Intelligent control method and system for high-energy hydrogen atom generator based on cloud platform

Abstract

The invention relates to the technical field of intelligent control of high-energy hydrogen atom generators, and discloses an intelligent control method and system for a high-energy hydrogen atom generator based on a cloud platform, and the method comprises the steps: obtaining the instantaneous color change data and real-time environment parameters of the high-energy hydrogen atom generator; performing image enhancement processing, feature extraction, standard value comparison operation and cloud platform data analysis according to the instantaneous color change data to obtain a parameter adjustment instruction; performing classification verification and feedback signal analysis according to the real-time environment parameters and the parameter adjustment instruction to obtain a deviation correction value; and adjusting the power parameter of the generator according to the deviation correction value, and carrying out output monitoring to obtain the hydrogen atom output energy level. According to the method, intelligent control and dynamic optimization of the output energy of the high-energy hydrogen atom generator can be realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for high-energy hydrogen atom generators, and in particular to an intelligent control method and system for high-energy hydrogen atom generators based on a cloud platform. Background Technology

[0002] Currently, high-energy hydrogen atom generators play a crucial role in the energy and materials processing fields, and their stable output directly affects the efficiency of hydrogen fuel cells and the reliability of industrial hydrogen supply. With the rapid growth in demand for clean energy, ensuring that generators efficiently and stably produce high-energy hydrogen atoms has become a key challenge for industry.

[0003] In existing technologies, control systems based on fixed parameter settings are typically employed, or monitoring relies on laboratory-grade precision instruments. Some solutions attempt to upload monitoring data to a cloud platform for analysis using sensor arrays and wireless networking technologies. However, these methods largely depend on fixed monitoring thresholds and cannot adapt to the complex dynamic environmental changes (such as temperature fluctuations and impurity interference) during hydrogen atom generation. More importantly, they ignore the real-time variability of the energy state within the hydrogen atom, particularly the instantaneous and subtle color changes caused by energy transitions. Due to insufficient sensor resolution and delays in data transmission and analysis mechanisms in the cloud, the control system struggles to capture these crucial characteristics, leading to inaccurate judgments of the energy state and consequently, generator overload or energy waste.

[0004] In summary, existing technologies suffer from the inability to analyze instantaneous color changes in real time to accurately map the energy state of hydrogen atoms, which leads to inaccurate control of generator power parameters and large fluctuations in output energy. Summary of the Invention

[0005] This invention provides a cloud-based intelligent control method and system for a high-energy hydrogen atom generator to solve the technical problems of inaccurate generator power parameter control and large fluctuations in output energy.

[0006] Firstly, to address the aforementioned technical problems, this invention provides an intelligent control method for a high-energy hydrogen atom generator based on a cloud platform, comprising: Instantaneous color change data from a high-energy hydrogen atom generator are collected, and image enhancement processing is performed on the instantaneous color change data to obtain a color feature sequence; Feature extraction is performed on the color feature sequence to determine the energy state mapping relationship; The energy state mapping relationship is compared with the standard value to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain the parameter adjustment instruction. Acquire real-time environmental parameters, classify and verify them according to the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate feedback signals; The feedback signal is analyzed to obtain the deviation correction value, and the power parameters of the generator are adjusted according to the deviation correction value. The output is monitored to obtain the hydrogen atom output energy level. If the energy level of the hydrogen atom output fluctuates, the color change monitoring process is called repeatedly and iterative analysis is performed to determine a dynamic optimization scheme. According to the dynamic optimization scheme, the monitoring threshold of the sensor array is updated, and the flow control device in the hydrogen supply pipeline is adjusted in real time to obtain a stable hydrogen source output state.

[0007] Secondly, the present invention provides an intelligent control system for a high-energy hydrogen atom generator based on a cloud platform, comprising: The data acquisition module is used to acquire instantaneous color change data from the high-energy hydrogen atom generator and perform image enhancement processing on the instantaneous color change data to obtain a color feature sequence. The state mapping module is used to extract features from the color feature sequence and determine the energy state mapping relationship; The instruction acquisition module is used to perform standard value comparison calculation on the energy state mapping relationship to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain the parameter adjustment instruction. The instruction verification module is used to acquire real-time environmental parameters, classify and verify the parameters according to the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate feedback signals. The closed-loop control module is used to analyze the feedback signal, obtain the deviation correction value, adjust the power parameters of the generator according to the deviation correction value, and monitor the output to obtain the hydrogen atom output energy level. The iterative optimization module is used to repeatedly call the color change monitoring process and perform iterative analysis to determine the dynamic optimization scheme if the output energy level of the hydrogen atom fluctuates. The synchronous adjustment module is used to update the monitoring threshold of the sensor array according to the dynamic optimization scheme, and to adjust the flow control device in the hydrogen supply pipeline in real time to obtain a stable hydrogen source output state.

[0008] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention acquires instantaneous color change data from a high-energy hydrogen atom generator using a sensor array, and processes the original signal using image enhancement techniques (such as wavelet transform denoising and bicubic interpolation) to improve resolution, resulting in a clear color feature sequence. Then, a convolutional neural network model is used to extract features from this sequence, particularly identifying the "light blue to purple slight change pattern" related to energy transitions, thereby determining the precise mapping relationship between color features and energy states. This method effectively solves the problem in existing technologies where the instantaneous and weak color changes lead to insufficient sensor resolution and are easily misjudged as noise, achieving accurate capture and mapping of the internal energy state of hydrogen atoms.

[0009] (2) After obtaining parameter adjustment instructions from the cloud platform, this invention does not execute them directly. Instead, it first obtains real-time environmental parameters such as temperature fluctuations and impurity interference, and uses a support vector machine algorithm to analyze these environmental factors and classify and verify the cloud instructions to determine their applicability in the current dynamic environment. This design overcomes the shortcomings of existing control methods that rely on fixed parameters and cannot adapt to complex dynamic environmental changes. Through "secondary verification" of the instructions, it ensures the robustness and environmental adaptability of the adjustment strategy and avoids control failure caused by environmental interference.

