48 results about "Cyclic network" patented technology

The invention discloses a charging and moisture regaining equipment temperature prediction method based on a time sequence fusion network model, and relates to the field of production process control, and the method comprises the steps: collecting time sequence data in a charging and moisture regaining equipment production environment, and carrying out the preprocessing; dividing the data set into a training set, a verification set and a test set, and injecting Gaussian noise into the training set; a prediction model for predicting the outlet temperature is constructed, and the prediction model is a time sequence fusion network model and comprises a residual TCN time sequence convolutional network, an SK-Net multi-scale attention network and a BiLSTM bidirectional circulation network; pre-training the prediction model by using the training set; utilizing the trained prediction model to predict the outlet temperature of the feeding and moisture regaining equipment; and evaluating a prediction result, and if an evaluation index is greater than a threshold value, starting an incremental training process to re-train the prediction model. According to the invention, through a multi-module combined deep learning model, the prediction accuracy of the outlet temperature of the charging and moisture regaining equipment can be improved in a complex and changeable industrial environment.

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention discloses a complex product process route flexible planning method based on a deep cycle Q network. The method is suitable for efficient process route optimization in a dynamic manufacturing environment. The method comprises the following steps: firstly, modeling process characteristics, processing operation, resources and constraint relationships of a part, and constructing a state space and reward mechanism based on a partial observable Markov decision process; and then constructing a long short-term memory network to extract time sequence characteristics in a processing state, and constructing a DRQN on the basis to realize an optimal decision of processing operation. In order to adapt to variable process targets and environments, a self-adaptive reward function adjustment strategy is introduced, and dynamic optimization is realized by adjusting weights such as processing quality, time and energy consumption; and meanwhile, a selective forgetting mechanism is constructed, and model parameters are suppressed and updated by utilizing a Fisher information matrix, so that efficient forgetting of outdated process knowledge is realized, and the migration ability and generalization performance of the model are improved.

The invention discloses a supply chain order business automatic processing system and a method thereof. The system comprises a dynamic intelligent center, an elastic process engine, a green loop network and a trusted security layer, the dynamic intelligent center dynamically optimizes decisions through federated learning and generative AI, and the elastic process engine realizes flexible recombination of processes based on micro-service and a low-code platform. The green circulation network utilizes a block chain to track carbon footprints and manage reverse resources, and the credible security layer adopts quantum encryption and dynamic authority to guarantee data credibility. The corresponding method comprises the steps of combining a multi-party data training model and generating a multi-scheme, performing a micro-service splitting and low-code configuration process, driving intelligent contract transaction by a carbon label, and performing quantum encryption evidence storage and anomaly detection. Through the edge-cloud collaborative architecture, the scheme synchronously improves the efficiency, flexibility and sustainability, solves the problems of insufficient intelligence, framework stiffness and security defects in the prior art, and is suitable for complex scenes such as e-commerce and manufacturing.

The application relates to a traffic flow prediction method based on a multi-mode dynamic memory graph convolution network, and belongs to the technical field of intelligent transportation. At present, the time-space correlation characteristics of the graph convolution neural network method are insufficient, the periodic characteristics are not fully considered, and the dynamic evolution correlation is not accurately captured. In order to solve the above problems, firstly, a time sequence feature extraction module and a bidirectional memory cycle network module are established, and the captured time sequence features are fused to form a comprehensive feature vector. Then, in the dynamic graph convolution module, the spatial correlation between nodes is captured through the fusion mode of the diffusion graph convolution neural network, the attention mechanism and the typical traffic mode, and a new dynamic adjacency matrix is generated. The dynamic adjacency matrix can reflect the node connection relationship changing with time. The application dynamically updates the adjacency matrix through the multi-mode dynamic memory graph convolution network, better responds to emergencies and abnormal situations, and effectively improves the accuracy and robustness of traffic flow prediction.

