transmission analysis module is used to perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data, and to perform processing transmission analysis on the preliminary processed data to obtain processed transmission analysis data. The health analysis module is used to perform charger data trend analysis based on the processed transmission analysis data, obtain charger status trend analysis data, and perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. The identification and storage module is used to identify, store, and analyze data based on health status analysis data to obtain charger data processing information.

[0013] The beneficial effects of this invention are: it solves the problems of traditional chargers having a single data processing method, crude judgment logic, and susceptibility to external interference leading to malfunctions; it realizes full-process digital and intelligent control of the charging process; it improves the reliability of charging status identification and the accuracy of battery health assessment; it reduces the risk of control anomalies caused by data distortion and interference glitches; and it provides a foundation for algorithm optimization and fault tracing through structured data accumulation, enhancing the stability and adaptability of the system in long-term use. Attached Figure Description

[0014] Figure 1 A schematic diagram of a data processing method for a charger; Figure 2 This is a schematic diagram of data acquisition. Figure 3 This is a schematic diagram of the frequency adjustment process; Figure 4 This is a schematic diagram of state transition combinations. Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] In one embodiment of the present invention, a data processing method and apparatus for a charger are provided, the method comprising: S1. Data is collected from the charger through a preset data acquisition device to obtain the charger's collected data. The collected frequency is analyzed and adjusted based on the charger's collected data to obtain frequency analysis and adjustment data, and then the collected update data is obtained. S2. Perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data. Based on the preliminary processed data, perform processing data transmission analysis to obtain processing data transmission analysis data. S3. Perform charger data trend analysis based on the processed transmission analysis data to obtain charger status trend analysis data. Perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. S4. Based on the health status analysis data, perform data identification, storage, and analysis to obtain charger data processing information, such as... Figure 1 As shown.

[0017] First, data is collected and the collection frequency is adjusted. Then, the data is processed and transmitted. Next, trends and health status are analyzed. Finally, the results are identified and stored. Collection frequency analysis and adjustment determine how often data should be collected based on the rate of data change. Data processing information leads to the final conclusions regarding whether charging is normal, the battery's condition, and any abnormalities. Overall, it transforms chaotic data into usable judgment results.

[0018] The working principle and technical effects of the above solution are as follows: A stable communication channel is established between the preset data acquisition device and the charger and charging equipment to synchronously acquire multi-dimensional core parameters such as input and output voltage, current, battery temperature, internal resistance, and SOC, and record the time sequence timestamps. Based on the acquired data, the system performs fluctuation analysis to determine the magnitude and stability of data changes, and then dynamically adjusts the acquisition frequency according to the degree of fluctuation, ensuring that the sampling frequency adapts to the speed of data changes in real time, ultimately outputting accurate and updated acquisition data.

[0019] The system clusters the collected and updated data, identifies normal and abnormal data points, and extracts adjacent normal and abnormal points to form state transition combinations, which are used to characterize the state transitions during the charging process. The combinations are then subjected to validity screening and noise reduction to eliminate false transitions and retain valid data that truly reflects changes in the charging stage. A transmission sequence is then constructed according to data type, quality level, and time sequence, and after fragmentation and error correction, reliable transmission is achieved.

[0020] The system extracts features from the transmitted data, constructs a trend curve by combining it with time-series information, identifies trend anomalies and trend transition combinations, calculates the trend difference and compares it with a preset threshold, thereby achieving a comprehensive assessment of the charger's working status and battery health, and obtaining quantitative health status analysis data.

[0021] The system categorizes data into normal and abnormal information based on health status results, performs feature association and binding, stores the data in categories, and establishes interconnected archives to form traceable and queryable structured data processing information.

[0022] Achieve adaptive data acquisition, balancing accuracy and energy consumption. Dynamically adjust the acquisition frequency based on data fluctuations to avoid losing critical inflection points, while reducing system overhead from invalid sampling, thus improving acquisition quality and overall operational efficiency. Enhance data anti-interference capabilities and reduce false alarm rates. Effectively distinguish between electromagnetic interference and genuine faults through state transition combination filtering and anomaly interval judgment, preventing charger false protection and malfunctions, and improving system stability and safety. Optimize data processing and transmission efficiency. Through clustering, noise reduction, priority transmission, and fragmented verification, make data more organized, reliable, and orderly, improving real-time transmission and link utilization, and reducing the risk of delays or loss of critical information. Achieve trend-based health assessment and early warning of potential problems. No longer relying on single-point numerical values ​​to determine status, but using trend changes and difference analysis to accurately identify potential problems such as battery aging and charger anomalies, achieving proactive warnings and enhancing safety assurance capabilities. Build a structured and traceable data management system. Classify, store, and archive normal and abnormal data, making charging status traceable and faults reproducible, providing data support for continuous system optimization and strategy iteration, and enhancing the long-term intelligence and stability of the charger.

