Energy storage battery cabin control method based on high-safety performance multi-source data acquisition and transmission

Abstract

The invention provides an energy storage battery cabin control method based on high-safety-performance multi-source data acquisition and transmission, and relates to the technical field of energy storage battery management and control. Original multi-source data in an energy storage battery cabin and in an environment where the energy storage battery cabin is located are acquired; preprocessing the original multi-source data through a cabin-mounted edge controller to obtain a calibrated multi-source data set so as to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data; constructing a multi-objective optimization model according to the short-term load, the renewable output and the transmission feature vector; a management strategy corresponding to the transmission feature vector is solved based on a multi-objective optimization model, and a regulation and control instruction corresponding to the management strategy is encrypted and signed through a cabin-mounted hardware security module, so that the regulation and control instruction is issued to a dispatching center and allocated to a cabin-mounted edge controller; according to the energy storage battery compartment control method and the energy storage battery compartment control system, the response speed of the energy storage battery compartment can be improved, and the fault-tolerant capability of the energy storage battery compartment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage battery management and control, and in particular to a method for controlling an energy storage battery compartment based on high-safety multi-source data acquisition and transmission. Background Art

[0002] In scenarios with a high penetration of renewable energy, battery energy storage power stations are core facilities for critical auxiliary services such as grid peak shaving, frequency regulation, and black starts. However, due to the high energy density of individual cells and the complex coupling of multiple physical fields, the risks of thermal runaway and explosion during operation are prominent. As the installed capacity of energy storage power stations advances from tens of megawatt-hours to gigawatt-hours, and energy storage technology and operation and maintenance scenarios continue to evolve, traditional strategies based on periodic maintenance and empirical threshold warnings are no longer adaptable to changing operating conditions, placing higher demands on safety, stability, and operation and maintenance efficiency.

[0003] The Chinese patent publication number CN118707369A discloses a method for testing an energy storage system, a controller, an energy storage system, and a storage medium. The method includes: constructing a battery compartment test pair of at least one battery compartment to be tested based on the number of at least one battery compartment to be tested, controlling the charge and discharge modes of the energy storage converter corresponding to the first battery compartment and the second battery compartment, and testing the charge and discharge modes of the energy storage converter to perform an online test. When all the battery compartment test pairs of at least one battery compartment to be tested are successfully online, the first battery compartment and the second battery compartment are controlled to have a preset remaining power according to the remaining charge and discharge power of the first battery compartment and the second battery compartment to complete the test of the energy storage system. However, the above solution only supports online testing and resetting the remaining power of paired battery compartments, lacks continuous monitoring and management of the battery operating status, resulting in insufficient response speed and fault tolerance of the battery compartment. Therefore, it is very necessary to provide an energy storage battery compartment control method based on high-security multi-source data acquisition and transmission to enhance the response speed and fault tolerance of the energy storage battery compartment. Summary of the Invention

[0004] In light of this, this paper proposes a battery storage compartment control method based on high-security multi-source data acquisition and transmission. By employing an onboard edge controller to preprocess and calibrate the multi-source raw data, aligning it based on timestamps, and introducing a long short-term memory (LSTM) neural network, combined with multi-source transmission feature vectors, this method ensures rapid response to sudden load fluctuations or environmental changes, effectively improving response speed and fault tolerance.

[0005] The present invention provides a method for controlling an energy storage battery compartment based on high-security multi-source data acquisition and transmission, the method comprising:

[0006] Collecting original multi-source data of the interior of the energy storage battery compartment and the environment within the energy storage battery compartment;

[0007] Preprocessing the raw multi-source data through an onboard edge controller to obtain a calibrated multi-source data set, and performing cross-source time series alignment on the calibrated multi-source data based on timestamps to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data;

[0008] Predicting short-term load and renewable output based on a long short-term memory neural network and the transmission characteristic vector, and constructing a multi-objective optimization model based on the short-term load, the renewable output and the transmission characteristic vector;

[0009] Solving a management policy corresponding to the transmission eigenvector based on the multi-objective optimization model, and encrypting and signing a control instruction corresponding to the management policy through an onboard hardware security module, so as to issue the control instruction to a dispatch center and distribute it to the onboard edge controller;

[0010] The onboard edge controller drives the charging and discharging device, DC / DC converter, cooling module and balancing circuit according to the control instructions, and periodically reports status and alarm signals to the host computer.

[0011] Based on the above technical solution, preferably, the preprocessing of the original multi-source data by the onboard edge controller to obtain a calibrated multi-source data set specifically includes:

[0012] The original multi-source data is smoothed using a Kalman filter, and abnormal data in the smoothed original multi-source data is identified based on a clustering forest algorithm and abnormal points are marked, and neighboring value interpolation is performed on the abnormal points to compensate for them, thereby obtaining transitional multi-source data;

[0013] The timestamps in the transition multi-source data are verified and corrected by GPS, and Z-score normalization is performed according to a preset range. The normalized transition multi-source data are merged in timestamp order to output a calibration multi-source data set.

[0014] On the basis of the above technical solution, preferably, the cross-source time sequence alignment of the calibration multi-source data based on the timestamp is performed to fuse and generate a transmission feature vector corresponding to the calibration multi-source data, specifically including:

[0015] Generate a sequence of equally spaced reference timestamps within a target time range according to a preset sampling period;

[0016] If there is no original data point corresponding to the reference timestamp sequence in the calibration multi-source data, then based on the linear interpolation algorithm, the data estimation value of the original data point is calculated, and the interpolation results of all the calibration multi-source data are mapped to the same reference timestamp;

[0017] Taking the reference moment in each reference timestamp sequence as the center, the statistics of each calibration multi-source data in the preset sliding time window are calculated respectively through the preset sliding time window;

[0018] splicing the original values corresponding to the original data points at each reference moment and the statistics output by the preset sliding time window in a predetermined order to form a reference time vector;

[0019] Each reference time is Z-score normalized, and the normalized reference time vector is encoded as a transmission feature vector.