The invention provides a traditional Chinese medicine data classification processing method and system based on a graph loop network, and a medium. The method comprises the following steps: collecting traditional Chinese medicine data; preprocessing the collected traditional Chinese medicine data; constructing a graph loop network; and inputting the preprocessed traditional Chinese medicine data into the graph loop network to obtain a traditional Chinese medicine data classification result. In the application, the first-level graph convolution layer focuses on local feature extraction and enhancement of the perception ability of the model, and the high-level graph convolution layer integrates global information, captures detail information around nodes and enhances the understanding ability of the model. And the feedback mechanism layer adaptively adjusts the mechanism, monitors data in real time, adjusts network parameters, and reduces the over-fitting risk. The traditional Chinese medicine knowledge is fused into the graph structure construction process, multi-dimensional features can be extracted from the traditional Chinese medicine data, local and global information can be comprehensively captured, the classification and screening performance of the unstructured traditional Chinese medicine data is improved, and rapid data selection is provided for new medicine research and development or medicine compatibility and the like aiming at diseases.

The invention provides a service logic vulnerability detection method and device, equipment, a medium and a program product, and relates to the technical field of network security. The method comprises the following steps: constructing a business process state machine through a key entity obtained by performing natural language analysis on a business document; the business process state machine is used for describing state changes of the business process in different stages; obtaining an operation sequence generated based on the interaction operation through a flow agent deployed in a network environment; carrying out modeling and feature extraction on the operation sequence based on a hierarchical attention circulation network to obtain operation behavior features; comparing the operation behavior characteristics with a business process state machine to obtain a vulnerability detection result; the vulnerability detection result comprises permission violation and / or flow abnormality.

The invention relates to the technical field of intelligent peripherals, in particular to a user-oriented interaction information expansion method, which comprises the following steps that: each keycap acquires displacement and pressure, generates an event packet, uploads the event packet through an optical wireless link, and obtains a clock and supplies power; constructing a keycap graph by a master controller, obtaining an intention vector through graph convolution and pooling, inputting the intention vector into a cyclic network to generate a pixel matrix and a phase matrix, and downlink after Bernoulli sparse coding; performing key cap side orthogonal matching pursuit decoding and four-round gradient optimization, and outputting a differential pixel set, a differential phase set and a synchronization mark; the master control refreshes the digital micromirror array and the ultrasonic phased array in a differential manner, locks a frame-level time sequence through a synchronization pulse, compensates link delay and hardware drift in real time based on Kalman filtering, and keeps an optical-acoustic alignment error to be less than 100 microseconds; degradation monitoring enables a link to be automatically switched to a pre-carved character layer and to be quickly recovered when the link is abnormal, and a keyboard interaction information expansion scheme with low delay and high consistency is provided.

The application discloses a deep learning-based time-space fusion early warning method for deep rock burst, and constructs a dual-branch Transformer-CNN time-space feature fusion early warning model. The model captures local mutation features in a microseismic sequence through a parallel multi-scale CNN network branch, combines a Transformer network branch to extract global evolution features, realizes comprehensive mining of rock burst precursor information and identification of key precursor information, and further enhances the dynamic adaptability of the early warning model to complex microseismic sequences by using a self-adaptive gating fusion mechanism, thereby enhancing the adaptability of the early warning model in a complex monitoring environment. In addition, a learnable position coding is introduced to replace a traditional fixed coding mode, so that the early warning model can more flexibly capture time sequence dependence in mining stress evolution, and the problems of gradient vanishing and low calculation efficiency of a traditional cyclic network in long sequence modeling are overcome. Finally, the classification accuracy of the rock burst danger level and the early warning reliability are effectively improved.

The embodiment of the invention provides a logistics congestion early warning method and system based on space-time coupling and track matching, and belongs to the technical field of logistics management. The early warning method comprises the following steps: acquiring real-time distribution data of smoke box logistics trolleys; constructing and training a distribution prediction network model; inputting the real-time distribution data into the prediction network model to obtain real-time prediction distribution data; acquiring real-time track data of the trolley according to the real-time prediction distribution data; and calculating a track congestion index according to the real-time track data and carrying out congestion early warning. According to the method, the distribution prediction model is constructed through the graph convolution network and the circulation network, accurate matching of the trolley track is realized through combination of depth-first search and the Hungary algorithm, the congestion index is calculated through historical track data so as to realize congestion early warning without scheduling dependence, and dependence on manual judgment is remarkably reduced.

