1742 results about "Training time" patented technology

The invention belongs to the technical field of speech recognition. The invention provides a low-resource language self-adaptive speech recognition method based on AdLoRA Plus. The low-resource language self-adaptive speech recognition method comprises the following steps of: obtaining a speech According to the embodiment of the invention, the AdLoRA Plus fine tuning technology is introduced, so that the speech recognition precision of the Whisper model is remarkably improved in a low-resource language environment. Through a low-rank adapter layer and dynamic learning rate adjustment, efficient parameter updating is realized under limited training data. According to the method, a strategy of freezing basic parameters is adopted, only an AdLoRA Plus adapter layer is trained, calculation overhead and training time are remarkably reduced, pre-training knowledge is kept, and overfitting is prevented. The method has wide adaptability and expansibility, and is ensured to be outstanding in practical application. The voice recognition precision, the reasoning efficiency and the training efficiency are effectively improved, multiple challenges in application in small languages and specific fields in the prior art are solved, and the method has important practical application value.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention discloses a magneto-rheological damper modeling method based on a Gaussian process regression model. The magneto-rheological damper modeling method comprises the following steps: step 1, acquiring and preprocessing data based on a mechanical property test of a magneto-rheological damper; 2, determining Gaussian process regression prior distribution of the magnetorheological damper; 3, calculating a posteriori mean value and a posteriori covariance of the Gaussian process regression model; 4, evaluating the Gaussian process regression model of the magnetorheological damper; 5, performing hyper-parameter optimization calculation; and step 6, final evaluation and application of the Gaussian process regression model of the magnetorheological damper. On the premise of ensuring the model precision, the problems of insufficient computing resources, increased training time cost and the like possibly caused when a non-parameterized model represented by a neural network model faces a large-scale data set are solved; meanwhile, for the problem of model overfitting possibly caused by noise interference in training data, a kernel function with a noise interference suppression effect is provided so as to improve the modeling precision of the model.

The invention discloses an intelligent fusion classification and grading advanced early warning method for major disasters of a coal mine. Firstly, coal mine safety monitoring data are collected to construct a data set; secondly, an Angle-Transform model and an E2GAN model are trained, abnormal values and missing values in the data are recognized and interpolated, and data preprocessing is achieved; and thirdly, a new multi-index fusion prediction model is introduced in combination with a BiLSTM and MMOE multi-task learning network, and the future trend of each index can be synchronously predicted after training optimization. And then, improving an AdaTT network to realize a multi-disaster risk fusion early warning model, and optimizing training times and an early warning threshold value by using an F1 index. And inputting real-time monitoring and fusion prediction data into the optimized early warning model to obtain the risk probability of each disaster. And finally, in combination with the optimal early warning threshold and the risk probability, providing a multi-disaster grading early warning method, and realizing grading identification of different disaster risks. According to the method, disaster risks such as gas, fire disasters, dust and roofs can be effectively warned, risk information is provided in advance, and the method is of great significance to advanced management and control of multiple disaster risks of a coal mine.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

The invention relates to a multi-vehicle intelligent driving cooperative training method based on a block chain in the Internet of Vehicles, and belongs to the technical field of mobile communication. Aiming at the problems of data privacy leakage, too high synchronous federated learning time delay, inaccurate node contribution degree evaluation, insufficient single-chain block chain expandability and the like existing in intelligent driving cooperative training in the Internet of Vehicles, an asynchronous federated learning framework with fusion of a cloud-edge-end three-layer architecture and a double-layer block chain is constructed. A local training strategy is optimized through multi-agent reinforcement learning to minimize the total time delay of a system, a dynamic reputation evaluation mechanism based on training interaction timeliness, confirmed site occupancy and model quality contribution is designed to screen high-reputation nodes, and asynchronous model verification and PBFT main chain global aggregation are realized by adopting a DAG block chain. According to the method, on the premise of guaranteeing data privacy, the model sharing efficiency is improved by more than 30%, the training time delay is reduced by 40%, the convergence stability of a global model is effectively enhanced, and the expansibility bottleneck of a traditional architecture is solved.

