15 results about "Entity relationship modelling" patented technology

The invention relates to the field of data processing, and provides an intelligent prediction recommendation method and system based on full-link data of a supply chain. The method comprises the steps of collecting multi-source heterogeneous supply chain data and performing standardization processing to obtain a standardized data set; according to the standardized data set, performing quantitative modeling on the supply chain state features to obtain a supply chain state feature vector; on the basis of the supply chain state feature vector, performing supply chain entity relationship modeling through a heterogeneous graph neural network to obtain a graph embedding vector; according to the graph embedding vector, performing dynamic generation on a candidate set through a multi-objective optimization algorithm to obtain a dynamic candidate set; and performing personalized matching of user requirements based on the dynamic candidate set to obtain a recommendation result. According to the invention, intelligent recommendation of the supply chain is realized, the decision-making efficiency is improved, and the operation cost is reduced.

The invention discloses a sarcopenia risk assessment method based on a knowledge graph, and relates to the field of medical large models. Full-dimensional multi-source data of a patient is collected and preprocessed; defining a six-tuple entity relationship model, introducing a dynamic entity life cycle and heterogeneous relationship type system, and realizing multi-modal feature fusion through modal adaptive weight learning, knowledge conflict resolution and entity evolution embedding; based on an enhanced space-time diagram convolutional network, through heterogeneous relation attention space convolution, space-time adaptive time convolution and triple contrast learning tasks, enhanced entity embedding is output, and the enhanced entity embedding comprises a historical state, a current state, an evolution rate and a future trend. Through multi-modal space-time alignment and enhanced knowledge graph construction, deep fusion of multi-source heterogeneous data and space-time dynamic relationship modeling are realized, and comprehensiveness and accuracy of sarcopenia risk assessment are remarkably improved.

Systems, methods, and computer-readable storage media for exchange modeling. One system includes a processing circuit. The processing circuits includes one or more processors and memory storing instructions that, when executed, cause the processing circuit to train a plurality of protection models of an entity using a training input to output a plurality of protection responses. The instructions further cause the processing circuit to receive exchange data of an exchange of a first entity. The instructions further cause the processing circuit to model, using an entity protection model, the exchange data and third-party data to generate an entity protection response. The instructions further cause the processing circuit to model, using at least one of an entity relationship model or an entity sector model, to generate at least one strategy response. The instructions further cause the processing circuit to receiving a feedback response to the entity protection response or the strategy response.

This invention discloses an event prediction method and apparatus based on subgraph segmentation and entity relationship model. It transforms an academic knowledge graph into a sequence of temporal historical subgraphs. Segmentation operations determine the segmentation points of the temporal historical subgraph sequence and the number of segments corresponding to different times. The number of continuous segments is also determined. A DQN network is used to perform secondary path search through target operations to obtain the final search path. The entity in the last node is the object to be predicted. This invention achieves this by segmenting the temporal historical subgraph sequence to give the input data a focus, enabling better selection of answer nodes. Secondly, the use of a DQN network better considers the intrinsic connections between entities and relationships. Finally, the global nature of convolutional neural networks captures the global features of the search path, significantly improving prediction accuracy.

The invention provides a software defect governance method and system based on a dynamic knowledge graph and multi-modal reasoning, and relates to the technical field of software defect governance, and the method comprises the following steps: collecting a plurality of entity parameters from each source system, including defect, code, test and environmental data, and carrying out standardization processing; a dynamic four-dimensional entity relation model is constructed, and the dynamic four-dimensional entity relation model is used for forming a knowledge graph based on four-dimensional entities, and structured storage and real-time updating of knowledge are achieved; performing software defect root cause analysis and repair suggestion generation through the intelligent reasoning module; and full-process management of defect treatment is carried out. The method has the advantage that the defect positioning efficiency and accuracy can be improved.

