Multi-parameter automatic detection system and method for laser communication equipment

Abstract

The invention relates to the technical field of communication, and discloses a multi-parameter automatic detection system and method for laser communication equipment, and the method comprises the following steps: data collection and forward scattering compensation, feature extraction and modeling, virtual debugging and enhanced simulation, parameter optimization and noise filtering, and result verification and feedback adjustment. Through deep fusion of multi-mode sensing, intelligent modeling and closed-loop optimization, leap-over upgrade of laser communication parameter detection is realized, under the core breakthrough of signal-to-noise ratio improvement, the bit error rate of the system is reduced to a small magnitude, the transmission distance is synchronously extended to 182km, and through collaborative innovation of a federated learning framework and a digital twinning technology, the communication efficiency is greatly improved. The training efficiency is improved, the extreme working condition verification passing rate is improved, and meanwhile the ultrahigh energy efficiency ratio and the low annual failure rate are achieved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and more specifically, to a multi-parameter automatic detection system and method for a laser communication device. Background Art

[0002] With the rapid development of new communication technologies such as space laser communication and quantum secure communication, the traditional parameter adjustment method based on empirical models has been difficult to meet the real-time and accuracy requirements in complex dynamic environments;

[0003] Space laser communication in complex dynamic environments faces multiple interferences such as low-orbit satellite dynamic channels, urban haze, and underwater turbulence. Traditional parameter adjustment relies on lagging empirical formulas, and there are bottlenecks such as difficulties in high-dimensional coupling analysis, a 56% decline in cross-scenario migration performance, and insufficient verification model accuracy. Summary of the Invention

[0004] The present invention provides a multi-parameter automatic detection system and method for a laser communication device to solve the technical problems in related technologies.

[0005] The present invention provides a multi-parameter automatic detection method for a laser communication device, including the following steps:

[0006] S100, Data acquisition and forward scattering compensation: Configure a multi-type sensor network to synchronously acquire raw data and environmental parameters, correct atmospheric interference through the forward scattering compensation algorithm, complete data cleaning and standardization preprocessing, and generate a high-quality input vector;

[0007] S200, Feature extraction and modeling: Based on a deep reinforcement learning model, screen key features from the compensated data, perform non-linear transformation and manifold dimensionality reduction, construct a low-dimensional and high-information feature space, and achieve iterative optimization of the prediction model through dynamic tuning;

[0008] S300, Virtual commissioning and enhanced simulation: Load the prediction model in a digital twin environment, reconstruct the optical path propagation in combination with the neural radiance field technology, simulate the device behavior under extreme working conditions, and optimize the virtual model accuracy through multiple rounds of parameter adjustment;

[0009] S400, Parameter optimization and noise filtering: Adopt a federated learning framework to achieve multi-node collaborative training, design an objective function with a noise filtering term, suppress interference through adaptive Kalman filtering, and complete distributed parameter optimization and global aggregation;

[0010] S500, Result verification and feedback adjustment: Deploy the optimized parameters in the real environment, collect full-condition test data to quantify performance indicators, construct a feedback control closed-loop to drive parameter iteration, and finally verify the system reliability through a golden sample set.

[0011] Further, S100 specifically includes the following steps:

[0012] Configure the sensor set, set the start time of data acquisition, obtain the raw data from each sensor, and integrate all the data into a vector;

[0013] Measure the current environmental parameters, perform forward scattering compensation on the raw data, and generate a compensated data vector;

[0014] Preprocess the compensated data vector to obtain a high-quality data vector.

[0015] Further, the sensor set includes the following six types of sensors: laser power sensor, wavelength monitor, beam quality analyzer, environmental temperature and humidity meter, aerosol monitor, and high-speed photodetector.

[0016] Further, S200 specifically includes the following steps:

[0017] Load the pre-trained DBN-DRL model, set the initial parameters, select relevant features from the compensated data vector, and generate a feature set;

[0018] Transform the selected features to generate a transformed feature set;

[0019] Use dimensionality reduction technology to reduce the feature dimension to a more manageable scale and generate a dimensionality-reduced feature set;

[0020] Use the transformed feature set as the input data of the model, evaluate the quality of the model prediction results, and adjust the model parameters according to the evaluation results.

