171 results about "Reasoning algorithm" patented technology

The invention provides a network attack link tracking and threat situation reasoning method based on a knowledge graph, and relates to the technical field of network security, and the method comprises the steps: collecting and processing original data; extracting features and constructing a network attack knowledge graph; performing representation learning by adopting a graph neural network; identifying a time sequence dependency relationship; generating and analyzing candidate attack links; constructing a dynamic Bayesian network model; performing parameter learning by adopting a variational reasoning algorithm; future attack targets and means are predicted. According to the method, the network attack link can be effectively tracked, the future threat situation is accurately predicted, and the network security defense capability is improved.

The invention provides an unmanned intelligent inspection equipment cooperative scheduling method and system in a photovoltaic power generation scene, and relates to the technical field of photovoltaic power station intelligent inspection, and the method comprises the steps: carrying out the task priority classification and time sequence correlation analysis based on a dynamic adaptive neural network and a sequence predictor, constructing a task dependency graph, and carrying out the task priority classification and time sequence correlation analysis; a candidate task allocation scheme is generated based on a deep reinforcement learning framework, the candidate scheme is optimized in combination with a collaborative decision-making system constructed by a multi-target genetic algorithm and a graph attention mechanism by taking minimization of completion time, energy consumption and maximization of coverage rate as targets, and task allocation constraints are updated by using a confidence propagation network; and an abnormal condition is judged through a Bayesian reasoning algorithm, and an optimal scheduling scheme is finally output, so that the inspection efficiency of the photovoltaic power station can be effectively improved, the energy consumption is reduced, and the inspection coverage rate is improved.

The invention discloses an intelligent visualization and text association method for a multi-modal knowledge graph, and relates to the technical field of information processing. According to the intelligent visualization and text association method for the multi-modal knowledge graph, multi-modal data in an internal database of an enterprise is obtained, and a multi-modal feature vector is generated by using a data preprocessing and feature extraction technology; the feature vectors are mapped to a unified vector space through a cross-modal embedding algorithm, semantic alignment, association and fusion among multi-modal data are realized, and a complete multi-modal knowledge graph is constructed. Through bidirectional association and multi-modal joint query, the information acquisition efficiency is improved, and visual display of the knowledge graph is realized by using the graph neural network. Meanwhile, the knowledge graph is dynamically updated through a graph reasoning algorithm, and the timeliness and accuracy of the knowledge graph are ensured. A more efficient solution is provided for an enterprise in the aspects of management and application of multi-modal data, and the intelligent process of knowledge management is promoted.

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

The invention relates to a brilliance large-scale model culture knowledge generation and retrieval method combined with a knowledge graph. The method comprises the steps that entity extraction, relation modeling and dynamic completion processing are carried out according to multi-modal culture data, a culture knowledge graph is constructed in combination with a fuzzy time reasoning algorithm and a cross-modal alignment mechanism, and the multi-modal culture data comprises an unstructured culture text, image data and space-time metadata; the space-time metadata comprises text space-time metadata and image moment metadata; performing semantic alignment processing by combining a large model through a user query statement and a culture knowledge graph to obtain joint representation of cross-modal semantic alignment; and performing multi-modal retrieval-generation joint optimization based on the joint representation to obtain cultural knowledge description and associated multi-modal content. According to the method, through multi-modal data fusion, dynamic knowledge completion and fuzzy time reasoning, culture knowledge description can be accurately generated, associated multi-modal content can be retrieved, and the accuracy and the intelligent level of culture knowledge service are improved.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

The invention discloses a medical record quality management system and method, and relates to the technical field of medical information processing, and the method comprises the steps: collecting medical record data, and carrying out the preprocessing of the collected medical record data; analyzing the preprocessed medical record data by adopting an NLP, and extracting a medical record core field through an NER; constructing a dynamic medical knowledge graph, and matching the dynamic medical knowledge graph with the medical record core field; after matching is completed, a graph reasoning algorithm is used for conducting logic consistency verification on the medical record, logic contradiction points are marked, and a verification result is generated; and generating a medical record quality auditing report according to the verification result. According to the method, the dynamic medical knowledge graph is constructed and matched with the core field of the medical record, and the graph reasoning algorithm is adopted to perform logic consistency verification on the medical record, so that the intelligence and precision of medical record quality management are realized.

