335 results about "Back propagation algorithm" patented technology

The invention discloses a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and relates to the technical field of fault diagnos.The method comprises the steps that a graph neural network with a liquid time constant network unit as a node is constructed through a dynamic topology dependency relationship and a multi-dimensional key performance index flow; each unit describes state evolution through a coupled ordinary differential equation system, and a liquid state time constant can be adaptively adjusted. A time back propagation algorithm is adopted to train a model to learn a normal behavior track contour reference, and anomaly is detected through a dynamic time warping distance. And determining a fault propagation path and a root cause through anti-fact intervention and forward integral solution. And generating an optimal diagnosis action sequence in a liquid graph neural network simulation environment, and calculating a reward value based on execution efficiency, accuracy and a repair effect to carry out strategy optimization. The abnormal detection accuracy and the root cause positioning precision are improved, the fault repair time is shortened, the operation and maintenance cost is reduced, and an intelligent fault diagnosis solution is provided for a complex information technology system.

The invention provides a lightweight visible light ship target detection method based on edge feature guidance, and relates to the technical field of ship detection image data processing, and the method comprises the steps: collecting remote sensing satellite images, and carrying out the random distribution of the images after screening and marking, and obtaining a training set and a verification set; the backbone network module comprises a plurality of Conv modules and C3k2 modules which are mutually stacked; the neck module comprises a detail-enhanced convolution module and a hierarchical pyramid module based on dynamic feature aggregation; in the head module, after the features of all detection layers are subjected to independent convolution processing, feature transformation is carried out through a multi-branch detail enhancement convolution module; performing data enhancement on the training set; and obtaining a trained ship target detection model through a back propagation algorithm and a gradient descent optimization method. According to the invention, the lightweight and precision improvement of the detection head are realized, the robustness of the model to the illumination change is enhanced, and the global semantic information and the local detail features are fused to balance the detection of the small target and the large target.

The invention relates to the technical field of intelligent communication, and discloses an end-to-end optimization method, a signal constellation geometry and probability joint shaping optimization method in an end-to-end intelligent communication system, and a communication device based on a neural network. According to the method, a transmitter is constructed by using a neural network, and a received signal passes through a two-stage equalization processing system to form an end-to-end optimization framework without assistance of a channel model. The transmitter adopts a modular design, can realize joint optimization of constellation probability shaping (PS) and geometric shaping (GS), and performs joint updating on all trainable parameters of the system based on a total loss function containing an equilibrium error and a decoding error through a back propagation algorithm. According to the invention, computing resources are effectively saved; the two-stage equalization system can accurately compensate channel damage, the realized PS and GS joint optimization can significantly improve the performance of the communication system and reduce the bit error rate, and the method has good performance advantages and practical value in an actual optical fiber communication system.

The invention provides a PCB manufacturability intelligent analysis and early warning method and system based on artificial intelligence, and the method comprises the steps: collecting and marking the multi-source time sequence process parameter data in the PCB design and manufacturing process under working conditions, building a dynamic causal graph model with direction and time lag marks through a sliding window and standardization processing by applying a causal discovery algorithm, and carrying out the calculation of the dynamic causal graph model. Dynamic expression of causal relationships among process variables is realized; when manufacturing abnormity is detected, abnormity attribution is carried out by combining a Bayesian back propagation algorithm, high-contribution-degree root dependent variables are screened, the effectiveness of root causes is verified through virtual intervention simulation and statistical test, and finally verification results and a causal mode are stored in a knowledge base to support subsequent rapid matching and reasoning. According to the method, the accuracy, efficiency and interpretability of PCB manufacturing abnormity attribution are improved, and process optimization and preventive intervention are facilitated.

The invention discloses a generative defense method and system for resisting attacks, relates to the technical field of network security, and aims to solve the problems that an existing defense scheme is high in calculation overhead and poor in real-time performance, static defense is easy to bypass, and robustness and accuracy are difficult to balance. The method comprises the following steps: constructing an adversarial network model by taking a pre-trained target model as a discriminator and a generative model as a generator; constructing a total loss function by combining a defensive loss function and an accuracy loss function, generating a defensive benign sample by adding defensive disturbance into a benign sample, generating a defensive confrontation sample by adding defensive disturbance after generating a confrontation sample based on the benign sample, and inputting the three types of samples into a target model to obtain total loss; and training the generative model to convergence by using a back propagation algorithm to obtain a trained adversarial network model for classification of defense disturbance samples. Defense generation network training is completed in the training stage, only defense disturbance needs to be overlaid in the reasoning stage, the real-time requirement is met, the robustness and accuracy of the model can be balanced, and the method is suitable for various attack scenes.