[0010] (3) This invention constructs a closed-loop iterative optimization mechanism. After adjusting the power parameters based on the feedback signal, if fluctuations in the output energy level are still detected, the system will repeatedly call the color change monitoring process and perform iterative analysis in conjunction with historical data sequences to determine further dynamic optimization schemes. Furthermore, this dynamic optimization scheme will be used to update the monitoring thresholds of the sensor array and to make real-time adjustments to the hydrogen source supply chain (such as flow control valves). This adaptive closed-loop control and iterative optimization capability ensures that the system can continuously improve itself, significantly enhancing the long-term stability of the generator operation and the accuracy of energy output. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a cloud-based intelligent control method for a high-energy hydrogen atom generator provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a cloud-based intelligent control system for a high-energy hydrogen atom generator, provided in the second embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Reference Figure 1 The first embodiment of the present invention provides an intelligent control method for a high-energy hydrogen atom generator based on a cloud platform, comprising the following steps: S11, collect instantaneous color change data from the high-energy hydrogen atom generator, and perform image enhancement processing on the instantaneous color change data to obtain a color feature sequence; S12, extract features from the color feature sequence to determine the energy state mapping relationship; S13, perform a standard value comparison operation on the energy state mapping relationship to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, upload the energy state mapping relationship and the mapping deviation value to the cloud platform, and parse the data returned by the cloud platform to obtain parameter adjustment instructions. S14, acquire real-time environmental parameters, classify and verify the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate a feedback signal; S15, analyze the feedback signal to obtain the deviation correction value, adjust the power parameters of the generator according to the deviation correction value, and monitor the output to obtain the hydrogen atom output energy level; S16, If the output energy level of the hydrogen atom fluctuates, the color change monitoring process is called repeatedly and iterative analysis is performed to determine the dynamic optimization scheme. S17. According to the dynamic optimization scheme, update the monitoring threshold of the sensor array and adjust the flow control device in the hydrogen supply pipeline in real time to obtain a stable hydrogen source output state.

[0014] In step S11, instantaneous color change data from the high-energy hydrogen atom generator is acquired, and image enhancement processing is performed on the instantaneous color change data to obtain a color feature sequence, including: The high-energy hydrogen atom generator is subjected to real-time acquisition of spectral signals to obtain the original signal matrix; The original signal matrix is ​​denoised to obtain a purified spectral data sequence; The purified spectral data sequence is subjected to density enhancement processing to determine high-resolution color change trajectories; Based on the high-resolution color change trajectory, spectral matching processing is performed to obtain the color feature sequence.

[0015] It should be noted that this invention is based on a technological discovery: during the operation of a high-energy hydrogen atom generator, the critical energy state transitions within it are accompanied by a specific and subtle spectral change pattern, specifically a "light blue to purple micro-change pattern" shifting from light blue (e.g., approximately 480 nm) to purple (e.g., approximately 420 nm). Through high-precision, high-temporal-resolution spectral acquisition of the generator output, strict control of environmental variables (such as temperature, pressure, and hydrogen purity), and comparison with the introduction of known interference sources (such as trace amounts of nitrogen and oxygen), it was confirmed that the correlation between this color change pattern and the hydrogen atom energy state is significantly stronger than that of other interfering factors. Furthermore, principal component analysis and partial least squares regression analysis were used to determine the characteristic bands most relevant to the energy state in this composite spectral pattern, thus laying a solid physical foundation for subsequent feature extraction based on image and signal processing. Therefore, the technical premise for establishing the "energy state mapping relationship" in subsequent steps of this invention (such as S12) is the accurate capture of this specific micro-change pattern. The purpose of this step (S11) is to acquire and quantify this specific color change pattern through high-resolution spectral acquisition and image enhancement techniques, so as to provide clear and reliable input data for subsequent energy state mapping, rather than acquiring all the original light signals that reflect the energy state.

[0016] It should be noted that this step aims to acquire the raw optical signal reflecting the energy state of hydrogen atoms. In this embodiment, a sensor array containing multiple (e.g., 256) photodetectors is used to perform multi-channel parallel detection of the spectral signal generated during the operation of the high-energy hydrogen atom generator. When the spectral intensity detected by the sensor array exceeds a preset spectral intensity threshold, the system immediately records the current instantaneous color change data.

[0017] It is worth noting that the preset spectral intensity threshold (e.g., 1000 ADC counts) is determined based on a large number of historical background noise experiments. Setting this threshold aims to effectively distinguish the effective spectral signal generated by the energy transition of hydrogen atoms from environmental background noise and sensor dark current, ensuring that data recording is triggered only when the generator actually produces high-energy hydrogen atoms.

[0018] For example, the data collected by the system is constructed into a raw signal matrix, in which each row represents a data point, containing a timestamp accurate to the microsecond level and a spectral intensity value ranging from 0 to 65535, providing high-precision raw input for subsequent signal processing.

[0019] It should be noted that this step aims to eliminate random noise and interference present in the original signal matrix obtained in the previous step. In this embodiment, wavelet transform is used to process the original signal matrix. Wavelet transform (e.g., using Daubechies wavelet basis functions) can decompose the signal into sub-bands of different frequencies, thereby effectively separating high-frequency noise components from the effective signal.

[0020] It is worth noting that the system calculates the signal-to-noise ratio (SNR) of the signal. If the SNR is lower than a preset SNR standard (e.g., 20dB), an adaptive filter is activated to further optimize the signal quality. The preset SNR standard is set based on knowledge in the field of signal processing; signals below this standard cannot guarantee the reliability of subsequent feature extraction.

[0021] For example, when the original signal-to-noise ratio (e.g., 18 dB) is detected to be lower than the trigger threshold (20 dB), an adaptive filter is activated. Once activated, the filter is iteratively optimized to suppress high-frequency noise while balancing signal fidelity, in order to avoid over-filtering that could distort key spectral features, ultimately resulting in a purified spectral data sequence.

[0022] It should be noted that this step aims to improve the temporal resolution of the purified spectral data sequence to capture instantaneous changes at the millisecond or even microsecond level. In this embodiment, a bicubic interpolation algorithm is used to enhance the density of the data points. This algorithm calculates the interpolation point value by utilizing information from 16 neighboring data points around a data point, ensuring the smoothness and continuity of the interpolation results.

[0023] It is worth noting that when the time interval between adjacent data points in the purified spectral data sequence is detected to be greater than a preset interval (e.g., 10 microseconds), the system triggers interpolation calculation. The preset interval is determined based on the system's highest sampling frequency and the shortest duration of the instantaneous change to be captured, aiming to ensure that the data density is sufficient to reconstruct minute color change events.

[0024] For example, by using bicubic interpolation, data that was originally sampled once every 100 microseconds can be enhanced to one data point every 10 microseconds, increasing the data density by 10 times, thereby determining a high-resolution color change trajectory that can finely describe color changes.

[0025] It should be noted that this step aims to convert the physical spectral trajectory into standardized color features that can be used for subsequent model analysis. In this embodiment, a spectral matching method is used to compare the high-resolution color change trajectory determined in the previous step with a pre-established standard spectral library. The standard spectral library stores standard spectral data of characteristic wavelengths such as the Balmer series of hydrogen atoms (e.g., red light at 656.3 nm, blue-green light at 486.1 nm, etc.).

[0026] It is worth noting that the matching process is accomplished by calculating the correlation coefficient between the measured spectrum and the spectra in the standard spectral library. When the correlation coefficient exceeds a preset correlation threshold (e.g., 0.95), the system confirms a successful match. This correlation threshold is determined based on statistical analysis and aims to ensure that the matching result has a confidence level higher than 95% to exclude erroneous matches.