The invention discloses a dispatching automation fault intelligent processing system based on knowledge reasoning navigation. The dispatching automation fault intelligent processing system comprises a dispatching automation system environment sensing module which obtains machine room environment multi-source data in real time to serve as a data source; the intelligent analysis module performs preprocessing and labeling on the image type data and the text type data, then performs knowledge extraction to obtain entities and relationships among the entities, performs data fusion on the obtained entities, constructs an automatic duty knowledge graph, forms a scheduling automation system fault research and judgment mechanism reasoning model, and performs fault diagnosis on the scheduling automation system. A fault alarm event is generated, a knowledge reasoning environment is constructed, and a fault research and judgment conclusion is obtained through reasoning of a knowledge reasoning algorithm; an auxiliary decision-making module performs reasoning matching on the fault alarm event and the automatic duty knowledge graph through an RE2 model to obtain a fault handling auxiliary decision-making opinion; and constructing a fault exception handling flow navigation chart, and generating a fault handling coping strategy. The operation and maintenance management level of the dispatching automation machine room is improved.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention discloses a building engineering automatic compliance examination method based on AI and BIM, and the method comprises the steps: obtaining a to-be-examined standard text, carrying out the natural language processing, and generating a semantic information group; by adopting a semantic mapping rule, mapping the semantic information group subjected to the information conversion algorithm and entity attributes in the IFC ontology structure, and by utilizing a logic rule generator, converting a mapped target NLP deep learning rule knowledge model into a logic rule; obtaining a to-be-examined BIM model, constructing a plurality of specific IDM views, extracting entity attributes corresponding to a specific field from the to-be-examined BIM model, mapping the entity attributes in each specific IDM view and the entity attributes in the IFC ontology structure, and converting the mapped target IFC engineering feature semantic model into a logic fact by using a preset conversion tool; and performing automatic compliance review on the logic rules and the logic facts by utilizing a rule engine through a rule reasoning algorithm and an ontology semantic reasoning algorithm to obtain a rule check report.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention discloses an engineering project progress multi-dimensional monitoring system and method based on an information technology, and belongs to the field of monitoring. The engineering project progress multi-dimensional monitoring system comprises an initial construction plan construction module, a data acquisition and environment monitoring module, a data analysis and plan optimization module, a construction progress prediction module and a risk management module. By collecting multi-dimensional data of a construction site in real time and applying the intuitionistic fuzzy reasoning algorithm, a construction plan can be dynamically adjusted to cope with constantly changing site conditions and external environment factors, the intuitionistic fuzzy reasoning algorithm is utilized to analyze requirements and bottlenecks of a construction project, and the construction efficiency is improved by combining construction logic and physical constraints. Resource configuration can be carried out more accurately; fuzzy and uncertain conditions are processed through the fuzzy logic controller, it is ensured that a project meets all related building and safety standards, and project quality and safety are ensured through continuous monitoring and timely adjustment.

The invention discloses an intelligent security and protection management system based on an AI large model, and relates to the technical field of security and protection management, the intelligent security and protection management system comprises a modal large model and a chained reasoning module, the chained reasoning module comprises a distributed chained reasoning sub-module, and a preset chained reasoning algorithm process is adopted to execute the following steps: scene segmentation and target detection; quantifying risk factors; constructing a causal relationship; threat scene simulation; and risk assessment calculation. According to the method, the multi-modal large model and the chain reasoning module are designed, multi-modal information is extracted and fused in real time to form the risk state matrix, the risk situation is deduced step by step, the problem that the multi-causal relationship and dynamic change in a complex scene are difficult to capture in traditional single-layer anomaly detection is solved, the risk identification accuracy is remarkably improved, and the risk identification efficiency is improved. The false alarm and the missing alarm are reduced, and the accurate prejudgment of the global security situation is provided for the system.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

