136 results about "Semantic mining" patented technology

The invention discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which comprises the following steps of: acquiring point cloud data of a building component through three-dimensional scanning equipment, associating text information, and constructing a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine tuning module; a cross-modal contrast loss and task instruction fine tuning strategy is adopted in model training, and finally semantic recognition, attribute completion and historical background analysis results of the building components are output. The method is suitable for building heritage digital protection, intelligent building process monitoring and three-dimensional digital archive management, component function recognition precision and cultural semantic mining capability in a complex scene can be improved, and real-time semantic updating and interactive response of a dynamic construction environment are supported.

The invention discloses an automatic driving test case self-evolution repairing method based on a large language model, and belongs to the technical field of testing and performance optimization of an automatic driving system. According to the automatic driving test case self-evolution repairing method based on the large language model, deep semantic mining is conducted on a performance log of the model by means of semantic comprehension and recommendation capacity of the large language model, and a high-quality repairing scene is recommended from a structured test scene library. The method comprises the steps of performance log collection and pre-evaluation execution, failure mode semantic extraction, a scene semantic retrieval mechanism, initial recommendation construction, reflection type semantic optimization, few-sample fine adjustment adaptation and a closed-loop self-evolution mechanism. The method has obvious advantages in the aspects of automatic driving test case recommendation, failure mode recognition, rapid model capability adaptation and system closed-loop self-evolution, and the safety, generalization capability and test maintainability of the automatic driving system can be remarkably improved.

The invention provides a dialogue interaction method and system based on long historical dialogue semantic understanding, and belongs to the technical field of natural language process.The dialogue interaction method comprises the steps that firstly, a historical dialogue information set containing multiple rounds of user consultation and customer service response of an e-commerce platform target user is obtained, and then deep semantic mining is conducted on the historical dialogue information set; dialogue intention features and dialogue theme association features are obtained, a historical dialogue semantic understanding result containing user core demand induction and a dialogue theme development knowledge path is generated according to the two features, an adaptive dialogue interaction strategy is generated according to the historical dialogue semantic understanding result, and the adaptive dialogue interaction strategy comprises response emphasis, guide direction and rhythm control information. And finally, executing the strategy to realize dialogue interaction, outputting a response statement and receiving new input of the user, thereby improving customer service quality and user experience of the e-commerce platform.

The invention relates to a ship user behavior self-learning recommendation system based on a large model, and relates to the technical field of ship informatization. According to the system, through collection and fusion of multi-source heterogeneous ship user behavior data, a large-scale pre-training language model (large model) is utilized to carry out deep understanding and semantic mining on massive ship field text information and user behavior sequences, and a ship field knowledge graph or semantic vector space is constructed. The large model can identify and predict potential demands, behavior patterns and preference changes of ship users, and generates highly personalized, accurate and prospective ship service, product, route or information recommendations in combination with real-time operation data and external environment factors. Besides, a user feedback self-learning mechanism is introduced into the system, recommendation strategies and model parameters are continuously optimized according to interaction behaviors and explicit evaluation of the users in modes of reinforcement learning or continuous learning and the like, and intelligent iteration of the system and continuous improvement of the recommendation effect are achieved. According to the method, the challenges of a traditional recommendation system in the aspects of data complexity, semantic gaps and dynamic demand adaptability in the ship field are effectively solved, and the ship operation efficiency and the user satisfaction degree are remarkably improved.

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 relates to the technical field of false news detection, in particular to a false news detection method, equipment and device based on user emotion and comment semantic mining and a computer storage medium. According to the false news detection method, firstly, initial feature representation is carried out on news texts, comments and user emotions, secondly, news representation associated with context is learned according to sentence-level news initial embedding, and news fine-grained semantics are captured for reinforcement learning of subsequent comments at the same time. Thirdly, integrating consideration of different attention of news text sentences to obtain comment semantic embedding of news enhancement. Fourthly, by taking the comments as units, designing a gating mechanism to perform feature fusion on comment semantics and user emotions, and mining associated features of the comments and the user emotions; and 5, capturing change modes of comment semantics and user emotions in the comment area through a long-short-term memory network. And finally, integrating features extracted by each module, and carrying out news category judgment. The accuracy of false news detection is improved.

