An unattended hydrogen ion battery charging station

Abstract

The application discloses an unattended hydrogen ion battery charging station, and relates to the technical field of energy management, which comprises the following steps: monitoring battery state parameters and environmental conditions through a sensor, collecting multidimensional data; transmitting the multidimensional data to an automatic operation platform for cleaning and feature extraction, and training an adaptive learning model; determining a charging strategy by applying an optimization algorithm according to a charging demand prediction result; converting the charging strategy into control instructions and sending the control instructions to a controller in the automatic operation platform; adjusting the working mode of a charging device after the controller receives the instructions; and detecting the running state of the battery in real time, and triggering an early warning mechanism when potential faults are detected. The application determines the optimal charging strategy by using a differential evolution algorithm, comprehensively considers multiple factors such as charging demand intensity, power level and battery health, quickly finds the global optimal solution, improves the charging efficiency and resource utilization rate, and reduces the operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, in particular to an unmanned hydrogen ion battery charging station. Background Art

[0002] With the world's increasing emphasis on renewable energy and a low-carbon economy, the development of electric vehicles (EVs) and their supporting infrastructure has become an important part of energy policies in various countries. Hydrogen-ion batteries, as new energy storage devices, have shown significant advantages in energy density, charging speed, and environmental friendliness, and have therefore received widespread attention in the electric vehicle industry. At the same time, advances in smart grid technology have provided solid technical support for distributed energy management, making power distribution more efficient and flexible. However, traditional charging stations mostly rely on manual operation, which not only increases operating costs, but also makes it difficult to meet the growing demand for automation and service quality requirements. In recent years, unmanned charging stations have gradually become a research hotspot. They combine advanced technologies such as the Internet of Things (IoT), big data analysis, and machine learning to achieve complete automation of the charging process, improve charging efficiency, and reduce maintenance costs.

[0003] Although some unattended charging solutions already exist in the existing technology, they have obvious limitations when dealing with complex and changeable actual application scenarios. Traditional charging methods usually use fixed charging algorithms. This approach cannot dynamically adjust the charging strategy according to the battery status and external environment. The poor flexibility of the charging strategy may lead to extended charging time or shortened battery life. Insufficient data quality and information security are another key issue that needs to be addressed urgently. Most current charging methods fail to fully consider the importance of data encryption during communication. Once a network attack occurs, the user's sensitive information, such as charging records and personal identity information, may be leaked. When the equipment has an abnormal condition, it is often not discovered and handled in time, which in turn affects the stable operation of the entire system. Existing unattended charging stations rarely involve the application of deep learning models, and research on how to use historical data for accurate prediction and optimized decision-making is still insufficient, which limits the further improvement of their intelligence level and service capabilities. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an unmanned hydrogen ion battery charging station to solve the problems of insufficient data quality and information security and poor charging strategy flexibility in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an unmanned hydrogen ion battery charging station, which includes monitoring battery status parameters and environmental conditions through sensors and collecting multi-dimensional data; the specific steps of monitoring battery status parameters and environmental conditions through sensors and collecting multi-dimensional data are as follows:

[0008] Select and install current, voltage, temperature, humidity, light intensity, and grid load sensors;

[0009] Configure the sensor's operating mode and set the measurement range and accuracy;

[0010] Connect the sensor to the automated operation platform, configure the communication protocol and set the sampling frequency;

[0011] The automated operation platform triggers data collection at regular intervals, obtaining multi-dimensional data on current, voltage, temperature, internal resistance, charging rate, discharge depth, and grid load from sensors.

[0012] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, wherein: the multi-dimensional data is transmitted to the automated operation platform for cleaning and feature extraction, and the adaptive learning model is trained, the specific steps are:

[0013] Use encrypted communication protocols to transmit sensor data to the automated operation platform in real time. The automated operation platform receives and stores data packets through HTTP protocol POST requests.

[0014] Apply low-pass filtering and smoothing, and store the data packets in a distributed database;

[0015] The automated operation platform identifies data packets in the distributed database and removes outliers and fills in missing values;

[0016] Extract features from processed data packets and select predictive features to optimize the input of the adaptive learning model;

[0017] Initialize the long short-term memory model network parameters, set the initial learning rate and batch size, use historical data to train the adaptive learning model, and dynamically adjust the hyperparameters.

[0018] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, wherein: the adaptive learning model based on training automatically adjusts the prediction parameters according to real-time environmental changes, the specific steps are:

[0019] Extract historical data and real-time sensor data from distributed databases and perform normalization and standardization;

[0020] Combining historical feature changes and current environmental impacts, the i-th predicted feature value f of the historical data and real-time sensor data that have been normalized and standardized over time is calculated. i (t), the jth environmental factor value e that affects charging demand in the real-time environment j and the weight w corresponding to the i-th prediction feature i Introduce an expression to calculate the dynamically adjusted hyperparameter α′;

[0021] Using the initial hyperparameter α, the long short-term memory model is used to predict future charging demand and optimize the dynamically adjusted hyperparameter α′.

[0022] Apply the obtained dynamically adjusted hyperparameter α′ to the initial parameter setting of the optimization algorithm;

[0023] Update the population parameters in the optimization algorithm and dynamically adjust the initial learning rate and batch size based on the dynamic adjustment hyperparameter α′.

[0024] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, wherein: the charging strategy is determined by applying an optimization algorithm based on the charging demand prediction result, and the specific steps are as follows:

[0025] Using the dynamically adjusted hyperparameter α′, the long short-term memory model generates charging demand forecast results, obtaining the charging demand intensity Q and power level P at the time point;

[0026] Combined with extracting the demand characteristic value g that changes over time from multi-dimensional data i (t), and assign weight d i Form the characteristic value of total demand;

[0027] Extract the health feature value k that affects charging demand from the real-time environment j (t), and assign weight b j Forming the total health characteristic value;

[0028] The charging demand intensity Q and power level P are introduced into the expression to calculate the optimal charging strategy S * ;

[0029] Differential evolution algorithm is used to solve the charging strategy S * , initialize the population parameters and dynamically adjust them, iteratively update the population individuals until they converge to the optimal solution.

[0030] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, wherein: the differential evolution algorithm is used to solve the charging strategy S * , initialize the population parameters and dynamically adjust them, iteratively update the population individuals until they converge to the optimal solution. The specific steps are:

[0031] The automated operation platform generates multiple candidate charging strategies based on the charging demand forecast results;

[0032] The automated operation platform defines comprehensive evaluation criteria, analyzes each candidate charging strategy, and assesses its performance in terms of meeting charging requirements, grid load balancing, and battery health, and generates evaluation results.

[0033] The automated operation platform uses a differential evolution algorithm to iteratively optimize the population. It generates new individuals through selection, mutation, and crossover operations, and replaces existing individuals, gradually approaching the optimal solution.