The invention relates to the technical field of data processing and governance, in particular to a monitoring method and system for a data governance process, and the method comprises the steps: constructing a quantum distribution model through extracting multi-modal features, so as to represent a complex relation between feature variables; analyzing the abnormal propagation path based on a causal chain model and a recursive reasoning algorithm, and generating basic abnormal feature data; performing parameter optimization on the governance rule by using a memetic evolutionary algorithm to generate optimized rule data; model parameters of the monitoring system are updated through multi-round iteration, and dynamic optimization of the system state is achieved. According to the method, the depth and precision of anomaly detection are remarkably improved, meanwhile, efficient iteration of a monitoring system is achieved, the treatment requirement under the complex multi-modal data environment is met, and the key problems that in the prior art, the analysis depth is insufficient, the real-time performance is poor, and the traceability is limited are solved.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention discloses a building environment dynamic regulation and control method and system based on Internet of Things driving, particularly relates to the field of building environment intelligent regulation and control, and is used for solving the problems of multi-region environment dynamic regulation and control and fine management. The environment data of each sub-region of the building is accurately acquired and synchronized, real-time region division is realized in combination with an adaptive clustering algorithm, and the accurate capture of environment features and the reasonability of space division are ensured. On the basis, the independence and interaction strength of the sub-regions are comprehensively evaluated by calculating an environmental independence index, and a scientific basis is provided for refined and coordinated regulation and control. Based on a rule engine and a decision tree reasoning algorithm, an optimal regulation and control strategy is automatically generated, and continuous optimization is performed in combination with a Q learning algorithm, so that the system can adapt to environmental changes, dynamically adjust regulation and control actions, realize intelligent optimization and self-learning of the regulation and control strategy, and remarkably improve the regulation and control efficiency and accuracy of the building environment.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention relates to the technical field of power monitoring, in particular to a method and system for detecting and positioning partial discharge of a switch cabinet, and the system comprises a signal collection module, a signal preprocessing module, a feature extraction module, a data fusion module, a discharge source positioning module and a classification and alarm module. The signal acquisition module simultaneously acquires partial discharge signals through a plurality of sensors installed in the switch cabinet, the signal preprocessing module performs noise filtering and signal enhancement on the acquired signals, and the feature extraction module screens out most representative discharge features based on a deep learning algorithm. The data fusion module fuses data of multiple sensors through Kalman filtering and a Bayesian reasoning algorithm, the discharge source positioning module accurately calculates the position of a discharge source through a multi-point positioning algorithm, and the discharge source is classified through the classification and alarm module. The partial discharge detection precision and the positioning accuracy of the switch cabinet can be effectively improved, and safe operation of a power system is ensured.

The invention discloses a drilling and blasting method tunnel construction safety risk evolution analysis method based on a knowledge graph, and the method comprises the steps: obtaining and preprocessing the text data of a drilling and blasting method tunnel construction accident case, and obtaining a safety risk corpus; a mixed deep learning model is constructed, the safety risk corpus is trained through the mixed deep learning model, and the risk factors and the incidence relation of drilling and blasting method tunnel safety are obtained; constructing a drilling and blasting method tunnel construction safety risk coupling evolution analysis knowledge graph based on the risk factors and the incidence relation, and obtaining a key accident chain and a key edge in combination with a knowledge query and path reasoning algorithm; and quantitatively evaluating independent values and coupling effect intensity of the risk factors by using an interaction matrix algorithm, calculating comprehensive importance scores of the risk factors, and determining key risk points according to the comprehensive importance scores of the risk factors. The problems that in a traditional method, attention to the risk factor coupling effect is insufficient, and accident case data are not fully utilized are effectively solved.

