Data processing method and device for adaptive adjustment of charging and discharging parameters of super capacitor

Abstract

The invention provides a data processing method and device for adaptive adjustment of charging and discharging parameters of a super capacitor, and is applied to the technical field of data processing. According to the method, a full-process self-adaptive system is constructed around the requirement of a variable pitch backup power supply of a wind turbine generator set: firstly, real-time state data of a super capacitor are collected, and overall operation real-time data and preliminary evaluation are generated through anomaly elimination and trend analysis processing; setting a multi-level threshold value by combining the environment temperature and the charging and discharging frequency, applying capacitance characteristic constraint and a variable pitch safety target, and generating an alarm and state report; the charging current is automatically adjusted according to the alarm level, the floating charge voltage is adapted, and parameters are dynamically adjusted to form an automatic scheme; mining a historical data capture performance attenuation rule, predicting a future state and optimizing parameters; finally, multi-link information is integrated, evaluation dimensions and indexes are set, comprehensive evaluation information is generated, the stability of the variable pitch system is guaranteed, and the operation and maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a data processing method and device for adaptive adjustment of super capacitor charging and discharging parameters. BACKGROUND

[0002] In the current variable pitch system of wind turbine generators, the super capacitor is often used as a core component of the backup power supply, and the rationality of its charging and discharging parameters directly affects the safe operation of the variable pitch system and the service life of the capacitor. In the prior art, the charging and discharging parameters of the super capacitor are mostly set in a fixed mode, or are simply adjusted according to a single operating condition index, and no full-process adaptive control system has been formed.

[0003] In the data processing link, the existing scheme is not comprehensive enough in the collection dimension of real-time state data of the super capacitor, often ignoring the associated influence of environmental temperature, charging and discharging frequency and capacitor performance, and lacking effective mechanisms for outlier rejection and data smoothing processing, resulting in insufficient data accuracy. At the same time, the prior art does not set differentiated multi-level operating thresholds for different environmental temperatures (such as low temperature ≤-20℃ and high temperature ≥60℃) and charging and discharging frequencies (such as high frequency ≥5 times / hour), and the threshold setting lacks deep coupling with the characteristics of the super capacitor (such as temperature resistance range-40℃~+65℃ and cycle life) and the safety goals of the variable pitch system (such as emergency feathering response time and safe shutdown trigger conditions).

[0004] In terms of parameter adjustment and prediction, the response measures of the prior art are relatively single, and no specific charging current adjustment, floating charging voltage adaptation and charging start-stop control strategy is formulated according to the alarm level (mild, moderate and severe), and the historical operating data is not deeply mined, which cannot accurately capture the performance degradation law of the capacitor, making it difficult to effectively predict the future operating state and optimize the parameters, resulting in insufficient stability of the variable pitch system and high operation and maintenance cost of the super capacitor.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the application.

[0007] According to an aspect of the present application, a data processing method for adaptive adjustment of super capacitor charging and discharging parameters is provided, comprising: obtaining real-time state data of the super capacitor; processing the obtained real-time state data of the super capacitor to capture the usage state and change trend of the capacitor, and generate real-time data and preliminary evaluation information of the overall operation of the capacitor; processing the real-time data and preliminary evaluation information of the overall operation of the capacitor, setting multi-level operation threshold values in combination with the usage mode and actual working condition of the capacitor under different environmental temperatures and charging and discharging frequencies, applying super capacitor characteristic constraints and safety operation objective functions of emergency feathering and safety shutdown of the variable pitch system, generating corresponding level alarm signals and capacitor state detailed reports containing capacitor voltage fluctuation, temperature change and loop integrity; processing the alarm signals and capacitor state detailed reports of different levels, automatically executing response measures of charging current adjustment, floating charging voltage adaptation and charging start-stop control in combination with the alarm level, dynamically adjusting the charging and discharging parameters, and generating an automatic adjustment scheme for ensuring stable operation of the variable pitch system; processing the automatic adjustment scheme, using data analysis technology to mine historical operation data, capturing capacitor performance degradation rules, identifying capacitor performance change rules, generating future capacitor operation state prediction and charging and discharging parameter optimization suggestions; processing the real-time data and preliminary evaluation information of the overall operation of the capacitor, multi-level alarm signals and capacitor state detailed reports, automatic adjustment scheme, future capacitor operation state prediction and charging and discharging parameter optimization suggestions, setting evaluation dimensions and quantitative indicators, applying adaptability constraints of evaluation results and actual working conditions, and generating comprehensive evaluation information of the adaptive adjustment effect of super capacitor charging and discharging parameters.

[0008] Another aspect of the present application, a data processing device for adaptive adjustment of super capacitor charging and discharging parameters, comprising: an acquisition module for acquiring real-time state data of the super capacitor; a processing module for processing the acquired real-time state data of the super capacitor, capturing the usage state and change trend of the capacitor, and generating real-time data and preliminary evaluation information of the overall operation of the capacitor; processing the real-time data and preliminary evaluation information of the overall operation of the capacitor, setting multiple levels of operation thresholds in combination with the usage mode and actual working condition of the capacitor under different environmental temperatures and charging and discharging frequencies, applying super capacitor characteristic constraints and safety operation objective functions of emergency feathering and safety shutdown of the variable pitch system, generating corresponding level alarm signals and capacitor state detailed reports containing capacitor voltage fluctuation, temperature change and loop integrity; processing the alarm signals and capacitor state detailed reports of different levels, combining the alarm levels, automatically executing response measures of charging current adjustment, floating charging voltage adaptation and charging start-stop control, dynamically adjusting the charging and discharging parameters, and generating an automatic adjustment scheme to ensure stable operation of the variable pitch system; processing the automatic adjustment scheme, using data analysis techniques to mine historical operation data, capturing capacitor performance degradation rules, identifying capacitor performance change rules, and generating future capacitor operation state prediction and charging and discharging parameter optimization suggestions; processing the real-time data and preliminary evaluation information of the overall operation of the capacitor, the multi-level alarm signals and the capacitor state detailed reports, the automatic adjustment scheme, the future capacitor operation state prediction and the charging and discharging parameter optimization suggestions, setting evaluation dimensions and quantitative indicators, applying adaptability constraints of evaluation results and actual working conditions, and generating comprehensive evaluation information of the adaptive adjustment effect of the super capacitor charging and discharging parameters.

[0009] According to another aspect of the present application, an electronic device comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the executable instructions to implement the above-mentioned data processing method for adaptive adjustment of super capacitor charging and discharging parameters.

[0010] According to another aspect of the present application, an electronic device comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the executable instructions to implement the above-mentioned data processing method for adaptive adjustment of super capacitor charging and discharging parameters.

[0011] The application provides a data processing method and device for adaptive adjustment of super capacitor charging and discharging parameters, and aims to build a full-process adaptive data processing system, accurately collect and process real-time state data, set multi-level working condition adaptive threshold values, develop a graded response adjustment scheme, and realize prediction optimization by mining performance degradation rules, so as to finally ensure stable operation of a wind turbine variable pitch system and reduce operation and maintenance costs.

[0012] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 A flowchart of a data processing method for adaptive adjustment of super capacitor charging and discharging parameters provided by an embodiment of the application is shown. Figure 2 A structural schematic diagram of a data processing device for adaptive adjustment of super capacitor charging and discharging parameters provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0014] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the application, and are not used to limit the application.

[0015] The data processing method for adaptive adjustment of super capacitor charging and discharging parameters according to the exemplary embodiments of the application is described below in conjunction with Figure 1 It should be noted that the following application scenarios are only shown for the purpose of facilitating understanding of the spirit and principles of the application, and the embodiments of the application are not limited in this respect. On the contrary, the embodiments of the application are applicable to any applicable scenarios.

[0016] In one embodiment, Figure 1 A flowchart of a data processing method for adaptive adjustment of super capacitor charging and discharging parameters according to an embodiment of the application is shown.

[0017] S101, real-time state data of the super capacitor is acquired.

