1665 results about "Backpropagation" patented technology

The invention discloses a power load prediction method and system based on a meteorological prediction model, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical power load data and corresponding multi-dimensional meteorological data, constructing a meteorological coupling effect analysis model, calculating a dynamic coupling weight matrix among the meteorological parameters through an attention mechanism network; performing feature fusion on the coupling weight matrix and the multi-dimensional meteorological data through a gating circulation unit to generate a space-time coupling feature vector; establishing a power load prediction model, and extracting a short-term fluctuation feature and a long-term trend feature of the power load from the space-time coupling feature vector based on a deep residual network architecture and a parallel convolution channel; the dynamic coupling weight matrix is updated based on prediction error back propagation, and adaptive adjustment of the meteorological parameter coupling relation is achieved. According to the method, dynamic fusion of the spatial-temporal characteristics is realized through the cross gating unit, so that the model can perform power load prediction by using spatial-temporal information, and the prediction precision is improved.

The invention discloses an artificial intelligence-based engineering resource allocation optimization method and system, and relates to the technical field of artificial intelligence and resource management crossing. The problems of resource configuration dynamic change and resource conflict coordination are solved. And processing the multi-modal heterogeneous data in the target engineering scene through space-time alignment and semantic coding to generate a dynamic data stream. A task, resource and environment node relationship is constructed based on a dynamic heterogeneous graph network, and a node dependency weight is updated through an event-driven mechanism. Two-stage collaborative optimization is executed, in the first stage, a baseline scheme is generated through Monte Carlo sampling, and in the second stage, resource conflicts are eliminated through back propagation negotiation. The resource dynamic entropy is monitored in real time, a rebalance algorithm is triggered during local overload, and a distribution scheme is adjusted in combination with security constraints. And finally, outputting the optimization scheme to an engineering management system for execution, dynamically returning updated graph network parameters, forming a data acquisition, optimization decision and feedback closed loop, and improving the intelligence and robustness of resource allocation.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based image segmentation and dynamic target recognition method, which comprises the following steps of: accurately positioning a candidate region through multi-modal space-time fusion and dynamic confidence coefficient screening; strengthening spatial-temporal feature expression in a layering manner through a multi-level feature decoupler, and generating a multi-dimensional feature enhanced spatial-temporal candidate region; through a deformable segmentation network, a deformation convolution kernel and edge motion matching loss are combined, joint optimization of a geometric boundary and motion continuity is realized, and the segmentation robustness of a flexible target is improved; through optical flow back propagation dynamic correction and confidence coefficient propagation, high-precision segmentation masks with consistent time and space are output; and through a target trajectory re-identification and completion mechanism driven by a graph attention network, and in combination with optical flow deformation prediction, stable tracking in a shielding scene is realized.

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention discloses an underwater small target detection method based on YOLOv8 improvement, and aims to solve the problem of small target detection caused by illumination variation, background complexity and target scale difference in an underwater environment. According to the method, optimization is carried out based on a YOLOv8 architecture, firstly, a star convolution StarsBlock in a StarNet backbone network is combined with a C2F module of the YOLOv8, an improved C2FStarsBlock module is obtained, and the diversity and robustness of feature extraction are remarkably improved. Secondly, a CARAFEEnhanced module is adopted to replace up-sampling operation of a YOLOv8 neck, and dynamic convolution, grouping convolution and a nonlinear feature enhancement strategy are introduced, so that the calculation efficiency and adaptability of the model are optimized; and finally, integrating a gradient-guided multi-scale convolution attention mechanism GMSAA, introducing the gradient-guided attention mechanism in a back propagation stage, accurately focusing on a target area, and reducing background interference. The method effectively improves the detection precision of the underwater small target, and is especially suitable for efficient automatic detection of small underwater targets such as marine organisms.

