1318 results about "Graph generation" patented technology

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention relates to the technical field of deep learning, in particular to a stress distribution monitoring and crack risk prediction method for a silicon carbide part, which realizes comprehensive sensing of the stress state of the silicon carbide part, accurate positioning of a risk area and advanced early warning of a crack fault. The method comprises the following steps: synchronously acquiring multi-modal data through multiple types of sensors, and realizing cross-modal time sequence synchronization through feature alignment; designing a crack risk multi-branch feature extraction module, and respectively extracting general depth features and risk features oriented to thermal stress mismatch, microcrack evolution and structural instability through a shared backbone network and a special branch network; constructing a stress nephogram generation and risk area positioning module based on a graph neural network, and realizing visual reasoning and risk area marking from discrete features to full-field stress distribution; and designing a crack risk comprehensive prediction module based on multi-dimensional risk feature fusion, fusing an instantaneous state and an evolution trend, outputting a multi-risk confidence vector and triggering graded early warning.

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

The invention discloses a marketing strategy optimization management system based on six elements of order transaction, and belongs to the field of communication management systems, in terms of data acquisition, multi-source comprehensive information collection enables enterprises to perceive consumer demands in all directions and no longer blindly grope, a dynamic feature modeling unit fuses various kinds of data into six-dimensional feature vectors, and the six-dimensional feature vectors are integrated into a database; multiple factors of commodities, users and festivals are balanced and considered, a solid foundation is laid for a marketing strategy, a commodity-festival-user ternary association graph constructed by a festival graph generation engine enables commodities to be pushed in a targeted manner, a personalized strategy is generated by an intelligent decision module, the matching degree of the commodities and consumers is improved, and the marketing efficiency is improved. The overall continuous feedback optimization mechanism of the system enables the marketing strategy to be like a continuously evolved life entity, can better adapt to the market change, and improves the sales probability of commodities.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an artificial intelligence operation and maintenance decision support method and system for multi-source information fusion. The method comprises the following steps: acquiring multi-source operation and maintenance data to perform multi-dimensional feature extraction to obtain multi-dimensional operation and maintenance feature data; performing continuous spatial cross-modal embedding according to the multi-dimensional operation and maintenance feature data to obtain cross-modal embedded data; performing heterogeneous feature coupling graph generation on the cross-modal embedded data to obtain coupling graph data; performing heterogeneous space fusion coding according to the coupling graph data to obtain fusion coding data; performing expert knowledge driving graph embedding on the fusion coding data to obtain operation and maintenance fusion graph data; performing root cause positioning reasoning according to the operation and maintenance fusion graph data to obtain root cause positioning data; and performing operation and maintenance decision generation according to the root cause positioning data to obtain operation and maintenance decision data. Through multi-source information fusion and intelligent reasoning, the root cause positioning accuracy and intelligent operation and maintenance decision efficiency of the system can be effectively improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention provides a multi-dimensional map generation method and device based on multi-sensor tight coupling SLAM. The method comprises the following steps: acquiring image data, point cloud data and inertial data acquired by using a predetermined sensor combination; state propagation is carried out based on the inertial data, and positioning output under a global coordinate system and an estimation result used for point cloud position correction are obtained; generating a three-dimensional map and a semantic map; geometric elements are extracted based on the three-dimensional map, and a geometric annotation map is formed by global coordinates according to the geometric elements; projecting the spatial information to a plane, and superposing ground color textures from the image data to generate a two-dimensional texture grid map; and associating the positioning output, the three-dimensional map, the semantic map, the geometric annotation map and the two-dimensional texture grid map, and providing an association result for a positioning and path planning module of the mobile equipment to call. The multi-dimensional map can be synchronously output through one-time mapping, the coordinates of the four layers are uniformly updated in real time, and the manual labeling cost is remarkably reduced.