[0020] More preferably, solving the management strategy corresponding to the transmission feature vector based on the multi-objective optimization model and encrypting and signing the control instructions corresponding to the management strategy through the onboard hardware security module specifically includes:

[0021] Generate a set of optimal management strategies based on the Pareto optimal evolutionary algorithm, and select a management strategy from the set of optimal management strategies according to the preset risk preference;

[0022] The management strategy is mapped into a control instruction set, and the control instruction set is encrypted and signed according to the onboard hardware security module, wherein the control instruction set includes charge and discharge commands, voltage setting values, current setting values, and cooling module and balancing circuit control quantities.

[0023] More preferably, the cryptographically signing the control instruction set according to the onboard hardware security module specifically includes:

[0024] The control instruction set, the timestamp corresponding to any control instruction in the control instruction set, and the random number are concatenated to calculate a summary, and the summary is digitally signed using the private key securely stored in the onboard hardware security module to obtain signature data.

[0025] More preferably, it also includes:

[0026] According to the status and alarm signals received by the cloud database, intelligent operation and maintenance of cloud services and intelligent alarm are performed using the data stored in the cloud database and a predetermined cloud service operation and maintenance algorithm;

[0027] Based on the data analysis results of the cloud service and a predetermined prediction algorithm, battery parameter identification, battery state of charge estimation, battery health state estimation, battery life estimation, battery consistency estimation, battery reliability estimation, battery operation index calculation, battery economic index calculation, or battery environmental index calculation are performed to generate battery status data;

[0028] The energy storage battery compartment is predicted based on the battery status data to generate cloud service prediction data, wherein the cloud service prediction data includes battery status change trend data, battery safety indicators, battery economic indicators, and battery environmental indicators.

[0029] More preferably, the original multi-source data includes one or more of battery cell voltage, battery cell current, battery cell temperature, battery state of charge, battery health status, temperature inside the energy storage battery compartment, humidity inside the energy storage battery compartment, main grid voltage, main grid frequency, load power, photovoltaic output and wind power output.

[0030] In a second aspect of the present application, a battery storage compartment control system is provided based on high-security multi-source data acquisition and transmission, the battery storage compartment control system includes a data acquisition module, an instruction distribution module and a modulation drive module, wherein:

[0031] The data acquisition module is used to collect original multi-source data inside the energy storage battery compartment and the environment inside the energy storage battery compartment;

[0032] The instruction distribution module is used to preprocess the original multi-source data through the onboard edge controller to obtain a calibrated multi-source data set, and perform cross-source time series alignment on the calibrated multi-source data based on the timestamp to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data, predict the short-term load and renewable output based on the long short-term memory neural network and the transmission feature vector, and construct a multi-objective optimization model based on the short-term load, the renewable output and the transmission feature vector, solve the management policy corresponding to the transmission feature vector based on the multi-objective optimization model, and encrypt and sign the control instructions corresponding to the management policy through the onboard hardware security module to send the control instructions to the dispatch center and distribute them to the onboard edge controller;

[0033] The modulation drive module is used to enable the onboard edge controller to drive the charging and discharging device, DC / DC converter, cooling module and balancing circuit according to the control instructions, and periodically report status and alarm signals to the host computer.

[0034] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0035] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to implement the steps of a method for controlling an energy storage battery compartment based on high-safety multi-source data acquisition and transmission.

[0036] The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission provided by the present invention has the following beneficial effects compared with the prior art:

[0037] (1) By using the onboard edge controller to preprocess, calibrate and align the multi-source raw data based on the timestamp, the timing deviation and range mismatch between sensors are eliminated, and the input data is improved to high precision and high reliability. In addition, the long short-term memory (LSTM) neural network is introduced, combined with the multi-source transmission feature vector, to jointly predict the short-term load and renewable power output, effectively capturing the nonlinear dynamic changes of load and output, and significantly reducing the prediction error. At the same time, based on the prediction results and real-time transmission characteristics, a multi-objective optimization model is constructed, which can coordinate the charging and discharging, DC / DC conversion, cooling and balancing circuit operations under multiple constraints to achieve global optimal scheduling. All key decisions are made at the onboard edge, which significantly reduces the data round-trip delay and ensures a rapid response to sudden load fluctuations or environmental changes. It can effectively improve the response speed and fault tolerance. The edge controller periodically reports status and alarm signals to the upper computer. Combined with cross-validation and trend analysis of multi-source data, it can provide early warning of risks such as battery pack overtemperature, balancing imbalance, hardware failure, etc., thereby improving operational safety.

[0038] (2) By using Kalman filtering to smooth the original multi-source data and suppress random noise, the outlier detection and neighboring value interpolation based on clustering forest are effectively eliminated and compensated for sudden outliers. The GPS is used to correct and verify the timestamps of each data source to ensure the strict alignment of multi-source data. The equally spaced reference timestamps are generated to build a regular sampling framework. The data points at the missing moments are filled by linear interpolation to ensure the continuity and integrity of the sequence. At the same time, within the sliding window around each reference moment, the mean, variance, extreme value and other statistics are calculated to capture short-term dynamic changes. The original value is spliced with the window statistics to form a context-rich reference time vector. Each reference time vector is again Z-score normalized to eliminate the distribution differences in each dimension. The final output transmission feature vector not only retains the original information of the multi-source signal, but also incorporates the statistical characteristics of the time series. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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.

