84 results about "Semantic tree" patented technology

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention discloses a human resource data natural language query and SQL generation method and system, and relates to the technical field of SQL generation, and the method comprises the steps: preprocessing a natural language query text input by a user, and constructing a query semantic tree; traversing the query semantic tree to generate a candidate entity set, determining an unambiguous entity through a three-layer ambiguity resolution mechanism of semantic scoring, service verification and cross validation, identifying a composite intention, disassembling the composite intention into a sub-intention sequence, and generating an SQL operation instruction set in combination with a preset SQL operation mapping rule; based on the SQL operation instruction set and the human resource data association model, determining a data table set and an association path, generating a dynamic calculation logic and structured condition expression, and constructing a structured query skeleton; according to the method, the personalized SQL template is dynamically generated according to the query skeleton, after filling, grammar verification, strategy optimization and logic consistency verification are performed, and the optimized SQL statement is output, so that the query accuracy and performance are remarkably improved.

The invention belongs to the technical field of image processing, and discloses a remote digital image analysis cooperation system. The virtual diagnosis module is used for collecting a continuous pathological section sequence and carrying out space registration to obtain a registration section; the model building module is used for performing three-dimensional voxel reconstruction on the registration slices to obtain a three-dimensional voxel reconstruction model, and performing semantic enhancement to obtain a three-dimensional pathological voxel model; the collaborative interaction module is used for generating virtual avatars of G experts based on the VR technology, labeling the three-dimensional pathological voxel model and obtaining space anchor point labeling data of the experts; the graph driving module is used for constructing a structured semantic tree according to the space anchor point annotation data, detecting annotation conflicts and obtaining an annotation conflict detection result; the conflict resolution module is used for performing conflict resolution on the marking conflict detection result to obtain a consensus suggestion; the collaborative efficiency and conclusion reliability of remote diagnosis are improved, and a systematic solution is provided for precise diagnosis of complex pathological cases.

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention provides an intelligent customer service query system and method based on natural language understanding, and relates to the technical field of natural language processing. A query intention and a core entity of a natural language query request input by a user side are extracted; performing hierarchical deconstruction on the query intention, and constructing a query semantic tree according to a hierarchical deconstruction result and a semantic association relationship between core entities; generating a decision tensor representing a query path priority according to a node semantic weight of the query semantic tree and an access delay feature of the multi-source database; further adjusting an access sequence and a connection strategy of the multi-source data through a decision tensor and a dynamically updated query cost model to obtain a query execution link adaptive to the current query request; and calling a corresponding data source interface according to the query execution link to execute data retrieval and integration operation to obtain a target result set responding to the query demand of the user. By the adoption of the scheme, multi-source data optimal cost retrieval based on the natural language can be achieved.

The invention discloses a bug repairing method and system based on a grammar semantic tree, and the method comprises the steps: extracting a repairing mode fusing a grammar structure and a semantic intention from a historical bug repairing case, and guaranteeing the accuracy of the mode through program slicing guided by a key entity and iterative verification; on the basis of a double-constraint mechanism of grammar mergibility and semantic consistency, a specific mode is generalized to construct a hierarchical grammar semantic tree knowledge base; a knowledge base is used for generating a plurality of candidate patches for new vulnerabilities, single high-quality patches are generated through structure, grammar and semantic three-dimensional clustering and in-cluster integration and fusion, and the patches are output after heuristic sorting. According to the method, the defects that in the prior art, semantic understanding is shallow, and patch quality is unstable are overcome, and repairing accuracy, generalization ability and engineering practicability are remarkably improved.

The invention provides a database query method and device with a private protection characteristic, equipment and a medium. The method comprises the following steps: receiving a to-be-processed query statement; generating a query block set carrying a protection mark according to the query statement and the target keyword in the query statement so as to construct a target syntax tree structure carrying the protection mark; in the process of constructing a semantic tree structure based on the target syntax tree structure, maintaining a hierarchical action domain stack structure, and performing cross-hierarchical visibility detection by using the hierarchical action domain stack structure to obtain a semantic tree structure passing the cross-hierarchical visibility detection; and generating a response result corresponding to the query statement according to the semantic tree structure. According to the invention, the private protection characteristic is introduced to realize the fine control of the CTE action.