The application provides a power distribution network carrying capacity evaluation method and system fusing GNN and deep learning, in the method, a graph neural network model of the power distribution network is established based on power distribution network topological structure data and historical parameter data; global topological electrical coupling features in the graph neural network model are extracted based on a graph attention network and a gated recurrent network; future power data predicted by an LSTM is taken as input of power flow calculation in the graph neural network model, so that node state parameter features are obtained; the node state parameter features and the global topological electrical coupling features are taken as input, a PPO algorithm is used to adaptively adjust an operation strategy, and control actions of power distribution network equipment are output; the equipment actions output by the PPO algorithm are fed back to the graph neural network, power flow is recalculated and node state parameters are updated; and a power distribution network carrying capacity evaluation result is obtained based on the updated node parameter state, so that the efficiency and reliability of power distribution network carrying capacity evaluation are improved.

The application discloses a robot intelligent control method and system based on a causal mechanism, and relates to the technical field of intelligent robots, which comprises the following steps: inputting an operation task and an environment state of a target robot at a current time into a causal structure model to determine each candidate action for pushing a target object from a current time position to a next time position; inputting the candidate action into a graph recurrent network to obtain a predicted position of the target object under the candidate action at the next time; screening an execution action of the target robot at the next time according to the predicted position of the target object under each candidate action and a target position of the target object; and updating the predicted position corresponding to the execution action of the target robot at the next time to the current time position until the target object reaches the target position. The application can effectively improve the intelligent control capability of the robot by determining the action to be executed according to the predicted position of the target object under the execution action and the target position based on the causal structure model and the graph recurrent network.

This invention relates to the field of storm surge disaster monitoring and intelligent forecasting technology, and provides a regional collaborative storm surge rise forecasting method using coupled graph attention and gated cyclic networks. The method includes: using a hybrid wind field-driven two-dimensional hydrodynamic model formed by fusing typhoon parameterization and reanalysis data to obtain storm surge rise residual field data; registering the rise residual field data with the observation time series in time and space, and using it as a physical prior input; constructing a dynamic edge-weighted graph structure based on a spatiotemporal causal correlation analysis method to adaptively update the dependencies between multiple observation stations; using the physical prior and key features as node inputs, fusing the graph attention mechanism of GATv2 and the temporal feature extraction capability of GRU to perform spatiotemporal coupled modeling of storm surge rise; conducting multi-site joint prediction through regional collaboration, performing model evaluation and result optimization, and outputting storm surge rise forecast sequences with a 15-minute time step and a lead time of 15 minutes to 4 hours.

The invention provides a short-term power load prediction method and system based on a KAN cycle network, and the method comprises the steps: carrying out the feature enhancement operation of historical multi-time scale variables of a to-be-predicted region based on a Pearson's correlation coefficient analysis method, so as to determine the correlation between each variable in the historical multi-time scale variables and a short-term power load, obtaining a historical vector sequence; training a preset KAN loop initial network based on the historical vector sequence to obtain a target KAN loop network; obtaining a current multi-time scale variable, and constructing a current vector sequence; and taking the current vector sequence as the input of the target KAN cycle network, so that the target KAN cycle network outputs a short-term power load prediction result. According to the short-term power load prediction method provided by the invention, the short-term power load prediction in different scenes can be realized by utilizing the feature enhancement and the KAN circulation network, the prediction precision is improved, and the power supply and demand balance and the operation stability of a regional power grid are effectively guaranteed.

The invention discloses an equipment state prediction method based on a parallelization loop network. The method comprises the following steps: S1, improving a GRU network architecture; s2, extracting an input time sequence deep feature mode by using a GELU feedforward network; s3, adding a noise module to realize consistency of parallel training and sequence prediction; s4, improving the efficiency and the stability of the model by using RMSNorm; s5, capturing long-period causal time sequence information by adopting causal convolution; s6, the modules are deepened, stacked and combined through residual connection; s7, performing optimization by using an ADAMW optimizer, superposition dynamics and prediction loss; s8, inputting historical and future control quantity information, outputting differential historical and future prediction, and calculating mean square error improvement performance; and S9, carrying out equipment state early warning according to a prediction result. Compared with a traditional LSTM model, the method has the advantages that the prediction error is reduced, the single-sequence reasoning time is greatly shortened, and a powerful solution is provided for accurate and efficient prediction of the state of the industrial equipment.