The invention relates to a geological analysis technology, and discloses a multi-head attention model training method fused with geological rules, which is used for improving the scientificity and accuracy of geological body model prediction and improving the interpretability of a geological analysis process. According to the scheme, firstly, feature vectors of geological attribute data and geological coordinate data are spliced to obtain a fusion feature vector, then spatial locality and directivity are considered at the same time, geomorphic multi-head attention calculation is conducted on the fusion feature vector, and an attention feature sequence is obtained; then extracting multi-scale features from the input geological map, constructing a cross-fault weight mask matrix based on fault constraints, and obtaining comprehensive features through fusion; calculating a loss value by adopting a joint loss function containing prediction loss and fault non-penetrability constraint terms, and updating model parameters through a back propagation algorithm to complete model training; and finally, drawing an attention thermodynamic diagram for visual display, and superposing the attention thermodynamic diagram with the three-dimensional geologic model.

The invention discloses a rapid nondestructive testing method and system for the lipid content and deterioration degree of red pine nuts based on hyperspectral imaging and deep learning, and belongs to the technical field of nondestructive testing of the quality and safety of agricultural and forestry products. The method is provided for solving the problems that an existing method for detecting the lipid content and the oxidation degree in the red pine nut kernels is generally complex in operation process, high in large-batch detection cost, long in consumed time and difficult to achieve detection in the whole storage and transportation process. The method is characterized by comprising the following steps: acquiring original near infrared spectrum data of a pine nut sample through a collected hyperspectrum; determining the lipid reference content truth value and the oxidation deterioration reference degree of the pine nut samples at different sampling times, and establishing a database according to the values; the method comprises the following steps: preprocessing collected original near infrared spectrum data of red pine nuts, and dividing red pine nut sample data collected in different batches into a training set and a verification set; and designing an improved one-dimensional cavity convolutional network based on a dynamic weight distribution module to construct a deep learning model, wherein the deep learning model is used for constructing a deep learning structure suitable for spectral feature analysis of the red pine nuts. And a back propagation algorithm is adopted to train the constructed model, and reverse updating of network weight parameters is realized by minimizing a loss function. When the performance of the constructed model meets the rapid detection requirement, the method is used for efficient and lossless synchronous detection of the lipid content and the oxidative rancidity degree of the red pine nuts.

The invention relates to the technical field of ship design optimization, and discloses a hull shape optimization method based on neural network modeling. In the data acquisition stage of the method, initial appearance parameters and hydrodynamic performance data of a ship body are obtained, the appearance parameters comprise geometric dimensions and shape features, and the performance data comprise resistance coefficients and wave-making resistance values. In the neural network construction stage, a neural network model with a multi-layer perceptron structure is trained by using collected data, weights are updated through a back propagation algorithm, and a nonlinear mapping relation between appearance parameters and hydrodynamic performance indexes is established. In the shape optimization stage, the trained neural network model is used for carrying out iterative adjustment on the shape of the ship body, fluid dynamic performance indexes are recalculated through the model after each adjustment until preset convergence conditions are met, and finally optimized ship body shape data are output. According to the method, partial complex calculation is replaced by the neural network, and intelligent optimization of the hull appearance is realized.

The invention provides a model training method and device, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of neural network models and model training. The specific implementation scheme is as follows: a calculation unit executes quantization matrix multiplication based on Hadamard pre-transformation on an activation tensor and a weight tensor of a target model stored in a memory so as to generate an output tensor of a linear layer based on a low-precision tensor with smaller data bit width; using the output tensor and a subsequent network layer of the target model to complete forward propagation so as to obtain a loss value; and according to the loss value, updating model parameters of the target model stored in a memory through a back propagation algorithm. By means of the technical scheme, on the premise that the model training precision is guaranteed, memory resource occupation and the calculation amount in the calculation process can be remarkably reduced, and the training cost is reduced.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention provides an atom pair distribution function calculation method based on a back propagation algorithm, and the method comprises the steps: building a micromapping relation between a material microstructure and experimental PDF data through constructing a physical calculation layer containing lattice parameters and atom displacement; defining a loss function by taking experimental data as a supervision signal, and synchronously optimizing tens of thousands of atomic positions and lattice parameters by utilizing gradient back propagation of the loss function covering the experimental data and model calculation result deviation; and a special optimization algorithm CrystalAdam and dynamic learning rate scheduling are combined, so that global efficient search of a high-dimensional parameter space is realized. Therefore, compared with a traditional modeling method, the method provided by the invention not only can remarkably improve the optimization efficiency, but also can provide finer local adjustment in a complex structure space, thereby achieving higher precision.

