11 results about "Semantic dictionary" patented technology

The invention discloses an artificial intelligence proposition method, device and equipment and a storage medium, and the method comprises the steps: constructing a multidisciplinary knowledge base based on an education normative document and a subject knowledge document and according to a multidisciplinary knowledge ontology framework; based on the structured semantics of the multidisciplinary knowledge base and the hierarchical semantics of the general semantic dictionary, calculating the comprehensive semantic similarity between the interdisciplinary knowledge points, and screening candidate knowledge point pairs according to the comprehensive semantic similarity; based on the comprehensive semantic similarity and a graph topological structure in the multidisciplinary knowledge base, performing association strength evaluation on the candidate knowledge point pairs through multi-hop path reasoning and graph neural network model conjoint analysis, and determining target knowledge point pairs meeting interdisciplinary proposition availability requirements; and returning the target knowledge point pair. Therefore, a complete technical path is provided for high-quality, explainable, compliant and controllable interdisciplinary intelligent propositions by constructing a multi-disciplinary knowledge base for education alignment and fusing structured semantics and map reasoning.

This invention relates to the field of text processing technology and discloses a dynamic template generation system for test data of mobile maintenance of pumped storage power stations. The system includes: generating process activity degrees; dividing into stages; combining and generating stage feature vectors; generating a mask matrix; generating a set of patterns and stage grouping; constructing a two-dimensional rectangular grid and row mapping rules; generating a semantic dictionary; and generating acquisition results. This invention avoids structural redundancy caused by mechanism-selective column cluster holes by dynamically generating a two-dimensional rectangular grid template without complex topology. The strong binding between the globally unique primary key and the semantic dictionary ensures that semantic anchors remain stable during cross-platform conversion, preventing the loss of data physical meaning due to layout reflow. Simultaneously, the system can flexibly adapt to different test conditions and unit types, reducing manual template adjustments and providing convenient data acquisition support for mobile maintenance of pumped storage power stations, improving the standardization and efficiency of test data acquisition.

The invention belongs to the technical field of big data customer information matching, and discloses an intelligent customer information matching method based on big data, which comprises the following steps of: firstly, establishing a scene customer behavior semantic dictionary according to three groups of new customers, high-value customers and loss early-warning customers, and determining behavior label mapping rules of different customer types; for example, the browsing duration of a new customer exceeds 3 minutes, the consultation frequency exceeds 2, the behavior is defined as a high-intention behavior, the semantic dictionary is dynamically updated every 24 hours based on cross-platform semantic conflict feedback, and the cross-platform data semantic standard is unified; secondly, screening weak association features by adopting a mutual information and customer value double-weight algorithm, setting weights according to customer value differences, and preferentially retaining features with higher association degree with customer demands to generate structured feature vectors; and finally, deploying a preprocessing effect verification module at an edge computing node, performing field secondary labeling, null interpolation complementation and time format unification operation on the data, and improving the data standardization level.

The invention discloses a polyphone disambiguation method based on a pointer network and context adaptive modeling, and relates to the technical field of natural language processing and intelligent text processing. According to the method, context range labels and category labels associated with polyphones are predicted at the same time through a pointer network, so that self-adaptive recognition of context granularity required by decision making is achieved. According to different category labels, a local disambiguation process, a global disambiguation process and a proper noun processing process are respectively constructed according to the method: the local process realizes pronunciation judgment of adjacent word levels based on dictionary retrieval and semantic similar word acquisition; the global process is combined with a large language model and a hierarchical semantic dictionary to realize sentence-level semantic-driven pronunciation generation; and the name and place name categories are deduced by directly utilizing entity knowledge of the language model. In addition, the invention further provides a paraphrase enhancement module oriented to an ancient language scene, and through combined modeling of comparative learning and example learning of sentences of the same pronunciation, the model can stably distinguish different pronunciations in an ancient language text and generate corresponding paraphrases. According to the method, the optimal disambiguation path can be automatically selected according to the context dependence characteristics of different polyphones, the accuracy and generalization ability of polyphone pronunciation prediction are effectively improved, and the method is suitable for various text scenes such as modern Chinese and ancient Chinese.

The embodiment of the application provides a power data and third-party database fast sharing method and system, the method comprises the following steps: accessing at least one kind of power data source to obtain original power data, relying on a power data semantic dictionary to complete semantic analysis and standardization conversion, and outputting standardized intermediate data. The third-party target database is connected, the table structure is automatically identified, the intermediate data is adapted to the target format matched with the table structure of the format conversion rule library. Configure data sharing strategy and access permission rule, clear sharing range, transmission frequency and priority. Establish field mapping relationship, combine system load to dynamically schedule data transmission. Whole process monitoring, abnormal detection and hierarchical treatment according to the preset abnormal threshold, guarantee the stability and standardization of power data cross-database sharing transmission. The application can realize efficient, safe and intelligent sharing of power data and heterogeneous third-party databases, reduce development and maintenance cost, and guarantee the real-time, reliability and security of data sharing.