[0027] For example, after a successful match, the system converts the high-resolution color change trajectory into a structured vector containing parameters in three dimensions: hue, saturation, and brightness (HLS). This complete sequence containing the three-dimensional HLS parameters is the final color feature sequence.

[0028] In step S12, feature extraction is performed on the color feature sequence to determine the energy state mapping relationship, including: Time series data is obtained from the color feature sequence, and frequency domain transformation is performed on the time series data to obtain frequency-enhanced time series data; Based on the frequency-enhanced time-series data, spatial features are extracted to obtain a high-dimensional feature vector; The high-dimensional feature vector is reduced in dimensionality to determine the reduced feature vector. Based on the reduced dimensionality feature vector, a mapping transformation is performed, and an energy state determination is made to obtain the energy state mapping relationship.

[0029] It should be noted that this step aims to extract frequency domain features reflecting the periodic changes of the color feature sequence (including hue, saturation, and brightness). First, the system arranges the values ​​of hue, saturation, and brightness in timestamp order, forming time series data containing three dimensions. Subsequently, a Fourier transform is used to perform a frequency domain transformation on the time series data to identify the periodic components in the signal.

[0030] It is worth noting that the system determines the significance of frequency components by using a preset dominant frequency threshold. This threshold is determined based on statistical analysis of a large number of historical stable signals. For example, when the amplitude of a certain frequency component (such as 50 Hz) exceeds three times the average amplitude of the data, the system determines it to be a dominant frequency with significant periodic characteristics. The system then uses a filter to retain this dominant frequency and its harmonic components, thereby obtaining frequency-enhanced time series data with a higher signal-to-noise ratio (e.g., increased to over 40 dB).

[0031] It should be noted that this step aims to automatically extract complex patterns related to energy transitions from the frequency-enhanced time-series data obtained in the previous step using a deep learning model. In this embodiment, a pre-built Convolutional Neural Network (CNN) model is used for spatial feature extraction.

[0032] It is worth noting that the core structure and training process of the convolutional neural network model includes two parts: model structure and model usage. The model structure adopts a multi-layer convolutional architecture. This embodiment uses a deep network architecture containing three one-dimensional convolutional layers, one max-pooling layer, and two fully connected layers. The first convolutional layer has 64 kernels (kernel size 5), the second layer has 128 kernels (kernel size 3), and the third layer has 256 kernels (kernel size 3). All convolutional layers use ReLU (Rectified Linear Unit) as the activation function, followed by a max-pooling layer with a pooling size of 2 for downsampling and feature dimensionality reduction.

[0033] The model was trained offline on a labeled dataset containing 500,000 historical operating samples, covering all known energy transition modes and their corresponding spectral noise. By precisely adjusting the input power, gas flow rate, and pressure of the high-frequency pulse discharge generator, an energy state covering its entire normal operating range and some boundary conditions was artificially created. For each set condition, the raw spectral signal collected by the sensor array was recorded synchronously, and the actual output of the generator under this condition could be accurately measured using an Ocean Optics HR4000 high-resolution spectrometer, which served as the true "energy state" label corresponding to that set of spectral data. Through the above methods, massive amounts of data were collected and labeled in continuous experiments, thereby constructing a high-quality dataset for training the CNN model.

[0034] The training process uses the Adam optimizer and sparse classification cross-entropy as the loss function. Iterative training is performed through backpropagation until the model converges. When the model is used, the frequency-enhanced time series data is input into the CNN model. After multiple layers of convolution, activation and pooling, the first fully connected layer maps it to (e.g., 1024 dimensions), and finally outputs a high-dimensional feature vector, which is a highly abstract representation of the original color change pattern.

[0035] It should be noted that this step aims to compress the dimensionality of the high-dimensional feature vector obtained in the previous step, reducing the complexity of subsequent calculations while retaining key information. In this embodiment, Principal Component Analysis (PCA) is used to reduce the dimensionality of the high-dimensional feature vector. PCA achieves dimensionality reduction by calculating the covariance matrix of the feature vector and extracting the principal components.

[0036] It is worth noting that the target dimension for dimensionality reduction is determined based on a preset variance retention threshold (e.g., 90%). This threshold, based on information theory, aims to ensure that the dimensionality-reduced data still retains most (e.g., 95.2%) of the variance of the original information.

[0037] For example, the system compresses the 1024-dimensional high-dimensional feature vector to 64 dimensions using the PCA algorithm and confirms that it retains more than 90% of the variance information. This 64-dimensional vector is the final determined dimensionality-reduced feature vector.

[0038] It should be noted that this step aims to transform the dimensionality-reduced, abstract feature vector into an energy state with explicit physical meaning. In this embodiment, this transformation is performed using a pre-established mapping function.

[0039] It is worth noting that the mapping function is constructed based on a feature-state benchmark dataset containing a massive number of labeled samples. To construct this dataset, the present invention simultaneously performs the following two operations in a controlled laboratory environment: First, a PCA dimensionality reduction process is run to obtain the dimensionality-reduced feature vector (e.g., 64 dimensions); next, a high-precision spectrometer is used to measure the same energy event, and the measurement result is rigorously compared with known spectral lines such as the Balmer series in the Bohr's model of hydrogen atom energy levels, assigning a precise physical label to the event (e.g., "transition from n=6 to n=2"). The mapping function is generated by using a multivariate polynomial regression algorithm to fit the nonlinear correspondence between millions of "feature vectors" and "physical labels" in the benchmark dataset, thereby establishing a mathematical correspondence between the values ​​of a specific dimension in the dimensionality-reduced feature vector and the specific energy state of the hydrogen atom.

[0040] For example, the system inputs the dimensionality-reduced feature vector (e.g., its 15th dimension has a value of 0.82 and its 23rd dimension has a value of 0.67) into the mapping function. After calculation, the function outputs a clear energy state determination result, such as "the transition from n=6 to n=2". This final determination result is the energy state mapping relationship.

[0041] In step S13, a standard value comparison operation is performed on the energy state mapping relationship to determine the mapping deviation value. If the mapping deviation value exceeds a preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain parameter adjustment instructions, including: The energy state mapping relationship is compared with standard values ​​to obtain the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, a communication link is established with the cloud platform server, and the mapping data is encapsulated using an encrypted transmission protocol for real-time data upload to obtain the processing result information. The processing result information is format-converted and content-verified to obtain the parameter adjustment instruction.

[0042] It should be noted that this step aims to quantify the degree of deviation between the energy state mapping relationship and the ideal state. In this embodiment, the difference calculation method is used to compare the real-time mapping result determined in S12 (e.g., the measured energy difference is 3.10 electron volts) with the theoretical standard value in a pre-established standard reference value database (e.g., the standard energy difference for the transition from n=6 to n=2 is 3.04 electron volts).