The invention relates to a four-body coupling vehicle base vibration noise prediction optimization method and system, and relates to the technical field of rail transit operation and maintenance optimization, and the method comprises the steps: obtaining a vehicle base multi-physics field correlation monitoring data set; carrying out system level assembly in a multi-physics field co-simulation environment to generate a coupled system state parameter matrix; generating sound ray bending trajectory data; loading the vehicle operation condition time sequence data to a wave equation solver, and outputting vibration noise propagation characteristic field distribution data; building a network topology structure to form a prediction model, and executing a back propagation algorithm to train network parameters to a convergence state; designing a vehicle scheduling scheme chromosome coding structure to generate a candidate solution population; and performing selection, crossover and variation genetic operations on the primary solution set, and outputting a vehicle base operation and maintenance scheduling real-time optimization scheme. The method has the beneficial effects that the precision and generalization ability of vibration noise prediction in the future time period are greatly improved, and the fundamental transformation from passive vibration isolation treatment to active prediction optimization is realized.

The invention relates to the technical field of pressure control, in particular to a butterfly valve pressure self-adaptive adjusting method, device and equipment and a storage medium. According to the method, a pre-constructed neural network model is introduced, and user set pressure, actual measurement pressure, current control signals and error parameters serve as input variables; dynamic generation and real-time self-adaptive adjustment of PID control parameters are realized, the technical bottlenecks that parameter setting of a traditional PID controller is complex and adaptability to non-linear and time-varying systems is insufficient are effectively solved, and the parameter adjustment period is shortened; the weight parameter of the neural network is continuously optimized through a back propagation algorithm, so that the inhibition capability of the system on external disturbance is remarkably enhanced, the overshoot is reduced, and the long-term stability and control precision of pressure control are improved; the target step number is calculated in combination with butterfly valve hardware parameters, the accuracy and rapidity of stepping motor driving are ensured, and the problem of response delay of traditional PID control is effectively solved.

The invention relates to the technical field of image classification, in particular to an edge-cloud collaborative rice disease monitoring method and system for precision agriculture, and the method comprises the steps: constructing a rice disease recognition network; a squeezing-incentive attention module and a multi-scale convolution-space attention module are introduced into a backbone network and a neck network of the rice disease recognition network; replacing standard convolution in the backbone network and the neck network with deep separable convolution; training the improved and optimized rice disease recognition network to obtain a lightweight student model; soft label distillation loss is constructed based on prediction distribution output by a pre-trained deep teacher model and a student model; a total loss function is constructed in combination with cross entropy loss, training of student models is guided through a back propagation algorithm, and a rice disease lightweight recognition model is obtained and used for recognizing rice diseases. Through edge end deployment of a lightweight model obtained through distillation, rapid reasoning of a high-precision model is realized on low-power-consumption edge equipment.

The invention relates to the technical field of laser control, in particular to an intelligent control method and system of an MOPA laser. The method comprises the following steps: constructing a target training data set and a one-dimensional convolutional neural network model; training a one-dimensional convolutional neural network model based on the target training data set in combination with a target loss function and a back propagation algorithm to obtain a target model; acquiring current operation parameters of the MOPA laser; inputting the current operation parameters into a target model, and outputting target regulation and control parameters by the target model; the target regulation and control parameters comprise the working current of a pumping module in the MOPA laser and the working temperature of a doped fiber; based on the target regulation and control parameters, the working current of a pumping module in the MOPA laser and the working temperature of a doped optical fiber are adjusted, the working temperature of the doped optical fiber comprises a plurality of different target temperatures, and each target temperature corresponds to a different position on the doped optical fiber. In this way, the problems caused by the nonlinear effect generated under high-power pumping can be reduced.