The invention discloses an industrial data prediction method and device based on a robust cyclic random configuration network, and aims to solve the problem that the modeling precision and robustness are insufficient when an existing model processes industrial data which is uncertain in input order, strong in time sequence and contains noise and abnormal values. Through combination of a robust learning mechanism and a recursive structure, order identification is not needed, reserve pool nodes are randomly generated based on a supervision mechanism, a network structure is constructed, and the global approximation capability of any nonlinear mapping function is ensured. The method not only inherits the efficient modeling capability of a cyclic network for time sequence characteristics, but also significantly enhances the stability and generalization capability of the model in a complex interference environment, and is suitable for accurate prediction and intelligent decision support scenes in a complex dynamic industrial process.

The invention relates to the technical field of boiler fault diagnosis, in particular to a digital fault early warning method for a through-flow boiler and a remote operation and maintenance platform. Comprising the following steps that boiler operation parameters are collected through a distributed sensor network, and a full-dimension monitoring system is constructed; performing noise reduction, normalization and missing value interpolation on the original data at an edge computing node, constructing a working condition feature library in combination with historical operation data, and matching a current working condition type through a dynamic time warping algorithm; a space-time attention mechanism is adopted to carry out feature fusion on multi-source data, a sensor space association graph is constructed through a graph convolutional network, time sequence features are captured in combination with a gated loop network structure, and the feature weight of each sensor is adaptively adjusted based on data dynamic features. According to the method and the device, noise reduction, normalization and missing value processing are performed on the acquired data on the edge side in real time, so that transmission delay caused by centralized cloud processing of the data is avoided, and the timeliness and the efficiency of data processing are improved.

The invention relates to the technical field of transformer gas detection, and discloses an intelligent gas leakage detection system and method for a transformer, and the method comprises the steps: collecting multi-source operation data, such as gas pressure, oil level and temperature, carrying out the standardization processing, and building a unified feature matrix to eliminate the dimensional difference. Then extracting space-time correlation characteristics of gas leakage, and constructing a supervisible training sample in combination with historical records; a deep learning model is utilized to fuse a convolutional network and a cyclic network structure, local change features and global dynamic features are captured respectively, and common working conditions and extreme working conditions are distinguished through a multi-branch classifier. And angle subareas are divided according to the wind direction of the whole field, a correction curve between historical gas concentration and actual gas concentration is established, and space subarea correction is achieved. And finally, obtaining optimal gas leakage state evaluation through weighted fusion of the branch prediction value and the comprehensive correction value. According to the invention, an intelligent detection principle of data driving, space-time coupling and working condition self-adaption is effectively realized.

The invention provides a fault-tolerant perception task unloading method in an end-side cloud cooperative environment, which relates to the technical field of wireless communication networks and comprises the following steps: receiving tasks generated by a plurality of mobile devices; calculating a fault recovery time expected value of a node and a task processing total cost model based on an edge server, and independently determining a distributed unloading strategy of a task for each mobile device by using a deep cycle Q network algorithm; in a task execution process, monitoring whether a computing node of the edge server has a fault or not; when it is detected that the computing node of the edge server has a fault, fault recovery is carried out by adopting a fault-tolerant technology; according to the fault recovery condition, the execution strategy of the influenced task is dynamically adjusted, it is ensured that the task is completed before the maximum tolerance time delay, the total task processing cost is minimized, and the total task processing cost is the weighted sum of task execution time delay and energy consumption. It is ensured that the task can still be completed at a certain success rate before the maximum allowable deadline when a fault occurs, and the user experience quality is improved.

The invention relates to the technical field of ship BOG prediction, and particularly discloses a thin film type LNG ship BOG prediction method, system, equipment and medium, and the method comprises the steps: S1, obtaining sample operation data in the operation process of a thin film type LNG ship; s2, performing data processing on the sample operation data to obtain training data; s3, constructing a circulating network model fusing GTT-BOR physical constraints, and training the circulating network model by adopting a dual-stage optimization strategy based on the training data to obtain a BOG prediction model; and S4, monitoring actual operation data in the operation process of the thin film type LNG ship in real time, inputting the actual operation data into the BOG prediction model, and calculating to obtain a BOG prediction value of the thin film type LNG ship. According to the method, the BOG change trend prediction precision is improved, the modeling difficulty and cost are reduced, and the adaptability of the model to the working condition change is enhanced.