[0018] In one implementation, the focus is on key dimensions of supercapacitor operation to determine the types and coverage of data to ensure comprehensiveness and relevance. Core electrical parameters of the capacitor include: current voltage, charging / discharging current, remaining capacity, internal resistance, voltage equalization circuit status, and charging / discharging circuit continuity. Environmental adaptation parameters include: operating temperature of the capacitor module, humidity inside the battery cabinet, and cabinet corrosion protection status. Operating history parameters include: cumulative number of charge / discharge cycles, duration of each charge / discharge cycle, charging / discharging frequency distribution over the past 30 days, and the number and type of faults. Related system parameters include: pitch system power supply status, emergency feathering trigger records, and generator speed (self-check and fault scenarios).

[0019] Relying on the upgraded hardware system and equipped with dedicated data acquisition equipment, the accuracy and stability of data acquisition are ensured. High-precision voltage sensors (error ≤ ±0.5%) are installed at the positive and negative output terminals of the supercapacitor module to collect real-time voltage data for individual cells and the total voltage of the module. A current acquisition device (product model: 18μ420 / 20) installed after the upgrade is reused and connected in series in the charging and discharging circuit to capture current changes during charging, discharging, and standby. A PT100 temperature sensor is fixed on the surface of the capacitor module and inside the battery cabinet, with a data acquisition range covering -40℃ to +80℃, adapting to the supercapacitor's operating environment. Through loop detection sensors and a voltage equalization circuit monitoring module, the on / off status of the charging and discharging circuit and the operating status of the voltage equalization circuit are fed back in real time. Utilizing the unit's existing communication lines and an industrial-grade RS485 bus, real-time transmission of acquired data to the main control system is achieved, with a transmission delay ≤ 100ms.

[0020] Based on the requirements of operating conditions and data timeliness, differentiated acquisition frequencies are set to balance data accuracy and system load. Under normal operating conditions (capacitors are in standby or normal charging and discharging state): voltage, current, and ambient temperature are collected once every 100ms; charge / discharge count and circuit status are collected once every 1s. Under special operating conditions (low temperature ≤ -20℃, high temperature ≥ 60℃, or charge / discharge frequency ≥ 5 times / hour): the acquisition frequency of voltage, current, and ambient temperature is increased to 50ms / time; the status of the equalizing circuit and battery cabinet humidity are collected once every 500ms. Under fault and self-test conditions: when an alarm signal is triggered or the self-test process is entered, the acquisition frequency of all data is increased to 20ms / time, and acquisition continues until the fault is cleared or the self-test is completed.

[0021] The system initiates a self-test for the data acquisition equipment, calibrates the accuracy of voltage, current, and temperature sensors, confirms the communication link is functioning correctly, initializes the data storage buffer, and sets the acquisition frequency and data filtering rules. Example: After the unit powers on, the main control system sends self-test commands to each acquisition module. The voltage sensor is calibrated to an error of ±0.3%, the current acquisition unit completes signal input range (0~5A) adaptation, and after confirming no packet loss in the communication link, the acquisition frequency and data storage path under normal operating conditions are set. According to the set frequency, target data is captured through each sensor and monitoring module, and the acquisition timestamp is recorded synchronously to ensure accurate correlation between data and time. Example: Under normal operating conditions, the voltage sensor collects the total voltage of the supercapacitor module every 100ms and records it as "2025-06-01 10:00:00.000, total module voltage: 54.2V"; the PT100 sensor simultaneously collects the capacitor surface temperature and records it as "2025-06-01 10:00:00.000, module surface temperature: 25.3℃"; the current acquisition device captures the charging current and records it as "2025-06-01 10:00:00.000, charging current: 1.8A".

[0022] The collected raw data undergoes validity verification, outliers are removed (such as values ​​exceeding the sensor's range or sudden data jumps outside the reasonable range), and slightly fluctuating data is smoothed to ensure data accuracy. Example: Voltage data shows "54.2V, 54.3V, 102.5V, 54.1V," with 102.5V exceeding the supercapacitor's rated voltage (55V), thus identified as an outlier and removed. The remaining data is processed using a moving average algorithm to obtain the smoothed voltage data "54.2V." Current data, collected three times consecutively, shows "1.8A, 1.78A, 1.82A," which is considered valid data and the original record is retained.

[0023] The pre-processed valid data is transmitted to the unit's main control system via a communication bus, and simultaneously stored in a local database and a cloud server, supporting historical data review and analysis. Example: Pre-processed voltage, current, and temperature data are transmitted to the PLC main control system via an RS485 bus. The local database stores data categorized by "date-unit number-capacitor number," and the data is synchronously backed up in the cloud. The data retention period is set to 5 years to facilitate subsequent analysis of performance degradation patterns.

[0024] The system monitors the operational status of the data acquisition equipment in real time. If sensor malfunctions, communication interruptions, or persistent data anomalies occur, an acquisition anomaly alarm is immediately triggered, and the system switches to a backup acquisition mode (e.g., activating a backup sensor or reducing the acquisition frequency to ensure core data acquisition). Example: If the PT100 temperature sensor continuously displays "-50℃" (outside the reasonable range), the main control system determines the sensor is faulty, triggers a "temperature acquisition anomaly" alarm, and uploads it to the host computer. Simultaneously, the backup temperature sensor is activated, and the acquisition frequency is adjusted to 500ms / time to ensure uninterrupted ambient temperature data.

[0025] S102 processes the acquired real-time status data of the supercapacitor, captures the usage status and changing trends of the capacitor, and generates real-time data and preliminary evaluation information on the overall operation of the capacitor.

[0026] In one implementation, the acquired real-time status data of the supercapacitor is processed based on preset data preprocessing rules. Outlier removal and data smoothing algorithms are used to filter the raw data, generating a preprocessed, well-structured dataset and a data quality verification report. The effective range, reasonable fluctuation range, and anomaly judgment criteria for each type of data are clearly defined. For example, voltage data must be within ±10% of the supercapacitor's rated voltage, and temperature data must be within -40℃ to +65℃ (the supercapacitor's operating temperature range). The outlier removal algorithm combines the 3σ principle (for normally distributed data) with the absolute deviation method to determine and remove data that exceeds the reasonable range or exhibits abrupt changes. Data segments where the abrupt change exceeds a set threshold (e.g., voltage change ≥ 5V, temperature change ≥ 10℃) in three consecutive data acquisitions are identified as abnormal data segments and marked.

[0027] The data smoothing algorithm uses a moving average (with a window size of 5 acquisition cycles) to smooth the valid data after outlier removal, reducing the impact of random fluctuations on data trends. A verification report is generated based on three dimensions: data integrity (acquisition success rate ≥99%), validity (outlier data percentage ≤0.5%), and consistency (data deviation ≤±1% across different acquisition channels for the same parameter).

[0028] Assuming the supercapacitor's rated voltage is 55V, the original voltage data collected are: 54.2V, 54.3V, 102.5V, 54.1V, 53.9V, 54.0V, 89.7V, and 54.2V. 102.5V and 89.7V exceed the rated voltage ±10% (49.5V~60.5V) range and are therefore identified as outliers and removed. The remaining data are 54.2V, 54.3V, 54.1V, 53.9V, 54.0V, and 54.2V. Using a 5-period moving average algorithm, the smoothed data are calculated as follows: 54.1V (average of the first 5 data points) and 54.1V (average of the last 5 data points). Quality verification: A total of 8 data points were collected, with 6 valid data points, a collection success rate of 75%, and an abnormal data rate of 25%. A verification report was generated indicating "data integrity not up to standard (collection success rate below 99%)" and "abnormal data rate exceeding standard", and a "collection device communication check" prompt was triggered simultaneously.

[0029] Data feature extraction rules were designed based on the operating characteristics of supercapacitors and the monitoring requirements of the pitch system. Focusing on core dimensions such as voltage fluctuation amplitude, temperature change rate, and charge-discharge cycle stability, key state feature sets and feature quantification indicators were generated. By combining the operating characteristics of supercapacitors with the monitoring requirements of the pitch system, key features reflecting the state and changes of the capacitor were extracted from the regularized dataset, and feature indicators were quantified to provide core input for trend analysis. Feature extraction dimensions were determined around three core aspects: "capacitor performance state," "environmental adaptability state," and "operational stability," including voltage fluctuation amplitude, temperature change rate, charge-discharge cycle stability, internal resistance change trend, and voltage equalization circuit operating status.