The invention discloses an original script-oriented AI autonomous plot structure adaptive generation system, and relates to the technical field of creation assistance, and the system specifically comprises the following modules: a structure extraction analysis module, an emotional role analysis module, a plot inference module, a scene generation optimization module, a conservation target generation module, an adaptive control module, and a constraint punishment module. According to the method, a multi-level narrative structure and a causal relationship graph are constructed to form an emotion vector and a trajectory curve, an optimal causal path is generated based on a graph neural network, a graph convolution / attention mechanism and a graph generation algorithm, and emotion toning and conservation target driven text generation are performed on scene and dialogue levels. Structural consistency and emotional arcs are optimized in real time in combination with self-adaptive control, plot path weighting and rewriting triggering are performed by utilizing a multi-dimensional emotional space and a neural oscillator network, intelligent structured management of a script is realized, and script creation efficiency and quality are improved.

The invention relates to the technical field of gift packaging three-dimensional modeling, and discloses a construction method of a gift box packaging design three-dimensional model. The method comprises the following steps: collecting natural language description, a freehand sketch image and a physical material attribute parameter; analyzing a natural language to construct a design intention map, and analyzing a sketch to generate a geometric feature code; performing knowledge alignment fusion on the two to form enhanced design semantic representation; generating an initial three-dimensional design framework by using a generative design algorithm; material parameters are integrated, compatibility simulation test is carried out, parameter self-adaptive adjustment circulation is started according to a feasibility evaluation report, and component parameters are dynamically optimized; and rendering and outputting a final model. Through deep fusion of multi-modal design information and preposed dynamic optimization of physical attributes, the accuracy of design intention expression and the producibility of a three-dimensional model are improved, and automation and intelligentization of a design process are realized.

The invention discloses a digital twinborn monitoring method for a self-evolution concrete-filled steel tube arch bridge, and the method comprises the steps: collecting multi-source monitoring data through deploying a multi-mode sensor at a key component of a bridge, and constructing an initial structure topological graph; and dynamic evolution and anomaly detection of the structure topology are realized by using a graph neural network and a Transform structure. Intelligent recognition and fault diagnosis of a structure state are realized by constructing a multi-physical field five-dimensional tensor and a health knowledge graph. And finally, generating and optimizing a structure alternative scheme by adopting a graph generative adversarial network and a genetic algorithm, verifying the performance of the scheme through finite element simulation, and realizing intelligence of bridge health monitoring and maintenance decision making. According to the method, real-time sensing, dynamic modeling and intelligent diagnosis of the structural state of the concrete-filled steel tube arch bridge are achieved by fusing multi-modal sensor data, multi-physical field coupling analysis, graph neural network modeling and an intelligent reasoning mechanism, the real-time performance and accuracy of monitoring are improved, and a scientific basis is provided for structural optimization design of the bridge.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

The invention relates to the field of water environment monitoring and treatment, in particular to a lake and reservoir pollution flux dynamic evaluation and optimization system and method.According to the system, hydrological, water flow, meteorological and remote sensing data are collected in real time through a data processing module, and a pollution flux space-time matrix is generated; the atlas generation module constructs a mechanism model based on the matrix and outputs a pollution flux thermodynamic atlas; the scheme simulation module determines a pollution source emission reduction scheme according to the map and simulates the influence of the pollution source emission reduction scheme on lake and reservoir water quality and ecology; and the feedback optimization module performs treatment efficiency scoring on the simulation result, compares the evaluation result with a preset threshold value, and adjusts the emission reduction scheme and updates the thermodynamic map when the evaluation result does not reach the standard. According to the method, through data driving, map visualization, scene simulation and dynamic feedback closed loop, accurate evaluation and optimal regulation and control of the lake and reservoir pollution flux are realized, and a scientific basis is provided for pollution abatement decision.

The invention belongs to the technical field of data visualization, and particularly relates to a dynamic visualization generation method, system and device based on user behaviors and context awareness and a medium, and the method comprises the steps: generating a structured operation sequence after standardizing original input data; performing time sequence modeling on the operation sequence to generate a user behavior semantic vector, and generating a joint feature vector by adopting an attention mechanism; pre-loading an input data set, and generating a candidate view set in combination with the user intention preference in the joint feature vector; calculating view scores through a layout optimization function, and screening an optimal view scheme with the highest score; dynamically simplifying or enhancing the chart component according to the equipment performance and context parameters in the joint feature vector; calling a rendering engine to output a view, and monitoring a user interaction event; and updating the strategy network through a PPO algorithm, and optimizing a subsequent view generation path. The problem of unsmooth interaction caused by a static template is avoided, and the user experience is remarkably improved.