The invention provides an intelligent quality analysis method and system for an AC / DC rectifier module, and relates to the technical field of computers. According to the method, firstly, in the process of testing the AC / DC rectifier module, target image data and target vibration data which correspond to the AC / DC rectifier module and are formed through image acquisition and vibration monitoring are obtained; secondly, performing interference removal processing on the target vibration data to form interference-removed vibration data; then, respectively carrying out semantic mining on the target image data and the interference removal vibration data, and outputting an image semantic vector and a vibration semantic vector; further, fusing the image semantic vector and the vibration semantic vector, and outputting a module multi-modal vector; and finally, performing quality analysis based on the multi-modal vector of the module, and outputting target quality data. Based on the method, the problem that in the prior art, the reliability of quality analysis of the AD / DC rectifier module is relatively low can be solved.

The invention discloses an enterprise type identification method fusing multi-source data. The method comprises the following steps: acquiring map POI point of interest information, enterprise registration information and enterprise business brief introduction information; matching the POI name with the enterprise registration name to obtain matched enterprise data; cleaning the brief introduction information of the enterprise business and the matched enterprise data to obtain cleaned data; performing semantic information extraction on brief introduction information in the cleaned data to obtain extracted data; fusing the enterprise data in the cleaned data with the extracted data to form a fused data set; and clustering the fused data set to obtain theme distribution of each enterprise. According to the method, semantic mining, LDA topic modeling, keyword recognition and regional clustering analysis can be combined, so that accurate subdivision of enterprise types and intelligent recognition of regional dominant industries are realized, and regional industry strategy making and industrial park planning layout are supported.

The invention relates to a multi-mode large model and light-weight small model collaborative road surface ice condensation state prediction method, and belongs to the field of road traffic safety monitoring and prediction. The method aims at solving the problem that real-time early warning and accurate prevention and control are difficult in the prior art. According to the invention, a multi-modal data coding system is constructed, road surface monitoring images, meteorological time sequence data and historical ice condensation text cases are integrated, and feature fusion is realized by adopting visual-physical feature joint coding, meteorological time sequence feature enhancement and text semantic mining; a pseudo label is generated through large model zero sample reasoning, and a lightweight small model is trained through cross-modal knowledge distillation; and finally, on the basis of a dynamic trigger type double-model reasoning framework, calling a cloud large model for fine judgment when the small model is low in confidence coefficient or high in scene complexity, and outputting the icing starting moment and thickness through confidence coefficient weighted fusion. According to the method, the prediction accuracy and real-time performance are improved, the model generalization ability is enhanced, and reliable support is provided for road traffic control in winter.

The invention relates to the technical field of form construction, in particular to a form construction method and system based on dragging operation and a storage medium. The method comprises the following steps: identifying a target form dragged by a mouse of a user, and performing form content structure deep analysis to obtain a form information structure identification result; obtaining a user historical form operation data set to perform operation behavior semantic mining, and performing historical page layout behavior clustering analysis on a form information structure identification result to obtain form layout preference features; performing layout demand prediction according to the form layout preference features, and performing feasibility evaluation to obtain an accurate layout demand prediction result; and identifying an affiliation page to which the user drags the target form, performing spatial layout constraint analysis, and performing available space size evaluation, thereby obtaining the available space of the current page. According to the method, efficient and convenient form self-adaptive construction is realized, the user development efficiency is improved, and multi-terminal operation is adapted.