The invention provides a computer document intelligent compliance detection system based on deep learning, and relates to the technical field of data processing, and the system comprises the steps: carrying out the topological structure analysis of an adjacent matrix through a graph neural network, so as to detect an abnormality, obtaining a structure deviation degree, and analyzing a decision path node sequence through a pre-training language model to generate semantic conflict features; fusing the structure deviation degree with the semantic conflict feature to generate a risk feature vector; generating an augmented rule set according to the risk feature vector, and updating the formalized rule base; updating parameters of the feature coding component through gradient back propagation; optimizing a differentiable logic layer judgment threshold value and the weight of an analysis module; and obtaining the updated feature coding component, the rule base version and the risk quantification parameter. According to the invention, the efficiency of document compliance detection is effectively improved.

The invention discloses an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

The invention provides a Monte Carlo tree search and multi-agent cooperation-based knowledge graph question and answer method, which comprises the following steps that: a plurality of agents are initialized, and each agent has an initial process, a state and a target; a graph query statement is generated through multi-agent cooperation, and a complete track with scores is obtained; and utilizing a Monte Carlo tree search method, optimizing a query track through the steps of selection, expansion, simulation and back propagation according to the query track generated through multi-agent cooperation, generating an executable graph query statement, and finally outputting an answer set of the question. According to the method, the advantages of an end-to-end method and a step-by-step reasoning method are combined, meanwhile, the defects that the knowledge graph environment perception ability is insufficient and the search space is too large are overcome, dynamic generation, evaluation and optimization of the query path are achieved by designing a division and cooperation mechanism of the expansion agent, the simulation agent and the valuing agent, and the search efficiency is improved. Therefore, the accuracy and robustness of the question answering system are improved.

The invention provides an automatic heuristic algorithm planning method based on a large language model, and the method comprises the following steps: carrying out the initialization and problem modeling, starting from a basic heuristic mode through guiding the large language model, generating a candidate algorithm set in combination with a plurality of cognitive perspectives, and providing diversified starting points for a search space; configuring core parameters of Monte Carlo tree search; in each iteration process, planning is started in a heuristic space by utilizing Monte Carlo tree search, and the process is composed of five core stages of selection, reflection, expansion, simulation and back propagation; and after all iterations are completed, the path with the highest average reward and the corresponding optimal heuristic algorithm are returned, and the global optimality of the final solution is ensured. According to the method, the effective experience can be automatically extracted from the heuristic strategy generated historically, and real-time feedback adjustment and strategy induction optimization of the heuristic structure are realized, so that the knowledge migration and generalization ability in the search process is remarkably enhanced.

The invention relates to the technical field of data processing, in particular to a multi-source target trajectory fusion method and system based on deep learning. Comprising the following steps: constructing a space-time alignment training set containing radar echo data, satellite telemetry data and truth value trajectory data; designing a multi-channel deep learning network architecture which comprises a radar echo data feature extraction channel and a satellite telemetry data feature extraction channel, and establishing dual-channel feature association through an attention mechanism; optimizing network parameters through back propagation to obtain a weight prediction model; radar echo data and satellite telemetry data are received in real time, and space-time alignment of sampling data is achieved through a sliding window mechanism and coordinate system conversion; inputting the preprocessed double-source features into the trained weight prediction model, and generating a radar-telemetering fusion weight coefficient; and performing weighting processing and optimization on the double-source trajectory coordinates, and outputting a fused target motion trajectory. The problem that a traditional single-source track measurement system is insufficient in precision in a complex environment is solved.