[0030] Each extracted dimension is given a clear quantitative calculation method. For example, voltage fluctuation amplitude = (maximum voltage - minimum voltage within the acquisition cycle) / rated voltage × 100%; temperature change rate = (current temperature - temperature of the previous acquisition cycle) / acquisition interval; charge-discharge cycle stability = average voltage fluctuation amplitude during a single charge-discharge cycle. Features with weak correlation to capacitor operating status (correlation coefficient < 0.3) are removed, and the core feature set is retained.

[0031] Based on the preprocessed regularized dataset (voltage: 54.2V, 54.1V, 54.0V, 53.9V, 53.8V, 53.9V; temperature: 25.3℃, 25.5℃, 25.7℃, 25.6℃, 25.8℃, 25.9℃; acquisition interval 100ms), core features were extracted: voltage fluctuation amplitude: (54.2V-53.8V) / 55V×100%≈0.73%; temperature change rate: (25.9℃-25.3℃) / (5×100ms)=12℃ / s; charge-discharge cycle stability: the voltage fluctuation amplitudes of the three sliding windows during this discharge process were 0.73%, 0.55%, and 0.61%, with an average of 0.63%; a key state feature set was generated, {voltage fluctuation amplitude 0.73%, temperature change rate 12℃ / s, charge-discharge cycle stability 0.63%}, and the quantitative units and calculation basis of each feature were labeled.

[0032] The time-series trend analysis module processes the key state feature set, integrating sliding window analysis and trend fitting algorithms to capture the changing patterns of capacitor usage status, generating capacitor state trend curves and correlation data between feature changes. The time-series trend analysis module loads sliding window analysis and trend fitting algorithms. Sliding window analysis captures short-term trends (window size set to 30 acquisition cycles), while the trend fitting algorithm combines linear regression and exponential fitting to adapt to different feature change patterns (e.g., voltage decay is linear, internal resistance growth is exponential). Trend curve generation: For each key feature, a time-series trend curve is generated with acquisition time on the horizontal axis and feature quantization value on the vertical axis, annotating key information such as trend slope and inflection points. Feature change correlation analysis: Correlation coefficients between different features are calculated, such as the correlation between voltage fluctuation amplitude and temperature change rate, and the correlation between charge / discharge cycle stability and the number of charge / discharge cycles, generating a feature correlation matrix.

[0033] Based on 30 minutes of continuous key feature data (collected once every 100ms, totaling 18,000 data points), time-series trend analysis was performed. Using 30 acquisition cycles (3 seconds) as windows, the average voltage fluctuation amplitude within each window was calculated. It was found that the average fluctuation amplitude remained between 0.5% and 0.7% in the first 10 minutes, gradually increasing to 0.8% to 1.0% in the following 20 minutes, showing a slow upward trend in the short term. Linear regression fitting was performed on the temperature change rate data, yielding the trend equation y = 0.02x + 10 (x is the acquisition time in minutes; y is the temperature change rate in °C / s). The slope is positive, indicating that the temperature change rate increases slowly over time. The correlation coefficient between voltage fluctuation amplitude and temperature change rate was calculated to be 0.85, indicating a strong positive correlation, suggesting that an increase in the temperature change rate may lead to an increase in voltage fluctuation amplitude. Voltage fluctuation trend curves, temperature change rate trend curves, and a feature correlation matrix were generated (with a correlation coefficient of 0.85).

[0034] The system integrates and standardizes the dataset, key state feature set, state trend curve, and related data on feature changes, then launches the data fusion evaluation engine to generate real-time data and preliminary evaluation information on the overall operation of the capacitor. The data fusion evaluation engine starts by loading a pre-defined fusion evaluation model, which includes state judgment rules (such as whether feature quantification indicators are within the normal range and whether trend changes meet expectations) and weight allocation (60% weight for safety-related features and 40% weight for performance-related features). It integrates real-time values ​​from the standardized dataset, quantification indicators of key features, slopes and inflection points of trend curves, and correlation coefficients of feature association matrices to form a unified evaluation dataset. Evaluation conclusions are output from three dimensions: "Operating Status (Normal / Warning / Abnormal)," "Performance Trend (Stable / Degradation / Improvement)," and "Safety Risk (Low / Medium / High)," clearly indicating core data indicators and the reasons for trend changes.

[0035] Integrating data from the aforementioned stages, the fusion evaluation engine was activated, and the voltage data was normalized to 53.8V~54.2V (within the rated voltage range), voltage fluctuation amplitude of 0.73% (normal range ≤1%), temperature change rate of 12℃ / s (normal range ≤20℃ / s), trend curve slope of 0.02 (slow increase), and voltage-temperature change correlation coefficient of 0.85 (strong positive correlation). All core characteristic indicators were within the normal range, showing a slow decay trend, indicating low safety risk. The preliminary assessment conclusion was that "the supercapacitor is currently operating normally, voltage fluctuation amplitude and temperature change rate are strongly positively correlated, short-term performance trend is stable, and there is no significant safety risk. It is recommended to continuously monitor the impact of temperature changes on voltage stability." Real-time overall capacitor operation data (including current voltage 54.0V, temperature 25.8℃, internal resistance 2.5mΩ, and other core parameters) and preliminary assessment information (including operating status, performance trend, safety risks, and recommendations) were generated.

[0036] S103 processes real-time data and preliminary assessment information on the overall operation of the capacitor. It sets multi-level operating thresholds based on the capacitor's usage patterns and actual operating conditions under different ambient temperatures and charging / discharging frequencies. It applies supercapacitor characteristic constraints and safety operation objective functions for emergency feathering and safe shutdown of the pitch system, and generates alarm signals of corresponding levels and detailed capacitor status reports including capacitor voltage fluctuations, temperature changes, and circuit integrity.

[0037] In one implementation, real-time data on the overall operation of the capacitor and preliminary assessment information are standardized and integrated to construct a capacitor state-operating condition correlation data sequence, generating a basic dataset for threshold setting. Dispersed real-time capacitor operation data and preliminary assessment information are integrated in a unified format to establish the correlation between state parameters and operating conditions, generating a structurally sound and logically coherent basic dataset for threshold setting. The units, precision, and storage format of various data types are unified according to a preset format; for example, voltage data is retained to one decimal place (unit: V), temperature data is retained to one decimal place (unit: ℃), and charging / discharging frequency is uniformly converted to "times / hour". Using the "collection timestamp" as the core correlation key, real-time capacitor state data (voltage, current, temperature, etc.), preliminary assessment information (operating status, performance trends, etc.), and operating conditions (ambient temperature, charging / discharging frequency, etc.) are mapped one-to-one, forming a three-dimensional data sequence of "time-state-operating condition". Data entries with incomplete correlations or failed standardization are removed to ensure the integrity (data entry integrity rate ≥ 99.5%) and consistency (no conflicts in correlation fields) of the basic dataset.

[0038] The threshold setting dataset was imported into the supercapacitor operation simulation software for simulation calculations. This simulated capacitor performance degradation and safe operating boundary conditions under different combinations of ambient temperature and charge / discharge frequency, generating multi-dimensional operating condition threshold simulation data. Simulation parameter settings: Based on the basic dataset, ambient temperature (-40℃~+65℃, 5℃ interval) and charge / discharge frequency (0.5~10 times / hour, 0.5 times / hour interval) were selected as core variables. The simulation duration was set to 1000 hours, and the simulation step size was 1 hour. The simulation content was defined as follows: focusing on simulating the voltage decay rate, temperature rise rate, internal resistance growth rate, and capacitance retention rate of the capacitor under different operating condition combinations, clarifying the critical value of capacitor performance degradation and the safe operating boundary. Simulation results were output according to the "ambient temperature-charge / discharge frequency" combination, recording the safe operating parameter range of the capacitor under each combination (such as maximum allowable voltage, maximum allowable temperature, etc.).