The invention provides a low-temperature-resistant performance evaluation method for a composite material, and belongs to the technical field of material performance determination.The method comprises the steps that firstly, in a low-temperature environment box, a temperature sensor is used for accurately controlling the temperature of a sample, and meanwhile, a high-frequency strain sensor is used for collecting data; then, wavelet transform is adopted to carry out denoising and decomposition on the strain signals, and key low-temperature characteristic parameters are extracted; and constructing a performance evaluation model based on deep learning, inputting the temperature field, the strain characteristics and the material basic parameters into the model, and predicting the low-temperature strength. A prediction result is verified through a standard test, a prediction error is calculated, an error distribution matrix is established, and an improved back propagation algorithm is adopted to optimize the model. Finally, a fuzzy comprehensive evaluation method is applied, indexes such as strength retention rate and strain stability are comprehensively considered, and the low temperature resistance grade of the composite material is scientifically evaluated. The method solves the problem that the actual mechanical properties of the composite material in the low-temperature environment are difficult to comprehensively reflect in the prior art.

The invention discloses a flood peak enhanced physical base flow and residual error correction collaborative runoff prediction method, which belongs to the field of hydrological prediction, and comprises the following steps of: dividing a training set and a verification set according to a proportion, performing oversampling processing on flood peak samples, and constructing a time sequence window; a Xinanjiang model is discretized and expressed by adopting an ordinary differential equation, rainfall and potential evaporation data are input, and intermediate variables are obtained. A physical base flow and residual error correction dual-channel module is constructed, and physical base flow and residual error correction is calculated through two full-connection networks. And calculating a final runoff predicted value by adopting a residual connection structure, taking basic NSE loss as a core, superposing a flood peak sample error weighted item, strengthening flood peak fitting precision, and updating physical parameters and neural network weight through a back propagation algorithm. And verifying the model, and respectively calculating prediction indexes of the training set and the verification set. According to the method, fusion of a traditional hydrological model and a deep learning method is realized, the physical interpretation of the model is enhanced, and the basin runoff prediction precision is improved.

The invention belongs to the technical field of wind generating set operation parameter monitoring, and aims to solve the problem that information propagation time is different due to the fact that the distance of the relation between devices is different. The invention discloses a wind generating set time sequence data anomaly detection system and method. The system comprises a multi-scale generation module, a graph structure self-learning module, a scale adaptive fusion module and a decoding module. The method comprises the steps of generating data representations of different time scales, representing a relationship between sensors in a graph structure form, self-learning a weight coefficient of each scale layer through a neural network training mode, mapping features of a potential space back to an original space through a three-layer full-connection layer, generating reconstruction data, and obtaining the reconstructed data. The loss is calculated through the mean square error of the reconstructed data and the original data, and the model parameters are optimized by using a back propagation algorithm. According to the invention, diversified interaction modes between sensors can be effectively captured, so that the accuracy and robustness of anomaly detection are improved.

The embodiment of the invention provides a training method, a reasoning method and a related device for a stream matching generation model, which are used for improving the accuracy of an action sequence predicted by the trained stream matching generation model. The method provided by the embodiment of the invention comprises the following steps: acquiring noise, a moment t and an environment characteristic of a first action, wherein the environment characteristic of the first action at least comprises an observation value of the first action; inputting the noise, the moment t and the environment characteristics into an initialized flow matching generation model to obtain an output predicted velocity field vector of the conditional probability path at the moment t + 1; calculating the loss between the predicted velocity field vector and the real velocity field vector by using a preset loss function, wherein the preset loss function comprises at least one of a first loss function and a second loss function, and a third loss function; and training the initialized stream matching generation model by using a loss and back propagation algorithm until the stream matching generation model converges to obtain a trained stream matching generation model.

The invention discloses a diffraction neural network optical classification method and system based on dual-wavelength differential modulation, and relates to the technical field of optical calculation and optical information processing, and the method comprises the steps: S1, optimizing the phase distribution of a single diffraction layer through a deep learning back propagation algorithm; s2, loading a code corresponding to the input information through an input layer; s3, introducing two coherent light sources with different wavelengths to irradiate the codes in sequence or in parallel; s4, after the single diffraction layer modulates the input light field, an output light field is detected through a detector; and S5, performing differential normalization operation on the light intensities of different wavelengths to obtain a discrimination signal Sc of each category, and completing optical classification. According to the invention, the adjustment error caused by a multilayer device is eliminated through a single-layer diffraction structure, a dual-wavelength differential detection mechanism is introduced, and the non-negativity limitation of a light intensity signal is broken through, so that the classification precision and robustness are remarkably improved while the structure simplification is ensured, and the method has important engineering value and application prospect.

