530 results about "Counter propagation" patented technology

The invention discloses a base station facility abnormity intelligent monitoring method and system, and the method comprises the steps: employing a multi-scale wavelet fusion pulse neural network to construct a dynamic signal perception topology according to the real-time data of a base station physical layer, and generating a space-time aligned base station full-dimension feature tensor; inputting the full-dimensional feature tensor of the base station into a fault mode confrontation distillation module, reconstructing a normal working condition manifold of the base station based on a physical constraint variational auto-encoder, and outputting an abnormal feature vector with a fault fingerprint identifier; fault propagation graph modeling is carried out on the abnormal feature vectors, and a base station fault root cause topological graph is generated in combination with back propagation credibility verification; and inputting the base station fault root cause topological graph into a toughness self-healing strategy generator, and finally outputting a base station facility self-healing strategy set meeting real-time constraint. By utilizing the embodiment of the invention, the accuracy and the real-time performance of abnormity monitoring can be improved, and the operation reliability and the communication service quality of the base station are improved.

The invention discloses a user behavior prediction system and method based on multi-modal data fusion, and particularly relates to the field of user behavior prediction, and the system comprises a multi-modal data collection module, a preprocessing and feature extraction module, a cross-modal fusion module, a user behavior prediction module, and a model optimization and feedback module. According to the system, multi-dimensional original data such as visual sense, auditory sense, text, physiological signals and environment context of a user are acquired in real time through a multi-modal data acquisition module; then, deep networks such as ResNet, VGGish and BERT are adopted to extract high-dimensional feature vectors of all modals, and contribution weights of features of different modals are dynamically learned through an attention mechanism; and finally, based on a time sequence model of Transform and LSTM, analyzing fusion features, and outputting probability distribution of future behavior intentions. And parameter joint optimization and continuous learning are realized through a multi-objective loss function and end-to-end back propagation.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The embodiment of the invention provides an assembly line optimization method and device for multi-modal large model training, and relates to the technical field of artificial intelligence. According to the method, each model slice of a to-be-trained multi-modal large model is deployed to different training devices, each training batch is divided into a plurality of micro-batches, for each training batch, training sequences corresponding to different training devices are determined according to a parallel scheduling strategy, and the training sequences are used for training the multi-modal large model. The training sequence comprises a forward propagation position, an input back propagation position and a weight back propagation position of each micro-batch, and finally, on each training device, a training process of a corresponding training batch is executed based on the training sequence until training is completed. A back propagation process is divided into back propagation of an input matrix and back propagation of a weight matrix, three calculation stages are jointly formed by the back propagation process and forward propagation, peak shifting calculation can be carried out, bubble time can be effectively filled, idle duration can be reduced, the bubble proportion of an assembly line can be remarkably reduced, and training throughput can be greatly improved.

The invention discloses a classification model training method based on big data distributed computing, and relates to the technical field of big data processing and machine learning, and the method comprises the steps: S1, obtaining communication delay data between distributed computing nodes, and analyzing delay characteristics to generate regulation and control parameters, the method comprises the steps of S1, dynamically determining the batch size of each training batch based on regulation and control parameters, and ensuring the optimization of the synchronization efficiency, S3, distributing the adjusted batch size to each node, and executing the forward and reverse propagation calculation process of the model, and S4, collecting the calculation result of each node to perform parameter aggregation updating, and completing the iterative training of the classification model. According to the classification model training method based on big data distributed computing, the batch size is regulated and controlled according to the communication delay between the nodes so as to solve the problem that the distributed synchronization efficiency is low.

The invention relates to a robot decision control method based on gradient rarefaction and a robot. The method comprises the following steps: acquiring multi-modal sensor data of a robot; based on a preset sparsification strategy, generating a dynamic mask corresponding to the gradient matrix of the multi-modal large model; based on the generated dynamic mask, screening an effective gradient in a back propagation process of the dynamic mask; updating parameters corresponding to the effective gradient in real time, and obtaining the output of the multi-modal large model based on the updated parameters; according to the obtained multi-modal sensor data and the output of the multi-modal large model based on the updated parameters, feature fusion is carried out, and a combined state code including an environment state, a robot body state and historical decision information is generated; and according to the determined joint state code and based on a time sequence model, generating an action sequence, a force control parameter and a path planning dynamic decision instruction of the robot, so that the robot can act based on the generated dynamic decision instruction, thereby realizing decision control of the robot.