[0034] Improve optimization efficiency and robustness according to the adaptive differential evolution algorithm parameters;

[0035] When the maximum number of iterations is reached, the adaptive differential evolution algorithm stops running and outputs the final optimal charging strategy.

[0036] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, the charging strategy is converted into a control instruction and sent to the controller in the automated operation platform. After receiving the instruction, the controller adjusts the working mode of the charging device. The specific steps are as follows:

[0037] According to the final optimal charging strategy, it is organized into control instructions in JSON format;

[0038] Use TLS / SSL encryption protocol to send control instructions from the automated operation platform to the controller via the HTTP protocol POST request method. The controller receives and parses the instructions and extracts parameters.

[0039] The controller adjusts the operating mode of the charging device according to the extracted parameters;

[0040] The controller executes the adjusted working mode and monitors the charging effect through sensors;

[0041] The controller continuously monitors the charging process through sensors. When an abnormal situation is detected, the controller feeds back the information to the automated operation platform, which records the charging data.

[0042] As a preferred solution of the unmanned hydrogen ion battery charging station of the present invention, wherein: the real-time detection of the operating status of the battery, when a potential fault is detected, triggering the early warning mechanism, the specific steps are:

[0043] The controller continuously monitors the battery's operating parameters through sensors and transmits the data to the automated operation platform;

[0044] The automated operations platform conducts comparative analysis based on historical and real-time data to identify abnormal patterns;

[0045] If a potential fault is displayed, the early warning mechanism is triggered;

[0046] If no potential fault is detected, the current charging state is maintained and the battery operating status continues to be monitored;

[0047] When the early warning mechanism is triggered, the automated operation platform pushes it to relevant personnel via email, SMS and application.

[0048] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the unmanned hydrogen ion battery charging station as described in the first aspect of the present invention is implemented.

[0049] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the unmanned hydrogen ion battery charging station as described in the first aspect of the present invention is implemented.

[0050] The beneficial effects of the present invention are: by deploying high-precision sensors to monitor battery status and environmental conditions in real time, multi-dimensional data is collected, the quality and reliability of input data are ensured, the adaptability of the automated operation platform to complex environments is enhanced, and instant response changes are achieved to optimize charging strategies. The data is securely transmitted to the automated operation platform using an encrypted communication protocol, cleaned and feature extracted on the platform, and the adaptive learning model is trained using a long-short-term memory model, thereby improving the accuracy and security of data analysis. Based on the trained model, the automated operation platform automatically adjusts the prediction parameters according to real-time environmental changes, generates accurate charging demand prediction results, reduces prediction errors and enhances the flexibility and reliability of the automated operation platform. Finally, the optimal charging strategy is determined by the differential evolution algorithm, and comprehensive consideration of multiple factors such as charging demand intensity, power level and battery health is used to quickly find the global optimal solution, improve charging efficiency and resource utilization, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flow chart of the unattended hydrogen ion battery charging station in Example 1.

[0053] Figure 2Flowchart for collecting multi-dimensional data in Example 1. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0055] Example 1, refer to Figure 1 For the first embodiment of the present application, the embodiment provides an unattended hydrogen-ion battery charging station, including the following steps:

[0056] S1, monitor the battery state parameters and environmental conditions through sensors, collect multi-dimensional data.

[0057] Further, select and install current, voltage, temperature, humidity, light intensity and power grid load sensors;

[0058] Specifically, in order to ensure that the unattended hydrogen-ion battery charging station can comprehensively monitor the battery state and its operating environment, professional sensors suitable for different application scenarios are selected and accurately installed and calibrated, which specifically includes,

[0059] Current sensor: used to measure the charging current;

[0060] Voltage sensor: used to detect the battery terminal voltage;

[0061] Temperature sensor: used to monitor the internal and external temperature of the battery;

[0062] Humidity sensor: used to evaluate air humidity;

[0063] Light intensity sensor: used to record the changes of natural light source;

[0064] Power grid load sensor: used to monitor the power supply situation.

[0065] By selecting and installing current, voltage, temperature, humidity, light intensity and power grid load sensors, the automatic operation platform realizes accurate measurement of the battery state and environmental conditions. High-precision sensors ensure the reliability and accuracy of data, providing a solid foundation for subsequent data analysis. Diversified sensors cover physical quantities and environmental variables, enhancing the environmental adaptability of the automatic operation platform, allowing it to maintain stable performance under complex conditions.

[0066] Real-time monitoring and dynamic adjustment enable the automated operation platform to respond to changes in real-time, optimize charging strategies, improve efficiency, and reduce energy waste. Comprehensive monitoring also helps identify potential problems in advance, trigger early warning mechanisms, and perform preventive maintenance, enhancing the safety and reliability of the automated operation platform. Ultimately, this approach builds an efficient, intelligent, and secure automated operation platform, significantly improving the performance of unmanned hydrogen-ion battery charging stations and demonstrating the value of technological innovation.

[0067] Configure the working mode of the sensor, set the measurement range and accuracy;

[0068] It should be noted that by configuring the working mode of the sensor, setting the measurement range and accuracy, precise monitoring of the battery state and environmental conditions is achieved, which specifically includes,

[0069] Current sensor: set the measurement range to 0-50A, accuracy ±0.5%;

[0070] Voltage sensor: set the measurement range to 0-400V, accuracy ±0.2%;

[0071] Temperature sensor: set the measurement range to -40℃ to +125℃, accuracy ±0.3℃;

[0072] Humidity sensor: set the measurement range to 0%-100%RH, accuracy ±2%;

[0073] Illuminance sensor: set the measurement range to 0-200,000 lux, accuracy ±5%;

[0074] Grid load sensor: set the measurement range to 0-500kW, accuracy ±1%.

[0075] Reasonable measurement range ensures that the sensor works stably under expected operating conditions, while high accuracy standards guarantee data accuracy, providing a reliable foundation for subsequent data analysis. This helps develop scientific and reasonable charging strategies, sets appropriate measurement ranges to avoid resource waste, improves resource utilization, reduces unnecessary energy consumption and equipment redundancy, and lowers operating costs.

[0076] High-quality data input enables machine learning models to more accurately predict future demand and behavior patterns, guiding more intelligent charging strategies and improving the intelligent level of the automated operation platform. High-precision measurement enables the automated operation platform to maintain stable performance in complex environments, enhancing the environmental adaptability of the automated operation platform. Accurate data input helps identify potential problems in advance and take timely measures, ensuring the stability and safety of the automated operation platform. Reasonable measurement range not only improves resource utilization but also reduces hardware costs, unnecessary energy consumption, and improves economic efficiency.