An improved YOLOv8-based industrial pipeline defect detection method and system are provided. The method includes: acquiring a pipeline surface image; and recognizing a defect position and a defect type in the pipeline surface image by using a pipeline defect detection model. The model is based on an improved YOLOv8 network, which replaces the original means with WIoU loss and a Sophia optimizer during training, and a final model can quickly and accurately recognize the defect position and the defect type in the pipeline surface image. Compared with a conventional YOLOv8 algorithm, training stability, convergence speed, and recognition accuracy are improved by replacing a CIoU loss function with a WIoU loss function. An official AdamW optimizer is replaced with a Sophia optimizer, training time of the model can be greatly shortened, a lot of computing resources can be saved, and less memory is occupied.

The invention provides a VR-based dynamic visual function training method and device, a medium, a program product and a terminal. An initial training task type and configuration parameters are generated according to a visual evaluation result of a patient, a sighting mark and a stimulation signal for visual function training are constructed, and training is executed through a VR unit. In the training process, feedback data of a patient is collected in real time, a real-time evaluation result is analyzed and generated, task types, configuration parameters and sighting marks are dynamically adjusted, and new stimulation signals are generated to continue training, so that dynamic optimization of training content is achieved. Through real-time evaluation and dynamic adjustment, the problem that the training content is not matched with the requirements of the patient is solved, the defects of a traditional method in the aspects of individuation and adaptability are overcome, the training effect is remarkably improved, the requirements of the patient are better met, the training time is shortened, and the efficiency is improved. The device is especially suitable for rehabilitation training of various visual function defects, and has remarkable advantages in the aspect of dynamic visual function recovery.

The invention relates to the technical field of computer vision image enhancement, and provides a zero sample learning defogging image enhancement method and device based on image decomposition, and the method comprises the steps: introducing three initial image layer generation networks: an initial clean image layer generation network, an initial atmospheric light image layer generation network, and an initial transmissivity image layer generation network; a target atmospheric light layer generation network and a target transmissivity layer generation network are obtained through joint training and are respectively used for determining a target atmospheric light layer and a target transmissivity layer of a to-be-defogged image, and a defogged image corresponding to the to-be-defogged image is solved by combining an atmospheric scattering model. In the joint training process of the three initial layer generation networks, only the to-be-defogged image is needed, a large number of training samples do not need to be introduced, the problem that in the prior art, the training samples of an image defogging model are difficult to obtain can be avoided, the calculated amount in the training process can be reduced, the training duration of the network can be shortened, and the training efficiency of the image defogging model is improved. And the training time cost of the network is reduced.

The invention discloses a CXL-based training time consumption prediction method and device in heterogeneous computing power, and relates to the technical field of heterogeneous computing, and the method comprises the steps: obtaining task description information of a distributed training task; determining a neural layer calculation sequence of a neural network in the distributed training task and each heterogeneous computing power corresponding to the distributed training task according to the task description information; according to the neural layer calculation sequence, initial calculation process diagrams corresponding to the heterogeneous calculation power are constructed; adding a CXL-based parameter synchronization process to a backward transmission calculation node in the initial calculation process diagram to obtain each target calculation process diagram; respectively obtaining neural layer calculation time consumption and memory access time consumption of each node in the target calculation process graph; and according to the calculation time consumption of each neural layer and the access time consumption of each memory, predicting the total time consumption of training the distributed training task. The method solves the problems of long time consumption of parameter synchronization, low time consumption prediction accuracy and the like, reduces the time consumption of model parameter synchronization, and improves the accuracy of training time consumption prediction.

The present invention relates to the field of power automation. Disclosed are a power grid regulation and control model training method and system, a computer device, and a storage medium. The method comprises: acquiring training data of a power grid regulation and control model, and grouping the training data to obtain a plurality of training data groups; training a plurality of preset learning bodies in parallel by means of the plurality of training data groups on the basis of a one-to-one correspondence between the training data groups and the learning bodies to obtain a plurality of execution bodies, wherein when the learning bodies are trained by means of the training data groups, the training data groups are divided into a plurality of training data units, a training effect of each training data unit is acquired to obtain a shared training data unit, and the shared training data unit is shared with other training data groups than the training data group to which the shared training data unit belongs; and finally, combining the plurality of execution bodies to obtain a power grid regulation and control model. By means of parallel training and parallel prediction, quality is improved on the basis of quantity, and shared learning of high-quality sample data is achieved, thereby effectively improving a training effect, and being capable of effectively reducing the training time of the power grid regulation and control model under a big data condition.