The invention discloses a deep learning table recognition model evolution-oriented regression defect relieving method, which comprises the following steps of: firstly, screening new features by applying a CONMI evaluation function in combination with specific hyper-parameters in a feature selection stage, and taking a new feature set obtained by combining the new features with an old feature set as model training data to realize contribution degree control-based retraining; on one hand, the feature contribution degree is calculated through the expected gradient, on the other hand, an overall loss function containing cosine similarity and a cross entropy loss function is designed to stabilize the old feature contribution degree and learn new features, and a model is trained through forward propagation and back propagation processes based on a new feature set; and under the condition that the accuracy rate of the table recognition model of the new version meets the condition, constructing a feature evolution scene, realizing a model evolution process according to a regression mitigation strategy, and taking the accuracy rate of the table recognition model of the new version as an evaluation index to obtain an evolution process regression defect mitigation strategy. The method is beneficial to improving the quality and reliability of a deep learning table recognition model.

The invention discloses a multivariable time sequence prediction method and system based on an implicit neural network. The method comprises the following steps: 1) collecting data and preprocessing the data; 2) performing window division on the standardized or normalized multivariable time sequence and determining the length of a to-be-predicted window; 3) performing variable correlation coding on the input window to obtain a variable-level feature vector; 4) the implicit neural network based on time attention predicts target parameters by using the variable features in the step 3), and implicit neural representation of the target sequence is modeled through the parameters; 5) taking the output of the implicit nerve representation and the original input window as the input of the multi-head attention predictor, and obtaining a prediction result through cross-sequence cross attention calculation performed in the implicit space and multi-layer perceptron conversion output dimension; and 6) training and optimizing model parameters, calculating a mean square error of a prediction result and a real result, taking the mean square error as a loss function, carrying out back propagation to optimize trainable parameters of the variable correlation coding module, the implicit neural network module multi-head attention predictor and the multi-layer perceptron, and then repeating the steps 3) to 6) to obtain the multi-head attention predictor. Until the preset number of iterations is reached or the error of the model on the verification set meets the requirement of early stop; and 7) performing prediction by using a model of training convergence, and performing reverse normalization on a prediction result to obtain a final prediction result. The method has good generalization, and meanwhile, the interpretability of the attention mechanism is remarkably improved by generating the hidden space characteristics of the trend component and the season component.

The invention relates to a vision and GNSS fusion displacement monitoring method and system based on deep learning. The method comprises the following steps: respectively acquiring visual data and GNSS data, preprocessing the visual data and the GNSS data, and synchronizing the visual data and the GNSS data to obtain synchronized visual data and synchronized GNSS data; performing feature extraction and combination to obtain a modal combination feature sequence; constructing a dynamic weight adaptive fusion network; and training the dynamic weight adaptive fusion network to obtain a displacement monitoring model, and inputting the modal joint feature sequence into the displacement monitoring model to complete displacement monitoring. According to the invention, through a feature extraction network of joint learning, time sequence correlation modeling of vision and GNSS in the same embedding space is realized. And dynamic self-adjustment of the modal weight is realized through a dynamic weight adaptive fusion network. All modules can be micro, and end-to-end back propagation training is supported. And when the visual signal is interfered or the GNSS is interrupted for a short time, the system can carry out self-adaptive compensation, and continuous and stable displacement output is kept.