[0077] Connect the sensor to the automated operation platform, configure the communication protocol and set the sampling frequency;

[0078] It should be noted that the current, voltage, temperature, humidity, light intensity and grid load sensors are connected to the central automation operation platform via wired or wireless means, and a secure encrypted communication protocol (such as TLS / SSL) is selected to ensure the security and integrity of data transmission. The sampling frequency is set as follows:

[0079] Current sensor: collects data once per second (1Hz) to ensure real-time monitoring of current changes during charging;

[0080] Voltage sensor: collects data once per second (1Hz) to capture voltage fluctuations and ensure charging stability;

[0081] Temperature sensor: collects data once every minute (1 / 60Hz) to monitor battery and ambient temperature to prevent overheating;

[0082] Humidity sensor: collects data every 10 minutes (1 / 600Hz) to assess the impact of ambient humidity on the battery;

[0083] Light intensity sensor: collects data every 30 minutes (1 / 1800Hz), records changes in natural light sources, and assists in predicting solar charging efficiency;

[0084] Grid load sensor: collects data every 5 minutes (1 / 300Hz) to monitor power supply and optimize resource allocation.

[0085] Through secure encrypted communication protocols, data transmission from sensors to the automated operation platform is ensured to be fast and secure, preventing data tampering or leakage, and flexibly adapting to different needs: different sampling frequency settings are adjusted according to actual needs to ensure high-frequency monitoring of key parameters, while reducing unnecessary data volume, improving the operational efficiency of the automated operation platform, optimizing resource utilization and reducing latency: reasonable sampling frequency avoids data overload, reduces the processing burden of the automated operation platform, reduces the response time of the automated operation platform, and improves overall operational efficiency.

[0086] Real-time transmission and high-precision data collection provide a solid foundation for subsequent data analysis, enhance the stability and reliability of the automated operation platform, and strengthen decision-making support capabilities: high-quality data input enables machine learning models to more accurately predict future demand and behavior patterns, guide more intelligent charging strategies, enhance the intelligence level of the automated operation platform, improve operational efficiency and reduce costs: by optimizing resource utilization, it reduces unnecessary energy consumption and equipment redundancy, reduces hardware costs and maintenance costs, and improves economic benefits.

[0087] The automated operation platform triggers data collection at regular intervals, obtaining multi-dimensional data on current, voltage, temperature, internal resistance, charging rate, discharge depth, and grid load from sensors.

[0088] It should be noted that the automated operation platform automatically triggers data collection tasks according to preset time intervals (such as every second, every minute or longer), and acquires multi-dimensional data: key parameters including current (1Hz), voltage (1Hz), temperature (1 / 60Hz), internal resistance (1 / 300Hz), charging rate (1 / 60Hz), discharge depth (1 / 3600Hz) and grid load (1 / 300Hz) are collected from different types of sensors.

[0089] Timed-triggered data collection ensures all-round monitoring of the battery and its environmental conditions, provides real-time, continuous data flow, and provides detailed basic information for subsequent analysis and decision-making. Through timed collection, data loss or omissions are avoided, data integrity and consistency are guaranteed, and the reliability and stability of the automated operation platform are enhanced. The acquisition of multi-dimensional data enables the automated operation platform to capture more influencing factors, providing rich input for machine learning models and optimization algorithms, and supporting more intelligent and accurate charging strategy formulation.

[0090] S2. Transmit multi-dimensional data to the automated operation platform for cleaning and feature extraction, and train the adaptive learning model.

[0091] Furthermore, an encrypted communication protocol is used to transmit sensor data in real time to the automated operation platform, which receives and stores data packets via a POST request of the HTTP protocol;

[0092] Specifically, encrypted communication protocols such as TLS / SSL are selected to ensure that the security and confidentiality of data are not threatened during the data transmission from sensors to the automated operation platform. The data collected by the sensors are transmitted to the automated operation platform in real time through encrypted channels, ensuring the immediacy and continuity of the data. The automated operation platform uses the POST request of the HTTP protocol to receive data packets and store them in a distributed database to ensure effective data management and subsequent analysis.

[0093] The encrypted communication protocol prevents data from being stolen or tampered with during transmission, enhancing the security of the automated operation platform. It is particularly suitable for unattended environments. Real-time transmission enables the automated operation platform to immediately obtain the latest battery status and environmental parameters, adjust charging strategies in a timely manner, and improve the response speed of the automated operation platform. The HTTP POST request method simplifies the data transmission process, ensuring that data packets can reach the automated operation platform quickly and reliably and are effectively stored for subsequent processing and analysis.

[0094] Encrypted communication ensures the security of data transmission, reduces problems caused by data loss or errors, and enhances the stability and reliability of the automated operation platform. Real-time data transmission provides the latest and most accurate input for machine learning models, supports more intelligent and precise charging strategy formulation, and improves the intelligence level of the automated operation platform. Efficient transmission and storage mechanisms reduce the burden on the automated operation platform, reduce unnecessary hardware costs and maintenance work, and improve operational efficiency. Real-time monitoring and immediate response mechanisms enable the automated operation platform to discover and solve problems in the first time, ensuring service continuity and the quality of user experience.

[0095] Apply low-pass filtering and smoothing, and store the data packets in a distributed database;

[0096] It should be noted that a low-pass filter is applied to the data collected by the sensor to remove high-frequency noise and instantaneous fluctuations and retain useful signals. A smoothing algorithm (such as the moving average method) is further used to reduce random disturbances in the data, making the data more stable and reliable. The processed data packets are stored in a distributed database to ensure data security, scalability and efficient access.

[0097] Low-pass filtering and smoothing processing effectively remove noise and outliers from the data, improve data quality and reliability, and provide clean basic data for subsequent analysis. High-quality data input enables machine learning models and prediction algorithms to more accurately capture trends and patterns, supporting more scientific and reasonable charging strategy formulation. Distributed databases not only provide high availability and fault tolerance, but also support rapid access and processing of large-scale data, enhancing the flexibility and responsiveness of the automated operation platform.

[0098] Through purification processing, noise interference is reduced, making the data more accurate and stable, thereby improving the reliability of decisions made based on this data. After removing outliers and smoothing processing, the automated operation platform can better respond to environmental changes and emergencies, enhancing overall robustness and stability. The use of distributed databases not only improves the efficiency of data storage and retrieval, but also reduces the risk of single point failures, reduces maintenance costs, and improves operational efficiency. The processed data can more realistically reflect the battery status and environmental conditions, helping to identify potential problems in advance and take preventive measures, thereby ensuring the long-term performance and reliability of the automated operation platform.

[0099] The automated operation platform identifies data packets in the distributed database and removes outliers and fills in missing values;

[0100] It should be noted that the automated operation platform extracts data packets transmitted by sensors from the distributed database to ensure that all data are correctly received and stored, applies statistical methods or machine learning algorithms to identify and eliminate abnormal data points that are beyond the normal range, prevents these outliers from affecting subsequent analysis, and uses interpolation, mean filling or prediction models to fill in missing data caused by collection failure or other reasons to ensure data continuity and integrity.