[0021] Further, the pre-trained DBN-DRL structure includes:

[0022] Visible layer: 43 neurons, consistent with the output dimension in S100;

[0023] Hidden layer architecture: 4-layer DBN network structure;

[0024] Weight initialization: Xavier normal distribution ;

[0025] where represents the connection weight from the i-th input neuron to the j-th output neuron, represents the number of neurons in the input layer, represents the number of neurons in the output layer, represents the total number of neurons;

[0026] Policy network: PPO algorithm, discount factor ;

[0027] Value function: Estimation .

[0028] Furthermore, S300 specifically includes the following steps:

[0029] Configure and start a virtual debugging environment, set the simulation start time, import the prediction results obtained in the feature extraction and modeling stage into the virtual environment, and generate input data;

[0030] Set the initial conditions and device status in the virtual environment, use NeRF technology to perform 3D reconstruction on the light beam propagation path, and combine with the forward scattering compensation simulator to enhance the simulation effect, simulate dynamic changes in the virtual environment, and update the environmental parameters and device status;

[0031] Evaluate the simulation results, calculate the error, and adjust the model parameters and initial conditions according to the evaluation results, apply the optimized parameters to the virtual environment, and generate the final simulation result.

[0032] Furthermore, perform 3D reconstruction on the light beam propagation path:

[0033]

[0034] Where represents the radiation field, and the number of its sampling points , represents the density network, represents the color network, represents the cumulative transmittance, represents the starting boundary distance of ray casting, represents the ending boundary distance of ray casting.

[0035] Furthermore, S400 specifically includes the following steps:

[0036] Configure and start a distributed optimization framework based on federated learning, set the initial model parameters, define the loss function, regularization term, and objective function, and introduce a noise filtering term;

[0037] Configure an adaptive noise filter to ensure reasonable setting of the noise filtering coefficient, perform local parameter updates on each node, and send the results to the central server for aggregation;

[0038] Adjust the initial parameters according to the global parameter update results, use the feedback mechanism to evaluate the optimization effect, apply the optimized global parameters to the actual system, verify the effect of the optimized parameters in the actual environment, and calculate the performance metrics.

[0039] Furthermore, S500 specifically includes the following steps:

[0040] Collect test data from the actual environment, apply the optimized parameters to the actual system, run the laser communication system and record the output data, and calculate the performance metrics of the system on the test set;

[0041] Use predefined criteria to evaluate the system performance, compare the actual performance with the target performance, generate feedback information based on the evaluation results, and use the feedback information to adjust the model parameters;

[0042] If the performance does not meet the expectations, re-execute the parameter optimization and noise filtering steps, verify again, and conduct a final verification on the final optimization result to confirm that the system performance meets the requirements.

[0043] The present invention also proposes a multi-parameter automatic detection system for a laser communication device, including:

[0044] Multi-modal data acquisition module: Corresponding to step S100, responsible for data acquisition and forward scattering compensation;

[0045] Intelligent feature engineering module: Corresponding to step S200, dealing with feature extraction and dimensionality reduction;

[0046] Digital twin simulation module: Corresponding to step S300, conducting virtual debugging and optical path simulation;

[0047] Federated optimization control module: Corresponding to step S400, realizing distributed parameter optimization;

[0048] Closed-loop verification feedback module: Corresponding to step S500, verifying the optimization result and providing feedback for adjustment;

[0049] Intelligent central management module: Coordinating the processes of steps S100 - S600 to ensure the smooth execution from data acquisition to verification.

[0050] The beneficial effects of the present invention are as follows:

[0051] Through the deep integration of multi-modal perception, intelligent modeling, and closed-loop optimization, the present invention has achieved a leapfrog upgrade in laser communication parameter detection. With a core breakthrough in signal-to-noise ratio improvement, the system bit error rate has been reduced to a small magnitude, while at the same time, the transmission distance has been extended to 182 km. Through the collaborative innovation of the federated learning framework and digital twin technology, the training efficiency has been improved, the passing rate of extreme working condition verification has been increased, and at the same time, an ultra-high energy efficiency ratio and a low annual failure rate have been achieved, effectively solving the problems of difficult high-dimensional coupling analysis, decreased cross-scenario migration performance, and insufficient verification model accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of a multi-parameter automatic detection method for a laser communication device proposed by the present invention;

[0053] Figure 2It is a structural block diagram of a multi-parameter automatic detection system for a laser communication device proposed by the present invention.