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

Disclosed in the present invention is a lightweight defect detection method based on hybrid multi-scale knowledge distillation. The method comprises: constructing a dataset; constructing a teacher network model and a lightweight student network model; using the dataset to train the teacher network model, and saving a weight file of the trained teacher network model; and loading into the teacher network model the saved weight file of the teacher network model, inputting defect images in the dataset into the teacher network model and the student network model to respectively obtain first multi-scale features and second multi-scale features, respectively inputting the first multi-scale features and the second multi-scale features into a cascaded knowledge blending module to obtain final deeply fused first multi-scale features and final deeply fused second multi-scale features, then calculating a hybrid multi-scale knowledge loss, and in combination with the prediction loss of the student network model, using a backpropagation algorithm to update network parameters, so as to obtain a trained lightweight student network model for implementing defect detection of intelligent manufacturing products. The cognitive ability and recognition performance for defects of different scales are improved.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

The invention relates to a training method, a data processing method, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The training method comprises the steps that a training sample is input into a to-be-trained model, forward propagation is executed, output of the to-be-trained model is obtained, the to-be-trained model comprises a plurality of expert layers, and a specific activation value is not stored in the forward propagation process; determining the value of a loss function according to the output of the to-be-trained model; according to the value of the loss function and the loss function, back propagation is executed, in the back propagation process of each expert layer, a recalculation task of a specific activation value and a first communication task are executed in an overlapping mode, and for each expert layer, the first communication task comprises a communication task of data interaction among multiple devices corresponding to the expert layer.

Systems and techniques are described herein for training a machine learning (ML) model. For instance, a process can include obtaining, from a teacher ML model, a first prediction based on an input. The process can further include obtaining, from a student ML model, a second prediction based on the input. The process can include determining a loss based on a difference between the second prediction from the first prediction. For instance, the loss can include a variance reduced total variation distance (TVD) loss based on an unbiased gradient estimate of the loss. The process can further include backpropagating the loss through the student ML model to train the student ML model.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention discloses a network intrusion detection method and system based on edge attention learning, and a storage medium, and the method comprises the steps: converting an original network flow into a network flow diagram, and constructing a training diagram and a test diagram under the condition that a coarse-grained label and a fine-grained label are reserved; edge embedding representation is obtained through edge feature reservation, adaptive weight distribution and multi-layer feature extraction of the training graph and the test graph; based on the edge embedding representation, performing coarse-grained detection to identify a basic attack category, and performing fine-grained classification by using multi-scale feature fusion related to global graph attributes; and adversarial training: through initializing adversarial disturbance and optimizing disturbance based on loss function gradient iteration, superposing final disturbance into the training graph, and based on loss function back propagation updating, obtaining a trained network intrusion detection model. The method provided by the invention can effectively capture the depth characteristics of the key attack and maintain the stable detection performance in the confrontation environment.

The embodiment of the invention provides an attention mechanism calculation optimization method and device, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: executing a forward re-calculation operator based on a query block and a key block, and obtaining a forward normalization result. Executing a first matrix multiplication operator based on the output gradient block and the forward normalization result to obtain a value intermediate result, and executing a point multiplication summation operator in parallel based on the forward output block and the output gradient block to obtain an intermediate tensor; executing a first type of transformation operator on the value intermediate result to obtain value gradient blocks; and meanwhile, a gradient back propagation operator is executed in parallel based on the output gradient block, the value block, the intermediate tensor and the forward normalization result, and a reverse normalization result is obtained. And finally, on the basis of a reverse normalization result, the key blocks and the query blocks, obtaining query gradient blocks and key gradient blocks, so that effective pipeline processing is performed on different operators, and the overall performance of attention mechanism calculation is improved.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention provides a lithium battery SOH estimation method based on a time sequence diagram neural network under physical constraints. The method comprises the following steps: collecting battery charge-discharge cycle data; time-related statistical characteristics and IC curve characteristics are extracted from the battery charge-discharge cycle data, and analysis characteristics are obtained after PCC analysis; calculating a time sequence distance between analysis features based on a sliding window and multi-channel collaborative dynamic time warping, and constructing a graph structure; constructing a time sequence diagram neural network, firstly extracting spatial features through diagram convolution, and then modeling a degradation law through time convolution to obtain a preliminary SOH predicted value; defining a physical residual error loss and a monotonicity regular term, and performing weighted combination on the physical residual error loss and the monotonicity regular term and data fitting loss to construct a joint optimization objective function; based on the optimization target, a final constrained SOH predicted value is obtained through a back propagation training model; therefore, in combination with a physical information constraint mechanism, high-precision and physically consistent SOH prediction is realized.