[0101] By eliminating outliers, the impact of noise and erroneous data on the automated operation platform is eliminated, the quality and reliability of the data are improved, and missing values ​​are filled to keep the data series complete, avoiding analytical bias caused by data discontinuity, and providing a reliable foundation for subsequent processing. High-quality and complete data input enables machine learning models and prediction algorithms to more accurately capture trends and patterns, supporting more scientific and reasonable charging strategy formulation.

[0102] The purified data reduces sources of error, making decisions based on the data more accurate and reliable, and improving the intelligence level of the automated operation platform. After removing outliers and smoothing, the automated operation platform can better respond to environmental changes and emergencies, enhancing overall robustness and stability. The complete data sequence helps to more accurately assess battery status and environmental conditions, thereby optimizing charging strategies and reducing unnecessary energy consumption and equipment wear. The processed data can more realistically reflect the actual situation, help identify potential problems in advance and take preventive measures, and ensure the long-term performance and reliability of the automated operation platform.

[0103] Extract features from processed data packets and select predictive features to optimize the input of the adaptive learning model;

[0104] It should be noted that the automated operation platform conducts in-depth analysis of data packets that have undergone low-pass filtering and smoothing, extracts key features such as current, voltage, temperature, internal resistance, charging rate, etc., and screens out the most representative and predictive features based on domain knowledge and statistical analysis to ensure that the data input into the adaptive learning model is highly relevant and representative. The selected predictive features are combined into the optimal input set to provide high-quality data support for training the adaptive learning model.

[0105] Through feature extraction, the automated operation platform can focus on the key parameters that best reflect battery status and environmental changes, enhancing the explanatory power of the data and the accuracy of the model. Selecting predictive features enables the model to more accurately capture the factors affecting charging demand, improving the accuracy of predicting future charging demand. High-quality input data helps train more efficient adaptive learning models, reduce the risk of overfitting, and improve the model's generalization ability and robustness.

[0106] By optimizing feature input, the interference of irrelevant or redundant information is reduced, making the model output more accurate and reliable, and guiding the formulation of more scientific and reasonable charging strategies. Accurate feature selection and optimized input significantly improve the performance of the adaptive learning model, enabling the automated operation platform to dynamically adjust the charging strategy according to real-time environmental changes, improving the overall intelligence level. The optimized model can more accurately evaluate battery status and environmental conditions, thereby optimizing the charging strategy, reducing unnecessary energy consumption and equipment wear, and improving operational efficiency. High-quality feature input and optimized models help to identify potential problems in advance and take preventive measures, ensuring the long-term performance and reliability of the automated operation platform.

[0107] Initialize the long short-term memory model parameters, set the initial learning rate and batch size, use historical data to train the adaptive learning model, and dynamically adjust the hyperparameters;

[0108] It should be noted that setting the initial weights and biases ensures a good starting point for the model and avoids vanishing or exploding gradients. Choosing an appropriate learning rate (e.g., 0.001) determines the speed of parameter updates. A higher learning rate can accelerate convergence but may lead to instability, while a lower learning rate ensures more accurate gradient estimates but may increase training time. Setting an appropriate batch size (e.g., 64) balances memory usage with the accuracy of gradient estimates. Larger batches provide more stable gradient estimates but require more memory; smaller batches conserve memory but increase gradient estimate volatility.

[0109] Reasonable initialization parameters help speed up the training process, avoid falling into local optimal solutions, and improve the efficiency of model training. Appropriate learning rate and batch size settings can ensure the accuracy of the model while ensuring training speed, thereby improving the overall training effect.

[0110] By carefully setting the initial learning rate, the initial oscillations in training are reduced, the model enters a stable state more quickly, and the training time is significantly shortened. Reasonable learning rate and batch size configuration enable the model to better capture data features during training, enhancing its generalization ability and robustness.

[0111] Use processed multi-dimensional historical data for training and dynamically adjust hyperparameters to optimize model performance:

[0112] The preprocessed and feature-extracted data packets are input into the long-short-term memory model for training, enabling it to learn the changing patterns of battery status and environmental conditions. During training, hyperparameters such as learning rate and batch size are dynamically adjusted based on the performance on the validation set to ensure that the model achieves optimal performance.

[0113] Through training with historical data, the model can learn the changing patterns of battery status and environmental conditions, enhancing its ability to predict future charging needs. Dynamic adjustment of hyperparameters enables the model to continuously improve its performance, adapt to the needs under different working conditions, and improve the model's robustness and generalization capabilities.

[0114] The optimized long-short-term memory model can more accurately predict future charging demand, reduce prediction errors, and guide the formulation of more scientific and reasonable charging strategies. The precise prediction capability enables the automated operation platform to dynamically adjust the charging strategy according to real-time environmental changes, significantly improving the intelligence level and response speed of the automated operation platform. The optimized model can more accurately assess battery status and environmental conditions, thereby optimizing charging strategies, reducing unnecessary energy consumption and equipment wear, and improving operational efficiency. High-quality prediction and optimization mechanisms help to identify potential problems in advance and take preventive measures, ensuring the long-term performance and reliability of the automated operation platform.

[0115] S3. Based on the trained adaptive learning model, the prediction parameters are automatically adjusted according to real-time environmental changes to generate charging demand prediction results. Charging strategy;

[0116] Furthermore, historical data and real-time sensor data are extracted from the distributed database and normalized and standardized;

[0117] Specifically, normalization and standardization processing enable data of different dimensions to be compared and analyzed under unified standards. The processed data is more in line with the requirements of machine learning models, reduces the impact of outliers and noise, and improves the quality of model input.

[0118] Consistent data input helps improve the stability and prediction accuracy of the model, reduces errors caused by data differences, and standardized data facilitates subsequent feature extraction and model training, reduces processing complexity, and improves overall efficiency.

[0119] Combining historical feature changes and current environmental impacts, the i-th predicted feature value f of the historical data and real-time sensor data that have been normalized and standardized over time is calculated. i (t), the jth environmental factor value e that affects charging demand in the real-time environment j and the weight w corresponding to the i-th prediction feature i The expression is introduced to calculate the dynamically adjusted hyperparameter α′, which is:

[0120]

[0121] Where λ is the attenuation factor, α is the initial hyperparameter, n is the total number of features, γ is the environmental factor weight, m ​​is the total number of environmental factors, j is the accurate value in time change, i is the accurate value in charging demand, and t is the time variable;

[0122] It should be noted that the attenuation factor λ is used to control the influence of the historical hyperparameter α and balance the ratio of new and old information.

[0123] The initial hyperparameter α represents the predicted feature value that changes over time, reflecting the change of battery state.

[0124] The total number of features n is the predicted feature value that changes over time, reflecting the change of battery status.