The application discloses a kind of based on deep cycle Q network's intelligent workshop real-time rescheduling method, it is related to intelligent workshop rescheduling technical field, the based on deep cycle Q network's intelligent workshop real-time rescheduling method includes the following steps: establishing real-time rescheduling sequence decision model;Mapping relationship between real-time state and rescheduling decision of production system is obtained using deep cycle Q network training rescheduling agent;Rescheduling repair action matched with real-time state of production system is executed using trained rescheduling agent;Rescheduling strategy experience data is collected from the replay memory of intelligent workshop and stored in rescheduling agent.The based on deep cycle Q network's intelligent workshop real-time rescheduling method provided by the application can identify the rescheduling point of dynamic production process in real time, and make adaptive rescheduling decision flexibly to offset the negative effects caused by various disturbances.

This invention provides a method for constructing a hierarchical classification model for government procurement items. The method first constructs and encodes a hierarchical structure for the procurement items. Then, it performs text cleaning and word segmentation on the procurement project names, using a continuous bag-of-words model to train the corpus to obtain word vectors, or directly obtaining word vectors using a BERT Chinese pre-trained model, thus obtaining an initial text representation. Finally, the text representation and label representation are fed into the model for training. The three-layer network contained in the HA-BiGRU model can effectively solve the above problems. The text encoding layer with a BiGRU network as the encoder can further extract semantic information from the context; the hierarchical attention recurrent network layer can model the dependencies between layers and enhance the association between text and labels through a text-label attention module; the hybrid prediction layer integrates the local and global losses of hierarchical labels, optimizing the model by reducing the total loss.

This invention belongs to the field of information technology and relates to a method and system for temporal knowledge hypergraph reasoning based on a two-layer linear attention recurrent network. The method includes: obtaining node representations in subgraphs with different timestamps under the original time series based on a relation-aware graph convolutional network (RGCN), and integrating historical temporal information through a first-layer linear attention recurrent network (RWKV) to obtain a spatiotemporal fusion representation; constructing a hypergraph based on density peaks based on the spatiotemporal fusion representation and obtaining the non-qualitative association matrix of nodes relative to hyperedges; encoding the spatial features of the hypergraph based on a hyper-relation-aware graph neural network (HRGNN), and encoding temporal features through a second-layer linear attention recurrent network (RWKV) to achieve spatiotemporal information encoding; and using a decoder for connection prediction to achieve knowledge reasoning. This invention fully considers and designs various aspects of hypergraph construction, learning, and processing, enabling the extraction of temporal evolution patterns from historical information and achieving better reasoning about future events.

This invention relates to the field of communication equipment operation and maintenance management and digital twin technology, and discloses a digital twin system and method for monitoring the operation of communication tower equipment. The method includes multi-source heterogeneous sensor data acquisition, spatiotemporal alignment and four-dimensional reliability scoring, state representation vector generation, fault probability prediction based on a bidirectional gated cyclic network, root cause diagnosis driven by an extreme gradient boosting model, and dual-deep Q-network optimization of programmable optical transceiver configuration. The effectiveness of the strategy is verified through bidirectional LSTM simulation, and finally, a full lifecycle archive is constructed, enabling collaborative traffic offloading between neighboring towers. This application can achieve high-precision early warning, automatic root cause localization, and cross-tower resource coordination, improving the autonomy and resilience of optical communication networks.

The application discloses a power distribution network voltage prediction method based on a space-time graph neural network, comprising the following steps: abstracting a power distribution network into a dynamic graph structure; obtaining a historical operation data sequence; and inputting the historical operation data sequence into a pre-trained space-time graph neural network model for prediction. The model extracts spatial features through multi-relation graph convolution and electrical guide graph attention network, extracts time features through a recurrent network, fuses the two by using a space-time cross attention mechanism, and finally decodes and outputs future multi-step voltage prediction values and uncertainties. By explicitly modeling the space-time coupling characteristics of the power grid and embedding physical constraints, the application significantly improves the accuracy, robustness and interpretability of voltage prediction under high-proportion distributed energy access, and can provide proactive decision support for active voltage control.