This application provides a knowledge graph-based method and system for battlefield situation semantic understanding. The method acquires multi-dimensional visual and electromagnetic wave data from the battlefield, extracts enemy equipment outlines and environmental occlusion features through distributed processing; optimizes the capture range of thermal radiation features using vegetation density, and identifies equipment types using an equipment database; integrates terrain, thermal radiation features, and equipment location to establish an entity relationship model and combat unit network; and finally, dynamically adjusts spatial weights based on equipment type feedback to establish an equipment-terrain adaptation relationship to optimize network semantic expression and output the understanding results. The technical solution provided in this application significantly improves the accuracy and semantic level of battlefield situation understanding through multi-source data fusion, environmentally adaptive optimization of feature extraction and equipment identification, and dynamic construction and optimization of the equipment-terrain association model, providing precise support for decision-making.

The invention relates to a method and a system for dynamically dividing a natural resource right confirmation registration unit in combination with AI image segmentation. The method comprises the following steps: preprocessing multi-modal evidence data covering a to-be-confirmed area to obtain standardized boundary information and an evidence source entity; obtaining a multi-modal spatial knowledge graph through entity relationship modeling and spatial correlation analysis; based on the multi-modal space knowledge graph, calculating the space inconsistency measurement between the boundary line segment pairs and carrying out space-time logic analysis to obtain the conflict relation of the boundary line segment pairs; inputting the conflict region set into a pre-trained dynamic weight learning fusion network to obtain a candidate boundary line and an evidence weight map of each conflict region; and based on hybrid reasoning combined with a preset rule and graph neural network learning, conflict resolution and decision are carried out in combination with the candidate boundary line, the evidence weight graph and the conflict relationship, and a final vector boundary of the natural resource right confirmation registration unit is obtained. By adopting the method, the high-reliability right confirmation registration unit boundary can be generated.

This invention relates to a medical consultation dialogue system and method based on medical entity knowledge prediction and reasoning. Its technical features include: the system comprising a dialogue history encoding module, a medical entity prediction module, a medical entity reasoning module, and a knowledge-guided dialogue generation module; the method comprising: encoding the doctor-patient dialogue history using a neural network model; introducing an entity prediction module to obtain potential medical entities for the next round of dialogue; constructing an entity reasoning module to detect strong coupling relationships between medical entities; and using the knowledge-guided dialogue generation module to generate diagnostic responses. This invention is rationally designed and overcomes the problems of entity guidance deficiencies and insufficient entity relationship modeling, thereby enabling the medical dialogue system to generate responses that conform to dialogue history consistency and entity logical reasoning.

The embodiment of the application provides a large model data processing method, device and equipment and a storage medium, and relates to the technical field of network security. The method comprises the following steps: constructing a threat entity relationship model according to at least an asset knowledge base, a vulnerability knowledge base and an attack behavior knowledge base; inputting the threat entity relationship model into an encoder to perform semantic coding to obtain an encoding vector corresponding to each node; inputting the encoding vector into a semantic fusion layer to perform semantic enhancement to obtain an enhanced semantic representation corresponding to each node; generating a plurality of entity samples according to the enhanced semantic representation, and constructing a knowledge guide parameter matrix based on the entity samples; inputting the entity samples and the knowledge guide parameter matrix into an initial large model to perform feature representation to obtain a predicted feature representation; and fine-tuning the initial large model according to the predicted feature representation to obtain a target large model. Through the overall design, the accuracy of complex network security data processing is ensured, the reasoning efficiency is taken into account, and the data processing performance of the large language model in the field of network security is improved.

The application provides a kind of data sharing method and system based on embedded rule, extracts entity under each subject domain and the association between entities, constructs entity relationship model, and embeds data constraint rules in entity relationship model, maps entity relationship model into physical data model, after extracting the business data required by customer, the rules embedded in physical data model can be directly called to check data. Based on the traditional mode, the business rules are scattered in the application code, database trigger or artificial experience, it is difficult to extract and reuse. Through systematic analysis, the core business rules (verified by pilot) are coded into the entity relationship model, and the data is issued together. After receiving, customers can directly call these preset rules for data verification, without starting from scratch to build a verification system, significantly reducing implementation costs.