The present application relates to the technical field of autonomous driving, and discloses a BEV feature decoding method and apparatus, an electronic device, and a storage medium. The method comprises: first, acquiring a BEV feature map corresponding to an image at the current moment; and then, inputting the BEV feature map into a preset multi-dimensional attention decoding model for decoding, to obtain a multi-task decoding result. During the training of the multi-dimensional attention decoding model, the multi-dimensional attention decoding model is configured to comprise N attention mechanism network layers; losses of outputs of the N attention mechanism network layers would be independently calculated, and the losses of the layers are summed at the last attention mechanism network layer as a total loss, so that the back propagation process of each attention mechanism network layer is optimized, making global training convergence faster; in addition, model training is not prone to being trapped in local optima, and a large deviation would hardly occur in a result.

The invention relates to the technical field of metal processing detection, in particular to a copper bar drawing residual stress detection method and system, and the method comprises the steps: collecting production line process state parameters in real time, and mapping the parameters to a predefined multi-dimensional process parameter space to generate a dynamic state vector; adjusting a key coefficient of a material constitutive equation based on the dynamic state vector, and generating a reference stress field; synchronously acquiring multi-physical field data on the surface of the copper bar; constructing a physically constrained machine learning model, and generating a cross-scale fusion stress field; an adjoint optimization equation is constructed based on dynamic process constraints, and model weight parameters are updated through real-time back propagation gradient; and outputting a new cross-scale fusion stress field according to the updated machine learning model, and obtaining related indexes of residual stress detection. According to the invention, the defects of static stiffness and insufficient data fusion of the model in the prior art are effectively overcome, and the real-time detection of the residual stress with adaptive technological parameters is realized.

The invention provides a training method and device for a VLA model, and relates to the technical field of intelligent robots with bodies, and the method comprises the steps that the VLA model outputs a joint angle sequence of a mechanical arm of a robot with a body based on visual input and language input; the differentiable forward kinematics module outputs a real-time pose of an end effector of the mechanical arm in a task space; based on joint space loss formed by the joint angle sequence and the joint angle in the teaching data, task space loss formed by the real-time pose and the tail end pose in the teaching data, and task space constraint loss, a multi-objective loss function is constructed, and total loss is calculated; calculating the gradient of the total loss to the joint angle sequence and the real-time pose through back propagation, and optimizing preset parameters of the VLA model; the above steps are repeatedly executed until the total loss meets the expectation, and a trained target VLA model is obtained; the technical problems that when a VLA model is trained in a joint space, the hardware coupling performance is high, and the generalization ability is insufficient are solved.

The invention discloses a voice model compression method, electronic equipment and a storage medium. The method is specially designed for a large-scale sequence-to-sequence speech recognition model with an encoder-decoder architecture. According to the method, the decoder and the encoder are pruned in sequence, so that heavy back propagation calculation is avoided. According to the method, under the condition of no back propagation or retraining, the parameters of the Whisper-large model can be reduced by about 60%, and the performance of the model on various data sets is hardly influenced. Meanwhile, the method is suitable for a multi-language data set, and the pruned model keeps good robustness and generalization in the aspect of multi-language ability. According to the innovation, the threshold of large-scale model deployment is greatly reduced, and the method is easier to apply in a resource-limited environment.

The invention provides an online video instance segmentation method and system based on mask propagation, and the method comprises the following steps: S1, achieving the cross-frame propagation of a target mask through a mask propagation model, obtaining a prediction mask of a current frame, and achieving the correlation of a target through the calculation of the intersection-union ratio of the prediction mask; and S2, performing back propagation on the target by using the mask propagation model so as to complement the missed target mask and enhance the continuity of the track. Objects between different frames are associated on the basis of a mask propagation mechanism and in combination with the intersection-to-union ratio of masks, and meanwhile, in order to relieve the scene that a segmentation model fails to segment shielded objects, non-significant objects and the like, a reverse mask propagation technology is provided to complement the failed frames. The method aims at performing fine segmentation and continuous tracking on various scenes such as intelligent monitoring, automatic driving, augmented reality and video editing on the target in the video, and has wide research and application prospects.