The invention provides a deep-buried tunnel dynamic disturbance field three-dimensional positioning method based on multi-source heterogeneous sensing, and relates to the technical field of data analysis. The method comprises the following steps: firstly, obtaining a plurality of disturbance monitoring data, and determining three-dimensional coordinate data of a sensor; secondly, performing multi-level semantic mining on frequency domain information of the disturbance monitoring data to form a target disturbance semantic vector; then, fusing semantic information corresponding to the three-dimensional coordinate data of the sensor and semantic information corresponding to the disturbance start / end time data into a target disturbance semantic vector to form a target monitoring semantic vector; further, semantic information association fusion is carried out on the multiple target monitoring semantic vectors, and a global monitoring semantic vector is formed; and finally, performing positioning output based on the global monitoring semantic vector to obtain three-dimensional coordinate data of the disturbance field. On the basis of the content, the problem that in the prior art, the precision of three-dimensional positioning of the dynamic disturbance field of the deep-buried tunnel is relatively low can be solved.

The application discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which collects point cloud data of building components through a three-dimensional scanning device and associates text information, constructs a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine-tuning module; model training adopts a cross-modal contrast loss and a task instruction fine-tuning strategy, and finally outputs semantic recognition, attribute completion and historical background analysis results of the building components. The application is suitable for digital protection of building heritage, intelligent construction process monitoring and three-dimensional digital archive management, can improve component function recognition accuracy and cultural semantic mining capability in a complex scene, and supports real-time semantic updating and interactive response of a dynamic construction environment.

The invention provides an environment-friendly public toilet equipment state monitoring method, device and equipment, and relates to the technical field of equipment state monitoring. The method comprises the following steps: firstly, acquiring multi-dimensional state time sequence data of target environment-friendly public toilet equipment; secondly, performing cross-dimensional state time sequence semantic mining on the multi-dimensional state time sequence data to form multi-dimensional state time sequence features corresponding to the multi-dimensional state time sequence data; and then, network state prediction is carried out based on the multi-dimensional state time sequence characteristics, and a target network state prediction result is obtained. On the basis of the content, the problem that in the prior art, the reliability of equipment state monitoring is relatively low can be solved.

The invention discloses an industrial Internet of Things production line safety analysis method, system and device and a medium, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: obtaining the operation environment time sequence data of a target production line and the corresponding fault record data; through a semantic mining model included in a target production line data analysis network, respectively performing semantic coding on the operation environment time sequence data and the fault record data to obtain corresponding operation environment features and fault features; performing cross-domain association semantic coding on the operating environment features and the fault features through the semantic analysis model to obtain corresponding environment fault association features; and performing safety situation assessment on the target production line according to the environment fault correlation features through a safety assessment model to obtain a corresponding operation safety assessment result. The method has the effect of improving the accuracy of safety analysis of the production line.

The invention relates to a cross-modal medical data hash retrieval method assisted by a large language model, and belongs to the field of artificial intelligence information retrieval in an intelligent medical system. Designing an image description type prompt template, a diagnosis report summary type prompt template and a combined modal reasoning type prompt template in combination with the semantic generalization ability of the large language model to perform modal data enhancement, and realizing deep semantic mining of medical image and diagnosis report data; a structured coding layer is utilized to ensure that the features of the image and the text can be accurately matched in a unified embedding space; finely adjusting the large language model by adopting a mode of combining soft prompt and hard prompt to realize alignment of image and text features; a Gaussian binary restricted Boltzmann machine is introduced to carry out probabilistic Hash mapping, data structure information is effectively reserved, and Hash codes are generated to carry out cross-modal Hash retrieval. Semantic similarity retrieval of medical cases among different modes can be realized, high retrieval efficiency is realized, and accurate and personalized diagnosis and treatment schemes are provided for patients.

The invention relates to an urban traffic travel evaluation method and system fused with a large-scale language model. The method comprises the following steps: establishing a multi-dimensional index library for urban traffic evaluation, performing semantic mining on social media data by using the large-scale language model, identifying emerging evaluation dimensions, and generating a dynamically updated comprehensive evaluation index system; classifying the social media data by using a pre-training language model, and generating a text set corresponding to each evaluation index; performing sentiment intensity and polarity analysis on the text set by using a dictionary-based sentiment analysis model; inputting the emotion intensity and polarity analysis result and the traffic sensor data into a large language model for multi-modal causal reasoning, and generating a causal association report; and finally, verifying the evaluation result based on the sentiment analysis, and generating an urban traffic intelligent evaluation report. Compared with the prior art, the method has the advantages that the multi-dimensional emotion information in the social media data can be fully explored, and the accuracy of urban traffic travel evaluation is improved.