The invention relates to the technical field of large model computing clusters, in particular to a large model computing cluster service reliability evaluation and optimization method, device and equipment based on parallel policy scheduling and a computer storage medium. According to the large model computing cluster service reliability evaluation and optimization method, the flow line parallel behavior is taken as the core, the influence of the performance difference between different GPUs, the data transmission time and the uncertainty factors on the training time in the training process is comprehensively considered, the existing staggered and non-staggered flow line parallel strategies are combined, and the reliability of the large model computing cluster service is evaluated and optimized. According to the method, a model which fully considers inconsistency of training time and combines various pipeline parallel strategies is established, specific constraints of a training process are given for specific characteristics of large model training, and the model is solved through integer programming so as to realize efficient and reliable training of a large model computing cluster.

The invention provides a parallel training method, device and equipment based on adaptive re-calculation and load division and a computer readable medium. The method comprises the following steps: firstly, converting a dynamic calculation graph in a deep learning framework into a static calculation graph data structure; then, the calculation sequence is divided, and calculation sub-graphs are generated; constructing a mixed integer programming model according to the calculation time of the operator, the transfer cost of the operator and the constraint condition; then, solving the scheduling optimization problem of the operator by using a solver to generate a video memory optimization strategy; aiming at the generated video memory optimization strategy, evaluating the execution time of each calculation sub-graph, and calculating to obtain a load balancing rate; and finally, according to a calculation result of the load balancing rate, determining a calculation sub-graph which is finally divided, and performing parallel training by utilizing the calculation sub-graph which is finally divided. Through the method provided by the invention, the final operator execution sequence can be found, so that the peak video memory consumption during the operation of the computational graph is within a given limit, and the training time of the neural network model is minimized.

The invention relates to a parameter updating method based on low-rank decomposition, and aims to solve the problems of high consumption of computing resources and difficulty in balancing model performance and complexity when large-scale financial data is processed in the prior art. According to the method, the pre-training model is determined, the source data set is obtained, and the source data set is utilized to train the pre-training model to obtain the source model; fixing parameters of all layers of the source model, and inserting a preset number of data processing layers into the source model; and obtaining a training data set, training the inserted data processing layer by using the training data set, updating parameters of the data processing layer in an incremental mode, and finally obtaining a target model. The method is suitable for processing a scene of a large-scale data set, the problems that in the model training process, computing resource consumption is too large, and training time is too long are solved, the practicability and response speed of the model are remarkably improved, and meanwhile the performance and adaptability of the model on specific tasks are improved.

According to an embodiment, a performance benchmark database is obtained, where the performance benchmark database at least includes structural data of one or more deep neural network models, time performance data and computing resource consumption data of a plurality of deep learning applications based on the one or more deep neural network models; a training dataset is extracted based on the performance benchmark database, where the training dataset has a plurality of parameter dimensions, the plurality of parameter dimensions including: structures of deep neural network models of the plurality of deep learning applications, resource configuration of the plurality of deep learning applications, and training time of the plurality of deep learning applications; and correspondence among the parameter dimensions of the training dataset is created so as to create an estimation model for estimating resources utilized by deep learning applications.

The invention discloses an unsupervised domain adaptive semantic segmentation method, system and device based on inter-domain consistency and a medium. The method comprises the following steps: translating source domain image data to a target domain style while keeping the content by using a GAN-based multi-scale generative adversarial network; inputting the source domain image data into an inter-domain consistency training network based on a Transform model for adaptive training, wherein the source domain image data subjected to data enhancement is sent to a student network for source domain semantic segmentation training, and an EMA strategy is used for updating a teacher network weight; inputting the target domain label-free image data into a teacher network to generate a pseudo label so as to further guide students to train the network; randomly combining the source domain image data and the target domain image data by using a hybrid strategy to guide students to learn domain invariant knowledge in a network; and finally, adjusting and optimizing the student network according to the consistency of the attention feature map, outputting an unsupervised field adaptive semantic segmentation model, and completing segmentation of target domain image data under the condition of not using a target domain label. According to the method, a model with excellent performance can be trained by using fewer real image data sets in the unmanned driving field, so that the training time and cost are greatly reduced, and the working efficiency is improved.