The invention provides an airport noise prediction method based on a back propagation neural network and a support vector machine, and belongs to the technical field of air traffic transportation noise, and the method comprises the steps: collecting airport noise historical data, obtaining flight historical data of airplanes with different powers, and obtaining a prediction input variable and a prediction output variable; according to the input and output variables and an elastic back propagation algorithm, in combination with a gradient time sequence dynamic rarefaction method and an adaptive structure adjustment method, establishing a back propagation neural network prediction model; constructing an airport noise kernel function, and establishing a support vector machine prediction model in combination with a cross validation method; predicting by using a back propagation neural network prediction model and a support vector machine prediction model, comparing and analyzing prediction results in combination with evaluation indexes, and selecting an optimal prediction result as an airport noise prediction result; the problems that an existing noise prediction algorithm depends on a large amount of historical data, the calculated amount is huge, and the noise prediction efficiency is low are solved.

The invention relates to a post personnel recommendation interpretation method and device based on gradient features and a community structure. The method comprises the steps of obtaining a pre-trained and converged graph neural network and original embedded features, carrying out subsequent operation according to a knowledge graph in which entities such as personnel, skills, posts and services are stored, and recommending a network for on-duty personnel by using the graph neural network, and combining the original embedded features with gradient vectors obtained by the graph neural network through a back propagation algorithm to form gradient features. And performing community division on the knowledge graph nodes, and generating node community tags by maximizing modularity. Finally, the gradient features, the node community labels and the graph neural network model are input into an interpretation framework together, the graph masks and the feature masks are optimized to maximize information quantitative indexes, and post personnel recommendation results containing the key sub-graphs and the feature masks are generated. By adopting the method, the on-duty personnel recommendation interpretability in different scenes can be continuously improved.

The invention belongs to the technical field of geophysical exploration, and discloses a transient electromagnetic vertical component detection data one-dimensional real-time accurate inversion method and system. Processing the established stratum model, training the constructed Seq2seq network by using a back propagation algorithm, and training transient electromagnetic optimal inversion models with different numbers of layers; performing background field extraction on all measurement points of the actually measured data, constructing simulated background field data, and extracting pure abnormal field ratio data; multiplying the obtained actually measured pure abnormal field ratio data by the obtained simulated background curve to reconstruct brand new total field data; the reconstructed brand-new total field data is corrected to be consistent with the time domain of the sample library; an inversion model of the corresponding layer number is selected, and the resistivity-thickness of the stratum is calculated in real time; and carrying out visual display on an inversion result. According to the invention, inversion precision is improved, and real-time inversion of transient electromagnetic detection data is realized.

The invention discloses a myopia image deep learning recognition model training method, particularly relates to the technical field of medical image processing and deep learning, and is used for solving the problem that an existing deep learning model lacks anatomical structure priori knowledge guidance in myopia eye bottom image analysis. The method comprises the following steps: acquiring a myopia eye bottom image and anatomical structure priori knowledge data, extracting a multi-scale feature map by using a deep learning model, analyzing the geometric morphology of a key anatomical component based on standard spatial relationship information, and generating a spatial constraint loss item; according to the method, key anatomical path topology coherence is evaluated based on topology connection information, topology constraint loss items are generated, a loss item fusion strategy is dynamically adjusted according to a training stage, finally, a model is iteratively trained to convergence through a gradient back propagation algorithm, and organic combination of medical priori knowledge and a deep learning model is realized. And the clinical rationality and reliability of model output are improved.

The invention relates to the technical field of power fault diagnosis, and particularly discloses a wall bushing fault intelligent diagnosis method based on a physics-causal fusion diagnosis method, and the method comprises the steps: multi-physics field signal collection: deploying a multi-physics field sensor around a wall bushing to collect corresponding data; characteristic quantity extraction of multi-source signals is realized through a physical model; fault reason reasoning is carried out, and fault tracing is realized through a multi-level causal network and a physical constraint back propagation algorithm; and outputting a self-adaptive result, and outputting the possible fault type and the corresponding probability of the wall bushing. According to the wall bushing fault intelligent diagnosis method based on the physics-causal fusion diagnosis method provided by the invention, the technical problem that the multi-physics field coupling fault of the wall bushing is difficult to accurately trace can be effectively solved.