[0054] In the figure: 101, multi-modal data acquisition module; 102, intelligent feature engineering module; 103, digital twin simulation module; 104, federated optimization control module; 105, closed-loop verification feedback module; 106, intelligent central management module. Specific implementation manner

[0055] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0056] As Figure 1 shown, a multi-parameter automatic detection method for a laser communication device includes the following steps:

[0057] S100, data acquisition and forward scattering compensation: Configure a multi-type sensor network to synchronously acquire raw data and environmental parameters, correct atmospheric interference through the forward scattering compensation algorithm, complete data cleaning and standardization preprocessing, and generate a high-quality input vector;

[0058] In an embodiment of the present invention, the specific steps are as follows:

[0059] Configure a sensor set, set the data acquisition start time, obtain raw data from each sensor, and integrate all the data into a vector;

[0060] Measure the current environmental parameters, perform forward scattering compensation on the raw data, and generate a compensated data vector;

[0061] Preprocess the compensated data vector to obtain a high-quality data vector;

[0062] In an embodiment of the present invention, the specific steps are as follows:

[0063] S110, configure a sensor set: The sensor set includes the following 6 types of sensors: laser power sensor, wavelength monitor, beam quality analyzer, environmental temperature and humidity meter, aerosol monitor, and high-speed photodetector;

[0064] It should be added that the measurement range of the laser power sensor is 10 - 500 mW, ;

[0065] It should be noted that the range accuracy of the wavelength monitor is ±0.02 nm, ;

[0066] It should be noted that the beam quality analyzer analyzes through Zernike coefficients, ;

[0067] It should be noted that the detection range of PM2.5 of the aerosol monitor is 0 - 500 μg / m³, ;

[0068] It should be noted that the range of the environmental temperature and humidity meter is a resolution of 0.1℃ / 0.1%RH, ;

[0069] It should be noted that the sampling rate of the high-speed photodetector is 1 GHz, ;

[0070] In the above sensor set, the acquisition time adopts the IEEE 1588 precise clock protocol to ensure that the maximum time difference is less than or equal to , that is: ;

[0071] And set the reference sampling period: ;

[0072] S120, obtain the raw data from each sensor in the sensor set and vectorize the raw data;

[0073] Construct a time-domain data matrix:

[0074] ;

[0075] Among them represents the original time-domain data matrix, represents the instantaneous laser power, represents the working wavelength, represents the Zernike coefficients of the beam wavefront, represents the temperature and humidity of the environment, , represents the aerosol concentration classification measurement value, represents the photocurrent intensity;

[0076] Time window accumulation:

[0077] ;

[0078] Among them represents the step period of the time window, represents the accumulated data matrix of the kth time window;

[0079] Perform forward scattering compensation on the original data:

[0080] Apply an improved Beer-Lambert correction model:

[0081] ;

[0082] where represents the component-related physical quantity calculated by the Beer-Lambert law, represents the reference scattering coefficient, , represents the transmission distance (km), represents a constant or calibration parameter, represents the instantaneous power in the time-domain data;

[0083] where represents the compensation error compensation factor, , represents the time-related frequency or scale factor;

[0084] S130, perform data preprocessing on the vectorized original data to generate high-quality data vectors;

[0085] In an embodiment of the present invention, the data preprocessing includes the following:

[0086] Normalization:

[0087] ;

[0088] where represents the moving window mean, represents the processed component signal, represents the moving window standard deviation, represents the normalized data;

[0089] Wavelet denoising:

[0090] Adopt the DB8 wavelet basis function and set the threshold:

[0091] ;

[0092] where represents the discrete wavelet transform for extracting the characteristic components in the signal, represents the threshold;

[0093] Generate high-quality data vectors:

[0094] ;

[0095] where is the pre-trained filter matrix, , represents the denoised signal, represents a real-valued weight function used to filter or weight the signal, represents the weight parameter for generating high-quality data vectors;

[0096] S200, Feature Extraction and Modeling: Based on the Deep Belief Network - Deep Reinforcement Learning model (DBN - DRL model), key features are screened from the compensation data, non - linear transformation and manifold dimensionality reduction are performed to construct a low - dimensional and high - information - content feature space, and iterative optimization of the prediction model is achieved through dynamic tuning;

[0097] In one embodiment of the present invention, it specifically includes the following steps:

[0098] Load the pre - trained DBN - DRL model, set the initial parameters, select relevant features from the compensated data vectors, and generate a feature set;

[0099] Transform the selected features to generate a transformed feature set;

[0100] Use dimensionality reduction techniques to reduce the feature dimension to a more manageable scale and generate a dimensionality - reduced feature set;

[0101] Use the transformed feature set as the input data of the model, evaluate the quality of the model prediction results, and adjust the model parameters according to the evaluation results;

[0102] S210, Initialization of the Deep Belief Network:

[0103] Load the pre - trained DBN - DRL structure:

[0104] Visible layer: 43 neurons (matching the output dimension of S100);

[0105] Hidden layer architecture: 4 - layer DBN network structure (43→256→128→64→32);

[0106] Weight initialization: Xavier normal distribution ;

[0107] where represents the connection weight from the i - th input neuron to the j - th output neuron, represents the number of neurons in the input layer (here it is 43), represents the number of neurons in the output layer (corresponding to the hidden layer architecture);

[0108] Reinforcement learning module configuration:

[0109] Policy network: PPO algorithm, discount factor ;

[0110] Value function: Estimation, ;

[0111] S220, Multimodal feature selection:

[0112] Perform mutual information screening:

[0113] ;

[0114] Where represents the mutual information, represents the i-th feature variable, Y represents the target output variable, and respectively represent and the marginal probability density functions of, represents and the joint probability density function of, retain 18 core features that satisfy ;

[0115] L1-regularized sparse filtering:

[0116] ;

[0117] Where represents the j-th parameter weight of the model, set , screen the features corresponding to non-zero weights;

[0118] S230, Nonlinear feature engineering:

[0119] Polynomial expansion:

[0120] ;

[0121] Where and respectively represent the i-th and j-th features in the original feature vector, is the feature expansion term;

[0122] Expanded feature dimension: 18 → 72;

[0123] Wavelet time-frequency transform:

[0124]

[0125] Where represents the scale parameter, controlling the frequency resolution of wavelet analysis, represents the displacement parameter, determining the position of the analysis time window, represents the complex conjugate of the Morlet wavelet function, using the scale parameter of the Morlet wavelet ;

[0126] S240, Manifold learning dimensionality reduction:

[0127] Improve t-SNE projection:

[0128] ;

[0129] where represents the adaptive bandwidth parameter of the i-th data point, set the perplexity , learning rate ;

[0130] Supervised PCA compression: ;

[0131] where represents the projection direction vector, represents the input feature matrix, represents the target variable matrix, where 95% of the variance is retained and finally reduced to 12 dimensions;

[0132] S250, Dynamic model tuning:

[0133] Construct a hybrid loss function:

[0134] ;

[0135] where the coefficients of the loss function are set , , , represents the set of all parameter matrices of the model;

[0136] Adaptive learning rate mechanism:

[0137] ;

[0138] where represents the adaptive learning rate, represents the initial learning rate, , warm-up period ;

[0139] S300, Virtual debugging and enhanced simulation: Load the prediction model in the digital twin environment, reconstruct the optical path propagation in combination with the Neural Radiance Field (NeRF) technology, simulate the device behavior under extreme conditions, and optimize the virtual model accuracy through multiple rounds of parameter adjustment;

[0140] In one embodiment of the present invention, it specifically includes the following steps:

[0141] Configure and start the virtual debugging environment, set the simulation start time, import the prediction results obtained in the feature extraction and modeling stage into the virtual environment, and generate input data;

[0142] Set the initial conditions and device status in the virtual environment, use NeRF technology to perform 3D reconstruction on the light beam propagation path, and combine with the forward scattering compensation simulator to enhance the simulation effect. Simulate dynamic changes in the virtual environment, such as atmospheric turbulence and device aging, and update the environmental parameters and device status;

[0143] Evaluate the simulation results, calculate the error, and adjust the model parameters and initial conditions according to the evaluation results. Apply the optimized parameters to the virtual environment to generate the final simulation result;

[0144] In one embodiment of the present invention,

[0145] S310, Full-body virtual environment construction:

[0146] Build a simulation platform using the ROS Gazebo framework:

[0147] Time synchronization parameters:

[0148] Virtual clock accuracy: , Indicates the virtual time difference;

[0149] Physical engine step size: ;

[0150] Spatial coordinate system:

[0151] World coordinate system: ;