The invention discloses a bid invitation demand analysis and local file matching method and system based on a large model, and the method comprises the steps: building a local credit file library, classifying credit big data through a semantic model, extracting the data, constructing a knowledge base, and uploading the data to a cloud large model; inputting a bid invitation file to a cloud large model to identify and analyze business demands, extracting and standardizing a demand file in combination with a knowledge base, and packaging and downloading a corresponding file; when the knowledge base is constructed, a graph structure of the knowledge base is used as a classification mode, and a word2vec model is used for constructing a word vector model to capture a semantic relationship; mapping the semantic relationship to a graph entity relationship model, adding relationship data, and screening keyword vectors to construct a decision tree classification model; according to the graph structure, credit big data entity word vectors and classification information are obtained to construct decision training data, the decision training data are imported into a classification model for training, and rapid identification, information classification and qualification analysis of bid invitation texts are realized through the classification model, so that the file matching efficiency is effectively improved.

The invention discloses an accounting electronic certificate encryption method, and relates to the technical field of data encryption, and the method comprises the following steps: S1, constructing an entity relation model for an accounting electronic certificate of a terminal; s2, determining a time convergence factor according to the timestamp of the accounting electronic certificate; s3, determining a space verification label of the accounting electronic certificate according to an association relationship entity contained in the entity relationship model; and S4, completing access to the accounting electronic certificate according to the time convergence factor and the space verification label. According to the space verification tag, the service association entity and the storage data block are combined, tampering of any link (such as voucher content and storage position) can cause verification tag failure, and dual integrity protection of service logic and a storage carrier is achieved; the spatial verification tag is used as a digital fingerprint, and the fine-grained access control requirement of an enterprise on the electronic certificate is met.

The application discloses a sarcopenia risk assessment method based on a knowledge graph, relates to the field of medical large models, collects full-dimension multi-source data of a patient and carries out pretreatment; a six-tuple entity relationship model is defined, a dynamic entity life cycle and a heterogeneous relationship type system are introduced, multi-modal feature fusion is realized through modal adaptive weight learning, knowledge conflict resolution and entity evolution embedding; based on an enhanced spatiotemporal graph convolution network, through heterogeneous relationship attention space convolution, spatiotemporal adaptive time convolution and triple contrast learning tasks, enhanced entity embedding is output, and the enhanced entity embedding comprises a historical state, a current state, an evolution rate and a future trend. Through multi-modal spatiotemporal alignment and enhanced knowledge graph construction, the application realizes deep fusion of multi-source heterogeneous data and spatiotemporal dynamic relationship modeling, and significantly improves the comprehensiveness and precision of sarcopenia risk assessment.

The invention provides a unit fault risk scoring method based on a creative technology stack, and the method comprises the steps: carrying out the data entity modeling, constructing an entity relation model of multi-source heterogeneous data, defining a structured data set, carrying out the recursive automatic construction through aggregation and conversion primitives, generating a high-order candidate feature set, and carrying out the evaluation of a unit fault risk. Learning a causal graph among the features in the high-order candidate feature set based on a microcausal discovery algorithm, generating a causal graph among the features, identifying Markov coverage of a target variable according to the causal graph among the features, screening an effective causal feature subset, constructing an association graph by taking the effective causal feature subset as a node, and aggregating feature interaction information to generate a global fusion feature; dynamically and quantitatively calculating a real-time fault risk score of the computer unit based on the global fusion features; according to the method, the effects of deep utilization of the multi-source data and quantitative sorting of the fault risks are achieved through the creative and creative adaptive process and the continuous differentiated scoring model.

The application discloses an accounting electronic voucher encryption method, and relates to the technical field of data encryption, and comprises the following steps: S1, constructing an entity relationship model for the accounting electronic voucher of a terminal; S2, determining a time convergence factor according to the time stamp of the accounting electronic voucher; S3, determining a space verification tag of the accounting electronic voucher according to the associated relationship entity contained in the entity relationship model; and S4, completing access to the accounting electronic voucher according to the time convergence factor and the space verification tag. The space verification tag combines the business associated entity and the storage data block, and tampering (such as the voucher content and the storage position) at any link will cause the verification tag to fail, so that double integrity protection of the business logic and the storage carrier is realized; and the space verification tag serves as a digital fingerprint, and meets the fine-grained access control requirement of enterprises on the electronic voucher.