[0040] Figure 1 A flow chart of a method for controlling an energy storage battery compartment based on high-security multi-source data acquisition and transmission provided by the present invention;

[0041] Figure 2 A schematic diagram of the structure of the energy storage battery compartment control system provided by the present invention;

[0042] Figure 3 This is a schematic structural diagram of the electronic device provided by the present invention.

[0043] Explanation of the accompanying symbols: 1. Energy storage battery compartment control system; 11. Data acquisition module; 12. Instruction distribution module; 13. Modulation drive module; 2. Electronic equipment; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. DETAILED DESCRIPTION

[0044] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The present invention discloses a method for controlling an energy storage battery compartment based on high-security multi-source data acquisition and transmission. Figure 1 , the steps of the method include S1 to S5.

[0046] Step S1: collecting original multi-source data of the interior of the energy storage battery compartment and the environment within the energy storage battery compartment.

[0047] In this step, the original multi-source data includes one or more of battery cell voltage, battery cell current, battery cell temperature, battery state of charge, battery health status, energy storage battery compartment temperature, energy storage battery compartment humidity, main grid voltage, main grid frequency, load power, photovoltaic output, and wind power output.

[0048] Furthermore, it also includes battery parameter acquisition, cabin environmental parameter acquisition, and cabin status acquisition. Battery parameter acquisition uses a voltage sensor to obtain the terminal voltage of each string / cell battery, a current sensor to obtain the charge and discharge current, and a temperature sensor to obtain the battery cell temperature and busbar temperature. Cabin environmental parameter acquisition uses a temperature sensor to obtain the cabin air temperature, a humidity sensor to obtain the cabin relative humidity, a pressure sensor to obtain the cabin atmospheric pressure, and a combustible gas / smoke sensor to obtain the concentration of hazardous gases or smoke. Cabin status acquisition uses an acceleration sensor and a vibration sensor to obtain cabin vibration and acceleration, and an inclination sensor to obtain the cabin installation inclination angle. The analog or digital signals output by each of the above sensors are respectively transmitted to the cabin edge controller through an analog front-end acquisition module (AFE) or a digital interface module after A / D conversion via an industrial bus (such as CAN, Modbus) or a wireless communication module (such as Wi-Fi, LoRa).

[0049] In one example, the internal battery compartment needs to collect the voltage, current, temperature, SOC / SOH, and cell imbalance of each battery cell; the external environment needs to collect the internal and external temperature, humidity, air pressure, vibration, wind speed (strong wind or cooling fan working conditions), toxic or flammable gas concentration, etc.; the electrical cabinet / vehicle environment (such as used in vehicle scenarios) needs acceleration, tilt angle, EMI / EMC electromagnetic environment indicators, etc. The voltage and current sensors use high-precision Hall / shunt resistors, and the temperature sensor uses PT100 / NTC thermistor or digital temperature sensor (I 2 C / SPI interface), the humidity and pressure sensor adopts a digital environmental sensor module, the acceleration sensor adopts a MEMS accelerometer, and the gas sensor adopts a semiconductor or electrochemical gas sensor.

[0050] In step S2, the original multi-source data is pre-processed by the onboard edge controller to obtain a calibrated multi-source data set, and the calibrated multi-source data is time-series aligned across sources based on the timestamp to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data.

[0051] This step also includes steps S21 to S27.

[0052] In step S21, the original multi-source data is smoothed using Kalman filtering, and abnormal data in the smoothed original multi-source data is identified based on the clustering forest algorithm and abnormal points are marked. The abnormal points are compensated by performing neighboring value interpolation to obtain transitional multi-source data.

[0053] In this step, an initial state estimate and estimation error are set for each time series signal (each sensor channel) at the beginning. At each new observation moment, the state estimate at the previous moment is first used to make a prediction to obtain a prior estimate of the state at this moment. The difference between the predicted estimate and the observed value is calculated, and a Kalman gain is generated by combining the pre-set process noise and observation noise weights. The predicted estimate and the observed value are weighted and fused using the Kalman gain to update the new state estimate. The estimation error is also updated at the same time, and the state estimate at this moment is output as the smoothed data. The above prediction-update process is repeated until all time points are processed.

[0054] The smoothed data of all channels are combined into a multidimensional feature vector at each moment, and a forest model is constructed using multiple random clustering trees (each tree splits the sample at a randomly selected subset dimension and threshold). The average path length at each time point is calculated and compared with the expected path length of a normal sample to obtain the anomaly score of the point. A score threshold is set in advance, and any moment above the threshold is marked as an anomaly point.

[0055] For each channel marked as abnormal, the nearest normal point is searched forward and backward in the smoothed sequence of the same channel, and linear interpolation is performed in proportion to the time distance between them to calculate the compensation value of the abnormal point. If it is found that the normal points before and after are too far apart, constant filling (taking the nearest normal value) or higher-order polynomial interpolation can be used. The compensation value obtained by interpolation replaces the original abnormal observation, and the compensated data of all channels are output as transitional multi-source data.

[0056] Step S22: verify and correct the timestamps in the transition multi-source data using GPS, perform Z-score normalization according to a preset range, merge the normalized transition multi-source data in timestamp order, and output a calibrated multi-source data set.