The present invention provides a method, device and electronic equipment for assessing the outage risk of a distribution network. The method, device and electronic equipment for assessing the outage risk of a distribution network provided by the present invention determine whether the original network diagram of the distribution network is a cyclic network diagram, and perform tree decomposition on the original network diagram that is a cyclic network diagram to obtain a tree network diagram under each decomposition scenario. The scene existence probability of each decomposition scenario and the power-on probability of each node in the tree network diagram under the scenario are used to assess the outage risk of each node in the original network diagram, thereby achieving outage risk assessment of the target distribution network, thereby utilizing the tree decomposition principle and the node power-on probability solution principle to achieve accurate and reliable distribution network outage risk assessment.

The embodiment of the invention discloses a differential protection fault identification method and device based on a machine learning model, and relates to the technical field of power system relay protection, and the method comprises the steps: obtaining current signals at two sides of a protected device, and extracting a time sequence feature sequence; a feedforward neural network comprising a memory module is adopted for processing, and the memory module generates historical information representation by applying learnable non-cyclic transformation to the historical hidden state of the hidden layer; the historical information representation is spliced with the current hidden state, and a result is transmitted to the next layer; and finally, training by adopting a standard error back propagation algorithm. The invention further provides a corresponding device. By introducing a full feed-forward structure of the memory module, time sequence characteristics of current signals can be effectively captured, meanwhile, the gradient problem of a cyclic network is avoided, stable and efficient training is achieved, high accuracy and strong generalization ability are achieved in a small sample scene, and the reliability of differential protection is remarkably improved.

The invention discloses a power distribution network fault power supply recovery method and system based on a graph attention mechanism, and belongs to the technical field of intelligent operation and maintenance of a power distribution network. The method comprises the following steps: performing Thiessen polygon division on a power distribution network region according to a fault point to obtain an initial fault division region; carrying out island division according to the initial fault division area; according to an island division result, a graph attention mechanism is combined with a bidirectional deep cycle Q network, and an optimal power distribution network power supply recovery strategy is solved; and executing the optimal power distribution network power supply recovery strategy to realize power distribution network fault power supply recovery based on the graph attention mechanism. According to the method, the convergence efficiency of the reinforcement learning strategy is improved, the action search space is reduced, and the calculation amount of the global search strategy is reduced; according to the method, the graph attention mechanism and the bidirectional deep cycle Q network are fused, the defect that topological relevance modeling is insufficient in a traditional algorithm is overcome, the recessive coupling relation of power interaction between islands is accurately captured, and the strategy robustness is improved.

The application provides a data processing method and device, and relates to the field of data processing. The method comprises the following steps: a sending end generates tree structure to-be-compressed data according to original data, and determines data possession information of the original data in the tree structure by using a recurrent network layer contained in a data compression model. The data possession information is used to indicate data distribution of the original data in the tree structure. Then, the sending end compresses the to-be-compressed data according to the data possession information to obtain compressed data. A receiving end determines data possession information of the compressed data in the tree structure by using the recurrent network layer contained in the data compression model. The compressed data is decompressed according to the data possession information to obtain decompressed data. The application uses one recurrent network layer to replace a multi-layer MLP network in the prior art for context prediction, reduces the complexity of the data compression model, and reduces the required computing resources when the context prediction obtains the data possession information.

The application provides a multi-step time series prediction method based on a quantum bidirectional recurrent network of causal convolution. The application can capture longer time dependence in a relatively shallow network by using a quantum causal convolution network based on time series perception, and can realize fast capture and processing of long-time dependence, causality and time sequence correlation between data by virtue of the advantages of quantum computing. The application can enhance the expression of time series information and effectively improve the ability to process complex time-varying relationships by fusing time series state information and time series feature information through a quantum bidirectional recurrent network guided by an attention mechanism. The application can capture and integrate important information of long-time span by updating the important information of the hidden layer of the bidirectional recurrent unit by using a multi-head attention mechanism. Compared with the prior art, the application effectively improves the performance of multi-step time series prediction.