[0023] This method enables chargers to have higher precision, stronger anti-interference, more intelligent judgment, and safer and more reliable data processing capabilities. It solves the core pain points of traditional solutions, such as poor adaptability, high misjudgment rate, analysis lag, and extensive management, and significantly improves the operational safety, reliability and long-term intelligence level of chargers.

[0024] In one embodiment of the present invention, S1 includes: A sensor array is built using a pre-set data acquisition device, and the sensor array is bidirectionally connected to both the charger and the charging equipment, such as... Figure 2 As shown; The charger collects charging data through a sensor array, specifically including charging input / output voltage, charging input / output current, real-time battery temperature, remaining battery capacity (SOC), battery internal resistance, charging duration, and time sequence timestamp. Data fluctuation analysis is performed on the data collected from the charger to obtain charging fluctuation analysis data; Based on the charging fluctuation analysis data, frequency regulation analysis is performed to obtain frequency regulation analysis data. Data is collected and updated based on frequency adjustment data to obtain the updated data.

[0025] A sensor array consists of multiple sensors for voltage, current, temperature, etc., working together; two-way communication allows the charger and the device being charged to exchange data. Charging fluctuation analysis examines the stability and magnitude of data fluctuations; subsequently, the sampling frequency is adjusted based on the magnitude of the fluctuations.

[0026] The working principle and technical effects of the above solution are as follows: By constructing a data acquisition array containing multiple types of sensors such as voltage, current, temperature, and internal resistance, a two-way communication link is established with the charger and charging equipment to ensure stable and reliable data interaction. After collecting key parameters throughout the charging process, the data fluctuations are analyzed to determine the rate and stability of data changes. Based on the fluctuation results, the acquisition frequency is dynamically adjusted to match the sampling rhythm with the actual charging state, ultimately generating high-quality acquired and updated data.

[0027] It solves the problems of fixed sampling methods, easy loss of key change information, and resource waste during stable phases in traditional chargers; it achieves multi-dimensional and full-coverage data collection; it improves the targeting and effectiveness of data collection; it reduces system power consumption and computational pressure caused by invalid sampling; and it enhances the adaptability of the collection device to different charging scenarios and different devices.

[0028] In one embodiment of the present invention, the step of performing data fluctuation analysis on the charger-collected data to obtain charging fluctuation analysis data includes: The data range is divided based on the data collected by the charger to obtain the baseline range data and the fluctuation range data. Calculate the average value of the baseline range data to obtain standard data; Calculate the difference between each fluctuation range data and the standard data to obtain the fluctuation difference data; Based on the fluctuation difference data, fluctuations are labeled for each fluctuation node to obtain fluctuation node labeling data; Time-annotated fluctuation node data is obtained by performing time-annotated fluctuation node data. The fluctuation difference data of the time fluctuation node labeled data is compared with the preset fluctuation difference threshold to obtain the fluctuation comparison result; Based on the fluctuation comparison results, anomaly detection is performed on the time fluctuation node labeled data to obtain anomaly detection nodes; Obtain the interval between adjacent abnormal judgment nodes and the number of normal judgment nodes; The number of nodes that are judged to be normal at intervals is compared with the preset threshold for nodes that are judged to be normal at intervals to obtain the interval comparison result; Data fluctuations are determined based on the interval comparison results, and charging fluctuation analysis data is obtained.

[0029] The baseline range and fluctuation range separate stable and variable data; the standard data is the average value of the stable period. Anomaly detection nodes are data points that deviate too much; the key is to examine how many normal points are between two anomaly points to distinguish between interference and a genuine fault. Simply put, the number of normal detection nodes between them means: anomaly points must be close together for a genuine anomaly to be considered true; a large gap indicates interference.

[0030] The working principle and technical effect of the above technical solution are as follows: The collected data is divided into a stable baseline segment and a fluctuating segment. The average value of the baseline segment is used as a reference standard to calculate the deviation of each fluctuation point from the standard value, and time-series information is marked for each fluctuation node. By comparing the fluctuation difference with a preset threshold, abnormal points are initially identified. Then, the number of normal points between adjacent abnormal points is further determined to distinguish between transient interference and real continuous anomalies, ultimately forming a reliable fluctuation analysis result.