The invention provides a distribution network equipment operation state evaluation method and system based on data analysis, and relates to the technical field of data analysis. The method comprises the following steps: firstly, acquiring state data of a plurality of pieces of distribution network equipment; secondly, for each piece of distribution network equipment state data, extracting time domain state characteristics of the distribution network equipment state data, and converting the distribution network equipment state data into equipment state frequency domain data; then, based on semantic information of the time domain state features, semantic mining is carried out on the semantic information of the equipment state frequency domain data, and a first state semantic vector is formed; further, fusing the first state semantic vector corresponding to each piece of distribution network equipment state data to form a second state semantic vector; and finally, performing state evaluation based on the second state semantic vector to obtain a running state evaluation result. Based on the above content, the problem that the reliability of the operation state evaluation of the distribution network equipment is relatively low in the prior art can be improved.

The invention provides a risk identification method and system based on video data and mobile phone signaling analysis, and relates to the technical field of data analysis. The method comprises the following steps: firstly, acquiring monitoring video data and mobile phone signaling data formed in a target time interval in a target area, and determining motion trail data of each mobile phone user based on the mobile phone signaling data; secondly, performing video semantic mining on the monitoring video data to form a video semantic vector; secondly, track semantic mining is carried out on the motion track data, track semantic vectors are formed, and in the track semantic mining process, different track complexities have different mining modes; further, aggregating the video semantic vector and the track semantic vector to form a video track aggregation vector; and finally, performing risk identification based on the video trajectory aggregation vector to obtain a risk identification result. Based on the method, the problem that the reliability of risk identification is relatively low in the prior art can be improved.

The application relates to the technical field of artificial intelligence, in particular to a retrieval method and device based on multiple agents, equipment and a medium. Applied to a financial scene, the application realizes the intellectualization and dynamicization of the retrieval process through a multiple-agent cooperative working mechanism. The retrieval core elements are accurately extracted and a dynamic cognitive graph is updated by analyzing the agents, deep semantic mining and correlation expansion are carried out based on the cognitive seeds of semantic agents, the dependence on keywords in traditional retrieval is effectively broken, and the breadth and depth of semantic understanding are improved. The query statement is optimized by combining the extended semantics and the dynamic cognitive graph of the context agent, so that the query is more in line with the real intention and context of the user. The retrieval agent efficiently retrieves the candidate path in the updated dynamic cognitive graph, the recommendation agent filters the optimal result through cognitive consistency scoring, and the accuracy, relevance and logical coherence of the retrieval result are ensured, and the accuracy of the complex information retrieval task is significantly improved.

The embodiment of the invention discloses a log analysis method and device, electronic equipment and a medium, and relates to the technical field of artificial intelligence. One specific implementation mode of the method comprises the steps that a historical log text and domain knowledge data associated with the historical log text are acquired, the domain knowledge data is used for indicating business logic, fault association rules and system operation specifications corresponding to the historical log text; semantic analysis is conducted on the historical log texts and the domain knowledge data, semantic vector distribution is constructed, and the semantic vector distribution is used for indicating potential correlation between the historical log texts; based on semantic vector distribution, performing multi-dimensional association retrieval on the to-be-analyzed log to generate a structured cognitive guidance package; and based on the structured cognitive guide package and a preset analysis model, analyzing the to-be-analyzed log to generate a log analysis result. Deep semantic mining of the log is realized, system behaviors, business logics and causal relationships implied in the log are effectively captured, and the log analysis precision is improved.