The invention discloses a sparse view angle three-dimensional reconstruction method based on 3DGS. The method comprises the following steps: providing a Gaussian field initialization model; according to the method, the FPN, the 2D U-Net and the MLP are used for forming a Gaussian parameter initialization network, the network integrates high-level and low-level semantic information, and the understanding of the network on multi-scale features is enhanced. And then a complete Gaussian field model is obtained by combining initial point cloud coordinates generated by DUSt3R. And then performing projection and rasterization under different visual angles on the 3D Gaussian ball by taking three-dimensional Gaussian splashing as a main body frame of an algorithm, performing adaptive adjustment of cloning and pruning on the 3D Gaussian ball with relatively large gradient change, and outputting an output result which is optical information and depth information of each visual angle image after rendering. Then, in gradient back propagation, pixel value absolute value deviation between the RGB image obtained by rendering and the RGB true value is supervised through L1 norm loss; the scene rendering quality is high, calculation is simple, and engineering implementation is easy.

The invention relates to the technical field of computer-generated holographic three-dimensional display and deep learning, in particular to an unsupervised deep learning method for realizing three-dimensional holographic display. The method comprises the steps of generating a depth map corresponding to a two-dimensional image; the depth image and the two-dimensional image are spliced in the channel dimension to serve as input of a hologram encoder, and a double-U-Net cascade neural network architecture serves as the hologram encoder; angular spectrum diffraction back propagation is carried out through the generated pure phase hologram to realize three-dimensional scene discretization reconstruction, a reconstructed image with a specified depth is obtained, a depth map is uniformly quantized to obtain a plurality of binary masks, loss calculation is carried out on the reconstructed image and a target image superposed with the corresponding depth binary masks, network parameter optimization is carried out, and a target image with the depth corresponding to the target image is obtained. And when the training of the double U-Net cascade neural network architecture is converged, the training stage is ended. According to the method, high-quality three-dimensional hologram reconstruction is realized through layered angular spectrum propagation, and the method has relatively high precision and detail reduction capability.

The invention provides an unmanned aerial vehicle inspection fault intelligent identification system and method, and relates to the technical field of unmanned aerial vehicle intelligent inspection, and the method comprises the steps: building a dual-target path model through historical fault data, carrying out the dynamic optimization of an inspection path of an unmanned aerial vehicle through the dual-target path model according to multi-source sensing data, and obtaining an optimized path and a sensing region; performing hierarchical identification on the multi-source sensing data to obtain a state prediction value of the unmanned aerial vehicle, and constructing an intelligent path guiding function based on the optimized path, the sensing area and the state prediction value; performing back propagation correction on the intelligent path guiding function to obtain fault type features and detection precision, and constructing a feature association weight matrix of the inspection path based on the fault type features and the detection precision; according to the method and the device, path reconstruction and fault intelligent identification based on identification feedback driving can be realized, so that the accuracy of an unmanned aerial vehicle inspection result is improved.