[0031] It solves the problem that a single threshold judgment can easily misjudge electromagnetic interference and instantaneous spikes as faults; it enables effective differentiation between real anomalies and false interference; it improves the anti-interference ability and judgment accuracy of data fluctuation identification; and it reduces the false judgment rate and the risk of frequent false triggers.

[0032] In one embodiment of the present invention, the step of collecting frequency regulation analysis based on charging fluctuation analysis data to obtain frequency regulation analysis data includes: Determine the preset sampling frequency reference value, and adjust the preset sampling frequency reference value according to the charging fluctuation analysis data to obtain the sampling frequency adjustment data; Based on the collected frequency adjustment data, feedback data of charging fluctuation analysis data is obtained to acquire fluctuation feedback data; The frequency adjustment data is updated and adjusted based on the fluctuation feedback data to obtain frequency analysis and adjustment data; Based on frequency analysis and adjustment data, data collection and updates are performed to obtain the collected and updated data.

[0033] The baseline value is the default sampling frequency; the sampling frequency adjustment data is the frequency initially adjusted based on fluctuations. Fluctuation feedback data is the data collected after the frequency adjustment, which is then used to verify the previous adjustment and make a final fine-tuning. The overall idea is: first coarse adjustment, then fine adjustment, to make the frequency more closely match the data changes.

[0034] The working principle and technical effect of the above technical solution are as follows: Using a preset reference frequency as the initial value, preliminary adjustments are made based on data fluctuation analysis results. Then, feedback information is generated from newly collected data after adjustment, allowing for further optimization and fine-tuning of the frequency, gradually bringing the sampling frequency closer to the optimal rhythm of current data changes. This two-stage adjustment forms a closed loop, ensuring that the sampling frequency always matches the intensity of charging data fluctuations.

[0035] It solves the problem that a fixed sampling frequency cannot adapt to the dynamic charging process; it achieves adaptive and closed-loop adjustment of the sampling frequency; it improves the data acquisition accuracy and the ability to capture key inflection points; it reduces power consumption when the data is stable and ensures that details are not lost when there are drastic changes; and it improves the overall data processing efficiency and system response speed.

[0036] In one embodiment of the present invention, S2 includes: The collected and updated data is clustered by state to obtain state clustering data. Data is divided into abnormal and normal data points based on state clustering. By combining adjacent abnormal data points with normal data points, a state transition combination is obtained, such as... Figure 4 As shown; The state transition combinations are preliminarily processed and analyzed to obtain the collected preliminary processing and analysis data. Based on the collected and pre-processed data, data transmission sequence analysis is performed to obtain transmission sequence analysis data. Based on the transmission sequence analysis data, the data is collected, preliminarily processed, and analyzed during transmission to obtain processed transmission analysis data.

[0037] Data state clustering divides data into two categories: normal and abnormal. State transition combination groups adjacent normal and abnormal points together to observe the state change points. In essence, state transition combination captures the segment of data where the charging state changes.

[0038] The working principle and technical effect of the above technical solution are as follows: Collected data is divided into normal and abnormal data through clustering. Adjacent normal and abnormal points are extracted to form state transition combinations, which are used to locate key locations where state transitions occur during charging. After filtering and processing these combinations, a transmission sequence is planned to ensure that data is transmitted in an orderly manner according to importance and timing, guaranteeing the priority and stable delivery of critical information.

[0039] It solves the problems of messy raw data, unclear state changes, and difficulty in locating stage transition points; it enables accurate capture of charging state change points; it eliminates redundant data and highlights effective features; it improves the focus and processing efficiency of the analysis; and it reduces the analysis errors caused by transmission pressure and data disorder.

[0040] In one embodiment of the present invention, the preliminary processing and analysis of the state transition combination to obtain preliminary processing and analysis data includes: The data point information of the state transition combination is compared with the preset combination data point range to obtain the combination comparison result; Based on the results of the combination comparison, the effectiveness of the combination is determined, and information on the effectiveness of the combination is obtained. The state transition combinations are filtered based on the combination validity determination information to obtain valid transition combinations; The effective transformation combinations are sorted according to the data acquisition time sequence to obtain the preliminary processed and analyzed data.

[0041] The range of combined data points is a preset reasonable range; exceeding this range indicates a false transition. Effective conversion combinations are data segments that truly reflect changes during the charging phase. Obvious and unreasonable transitions are filtered out, leaving only the true state changes, which are then arranged in chronological order. Simply put, determining the effectiveness of a combination involves filtering out interference and retaining useful change points.