[0039] Import the basic dataset into the supercapacitor simulation software, setting the ambient temperature variables to -40℃, -30℃, ..., +65℃, and the charge / discharge frequency variables to 0.5, 1.0, ..., 10.0 times / hour. Simulating an ambient temperature of 25℃ and a charge / discharge frequency of 2 times / hour, after 1000 hours, the capacitor voltage decays to 52.0V (a decay rate of 3.7%), the temperature stabilizes at 30℃, the internal resistance increases to 3.0mΩ, and the capacity retention rate is 95%. The safe operating boundaries are voltage ≥ 49.5V, temperature ≤ 65℃, and internal resistance ≤ 5.0mΩ. Output the simulation data corresponding to this operating condition combination: "Ambient temperature 25℃, charge / discharge frequency 2 times / hour, safe voltage range 49.5V~55.0V, safe temperature range -40℃~65℃, safe internal resistance range ≤ 5.0mΩ". Summarize the simulation results of all operating condition combinations to generate a multi-dimensional operating condition threshold simulation dataset containing 22×20=440 sets of data.

[0040] A hierarchical threshold partitioning algorithm is employed to subdivide capacitor operating thresholds in multi-dimensional operating condition threshold simulation data, generating scenario-based threshold configuration data. This algorithm breaks down the threshold setting logic into dimensions of capacitor lifespan protection, pitch safety assurance, and operational efficiency optimization. A partitioning model is established to address the threshold adaptation requirements and safety priority objectives for different operating conditions, including low-temperature stabilization periods, high-temperature fluctuation periods, and high-frequency charging and discharging periods. Specifically, the algorithm decomposes the data into three dimensions: "capacitor lifespan protection (priority 3)," "pitch safety assurance (priority 1)," and "operational efficiency optimization (priority 2)," prioritizing pitch system safety while also considering operational efficiency and capacitor lifespan.

[0041] The operating scenarios are divided into three categories: low-temperature stable period (ambient temperature -40℃ to 0℃, charge / discharge frequency ≤ 2 times / hour), high-temperature fluctuation period (ambient temperature 40℃ to 65℃, charge / discharge frequency 2 to 5 times / hour), and high-frequency charge / discharge period (charge / discharge frequency > 5 times / hour, ambient temperature 0℃ to 40℃). For each category, multi-level thresholds (early warning threshold, alarm threshold, and emergency threshold) are set based on priority targets. For example, the early warning threshold is 80% of the safety boundary, the alarm threshold is 90% of the safety boundary, and the emergency threshold is 100% of the safety boundary.

[0042] The system collects characteristic parameters of supercapacitors, such as temperature range, cycle life, and charge / discharge efficiency, as well as safety parameters of the pitch system, such as emergency feathering response time and safe shutdown trigger conditions. A characteristic-safety constraint library is constructed to generate threshold setting constraint data. This library specifically customizes constraints for the supercapacitor's operating range of -40℃ to +65℃, the pitch system's capacity requirements for two emergency feathering cycles, and charge / discharge parameter adaptation requirements. The inherent characteristic parameters of the supercapacitors and the safety requirements of the pitch system are collected to form a constraint library, providing a hard basis for the rationality of thresholds and preventing threshold settings from exceeding hardware limits or safety requirements. The system collects the supercapacitor's rated parameters (rated voltage, rated capacity) and inherent characteristics (temperature range -40℃ to +65℃, cycle life > 100,000 cycles, charge / discharge efficiency > 95%). The safety requirements of the pitch system are defined, including emergency feathering response time ≤ 15s, capacity requirements for two emergency feathering cycles, and safe shutdown trigger voltage thresholds. The parameters are organized into categories such as "characteristic constraints" and "safety constraints," and the types of constraints (range constraints and numerical constraints) and the consequences of violations are clearly defined to ensure that the threshold settings do not exceed the constraint range.

[0043] By integrating scenario-based threshold configuration data and threshold setting constraint data, a capacitor operating status-multi-level threshold coupling model is constructed. Through iterative optimization of the model, alarm signals of corresponding levels and detailed capacitor status reports including capacitor voltage fluctuations, temperature changes, and circuit integrity are generated. Using scenario-based threshold configuration data as variables and characteristic-safety constraint data as boundary conditions, a capacitor operating status-multi-level threshold coupling model is constructed, establishing a mapping relationship between "operating condition-threshold-alarm". The model is validated using historical operating data. If threshold constraints are exceeded or alarms are not timely, threshold parameters are adjusted (e.g., lowering the alarm threshold under high-temperature conditions), with at least 5 iterations to ensure model accuracy ≥99%. Based on the optimized model, the current capacitor operating data and operating conditions are matched in real time, triggering corresponding level alarm signals (early warning, alarm, emergency), and generating detailed status reports including voltage fluctuations, temperature changes, circuit integrity, alarm causes, and handling suggestions.

[0044] Using scenario-specific voltage thresholds (warning 54.0V, alarm 52.5V, emergency 51.0V) as variables and a safety constraint (emergency featherer capacity ≥ 50.0V) as the boundary, a coupled model was constructed. Validation with historical data revealed that under high-temperature conditions, when the voltage drops to 52.5V, the capacitor capacity is already close to 50.0V. Adjusting the high-temperature alarm threshold to 53.0V, after 5 iterations, the model accuracy reached 99.2%. When the current data of the capacitor is "voltage 52.8V, temperature 45℃, charge / discharge frequency 3 times / hour, circuit continuity normal", the model matches the operating conditions during the high temperature fluctuation period, triggers an alarm signal, and generates a detailed status report: "Current operating status of capacitor: alarm; voltage fluctuation: 52.8V (below the alarm threshold of 53.0V); temperature change: 45℃ (within the safe range of -40℃~65℃); circuit integrity: normal; alarm reason: the voltage decays to the alarm threshold under high temperature conditions, and the capacity is close to the lower limit of emergency feathering requirements; handling suggestion: appropriately increase the float charge voltage, reduce the charge / discharge frequency, and continuously monitor the changes in capacitor capacity."

[0045] S104 processes alarm signals and detailed capacitor status reports of different levels, and automatically executes response measures such as charging current adjustment, float charge voltage adaptation, and charging start / stop control based on the alarm level. It dynamically adjusts charging and discharging parameters and generates an automatic adjustment scheme to ensure the stable operation of the pitch system.

[0046] In one implementation, alarm signals and detailed capacitor status reports of different levels are functionally decomposed and categorized according to preset alarm classification response rules, generating dedicated processing rules and parameter adjustment threshold information for minor, moderate, and severe alarms. Alarm signals and status reports are decomposed and categorized according to alarm severity, clarifying the processing logic and parameter adjustment boundaries for different alarm levels, laying the foundation for accurate response. The criteria for determining minor, moderate, and severe alarms are clearly defined: minor alarms are those where parameters are close to the warning threshold but do not affect core functions; moderate alarms are those where parameters exceed the warning threshold and pose a risk of performance degradation; and severe alarms are those where parameters exceed the alarm threshold and threaten the safety of the pitch system. The core issue dimensions in the status reports are extracted according to alarm level: minor alarms focus on small parameter fluctuations; moderate alarms focus on performance deterioration trends; and severe alarms identify safety boundary breaches. Processing priorities, response times, and parameter adjustment thresholds are established for each alarm level to ensure that the rules are implementable and that the thresholds do not exceed the constraints of supercapacitor characteristics and pitch safety objectives.

[0047] The supercapacitor has a rated voltage of 55V, a safe voltage range of 49.5V~55.0V, a warning threshold of 54.0V, an alarm threshold of 52.5V, and an emergency threshold of 51.0V. A minor alarm occurs when the capacitor voltage is between 53.0V and 54.0V (close to the warning threshold) and the temperature is between 35℃ and 40℃ (the upper limit of the normal range). The specific rule is "prioritize fine-tuning parameters without interrupting normal operation," with parameter adjustment thresholds of charging current ±0.2A and float charge voltage ±0.5V. A moderate alarm occurs when the voltage is between 51.0V and 52.5V (exceeding the warning threshold) and the internal resistance is between 4.0mΩ and 5.0mΩ (close to the safety upper limit). The specific rule is "step-by-step parameter adjustment with continuous status monitoring," with parameter adjustment thresholds of charging current ±0.5A and float charge voltage ±1.0V. Critical alarm: When the voltage is below 51.0V (exceeding the emergency threshold) or the temperature is above 60℃ (approaching the upper limit of temperature resistance 65℃), it is judged as a critical alarm. The exclusive rule is "immediately activate emergency measures to ensure pitch safety". The parameter adjustment threshold is triggered by charging start-stop control and emergency power supply switching.