[0125] The environmental factor weight γ represents the importance or impact of an environmental factor on charging demand prediction. Each environmental factor (such as temperature, humidity, and light intensity) has a corresponding weight, reflecting its relative importance in the prediction model. By assigning different weights to different environmental factors, we can more accurately reflect their actual impact on charging demand. For example, temperature changes may have a significant impact on battery performance, so they are given a higher weight; while certain less important factors are given lower weights. Reasonable weighting helps improve the accuracy and reliability of the prediction model, ensuring that the model can better adapt to real-time environmental changes.

[0126] The total number of environmental factors, m, represents the number of environmental factors involved in the calculation. It indicates how many different environmental variables the automated operations platform considers to adjust the hyperparameter α′. The total number of environmental factors, m, determines how many environmental variables are considered by the model. More environmental factors provide more comprehensive information, making the model more intelligent and flexible. While increasing the number of environmental factors can improve model accuracy, too many factors also increase computational complexity. Therefore, choosing an appropriate value for m can ensure both prediction accuracy and computational efficiency.

[0127] The formula comprehensively considers the influence of historical data and real-time environment, so that the hyperparameter α′ can reflect the latest situation in real time, improving the adaptability and prediction accuracy of the model. The attenuation factor λ ensures that historical information is not completely ignored. At the same time, new environmental changes can also be taken into account in a timely manner, ensuring the stability of the model. The dynamically adjusted hyperparameter α′ can more accurately reflect the actual situation, reduce prediction errors, and improve the intelligence level of the automated operation platform. The model can be flexibly adjusted according to real-time environmental changes, enhancing the robustness and adaptability of the automated operation platform. Accurate hyperparameter adjustment enables the automated operation platform to maintain optimal performance in different environments, reducing unnecessary resource waste and improving overall operational efficiency.

[0128] Using the initial hyperparameter α, the long short-term memory model is used to predict future charging demand and optimize the dynamically adjusted hyperparameter α′.

[0129] It should be noted that the dynamically adjusted hyperparameter α′ is used as the initial parameter of the optimization algorithm to ensure that the optimization process starts from a better starting point. The initial learning rate and batch size are dynamically adjusted according to the hyperparameter α′, so that the optimization algorithm can converge to the optimal solution faster. The obtained dynamically adjusted hyperparameter α′ is applied to the initial parameter setting of the optimization algorithm. The dynamically adjusted hyperparameter α′ is used as the initial parameter of the optimization algorithm to ensure the efficiency of the optimization process. According to the results of the dynamic adjustment, the population parameters are gradually optimized, so that the optimization algorithm can converge to the optimal solution faster.

[0130] Dynamically adjusted initial parameters α and population parameters accelerate the convergence of the optimization algorithm and improve optimization efficiency. The optimized charging strategy can more accurately assess battery status and environmental conditions, reducing unnecessary energy consumption and equipment wear, and ensuring the long-term performance and reliability of the automated operation platform. Through dynamic optimization, the automated operation platform can adjust the charging strategy according to real-time environmental changes, further improving the intelligence level and response speed of the automated operation platform.

[0131] It should be noted that according to the requirements of the optimization algorithm, the individual parameters in the population are updated to ensure that each individual can perform effective search under the new conditions. According to the dynamically adjusted hyperparameter α′, the initial learning rate and batch size of the optimization algorithm are adjusted to make the optimization process more flexible and efficient.

[0132] The dynamically adjusted hyperparameter α′ serves as the initial parameter of the optimization algorithm, ensuring that the optimization process starts from a better starting point. Based on the results of the dynamic adjustment, the population parameters are gradually optimized, enabling the optimization algorithm to converge to the optimal solution faster. The dynamically adjusted initial learning rate and batch size accelerate the convergence speed of the optimization algorithm and improve the optimization efficiency. Through dynamic optimization, the automated operation platform can adjust the charging strategy according to real-time environmental changes, further improving the intelligence level and response speed of the automated operation platform.

[0133] The dynamically adjusted initial parameters and population parameters accelerate the convergence of the optimization algorithm, reduce the number of unnecessary iterations, and improve the overall optimization efficiency. The optimized charging strategy can more accurately evaluate the battery status and environmental conditions, reduce unnecessary energy consumption and equipment wear, and ensure the long-term performance and reliability of the automated operation platform. The automated operation platform can flexibly adjust the charging strategy according to real-time environmental changes, enhance the robustness and adaptability of the automated operation platform, and ensure that it can maintain optimal performance under different working conditions. The optimized charging strategy provides a reliable basis for formulating scientific and reasonable charging plans, and improves the intelligence level and management efficiency of the automated operation platform.

[0134] Update the population parameters in the optimization algorithm and dynamically adjust the initial learning rate and batch size based on the hyperparameter α′.

[0135] It should be noted that the initial learning rate of the optimization algorithm is adjusted according to the dynamically adjusted hyperparameter α′ to ensure that the optimization process starts from a better starting point. According to the requirements of the optimization algorithm, the batch size is dynamically adjusted to ensure that each iteration can make full use of the latest data information and improve the optimization efficiency.

[0136] The dynamically adjusted initial learning rate and batch size accelerate the convergence of the optimization algorithm, reduce the number of unnecessary iterations, and improve the overall optimization efficiency. The automated operation platform can flexibly adjust the charging strategy according to real-time environmental changes, enhance the robustness and adaptability of the automated operation platform, and ensure optimal performance under different working conditions. The optimized charging strategy can more accurately assess the battery status and environmental conditions, reduce unnecessary energy consumption and equipment wear, and ensure the long-term performance and reliability of the automated operation platform.

[0137] The optimized charging strategy provides a reliable basis for formulating scientific and reasonable charging plans, and improves the intelligence level and management efficiency of the automated operation platform. Through dynamic optimization, the automated operation platform can adjust the charging strategy according to real-time environmental changes, further improving the intelligence level and response speed of the automated operation platform. The optimized charging strategy reduces unnecessary waste of resources, improves overall operational efficiency and resource utilization efficiency, and reduces operating costs.

[0138] S4. Based on the charging demand prediction results, an optimization algorithm is applied to determine the charging strategy.

[0139] Furthermore, the dynamically adjusted hyperparameter α′ is used to generate charging demand forecast results through the long short-term memory model, obtaining the charging demand intensity Q and power level P at the time point;

[0140] Specifically, the demand characteristic value g that changes with time is extracted from multi-dimensional data i (t), and assign weight d i The total demand characteristic value is formed as follows:

[0141]

[0142] Where C is the total number of demand characteristics;

[0143] Extract the health feature value k that affects charging demand from the real-time environment j (t), and assign weight b j The total health characteristic value is formed as follows:

[0144]

[0145] Where R is the total number of health characteristics;

[0146] It should be noted that by using the dynamically adjusted hyperparameter α′, the long short-term memory model is used to predict future charging demand, and the charging demand intensity Q and power level P at the time point are obtained. The long short-term memory model can capture the dependencies in long time series data and accurately predict future charging demand. The dynamically adjusted hyperparameter α′ ensures that the model is always in the best state, improving the accuracy and stability of the prediction.