The invention discloses a government affair service digital human intelligent interaction method and system based on deep learning, and the method comprises the steps: firstly obtaining government affair service specification policy data and interaction behavior data, including voice, text, facial expression, limb movement and the like, of a user; performing spatio-temporal feature extraction and fusion on the original interaction data to generate a user behavior semantic vector, performing feature extraction on the policy data to generate a standard vector, analyzing the user behavior semantic vector based on a government affair policy cognition map to obtain a government affair handling intention vector, and optimizing an intention generation service execution vector in combination with the standard vector; and multi-modal behavior decoding and cross-modal alignment loss function optimization are carried out to obtain an interaction behavior sequence conforming to the specification, and finally, the digital human is controlled to carry out interaction feedback with the user according to the sequence. According to the invention, pertinence and normalization of interactive feedback of the government affair service digital human and the integrating degree with the actual demand of the user can be improved.

The invention provides a multi-mode intelligent control method and device for a robot body and electronic equipment. The multi-mode intelligent control method comprises the following steps: acquiring multi-view images and task texts of cameras at multiple parts of the robot; generating two-dimensional track supervision information and key point supervision information based on the two, and converting the two-dimensional track supervision information and the key point supervision information into a visual supervision graph; key point features are generated through the supervision graph, and track features are generated in combination with the multi-view image; extracting a depth image, optical flow information, a matched segmentation mask, an execution mechanism attitude and a motion frequency, and generating corresponding features; inputting each feature into a multi-modal fusion reasoning model, generating a relative action prediction feature and converting the relative action prediction feature into an action control instruction; and controlling an execution mechanism to complete the task. According to the method, track supervision, key point supervision and multi-modal prior are introduced under the vision-language condition, and perception integrity, robustness and execution precision are remarkably improved.

The invention discloses a remote sensing water body extraction method and device based on a U-Net and Transform fusion architecture, and relates to the technical field of remote sensing image intelligent processing and artificial intelligence, and the method comprises the steps: firstly obtaining a multispectral remote sensing image and knowledge-based product data, and carrying out the blocking, so as to obtain a multispectral image block and a space attention layer block; a U-Net encoder extracts spatial features, a Transform module fuses the spatial and temporal features and focuses on a water body area, and a decoder outputs a probability graph and processes the probability graph to generate a binary mask graph. And finally, splicing the mask graphs to generate a month-by-month data set. According to the method, multi-source data fusion and space-time modeling can be realized, the small water body and boundary identification precision is improved, large-range efficient coverage is realized under high resolution, and the requirements of high precision and high timeliness are met.

The invention relates to the technical field of cabin environment regulation and control, and discloses an immersive intelligent cabin environment regulation and control and energy consumption optimization system. The system multi-source sensing module is used for collecting environment multi-modal data streams through various sensors distributed inside and outside a cabin, and performing time alignment and space registration on the data to generate an environment data set; the dynamic graph generation module is used for performing multi-level feature analysis on the environment data set, extracting environment feature representation, constructing a parameter dependency graph and fusing time sequence dynamic information to generate an environment evolution graph; the core element extraction module is used for deriving an element vector of each dimension from the environment feature representation, calculating an element fluctuation index of each dimension, and screening out a core element sequence in combination with a topological relation in an environment evolution graph; and the intelligent decision-making module is used for generating an optimization control strategy according to the core element sequence and realizing self-adaptive adjustment and energy consumption management of the cabin environment.

The invention discloses an enterprise and policy service automatic matching method based on machine learning, and the method comprises the steps: carrying out the processing of enterprise multi-source data, constructing an enterprise portrait graph, and generating an enterprise graph embedding and structuring feature set; performing policy text analysis and condition recognition, constructing a policy condition graph and generating condition graph embedding representation; constructing a condition constraint field based on policy conditions, and generating a cross-graph alignment relationship and fusion features; integrating a multi-source feature input improved model to carry out joint modeling, and outputting a basic matching score; constructing a condition boundary manifold, and generating an anti-fact feature sample and a matching elasticity score; and based on the basic and elastic scores, generating a comprehensive score and outputting matching result information. According to the invention, by introducing graph structure perception alignment, conditional constraint guide interaction and a multi-channel scoring aggregation mechanism, high-precision, high-interpretability and intelligent reachability automatic matching between enterprises and policy services is realized.

