2525 results about "Network generation" patented technology

The invention provides an employee post matching and deploying method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an optimized employee capability feature vector based on employee historical work data, extracting an employee capability development trend, and combining team cooperation data to obtain an optimized employee capability feature vector; a cross-modal contrast learning network is utilized to obtain an employee comprehensive ability vector, a development potential index is calculated, a post skill knowledge graph is constructed based on post instruction data and historical recruitment data, and a post skill feature matrix and a team structure feature vector are generated through a dynamic heterogeneous graph neural network and an adaptive graph attention network. Calculating an employee-post matching degree score matrix in combination with an employee comprehensive ability vector, inputting the matching degree score matrix and a development potential index into a distributed multi-agent reinforcement learning model, constructing a reward function, generating and evaluating a deployment scheme through hierarchical task decomposition and Monte Carlo tree search, and finally outputting a deployment execution scheme.

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

The invention provides a chip production logistics optimization scheduling method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning. An improved bidirectional long-short-term memory network and a graph neural network are used for extracting a material flow time sequence rule, equipment operation cycle characteristics and logistics network topology characteristics, a fusion state representation is generated through a contrast learning network, a layered and collaborative deep reinforcement learning architecture is constructed, an improved encoder-decoder structure is used for generating a strategy vector, and the strategy vector is constructed. And performing hierarchical joint training through a multi-agent execution network, inputting a real-time state and a strategy vector into the trained deep reinforcement learning architecture, generating a scheduling instruction, and performing hierarchical model prediction control in combination with a Bayesian neural network ensemble model and a recurrent neural network prediction model to realize optimized scheduling of chip production logistics.

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention provides a mine ecological risk prediction method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source ecological monitoring data through a distributed sensor network disposed in a mine region, carrying out the time-space alignment processing to generate a time-space correlation feature set, calling a pre-trained ecological risk prediction model to predict the features, and carrying out the prediction of the features. Generating a risk conduction mode set including a risk propagation path, an initial node, a propagation direction and strength, constructing an ecological risk evolution network based on the risk conduction mode set, and displaying a topological structure and a time sequence dependency relationship of risk nodes, and according to the ecological risk evolution network, generating a risk early warning instruction set containing a risk grade division result and an ecological restoration strategy, and sending the risk early warning instruction set to an ecological management platform, thereby comprehensively and accurately predicting the ecological risk of the mine, providing an effective risk early warning and ecological restoration strategy, and ensuring the ecological safety of the mine.

The invention provides a large model deployment method and system based on a multi-level cache mechanism, and the method comprises the steps: building a first-level dynamic semantic cache, preferentially feeding back a reconstruction answer when a new request is matched, starting a second-level context association cache for a missed request, and starting a second-level context association cache for a missed request; the method comprises the following steps: extracting an implicit semantic trajectory vector and constructing a dynamic association graph to identify a potential context dependency relationship, realizing progressive answer splicing, if coverage is still not performed, activating a third-level intention decision cache, performing scene classification and state marking by utilizing a multi-level analysis structure, and finally performing classification and state marking by utilizing a multi-level analysis structure. Based on a knowledge topology network, a query instruction is generated, multi-modal response elements are extracted to generate a combined response, a system synchronously executes multi-level cache collaborative optimization, a cache strategy is adjusted according to user feedback, cache data are adaptively expanded and eliminated, and a multi-level cache is obtained. According to the technical scheme provided by the embodiment of the invention, the response speed, the context understanding capability and the personalized service level of large model deployment are remarkably improved.

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention discloses a multi-microgrid cooperative scheduling method and system based on the game theory, and the method comprises the steps: generating a dynamic game initial strategy set through a multi-agent strategy network according to the charge state of user energy storage equipment, the charge and discharge efficiency and the real-time scheduling demands of a power grid; based on the dynamic game initial strategy set, adopting an asymmetric Nash bargaining model to carry out distributed negotiation, and generating a balanced benefit distribution scheme; according to the equilibrium benefit distribution scheme, iteratively correcting the energy storage priority index by using a time decay type reinforcement learning algorithm, and generating a dynamic bidding rule containing a supply and demand elastic coefficient and risk compensation; and based on a dynamic bidding rule, energy storage resources are allocated in real time through a decentralized gradient consensus mechanism, and a final collaborative scheduling instruction is generated and synchronized to each micro-grid terminal. According to the embodiment of the invention, the operation efficiency and the self-adaptive capability of the multi-microgrid system can be improved, and the requirements of a future intelligent power distribution network are met.