[0048] Based on the charging and discharging parameter adjustment requirements, the analytical dimensions of the detailed capacitor status report are arranged and designed to obtain the feature extraction criteria, parameter adjustment types, and effective adjustment range information for each response measure, generating multi-dimensional response design information. Based on the charging and discharging parameter adjustment requirements, the status report analytical dimensions are broken down, clarifying the execution standards, parameter types, and adjustment ranges of each response measure, generating structured response design information. Following the logic of "problem location - parameter association - adjustment target," the status report analytical dimensions are arranged, including parameter anomaly type, anomaly degree, environmental conditions, performance degradation trend, etc. Extraction criteria are defined for each analytical dimension; for example, voltage anomalies are extracted as "current value, threshold deviation, fluctuation rate," and environmental conditions are extracted as "temperature, charging and discharging frequency." For the three core measures—charging current regulation, float charge voltage adaptation, and charging start / stop control—the parameter adjustment type (linear / stepped / switching), effective adjustment range (without exceeding the capacitor's rated parameters), and execution trigger conditions are clarified.

[0049] By combining the constraints of supercapacitor characteristics and the safety objectives of the pitch system, corresponding synchronous verification nodes are set for the multi-dimensional response design information of the three types of response measures. Verification nodes are set for the three types of response measures by combining the constraints of supercapacitor characteristics (such as temperature range and charge / discharge efficiency) and the safety objectives of the pitch system (such as emergency feathering capacity) to avoid parameter adjustment conflicts or exceeding safety boundaries. Characteristic constraint verification nodes: For the characteristics of supercapacitors, such as temperature range of -40℃ to +65℃ and charge / discharge efficiency >95%, verification nodes are set for the upper limits of temperature and current after parameter adjustment. Safety objective verification nodes: Verification nodes are set for capacitor capacity, power supply stability, etc., around the safety objectives of the pitch system, such as two emergency feathering operations and emergency shutdown. Verification logic settings: The verification method (real-time monitoring / periodic verification), judgment criteria (meeting standards / not meeting standards / requiring optimization), and anomaly handling mechanism (pausing adjustment / parameter rollback / activating emergency response) for each node are clearly defined.

[0050] Taking the stepped adjustment of charging current for a moderate alarm as an example, the settings are "adjusted charging current ≤ 3.0A (rated charging current of the capacitor)" and "adjusted charge / discharge efficiency ≥ 95%". Current and efficiency data are monitored in real time. If the current exceeds 3.0A, adjustment is immediately paused. The setting is "adjusted capacitor capacity ≥ 50.0V (meeting the needs of two emergency feathering operations)". The capacity is checked every 5 minutes. If the capacity is lower than 50.0V, the previous parameter level is reverted. If the check finds that parameter adjustment causes a continuous increase in temperature (exceeding 2°C every 10 minutes), adjustment is paused, and float charge voltage adaptation auxiliary cooling is activated.

[0051] The system processes the signal function breakdown results, multi-dimensional response design information, and synchronous verification nodes to generate an automatic adjustment scheme that includes response rule grouping information, multi-measure coordination schemes, and synchronous verification standards. For minor alarms, it focuses on fine-tuning the charging current and slightly adapting the float charge voltage; for moderate alarms, it implements stepped adjustment of the charging current and dynamic adaptation of the float charge voltage; and for severe alarms, it initiates charging start-stop control and emergency power supply switching, dynamically adjusting charging and discharging parameters to ensure stable operation of the pitch system. The system integrates alarm classification results, multi-dimensional response design information, and synchronous verification nodes to construct an automatic adjustment scheme that includes rules, schemes, and standards, dynamically adjusting charging and discharging parameters to ensure stable operation of the pitch system. All preceding information is integrated according to the logic of "alarm level - response measure - verification node" to ensure data consistency across all stages. The execution order, priority, and coordination logic of multiple measures under different alarm levels are clearly defined to avoid conflicting measures. Verification nodes are transformed into quantifiable standards, including allowable parameter adjustment deviations and conditions for achieving state recovery. A structured automatic adjustment scheme is generated, clearly defining the applicable scenarios, execution steps, parameter details, and verification requirements.

[0052] S105 processes the automatic adjustment scheme, uses data analysis technology to mine historical operating data, captures the law of capacitor performance decay, identifies the law of capacitor performance change, and generates predictions of future capacitor operating status and suggestions for optimizing charging and discharging parameters.

[0053] In one implementation, correlation analysis and trend extraction are performed on the automatic adjustment scheme execution data and the historical operating data of the supercapacitor to generate capacitor performance degradation characteristics and parameter adjustment effect correlation characteristics, forming basic information for trend analysis. By correlating the automatic adjustment scheme execution data with historical operating data, the laws of capacitor performance degradation and the actual effects of parameter adjustments are explored, forming basic feature information to support subsequent analysis. For example, a supercapacitor, designated C001, has historical operating data covering one year (300 charge-discharge cycles). The automatic adjustment scheme execution data is "charging current adjusted from 1.5A to 1.8A, float charge voltage adjusted from 54.5V to 55.0V". The voltage and internal resistance data before and after the adjustment are correlated with the corresponding parameters from the historical 300 cycles to form a "charge-discharge cycles - voltage - internal resistance - charging current - float charge voltage" correlation dataset. Time series analysis revealed that the voltage decay rate was 0.005V / cycle for the first 100 cycles, 0.008V / cycle for 100-200 cycles, and 0.01V / cycle for 200-300 cycles, with the decay rate gradually increasing. Correlation analysis showed that after increasing the charging current by 0.3A, the voltage recovery rate increased from 0.02V / min to 0.03V / min, with a correlation coefficient of 0.88.

[0054] The features are generated as follows: the performance degradation feature is "average voltage degradation rate of 0.0077V / cycle, entering a rapid degradation period after 200 cycles, with an estimated remaining lifespan of 800 cycles"; the parameter adjustment effect feature is "charging current adjustment sensitivity of 0.037V / (A・min), effect duration ≥72 hours, suitable for charging and discharging frequencies of 2-3 times / hour", forming the basic information for trend analysis.

[0055] Constraints and optimization objectives are set for the data analysis dimensional range and the fitting boundary of the decay law. Effective thresholds for data mining and priority parameters for pattern identification are generated, forming analytical constraint information. The dimensional range, fitting boundary, and optimization objectives of the data analysis are defined, clarifying the effective thresholds and rule priorities for data mining to prevent the analysis from deviating from core requirements. The analysis dimensions are limited to four core dimensions: voltage decay, internal resistance growth, capacity decay, and parameter adjustment response speed, excluding redundant dimensions unrelated to performance and adjustment effects.

[0056] The fitting boundary constraints for the decay law are set by defining the time range (last 6 months / 200 charge-discharge cycles) and operating condition range (ambient temperature -20℃ to +50℃, charge-discharge frequency 0.5-5 times / hour) of the fitted data to ensure that the fitting results closely match the actual operating scenario. The optimization objectives and priorities are set as follows: the core objectives are "extending supercapacitor lifespan (weight 60%)", "ensuring the safety and stability of the pitch system (weight 30%)", and "optimizing operating efficiency (weight 10%)", with data mining prioritizing the higher-weight objectives. Effective thresholds are set for each analysis dimension, such as an effective range for voltage decay rate of 0.001-0.02V / cycle and an effective range for internal resistance growth rate of 0.005-0.02mΩ / cycle. Data exceeding these ranges are considered abnormal and excluded from the law fitting process.