The invention discloses a multi-source text-based dynamic hotspot clue accurate extraction method, which realizes real-time adaptation and efficient training of a sensitive intelligence recognition model through dynamic label configuration and Prompt design in combination with a lightweight fine tuning technology (RsLoRA), and can accurately recognize in a changeable intelligence scene without re-training. Through human feedback reinforcement learning (PPO) and sorting training, clue texts output by the generative large model are optimized, event subjects and contents are accurately extracted, and a generated result is structured and highly summarized. A semantic vector model and a dynamic multi-layer DBSCAN clustering algorithm are combined, the problem that an existing clustering algorithm is insufficient in adaptability to semantic density difference scenes is solved, and high-precision clue aggregation and popularity statistics are achieved. The overall scheme reduces resource consumption and training time through the RsLoRA technology, comprehensively improves intelligence processing efficiency and analysis ability, and has significant technical advantages and wide application prospects in the fields of intelligent public security, government management and the like.

System and techniques to facilitate the translation of a sign language into another language are described herein. A modular architecture may be used in which the output of different classifiers may be used to produce intermediate representations, or final translations, of the sign language. These classifiers may be trained on different types of signs to enhance accuracy while reduce training time and complexity.

The invention relates to a multi-scale mask-based training-free defect detection method and defect detection equipment. The method comprises the following steps: S1, a data acquisition step: acquiring a to-be-detected image of a current detection product through an image acquisition device; s2, a text image alignment step: respectively carrying out multi-scale mask masking processing on the to-be-detected image, entering an image feature encoder according to a text-image pair pre-training model to carry out image feature encoding to obtain multi-scale image feature encoding information and text feature encoding information corresponding to the to-be-detected image, aligning the multi-scale image feature coding information with text feature coding information corresponding to the to-be-detected image; and S3, a product detection step: determining whether the to-be-detected image is a defective product image or not and marking the defect position of the to-be-detected image according to a coding information alignment result. According to the method, by means of the pre-trained model weight, large-scale model training starting from the beginning is avoided, so that the training time is shortened, the use of computing resources is optimized, and the high-performance detection capability can be kept.

The invention relates to the technical field of medical treatment, in particular to a child fracture rehabilitation management system based on virtual reality, which comprises a multi-modal sensing and data acquisition module, an intelligent rehabilitation training module, a multi-dimensional evaluation and feedback module and a data intelligent analysis and prediction module, compared with the defects that an existing rehabilitation training technology mainly depends on subjective experience of doctors to formulate a static scheme and lacks multi-source data dynamic feedback, the method comprises the steps that firstly, a comprehensive and accurate patient ability portrait is constructed; intelligent dynamic adjustment of training parameters is realized by utilizing an LSTM gating mechanism, a forgetting gate filters invalid historical data, an input gate fuses real-time action quality evaluation, an output gate generates a personalized difficulty gradient, and a patient ability threshold is continuously memorized in combination with a cell state, so that a training scheme has an adaptive evolution ability, and the training efficiency is improved. The parameter adjustment period of a traditional rehabilitation scheme is compressed from a week level to a real-time level, the accuracy rate of risk prediction of muscle strain and the like is improved, and the utilization rate of effective training time is increased.