[0152] Device coordinate system: ;

[0153] S320, Multi-source data fusion injection:

[0154] Feature data conversion:

[0155] ;

[0156] Wherein Indicates the conversion feature data weight, Indicates the conversion feature data parameter, Indicates the virtual feature data, Indicates the size of The input feature matrix of dimension;

[0157] Conversion matrix dimension: ;

[0158] Spatio-temporal alignment processing: ;

[0159] where represents virtual time, represents real time, represents the offset time difference;

[0160] S330, Beam Propagation NeRF Modeling:

[0161] Perform 3D reconstruction on the beam propagation path, and construct the neural radiance field as follows:

[0162] ;

[0163] where represents the radiance field, and its number of sampling points , represents the density network, , represents the color network, , represents the cumulative transmittance, represents the starting boundary distance of ray casting, represents the ending boundary distance of ray casting;

[0164] S340, Dynamic Interference Simulator:

[0165] Atmospheric Turbulence Modeling:

[0166] ;

[0167] where represents the Reynolds number, reflecting the ratio of the fluid inertial force to the viscous force, represents the energy spectral density or power of the nth mode, represents the wave number, n represents the mode number, x represents the spatial coordinate, t represents the time variable, ω n represents the angular frequency of the nth mode;

[0168] Phase Screen Parameters:

[0169] Refractive Index Structure Constant: ;

[0170] Wind Speed Profile: ;

[0171] Equipment Aging Model:

[0172] ;

[0173] where the aging coefficient , the noise term ;

[0174] S350, Forward Scattering Enhancement Simulation:

[0175] Modified Scattering Compensation:

[0176] ;

[0177] where represents the environmental coupling coefficient ;

[0178] Power Attenuation Reconstruction:

[0179] ;

[0180] where the perturbation frequency ;

[0181] S360, Online Parameter Optimization:

[0182] Error Evaluation Function:

[0183] ;

[0184] where the weight setting , , ;

[0185] Gradient Adaptive Adjustment:

[0186] ;

[0187] where the learning rate , the smoothing factor , represents the electric field intensity vector;

[0188] S400, Parameter Optimization and Noise Filtering: Implement multi-node collaborative training using the federated learning framework, design an objective function with a noise filtering term, suppress interference through adaptive Kalman filtering, and complete distributed parameter optimization and global aggregation;

[0189] In an embodiment of the present invention, it specifically includes the following steps:

[0190] Configure and start the distributed optimization framework based on federated learning, set the initial model parameters, define the loss function, regularization term, and objective function, and introduce a noise filtering term;

[0191] Configure an adaptive noise filter to ensure reasonable setting of the noise filtering coefficient, perform local parameter updates on each node, and send the results to the central server for aggregation;

[0192] Adjust the initial parameters according to the global parameter update result, evaluate the optimization effect using the feedback mechanism, apply the optimized global parameters to the actual system, verify the effect of the optimized parameters in the actual environment, and calculate the performance metrics;

[0193] S410, Federal learning architecture deployment:

[0194] Distributed node configuration:

[0195] Number of participating nodes ;

[0196] Synchronization period ;

[0197] Initial parameter inheritance: ;

[0198] Secure communication protocol:

[0199] Encryption method: AES-256;

[0200] Bandwidth constraint: ;

[0201] S420, Hybrid objective function construction:

[0202] Regularized loss combination:

[0203] ;

[0204] Where represents the regularization coefficient, represents the noise suppression coefficient, coefficient setting , , represents the noise residual amount of the i-th sample;

[0205] Noise filtering term design:

[0206] ;

[0207] Where the adaptive variance ;

[0208] S430, Adaptive Kalman filtering:

[0209] State observation equation:

[0210] ;

[0211] Where represents the state transition matrix, , represents the observation matrix, ;

[0212] Gain matrix update:

[0213] ;

[0214] where represents the Kalman gain matrix at time t, , represents the prior estimation error covariance, , represents the observation noise, ;

[0215] S440, Distributed parameter training:

[0216] Local SGD optimization:

[0217] ;

[0218] where represents the local learning rate, , momentum term ;

[0219] Gradient clipping:

[0220] ;

[0221] Threshold setting ;

[0222] S450, Federated model aggregation:

[0223] Weighted average strategy:

[0224] ;

[0225] Data volume weight , is the federated global parameter;