[0057] The interaction between capturing performance degradation patterns and optimizing charging and discharging parameters is investigated. This is combined with the objective functions of extending supercapacitor lifespan and ensuring the safety and stability of the pitch system for optimization calculations and performance evaluation. This generates predicted capacitor state characteristics and objective function fit parameters suitable for different operating conditions, providing optimization suggestions. By combining the dual objectives of extending supercapacitor lifespan and ensuring the safety and stability of the pitch system, the interaction between performance degradation patterns and parameter optimization is evaluated, generating predicted characteristics and optimization directions suitable for different operating conditions. The objective function is established as F = 0.6 × lifespan extension coefficient + 0.3 × safety and stability coefficient + 0.1 × efficiency optimization coefficient, where the lifespan extension coefficient is positively correlated with capacity retention, the safety and stability coefficient is positively correlated with emergency feathering capability, and the efficiency optimization coefficient is positively correlated with charging and discharging efficiency. A multivariate optimization algorithm is employed, using charging current and float charge voltage as optimization variables. Within the analysis constraints, different parameter combinations are traversed to calculate the objective function value and select the optimal parameter range. The evaluation is conducted from three dimensions: “the effect of parameter adjustment on the suppression of attenuation (the attenuation rate is reduced by ≥15%)”, “the objective function fit (≥0.8)”, and “the adaptability to operating conditions (covering more than 80% of actual operating conditions)”, and then the optimized suggestion features are generated after the target is met.

[0058] This system integrates basic trend analysis information, analytical constraint information, and optimization suggestion information to generate predictions of future capacitor operating states, optimization instructions for charge / discharge parameters, and a report on the compatibility of parameter adjustments with capacitor performance. It also integrates prior analysis information to generate clear predictions of future operating states, actionable parameter optimization procedures, and a report on the compatibility of parameters with performance, providing guidance for subsequent operation and maintenance. Based on decay patterns and optimized parameters, it predicts the capacitor state (voltage range, upper limit of internal resistance, capacity retention rate) for the next 6 months / 300 cycles, indicating the prediction's reliability (≥90%). It clearly defines the specific values ​​for parameter adjustments, the timing of adjustments (e.g., adjustments every 100 charge / discharge cycles), and key monitoring points after adjustments (e.g., voltage recovery, temperature changes). The compatibility analysis report includes the compatibility relationship between parameter adjustments and different operating conditions, verification data of adjustment effects, potential risk warnings (e.g., precautions for parameter adjustments under high-temperature conditions), and future optimization directions.

[0059] S106 processes real-time data and preliminary assessment information on the overall operation of the capacitor, multi-level alarm signals and detailed reports on capacitor status, automatic adjustment schemes, predictions of future capacitor operation status, and suggestions for optimizing charging and discharging parameters. It sets assessment dimensions and quantitative indicators, applies adaptability constraints between assessment results and actual operating conditions, and generates comprehensive assessment information on the adaptive adjustment effect of supercapacitor charging and discharging parameters.

[0060] In one implementation, real-time data on the overall operation of the capacitor, multi-level alarm signals, automatic adjustment schemes, and future operation prediction suggestions are grouped and categorized based on preset evaluation classification rules, generating evaluation types and associated quantitative parameter information corresponding to each rule. The entire process of capacitor operation is categorized according to preset rules, clarifying the evaluation dimensions and quantitative parameters corresponding to each type of information, laying the foundation for subsequent comprehensive evaluation. The preset evaluation classification rules divide evaluation types into "operation status," "alarm response," "parameter adjustment," and "trend prediction," with each rule specifying the inclusion criteria for information. Real-time data on the overall operation of the capacitor is categorized into the operation status category, multi-level alarm signals into the alarm response category, automatic adjustment schemes into the parameter adjustment category, and future operation prediction suggestions into the trend prediction category. Quantifiable core parameters are extracted from each type of information to ensure that the parameters are strongly correlated with the evaluation objectives and that the data format is consistent and comparable.

[0061] Taking the supercapacitor (rated voltage 55V) of an SL1500 unit in a wind farm as an example, the following parameters are used for operational status: Real-time voltage 54.0V, temperature 25.8℃, internal resistance 2.5mΩ, etc., with quantified parameters of "voltage deviation rate 1.8%, temperature compliance rate 100%, internal resistance control rate 50%" (internal resistance safety upper limit 5.0mΩ). Alarm response: Data on 1 minor alarm, 0 moderate alarms, and 0 severe alarms are included, with quantified parameters of "alarm occurrence rate 0.3%, alarm response timeliness rate 100%, and zero emergency alarms." Parameter adjustment: Data on charging current adjustment range of 0.3A and float charge voltage adjustment range of 0.5V are included, with quantified parameters of "parameter adjustment accuracy 98% and voltage recovery rate after adjustment 0.03V / min." Trend prediction: Data on the predicted capacity retention rate for the next 6 months of 88% and the predicted attenuation rate of 0.008V / time are included, with quantified parameters of "capacity attenuation control rate 85% and prediction reliability 92%."

[0062] Based on the requirements for full-process effect verification, the processing content of various input information is arranged and designed. Information on evaluation dimension extraction standards, indicator quantification methods, and result adaptation ranges is obtained to generate comprehensive evaluation processing design information. Based on the requirements for full-process effect verification, evaluation dimensions, quantification methods, and result ranges are designed to generate structured comprehensive evaluation processing information, ensuring that the evaluation is scientific and feasible. Around the four core dimensions of "monitoring effectiveness, adjustment timeliness, system security, and trend prediction," each dimension is broken down into 2-3 sub-indicators, covering the entire process from data collection to trend prediction. A quantification method combining "absolute value + relative proportion" is adopted. Absolute values ​​are used for directly measurable parameters (such as response time), and relative proportions are used for effect-related parameters (such as improvement rate). Referring to the constraints of supercapacitor characteristics and the safety objectives of the pitch system, excellent, good, qualified, and unqualified ranges are set for each indicator to ensure that the evaluation results have clear judgment criteria.

[0063] For example, the monitoring effectiveness dimension extracts "capacitor monitoring coverage" (coverage parameter percentage ≥ 95%) and "data accuracy" (effective data percentage ≥ 99%); the adjustment timeliness dimension extracts "parameter adjustment response efficiency" (response time ≤ 3s) and "adjustment effect achievement rate" (adjusted parameter percentage within the target range ≥ 98%). Capacitor monitoring coverage = number of monitored parameters / total number of core parameters × 100%; parameter adjustment response efficiency = average time from alarm trigger to parameter adjustment completion; pitch system safety improvement rate = (safety incident occurrence rate after modification - safety incident occurrence rate before modification) / safety incident occurrence rate before modification × 100%. The result applicability range is as follows: capacitor monitoring coverage ≥ 95% is excellent, 90%-94% is good, 85%-89% is acceptable, < 85% is unacceptable; parameter adjustment response efficiency ≤ 3s is excellent, 3-5s is good, 5-8s is acceptable, > 8s is unacceptable.

[0064] Based on the operating conditions of the supercapacitor and the safety and stability requirements of the pitch system, corresponding effect verification nodes are set for the comprehensive evaluation and processing design information of each rule. Verification nodes are set for each evaluation rule to ensure the traceability of the evaluation process and the authenticity and reliability of the results. Nodes are set according to "real-time verification, periodic verification, and scenario-based verification." Real-time verification corresponds to operational status information, periodic verification corresponds to parameter adjustment information, and scenario-based verification corresponds to trend prediction information. The verification timing, verification methods, and judgment criteria for each node are clearly defined. Verification methods include data comparison, operating condition simulation, and historical backtesting. If the verification results are unsatisfactory, a secondary verification process is triggered to investigate problems in the information collection or processing stages, ensuring that the evaluation results are unbiased.

[0065] For voltage and temperature data related to operational status, a "data verification every 100ms" node is set. The verification method is to compare the sensor-collected data with the standard calibration data. The judgment criterion is a deviation ≤ ±0.5%. In the example, the voltage of 54.0V deviates from the calibration value of 54.1V by 0.18%, and the verification passes. For parameter adjustment data, a "comprehensive verification once a day" node is set. The verification method is to backtrack the adjustment records and operational data of the day. The judgment criterion is that the parameter compliance rate after adjustment is ≥ 98%. In the example, all three adjustments on that day met the standard, and the verification passes. For capacity decay prediction data related to trend forecasting, a "operating condition simulation once a month" node is set. The verification method is to simulate the capacitor performance under different ambient temperatures. The judgment criterion is that the deviation between the predicted value and the actual value is ≤ ±5%. In the example, the predicted capacity retention rate of 88% deviates from the actual 87.2% by 0.91%, and the verification passes.