The invention provides a multi-modal-based advertisement recommendation method and a related device, and systematically solves the technical bottleneck of a traditional recommendation technology in a complex scene through multi-modal feature alignment, dynamic weight optimization and causal effect decoupling. The method comprises the following steps: firstly, respectively extracting modal features of a video, a text and a user behavior sequence, and constructing a cross-modal contrast loss function to strengthen multi-modal semantic alignment; secondly, dynamically updating each modal weight through back propagation, and realizing advertisement recall and sorting in combination with cross-modal similarity calculation; an anti-fact causal reasoning module is further introduced, interference of environment mixed variables on the recommendation effect is eliminated through tendency score estimation and anti-fact result prediction, and the real causal effect of advertisement exposure is accurately quantified. According to the scheme, representation learning, dynamic decision and causal inference are deeply fused, a generalized and anti-noise technical framework is provided for short video advertisement recommendation, and the performance boundary is remarkably superior to that of traditional collaborative filtering, matrix decomposition and other methods.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention belongs to the technical field of image processing and computer vision, and particularly relates to a foggy day data set synthesis method and device based on a depth estimation model in a power transmission scene. The method comprises the steps of performing meteorological screening on an original power transmission image through a convolutional neural network classifier to reserve a power transmission image of a cloudy / cloudy scene, and performing feature enhancement processing on the reserved power transmission image by using a python-piq library to obtain a target power transmission image; constructing a double-branch depth estimation network to extract macroscopic structure features of the power transmission equipment and microcosmic texture features of the conductor, and generating an optimized gray depth image through a confidence fusion algorithm constrained by span information; calculating a self-adaptive atmospheric light value by using an improved dark channel prior algorithm, and generating a physical fog field in combination with a depth-guided atmospheric scattering model; and constructing a DISTS-NEW evaluation model oriented to a tower structure, and dynamically adjusting the foggy day concentration coefficient of the physical fog field through a back propagation mechanism until the foggy day concentration coefficient meets a power transmission equipment recognizable degree threshold value.

The invention discloses an industrial chain multi-objective optimization solving method and a computer program product, and belongs to the technical field of data processing method.The industrial chain multi-objective optimization solving method comprises the steps that a mathematical model is constructed, and multiple objective functions of an industrial chain are quantized; constructing a swarm intelligence bat optimization algorithm; parameters in the swarm intelligence bat optimization algorithm are initialized; executing a search process, updating the speed and position of each bat individual, and comprehensively evaluating the fitness value of each bat individual by using a plurality of objective functions; a Pareto dominating strategy is adopted to optimize the multiple objective functions, and a Pareto non-dominated solution set is screened out; using a congestion distance algorithm to calculate a congestion distance between solutions in a Pareto non-dominated solution set, and rejecting over-concentrated solutions to form an optimal solution; carrying out back propagation on the optimal solution and the fitness value, and optimizing a next iteration result; and outputting an optimal solution when an iteration termination condition is satisfied. According to the method, the solving precision, the convergence speed and the global search capability of the industrial chain optimization problem can be improved.

The invention discloses a dual-channel time-frequency fusion driving Mama main power distribution network detection and classification method, which comprises the following steps: S1, collecting fault voltage and current signal data under different main power distribution network topologies, and constructing a training set; s2, constructing a model based on dual-channel time-frequency fusion driving Mama, wherein the model is used for calculating and obtaining the fault probability and the fault category label of the kth fault; s3, the training set trains the model by using a back propagation and gradient descent method to obtain a trained main and distribution network fault detection network which is used for inputting a fault data set and then mapping a corresponding fault classification label; and S4, inputting fault voltage and current signal data of the main power distribution network by using the trained model, and executing detection classification operation. According to the method, through multi-scale feature reconstruction and cross-modal fusion, the detection robustness in a complex noise environment is remarkably improved, and the fault starting moment, the duration time and the propagation path are accurately captured.

The invention discloses a social robot identification method and system based on deep learning, and relates to the technical field of robots, and the method comprises the steps: carrying out the smooth processing of a tweet through a large language model, and generating a tweet fused with expression semantics in combination with a natural language processing model; capturing an emotion expression difference between a robot account and a real user; generating a comprehensive feature vector of global context sensing; outputting fusion features; the output fusion features are mapped to a high-dimensional space through a linear layer, and the detection probability of a robot account is output through an activation function; and based on adaptive moment estimation, gradient back propagation is carried out by using a cross entropy loss function, and parameters of the graph convolutional network model are optimized to obtain a final classification result. According to the method, the problems of sparse text expression and emotion information loss are effectively relieved, and the separability of the social robot and the real user in emotion behavior modes is enhanced.