The invention discloses a heterogeneous computing system and a training time consumption prediction method and device thereof, a medium and a product, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring user training scheme data corresponding to a training task to be deployed, and acquiring maximum resource supply data provided by a heterogeneous computing system to meet the user training scheme data; and predicting the calculation amount and the memory reading amount of each neural layer based on the working process of each neural layer of the to-be-trained network model in the single iterative training process, and determining node time consumption information of each neural layer in combination with the maximum resource supply data. And according to the node time consumption information of each neural layer of the to-be-trained network model, determining single iteration time consumption of the heterogeneous computing system for training the to-be-trained network model. According to the method, the problems that time consumption prediction can only be carried out on actually deployed training tasks and the accuracy is not high in the prior art can be solved, and the time consumption of single iteration training of a single calculation unit can be predicted with high precision on the basis of not actually deploying the training tasks.

The invention relates to the field of mechanical arm motion planning, relates to a multi-mechanical arm motion planning method based on an improved artificial potential field method and deep reinforcement learning, and aims to solve the problems that in a multi-mechanical arm system, early-stage rewards are sparse, the training speed is low, and effective strategies are difficult to learn in deep reinforcement learning. The invention provides an improved dynamic artificial potential field expert demonstration scheme, and in the training process, an artificial potential field reward function and a dynamic artificial potential field expert demonstration mechanism which dynamically change along with the training process are added, so that the mechanical arm obtains higher learning efficiency and more positive samples in the early-stage training process; in later training, the mechanical arm is assisted to reach a high-precision target, and then the learning speed of the mechanical arm is increased. The training process mainly comprises the following steps of constructing a simulation environment, describing and modeling a multi-mechanical-arm path planning task, generating a training sample, executing the training task and performing simulation, dynamically adjusting expert data according to a simulation result, collecting experience and updating a strategy. Compared with a traditional method, the method has the advantages that the convergence speed is higher, the training time is shorter, the precision is higher, and particularly, the early-stage convergence speed is higher, and the fitting precision is higher.

The invention provides an adaptive optimization method and system for a training process of a multi-modal large model. The method comprises the following steps: receiving a submitted training execution script and a training strategy of the multi-modal large model; according to the training execution script and the training strategy, utilizing a pre-stored large model training script to execute a distributed training task so as to generate a multi-modal large model; acquiring real-time monitoring data generated in the process of executing the distributed training task; adjusting a training strategy based on the real-time monitoring data, and continuing to execute the distributed training task by utilizing the large model training script according to the large model training execution script and the adjusted training strategy until a trained multi-modal large model is obtained. The training strategy is dynamically adjusted in real time, unnecessary computing resource waste is avoided, the training time of the model is shortened, and the training efficiency is improved; the model can be adaptively optimized according to the characteristics of the monitoring data, and the accuracy and generalization ability of the finally obtained multi-modal large model are improved.

The invention discloses a Gaussian splash model training method and device, a medium and a program product, and relates to the technical field of computer graphics, and the method comprises the steps: carrying out the initialization of an original Gaussian splash model based on a sparse input sample image; according to the sparse input sample image and a preset Gaussian densification strategy, multiple rounds of iterative training are carried out on the original Gaussian splash model to obtain a progressive Gaussian splash model, and the Gaussian densification strategy comprises the following steps: when the number of times of iterative training of the original Gaussian splash model meets a preset number range, the number of times of iterative training of the original Gaussian splash model meets the preset number range; the densification threshold value used by the original Gaussian splash model in the iterative training process is inversely proportional to the number of iterative training times. According to the method, a relatively high densification threshold value is used at the initial stage of training, so that an over-fitting risk caused by over-high densification degree at the initial stage of training under a sparse input view angle is reduced.

The invention provides a light-weight road crack semantic segmentation algorithm fusing dense connection. The algorithm is an improved segmentation algorithm based on a DeeplabV3 + network model. The structure of a network body is similar to that of a DeeplabV3 + network, a lightweight network is adopted as a network trunk, densely connected cavity space pyramid pooling layers are designed, the output of each layer serves as the input of each later layer, the feature information of cracks is overlapped layer by layer, and finally the crack features under multiple scales are gathered. The improved DeeplabV3 + network model can be used for more accurately segmenting a crack fuzzy object image, can improve the feature multiplexing efficiency while ensuring the overall segmentation precision, solves the problem of slow training time caused by the complex structure of the original DeeplabV3 + network, and effectively captures information blur and low segmentation accuracy caused by the complex environment.