The invention provides a vertical furnace body temperature layout recommendation method, and belongs to the technical field of intelligent recommendation, and the method comprises the steps: collecting the internal temperature of a furnace body and the current production process parameters in real time through sensors distributed at different positions of the furnace body, and carrying out the cleaning and preprocessing of the collected data, dividing the preprocessed data into a training set and a test set according to a preset proportion; in combination with a back propagation algorithm and a gradient descent optimization algorithm, enabling the model to learn a complex mapping relation between each factor in the furnace body and the temperature layout; performing feature extraction on test set data by using the trained model, and obtaining key features of temperature distribution in the furnace body through multi-layer convolution and pooling operation of the model; and according to the key features and a preset temperature layout optimization target, an intelligent optimization algorithm is adopted to carry out optimization calculation on the furnace body temperature layout, and a recommended temperature layout scheme is generated. And the temperature control precision of the furnace body is obviously improved.

The invention discloses a satellite signal authentication method and device based on a complex valued neural network and a storage medium. The satellite signal authentication method comprises the following steps: acquiring a complex valued data sequence of a satellite signal to be authenticated; inputting the complex-valued data sequence of the to-be-authenticated satellite signal into a trained complex-valued neural network model to enable the trained complex-valued neural network model to output the radio frequency feature vector of the to-be-authenticated satellite signal, the trained complex-valued neural network model being obtained by training based on a preset triple loss function and a back propagation algorithm; calculating a target similarity score between the radio frequency feature vector of the satellite signal to be authenticated and the corresponding target anchor point sample, wherein the score is used for representing an average angular distance between the radio frequency feature vector and the target anchor point sample; and classifying and authenticating the satellite signal to be authenticated based on a preset score threshold and the target similarity score. According to the method, the recognition complexity can be reduced while the recognition precision of the satellite signal is improved.

The invention discloses a three-dimensional electromagnetic inverse scattering imaging method of a diffusion model based on physical constraint embedding. The method comprises the following steps: 1, in a specific three-dimensional electromagnetic inverse scattering imaging environment, defining a dielectric constant of an imaging area, arranging a transmitting and receiving antenna array at the periphery, and obtaining scattering field data of a target area for constructing an input and output sample pair of a diffusion model; 2, constructing a diffusion model on the three-dimensional grid space; 3, introducing a physical consistency constraint term based on a Maxwell equation set in the training stage of the diffusion model; and 4, synthesizing the generation loss and the physical constraint loss of the diffusion model, constructing a joint optimization objective function, and carrying out iterative updating on network parameters by adopting an end-to-end back propagation algorithm until the model converges. According to the method, the spatial resolution and continuity of three-dimensional imaging are remarkably improved, the stability and robustness of inverse scattering reconstruction are improved, and the physical consistency is enhanced. And high-precision and interpretable three-dimensional electromagnetic inverse scattering imaging can be realized.

The invention discloses an end-to-end scheduling method and system for a multi-source weather-driven urban integrated energy system. The method comprises the following steps: constructing an integrated energy system model; constructing a source load prediction model based on multi-source numerical weather forecast data in combination with historical photovoltaic output data and power load and thermal load data; the method comprises the following steps: establishing a comprehensive energy system optimization scheduling model with minimization of system operation cost as an optimization target, designing a differentiable optimization layer, and reversely transmitting the gradient of the optimization target in the scheduling model to a source load prediction model parameter to the source load prediction model through a back propagation algorithm by the differentiable optimization layer, the parameters of the driving source load prediction model are updated, and end-to-end linkage optimization from prediction to scheduling is achieved; and periodically obtaining updated multi-source numerical weather forecast data and source load data, readjusting prediction model parameters, and executing optimization solution of the integrated energy system optimization scheduling model. According to the invention, cooperative training and iterative optimization of the prediction model and the scheduling decision are realized.

The invention belongs to the field of flight evaluation, and provides a flight operation guarantee service quality evaluation analysis method and system, and the method comprises the steps: collecting multi-dimensional guarantee service index data in a flight operation guarantee process; collecting airport operation situation data; inputting the airport situation vector into a trained neural network model, and outputting a corresponding index weight correction factor to obtain a correction weight; performing weighted calculation on the standardized index matrix based on the correction weight to obtain a comprehensive service quality evaluation value; calculating a variance of the comprehensive service quality evaluation value in a preset time period, and performing optimization iteration on the neural network model by adopting a back propagation algorithm by taking the variance as a loss function until the loss function is converged; and outputting a comprehensive service quality evaluation result obtained based on the final correction weight.