[0057] In this step, each transition data record contains the timestamp t_local generated by the local clock and the reference timestamp t_local reported periodically by the timing module of the edge controller synchronized to the GPS. gps Calculate the deviation Δt=t for each record gps –t local If |Δt| is less than the preset single tolerance threshold (such as ±50ms), then directly set t corrected =t local +Δt; If multiple consecutive records of |Δt| are outside the threshold, it can be considered that the local clock has drifted. Take multiple (t local ,t gps ) samples, do linear fitting t gps ≈α·t local +β, after obtaining the drift parameters α and β, all tlocal Uniformly according to t gps ≈α·t local +β correction; use the corrected t corrected Replace the original timestamp to ensure that the time series of the entire transition dataset is consistent with the GPS absolute time.

[0058] For the transition multi-source data after time correction, extract all sample values for each channel (voltage, current, temperature,...), calculate the mean μ and standard deviation σ of each channel, record item by item to standardize the observed value x of each channel as z = (x - μ) / σ, and perform upper and lower bound pruning as required, for example, limit z within the interval [-R, +R] to prevent extreme values from affecting subsequent processing, and replace the original observed value with the standardized z value to generate standardized transition data. Sort the standardized transition data of all channels according to their corrected timestamp t corrected in ascending order. In the sorted records, horizontally splice the multi-source channel values at the same moment (or within the same reference time window) to form a complete multi-dimensional feature vector for each row, and sequentially output the vector sequences corresponding to all moments, that is, obtain the final calibrated multi-source dataset.

[0059] Step S23, generate an equally spaced reference timestamp sequence within the target time range according to the preset sampling period.

[0060] Step S24, if there are no original data points corresponding to the reference timestamp sequence in the calibrated multi-source data, then based on the linear interpolation algorithm, calculate the data estimation values of the original data points, and map the interpolation results of all calibrated multi-source data to the same reference timestamp.

[0061] In this step, for N records arranged in ascending order, each containing a timestamp t[i] and observed values x[i][1...D] of D channels, the reference timestamp sequence is M equally spaced moments T[...M]. Create an empty M×D table for the result matrix Z to store the interpolation results for each reference moment and each channel.

[0062] Furthermore, extract the original time series t and numerical series x of channel j j , repeat for k from 1 to M (each reference moment): Let τ = T[k], if there exists an i such that t[i] == τ, then directly let Z[k][j] = x j [i]; otherwise, perform a binary search in t to find indices i0 and i1 such that t[i0] < τ < t[i1] and i = i0 + 1; calculate the interpolation ratio α = (τ – t[i0]) / (t[i1] – t[i0]); let Z[k][j] = x j [i0] + α*(x j [i1] – x j[[i0]); If τ is outside the range of t (τ < t[1] or τ > t[N]), constant filling can be selected (Z[k][j] = x j [[1] or x j [[N]), or linear extrapolation can be performed (extrapolate after fitting a straight line with the two outermost points). After completion, each row in Z corresponds to a reference time, each column corresponds to a channel, and the values have been filled and mapped to T. Output Z and T, jointly constituting a complete calibrated multi-source dataset aligned to the reference timestamp.

[0063] Step S25, centered on the reference time in each reference timestamp sequence, and calculate the statistics of each calibrated multi-source data within the preset sliding time window through the preset sliding time window respectively.

[0064] In this step, the aligned calibrated multi-source dataset: each record contains the corrected + interpolated sample value x[k][j] (the kth reference time, the jth channel), and the corresponding reference timestamp T[k]. The sliding time window length is W (such as 2s, 5min, etc.), and the list of statistics within the window (such as mean, standard deviation, minimum value, maximum value, median, etc.). For each reference time T[k], define the time interval [T[k] – W / 2, T[k] + W / 2] centered on it. (If you want the window to be left-closed right-closed or left-closed right-open, it can be handled according to the agreement; if the data is sparse, it can also be changed to left-open right-closed or half-open half-closed in the front and back)

[0065] For k from 1 to M (M is the total number of reference times), determine the current window interval: T low = T[k] – W / 2, T high = T[k] + W / 2. Screen out all record index sets I low ≤ T[·] ≤ TT high in the aligned full data. For each channel j (1 ≤ j ≤ D), take out all sample values {x[i][j]|i ∈ I k} of channel j from the index set I k , and calculate the mean μ k (k), standard deviation σ j (k), minimum value min j (k), maximum value max j (k) and median med (k) respectively. If I j (k). If I j (k). If I kIf the window is empty (no samples), all statistics can be set to null (NaN), forward-filled with the statistics of the previous window, or replaced with global statistics (channel population μ, σ). The D channels × S selected statistics at the k-th reference time are concatenated into a row of sliding window feature vectors F[k]. Repeat this process from 1 to M, to obtain a sliding window statistical feature matrix with M rows and D × S columns. This matrix can be horizontally merged with the original alignment values x[k][j] as input for the next step of constructing the reference time vector.

[0066] Step S26 , concatenating the original values corresponding to the original data points at each reference moment and the statistics output by the preset sliding time window in a predetermined order to form a reference time vector.

[0067] Step S27: Perform Z-score normalization on each reference time, and encode the normalized reference time vector into a transmission feature vector.

[0068] In this embodiment, the original multi-source data is smoothed by using Kalman filtering to suppress random noise, and outlier detection + neighboring value interpolation based on clustering forest is used to effectively eliminate and compensate for sudden outliers. The timestamps of each data source are corrected and verified by GPS to ensure the strict alignment of multi-source data, generate equally spaced reference timestamps, build a regular sampling framework, and linear interpolation is used to fill in the data points at missing moments to ensure the continuity and integrity of the sequence. In the sliding window around each reference moment, statistics such as mean, variance, and extreme value are calculated to capture short-term dynamic changes. The original value is spliced with the window statistics to form a context-rich reference time vector. Each reference time vector is again Z-score normalized to eliminate the distribution differences in each dimension. The final output transmission feature vector retains the original information of the multi-source signal and incorporates the statistical characteristics of the time series.