The invention provides a knowledge graph construction and product recommendation method and system based on user data, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an initial knowledge graph through user historical behavior data, generating an optimization graph through a multi-layer neural network comprising a multi-granularity layer, a graph attention layer and a graph convolution layer, and performing bidirectional random walk sampling based on real-time behavior data of a target user to obtain a related sub-graph, calculating a product node importance score, and performing sorting pushing by adopting a multi-target optimization algorithm. According to the method, multi-dimensional accurate description of user interests can be realized, the recommendation accuracy and diversity are improved, and meanwhile, the commercial value is considered.

The invention provides a multi-level attribution and recommendation method and system for medical management decisions, and relates to the technical field of medical management decisions, and the method comprises the steps: extracting graph node evolution characteristics through a multi-level medical data knowledge graph and a decision knowledge base by employing a time sequence diagram convolutional network, constructing a decision influence propagation graph through a cross-layer interactive attention network, and carrying out the analysis of the decision influence propagation graph; calculating the contribution degree of each decision node to the medical effect, generating an attribution analysis result, determining a decision influence hierarchical system according to the attribution result, and generating and evaluating candidate decision paths in combination with a multi-objective optimization network, a hierarchical reinforcement learning network and a Monte Carlo search tree network. In conclusion, accurate attribution analysis and multi-objective optimization of the management decision can be realized, the hospital resource allocation efficiency is effectively improved, the operation cost is reduced, scientific decision support is provided for a hospital management layer, and the hospital fine management level and the overall operation efficiency are remarkably enhanced.

The invention relates to the technical field of image processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses an image sensitive character desensitization method which comprises the following steps: extracting a plurality of feature maps from a to-be-processed image, and inputting a feature pyramid network to generate pyramid features, fusing a spatial attention mechanism to enhance the response of a sensitive region in each level of feature map, generating candidate region coordinates through a region candidate network, mapping the candidate region coordinates to a to-be-processed image to obtain a local image, extracting text semantic information in the local image by using a codec based on a self-attention mechanism, and obtaining a local image; and performing sensitive word detection in combination with the semantic classification model, and performing local texture repair operation on the sensitive area according to a detection result to generate a desensitized image. According to the method, a space attention mechanism and a semantic understanding module are introduced on the basis of multi-scale feature fusion, so that the capability of detecting small-size sensitive characters in an image is improved.

The invention discloses an intelligent management method and system for port and navigation Internet of Things data, and the method comprises the steps: generating a standardized data flow through a multi-modal data fusion model according to the heterogeneous features of ship navigation data, port equipment operation data and cargo information; generating an anti-interference transmission channel based on the standardized data stream; according to the real-time data received by the anti-interference transmission channel, dynamically generating a tamper-proof storage index through a trusted execution environment; extracting multi-source data based on the storage index, and generating a ship arrival time prediction model and a port resource scheduling strategy; and according to the port resource scheduling strategy, constructing a cross-department data sharing network through a federated learning framework and a zero-knowledge proof protocol, and generating a verifiable shared data set. According to the embodiment of the invention, port and navigation Internet of Things data management with reliable transmission, safe storage and collaborative intelligence can be realized, and the data management efficiency is improved.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention discloses a scene-based emotional interactive accompanying doll system and method, and relates to the technical field of artificial intelligence, and the system comprises a data collection module, a feature analysis module, a portrait construction module, an interactive decision module, an execution control module and a database module. The data acquisition module is used for acquiring interaction data and scene data; the feature analysis module generates an emotion feature vector and a scene feature vector by using a multi-modal feature network, and determines a user emotion state and a scene type; the portrait construction module is used for constructing a user portrait; the interaction decision module comprises an emotion evolution unit, a scene interaction unit and a physiological regulation unit and is used for generating an execution instruction sequence; the execution control module is used for controlling the doll to complete emotion interaction behaviors. Through the scene perception and sentiment analysis technology, accurate recognition and personalized interaction response of the doll to the user sentiment state are achieved, and intelligent sentiment accompanying service is provided.

The invention discloses a rock debris image segmentation method based on multi-scale feature enhancement and edge perception gating. The method comprises the following steps: covering 13 * 13 to 3 * 3 pixel receptive fields through four-stage parallel depth separable cavity convolution; the dynamic weight generation network generates a space attention weight to realize self-adaptive fusion of multi-branch features; a traditional edge operator kernel is initialized in a shallow network, and 5 * 5 learnable convolution is adopted in a deep layer to enhance edge continuity. Through channel grouping initialization and a random disturbance strategy, the direction sensitivity of a traditional operator is reserved. A mixed weight is generated through channel attention and space attention, and edge features are injected into a main path in a residual scaling mode. The attention weight dynamically adjusts the contribution degree of the edge and semantic features, and high-precision segmentation of a boundary region is ensured; the shallow edge features are optimized preferentially by combining the loss function, and the robustness of the model to complex textures is improved.