[0043] It is worth noting that the standard reference database was pre-constructed using theoretical calculations of hydrogen atom energy levels and combined with extensive repeated experimental calibrations conducted under standard laboratory conditions (i.e., constant temperature and no impurity interference), ensuring its accuracy and reliability as a benchmark.

[0044] For example, by calculating the difference (3.10eV-3.04eV), the system obtains a quantized deviation value (e.g., 0.06eV), which is the mapping deviation value.

[0045] It should be noted that this step is a condition-triggered cloud reporting process. The preset mapping threshold (e.g., 0.05 eV) is determined based on the requirements for generator energy output stability, through statistical analysis of the correlation between historical operating data and downstream fuel cell lifespan. Setting this threshold aims to ensure that any significant energy deviations that may affect the performance of downstream equipment are promptly reported to the cloud for in-depth analysis.

[0046] In one embodiment, after receiving the mapping data (e.g., the 64-dimensional feature vector determined in S12 and the 0.06eV deviation value calculated in S13), the cloud platform analysis server deduces the optimal parameter adjustment instructions based on the mapping data by running a high-precision generator digital twin simulation model. The model described is a Physics-Informed Neural Network (PINN). This PINN model is pre-built by jointly training a finite element analysis (FEA) thermo-fluid dynamics (CFD) model of the coupled generator with massive historical operating data (e.g., pressure, temperature, and energy output data under different operating conditions). The massive historical operating data comes from continuous operating data records of more than 100 generators of the same model in industrial fields for more than one year. This data is anonymized and aggregated on a cloud platform, forming a super-large-scale dataset covering various real-world environmental disturbances and equipment aging conditions. The joint training can be built in ANSYS Fluen based on the generator's precise 3D drawings and material properties, and corrected using some laboratory measured data (such as cavity temperature distribution and outlet gas flow rate).

[0047] Upon receiving the mapping data, the cloud platform uses it as the initial input to the PINN model. Subsequently, within 100 milliseconds, the model performs forward simulation on dozens of potential parameter adjustment commands (e.g., "power +5%", "power +8%", "frequency -2Hz"), evaluating the convergence speed and stability of the generator state (e.g., energy deviation) under each command. The model ultimately selects the command that allows the energy deviation to converge to zero fastest and most stably as the optimal parameter adjustment command. Through the above simulation, the cloud platform finally encapsulates the optimal command (e.g., "adjust LED light source intensity to 90%)" into the processing result information and returns it to the local device. The cloud platform receives local data via a RESTful API, with the input format being a JSON-encapsulated feature vector. The model is invoked once each time a deviation threshold is triggered, with a response time of <100ms.

[0048] It is worth noting that when the mapping deviation exceeds the threshold, the local network connection will establish a communication link with the cloud platform analysis server (e.g., using a dynamic IP address allocation strategy). Subsequently, the system uses an encrypted transmission protocol (e.g., TLS 1.3) to perform multi-layer encapsulation processing on the mapping data (e.g., the 64-dimensional feature vector after dimensionality reduction in S12), converting it into encrypted data packets (e.g., controlled within 2KB), and uses a fragmented transmission mechanism (e.g., at a rate of 10Mbps) for real-time data upload.

[0049] For example, after the upload is completed, the data packet returned by the cloud platform analysis server is the processing result information.

[0050] It should be noted that this step aims to convert the processing result information returned from the cloud into instructions executable by the local device. In this embodiment, a data parser is used to perform this operation.

[0051] It is worth noting that the data parser first performs content verification on the processed result information, for example, verifying its digital signature to confirm the reliability of the source, and checking whether the parameter values ​​in the instructions are within a preset security range (e.g., the light source intensity is between 70% and 95%). After the verification is successful, the parser then performs format conversion on the information, for example, converting the XML format server instructions into binary control commands that the device can recognize.

[0052] For example, the parsed and verified, directly executable local parameter adjustment command (e.g., the instruction "adjust the LED light source intensity to 90%) is the final parameter adjustment instruction obtained.

[0053] In step S14, real-time environmental parameters are acquired, and the parameters are classified and verified according to the real-time environmental parameters and the parameter adjustment instructions. The applicability of the parameter adjustment instructions is determined, and a feedback signal is generated, including: Obtain real-time environmental parameters, and perform multi-point sampling on the real-time environmental parameters to obtain the original environmental dataset; The original environmental dataset is classified to determine the environmental state category; The applicability category of the instruction is determined by matching the environmental state category with the parameter adjustment instruction. Based on the applicable category of the instruction, perform format conversion and generate a set of encoded feedback signals; The encoded feedback signal set is subjected to content verification and consistency checks to obtain the feedback signal.

[0054] It should be noted that this step aims to acquire real-time physical environment data of the generator operation as a basis for subsequent instruction applicability verification. In this embodiment, a distributed sensor array is used to sample the real-time environmental parameters (mainly temperature fluctuations and impurity interference) at multiple points.

[0055] For example, the sensor array may include 12 temperature sensor nodes (e.g., platinum resistance thermometer PT100, with a measurement accuracy of 0.1 degrees Celsius) arranged in a ring monitoring network, and 8 laser scattering detection units (e.g., operating at a wavelength of 632.8 nanometers, for identifying particles larger than 0.3 micrometers). The sensor array acquires data at a preset frequency (e.g., 5 times per second) and integrates this multidimensional data containing timestamps (such as temperature change curves and particulate matter concentration distribution) to obtain the raw environmental dataset.

[0056] It should be noted that this step aims to quantify the original environmental dataset obtained in the previous step into a defined environmental state. In this embodiment, the system first compares the values ​​in the original environmental dataset with a preset environmental threshold.

[0057] It is worth noting that the preset environmental thresholds, such as the temperature fluctuation threshold (set to ±2 degrees Celsius) and the impurity interference threshold (particulate matter concentration exceeding 1000 particles per cubic centimeter), are determined through statistical analysis of historical fault data based on the requirement to ensure the stability of hydrogen atom spectral detection.

[0058] For example, if the detected data exceeds the threshold, the K-Means clustering algorithm (for example, setting the number of clusters to 3 to correspond to the three environmental state categories) is used to classify the original environmental dataset and determine the environmental state category (for example, "optimal detection environment", "slight interference environment" or "severe interference environment").

[0059] It should be noted that this step aims to perform joint analysis between the parameter adjustment instruction returned by S13 and the current environmental state category to determine the actual applicability of the instruction in the current physical environment. In this embodiment, a pre-trained support vector machine (SVM) model is used to perform this matching analysis.

[0060] It is worth noting that the SVM model uses a radial basis function (RBF) as its kernel function. The training process involves using a training set containing massive amounts of historical data. Each sample in the training set includes a "parameter adjustment instruction" feature vector, an "environmental state category" input, and an "instruction applicability category" (e.g., "execute immediately," "delay execution," "discard instruction") labeled by experts. This is achieved through supervised learning. The training set originates from post-event analysis of historical instruction execution records from the cloud platform. By recording each issued parameter adjustment instruction, a snapshot of the environmental state before instruction execution, and the stability changes in the hydrogen atom output energy level over a period after instruction execution, each record is labeled with an instruction applicability category according to preset rules (e.g., fluctuations below 2% within 30 minutes after execution are considered successful). This forms the supervised learning dataset used to train the SVM model.