[0042] The working principle and technical effect of the above technical solution are as follows: compare the state transition combination with the preset reasonable range to determine whether it is a real state jump, eliminate invalid combinations that are obviously caused by interference, retain only the valid combinations that can reflect the real changes in the charging stage, and reorder them according to the time sequence to make the data structure regular and the logic clear.

[0043] It solves the problems of mixed state transition information and false changes interfering with judgment; it achieves the purification and screening of valid state information; it improves data reliability and usability; it reduces the impact of abnormal mutations on subsequent trend analysis and health judgment; and it makes the identification of the charging stage more accurate and stable.

[0044] In one embodiment of the present invention, the step of performing data transmission sequence analysis based on the collected preliminary processing and analysis data to obtain transmission sequence analysis data includes: The collected, pre-processed, and analyzed data is processed to extract data types, data quality levels, and time-series timestamps to obtain preliminary extracted data. An initial transmission sequence is constructed from the initially extracted data based on the time sequence timestamps and the data quality level priority. The initial transmission sequence is preprocessed to obtain a preprocessed transmission sequence; The transmission sequence length is determined based on the preprocessed transmission sequence, and then the transmission fragmentation rules are determined. The long sequence is fragmented into segments with fixed frame lengths, and each segment is assigned a unique identifier and checksum, thereby obtaining transmission sequence analysis data.

[0045] Data quality priority means sending high-quality data first; preprocessing involves removing duplicate and useless data. Transmission fragmentation rules involve cutting long data into segments, adding a number and checksum to each segment to prevent packet loss and errors. The initial transmission sequence is arranged according to time order and importance, and then optimized before transmission.

[0046] The working principle and technical effect of the above technical solution are as follows: extract information such as data type, quality, and timing; construct a transmission queue according to time order and importance priority; eliminate duplicate and redundant content; then fragment long data and add identification and verification information to form an orderly and reliable transmission structure, ensuring efficient data transmission and reducing the likelihood of errors.

[0047] It solves the problems of disordered data transmission, delay of critical information, high packet loss and error rate; optimizes the transmission process and prioritizes the data transmission; improves data transmission efficiency and real-time performance; enhances the anti-interference and anti-packet loss capabilities of the transmission process; and reduces communication bandwidth usage and system transmission overhead.

[0048] In one embodiment of the present invention, S3 includes: Data feature extraction is performed on the processed transmission analysis data to obtain transmission feature extraction data; Based on the extracted transmission feature data and the preset acquisition time sequence information, the transmission feature trend information is determined; Determine abnormal trend points and normal trend points based on transmission characteristic trend information; By combining adjacent abnormal trend points with adjacent normal trend points, a trend reversal combination is obtained; Calculate the trend data difference of the trend transformation combination to obtain charger status trend analysis data; The charger status trend analysis data is compared with the preset trend analysis threshold to obtain the status trend comparison results. The health status of the charger is determined based on the comparison results of the status trends, and health status analysis data is obtained.

[0049] The trend transition combination is similar to the previous state transition combination; it identifies the inflection point of a trend change. The trend data difference indicates the magnitude of the change. By observing the data trend and comparing it to thresholds, we can determine the health of the charger and battery.

[0050] The working principle and technical effect of the above technical solution are as follows: extract change features from the transmitted data, construct a trend curve by combining time series information, identify abnormal points and normal points in the trend and combine them into a trend transformation combination, calculate the trend change amplitude, determine whether the current operating state is abnormal by comparing with a threshold, and then comprehensively evaluate the health status of the charger and battery.

[0051] It solves the problem that traditional chargers rely on single-point numerical values ​​for judgment and cannot identify trend-based degradation and potential faults; it realizes intelligent health status assessment based on trend changes; it improves fault early warning capabilities and the comprehensiveness of status judgment; it can detect potential hazards such as battery aging and charger abnormalities earlier; it reduces charging safety risks and extends battery life.

[0052] In one embodiment of the present invention, S4 includes: Based on health status analysis data, normal and abnormal health information are determined. By associating and binding features between normal and abnormal health information, normal health feature information and abnormal health feature information can be obtained; Multiple normal health characteristics are stored normally to obtain normal identification and storage data; Multiple abnormal health characteristic information are stored abnormally to obtain abnormal identification and storage data; The normal identification and storage data and the abnormal identification and storage data are associated and stored together to obtain charger data processing information.