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention relates to a power industry protocol semantic perception abnormity analysis method, and relates to the field of industrial control system security, the method comprises the following steps: constructing an industrial protocol semantic tree model, carrying out field decoding on collected original message data of a power industry protocol, and obtaining a semantic tree model of the industrial protocol; the decoded field values are organized and filled into the industrial protocol semantic tree model, and a structured message semantic tree is generated; according to the structured message semantic tree, judging whether the protocol interaction sequence, the command logic, the control frequency, the access authority and the authenticity of the original message data are abnormal or not; and when the original message data is abnormal, generating a standardized semantic annotation result, and constructing an abnormal event structural body. The method is used for constructing an industrial exception analysis engine with protocol semantic perception capability, state recognition capability and behavior judgment capability so as to adapt to the security requirements of a new-generation dispatching automation system on'visibility, manageability and controllability '.

The embodiment of the invention provides a power supply state fault positioning method and system, electronic equipment and a storage medium, and belongs to the technical field of fault positioning. The method comprises the following steps: carrying out residual calculation according to an original time domain electromagnetic signal and a reference frequency spectrum template to obtain a frequency spectrum residual matrix; and performing cross-frame disturbance integral scoring on the spectrum residual vector according to the integral residual scoring function to obtain disturbance scoring data. And obtaining a disturbance event set according to the disturbance score data and the disturbance threshold. And fusing the disturbance event set and the operation log to obtain a state event sequence. And constructing a state semantic tree according to the state event sequence and calculating an edge weight. And scoring each path according to the path scoring function and the edge weight to obtain path scoring data. And selecting a path corresponding to the maximum path score data as a main cause path, wherein a head node of the path is a fault root cause node. And entering a preset execution output process according to the main cause path and the fault root cause node. And the accuracy of power supply fault positioning is improved.

The invention discloses a reverse guidance type intelligent interaction method and system for realizing dynamic information assessment. The system comprises an information sufficiency assessor, an information sufficiency assessment device and a dynamic information assessment device, wherein the information sufficiency assessor is used for dynamically and quantitatively assessing and identifying key information gaps on the basis of an information entropy theory; the reverse questioning strategy generator is used for generating an optimal reverse guidance questioning sequence for supplementing a key information gap based on a Markov decision process model; the hierarchical problem decomposition engine is used for recursively decomposing a complex problem into a plurality of sub-problems by adopting a semantic tree decomposition technology, and fusing solution results of the sub-problems by utilizing an evidence theory fusion mechanism to form a problem solution; the personalized solution synthesizer is used for analyzing the cognitive level, learning style and attention characteristics of the user portrait, performing matching optimization on a problem solution and the user portrait, and calculating and measuring the semantic matching degree of the problem solution and the user portrait; and applying reinforcement learning to continuously optimize an output strategy according to user feedback to generate a personalized solution.

The invention discloses a semantic tree index-based reasoning enhanced RAG retrieval method and system. The method comprises the following steps of: S1, reading and analyzing a document; s2, structuring the semantic knowledge tree by the document; s3, carrying out tree search and retrieval; and S4, enhancing the answer by the RAG system. According to the method, a long document of a specific field task is structured into an indexable semantic knowledge tree, and a large language model is guided to perform multi-step reasoning and path search on the tree in a retrieval process, so that a logic node which is most matched with query is dynamically locked, fundamental transformation from semantic similarity matching to structured logic positioning is realized, and the search efficiency is improved. The method does not need to depend on a vector database and embedded model training.

The invention relates to the technical field of data processing, and discloses an IPTV education content intelligent navigation method and system based on voice recognition. The method comprises the following steps: constructing a knowledge point-course-subject three-level semantic tree through an educational knowledge graph neural network; performing cognitive modeling on the learning behaviors of the user to generate a user cognitive file; semantic analysis is performed on the voice data, and a query vector is generated in combination with a historical track; matching the query vector with the semantic tree, and screening candidate contents in combination with the cognitive archive; and performing continuity evaluation based on the session state to generate a navigation result. According to the method and the device, the problem that the recommended content is lack of systematicness and individuation due to the fact that an existing IPTV education platform cannot understand the education content knowledge hierarchical structure and the user cognitive development law is solved. And the education content recommendation accuracy and the learning path continuity are improved.

The invention relates to the technical field of intelligent water transportation information, in particular to an intelligent water transportation standard system construction method based on a four-dimensional level model. The method comprises the following steps: learning decision weights of different development value orientations according to idea features and execution features of a case, and determining a target development value of an actual water transportation service at a value dimension level; a spatial semantic tree is constructed by adopting a semantic layering mode, and traversal of the tree is driven by a target development value, so that matching of key element-scene requirements under a spatial dimension is realized. The method comprises the following steps of: partitioning a whole-cycle flow of a service, and performing time-dimension sequence pattern analysis on standard events at each stage to obtain corresponding time sequence construction requirements; and carrying out analysis and decision-making on operation paths among different elements from a logic dimension level to form a target implementation path and a support strategy. According to the method, a full-factor, full-period and full-scene intelligent water transportation standardization system can be constructed, and reliable multi-dimensional standard guidance is effectively provided for water transportation engineering construction.