The invention discloses a training method of a voice recognition model based on noise deconstruction, a voice recognition method, a device, equipment and a medium. According to the method, a staged training strategy that a noise unwrapping module is firstly isolated and trained and then a Conformer-Transducer architecture is finely adjusted and trained is adopted, so that high calculation complexity and training difficulty caused by simultaneous training of a plurality of complex modules are avoided. In the isolation training stage, the performance of the noise unwrapping module can be quickly optimized; in the fine tuning training stage, the trained noise unwrapping module is utilized to concentrate on optimizing the Conformer-Transducer architecture, so that the training efficiency is improved, and the training time and the consumption of computing resources are reduced. In the isolation training and fine tuning training process, the parameters of part of modules are frozen, the number of parameters needing to be optimized is reduced, and therefore the calculation complexity is reduced. Noise and pure voice in a voice signal are deconstructed through the noise unwrapping module, and accurate semantic understanding is carried out in combination with a Conformer-Transducer architecture, so that the whole voice recognition model has higher robustness to noise.

The invention discloses an unmanned aerial vehicle double-layer conflict resolution method for an urban low-altitude dense traffic flow airspace, and the method comprises the steps: constructing a mixed integer nonlinear programming model based on a conflict resolution strategy before an intelligent unmanned aerial vehicle takes off, and optimizing the mixed integer nonlinear programming model through an improved random fractal search algorithm; after the intelligent unmanned aerial vehicle takes off, a dual depth Q network is constructed and network training is carried out on the dual depth Q network, and the trained dual depth Q network is utilized to enable the intelligent unmanned aerial vehicle to output conflict-free maneuvering actions at each time step. According to the method, a double-layer conflict resolution framework is provided in combination with deep reinforcement learning, and conflict resolution before flight and real-time conflict resolution are combined, so that the occurrence of air collision is greatly reduced compared with that of any method which is independently used, and the intelligent unmanned aerial vehicle can avoid the current obstacle in an ultra-low-altitude urban dense traffic flow environment, thereby improving the traffic flow resolution efficiency. And meanwhile, secondary conflicts are reduced, and the training time of the real-time conflict solving method based on reinforcement learning is also remarkably shortened.

The invention belongs to the field of distributed optical fiber sensing, and provides a Brillouin temperature extraction method and system based on a self-attention recurrent neural network, and the method comprises the steps: collecting real Brillouin gain spectrum data through a Brillouin optical time domain analysis system which is built in advance, and carrying out the preprocessing of the real Brillouin gain spectrum data; based on the preprocessed Brillouin gain spectrum data, utilizing the trained self-attention recurrent neural network model to obtain a corresponding Brillouin temperature; a meta-learning algorithm is introduced in the process of a self-attention recurrent neural network model, so that the network model can share initialization parameters among a plurality of tasks, the parameters are optimized through meta-training, the network model can quickly adapt to a new task, training from zero is not needed in the face of multi-task learning or the new task, and the training efficiency is improved. The model can be quickly adjusted through a small amount of gradient updating, the training time is shortened, the performance is improved, and the problems of long time, poor real-time performance, poor adaptability and the like of curve fitting are solved.

The invention discloses a runoff prediction method for optimizing a neural network based on a hybrid optimization algorithm, and belongs to the technical field of hydrological forecasting. Neural network parameters are optimized through an SAPSO algorithm (simulated annealing particle swarm optimization), and daily runoff prediction is further carried out. The method comprises the following steps: setting parameters of a neural network and an optimization algorithm, including the number of input layer nodes, the number of hidden layer nodes, the number of training times, the population scale and the like; the method comprises the following steps: dividing ten-year daily runoff actual measurement data of a Taojiang station into a training set and a test set, and carrying out normalization processing to prepare for training and testing of a neural network; the weight and the threshold of the neural network are randomly initialized, parameters are transmitted to a simulated annealing particle swarm optimization algorithm for iterative optimization, optimal network parameters are obtained, and a prediction network model is established; and training the network model by using the grouped training set data, and predicting the runoff volume of the Taojiang station by using the training model. And the prediction precision of the runoff volume is obviously improved.