[0053] Feature association and binding involves tagging normal / abnormal data and associating it with time and device information. Normal and abnormal identification data are stored separately. Association storage binds these two types of data together, making it easy to check whether a particular charging session was normal or abnormal later.

[0054] The working principle and technical effect of the above technical solution are as follows: the health analysis results are divided into normal information and abnormal information, features are extracted and labeled respectively, and stored separately according to category. At the same time, the two types of data are correlated to form a traceable and queryable structured data archive, which is convenient for calling, analysis and model optimization.

[0055] It solves the problems of unclassified, unmanaged, and difficult-to-trace historical status of charging data; realizes the structured and systematic storage of normal and abnormal information; improves the convenience of data retrieval, fault location and historical comparison; and enhances the intelligence level and long-term reliability of chargers.

[0056] In one embodiment of the present invention, the apparatus includes: The data acquisition and monitoring module is used to acquire data from the charger through a preset data acquisition device, obtain the charger's acquired data, analyze and adjust the acquisition frequency based on the charger's acquired data, obtain frequency analysis and adjustment data, and then obtain the acquired and updated data. The transmission analysis module is used to perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data, and to perform processing transmission analysis on the preliminary processed data to obtain processed transmission analysis data. The health analysis module is used to perform charger data trend analysis based on the processed transmission analysis data, obtain charger status trend analysis data, and perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. The identification and storage module is used to identify, store, and analyze data based on health status analysis data to obtain charger data processing information.

[0057] The working principle and technical effect of the above technical solution are as follows: the device completes data acquisition and frequency adjustment through the acquisition and monitoring module, realizes data cleaning and transmission planning through the transmission and analysis module, completes trend judgment and health assessment through the health analysis module, and realizes information classification, binding and archiving through the identification and storage module. The four modules work together to form a complete data processing hardware execution architecture.

[0058] It solves the problems of traditional chargers having limited hardware functions and lacking intelligent data processing capabilities; it achieves integrated data acquisition, processing, analysis, and storage; it improves the overall operating efficiency and scalability of the device; it reduces logical conflicts and data delays in the collaborative operation of multiple modules; and it makes the charger safer, smarter, and more stable.

[0059] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A data processing method for a charger, characterized in that, The method includes: S1. Data is collected from the charger through a preset data acquisition device to obtain the charger's collected data. The collected frequency is analyzed and adjusted based on the charger's collected data to obtain frequency analysis and adjustment data, and then the collected update data is obtained. S2. Perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data. Based on the preliminary processed data, perform processing data transmission analysis to obtain processing data transmission analysis data. S3. Perform charger data trend analysis based on the processed transmission analysis data to obtain charger status trend analysis data. Perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. S4. Based on the health status analysis data, perform data identification, storage and analysis to obtain charger data processing information.

2. The data processing method for a charger according to claim 1, characterized in that, S1 includes: A sensor array is built using a pre-set data acquisition device, and the sensor array is bidirectionally connected to the charger and charging equipment respectively. The charger collects charging data by using a sensor array, thus obtaining the charger's collected data. Data fluctuation analysis is performed on the data collected from the charger to obtain charging fluctuation analysis data; Based on the charging fluctuation analysis data, frequency regulation analysis is performed to obtain frequency regulation analysis data. Data is collected and updated based on frequency adjustment data to obtain the updated data.

3. The data processing method for a charger according to claim 2, characterized in that, The step of performing data fluctuation analysis on the charger-collected data to obtain charging fluctuation analysis data includes: The data range is divided based on the data collected by the charger to obtain the baseline range data and the fluctuation range data. Calculate the average value of the baseline range data to obtain standard data; Calculate the difference between each fluctuation range data and the standard data to obtain the fluctuation difference data; Based on the fluctuation difference data, fluctuations are labeled for each fluctuation node to obtain fluctuation node labeling data; Time-annotated fluctuation node data is obtained by performing time-annotated fluctuation node data. The fluctuation difference data of the time fluctuation node labeled data is compared with the preset fluctuation difference threshold to obtain the fluctuation comparison result; Based on the fluctuation comparison results, anomaly detection is performed on the time fluctuation node labeled data to obtain anomaly detection nodes; Obtain the interval between adjacent abnormal judgment nodes and the number of normal judgment nodes; The number of nodes that are judged to be normal at intervals is compared with the preset threshold for nodes that are judged to be normal at intervals to obtain the interval comparison result; Data fluctuations are determined based on the interval comparison results, and charging fluctuation analysis data is obtained.