The invention provides a control system and method for the working condition of a clean room, and relates to the technical field of data analysis. The method comprises the following steps: firstly, constructing a target adjacent matrix; secondly, performing semantic mining on the target environment data based on the target adjacency matrix to form a target environment semantic vector; performing semantic mining on the current environment data based on the target adjacency matrix to form a current environment semantic vector; further, semantic mining is carried out on the historical environment data based on the target adjacency matrix to form a historical environment semantic vector; further, fusing the historical environment semantic vector into the current environment semantic vector to obtain an enhanced environment semantic vector; and finally, determining target working condition data based on the target environment semantic vector, the enhanced environment semantic vector and the semantic vector corresponding to the current working condition data of each environment control device. Based on the method, the problem that in the prior art, the reliability of working condition control of the clean room is relatively low can be solved.

This application relates to the field of smart park management technology, and discloses a method for automatic triggering and closed-loop management of park tasks based on key semantic mining. It collects, cleans, and sorts park meeting audio and business text in real time to form a key data stream. Key semantics are extracted through lexical and syntactic analysis and converted into business feature tags through feature mapping. These tags are input into a pre-set task model for rule collision and constraint calculation to generate execution tasks. After trigger addressing and packetization, task distribution instructions are generated and delivered to vertical domain digital human agents via a collaborative distribution network to complete multi-agent collaborative execution and produce execution data. This solves the technical problem of lagging business perception and disconnected task execution in traditional park management.

The application discloses a kind of achievement transformation report intelligent generation method and system, comprising: constructing achievement transformation corpus;Based on corpus, use five-element prompt word framework to extract entity and relationship, check, implicit completion and weight update are carried out in combination with achievement transformation value evaluation model, and optimized knowledge graph is constructed;Knowledge cluster is divided to graph, and dual-mode index network of paragraph vector index and structured knowledge index is constructed;After receiving user input, core entity set is obtained by semantic coding and entity recognition;With the set as center, structured knowledge enhancement retrieval is executed, and evidence fragment is screened by fusing semantic similarity and knowledge correlation degree score;Finally, sort constructs input context, calls natural language generation model to generate structured report, simultaneously extracts reasoning path and is associated with evidence source and outputs.The application solves the problems of poor precision in manual writing, poor adaptation of general model and insufficient semantic mining, and improves the scientificity, pertinence and traceability of the report.

The invention discloses a city restaurant health level prediction and evaluation method in the technical field of health evaluation, and the method comprises the steps: obtaining a multi-source data set of restaurants in a target region, and the multi-source data set comprises a space vector data set and a health label data set; and performing fusion processing on the multi-source data to obtain a fused data set. According to the method, multi-source data sets are fused, space information and health information of a restaurant are fully utilized, the comprehensiveness and accuracy of prediction and evaluation are improved, meanwhile, multi-dimensional health evaluation feature vectors are extracted based on semantic mining and image recognition, richer input information is provided for a prediction model, and the prediction efficiency is improved. According to the method, semantic similarity completion is performed on the to-be-predicted set, so that the problems of data missing and incompleteness are effectively solved, health level prediction of a whole region level can be completed under the condition that data input into the restaurant health level prediction model is insufficient, and a technical data basis is provided for further analyzing a regional diet environment and healthy diet fairness.

The application discloses a version knowledge graph reasoning method and system based on large language model enhancement, relates to the technical field of dynamic knowledge graph, and comprises the following steps: adopting a semantic drift detection and compensation mechanism, comparing the context coding differences of the same entities in different versions in an initial knowledge graph, identifying drift, dynamically adjusting entity embedding vectors, and outputting a compensation update graph; adopting a multi-hop reasoning algorithm enhanced by an LLM, performing multi-hop reasoning on the compensation update graph, performing symbolic reasoning, vector reasoning and context reasoning in parallel in each hop, and obtaining entity relationship reasoning results through dynamic weight fusion; and superimposing the entity relationship reasoning results on the compensation update graph through a cloud collaborative node, adding entity edges and automatically maintaining version history logs, and obtaining a reasoning fusion version knowledge graph. Through the multi-hop reasoning algorithm enhanced by the large language model, the deep semantic mining capability and reasoning accuracy of the cross-version entity relationship are effectively improved.