The invention relates to the technical field of mine industry management, in particular to an industrial management system with an industrial configuration picture presentation function, which comprises a heterogeneous data acquisition module which is composed of a plurality of sensors and realizes data fusion through a multi-protocol converter; the three-dimensional dynamic configuration engine covers the geological model construction unit, the real-time data mapping unit and the visual rendering unit; the intelligent scheduling decision module is provided with a transportation path optimization sub-module, a resource allocation sub-module and an emergency response sub-module; and a man-machine interaction terminal. An edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model; the transportation path optimization sub-module uses an improved ant colony algorithm, the resource allocation sub-module uses an improved multi-objective optimization model, and the emergency response sub-module uses an improved rule reasoning algorithm. The problems of data fusion, path optimization, resource allocation, emergency response, visualization and the like in mine industry management are effectively solved, and the efficiency, the safety and the intelligent level of mine management are improved.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention discloses a multi-dimensional digital twinning modeling method. The method comprises the following steps: S1, performing dynamic fusion and real-time modeling on multi-source heterogeneous data in a complex scene; and high-precision multi-dimensional data expression is formed. S2, based on a cross-domain knowledge graph and a semantic reasoning algorithm, establishing semantic association among multi-dimensional data; and the cross-domain data fusion problem is solved. And S3, performing real-time evaluation and early warning on the system state through an anomaly detection and trend prediction technology. The defects of a traditional digital twin system in multi-scene data fusion and predictive analysis are overcome.

The invention relates to a fault judgment system, method and device based on a knowledge graph and a medium, and belongs to the technical field of reliability analysis, and the system comprises a data collection module which is used for obtaining to-be-diagnosed information, the to-be-diagnosed information comprises a device type, a working condition and real-time monitoring data of a target device, the real-time monitoring data comprises operation parameters and state information; the fault diagnosis reasoning module is used for inputting the to-be-diagnosed information into a pre-constructed knowledge graph and performing fault diagnosis on the to-be-diagnosed information by adopting a graph reasoning algorithm to obtain a reasoning result of the target equipment, and the reasoning result comprises a fault type, a fault reason and an influence factor of the target equipment; and the diagnosis result analysis module is used for evaluating the reasoning result to obtain an evaluation result, displaying the reasoning result and the evaluation result based on a visual tool, and generating a fault diagnosis report. According to the method, the accuracy of fault judgment is effectively improved, and misjudgment caused by insufficient experience in a traditional method is reduced.

The invention discloses a mesh belt furnace multi-equipment collaborative material tracking and graphical monitoring method, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: carrying out the dynamic feature extraction of material block data based on a dynamic twin database, carrying out the analysis through combining a causal reasoning algorithm, and generating an enhanced data set with a causal label; on the basis of the enhanced data set with the causal label, when material tracking abnormity is detected, constructing a dynamic causal graph, calculating the contribution degree of equipment operation parameters to the abnormity through a Bayesian network, and generating a repair instruction set; and the repair instruction set is issued to the PLC to adjust the equipment operation parameters, the state change of the material blocks is monitored in real time, and when the deviation exceeds the tolerance range, the causal graph is triggered to update and a regulation and control instruction is generated to regulate and control the equipment operation parameters. According to the method, the causal network node weight is updated in real time by adopting the dynamic time warping algorithm, and accurate positioning of an abnormal source and intelligent generation of a repair strategy are realized.

The invention belongs to the technical field of disaster information early warning, and particularly relates to a disaster information fusion and semantic reasoning algorithm based on a cross-modal diagram attention mechanism, which comprises a data preprocessing module, a cross-modal diagram construction module, a cross-modal diagram attention mechanism module, a semantic reasoning module and a result output module. According to the method, data sources from multiple modals can be effectively fused through a cross-modal graph attention mechanism, so that more comprehensive disaster information is provided, and meanwhile, the graph attention mechanism is realized through the graph attention mechanism, so that the model can dynamically and adaptively adjust the weight according to the correlation of different modal data, and the accuracy of information fusion is improved. The graph neural network is adopted for semantic reasoning of disaster events, the types and influences of disasters can be accurately predicted, decision support can be provided, disaster emergency management personnel can be helped to respond quickly, various different types of disaster data can be processed, and the method has high adaptability to data sources and formats.