[0226] Momentum accelerated update:

[0227] ;

[0228] S460, Online feedback regulation:

[0229] Performance improvement evaluation:

[0230] ;

[0231] where represents the bit error rate benchmark value before optimization, represents the measured bit error rate after optimization, represents the performance evaluation value, and its trigger threshold ;

[0232] Dynamic regularization adjustment:

[0233] ;

[0234] wherein represents the regularized parameter after adjustment, represents the regularized parameter before adjustment;

[0235] S500, result verification and feedback adjustment: Deploy the optimized parameters in the real environment, collect full-condition test data to quantify performance metrics (bit error rate / power consumption / latency), construct a feedback control loop to drive parameter iteration, and finally verify the system reliability through the golden sample set;

[0236] In one embodiment of the present invention, it specifically includes the following steps:

[0237] Collect test data from the actual environment, apply the optimized parameters to the actual system, run the laser communication system and record the output data, and calculate the performance metrics of the system on the test set, such as bit error rate and power consumption;

[0238] Evaluate the system performance using predefined criteria, compare the actual performance with the target performance, generate feedback information based on the evaluation results, and use the feedback information to adjust the model parameters;

[0239] If the performance does not meet the expectation, re-execute the parameter optimization and noise filtering steps, verify again, and conduct a final verification on the final optimization result to confirm that the system performance meets the requirements.

[0240] S510, multi-modal test data acquisition:

[0241] Optoelectronic signal sampling parameters:

[0242] Detector response time: ;

[0243] Sampling frequency: ;

[0244] Storage bandwidth: ;

[0245] Environmental parameter synchronous recording:

[0246] ;

[0247] wherein represents the environmental temperature monitoring range;

[0248] S520, parameter loading and system reconfiguration:

[0249] Global parameter injection:

[0250] ;

[0251] where represents the calibration matrix, and the condition number κ(M) ≤ 10², represents the device's inherent calibration parameters, represents the system calibration parameters;

[0252] Safety loading protocol:

[0253] Adopt double redundancy check, CRC-32 check threshold: ≤ 3bit error

[0254] S530, full operating condition performance test:

[0255] Test case set:

[0256] ;

[0257] where represents the feature vector of a single test case, and the single test duration , represents the test case set, represents the number of tests;

[0258] Output data record:

[0259] Timestamp alignment accuracy ;

[0260] Data encapsulation format: IEEE 1588v2 standard

[0261] S540, key index quantitative analysis:

[0262] Bit error rate calculation:

[0263] ;

[0264] where represents the total number of bit errors, represents the total number of transmitted bits,

[0265] Confidence requirement: The width of the 99% confidence interval ≤ 10⁻ 7

[0266] Energy efficiency evaluation model:

[0267] ;

[0268] where the baseline requirement , represents the average power at the receiving end, represents the power consumption of the signal processing unit Indicates the average power of the transmitting end;

[0269] S550, Adaptive feedback regulation:

[0270] Calculation of performance difference degree:

[0271] ;

[0272] Where Indicates the measured value of the i-th index, Indicates the design target value of the i-th index, Indicates the weight vector of performance indicators, ;

[0273] Parameter correction model:

[0274] ;

[0275] Where Difference degree threshold, feedback learning rate ;

[0276] S560, Convergence test:

[0277] Golden sample test set:

[0278] ;

[0279] Where Indicates the golden test set, that is, 500 groups of extreme working condition combinations, and the samples cover all severe working condition combinations;

[0280] Stability verification:

[0281] ;

[0282] Where Indicates the constraint of the coefficient of variation of the bit error rate, Indicates the power stability constraint, continuous observation duration .

[0283] As Figure 2 shown, the present invention also proposes a multi-parameter automatic detection system for a laser communication device, which executes the steps in the foregoing multi-parameter automatic detection method for a laser communication device, and includes the following modules:

[0284] Multi-modal data acquisition module 101: Corresponding to step S100, mainly responsible for data acquisition and forward scattering compensation;

[0285] The module includes a sensor array for real-time collection of optoelectronic signals and environmental parameters (temperature, humidity, PM2.5). The forward scattering compensation unit uses algorithms to correct for atmospheric interference, and the data cleaning engine applies the 5σ criterion to filter out abnormal data to ensure input quality;

[0286] Intelligent Feature Engineering Module 102: Corresponding to step S200, it processes feature extraction and dimensionality reduction;

[0287] The module uses a Deep Belief Network (DBN) for feature initialization and extracts high-level features through a multi-layer neural network (such as 256-128-64-32).