[0061] For example, when the parameter adjustment instruction is "increase power by 20%" and the environmental state category is "severe interference environment", the SVM model determines through matching analysis that the instruction is too risky in this environment, and finally outputs the instruction applicability category as "delay execution".

[0062] It should be noted that this step aims to convert the descriptive instruction applicability categories determined in the previous step into digital signals recognizable by the control system. In this embodiment, a signal encoder is used to perform this format conversion.

[0063] For example, the signal encoder uses binary encoding to digitize the applicability category (e.g., "execute immediately" corresponds to "00", and "delay execution" corresponds to "01"). The encoded feedback signal set also contains key quantitative information from the original environmental dataset, such as representing the temperature deviation value using an 8-bit binary number and encoding the impurity concentration using a 12-bit binary number.

[0064] It should be noted that this step is a critical quality control step to ensure that the signal is not damaged during internal transmission. In this embodiment, a verification processor is used to perform a multi-level verification process on the encoded feedback signal set.

[0065] It is worth noting that the verification process is as follows: First, content verification is performed to check whether the format of the encoded feedback signal set is complete and whether each data bit is valid; second, a consistency check is performed, which in this embodiment is achieved by adding a (for example, 4-bit) CRC redundancy check code to the end of the encoded feedback signal set.

[0066] For example, the receiving end verifies the accuracy of data transmission by recalculating the checksum. When an encoding error is detected, the system automatically requests a retransmission. Only signals that pass content verification and consistency checks are confirmed as the final feedback signal usable in S15.

[0067] In step S15, the feedback signal is analyzed to obtain a deviation correction value, and the power parameters of the generator are adjusted according to the deviation correction value. Output monitoring is then performed to obtain the hydrogen atom output energy level, including: The feedback signal is format-decoded and numerically separated to obtain the deviation correction value, and the deviation correction value is smoothed to obtain a standardized deviation correction parameter set. Based on the standardized deviation correction parameter set, the deviation is converted into power, and the generator power adjustment amount is calculated to obtain the power parameter injection command sequence. According to the power parameter injection instruction sequence, the power parameters of the generator are dynamically adjusted to obtain a controlled power output signal; The controlled power output signal is monitored in real time to obtain the output energy level of the hydrogen atom.

[0068] It should be noted that this step aims to convert the encoded feedback signal generated in S14 (e.g., a 32-bit signal set) into quantization parameters that can be used for power control. In this embodiment, a data parser module performs this operation. The parser first locates the start position of the data using (e.g., 16-bit) synchronization codes and then segments the feedback signal according to a predefined data dictionary, for example, reading the deviation correction value represented in two's complement form from bits 9 to 16.

[0069] It is worth noting that if the obtained deviation correction value exceeds a preset smoothing trigger threshold (e.g., a temperature deviation exceeding 3.5 degrees Celsius or an impurity concentration deviation exceeding 1500 particles per cubic centimeter), the system activates a mean filter (e.g., using a 7-point moving average algorithm) to smooth the value, thereby suppressing the impact of transient interference signals on system stability. The smoothing trigger threshold is determined based on statistical analysis of historical transient interference data, aiming to distinguish between genuine parameter drift and occasional signal spikes.

[0070] For example, the set of data output after decoding, separation, and smoothing by a mean filter is the standardized deviation correction parameter set.

[0071] It should be noted that this step aims to convert the standardized deviation correction parameter set (i.e., deviation values) obtained in the previous step into power adjustment commands that the generator can execute. In this embodiment, the deviation-to-power conversion is performed through a pre-established parameter mapping table.

[0072] It is worth noting that the parameter mapping table was established in advance through experimental calibration under controlled conditions (i.e., calibrating power output under different environmental deviations). The mapping table maps different deviation ranges (e.g., temperature deviation in 0.5 degrees Celsius intervals) to specific power compensation coefficients (e.g., 0.85, representing that the output power needs to be reduced to 85% of the standard value).

[0073] For example, when the values ​​in the standardized deviation correction parameter set do not fall precisely into the range of the mapping table, the system uses a linear interpolation algorithm to calculate the precise power adjustment amount through the linear relationship between two adjacent mapping points, thereby generating the power parameter injection command sequence.

[0074] It should be noted that this step is the core of the power regulation process. In this embodiment, a pulse width modulator (PWM) is used to inject a command sequence according to the power parameters, and the power parameters of the generator are dynamically adjusted by regulating the duty cycle of the output signal.

[0075] It is worth noting that the system has a preset safety range (e.g., a duty cycle adjustment range of 30% to 95%), which is determined based on the generator hardware’s rated power and Safety of Operations (SOO) specifications, and is designed to prevent damage to the equipment caused by excessive or insufficient power injection.

[0076] For example, upon receiving a command, the pulse width modulator (e.g., with an operating frequency set to 20 kHz) adjusts its duty cycle to the target value. If the calculated adjustment exceeds the safety range, a limiting protection mechanism is activated, restricting the output within the safety boundary. The final electrical signal output by the modulator is the controlled power output signal.

[0077] It should be noted that this step is a real-time verification of the dynamic adjustment effect of the previous step. In this embodiment, an energy detector (e.g., a photodiode array arranged in a 3x3 matrix) is used to perform multi-point synchronous monitoring of the hydrogen atom output driven by the controlled power output signal.

[0078] It is worth noting that the system measures the amplitude fluctuation of energy using a spectrum analyzer (e.g., frequency resolution 0.01 nm, amplitude measurement accuracy 0.1%) and compares this fluctuation value with a preset stability threshold (e.g., 2% of a standard value). This stability threshold is determined through experimental statistics based on the minimum process requirements for hydrogen source supply stability in downstream applications (such as fuel cells).

[0079] For example, when the energy detector and spectrum analyzer confirm that the energy fluctuation amplitude is lower than the stability threshold in consecutive (e.g., 10) sampling periods, the system confirms that the current state is the output energy level of the hydrogen atom.

[0080] In step S16, if the energy level of the hydrogen atom output fluctuates, the color change monitoring process is repeatedly invoked, and iterative analysis is performed to determine a dynamic optimization scheme, including: The energy level of the hydrogen atom is measured by colorimetry to obtain colorimetric data. Based on the colorimetric data and pattern matching, a set of historical optimization parameters is obtained. The dynamic optimization scheme is generated by iteratively calculating the set of historical optimization parameters.

[0081] It should be noted that this step is a secondary optimization process triggered when the control effect of S15 is unsatisfactory. Its purpose is to find a better control strategy by re-analyzing the current spectral characteristics. In this embodiment, a spectral color analyzer is activated to measure the colorimetric level of the hydrogen atom output energy level (i.e., its emission spectrum). The colorimetric data is, in a preferred embodiment, a spectral feature vector.