[0069] Step S3: Based on the long short-term memory neural network and the transmission characteristic vector, the short-term load and renewable output are predicted, and a multi-objective optimization model is constructed according to the short-term load, renewable output and the transmission characteristic vector.

[0070] In this step, the input to the LSTM neural network includes a sequence of transmission network feature vectors from the most recent T1 reference moments, or external information such as weather forecasts and holiday signs. The LSTM neural network outputs a load forecast for the next H1 moments and a renewable output forecast for the next H2 moments. The LSTM neural network consists of multiple LSTM layers to capture temporal correlations, with the final fully connected layer generating a joint output for both load and renewable output tasks. The LSTM neural network's loss is a proportionally weighted sum of the load forecast error and the output forecast error. Training samples are constructed using a sliding window approach, employing strategies such as the Adam optimizer, early stopping, and learning rate decay. The network features from the most recent T1 step, along with optional external features, are input into the model, which then outputs forecasts for the next H1+H2 steps.

[0071] The decision variables in the multi-objective optimization scheduling model include the battery charging and discharging power at each moment, the battery's current state of charge (SOC), and, if connected to the grid, the grid power. The objectives of the multi-objective optimization scheduling model are to minimize operating electricity costs, minimize battery cycle degradation costs (extending battery life), and maximize output smoothness and system reliability (suppressing power spikes). A compromise solution for these multiple objectives can be found using weighted sums, constraint methods, or evolutionary algorithms. The constraints of the multi-objective optimization scheduling model are that the load, renewable output, battery, and grid power must be balanced at each moment; the battery SOC must be dynamically updated with charging and discharging and maintained between upper and lower limits; and both charging and discharging power and grid power must be subject to maximum and minimum limits. If necessary, the grid-connected power or optimization objective weights can be dynamically adjusted based on current transmission network congestion or security levels. For continuous problems, the multi-objective optimization scheduling model can use interior point methods or sequential quadratic programming; for non-convex problems or those involving discrete decisions, genetic algorithms, particle swarm optimization, simulated annealing, and other algorithms can be used. Real-time scheduling can employ model predictive control, with continuous optimization within a rolling window.

[0072] In step S4, the management strategy corresponding to the transmission feature vector is solved based on the multi-objective optimization model, and the control instructions corresponding to the management strategy are encrypted and signed by the onboard hardware security module to send the control instructions to the dispatch center and distribute them to the onboard edge controller.

[0073] This step also includes steps S41 to S42.

[0074] Step S41 : Based on the Pareto optimal evolutionary algorithm, a set of optimal management strategies is generated, and a management strategy is selected from the optimal management strategy set according to a preset risk preference.

[0075] In this step, a complete time series of charging, discharging, and grid connection arrangements is encoded as individuals (chromosomes), with each chromosome corresponding to a management strategy. For each individual, simulations or performance metrics are performed across all objectives (such as operating costs, lifespan loss, and output smoothness) to obtain a set of target values. Individuals in the population are grouped according to non-dominated hierarchies: the first tier consists of mutually independent individuals in the current population, the second tier is the Pareto tier remaining after removing the first tier, and so on. Within each tier, the density of individuals in the target space is measured using a crowding metric, with boundary points retained to maintain diversity.

[0076] Parents are selected from the current population based on hierarchy and crowding rules. Crossover and mutation are used to generate offspring. Parents and offspring are then merged, and non-dominated sorting and crowding screening are performed again to create the next generation population of a fixed size. After repeating the evaluation → sorting → inheritance process for several generations (or until the maximum number of generations is reached), the first level (Pareto front) of the final population is taken as the optimal management strategy set.

[0077] The definitions of risk preference in selecting a management strategy based on risk preference include risk aversion, risk neutrality, and risk seeking. The risk aversion type focuses more on stability and safety margins under the worst-case scenario; the risk neutral type comprehensively considers all objectives and selects equilibrium solutions located in the middle of the frontier; the risk seeking type prefers greater returns, is willing to accept higher volatility, and tends to favor extreme solutions at one end of the frontier. For each solution in the Pareto set, each objective is weighted or scored based on risk preference. When risk aversion is used, the smoothness and lifespan dimensions can be emphasized; when risk seeking is used, the weight of the cost or benefit dimension can be increased. The solution with the highest weighted score is selected as the optimal management strategy; or a "sliding window" search can be performed on the Pareto frontier according to risk preference to find the local optimum that best suits the preference.

[0078] Step S42: Map the management strategy into a control instruction set, and encrypt and sign the control instruction set according to the onboard hardware security module, wherein the control instruction set includes charge and discharge commands, voltage setting values, current setting values, and cooling module and balancing circuit control quantities.

[0079] In this embodiment, the analytical strategy outputs the battery charging power P ch (t), discharge power P dc (t), grid-connected power P grid (t), cooling requirements (e.g. cooling pump speed or valve opening), and the bypass resistor or current distribution of the single-cell balancing circuit. Calculate the set value to convert the power command into a voltage / current setting. If the battery manager requires current mode, directly use I set = ±P / battery voltage; otherwise, set the target voltage V set, so that the charge and discharge power reaches P. The cooling module maps the cooling rate to the pump PWM duty cycle or fan speed according to the strategy; the balancing circuit writes the control quantity into each battery bypass resistor or balancing current channel according to the strategy.