The embodiment of the invention provides a generative model content control method and device, equipment and a storage medium, relates to the technical field of visual models, and is suitable for financial science and technology and medical health scenes. According to the method, a semantic dictionary is defined according to a de-embedding matrix of a generative model, specialization scores of attention heads in the generative model to sub-dictionaries in the semantic dictionary are calculated by adopting a greedy algorithm of synchronous orthogonal matching pursuit, and then key heads are selected according to the specialization scores. And then obtaining directional editing information and an output signal of the key head, and performing weighting coefficient calculation on the output signal by using the directional editing information, so that the generative model outputs a directional editing result. According to the method, the functions of the specific components in the generative model can be quantified through sparse decomposition, the content generation result of the model is efficiently controlled by manipulating very few key components, and reliable and accurate control over the output content of the model is achieved under the condition that additional training is not needed.

The application belongs to the technical field of automatic driving, and particularly relates to a driving behavior semantic library construction method, a scenario-based retrieval method and a system for automatic driving. The driving behavior semantic library constructed by taking the codebook of a trajectory segmenter as a driving behavior semantic dictionary fully taps the potential value of the codebook and solves the problem of insufficient semantic utilization in the prior art. By preprocessing, cutting and converting massive historical driving data into compact Token sequences, storage overhead is greatly reduced, while key behavior semantics are retained, and efficient utilization of historical data is realized. The semantic library based on multi-level indexing supports fast scenario-based retrieval, so that the system can retrieve the processing strategy of a similar historical scenario in real time when facing rare corner cases, and provide reliable online decision assistance for an end-to-end model. The retrieval result can be directly used to automatically generate diversified simulation test cases, changing the situation of inefficient and limited coverage of traditional manual design.

The application relates to the technical field of artificial intelligence, in particular to a customer service reply method and system of a multi-modal fusion architecture and a storage medium, the method comprises the following steps: obtaining initial input data of a user, preprocessing the initial input data to generate a standard data source; performing decision compliance analysis on the standard data source to generate a decision judgment result; and generating a corresponding decision scheme according to the decision judgment result. Through a space-semantic double-constraint graph network, the application effectively solves the technical defects of fragmentation of multi-modal data recognition and low standardization degree of traditional intelligent customer service, overcomes the problem of insufficient adaptation of general recognition technology to government affairs formats, in the semantic constraint dimension, relying on the node association mechanism of the graph network, the spatial features are deeply fused with the government and enterprise professional semantic dictionary, the conversion of multi-modal data to structured policy elements is automatically completed, and the recognition accuracy is improved by more than 40% compared with traditional NLP technology.

This invention belongs to the technical field of highway tunnels and BIM models, specifically a data interaction method and system for highway tunnel BIM models based on IFC (Integrated Functional Design). The method includes: acquiring a source model; mapping source elements to a target IFC model through a semantic mapping table, where the semantic mapping table includes a component object description table and a semantic dictionary; generating the target model through reverse mapping; performing dynamic consistency verification, correcting or issuing alarms when thresholds are exceeded; dynamically extracting sub-model views according to business scenarios; and performing multi-dimensional quality checks and outputting reports. This invention solves the problems of semantic loss, low efficiency, poor data consistency, inability to extract sub-models on demand, and lack of automated quality inspection during BIM model cross-software conversion.

This invention belongs to the field of intelligent building fire protection review technology, and discloses a method, system, and medium for intelligent review of fire protection BIM models. It acquires the original BIM model submitted by the user and converts it into IFC model data. The IFC model data is parsed to extract full fire protection data and stored in a tree-structured index according to spatial hierarchy. Based on MVDLite view definition rules, lightweight sub-model data containing only the entities and attributes required for fire protection review is quickly extracted from the IFC model data. A review rule base is constructed using Structured Natural Language (SNL) based on fire protection code texts, and a unified semantic dictionary aligned with the bsDD standard is established. The lightweight sub-model data is compared with the review rule base item by item to generate review results containing identifiers of non-compliant entities. Based on the review results, a fire protection review report is generated, including descriptions of non-compliance items, references to fire protection code provisions, and location markers for non-compliant entity models. The MVDLite view definition rules reduce the size of the model data to be reviewed.

A knowledge accelerator platform automatically builds and maintains a knowledge index of assets stored across a plurality of sources to allow consolidated searching of assets in the plurality of sources. The platform automatically processes the assets to extract metadata information and record labels for each asset in the index, without storing the content of an asset itself in the index, where a semantic dictionary maps labels to terminology in different domains. The knowledge accelerator platform further provides an internal user interface that allows internal users to evaluate the knowledge contained in the assets at a high level and thus identify knowledge gaps that can be mitigated, and promote quality assets within targeted user communities. The knowledge accelerator platform also provides an external user interface that recommends assets to users based on various factors such as their experience, status, and domain interests.