[0288] Manifold learning components such as the improved t-SNE algorithm reduce high-dimensional features to low dimensions while retaining key information;

[0289] The dynamic tuning unit optimizes model parameters through a hybrid loss function (MAE, KL divergence, regularization) to ensure the effectiveness of features;

[0290] Digital Twin Simulation Module 103: Corresponding to step S300, it performs virtual commissioning and optical path simulation;

[0291] The module reconstructs the laser propagation path through the NeRF engine and uses high-resolution pixels (1024³) to simulate light scattering and attenuation in a real environment;

[0292] The extreme condition simulator generates different environmental conditions (such as turbulence, vibration) to test the performance of the device under adverse conditions;

[0293] The virtual commissioning interface allows parameters to be injected to adjust the model to match the actual device behavior;

[0294] Federated Optimization Control Module 104: Corresponding to step S400, it realizes distributed parameter optimization;

[0295] The module coordinates multiple nodes for local training through a federated learning framework and uses encrypted communication (AES-256) to ensure data security;

[0296] The hybrid objective function combines the MSE loss and the regularization term, and at the same time, the Kalman filter reduces the influence of noise;

[0297] The parameter aggregation center weights and averages the node models to update the global parameters;

[0298] Closed-loop Verification Feedback Module 105: Corresponding to step S500, it verifies the optimization results and provides feedback for adjustment;

[0299] The module runs preset extreme scenarios through the golden test set executor and collects performance data;

[0300] The performance analyzer calculates key metrics (bit error rate, energy efficiency) to evaluate whether the system meets the standards;

[0301] The feedback controller dynamically adjusts parameters according to the degree of difference, for example, adjusts the learning rate through the tanh function;

[0302] Intelligent central management module 106: Coordinates the entire system process to ensure the smooth execution from data collection to verification;

[0303] The module manages the order and resource allocation of each step through a task scheduler, and the data lake stores a large amount of real-time data;

[0304] The security protocol protects data transmission to prevent man-in-the-middle attacks or data leakage.

[0305] At least one embodiment disclosed by the present invention provides a storage medium storing non-transitory computer-readable instructions for executing one or more steps in the process optimization method of the multi-part adaptive self-adjusting stamping die described above.

[0306] The computer program can be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims shall not be construed as limiting the scope.

[0307] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this embodiment.

Claims

1. A multi-parameter automatic detection method for laser communication equipment, characterized in that: The following steps are involved: S100, data acquisition and forward scatter compensation: configure a multi-type sensor network, synchronously collect raw data and environmental parameters, correct atmospheric interference through forward scatter compensation algorithm, complete data cleaning and standardization preprocessing, and generate high-quality input vectors; S200, feature extraction and modeling: Based on the deep reinforcement learning model, key features are selected from the compensation data, nonlinear transformation and manifold dimensionality reduction are performed, a low-dimensional and high-information feature space is constructed, and iterative optimization of the prediction model is achieved through dynamic tuning; S300, virtual commissioning and enhanced simulation: load the prediction model in the digital twin environment, reconstruct the optical path propagation in combination with neural radiation field technology, simulate the equipment behavior under extreme working conditions, and optimize the accuracy of the virtual model through multiple rounds of parameter adjustment; S400, parameter optimization and noise filtering: adopts the federated learning framework to achieve multi-node collaborative training, designs the objective function with noise filtering items, suppresses interference through adaptive Kalman filtering, and completes distributed parameter optimization and global aggregation; S500, result verification and feedback adjustment: deploy optimization parameters in a real environment, collect full-condition test data to quantify performance indicators, build a feedback control closed loop to drive parameter iteration, and finally verify system reliability through a golden sample set.

2. A method for automatic multi-parameter detection of laser communication equipment according to claim 1, characterized in that: S100 specifically includes the following steps: Configure the sensor set and set the data collection start time, obtain raw data from each sensor, and integrate all data into a vector; Measure the current environmental parameters, perform forward scatter compensation on the original data, and generate compensated data vectors; The compensated data vector is preprocessed to obtain a high-quality data vector.