The invention relates to the technical field of pet behavior data processing, and discloses a time-space sequence data processing method and system for pet abnormal behavior recognition, and the method comprises the steps: 1, obtaining multi-modal data, carrying out the time alignment, building a multi-scale scene semantic graph, and generating a grid occupancy frequency and a region transfer matrix; 2, constructing a normal behavior template library, and generating window-level spatio-temporal features; 3, establishing group normal behavior distribution by using a generative density model, calculating a residual error in combination with a time sequence prediction model, and obtaining individual model parameters; 4, performing statistics on historical rhythm distribution and calculating differences of the day to obtain rhythm deviations; 5, fusing multi-component anomalies to obtain a comprehensive anomaly score; step 6, introducing an Internet of Things event to generate a gating coefficient and adjusting an abnormal score; and 7, comparing the abnormal score after gating with a threshold value, and outputting an abnormal alarm. According to the invention, accurate identification and stable alarm of the abnormal behavior of the pet are realized.

The invention discloses a retrieval method based on a semantic enhanced knowledge graph, which relates to the technical field of information, and comprises the following steps: receiving a natural language query of a user, and carrying out deep analysis on the query, including named entity recognition and linking, relationship extraction and query intention classification; and based on an analysis result, extracting a related local sub-graph from the knowledge graph, generating a query context vector, and generating dynamic semantic embedding for the sub-graph through a query-perceived graph attention network to obtain a dynamic enhanced semantic graph. According to the retrieval method based on the semantic enhancement knowledge graph, the retrieval precision and the recall rate are remarkably improved, the limitation of static knowledge representation is solved through a dynamic semantic enhancement mechanism of query intention perception, so that the local semantic representation of the knowledge graph is highly aligned with the query intention of a specific user; and the ability of understanding and answering complex, fuzzy, ambiguous and multi-hop queries is improved.

The invention discloses an agent construction and development system based on a knowledge graph, and the system comprises a data collection and ontology construction module which is used for generating basic graph data; the polymorphic knowledge cell construction module is used for generating a machine-readable rule and an evidence template; the semantic mapping and deduction module is used for determining forward and reverse mapping modes and setting a consistency threshold value and a violation degree weight; the task analysis and planning module is used for outputting a feasible path and generating a plan sequence and tool arrangement; the execution and correction module is used for generating an evidence packet, constructing a forward chain, carrying out reverse reconstruction by utilizing reverse mapping, generating anti-fact candidates and writing back or backing back the anti-fact candidates; and the version and audit module is used for generating a version fingerprint for the polymorphic knowledge cellular graph which is written back and providing query and audit functions. According to the method, adaptive planning, reliable execution and traceable auditing of the intelligent agent in a dynamic task are realized by constructing polymorphic knowledge cells and cross-layer semantic mapping.

The invention discloses a map generation method, electronic equipment and a robot, and relates to the technical field of artificial intelligence. The method comprises the steps of generating a structured prior map representing an environmental space occupation state according to environmental geometric shape design data of a target physical task space; determining a simulation path of the intelligent mobile device moving in the passage area of the structured prior map, and performing simulation scanning operation on the virtual physical model of the target physical task space along the simulation path to obtain laser point cloud simulation data and motion state simulation data; and generating a navigation map based on a pose map according to the laser point cloud simulation data and the motion state simulation data. According to the method and the device, the problems of high dependence on manpower, time consumption and uncertain generation results during map generation in the prior art can be solved, the map with high accuracy can be efficiently and automatically generated at low cost, and the task execution efficiency and the task execution precision of the intelligent mobile equipment are improved.