[0147] Accurate prediction results provide a reliable basis for formulating scientific and reasonable charging strategies, and improve the intelligence level of the automated operation platform. Real-time prediction enables the automated operation platform to respond immediately, improving overall operating efficiency. The dynamically adjusted hyperparameter α′ reduces prediction error, improves prediction accuracy, and enhances the reliability of the automated operation platform.

[0148] Extracting demand characteristic values ​​g that change over time from multi-dimensional data i (t), such as current, voltage, temperature, etc., and assign corresponding weights di according to the importance of each demand feature to form the total demand characteristic value D. By introducing multiple demand characteristic values ​​and assigning different weights, the automated operation platform can more comprehensively evaluate the changes in charging demand. High-quality feature input helps to train more efficient adaptive learning models, reduce the risk of overfitting, and improve the generalization ability and robustness of the model. Comprehensive consideration of multiple demand characteristics makes the prediction more accurate and reduces the deviation that may be caused by a single feature. The weights are flexibly adjusted according to the importance of different features, which enhances the adaptability and flexibility of the model. Accurate demand assessment helps to optimize charging strategies, reduce unnecessary energy consumption and equipment wear, and improve operational efficiency.

[0149] Extract the health feature value k that affects charging demand from the real-time environment j (t), such as temperature, humidity, light intensity, etc. according to each health characteristic k j (t) is important, and the corresponding weight b is assigned j , forming the total health characteristic value H, by introducing the health characteristic value k j (t), the automated operation platform can monitor the health status of the battery and its environment in real time, identify potential problems in advance, and jBased on the evaluation results of (t), the automated operation platform can dynamically adjust the charging strategy to ensure optimal performance under different working conditions, monitor and evaluate the battery health status in real time, reduce the risk of failure due to environmental factors, ensure the long-term performance and reliability of the automated operation platform, identify potential problems in advance and take preventive measures, and improve the safety and stability of the automated operation platform. Accurate health assessment helps to discover and solve problems in a timely manner, reduce maintenance costs, and improve maintenance efficiency.

[0150] Furthermore, the charging demand intensity Q and power level P are introduced into the expression to calculate the optimal charging strategy S * , the expression is:

[0151]

[0152] Where η is the charging demand weight factor, u is the battery health weight factor, ω is the hyperparameter weight factor, ρ is the demand intensity weight factor, -u(P-P0) is the direction of change from power level P to initial power level P0, ξ is the battery health weight factor, e is the base of the natural logarithm, e -u(P-P0) Indicates the direction of change of the current power level P relative to the initial power level P0.

[0153] It should be noted that the charging demand intensity Q is considered to ensure that the charging strategy can meet the actual demand. By introducing the current power level P and combining it with the initial power level P o , evaluate the direction and degree of power change, use the Sigmoid function to smooth the impact of power change, and ensure that the impact of power change on charging strategy is gradual and controllable.

[0154] By introducing the charging demand intensity Q, the automated operation platform can more comprehensively assess changes in charging demand, ensuring that the charging strategy is both compliant and efficient. By introducing the Sigmoid function, the automated operation platform can smoothly handle the impact of power changes and avoid the impact of sudden power changes on the battery and the automated operation platform.

[0155] By comprehensively considering multiple factors, the charging strategy is made more scientific and reasonable, and the charging efficiency and resource utilization are improved. By smoothing power changes, the impact of power mutations on batteries and the automated operation platform is reduced, and the stability and security of the automated operation platform are enhanced. By introducing multiple factors for comprehensive evaluation, the automated operation platform can flexibly adjust the charging strategy according to real-time environmental changes, ensuring the long-term performance and reliability of the automated operation platform.

[0156] Furthermore, the differential evolution algorithm is used to solve the charging strategy S * , initialize the population parameters and dynamically adjust them, iteratively update the population individuals until they converge to the optimal solution.

[0157] It also includes the use of differential evolution algorithm to solve the charging strategy, initializing the population parameters and dynamically adjusting them, and iteratively updating the population individuals until they converge to the optimal solution. The specific steps are:

[0158] The automated operation platform generates multiple candidate charging strategies based on the charging demand forecast results;

[0159] The automated operation platform defines comprehensive evaluation criteria, analyzes each candidate charging strategy, and assesses its performance in terms of meeting charging requirements, grid load balancing, and battery health, and generates evaluation results.

[0160] The automated operation platform uses a differential evolution algorithm to iteratively optimize the population. It generates new individuals through selection, mutation, and crossover operations, and replaces existing individuals, gradually approaching the optimal solution.

[0161] Improve optimization efficiency and robustness according to the adaptive differential evolution algorithm parameters;

[0162] When the maximum number of iterations is reached, the adaptive differential evolution algorithm stops running and outputs the final optimal charging strategy.

[0163] Specifically, by generating multiple candidate strategies, the automated operation platform is guaranteed to have appropriate solutions under different working conditions. Different candidate strategies can flexibly respond to real-time environmental changes and demand fluctuations, thus enhancing the adaptability of the automated operation platform.

[0164] The diverse candidate strategies enable the automated operation platform to respond quickly in different situations, improving overall flexibility. By having multiple options, the risks that may be brought about by a single strategy are reduced, and the reliability of the automated operation platform is enhanced.

[0165] The automated operation platform defines comprehensive evaluation criteria covering charging demand, grid load balancing, and battery health impacts. Based on these criteria, it conducts a comprehensive analysis of each candidate charging strategy, evaluates its performance, and generates evaluation results.

[0166] Comprehensive evaluation criteria ensure that candidate strategies are fully considered across multiple key aspects, such as charging demand, grid load balancing, and battery health. Through comprehensive performance evaluation, the automated operations platform can select the charging strategy that best meets actual needs, improving the scientific and rationality of decision-making.

[0167] Comprehensive performance evaluation provides a reliable basis for selecting the optimal charging strategy and improves the intelligence level of the automated operation platform. By comprehensively considering multiple factors, the automated operation platform can more accurately assess battery status and environmental conditions, ensuring the long-term performance and reliability of the automated operation platform.