[0080] Furthermore, an onboard hardware security module (HSM) is used for encryption and signing, and a true random number nonce is obtained from the HSM. The current UTC time is used as the timestamp of the final instruction. The instruction message, timestamp, and nonce are concatenated into a first-level byte stream, and a secure hash (such as SHA-256) digest is calculated within the HSM. The HSM uses its internal private key to perform a signature operation on the digest. The original instruction message, timestamp, nonce, and signature are packaged into the final control package according to the protocol format and sent to the execution unit or BMS through a secure channel. In this way, anyone who receives the instruction package can recalculate the digest using the same format and verify the signature using the published public key to ensure that the instruction source is credible and has not been tampered with. The charging and discharging, voltage and current setting, cooling, and balancing execution units are then driven by field.

[0081] Furthermore, the control instruction set, the timestamp corresponding to any control instruction in the control instruction set, and the random number are concatenated to calculate the summary, and the summary is digitally signed using the private key securely stored in the onboard hardware security module to obtain signature data.

[0082] For example, within the hardware security module, a digest is first calculated by concatenating the generated control instruction set, a millisecond-accurate timestamp, and a random number. This digest is then digitally signed using the module's securely stored private key to generate the signature data. Next, the control instruction, timestamp, random number, and digital signature are packaged into a secure message. This entire message is encrypted with the dispatch center's public key and sent to the dispatch center via an encrypted communication channel. Upon receiving the message, the onboard edge controller first decrypts it using its own private key or session key, then verifies the validity of the signature and timestamp, confirming that the message is complete and has not been replayed. It then drives each subsystem to perform the corresponding charging, discharging, converter, cooling, and balancing operations according to the instructions.

[0083] In step S5, the onboard edge controller drives the charging and discharging device, DC / DC converter, cooling module and balancing circuit according to the control instructions, and periodically reports status and alarm signals to the host computer.

[0084] In this embodiment, the onboard edge controller monitors a communication bus (such as CAN, Ethernet, or serial port) to receive control command packets. It then uses the onboard hardware security module (HSM) to verify the signature, timestamp, and random number in the packet using the stored public key. If the verification or validity period fails, an alarm is immediately generated and the command is discarded.

[0085] The system extracts the charge and discharge mode and current / voltage settings, DC / DC converter output settings, cooling module (fan / pump) control variables, and battery balancing circuit current or bypass switch status from the verified instruction packet. This information is then sent to the charging and discharging device (BMS or power stage), DC / DC converter controller, cooling fan / pump driver, and battery balancer (bypass resistor or switch) via the underlying driver interface (PWM, DAC, GPIO, CAN-Tx, etc.). Sensor data such as current, voltage, and temperature are then read back locally. Built-in controllers such as PI / PID ensure that the setpoints are accurately reached. If an overflow, hardware error, or safety limit is exceeded, the system immediately switches to safe mode and reports an alarm.

[0086] Furthermore, a fixed reporting period (such as 1 second or 10 seconds) is set, and a timer or soft interrupt triggers status collection. The key information collected includes the actual charge and discharge current and voltage, DC / DC output voltage / current, cooling module speed or power, each battery cell voltage and balancing current, battery pack overall SOC, SOH and emergency alarm flags (overvoltage, undervoltage, overcurrent, overtemperature, etc.). At the same time, the message encapsulation frames the above status fields according to the agreed format, and adds the current timestamp and simple check (CRC). If necessary, an HSM signature can also be attached to prevent tampering. The status message is sent to the host computer or ground monitoring system through the reserved uplink channel (CAN-Rx, TCP / UDP, wireless link, etc.). If there is a high-priority alarm, immediately send an "interrupt message" to notify the host computer.

[0087] In one example, based on the status and alarm signals received by the cloud database, intelligent operation and maintenance and intelligent alarm of the cloud service are performed using the data stored in the cloud database and a predetermined cloud service operation and maintenance algorithm; based on the data analysis results of the cloud service and a predetermined prediction algorithm, battery parameter identification, battery state of charge estimation, battery health state estimation, battery life estimation, battery consistency estimation, battery reliability estimation, battery operation index calculation, battery economic index calculation or environmental index calculation are performed to generate battery status data; based on the battery status data, the energy storage battery compartment is predicted to generate cloud service prediction data, wherein the cloud service prediction data includes battery status change trend data, battery safety indicators, battery economic indicators, and battery environmental indicators.

[0088] In this embodiment, by using an onboard edge controller to preprocess and calibrate multi-source raw data and align them based on timestamp timing, the timing deviation and range mismatch between sensors are eliminated, and the input data is improved in terms of high precision and high reliability. In addition, a long short-term memory (LSTM) neural network is introduced, combined with multi-source transmission feature vectors, to jointly predict short-term load and renewable power output, effectively capturing the nonlinear dynamic changes of load and output, and significantly reducing prediction errors. At the same time, based on the prediction results and real-time transmission characteristics, a multi-objective optimization model is constructed, which can coordinate the operation of charging and discharging, DC / DC conversion, cooling and balancing circuits under multiple constraints to achieve global optimal scheduling. All key decisions are made at the onboard edge, significantly reducing data round-trip latency, ensuring a rapid response to sudden load fluctuations or environmental changes, and effectively improving response speed and fault tolerance.

[0089] Based on the above method, the embodiment of the present application discloses a storage battery compartment control system based on high-security multi-source data acquisition and transmission, referring to Figure 2 The energy storage battery compartment control system 1 includes a data acquisition module 11, an instruction distribution module 12 and a modulation drive module 13, wherein:

[0090] The data acquisition module 11 is used to collect original multi-source data inside the energy storage battery compartment and the environment inside the energy storage battery compartment;

[0091] The instruction distribution module 12 is used to pre-process the original multi-source data through the onboard edge controller to obtain a calibrated multi-source data set, and perform cross-source time series alignment on the calibrated multi-source data based on the timestamp to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data. Based on the long short-term memory neural network and the transmission feature vector, the module predicts the short-term load and renewable output, and constructs a multi-objective optimization model based on the short-term load, renewable output, and transmission feature vector. Based on the multi-objective optimization model, the module solves the management policy corresponding to the transmission feature vector, and encrypts and signs the control instructions corresponding to the management policy through the onboard hardware security module, so that the control instructions are sent to the dispatch center and distributed to the onboard edge controller.