[0082] In one embodiment, the spectral color analyzer is reactivated. To address the technical problem of low accuracy caused by the mismatch between the hydrogen atom spectrum (mainly discrete bright lines) and the traditional RGB color space model, this embodiment employs a quantization method based on spectral line peaks, rather than three-channel spectroscopic detection. The analyzer (e.g., with a spectral resolution of 0.05 nm) performs a full-band scan of the spectrum emitted at the current energy level and uses a peak detection algorithm based on Savitzky-Golay filtering to identify the top N (e.g., 3) most significant dominant spectral line peaks. The algorithm first smooths the raw spectral data obtained from the full-band scan using a Savitzky-Golay filter and calculates its first derivative. Then, it detects zero-crossing points in the first derivative sequence that change from positive to negative values ​​and marks them as "candidate peak points". Finally, it calculates the peak prominence of each "candidate peak point" and compares it with a preset prominence threshold (e.g., set to 0.1 of the total spectral normalization intensity). Only those points with prominence exceeding the threshold are retained as the peaks of the dominant spectral lines with the most significant energy.

[0083] It is worth noting that the preset spike threshold (e.g., 0.1) is set based on statistical analysis of (e.g.,) 1000 sets of sensor background noise data collected under signal-free (i.e., anechoic chamber) conditions to calculate the peak spike distribution of the noise signal; the present invention adds three times the standard deviation to the average value of this distribution. The value corresponding to (e.g., 0.1) is set as the threshold. This setting (i.e., the 3-sigma principle) aims to ensure that only peaks with a statistical confidence level higher than 99.7% that are significantly different from the background noise are identified as valid dominant spectral lines, thereby achieving robust noise suppression. Only those points with a prominence exceeding this threshold are retained as the peaks of the dominant spectral lines with the most significant energy.

[0084] For example, the peak detection algorithm based on Savitzky-Golay filtering does not perform RGB quantization, but instead extracts the "center wavelength" and its "normalized intensity" of each dominant spectral line. For instance, the system detects three peaks: {P1: 656.3 nm, Intensity: 0.92}, {P2: 486.1 nm, Intensity: 0.75}, and {P3: 434.0 nm, Intensity: 0.61}. This structured data, composed of multiple sets of (wavelength, intensity) key-value pairs, constitutes the spectral line feature vector, which can accurately characterize the current energy state of a hydrogen atom.

[0085] It should be noted that this step aims to find similar operating conditions and corresponding successful solutions from a historical experience database based on the spectral feature vector representing the current state obtained in the previous step. In this embodiment, this pattern matching process is performed through a time-series memory.

[0086] It is worth noting that the time-series memory is a local database deployed on the local control unit of the generator to ensure low-latency response of the S16 secondary optimization process. It uses a circular buffer structure to store the most recent 10,000 optimization records. When the data volume exceeds the limit, the oldest record is discarded according to its timestamp to ensure data real-time performance. Its data structure is built using a kd-tree, which effectively partitions the high-dimensional spectral feature vector space for fast indexing. Whenever the dynamic optimization scheme of S16 is successfully executed and confirmed by S17 (e.g., energy fluctuation converges to below 2%), the successful "spectral feature vector" and the corresponding "dynamic optimization scheme" will be written back and stored in the database as a new high-confidence entry, thereby achieving self-learning and iterative updates of the database.

[0087] For example, the pattern matching process employs a k-nearest neighbor algorithm, utilizing the kd-tree index to quickly query the most similar (e.g., K=5) historical vectors in high-dimensional space to the current spectral feature vector, and calculates their similarity scores. The system uses a preset similarity threshold (e.g., 0.85) to determine whether a match is successful. This similarity threshold (0.85) is determined through receiver operating characteristic (ROC) curve analysis on a benchmark dataset containing, for example, 10,000 labeled "successful matches" and "failed matches." The critical value of 0.85 is the point on the ROC curve that maximizes Youden's J statistic, representing an optimal balance between recall (ensuring successful recall of parameters) and specificity (excluding erroneous conditions), thus ensuring that the recalled parameter set is highly relevant to the current condition. When the similarity score exceeds 0.85, the corresponding historical adjustment records (e.g., historical power adjustment amplitude, adjustment time interval, etc.) are extracted to form the historical optimization parameter set.

[0088] It should be noted that this step aims to integrate multiple (potentially inconsistent) historical optimization parameters extracted in the previous step, and to generate a new, optimal solution suitable for the current operating conditions. In this embodiment, an iterative computation processor is used to perform this recursive operation.

[0089] It is worth noting that this iterative calculation first performs a weighted average on the historical optimization parameter set. The weighting coefficients are dynamically allocated based on the historical adjustment success rate of each record in the historical optimization parameter set. For example, parameter records with a success rate higher than 90% have a weighting coefficient of 1.0; records with a success rate between 70% and 90% have a weighting coefficient of 0.7.

[0090] For example, the iterative computation processor then employs an optimizer with an adaptive learning rate (e.g., the Adam optimizer) to perform recursive operations to optimize the tuning parameters, using the weighted average parameters as the initial point. This method addresses the problem of manually specifying the learning rate required by traditional gradient descent methods through internal momentum and adaptive learning rate adjustments. The recursive operation stops only when a preset convergence condition is met (e.g., the improvement after three consecutive iterations is less than 1%). The convergence condition is determined by performing a grid search on a validation set containing, for example, 10,000 sets of historical optimization data. This search process tested several different convergence thresholds from 0.1% to 5%, and ultimately selected 1% as the critical value because it provides the optimal Pareto optimality between computational efficiency (i.e., the fewest iterations) and optimization accuracy (i.e., the highest energy fluctuation convergence rate confirmed in S17). When the convergence condition is met, the final set of parameters output by the processor constitutes the dynamic optimization scheme.

[0091] In step S17, according to the dynamic optimization scheme, the monitoring threshold of the sensor array is updated, and the flow control device in the hydrogen supply pipeline is adjusted in real time to obtain a stable hydrogen source output state, including: Based on the dynamic optimization scheme, optimization scheduling is performed, and real-time adjustment parameters are extracted from the threshold configuration table; Based on the real-time adjustment parameters, parameter distribution processing is performed to generate a set of control commands; According to the set of control commands, the sensor array is subjected to monitoring threshold update processing to obtain the updated monitoring threshold; According to the set of control commands, the flow control device in the hydrogen supply pipeline is adjusted in real time and its performance is monitored to obtain a stable hydrogen source output state.

[0092] It should be noted that this step aims to convert the strategic dynamic optimization scheme generated in S16 into a set of executable, specific parameters. In this embodiment, this optimization scheduling process is executed by an optimization scheduler in the cloud platform. The optimization scheduler queries and extracts the corresponding parameter set from a pre-established threshold configuration table with a multi-dimensional index structure based on the dynamic optimization scheme (e.g., the scheme indicates switching to "high-efficiency mode").