The invention relates to a data parallel communication method and device in distributed training, a storage medium and a program product. The method comprises the following steps: for any computing device in a computing device cluster participating in distributed training of a target model, obtaining global latest parameters of the target model through global collection operation in a forward propagation process, according to the training data subsets corresponding to the computing devices and the global latest parameters of the target model, forward propagation calculation is carried out, loss values corresponding to the computing devices are obtained, and different computing devices in the computing device cluster carry out forward propagation calculation based on different training data subsets; and for any computing device, calculating a gradient corresponding to the computing device based on the loss value corresponding to the computing device in a back propagation process, and transmitting the gradient corresponding to the computing device to other computing devices in the computing device cluster through reduction and dispersion operation. The communication overhead can be reduced, and the efficiency of large-scale training can be improved.

The invention provides a segmentation federal learning method and system based on aggregation gradient broadcast, and the method comprises the steps: initializing a global model, and segmenting the global model into a server model and a client model according to the capability limitation of a local device; issuing the client model to all local equipment terminals; the multiple local devices execute forward propagation at the same time, and shredded data are obtained through calculation; transmitting the shredded data and the training data label to an edge server; performing forward propagation in parallel according to the shredded data, calculating the loss of each local equipment end in combination with a corresponding training data label, and performing back propagation to obtain a loss function and a gradient of the shredded data; aggregating the gradient of the shredded data and broadcasting to all local equipment; updating the server side model and the client side model, and aggregating the server side model; and the iterative learning loop is repeatedly executed until convergence or the maximum communication round is reached. According to the invention, the communication overhead in segmentation federal learning is saved, and the model training efficiency is effectively improved.

The invention relates to a large model training method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: for any item of training data of a target large model, segmenting the training data into a plurality of parts of segmented data, storing the plurality of parts of segmented data in a nonvolatile memory, and sequentially performing forward propagation calculation and back propagation calculation on the plurality of parts of segmented data; for any part of segmented data, reading the segmented data from a nonvolatile memory to a video memory, and executing forward propagation calculation on the segmented data through a GPU (Graphics Processing Unit) to obtain an activation value corresponding to the segmented data; and for any part of segmented data, executing back propagation calculation based on the activation value corresponding to the segmented data through the GPU to obtain gradient data corresponding to the segmented data, and moving the gradient data corresponding to the segmented data from the video memory to a nonvolatile memory or a CPU memory. The display memory occupation of the activation value can be reduced.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention discloses a multi-modal data feature representation optimization method based on comparative learning and two-stage masks. The method comprises the following steps of: firstly, aiming at an input single image-text pair, generating two groups of heterogeneous image-text data with mode missing as two inputs of a model by applying two random mask strategies with different mask areas and proportions; wherein the first group of data applies a high-proportion mask to the image and applies a low-proportion mask to the text; the second group applies a low-scale mask to the image and a high-scale mask to the text. Then, the two groups of data respectively pass through an image encoder and a text encoder which share parameters, and two different multi-modal fusion feature vectors are generated through a cross-modal fusion encoder; according to the method, masked modal information is recovered through a decoder, and the reconstruction loss of the difference between a recovery result and original data is calculated. Meanwhile, two multi-modal fusion feature vectors generated twice are subjected to comparative learning, and the comparative loss of feature representation distances under different enhanced views for approaching the same image-text pair is calculated. And finally, performing weighted summation on the reconstruction loss and the comparison loss to form an overall loss function, and optimizing model parameters through back propagation. When the trained encoder is used for a downstream multi-modal classification task, the classification precision and generalization ability of the model can be effectively improved.

The invention belongs to the technical field of target detection, and particularly relates to a teacher tensor knowledge distillation-driven anti-violation target detection method in an electric power safety supervision scene, and the method comprises the following steps: inputting image data to a student model and a pre-trained teacher model; performing forward propagation on the student model to obtain a student prediction tensor; associating differences between the real tags based on the student prediction tensor and the input image data; acquiring an original teacher output tensor by using the same input image data by calling a bottom layer forward propagation method of a teacher model; calculating knowledge distillation loss based on the difference between the student prediction tensor and the original teacher output tensor; the standard detection loss and the knowledge distillation loss are combined to form total loss; and performing back propagation updating on parameters of the student model based on the total loss so as to complete model training and real-time target detection and detection method updating optimization. According to the invention, the performance of the student model can be significantly improved, and the high efficiency of the model is maintained.