[0066] The information grouping results, comprehensive evaluation and processing design information, and effect verification nodes are processed to generate comprehensive evaluation information on the adaptive adjustment effect of supercapacitor charging and discharging parameters, including evaluation grouping information, quantitative schemes, and verification standards. Specifically, actual operating data is compared with preset target thresholds to generate effect deviation signals. A multi-dimensional weighted algorithm is then used to generate quantitative evaluation results for capacitor monitoring coverage, parameter adjustment response efficiency, and pitch system safety improvement rate, forming a full-process effect evaluation mechanism covering data acquisition, processing, adjustment, and prediction. By integrating grouping information, processing design information, and verification node results, and using deviation calculation and weighted algorithms, comprehensive evaluation information including quantitative evaluation results and verification standards is generated, constructing a full-process evaluation mechanism.

[0067] The actual operating data is compared with the preset target thresholds to calculate the absolute and relative deviations, generating deviation signals to identify non-compliant items and their degrees of deviation. A multi-dimensional weighted algorithm allocates weights according to "system security (40%), monitoring effectiveness (25%), adjustment timeliness (20%), and trend predictability (15%)" to calculate a comprehensive evaluation score. The evaluation results include an evaluation report containing evaluation group information, quantitative schemes, verification standards, comprehensive scores, and optimization directions, forming a full-process evaluation mechanism covering data collection, processing, adjustment, and prediction.

[0068] The preset capacitor monitoring coverage target was 95%, and the actual coverage was 96.5%, with a relative deviation of +1.5% (positive deviation). The parameter adjustment response efficiency target was 3s, and the actual response was 2.8s, with an absolute deviation of -0.2s. The pitch system safety improvement target was 30%, and the actual performance was 32%, with a relative deviation of +6.7%. The weighted algorithm calculated the system safety score as 92 (weight 40%, score 36.8), monitoring effectiveness score as 95 (weight 25%, score 23.75), adjustment timeliness score as 98 (weight 20%, score 19.6), and trend prediction score as 90 (weight 15%, score 13.5), for a comprehensive evaluation score of 93.65. The comprehensive evaluation information shows that "the adaptive adjustment effect of supercapacitor charging and discharging parameters is excellent, the capacitor monitoring coverage is 96.5%, the parameter adjustment response efficiency is 2.8s, the pitch system safety improvement rate is 32%, and the whole-process evaluation mechanism is operating effectively; the optimization direction is to further improve the trend prediction accuracy under low-temperature conditions."

[0069] In one implementation, such as Figure 2 As shown, this application also provides a data processing device for adaptive adjustment of supercapacitor charging and discharging parameters, comprising: The acquisition module 201 is used to acquire real-time status data of the supercapacitor; Processing module 202 is used to process the acquired real-time status data of the supercapacitor, capture the capacitor's usage status and changing trends, and generate real-time data and preliminary assessment information of the overall capacitor operation. It processes the real-time data and preliminary assessment information of the overall capacitor operation, sets multi-level operating thresholds based on the capacitor's usage patterns under different ambient temperatures and charging / discharging frequencies, and actual operating conditions. It applies supercapacitor characteristic constraints and safety operation objective functions for emergency feathering and safe shutdown of the pitch system, generating corresponding alarm signals and detailed capacitor status reports including capacitor voltage fluctuations, temperature changes, and circuit integrity. It processes alarm signals and detailed capacitor status reports of different levels, and automatically performs charging current adjustment based on the alarm level. The system implements response measures for float charge voltage adaptation and charging start / stop control, dynamically adjusts charging and discharging parameters, and generates an automatic adjustment scheme to ensure the stable operation of the pitch system. The automatic adjustment scheme is processed, and data analysis techniques are used to mine historical operating data, capture capacitor performance degradation patterns, identify capacitor performance change patterns, and generate predictions of future capacitor operating status and suggestions for optimizing charging and discharging parameters. Real-time data and preliminary assessment information on the overall capacitor operation, multi-level alarm signals and detailed capacitor status reports, automatic adjustment schemes, predictions of future capacitor operating status, and suggestions for optimizing charging and discharging parameters are processed. Evaluation dimensions and quantitative indicators are set, and adaptability constraints are applied between the evaluation results and actual operating conditions to generate comprehensive evaluation information on the adaptive adjustment effect of supercapacitor charging and discharging parameters.

[0070] The computer-readable storage medium provided in the above embodiments of this application and the data processing method for adaptive adjustment of supercapacitor charging and discharging parameters provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0071] The various embodiments in this application are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of the data processing method, electronic device, electronic device, and readable storage medium for evaluating the adaptive adjustment of supercapacitor charge and discharge parameters are basically similar to the embodiments of the supercapacitor charge and discharge parameter adaptive adjustment described above, and are therefore described simply. Relevant parts can be referred to in the descriptions of the embodiments of the supercapacitor charge and discharge parameter adaptive adjustment data processing method described above.

Claims

1. A data processing method for adaptive adjustment of supercapacitor charging and discharging parameters, characterized in that, include: Obtain real-time status data of supercapacitors; The acquired real-time status data of the supercapacitor is processed to capture the usage status and changing trends of the capacitor, and to generate real-time data and preliminary assessment information on the overall operation of the capacitor. The system processes real-time data and preliminary assessment information on the overall operation of the capacitor, sets multi-level operating thresholds based on the capacitor's usage patterns and actual operating conditions under different ambient temperatures and charging / discharging frequencies, applies supercapacitor characteristic constraints and safety operation objective functions for emergency feathering and safe shutdown of the pitch system, and generates alarm signals of corresponding levels and detailed capacitor status reports including capacitor voltage fluctuations, temperature changes, and circuit integrity. The system processes alarm signals and capacitor status reports of different levels, and automatically implements response measures such as charging current adjustment, float charge voltage adaptation, and charging start / stop control based on the alarm level. It dynamically adjusts charging and discharging parameters and generates an automatic adjustment scheme to ensure the stable operation of the pitch system. The automatic adjustment scheme is processed, and data analysis technology is used to mine historical operating data, capture the law of capacitor performance decay, identify the law of capacitor performance change, and generate predictions of future capacitor operating status and suggestions for optimization of charging and discharging parameters. The system processes real-time data and preliminary assessment information on the overall operation of the capacitor, multi-level alarm signals and detailed reports on capacitor status, automatic adjustment schemes, predictions of future capacitor operation status, and suggestions for optimizing charging and discharging parameters. It sets assessment dimensions and quantitative indicators, applies adaptability constraints between assessment results and actual operating conditions, and generates comprehensive assessment information on the adaptive adjustment effect of supercapacitor charging and discharging parameters.

2. The method as described in claim 1, characterized in that, The acquired real-time status data of the supercapacitor is processed to capture the capacitor's usage status and changing trends, generating real-time data and preliminary assessment information on the overall operation of the capacitor, including: The real-time status data of the supercapacitor is processed based on the preset data preprocessing rules. The original data is filtered by outlier removal and data smoothing algorithms to generate a preprocessed regular dataset and a data quality verification report. Based on the operating characteristics of supercapacitors and the monitoring requirements of pitch systems, data feature extraction rules are designed, focusing on the core dimensions of voltage fluctuation amplitude, temperature change rate, and charge-discharge cycle stability, to generate key state feature sets and feature quantification indicators. The time-series trend analysis module is called to process the key state feature set, and the sliding window analysis and trend fitting algorithm are combined to capture the change pattern of capacitor usage status and generate capacitor state trend curves and feature change correlation data. By integrating the regularized dataset, key state feature set, state trend curve and related data of feature changes, the data fusion evaluation engine is launched to generate real-time data and preliminary evaluation information on the overall operation of the capacitor.