The invention provides a content marketing semantic analysis method and system based on natural language processing, and relates to the technical field of natural language processing, and the method comprises the steps: constructing a marketing intention semantic tree through dependency syntax analysis, calculating audience perception vectors to form a conduction path graph, recognizing and demonstrating conflict unit pairs, and evaluating marketing persuasion. A double-layer optimization objective function is constructed for text reconstruction, scientific analysis and accurate optimization of marketing content are realized, the persuasion and the propagation effect of the marketing content are improved, the consumption of marketing resources is reduced, and the accuracy of a marketing strategy is enhanced.

The invention relates to the technical field of computer vision and geographic information, and particularly discloses a cross-view geographic positioning acceleration method based on semantic description, which comprises the following steps of: performing instruction fine tuning on an input query image and a database image by adopting a visual language model to generate natural language description; coding the natural language description into a low-dimensional semantic vector by utilizing a word embedding model; constructing a semantic keyword association graph based on a graph embedding technology, and fusing a general semantic knowledge base and a geographic domain ontology to construct a surface feature special semantic tree; screening candidate subsets according to a preset ground feature priority and a saliency detection result of the query image; extracting global features and local key point descriptors of the images in the candidate subsets by adopting a lightweight model; constructing a visual similarity calculation model based on the state space model, and modeling a feature sequence dependency relationship; and fusing the semantic matching score and the visual similarity, outputting a final matching result by adopting a weighted sorting strategy, and cooperatively accelerating according to the matching result in combination with a lightweight model.

The invention provides an image-text video generation method and system based on cross-modal semantic mapping, and relates to the technical field of data processing, and the method comprises the steps: 1, inputting a product description text, executing hierarchical semantic decoupling, extracting core object nouns, attribute adjectives and scene adverbs, and constructing a hierarchical structured semantic tree; and step 2, based on the hierarchical structured semantic tree, executing region exploration of fine-grained modifier meanings, identifying associated regions of attribute adjectives or scene adverbs, and generating a semantic adaptation correction factor for each associated region. According to the method, through hierarchical semantic decoupling, regional semantic mapping, cross-modal feature fusion and dynamic space-time calibration, automatic generation of a dynamic video with accurate semantics and coherent time sequence from a product description text is achieved, and it is ensured that the visual effect is consistent with the text semantics.

The embodiment of the invention discloses a buried point verification method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of buried points. The method comprises the following steps: acquiring a burying point application form; performing intelligent verification on the burying point application form to obtain a structured parameter table and business metadata; inputting the structured parameters and the business metadata into a semantic analysis engine to obtain a parameter semantic tree of the burying point application form; obtaining a dynamic verification rule generated by a dynamic rule generator; the dynamic rule generator is obtained by training based on a historical burying point log; and verifying the parameter semantic tree of the burying point application form based on the dynamic verification rule to obtain a burying point verification result. According to the scheme, buried point verification can be automatically carried out based on the dynamic verification rule.

The invention discloses a network interface dynamic configuration method, electronic equipment, a storage medium and a program product, and relates to the technical field of computers, and the method comprises the following steps: obtaining a user interface configuration model and target terminal equipment characteristics, analyzing a model unit to construct a semantic tree and a response type configuration object, interface conversion is completed in combination with equipment features and a preset layout strategy to obtain an adaptive component instance, and incremental rendering is achieved through a virtual interface node tree. According to the method, interface configuration logic is abstracted and unified through a standardized model, accurate terminal matching is achieved through a multi-terminal adaptation rule, redundant operation is reduced through an incremental updating mechanism, traditional development limitation is broken through, the problems that network interface multi-terminal adaptation consistency is poor, development and maintenance cost is high, and updating response efficiency is low are solved, and the network interface multi-terminal adaptation consistency is improved. And the technical effects of improving the interface adaptation precision and the development efficiency, reducing the resource consumption and optimizing the user interaction experience are achieved.