[0092] The modulation drive module 13 is used to enable the onboard edge controller to drive the charging and discharging device, DC / DC converter, cooling module and balancing circuit according to the control instructions, and periodically report status and alarm signals to the host computer.

[0093] In one example, the instruction distribution module 12 is used to smooth the original multi-source data using Kalman filtering, and identify abnormal data in the original multi-source data after smoothing based on the clustering forest algorithm and mark abnormal points, perform neighboring value interpolation to compensate for the abnormal points, and obtain transition multi-source data; verify and correct the timestamps in the transition multi-source data through GPS, and perform Z-score standardization according to a preset range, merge the standardized transition multi-source data in timestamp order, and output a calibrated multi-source data set.

[0094] In one example, the instruction allocation module 12 is used to generate a reference timestamp sequence with equal intervals within a target time range according to a preset sampling period; if there is no original data point corresponding to the reference timestamp sequence in the calibrated multi-source data, then based on a linear interpolation algorithm, the data estimation value of the original data point is calculated, and the interpolation results of all calibrated multi-source data are mapped to the same reference timestamp; with the reference moment in each reference timestamp sequence as the center, the statistics of each calibrated multi-source data in the preset sliding time window are calculated respectively through the preset sliding time window; the original values corresponding to the original data points at each reference moment and the statistics output by the preset sliding time window are spliced in a predetermined order to form a reference time vector; each reference time is Z-score standardized, and the standardized reference time vector is encoded as a transmission feature vector.

[0095] In one example, the instruction allocation module 12 is used to generate a set of optimal management strategy sets based on the Pareto optimal evolutionary algorithm, and select a management strategy from the optimal management strategy set according to a preset risk preference; map the management strategy into a control instruction set, and encrypt and sign the control instruction set according to the onboard hardware security module, wherein the control instruction set includes charging and discharging commands, voltage setting values, current setting values, and cooling module and balancing circuit control quantities.

[0096] In one example, the instruction distribution module 12 is used to concatenate the control instruction set, the timestamp corresponding to any control instruction in the control instruction set, and the random number to calculate the summary, and digitally sign the summary using the private key securely stored in the onboard hardware security module to obtain signature data.

[0097] In one example, it also includes:

[0098] Based on the status and alarm signals received by the cloud database, intelligent operation and maintenance of cloud services and intelligent alarms are performed using the data stored in the cloud database and pre-determined cloud service operation and maintenance algorithms;

[0099] Based on the data analysis results of the cloud service and a predetermined prediction algorithm, battery parameter identification, battery state of charge estimation, battery health state estimation, battery life estimation, battery consistency estimation, battery reliability estimation, battery operation index calculation, battery economic index calculation, or battery environmental index calculation are performed to generate battery status data;

[0100] The energy storage battery compartment is predicted based on the battery status data to generate cloud service prediction data, where the cloud service prediction data includes battery status change trend data, battery safety indicators, battery economic indicators, and battery environmental indicators.

[0101] In one example, the original multi-source data includes one or more of battery cell voltage, battery cell current, battery cell temperature, battery state of charge, battery health status, temperature inside the energy storage battery compartment, humidity inside the energy storage battery compartment, main grid voltage, main grid frequency, load power, photovoltaic output, and wind power output.

[0102] See Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 2 may include: at least one processor 21 , at least one network interface 24 , a user interface 23 , a memory 25 , and at least one communication bus 22 .

[0103] The communication bus 22 is used to realize the connection and communication between these components.

[0104] The user interface 23 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 23 may also include a standard wired interface and a wireless interface.

[0105] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0106] The processor 21 may include one or more processing cores. The processor 21 utilizes various interfaces and lines to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 25, and calling data stored in the memory 25, the processor 21 performs various server functions and processes data. Optionally, the processor 21 may be implemented in at least one hardware form selected from digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 21 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 21 and may be implemented separately on a single chip.

[0107] Among them, the memory 25 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 25 includes a non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 25 may also be optionally at least one storage device located away from the aforementioned processor 21. As Figure 3 As shown, the memory 25 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for an energy storage battery compartment control method based on high-security multi-source data acquisition and transmission.

[0108] exist Figure 3In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 21 can be used to call an application stored in the memory 25 for a method for controlling an energy storage battery compartment based on high-safety multi-source data acquisition and transmission. When executed by one or more processors, the electronic device executes one or more methods in the above embodiments.

[0109] A computer-readable storage medium stores instructions, which, when executed by one or more processors, cause the computer to execute one or more methods in the above-mentioned embodiments.

[0110] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0111] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0113] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0116] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not described in the present disclosure.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling an energy storage battery compartment based on high-security multi-source data acquisition and transmission, characterized in that: The method comprises: Collecting original multi-source data of the interior of the energy storage battery compartment and the environment within the energy storage battery compartment; Preprocessing the raw multi-source data through an onboard edge controller to obtain a calibrated multi-source data set, and performing cross-source time series alignment on the calibrated multi-source data based on timestamps to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data; Predicting short-term load and renewable output based on a long short-term memory neural network and the transmission characteristic vector, and constructing a multi-objective optimization model based on the short-term load, the renewable output and the transmission characteristic vector; Solving a management policy corresponding to the transmission eigenvector based on the multi-objective optimization model, and encrypting and signing a control instruction corresponding to the management policy through an onboard hardware security module, so as to issue the control instruction to a dispatch center and distribute it to the onboard edge controller; The onboard edge controller drives the charging and discharging device, DC / DC converter, cooling module and balancing circuit according to the control instructions, and periodically reports status and alarm signals to the host computer.

2. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1 is characterized in that: Preprocessing the original multi-source data by the onboard edge controller to obtain a calibrated multi-source data set specifically includes: The original multi-source data is smoothed using a Kalman filter, and abnormal data in the smoothed original multi-source data is identified based on a clustering forest algorithm and abnormal points are marked, and neighboring value interpolation is performed on the abnormal points to compensate for them, thereby obtaining transitional multi-source data; The timestamps in the transition multi-source data are verified and corrected by GPS, and Z-score normalization is performed according to a preset range. The normalized transition multi-source data are merged in timestamp order to output a calibration multi-source data set.

3. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1 is characterized in that: The performing cross-source time sequence alignment on the calibration multi-source data based on the timestamp to fuse and generate a transmission feature vector corresponding to the calibration multi-source data specifically includes: Generate a sequence of equally spaced reference timestamps within a target time range according to a preset sampling period; If there is no original data point corresponding to the reference timestamp sequence in the calibration multi-source data, then based on the linear interpolation algorithm, the data estimation value of the original data point is calculated, and the interpolation results of all the calibration multi-source data are mapped to the same reference timestamp; Taking the reference moment in each reference timestamp sequence as the center, the statistics of each calibration multi-source data in the preset sliding time window are calculated respectively through the preset sliding time window; splicing the original values corresponding to the original data points at each reference moment and the statistics output by the preset sliding time window in a predetermined order to form a reference time vector; Each reference time is Z-score normalized, and the normalized reference time vector is encoded as a transmission feature vector.

4. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1, characterized in that: Solving the management strategy corresponding to the transmission feature vector based on the multi-objective optimization model, and encrypting and signing the control instructions corresponding to the management strategy through the onboard hardware security module, specifically includes: Generate a set of optimal management strategies based on the Pareto optimal evolutionary algorithm, and select a management strategy from the set of optimal management strategies according to the preset risk preference; The management strategy is mapped into a control instruction set, and the control instruction set is encrypted and signed according to the onboard hardware security module, wherein the control instruction set includes charge and discharge commands, voltage setting values, current setting values, and cooling module and balancing circuit control quantities.

5. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1 is characterized in that: The encrypting and signing the control instruction set according to the onboard hardware security module specifically includes: The control instruction set, the timestamp corresponding to any control instruction in the control instruction set, and the random number are concatenated to calculate a summary, and the summary is digitally signed using the private key securely stored in the onboard hardware security module to obtain signature data.

6. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1 is characterized in that: Also includes: According to the status and alarm signals received by the cloud database, intelligent operation and maintenance of cloud services and intelligent alarm are performed using the data stored in the cloud database and a predetermined cloud service operation and maintenance algorithm; Based on the data analysis results of the cloud service and a predetermined prediction algorithm, battery parameter identification, battery state of charge estimation, battery health state estimation, battery life estimation, battery consistency estimation, battery reliability estimation, battery operation index calculation, battery economic index calculation, or battery environmental index calculation are performed to generate battery status data; The energy storage battery compartment is predicted based on the battery status data to generate cloud service prediction data, wherein the cloud service prediction data includes battery status change trend data, battery safety indicators, battery economic indicators, and battery environmental indicators.

7. The energy storage battery compartment control method based on high-security multi-source data acquisition and transmission according to claim 1 is characterized in that: The original multi-source data includes one or more of battery cell voltage, battery cell current, battery cell temperature, battery state of charge, battery health status, temperature inside the energy storage battery compartment, humidity inside the energy storage battery compartment, main grid voltage, main grid frequency, load power, photovoltaic output, and wind power output.

8. An energy storage battery compartment control system based on high-security multi-source data acquisition and transmission, characterized in that: The energy storage battery compartment control system (1) comprises a data acquisition module (11), an instruction distribution module (12) and a modulation drive module (13), wherein: The data acquisition module (11) is used to collect original multi-source data inside the energy storage battery compartment and the environment inside the energy storage battery compartment; The instruction distribution module (12) is used to pre-process the original multi-source data through the onboard edge controller to obtain a calibrated multi-source data set, and perform cross-source time series alignment on the calibrated multi-source data based on a timestamp to fuse and generate a transmission feature vector corresponding to the calibrated multi-source data, predict short-term load and renewable output based on a long short-term memory neural network and the transmission feature vector, and construct a multi-objective optimization model based on the short-term load, the renewable output and the transmission feature vector, solve a management strategy corresponding to the transmission feature vector based on the multi-objective optimization model, and encrypt and sign a control instruction corresponding to the management strategy through the onboard hardware security module to send the control instruction to the dispatch center and distribute it to the onboard edge controller; The modulation drive module (13) is used to enable the onboard edge controller to drive the charging and discharging device, the DC / DC converter, the cooling module and the balancing circuit according to the control instructions, and to periodically report status and alarm signals to the upper computer.

9. An electronic device, characterized in that: The electronic device (2) comprises a processor (21), a memory (25), a user interface (23) and a network interface (24), wherein the memory (25) is used to store instructions, the user interface (23) and the network interface (24) are used to communicate with other devices, and the processor (21) is used to execute the instructions stored in the memory (25) so that the electronic device (2) executes the method according to any one of claims 1 to 7.

10. 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 method according to any one of claims 1 to 7 are implemented.

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