The invention relates to the technical field of medical information, and discloses a clinical nursing data management method and system for clinical big data. The method comprises the following steps: acquiring structured physiological signs and unstructured nursing text data; extracting weak semantic signals through a deep semantic mining model and converting the weak semantic signals into high-dimensional feature vectors; mapping the features to a medical knowledge graph, and constructing a recessive nursing risk dynamic deduction network; predicting a target risk event probability by using a graph neural network and Bayesian reasoning; and generating graded early warning and intervention suggestions based on the probability distribution. The system comprises a data acquisition module, a deep semantic extraction engine, a risk deduction network construction module, a prediction analysis engine and an early warning intervention generation module. According to the method, the NLP and the knowledge graph are fused to realize prospective identification and active intervention of the hidden risk, and the early warning timeliness and the nursing decision accuracy are improved.

The data desensitization method and apparatus, equipment and storage medium based on artificial intelligence provided by the present application relate to the field of artificial intelligence technology. In the present application, first, the data to be desensitized is obtained; secondly, for each unit to be desensitized in the data to be desensitized, the semantic mining model included in the target sensitive information recognition network is used to mine the target desensitized semantic features corresponding to the unit to be desensitized based on the sensitive information knowledge graph; then, for each unit to be desensitized in the data to be desensitized, the sensitive information recognition model included in the target sensitive information recognition network is used to identify and output the target desensitized semantic features corresponding to the unit to be desensitized, and obtain the sensitive information recognition result corresponding to the unit to be desensitized; finally, each unit to be desensitized belonging to sensitive information is desensitized separately. Based on the above content, the problem of relatively low reliability of data desensitization in the prior art can be improved.

This invention proposes a collaborative evolutionary classification method based on cross-view structural attribute dual-domain semantic mining, belonging to the field of multi-view semi-supervised classification. This invention acquires multi-view features from training samples, constructs K-nearest neighbor graphs for each view, and adaptively prunes based on the shared support relationships of the graph structure to obtain a purified adjacency matrix. Subsequently, structural and attribute representations are extracted through a shared graph convolutional network, and the consistency between the two in a unified space is constrained using a structure-attribute consistency loss. Simultaneously, learnable view position encoding and masked self-attention mechanisms are introduced for progressive fusion, and cross-view evolutionary patterns are learned through adjacent view mapping to form an evolutionary loss. Finally, the evolutionary representations of each view are concatenated and mapped to a sample-level fused representation, which is then input into the classifier for prediction. The model is jointly optimized by the classification loss, consistency loss, and evolutionary loss. Compared with other methods, this invention significantly improves the accuracy of classification results with a small amount of labeled data.

The invention discloses a clinical test data report management system and method, and relates to the field of data report management, and the method comprises the steps: carrying out the deep semantic analysis of original clinical test table data, and carrying out the multi-dimensional semantic mining in combination with a preset medical analysis rule set, therefore, key discovery points with remarkable clinical significance can be automatically identified and screened out. Furthermore, the discovery points are subjected to priority ranking and pruning to form a structured core discovery point sequence, and the structured core discovery point sequence is injected into a prompt template to construct accurate guidance for a large language model. And finally, converting the guide instruction into an abstract text with clear logic and highlighted key points by utilizing a large language model. In this way, automatic conversion from an original data table to a high-quality narrative abstract can be achieved, and therefore the writing efficiency and the intelligent level of a clinical test report are remarkably improved.

The application provides an artificial intelligence-based PON gateway fault prediction method, device and equipment, and relates to the technical field of artificial intelligence.In the application, first, a target device temperature matrix is constructed based on temperature data formed by a plurality of gateway hardware devices at a plurality of time points;second, a semantic mining unit is used to perform a plurality of semantic mining operations on the target device temperature matrix to form a plurality of device temperature semantic vectors corresponding to the target device temperature matrix;then, a semantic fusion unit is used to fuse the plurality of device temperature semantic vectors to form a fused device temperature semantic vector corresponding to the target device temperature matrix;finally, a fault prediction unit is used to predict and output a gateway fault prediction result corresponding to the target PON gateway based on the fused device temperature semantic vector.Based on the above, the problem of not timely handling of PON gateway hardware faults in the prior art can be improved.