The invention provides a method and a device for automatically detecting the injection-writing consistency of automobile drawings through computing power by an intelligent computing cloud platform, and relates to the technical field of intelligent computing cloud platforms and computing power infrastructure, the method comprises the following steps: constructing a sample data set based on a preset consistency semantic tree, and dividing the sample data set into a training set and a verification set; the intelligent computing cloud platform performs N times of iterative training on the initial model based on the training set by using computing power, and determines the initial model after the Mth time of iterative training as a final model; and deploying the final model on the intelligent computing cloud platform so as to carry out injection-writing consistency detection on the received automobile drawings in batches. Therefore, the model can accurately position the injection and writing position and identify the abnormal injection and writing in the automobile drawing based on the characteristics fully trained in the early stage, thereby reducing the understanding difference in the process of manually reviewing the injection and writing consistency of the automobile drawing, and improving the detection efficiency and accuracy.

The invention relates to a software function point identification method fusing a large model and multiple clustering analysis, and solves the problems of subjective deviation, inconsistent naming and low auditing efficiency caused by function disassembly depending on artificial experience in traditional function point measurement and calculation. The method comprises the following steps: inputting a design scheme, and constructing a document level semantic tree; obtaining feature information described by corresponding function points in the semantic tree, and analyzing the feature information to determine candidate function points; fusing the candidate function points according to semantic tree hierarchy alignment; and carrying out differentiation processing on the candidate function points, and carrying out duplicate removal on the similarity frequency according to the candidate function points to obtain the function points. According to the method, based on construction and analysis of a document hierarchy semantic tree, feature positions and large model reasoning ability are combined, and accurate extraction and auditing of function points are achieved. And differentiation is formed by adopting multiple clustering, and a co-cooperation relation matrix is constructed through clustering cross validation, so that the function point de-duplication accuracy is greatly improved.

The invention belongs to the technical field of data processing, and discloses an intelligent geographic information text extraction method based on knowledge perception. Comprising the following steps: constructing a geographic knowledge ontology library, and constructing a corresponding semantic relationship network based on the geographic knowledge ontology library; obtaining a text geographic entity and a text semantic relationship corresponding to the input geographic information text; constructing a corresponding initial tree-shaped tree based on the obtained text geographic entity and the text semantic relationship in combination with a semantic relationship network; performing knowledge perception and semantic understanding on the corresponding initial tree based on the geographic knowledge ontology library to obtain a corresponding semantic tree; performing information extraction based on the obtained semantic tree to obtain required geographic information; by constructing the geographic knowledge ontology library and the semantic relationship network, deep understanding and intelligent extraction of geographic information texts are realized; and the accuracy and efficiency of geographic information extraction are improved.

The application belongs to the technical field of data processing, and discloses a geographic information text intelligent extraction method based on knowledge perception; comprising: constructing a geographic knowledge ontology library, and constructing a corresponding semantic relationship network based on the same; obtaining text geographic entities and text semantic relationships corresponding to input geographic information text; constructing a corresponding initial tree based on the obtained text geographic entities and text semantic relationships, and combining a semantic relationship network; performing knowledge perception and semantic understanding on the corresponding initial tree based on the geographic knowledge ontology library, and obtaining a corresponding semantic tree; performing information extraction based on the obtained semantic tree, and obtaining required geographic information; the application realizes in-depth understanding and intelligent extraction of geographic information text by constructing a geographic knowledge ontology library and a semantic relationship network, and improves the accuracy and efficiency of geographic information extraction.

The invention provides a method and system for querying a multi-source DPU flow table, a DPU cluster and computing equipment, and the method comprises the steps: receiving and analyzing a flow table operation statement which comprises a query target field and a filtering condition; converting the flow table operation statement into a structured semantic tree, and extracting the query target field and the filtering condition; according to the query target field, the filtering condition and DPU information obtained from a DPU information base, generating a flow table operation command of the corresponding DPU; respectively issuing the flow table operation commands of the corresponding DPUs to the corresponding DPUs to execute corresponding operations and obtain return values; and unifying the different return values as a final operation result of the logic flow table model field. According to the technical scheme, the problems that heterogeneous DPU flow table query modes are diversified, management is complex, and unified standard interfaces are lacked can be solved, the operation and maintenance efficiency is improved, the use threshold is lowered, and the consistency and intelligence of cross-manufacturer equipment flow table data management are enhanced.

The application discloses a large language model-based visual large-screen intelligent generation method and system, relates to the field of visual processing, and comprises the following steps: receiving a visual large-screen generation demand instruction, calling a large language model to generate an initial large-screen item containing multiple visual graph elements; receiving a modification instruction, determining the physical coordinates, layer depth and logical association relationship of all visual graph elements on the current canvas, and converting the same into a spatial semantic tree; inputting the combination of the modification instruction and the spatial semantic tree into the large language model to obtain a differential operation instruction set; analyzing the differential operation instruction set and applying the same to the current visual large screen; receiving a graph element redrawing instruction, determining whether there is a spatial conflict between the visual graph elements in the current visual large screen, and adaptively adjusting the positions of the visual graph elements that have the spatial conflict to eliminate the spatial conflict. The application can effectively guarantee the stability of the layout structure, graph element position and hierarchical relationship during the generation of the visual large screen.