The invention discloses a knowledge graph question-answer interaction method and system based on artificial intelligence, and the method comprises the steps: receiving a natural language question input by a user, carrying out the hierarchical analysis of entity words and intention words in the question through a dynamic weight distribution mechanism, and generating an enhanced semantic vector containing entity association strength and intention probability distribution; traversing the knowledge graph based on the enhanced semantic vector, and constructing a hierarchical query sub-graph comprising a core entity, an associated entity and an implicit relationship; performing reasoning calculation on the hierarchical query subgraph by adopting a multi-path collaborative reasoning algorithm to generate a candidate answer set; and performing semantic consistency verification and redundancy elimination on the candidate answer set, generating an adaptive answer and an association explanation path in combination with historical interaction preference of the user, and synchronously feeding back a newly discovered entity association relationship to the knowledge graph for incremental updating to form a question and answer interaction closed loop. By utilizing the embodiment of the invention, the accuracy and interpretability of questions and answers can be improved, and the user interaction experience and the information acquisition efficiency are improved.

The invention discloses an efficient secret state joint neural network inference method. The method comprises the following steps: S100, a data owner C and a model owner # imgabs0 # respectively send privacy data and a privacy model to computational nodes # imgabs1 # and # imgabs2 # in a secret sharing manner; s200, computing nodes # imgabs3 # and # imgabs4 # to run a secret state joint neural network reasoning algorithm, and respectively calculating secret shares Z0 and Z1 of reasoning results by sequentially executing secret state computing layer protocols, the secret state computing layer protocols comprising: S210: a secret state convolution layer algorithm # imgabs5 #; s220, a function layer algorithm # imgabs6 # is activated in a dense state; the invention further discloses an efficient dense state joint neural network inference system. According to the technical scheme, the ReLU algorithm with few communication rounds is designed based on the function secret sharing technology, so that the communication cost required for calculating the activation function is reduced, the convolutional neural network inference sub-protocol is designed by utilizing bilateral comparison and correct symbol discrimination, the inference accuracy is improved, and the inference result is consistent with a plaintext inference result.

The invention discloses a version knowledge graph reasoning method and system based on large language model enhancement, and relates to the technical field of dynamic knowledge graphs, and the method comprises the steps: employing a semantic drift detection and compensation mechanism, comparing context coding differences of same entities of different versions in an initial knowledge graph, recognizing drift, and dynamically adjusting entity embedding vectors, outputting a compensation update atlas; an LLM enhanced multi-hop reasoning algorithm is adopted, multi-hop reasoning is carried out on the compensation updating map, symbol reasoning, vector reasoning and context reasoning are carried out in parallel in each hop, and an entity relation reasoning result is obtained through dynamic weight fusion; and superposing an entity relationship reasoning result to the compensation updating graph through a cloud collaborative node, adding an entity edge and automatically maintaining a version history log, and obtaining a reasoning fusion version knowledge graph. Through a multi-hop reasoning algorithm enhanced by a large language model, the deep semantic mining capability and reasoning accuracy of a cross-version entity relationship are effectively improved.

The invention provides an index transaction attribution analysis method, electronic equipment, a storage medium and a product, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to an attribution analysis request for transaction of a target index, querying target data related to the target index; calling a plurality of agents, and analyzing the target data from a plurality of dimensions to obtain abnormal data under the plurality of dimensions; inputting the abnormal data under the plurality of dimensions into an attribution reasoning model, and outputting a plurality of candidate root causes causing the transaction of the target index; the multiple candidate root causes, the abnormal data under the multiple dimensions and data of a preset knowledge base are input into an attribution analysis model, an attribution analysis result of the target index transaction is output, and the preset knowledge base is used for storing description data of different indexes and dimensions. The method does not depend on a traditional reasoning algorithm, and the attribution analysis result reasoned by means of the model is more comprehensive and more accurate.