[0093] It is worth noting that the threshold configuration table was established in advance by statistical analysis and calibration of a large amount of historical operating data under different working modes (such as standard mode, high-efficiency mode, and energy-saving mode).

[0094] For example, when the dynamic optimization scheme is determined to be "high-efficiency mode", the real-time adjustment parameters extracted from the threshold configuration table by the optimization scheduling process may include: {sensor monitoring range: pressure 0.1-2.5 MPa, temperature 15-85 degrees Celsius; supply chain adjustment values: main pipeline flow coefficient X, buffer tank capacity ratio Y}.

[0095] It should be noted that this step aims to encapsulate the real-time adjustment parameters extracted in the previous step into standardized, transmittable instructions. In this embodiment, this parameter distribution process is performed by a parameter distributor. The parameter distributor (e.g., employing a multi-level caching mechanism) receives the real-time adjustment parameters and applies a priority sorting algorithm (e.g., based on the highest priority of urgent adjustment instructions) to convert them into a standardized data format.

[0096] It is worth noting that, to ensure the security of transmission, the parameter distribution process also includes using digital signature technology to securely verify the instructions, in order to prevent the instructions from being tampered with during transmission.

[0097] For example, the parameter distribution process encapsulates parameters such as {pressure 0.1-2.5 MPa} into an instruction containing a target device identifier code, operation type code, parameter value, and execution timestamp, which is part of the control instruction set.

[0098] It should be noted that this step is a specific implementation of the "update monitoring threshold" operation in step S11. The set of control commands is distributed to the local control unit of the sensor array. This unit parses the "sensor monitoring range" (e.g., pressure 0.1-2.5 MPa) portion carried in the command and performs the monitoring threshold update process.

[0099] For example, upon receiving a command, the local controller of the sensor array updates its internally stored pressure monitoring threshold from, for example, 2.0 MPa to 2.5 MPa. This updated, locally effective 2.5 MPa threshold is the updated monitoring threshold.

[0100] It should be noted that this step is a specific implementation of the operation of "real-time adjustment of the flow control device in the hydrogen supply pipeline" in step S11. The generator control unit receives the "supply chain adjustment value" portion carried in the control command set and performs real-time adjustment processing.

[0101] It is worth noting that the real-time adjustment process is achieved through an electric regulating valve (e.g., equipped with a high-precision stepper motor), and the adjustment process employs a gradual control strategy to avoid sudden flow changes impacting the generator.

[0102] For example, the generator control unit parses the command and adjusts the opening of the flow control valve (e.g., with an adjustment accuracy of 0.1 degrees) to a new set value. Subsequently, a performance monitor (e.g., collecting data such as pressure, flow rate, and temperature through multi-sensor fusion technology) begins performance monitoring. When all preset multi-dimensional indicators (e.g., flow stability, pressure consistency, and temperature field stability) meet their preset standards, the system confirms that the stable hydrogen source output state has been achieved.

[0103] Those skilled in the art will understand that the various complex algorithms integrated in this invention have undergone high engineering optimization during actual deployment to ensure the real-time performance of the system. In the local control unit, computationally intensive tasks (such as wavelet denoising in S11, CNN feature extraction and PCA dimensionality reduction in S12) are executed in parallel in a pipelined manner through dedicated hardware accelerators (such as DSP cores and parallel computing units integrated in FPGAs or ASICs), keeping the total processing time of the above steps within 10 milliseconds. For deep analysis on the cloud platform (S13), its core lies in a lightweight PINN model that has undergone extensive pre-training and scenario simplification. This model does not perform full-size C++ analysis for every request. Instead of FD simulation, it uses a pre-calculated response surface model covering the main operating conditions for fast interpolation queries, ensuring that optimization instructions are returned within 50 milliseconds. In addition, the k-NN search on which the local iterative optimization (S16) relies, due to the potential growth of its database size after long-term operation, adopts a strategy of periodic clustering and building representative case indexes to stabilize the time spent on each match below 5 milliseconds. The total latency of the entire intelligent control loop (from data acquisition to execution adjustment) can be stably kept below 100 milliseconds after optimization, meeting the control requirements of the high-energy hydrogen atom generator for second-level dynamic response. Among these, the selection of hardware accelerators and the implementation of optimization strategies are all known technologies in this field.

[0104] In summary, this invention constructs an intelligent control process of "color monitoring - energy mapping - cloud analysis - environmental verification - closed-loop adjustment." First, it collects instantaneous color change data from the high-energy hydrogen atom generator and establishes an energy state mapping. When the mapping deviates, it uses a cloud platform to obtain parameter adjustment commands and innovatively combines real-time environmental parameters to classify and verify these commands to ensure their applicability. Then, it corrects the generator's power parameters through feedback signals. Furthermore, this invention introduces a cyclic monitoring and iterative analysis mechanism. When the output energy level still fluctuates, it determines a dynamic optimization scheme and updates the sensor monitoring thresholds and adjusts the hydrogen source supply chain in real time accordingly. This solves the technical problems of existing technologies that rely on fixed parameters, cannot adapt to dynamic environmental changes, and lead to large fluctuations in output energy, achieving efficient, stable, and intelligent control of the high-energy hydrogen atom generator.

[0105] Reference Figure 2 The second embodiment of the present invention provides an intelligent control system for a high-energy hydrogen atom generator based on a cloud platform, comprising: The data acquisition module is used to acquire instantaneous color change data from the high-energy hydrogen atom generator and perform image enhancement processing on the instantaneous color change data to obtain a color feature sequence. The state mapping module is used to extract features from the color feature sequence and determine the energy state mapping relationship; The instruction acquisition module is used to perform standard value comparison calculation on the energy state mapping relationship to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain the parameter adjustment instruction. The instruction verification module is used to acquire real-time environmental parameters, classify and verify the parameters according to the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate feedback signals. The closed-loop control module is used to analyze the feedback signal, obtain the deviation correction value, adjust the power parameters of the generator according to the deviation correction value, and monitor the output to obtain the hydrogen atom output energy level. The iterative optimization module is used to repeatedly call the color change monitoring process and perform iterative analysis to determine the dynamic optimization scheme if the output energy level of the hydrogen atom fluctuates. The synchronous adjustment module is used to update the monitoring threshold of the sensor array according to the dynamic optimization scheme, and to adjust the flow control device in the hydrogen supply pipeline in real time to obtain a stable hydrogen source output state.

[0106] It should be noted that the cloud-based intelligent control system for a high-energy hydrogen atom generator provided in this embodiment of the invention is used to execute all the process steps of the cloud-based intelligent control method for a high-energy hydrogen atom generator described in the above embodiment. The working principles and beneficial effects of the two are one-to-one, and therefore will not be repeated.