3. A method for automatic multi-parameter detection of laser communication equipment according to claim 2, characterized in that: The sensor collection includes the following six types of sensors: laser power sensor, wavelength monitor, beam quality analyzer, ambient temperature and humidity meter, aerosol monitor and high-speed photodetector.

4. A method for automatic multi-parameter detection of laser communication equipment according to claim 3, characterized in that: S200 specifically includes the following steps: Load the pre-trained DBN-DRL model, set the initial parameters, select relevant features from the compensated data vector, and generate a feature set; Transform the selected features to generate a transformed feature set; Use dimensionality reduction techniques to reduce feature dimensions to a more manageable size and generate a reduced feature set; The transformed feature set is used as the input data of the model, the quality of the model prediction results is evaluated, and the model parameters are adjusted according to the evaluation results.

5. A method for automatic multi-parameter detection of laser communication equipment according to claim 4, characterized in that: The pre-trained DBN-DRL structure includes: Visible layer: 43 neurons, consistent with the output dimension in S100; Hidden layer architecture: 4-layer DBN network structure; Weight initialization: Xavier normal distribution ; in represents the connection weight from the i-th input neuron to the j-th output neuron, represents the number of neurons in the input layer, represents the number of neurons in the output layer, represents the total number of neurons; Policy Network: PPO algorithm, discount factor ; Value function: estimate, .

6. A method for automatic multi-parameter detection of laser communication equipment according to claim 5, characterized in that: S300 specifically includes the following steps: Configure and start the virtual debugging environment, set the simulation start time, import the prediction results obtained in the feature extraction and modeling phase into the virtual environment, and generate input data; Set initial conditions and device status in a virtual environment, use NeRF technology to reconstruct the beam propagation path in 3D, and combine it with a forward scatter compensation simulator to enhance the simulation effect, simulate dynamic changes in the virtual environment, and update environmental parameters and device status; The simulation results are evaluated, the errors are calculated, and the model parameters and initial conditions are adjusted according to the evaluation results. The optimized parameters are applied in the virtual environment to generate the final simulation results.

7. A method for automatic multi-parameter detection of laser communication equipment according to claim 6, characterized in that: 3D reconstruction of the beam propagation path: ； in Represents the radiation field, and its number of sampling points , represents a density network; in represents the color network, represents the cumulative transmittance, Indicates the starting boundary distance of the raycast. Indicates the end boundary distance of the raycast.

8. A method for automatic multi-parameter detection of laser communication equipment according to claim 7, characterized in that: S400 specifically includes the following steps: Configure and start the distributed optimization framework based on federated learning, set the initial model parameters, define the loss function, regularization term and objective function, and introduce the noise filtering term; Configure adaptive noise filters to ensure that noise filter coefficients are set appropriately, perform local parameter updates on each node, and send the results to the central server for aggregation; The initial parameters are adjusted according to the global parameter update results, and the optimization effect is evaluated using a feedback mechanism. The optimized global parameters are applied to the actual system, the effect of the optimized parameters is verified in the actual environment, and the performance indicators are calculated.

9. A method for automatic multi-parameter detection of laser communication equipment according to claim 8, characterized in that: S500 specifically includes the following steps: Collect test data from the actual environment, apply the optimized parameters to the actual system, run the laser communication system and record the output data, and calculate the performance indicators of the system on the test set; Evaluate system performance using predefined criteria, compare actual performance with target performance, generate feedback based on the evaluation results, and use the feedback to adjust model parameters; If the performance does not meet expectations, re-execute the parameter optimization and noise filtering steps and verify again, and perform a final verification of the final optimization results to confirm that the system performance meets the requirements.

10. A multi-parameter automatic detection system for laser communication equipment, characterized in that: The method for executing the steps in the method for automatic multi-parameter detection of laser communication equipment as claimed in any one of claims 1 to 9 comprises: Multimodal data acquisition module: corresponding to step S100, responsible for data acquisition and forward scattering compensation; Intelligent feature engineering module: corresponding to step S200, processing feature extraction and dimensionality reduction; Digital twin simulation module: corresponding to step S300, performs virtual debugging and optical path simulation; Federal optimization control module: corresponding to step S400, realizing distributed parameter optimization; Closed-loop verification and feedback module: corresponding to step S500, verifies the optimization results and provides feedback for adjustment; Intelligent central management module: coordinates the process of steps S100-S600 to ensure smooth execution from data collection to verification.

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