The invention discloses a layout generation method for load effect compensation and a related device, which are applied to the technical field of photoetching and comprise the following steps: acquiring photomask data; carrying out grid division on the mask graph according to the photomask data, and determining layout density in each grid; comparing the layout density of each grid with a target density, and determining a density difference value corresponding to each grid; determining a virtual graph corresponding to each grid according to the density difference value; and adding a corresponding virtual graph in a layout area corresponding to the grid to form a compensated mask plate graph. By performing grid division on the mask plate graph, calculating the specific layout density in each grid and adding the corresponding virtual graph to the layout area corresponding to the grid according to the difference value between the layout density and the target density, the graph density of the layout area in each grid can be effectively compensated, so that the load effect of each area in the layout is balanced, and the performance of the mask plate is improved. And the etching uniformity is improved.

The invention discloses a weak supervision pathological image tissue segmentation method based on text prompt learning. The method comprises the steps of feature extraction and initial class activation graph generation; using an MCRM module to optimize the initial class activation graph to obtain a refined class activation graph; and aggregating the plurality of refined class activation graphs to form a fused pseudo mask, taking the fused pseudo mask as a supervision signal, training a segmentation model, and after the training is completed, segmenting the new pathological image tissue by using the segmentation model. According to the method, a text prompt learning mechanism is utilized to focus the model on learning high-discrimination features, so that the influence of tissue co-occurrence is reduced. An initial class activation graph is optimized through a multi-mode class activation graph refining module, and the integrity of boundary segmentation is enhanced. Meanwhile, pseudo masks from different network layers are fused to train a segmentation model, and semantic segmentation of the pathological image is realized. According to the method, high-annotation data dependence is effectively relieved, and the generalization ability of the model is improved, so that application in the field of artificial intelligence-assisted medical treatment is promoted.

The invention provides a building information model compliance processing method and device based on artificial intelligence, electronic equipment and a storage medium, and the method comprises the steps: analyzing a standard document through a natural language processing model, and constructing a structured compliance knowledge graph containing a forced parameter, a naming rule, a geometric constraint and a data mapping relation; when the components are loaded, geometric feature vectors of the components are extracted, a hierarchical topological graph is modeled and nested, and a digital twin file containing geometric attributes, a parameter list and risk scores is generated; and matching and comparing the archive with the knowledge graph, dynamically generating a processing strategy sequence, and scheduling a geometric optimization and data encapsulation module to execute adaptive processing. Geometric optimization adopts a deep learning driven non-uniform grid simplification algorithm, key region details are reserved, and topological defects are repaired; and the data packaging realizes intelligent matching and binding from the custom parameters to the standard attribute set through semantic mapping. Through the method and the device, the efficiency, the accuracy and the automation level of cross-platform compliance conversion of the building information model can be remarkably improved.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

The invention relates to an ore control element extraction and weight determination method based on a knowledge graph, and the method comprises the following steps: constructing a geological mineral knowledge graph: collecting geological text data, extracting entities and semantic relationships in the geological text data, and constructing the geological mineral knowledge graph; network analysis and simplification: utilizing a community clustering algorithm to divide an ore deposit, and combing and simplifying a complex geological knowledge map in combination with a modularity algorithm process; subgraph generation: constructing a series of subgraphs according to community categories of nodes, and forming an information set which completely and structurally represents specific geologic features and metallogenic conditions; weight calculation and integration: quantifying the indicating significance of different geological entities on ore deposit prospecting through an entity-relation weight result, and defining the weight of ore control elements; and element screening: sorting according to the weight values, and rapidly screening out geological entities which have important influence on ore deposit mineralization as ore control elements. According to the invention, an intelligent and automatic decision support tool is provided for geological prospecting work.

The invention provides a state management and anomaly backtracking method and system for a multi-agent collaborative task, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining interaction data, state data and an execution log, building an initial task knowledge graph based on an ontology model, building a bidirectional causal chain through a Bayesian network, and forming a dynamic knowledge graph; and when an exception is detected, analyzing a state transition rule, identifying propagation nodes, constructing an exception propagation sub-graph, generating an influence chain, determining a recovery priority and triggering state rollback. According to the method, an abnormal source can be quickly traced, abnormal diffusion is effectively controlled, and the system robustness and the task recovery efficiency are improved.