[0107] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a cloud-based intelligent control program for a high-energy hydrogen atom generator. When the processor executes the computer program, it implements the steps described in the embodiments of the cloud-based intelligent control method for a high-energy hydrogen atom generator, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments, such as the data acquisition module.

[0108] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0109] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0110] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0111] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0112] If the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0113] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0114] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A cloud-based intelligent control method for a high-energy hydrogen atom generator, characterized in that, include: Instantaneous color change data from a high-energy hydrogen atom generator are collected, and image enhancement processing is performed on the instantaneous color change data to obtain a color feature sequence; Feature extraction is performed on the color feature sequence to determine the energy state mapping relationship; The energy state mapping relationship is compared with the standard value to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain the parameter adjustment instruction. Acquire real-time environmental parameters, classify and verify them according to the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate feedback signals; The feedback signal is analyzed to obtain the deviation correction value, and the power parameters of the generator are adjusted according to the deviation correction value. The output is monitored to obtain the hydrogen atom output energy level. If the energy level of the hydrogen atom output fluctuates, the color change monitoring process is called repeatedly and iterative analysis is performed to determine a dynamic optimization scheme. According to the dynamic optimization scheme, the monitoring threshold of the sensor array is updated, and the flow control device in the hydrogen supply pipeline is adjusted in real time to obtain a stable hydrogen source output state.

2. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The instantaneous color change data of the high-energy hydrogen atom generator is collected, and image enhancement processing is performed on the instantaneous color change data to obtain a color feature sequence, including: The high-energy hydrogen atom generator is subjected to real-time acquisition of spectral signals to obtain the original signal matrix; The original signal matrix is ​​denoised to obtain a purified spectral data sequence; The purified spectral data sequence is subjected to density enhancement processing to determine high-resolution color change trajectories; Based on the high-resolution color change trajectory, spectral matching processing is performed to obtain the color feature sequence.

3. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The step of extracting features from the color feature sequence and determining the energy state mapping relationship includes: Time series data is obtained from the color feature sequence, and frequency domain transformation is performed on the time series data to obtain frequency-enhanced time series data; Based on the frequency-enhanced time-series data, spatial features are extracted to obtain a high-dimensional feature vector; The high-dimensional feature vector is reduced in dimensionality to determine the reduced feature vector. Based on the reduced dimensionality feature vector, a mapping transformation is performed, and an energy state determination is made to obtain the energy state mapping relationship.

4. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The energy state mapping relationship is compared with a standard value to determine the mapping deviation value. If the mapping deviation value exceeds a preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain parameter adjustment instructions, including: The energy state mapping relationship is compared with standard values ​​to obtain the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, a communication link is established with the cloud platform server, and the mapping data is encapsulated using an encrypted transmission protocol for real-time data upload to obtain the processing result information. The processing result information is format-converted and content-verified to obtain the parameter adjustment instruction.

5. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The process of acquiring real-time environmental parameters, classifying and verifying the real-time environmental parameters and parameter adjustment instructions, determining the applicability of the parameter adjustment instructions, and generating feedback signals includes: Obtain real-time environmental parameters, and perform multi-point sampling on the real-time environmental parameters to obtain the original environmental dataset; The original environmental dataset is classified to determine the environmental state category; The applicability category of the instruction is determined by matching the environmental state category with the parameter adjustment instruction. Based on the applicable category of the instruction, perform format conversion and generate a set of encoded feedback signals; The encoded feedback signal set is subjected to content verification and consistency checks to obtain the feedback signal.

6. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The process of analyzing the feedback signal to obtain a deviation correction value, adjusting the power parameters of the generator based on the deviation correction value, and monitoring the output to obtain the hydrogen atom output energy level includes: The feedback signal is format-decoded and numerically separated to obtain the deviation correction value, and the deviation correction value is smoothed to obtain a standardized deviation correction parameter set. Based on the standardized deviation correction parameter set, the deviation is converted into power, and the generator power adjustment amount is calculated to obtain the power parameter injection command sequence. According to the power parameter injection instruction sequence, the power parameters of the generator are dynamically adjusted to obtain a controlled power output signal; The controlled power output signal is monitored in real time to obtain the output energy level of the hydrogen atom.

7. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, If the energy level of the hydrogen atom output fluctuates, the color change monitoring process is repeatedly invoked, and iterative analysis is performed to determine a dynamic optimization scheme, including: The energy level of the hydrogen atom is measured by colorimetry to obtain colorimetric data. Based on the colorimetric data and pattern matching, a set of historical optimization parameters is obtained. The dynamic optimization scheme is generated by iteratively calculating the set of historical optimization parameters.

8. The intelligent control method for a high-energy hydrogen atom generator based on a cloud platform according to claim 1, characterized in that, The step of updating the monitoring threshold of the sensor array according to the dynamic optimization scheme and adjusting the flow control device in the hydrogen supply pipeline in real time to obtain a stable hydrogen source output state includes: Based on the dynamic optimization scheme, optimization scheduling is performed, and real-time adjustment parameters are extracted from the threshold configuration table; Based on the real-time adjustment parameters, parameter distribution processing is performed to generate a set of control commands; According to the set of control commands, the sensor array is subjected to monitoring threshold update processing to obtain the updated monitoring threshold; According to the set of control commands, the flow control device in the hydrogen supply pipeline is adjusted in real time and its performance is monitored to obtain a stable hydrogen source output state.

9. A cloud-based intelligent control system for a high-energy hydrogen atom generator, characterized in that, include: The data acquisition module is used to acquire instantaneous color change data from the high-energy hydrogen atom generator and perform image enhancement processing on the instantaneous color change data to obtain a color feature sequence. The state mapping module is used to extract features from the color feature sequence and determine the energy state mapping relationship; The instruction acquisition module is used to perform standard value comparison calculation on the energy state mapping relationship to determine the mapping deviation value. If the mapping deviation value exceeds the preset mapping threshold, the energy state mapping relationship and the mapping deviation value are uploaded to the cloud platform, and the data returned by the cloud platform is parsed to obtain the parameter adjustment instruction. The instruction verification module is used to acquire real-time environmental parameters, classify and verify the parameters according to the real-time environmental parameters and the parameter adjustment instructions, determine the applicability of the parameter adjustment instructions, and generate feedback signals. The closed-loop control module is used to analyze the feedback signal, obtain the deviation correction value, adjust the power parameters of the generator according to the deviation correction value, and monitor the output to obtain the hydrogen atom output energy level. The iterative optimization module is used to repeatedly call the color change monitoring process and perform iterative analysis to determine the dynamic optimization scheme if the output energy level of the hydrogen atom fluctuates. The synchronous adjustment module is used to update the monitoring threshold of the sensor array according to the dynamic optimization scheme, and to adjust the flow control device in the hydrogen supply pipeline in real time to obtain a stable hydrogen source output state.

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