The invention relates to the technical field of optics and the like, in particular to a coupled system parameter prediction method based on a physical-data co-driven deep neural network CMT-NN. The method comprises the following steps: taking optical response spectral lines of a double-micro-ring coupling system under different physical parameters as the input of a CMT-NN model, and taking the output as the physical parameters of a coupling resonance system; the step of constructing the trained CMT-NN model specifically comprises the following steps: S1, constructing a physical model of a target coupling system; s2, constructing a CMT-NN model, bringing physical constraints and physical parameters of the coupling model into a loss function of CMT-NN at the same time, calculating physical loss in a back propagation process through the loss function through iterative training and back propagation, and if a preset condition is met, completing training to obtain the CMT-NN model for predicting the physical parameters of the coupling resonance system; and S3, verification of the CMT-NN model is completed, and a trained CMT-NN model is obtained.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The invention belongs to the technical field of optical calculation, and discloses an error-adaptive optical diffraction neural network in-situ training method, which comprises the following steps: based on the phase of each phase modulation layer, calculating the light field complex amplitude Uk of the input sample complex amplitude on the rear surface of each phase modulation layer and the light field complex amplitude Uo of the input sample complex amplitude on an output plane after forward propagation; calculating an error light field P based on Uo, carrying out three-dimensional central symmetry on a back propagation light path of the error light field P without changing the phase distribution of each phase modulation layer, and calculating the light field complex amplitude of the complex amplitude of the symmetrical error light field on the front surface of each phase modulation layer after forward propagation; the method comprises the steps of obtaining the light field complex amplitude Un + 1-k'of P back propagation to the rear surface of each phase modulation layer, calculating the gradient related to a loss function based on Uk and Un + 1-k 'so as to carry out updating, and carrying out the next round of training until a preset training round or loss convergence is reached. The method can adapt to errors existing in an optical system.

The invention provides a contrast learning sequence recommendation method based on large-scale language model view enhancement, which comprises the following steps of: 1, establishing a dynamic importance scoring mechanism, and calculating importance and generating a dynamic adjustment factor by utilizing a large-scale language model according to a hidden layer state of a user and an article historical interaction sequence; 2, generating an enhanced view containing a negative sample pair; step 3, constructing a sequence and project dual-granularity contrast learning method under multi-mode dual-view enhancement; 4, inputting the original user and article historical interaction sequence into a linear recursion unit; and 5, dynamically fusing a contrast learning method and a conventional sequence recommendation method, performing optimization through a weighted combination loss function, and jointly training the model through back propagation. According to the method, the resource utilization rate is improved, meanwhile, the robustness of the model in cold start item recommendation and long-tail distribution scenes is ensured, and an efficient and stable solution is provided for a dynamic recommendation system.

The invention discloses a high-resolution remote sensing image small target detection method, system and product based on point supervision, in a forward propagation stage, point labeling is used as supervision information, a feature aggregation strategy based on a gating mechanism is combined, shallow small target detail features are effectively reserved, feature response scores and edge response scores are combined at the same time, and the detection accuracy is improved. And generating a preliminary pseudo tag through a discrete second derivative method. Through a dynamic multi-instance learning and feature enhancement technology, the distinction degree between candidate frames is improved, and high-quality pseudo labels are screened out for training, so that the performance of a detector is improved. In the training stage, back propagation is carried out, multi-instance learning loss and intersection-parallel ratio loss joint optimization is adopted, and a dynamic weight distribution mechanism is introduced to balance various types of loss. According to the point supervision-based high-resolution remote sensing image small target detection method provided by the invention, the learning ability of a network to a small target sample under a point supervision normal form can be effectively enhanced, and the overall detection performance of a model to a small target is effectively improved.