[0168] Set the initial population size, mutation factor, crossover probability and other parameters to ensure that the optimization process starts from a reasonable starting point, generate new individuals through selection, mutation and crossover operations, and replace the original individuals to gradually approach the optimal solution;

[0169] The differential evolution algorithm is a global optimization algorithm that can effectively handle complex multi-dimensional optimization problems and is suitable for solving the best charging strategy. The process of iteratively updating the population individuals can quickly find the global optimal solution, reducing unnecessary waste of computing resources. Through selection, mutation and crossover operations, it ensures extensive coverage of the search space and avoids falling into local optimal solutions;

[0170] The differential evolution algorithm can quickly find the global optimal solution, reducing optimization time and improving overall efficiency. By dynamically adjusting population parameters, the automated operation platform can flexibly adjust according to real-time environmental changes, improving the intelligent level of the automated operation platform. The optimized charging strategy can more accurately assess battery status and environmental conditions, reducing unnecessary energy consumption and equipment wear and tear, and ensuring the long-term performance and reliability of the automated operation platform.

[0171] According to the feedback during the optimization process, dynamically adjust population parameters such as population size, mutation factor and crossover probability to adapt to different working condition requirements;

[0172] By dynamically adjusting population parameters, the algorithm can flexibly adjust under different working conditions, improving the robustness and adaptability of optimization. Dynamic adjustment of parameters ensures that the algorithm maintains optimal performance at different stages, improving the quality of optimization results;

[0173] Dynamic adjustment of population parameters enables the automated operation platform to flexibly adjust according to real-time environmental changes, enhancing the robustness and adaptability of the automated operation platform. By dynamically adjusting parameters, the optimization process can maintain optimal performance at different stages, improving the quality and reliability of optimization results.

[0174] When the maximum number of iterations is reached or the convergence condition is met, stop the optimization process and output the final best charging strategy for the automated operation platform to execute;

[0175] The termination condition ensures that the optimization process is completed within a reasonable time, avoiding unnecessary waste of computing resources. The output of the final best charging strategy provides clear guidance for practical applications;

[0176] The automatic termination condition ensures that the optimization process is completed within a reasonable time, improving overall efficiency. The output of the final best charging strategy provides clear guidance for practical applications, enhancing the practicality and operability of the automated operation platform.

[0177] S5. Convert the charging strategy into a control instruction and send it to the controller in the automated operation platform. After receiving the instruction, the controller adjusts the working mode of the charging device.

[0178] Furthermore, the final optimal charging strategy is compiled into control instructions in JSON format;

[0179] Specifically, the control instructions are sent from the automated operation platform to the controller via the POST request method of the HTTP protocol using the TLS / SSL encryption protocol. The controller receives and parses the instructions and extracts parameters.

[0180] The controller adjusts the operating mode of the charging device according to the extracted parameters;

[0181] The controller executes the adjusted working mode and monitors the charging effect through sensors;

[0182] The controller continuously monitors the charging process through sensors. When an abnormal situation is detected, the controller feeds back the information to the automated operation platform, which records the charging data.

[0183] Furthermore, the controller uses sensors to continuously collect battery operating parameters (such as voltage, current, temperature, etc.), and transmits the collected data to the automated operation platform in real time to ensure that the automated operation platform can obtain the latest battery status information;

[0184] Through continuous monitoring, the automated operation platform can keep abreast of the battery's operating status, ensuring that the battery's health status is known at all times. Real-time data transmission enables the automated operation platform to receive the latest information immediately, improving its response speed and decision-making efficiency.

[0185] Real-time monitoring and data transmission ensure transparency of battery status, reduce the risk of potential failures, and enhance the security of the automated operation platform. Continuous data collection and transmission provide a reliable foundation for subsequent analysis and improve the overall reliability of the automated operation platform.

[0186] The automated operation platform compares and analyzes historical data with real-time data to identify possible abnormal patterns. Through data analysis, the automated operation platform can identify abnormal situations that do not conform to normal patterns and discover potential problems in advance.

[0187] By comparing and analyzing historical data and real-time data, the automated operation platform can identify potential problems in advance and prevent them from escalating. Based on the results of data analysis, the automated operation platform can make more scientific and reasonable decisions, thereby improving management efficiency.

[0188] Early identification of abnormal patterns enables the automated operation platform to take preventive measures before problems occur, reducing the possibility of failures. Discovering potential problems in advance can reduce the need for emergency repairs and reduce maintenance costs and time.

[0189] Once a potential failure is identified, the automated operations platform immediately triggers an early warning mechanism and promptly notifies relevant personnel through multiple channels (e.g., email, SMS, and app push notifications) to ensure they can take prompt action.

[0190] The triggering of the early warning mechanism enables relevant personnel to be informed of potential risks at the first time and take timely response measures. Through multiple notification methods, the timeliness and accuracy of information transmission are ensured, thus improving the efficiency of emergency response;

[0191] Timely response to potential risks reduces the possibility of fault spread, enhances the emergency handling capabilities of the automated operation platform, and ensures the safety of personnel by notifying relevant personnel in a timely manner and ensuring that they take appropriate measures in the shortest possible time.

[0192] If no potential fault is detected, the automated operation platform will continue to maintain the current charging status to ensure the continuity of the charging process. Even if no fault is detected, the automated operation platform will continue to monitor the operating status of the battery to ensure that any changes are captured in a timely manner.

[0193] Maintaining the current charge status ensures the stable operation of the automated operation platform, avoiding unnecessary interruptions or adjustments. Even under normal circumstances, the automated operation platform continuously monitors the battery status to ensure that any potential problems can be discovered in a timely manner;

[0194] Maintaining the current charging status ensures the continuous operation of the automated operation platform, reduces unnecessary interference and adjustments, and continuous monitoring enables the automated operation platform to detect problems at any time, enhancing the overall reliability of the automated operation platform.

[0195] When the early warning mechanism is triggered, the automated operation platform notifies relevant personnel through various means such as email, SMS, and application push, ensuring that different types of users can receive early warning information in a timely manner, improving the coverage and efficiency of information transmission;

[0196] Through multiple notification methods, we ensure that early warning information can be quickly delivered to relevant personnel, improving the response speed. Multi-channel notification methods ensure that different types of users can receive information, improving the comprehensiveness and accuracy of information transmission;

[0197] The rapid transmission of early warning information enables relevant personnel to take action in the shortest possible time, reducing the possibility of fault expansion. Through multiple notification methods, the timeliness and accuracy of information transmission are ensured, improving communication efficiency and emergency response capabilities.

[0198] This embodiment also provides a computer device suitable for the unattended hydrogen ion battery charging station method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the unattended hydrogen ion battery charging station method proposed in the above embodiment.

[0199] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0200] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing an unattended hydrogen-ion battery charging station as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0201] In summary, the present invention deploys high-precision sensors to monitor battery status and environmental conditions in real time, collects multi-dimensional data, ensures the quality and reliability of input data, enhances the adaptability of the automated operation platform to complex environments, and realizes instant response to changes to optimize charging strategies. The data is securely transmitted to the automated operation platform using an encrypted communication protocol, cleaned and feature extracted on the platform, and the adaptive learning model is trained using a long short-term memory model, thereby improving the accuracy and security of data analysis. Based on the trained model, the automated operation platform automatically adjusts the prediction parameters according to real-time environmental changes, generates accurate charging demand prediction results, reduces prediction errors and enhances the flexibility and reliability of the automated operation platform. Finally, the optimal charging strategy is determined by the differential evolution algorithm, and comprehensive consideration of multiple factors such as charging demand intensity, power level and battery health is used to quickly find the global optimal solution, improve charging efficiency and resource utilization, and reduce operating costs.