The invention discloses an expert portrait description and dynamic updating method based on reinforcement learning, and the method comprises the following steps: S1, collecting and preprocessing expert data, and generating a knowledge vector, a behavior vector and a semantic vector; S2, constructing an expert cognitive representation tensor with a potential cognitive tag as a core; s3, inputting the expert cognitive representation tensor into a variable-dimension mimicry generation network to generate an expert portrait candidate body; s4, optimizing the expert portrait candidate body by adopting a multi-strategy enhanced whale optimization algorithm to generate an optimized expert portrait; s5, generating an expert portrait evolution path map according to the optimized expert portrait; and S6, collecting real behavior feedback data of the expert portrait, and generating a joint reward signal through a reward function in combination with the expert portrait evolution path map to perform bidirectional updating so as to realize dynamic updating and optimization evolution of the potential cognitive tag. According to the method, reinforcement learning and a label structure dimension changing method are fused, and dynamic description and updating of expert portraits are realized.

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention provides a scene adaptive adjustment method and system based on intelligent Internet of Things and meta-universe virtual-real fusion, and relates to the technical field of artificial intelligence and Internet of Things, and the method comprises the steps: collecting a scene image and environment parameter data, carrying out the decomposition and partitioning of the image, and recognizing the type of the scene, environment parameter tensors are constructed to form a digital twinborn model, the digital twinborn model is mapped to a virtual space, a proper feature vector is selected according to an identification result to generate a target feature vector, a projection position of a virtual object in an entity space is obtained, and the feature vector is optimized and then combined with the projection position to generate an enhanced feature and a mapping matrix; and generating rendering parameters through the parameter mapping network, rendering the virtual object, and displaying the virtual object on the entity space interface in an overlapping manner.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention relates to the technical field of subway train driving strategy optimization, and discloses a subway line real-time data driven DAS driving strategy optimization method, which comprises the following steps: acquiring multi-dimensional operation data including a train real-time position sequence, station passenger flow data and a trackside signal equipment state; extracting line operation characteristics through a convolution space-time encoder, generating a passenger flow fluctuation prediction map by using a time sequence decomposition algorithm, and performing characteristic fusion on the trackside signal equipment state to generate an equipment health degree evaluation matrix; inputting the multi-dimensional data into the driving strategy model to generate an initial driving instruction sequence, dividing operation control priorities through a regional clustering algorithm, and constructing a dynamic optimization strategy network in combination with a genetic algorithm to generate a final driving strategy scheme; and acquiring an execution state log in real time, and updating the strategy network through the abnormal decision detection model. According to the method, the dynamic optimization of the subway driving strategy is realized, and the operation safety, efficiency and comfort are improved.

The invention provides a high-speed traffic flow high-precision prediction method based on multi-source disturbance characteristics, and relates to the field of data prediction, and the specific steps are as follows: firstly, a multivariable entropy driving interaction field module maps the multi-source disturbance characteristics into a unified energy field, calculates joint information entropy density and constructs a joint interaction field; processing the original feature sequence; secondly, the collaborative disturbance reconstruction module adopts a learnable mapping matrix and a multi-scale mechanism to extract dynamic differences of features under different time scales, and generates enhanced disturbance response features through a decoupling network after global disturbance collaborative response is fused; then, a spatial manifold mapping and partitioning module realizes spatial expression and partitioning modeling of a traffic flow tension evolution trend; and then, the prediction module constructs an asymmetric prediction structure in combination with the disturbance amplitude factor and the weighted disturbance characteristics, adopts a mean square error, introduces a disturbance constraint term to train the model, and outputs a final traffic flow prediction result through the trained high-speed traffic flow prediction model.

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a natural language driven table processing method, device, equipment and medium. And generating and executing an operation action by utilizing the reinforcement learning strategy network, generating a reward signal according to execution result data and user feedback information, storing the state vector representation, the target table operation action and the reward signal into an experience playback queue, and updating network parameters of the reinforcement learning strategy network based on historical data in the experience playback queue. According to the method, the state vector is constructed through the reinforcement learning strategy network in combination with the natural language instruction and the table context, and the operation strategy is continuously optimized according to the operation result and the user feedback, so that intelligent understanding and action planning of the spreadsheet operation intention are realized, and the data processing efficiency and the interaction intelligence level of non-professional users are improved.