The application provides a kind of text and video generation method and system based on cross-modal semantic mapping, it is related to data processing technical field, the method includes: step 1, input product description text, execute hierarchical semantic decoupling, extract core object noun, attribute adjective and scene adverb, and construct hierarchical structured semantic tree;Step 2, based on hierarchical structured semantic tree, execute the area exploration of fine-grained modifier semantics, identify the associated area of attribute adjective or scene adverb, generate semantic adaptation correction factor for each associated area.The application realizes the automatic generation of product description text to semantic precision, time sequence coherent dynamic video by hierarchical semantic decoupling, area semantic mapping, cross-modal feature fusion and dynamic space-time calibration, ensures that visual effect is consistent with text semantics.

The application relates to the technical field of security risk standardization management, and discloses a project intelligent management and analysis device based on security risk standardization, which comprises the following modules: a hierarchical semantic space representation model construction module, which constructs a multilevel semantic representation and outputs a hierarchical semantic tree; a meta-learning driven cross-domain mapping function construction module, which receives the hierarchical semantic tree and constructs a cross-domain mapping function to realize cross-domain knowledge transfer; a semantic hierarchical prototype network construction module, which receives the output of the cross-domain mapping function and maps a complex risk structure; a sample rapid adaptation implementation module, which realizes efficient knowledge transfer of the output of the construction module; a standardized risk knowledge base construction and application module, which receives the output of the sample rapid adaptation implementation module and realizes risk standardization and application; the application reduces the sample quantity and sample demand required for new field adaptation by combining meta-learning and a semantic hierarchical prototype network, and shortens the construction period of new field risk standardization.

The application discloses a kind of repair manual and assembly body multimodal semantic alignment method and system, it is related to artificial intelligence and computer aided design cross technical field, including respectively analyzing standard repair manual and CAD model, extract hierarchical structure, construct text semantic tree and CAD topological graph;Definition hyperbolic space model and its Riemann geometry operation rule;Text semantic tree and CAD topological graph are mapped into hyperbolic embedding vector using hyperbolic neural network respectively;According to the hyperbolic space model and hyperbolic embedding vector established, by minimizing hyperbolic contrast loss function and hyperbolic implication cone loss function, jointly optimize text encoder and CAD encoder;Geodesic distance and implication relationship are calculated in hyperbolic space, for realizing accurate positioning from text to CAD entity.The application uses the above method and system, realizes the full-process automation processing from original repair manual and CAD model to accurate semantic alignment.

The application provides a kind of intelligent computing cloud platform through computing power and carries out automobile drawing annotation consistency automatic detection method and device, it is related to the technical field of wisdom calculation cloud platform and computing power infrastructure, this method includes: based on the consistency semantic tree of pre-set sample data set is constructed, and the sample data set is divided into training set and verification set;Intelligent computing cloud platform utilizes computing power, based on the training set, the initial model is iteratively trained N times, and the initial model after the Mth iteration training is determined as the final model;The final model is deployed on the intelligent computing cloud platform to perform annotation consistency detection on the received batch of automobile drawings.This way, the model based on the characteristics of the early training can accurately locate the annotation position and identify abnormal annotation in the automobile drawing, reducing the understanding differences in the process of manual annotation consistency review of automobile drawings, improving the detection efficiency and accuracy.

The present invention belongs to the technical field of financial data processing and discloses a network multi-source financial text big data processing method, comprising collecting multi-source financial text data, performing semantic-level unstructured analysis, separating language critical points and triggering semantic breakpoints in semantic changes, constructing a financial fluctuation language trigger factor matrix, and capturing key expression fragments; receiving the trigger factor matrix, adopting a language expression motivation modeling mechanism, performing structure extraction and configuration mapping on the key expression fragments, constructing a configuration transformation graph with language expression motivation as the axis, and outputting configuration equivalent semantic clusters; performing topological structure analysis based on the configuration equivalent semantic clusters, identifying semantic fission points and mutation connections, determining semantic faults, and performing concept reconstruction on both sides of the fault to generate a concept semantic tree; achieving deep mining and structural modeling of semantic information of financial texts, and having important application value.