3. The method as described in claim 1, characterized in that, The system processes real-time data and preliminary assessment information on the overall operation of the capacitor. It sets multi-level operating thresholds based on the capacitor's usage patterns under different ambient temperatures and charging / discharging frequencies, as well as actual operating conditions. It applies supercapacitor characteristic constraints and safety operation objective functions for emergency feathering and safe shutdown of the pitch system. This generates corresponding alarm signals and detailed capacitor status reports, including information on capacitor voltage fluctuations, temperature changes, and circuit integrity. The real-time data on the overall operation of the capacitor is standardized and integrated with the preliminary assessment information to construct a capacitor status-operating condition correlation data sequence and generate a basic dataset for threshold setting. Import the threshold setting dataset into the supercapacitor operation simulation software for simulation calculation, simulate the capacitor performance degradation and safe operation boundary conditions under different combinations of ambient temperature and charge / discharge frequency, and generate multi-dimensional operating condition threshold simulation data. A hierarchical threshold partitioning algorithm is used to subdivide the capacitor operation threshold in the multi-dimensional operating condition threshold simulation data to generate scenario-based threshold configuration data. The hierarchical threshold partitioning algorithm decomposes the threshold setting logic from the dimensions of capacitor life protection, pitch safety assurance, and operating efficiency optimization. It establishes a partitioning model for threshold adaptation requirements and safety priority targets for different operating conditions such as low temperature stabilization period, high temperature fluctuation period, and high frequency charging and discharging period. Collect characteristic parameters of supercapacitor temperature range, cycle life, and charge / discharge efficiency, as well as safety parameters of pitch system emergency feathering response time and safe shutdown triggering conditions. Construct a characteristic-safety constraint library and generate threshold setting constraint data. Among them, the characteristic-safety constraint library is customized for supercapacitor operating range requirements of -40℃ to +65℃, pitch system emergency feathering capacity requirements, and charge / discharge parameter adaptation requirements. By integrating scenario-based threshold configuration data and threshold setting constraint data, a capacitor operating status-multi-level threshold coupling model is constructed. Through iterative optimization of the model, alarm signals of corresponding levels and detailed capacitor status reports including capacitor voltage fluctuations, temperature changes, and circuit integrity are generated.

4. The method as described in claim 1, characterized in that, The system processes detailed reports on alarm signals and capacitor status at different levels. Based on the alarm level, it automatically executes response measures such as charging current adjustment, float charge voltage adaptation, and charging start / stop control. It dynamically adjusts charging and discharging parameters to generate an automatic adjustment scheme that ensures stable operation of the pitch system, including: According to the preset alarm classification response rules, the alarm signals and capacitor status detailed reports of different levels are functionally broken down and classified, and exclusive processing rules and parameter adjustment threshold information are generated for minor alarms, moderate alarms and severe alarms. Based on the charging and discharging parameter adjustment requirements, the analysis dimensions of the detailed capacitor status report are arranged and designed to obtain the feature extraction criteria, parameter adjustment type and effective adjustment range information of each response measure, and generate multi-dimensional response design information. Combining the constraints of supercapacitor characteristics and the safety objectives of the pitch system, corresponding synchronous verification nodes are set for the multi-dimensional response design information of the three types of response measures; The signal function decomposition results, multi-dimensional response design information, and synchronization verification nodes are processed to generate an automatic adjustment scheme that includes response rule grouping information, multi-measure coordination scheme, and synchronization verification standard. Among them, minor alarms focus on fine-tuning of charging current and slight adaptation of float charging voltage; moderate alarms implement step adjustment of charging current and dynamic adaptation of float charging voltage; and severe alarms initiate charging start-stop control and emergency power supply switching, dynamically adjusting charging and discharging parameters to ensure stable operation of the pitch system.

5. The method as described in claim 4, characterized in that, The automatic adjustment scheme is processed, and data analysis techniques are used to mine historical operating data, capture the laws of capacitor performance degradation, identify the patterns of capacitor performance change, and generate predictions of future capacitor operating states and suggestions for optimizing charging and discharging parameters, including: Correlation analysis and trend extraction are performed on the execution data of the automatic adjustment scheme and the historical operation data of the supercapacitor to generate capacitor performance degradation characteristics and parameter adjustment effect correlation characteristics, forming basic information for trend analysis. The constraints and optimization objectives are set for the data analysis dimension range and the fitting boundary of the decay law. Effective thresholds for data mining and priority parameters for pattern identification are generated to form analysis constraint information. The interaction between capturing the performance degradation law and optimizing the charging and discharging parameters is combined with the objective function of extending the supercapacitor's lifespan and ensuring the safety and stability of the pitch system to perform optimization calculations and performance evaluations, generating capacitor state prediction characteristics and objective function fit parameters suitable for different operating conditions, and forming optimization suggestion information; Integrating trend analysis data, constraint information, and optimization suggestions, the system generates predictions of future capacitor operating states, optimization information for charging and discharging parameters, and a report on parameter adjustments and capacitor performance compatibility analysis.

6. The method as described in claim 5, characterized in that, The system processes real-time data and preliminary assessment information on the overall operation of the capacitor, multi-level alarm signals and detailed capacitor status reports, automatic adjustment schemes, predictions of future capacitor operating status, and suggestions for optimizing charging and discharging parameters. It sets assessment dimensions and quantitative indicators, applies adaptability constraints between assessment results and actual operating conditions, and generates comprehensive assessment information on the adaptive adjustment effect of supercapacitor charging and discharging parameters, including: Based on preset evaluation and classification rules, the real-time data of the overall operation of the capacitor, multi-level alarm signals, automatic adjustment schemes and future operation prediction suggestions are grouped and classified to generate evaluation types and associated quantitative parameter information corresponding to each rule; Based on the requirements of full-process effect verification, the processing content of various input information is arranged and designed, the evaluation dimension extraction standard, indicator quantification method and result adaptation range information are obtained, and comprehensive evaluation processing design information is generated. Based on the operating conditions of the supercapacitor and the safety and stability requirements of the pitch system, corresponding effect verification nodes are set for the comprehensive evaluation and processing design information of each rule; The information grouping results, comprehensive evaluation and processing design information, and effect verification nodes are processed to generate comprehensive evaluation information on the adaptive adjustment effect of supercapacitor charging and discharging parameters, which includes evaluation grouping information, quantitative schemes, and verification standards. In this process, actual operating data is compared with preset target thresholds to generate effect deviation signals. A multi-dimensional weighted algorithm is used to generate quantitative evaluation results for capacitor monitoring coverage, parameter adjustment response efficiency, and pitch system safety improvement rate, forming a full-process effect evaluation mechanism covering data acquisition, processing, adjustment, and prediction.

7. A data processing device for adaptive adjustment of supercapacitor charging and discharging parameters, characterized in that, The device includes: The acquisition module is used to acquire real-time status data of the supercapacitor; The processing module processes the acquired real-time status data of the supercapacitor, captures the capacitor's usage status and changing trends, and generates real-time data and preliminary assessment information on the overall operation of the capacitor. It processes this data, sets multi-level operating thresholds based on the capacitor's usage patterns under different ambient temperatures and charging / discharging frequencies, and actual operating conditions. It applies supercapacitor characteristic constraints and safety operation objective functions for emergency feathering and safe shutdown of the pitch system, generating corresponding alarm signals and detailed capacitor status reports including voltage fluctuations, temperature changes, and circuit integrity. The module further processes alarm signals and detailed capacitor status reports at different levels, and automatically performs charging current adjustment and buoyancy control based on the alarm level. The system implements response measures for charging voltage adaptation and charging start / stop control, dynamically adjusts charging and discharging parameters, and generates an automatic adjustment scheme to ensure the stable operation of the pitch system. The automatic adjustment scheme is processed, and data analysis techniques are used to mine historical operating data, capture the laws governing capacitor performance degradation, identify patterns of capacitor performance change, and generate predictions of future capacitor operating status and suggestions for optimizing charging and discharging parameters. Real-time data and preliminary assessment information on the overall capacitor operating status, multi-level alarm signals and detailed capacitor status reports, automatic adjustment schemes, predictions of future capacitor operating status, and suggestions for optimizing charging and discharging parameters are processed. Evaluation dimensions and quantitative indicators are set, and adaptability constraints are applied between the evaluation results and actual operating conditions to generate comprehensive evaluation information on the adaptive adjustment effect of supercapacitor charging and discharging parameters.

8. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the data processing method for adaptive adjustment of supercapacitor charging and discharging parameters according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the data processing method for adaptive adjustment of supercapacitor charging and discharging parameters as described in any one of claims 1 to 6.

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