4. The data processing method for a charger according to claim 2, characterized in that, The step of collecting frequency adjustment analysis based on charging fluctuation analysis data to obtain frequency adjustment analysis data includes: Determine the preset sampling frequency reference value, and adjust the preset sampling frequency reference value according to the charging fluctuation analysis data to obtain the sampling frequency adjustment data; Based on the collected frequency adjustment data, feedback data of charging fluctuation analysis data is obtained to acquire fluctuation feedback data; The frequency adjustment data is updated and adjusted based on the fluctuation feedback data to obtain frequency analysis and adjustment data; Based on frequency analysis and adjustment data, data collection and updates are performed to obtain the collected and updated data.

5. The data processing method for a charger according to claim 1, characterized in that, S2 includes: The collected and updated data is clustered by state to obtain state clustering data. Data is divided into abnormal and normal data points based on state clustering. By combining adjacent abnormal data points and normal data points, a state transition combination is obtained; The state transition combinations are preliminarily processed and analyzed to obtain the collected preliminary processing and analysis data. Based on the collected and pre-processed data, data transmission sequence analysis is performed to obtain transmission sequence analysis data. Based on the transmission sequence analysis data, the data is collected, preliminarily processed, and analyzed during transmission to obtain processed transmission analysis data.

6. The data processing method for a charger according to claim 5, characterized in that, The preliminary processing and analysis of the state transition combinations to obtain the collected preliminary processing and analysis data includes: The data point information of the state transition combination is compared with the preset combination data point range to obtain the combination comparison result; Based on the results of the combination comparison, the validity of the combination is determined, and the information on the validity of the combination is obtained. The state transition combinations are filtered based on the combination validity determination information to obtain valid transition combinations; The effective transformation combinations are sorted according to the data acquisition time sequence to obtain the preliminary processed and analyzed data.

7. The data processing method for a charger according to claim 5, characterized in that, The step of performing data transmission sequence analysis based on the collected preliminary processing and analysis data to obtain transmission sequence analysis data includes: The collected, pre-processed, and analyzed data is processed to extract data types, data quality levels, and time-series timestamps to obtain preliminary extracted data. An initial transmission sequence is constructed from the initially extracted data based on the time sequence timestamps and the data quality level priority. The initial transmission sequence is preprocessed to obtain a preprocessed transmission sequence; The transmission sequence length is determined based on the preprocessed transmission sequence, and then the transmission fragmentation rules are determined. The long sequence is fragmented into segments with fixed frame lengths, and each segment is assigned a unique identifier and checksum, thereby obtaining transmission sequence analysis data.

8. The data processing method for a charger according to claim 1, characterized in that, S3 includes: Data feature extraction is performed on the processed transmission analysis data to obtain transmission feature extraction data; Based on the extracted transmission feature data and the preset acquisition time sequence information, the transmission feature trend information is determined; Determine abnormal trend points and normal trend points based on transmission characteristic trend information; By combining adjacent abnormal trend points with adjacent normal trend points, a trend reversal combination is obtained; Calculate the trend data difference of the trend transformation combination to obtain charger status trend analysis data; The charger status trend analysis data is compared with the preset trend analysis threshold to obtain the status trend comparison results. The health status of the charger is determined based on the comparison results of the status trends, and health status analysis data is obtained.

9. The data processing method for a charger according to claim 1, characterized in that, S4 includes: Based on health status analysis data, normal and abnormal health information are determined. By associating and binding features between normal and abnormal health information, normal health feature information and abnormal health feature information can be obtained; Multiple normal health characteristics are stored normally to obtain normal identification and storage data; Multiple abnormal health characteristic information are stored abnormally to obtain abnormal identification and storage data; The normal identification and storage data and the abnormal identification and storage data are associated and stored together to obtain the charger data processing information.

10. A data processing device for a charger, characterized in that, The device includes: The data acquisition and monitoring module is used to acquire data from the charger through a preset data acquisition device, obtain the charger's acquired data, analyze and adjust the acquisition frequency based on the charger's acquired data, obtain frequency analysis and adjustment data, and then obtain the acquired and updated data. The transmission analysis module is used to perform preliminary processing and analysis on the collected and updated data to obtain preliminary processed data, and to perform processing transmission analysis on the preliminary processed data to obtain processed transmission analysis data. The health analysis module is used to perform charger data trend analysis based on the processed transmission analysis data, obtain charger status trend analysis data, and perform charger health status analysis based on the charger status trend analysis data to obtain health status analysis data. The identification and storage module is used to identify, store, and analyze data based on health status analysis data to obtain charger data processing information.

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