The invention belongs to the technical field of wind power plant power prediction and intelligent operation and maintenance, and particularly relates to a wind power plant short-term wind power prediction method based on a space-time attention mechanism. According to the method, a dynamic graph evolved along with time is constructed through multi-source heterogeneous data fusion, and a time-varying adjacency matrix is output; obtaining space-time coupling characteristics through disturbance-sensitive space attention coding and wind speed-power coupling sensitivity driven time attention decoding; multi-scale features are output through dual-scale adaptive fusion; and finally, forming a continuously evolved prediction chain through edge end rolling learning and topological closed-loop correction by combining a loss back propagation optimization model of physical consistency constraint. According to the method, the problems of lack of physical constraints, insufficient dynamic adaptation and low efficiency of edge deployment in the prior art are solved, the prediction physical reasonability and dynamic adaptation are improved, efficient edge deployment is realized, and reliable support is provided for wind power plant scheduling.

A method, an apparatus, and a non-transitory computer-readable storage medium for video compression using a generative adversarial network (GAN) are provided. The method includes obtaining, by a generator of the GAN, a reconstructed target frame based on a reference frame and a raw target frame to be reconstructed; concatenating, by a transformer-based discriminator of the GAN, the reference frame, the raw target frame and the reconstructed target frame to obtain a paired data; determining, by the transformer-based discriminator of the GAN, whether the paired data is real or fake to guide reconstruction of the raw target frame; and determining a generator loss and a transformer-based discriminator loss, and performing gradient back propagation and updating network parameters of the GAN based on the generator loss and the transformer-based discriminator loss.

The invention provides a text-to-structured query statement method based on a large language model and reinforcement learning, and relates to the technical field of information, the method comprises the following steps: integrating a natural language problem and a database mode into a unified prompt template to obtain a candidate structured query, and generating a reference structured query by a reference model; constructing a multi-dimensional reward framework, and performing weighted aggregation on multi-dimensional rewards to obtain corresponding final reward scores; calculating a KL penalty value between the output of the strategy model and the output of the reference model based on the candidate structured query and the reference structured query to obtain a constraint of stable strategy update; updating the strategy model parameters through back propagation iteration to obtain an updated strategy model; and analyzing the prompt template by using the updated strategy model to obtain a text-to-structured query statement processing result, and completing the process from the text to the structured query statement. The problems that existing structured query generation is low in accuracy and semantic consistency is difficult to guarantee are solved.

The invention belongs to the technical field of edge devices, and particularly relates to a model quantitative perception training system on edge devices, which comprises a data acquisition module used for acquiring data from various edge devices; the data preprocessing module is used for cleaning, normalizing and enhancing preprocessing operation on the collected data so as to improve the quality and diversity of the data and provide a better data basis for subsequent model training; and the quantitative perception training module is used for applying a quantitative perception training technology in a training process so as to simulate a quantization operation in forward propagation of the model, performing quantization and inverse quantization on the weight and the activation value, and calculating a gradient by adopting a gradient approximation method in back propagation so as to optimize model parameters. According to the method, through quantitative perception training, the weight and the activation value of the model can be converted into low-precision representation, and the calculation amount when the model runs on edge equipment is greatly reduced.

The invention discloses a reservoir scheduling method and device based on multi-target reinforcement learning, and belongs to the technical field of reservoir scheduling, and the method comprises the steps: constructing a data set according to preprocessing data, inputting a group of training space state data into a preset multi-target deep reinforcement learning network, outputting predicted space state data, and outputting the predicted space state data; and performing parameter updating on the multi-target deep reinforcement learning network based on a back propagation mode according to the loss function, and taking the predicted spatial state data as new training spatial state data to obtain an updated target deep reinforcement learning network. And inputting other groups of training space state data in the plurality of groups of training space state data into the updated target deep reinforcement learning network to obtain a trained multi-target deep reinforcement learning network, and inputting the real-time collected space state data into the trained multi-target deep reinforcement learning network. And outputting a real-time optimized reservoir scheduling strategy. According to the invention, rapid and accurate reservoir scheduling is realized.