[0202] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An unmanned hydrogen ion battery charging station, characterized by: include, Monitor battery status parameters and environmental conditions through sensors to collect multi-dimensional data; Transmit multi-dimensional data to the automated operation platform for cleaning and feature extraction, and train the adaptive learning model; Based on the trained adaptive learning model, the prediction parameters are automatically adjusted according to the real-time environmental changes to generate the charging demand prediction results. The historical data after normalization and standardization and the first change over time in the real-time sensor data are combined with the historical feature changes and the current environmental impact. Predicted feature values , the first factor that affects charging demand in real-time environment Environmental factor values Hedi The weight corresponding to the prediction feature Introducing expressions to calculate dynamically adjusted hyperparameters , the expression is: ； in, is the attenuation factor, is the initial hyperparameter, is the total number of features, is the environmental factor weight, is the total number of environmental factors, is the accurate value in time change, For the precise value of charging requirements, is the time variable, the decay factor To control the history hyperparameters The degree of influence, balance the ratio of new and old information; Using initial hyperparameters , predict future charging demand through long short-term memory model, and optimize the dynamically adjusted hyperparameters ; The obtained dynamic adjustment hyperparameters Initial parameter settings applied to the optimization algorithm; Update the population parameters in the optimization algorithm and adjust the hyperparameters dynamically Dynamically adjust the initial learning rate and batch size; Using dynamically tuned hyperparameters , generate charging demand forecast results through long short-term memory model, and obtain charging demand intensity at a certain point in time and power levels , the charging demand intensity and power levels Introducing expressions to calculate the optimal charging strategy ; The charging strategy is converted into control instructions and sent to the controller in the automated operation platform. After receiving the instructions, the controller adjusts the working mode of the charging equipment; Real-time monitoring of battery operating status, triggering an early warning mechanism when a potential fault is detected; According to the charging demand prediction results, the optimization algorithm is applied to determine the charging strategy. The specific steps are as follows: Combined with extracting demand characteristic values ​​that change over time from multi-dimensional data , and assign weights Forming the characteristic value of total demand ; Extracting health characteristics that affect charging demand from real-time environment , and assign weights Forming the total health characteristic value ; The charging demand intensity and power levels Introducing expressions to calculate the optimal charging strategy : ； in, is the charging demand weight factor, is the power level adjustment factor, is the hyperparameter weight factor, is the demand intensity weight factor, For power level To the initial power level The direction of change, is the battery health weight factor, is the base of natural logarithms, Indicates the current power level Relative to the initial power level direction of change; Differential evolution algorithm is used to solve charging strategy , initialize the population parameters and dynamically adjust them, iteratively update the population individuals until they converge to the optimal solution.

2. The unmanned hydrogen ion battery charging station according to claim 1, characterized in that: The method monitors battery status parameters and environmental conditions through sensors and collects multi-dimensional data. The specific steps are: Select and install current, voltage, temperature, humidity, light intensity, and grid load sensors; Configure the sensor's operating mode and set the measurement range and accuracy; Connect the sensor to the automated operation platform, configure the communication protocol and set the sampling frequency; The automated operation platform triggers data collection at regular intervals, obtaining multi-dimensional data on current, voltage, temperature, internal resistance, charging rate, discharge depth, and grid load from sensors.

3. The unmanned hydrogen ion battery charging station according to claim 2, characterized in that: The multi-dimensional data is transmitted to the automated operation platform for cleaning and feature extraction, and the adaptive learning model is trained. The specific steps are: Use encrypted communication protocols to transmit sensor data to the automated operation platform in real time. The automated operation platform receives and stores data packets through HTTP protocol POST requests. Apply low-pass filtering and smoothing, and store the data packets in a distributed database; The automated operation platform identifies data packets in the distributed database and removes outliers and fills in missing values; Extract features from processed data packets and select predictive features to optimize the input of the adaptive learning model; Initialize the long short-term memory model parameters, set the initial learning rate and batch size, use historical data to train the adaptive learning model, and dynamically adjust the hyperparameters.

4. The unmanned hydrogen ion battery charging station according to claim 3, characterized in that: The differential evolution algorithm is used to solve the charging strategy , initialize the population parameters and dynamically adjust them, iteratively update the population individuals until they converge to the optimal solution. The specific steps are: The automated operation platform generates multiple candidate charging strategies based on the charging demand forecast results; The automated operation platform defines comprehensive evaluation criteria, analyzes each candidate charging strategy, and assesses its performance in terms of meeting charging requirements, grid load balancing, and battery health, and generates evaluation results. The automated operation platform uses a differential evolution algorithm to iteratively optimize the population. It generates new individuals through selection, mutation, and crossover operations, and replaces existing individuals, gradually approaching the optimal solution. Improve optimization efficiency and robustness according to the adaptive differential evolution algorithm parameters; When the maximum number of iterations is reached, the adaptive differential evolution algorithm stops running and outputs the final optimal charging strategy.

5. The unmanned hydrogen ion battery charging station according to claim 4, characterized in that: The charging strategy is converted into a control instruction and sent to the controller in the automated operation platform. After receiving the instruction, the controller adjusts the working mode of the charging device. The specific steps are: According to the final optimal charging strategy, it is organized into control instructions in JSON format; Use TLS / SSL encryption protocol to send control instructions from the automated operation platform to the controller via the HTTP protocol POST request method. The controller receives and parses the instructions and extracts parameters. The controller adjusts the operating mode of the charging device according to the extracted parameters; The controller executes the adjusted working mode and monitors the charging effect through sensors; The controller continuously monitors the charging process through sensors. When an abnormal situation is detected, the controller feeds back the information to the automated operation platform, which records the charging data.

6. The unmanned hydrogen ion battery charging station according to claim 5, characterized in that: The real-time detection of the battery's operating status triggers an early warning mechanism when a potential fault is detected. The specific steps are: The controller continuously monitors the battery's operating parameters through sensors and transmits the data to the automated operation platform; The automated operations platform conducts comparative analysis based on historical and real-time data to identify abnormal patterns; If a potential fault is displayed, the early warning mechanism is triggered; If no potential fault is detected, the current charging state is maintained and the battery operating status continues to be monitored; When the early warning mechanism is triggered, the automated operation platform pushes it to relevant personnel via email, SMS and application.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the unmanned hydrogen ion battery charging station according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the unmanned hydrogen ion battery charging station according to any one of claims 1 to 6 are implemented.

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