27 results about "Association model" patented technology

The invention discloses a business document design and user-defined field modeling method supporting dynamic expansion. The method relates to the technical field of custom field modeling, and comprises the following steps: constructing a core foundation and a metadata system; a non-intrusive expansion and data association model; performing high-performance query and index optimization; and performing dynamic verification and visual application. Structural stability and flexible expansion are realized through separate design of the core table and the expansion table, field definition, storage, verification and form rendering are driven by metadata, complex query and cross-receipt field multiplexing are supported, data consistency and full-link connection are guaranteed, business receipt design and self-defined field modeling expandability are improved, and the method is suitable for large-scale popularization and application. The problem of low expandability of business document design and custom field modeling in the prior art is solved.

The invention provides an intelligent updating method of a master-slave version linkage database, and belongs to the technical field of database updating, and the method comprises the steps: converting different key elements of a clothing version into quantitative correlation parameters based on a multi-dimensional parameterization correlation model, and building a real-time binding relationship between a master version and a slave version based on a mapping rule of a database layer; the method comprises the following steps: constructing a multi-modal clothing process knowledge graph, fusing multi-modal data to deeply associate clothing pattern data with key elements to obtain a pattern adjustment vector set, and adopting a multi-dimensional change type intelligent identification and priority judgment engine to automatically identify a master pattern change type and judge an adjustment priority based on a real-time binding relationship. And in combination with a preset AI self-optimization clothing structure mathematical formula library and a model adjustment vector set, the sub-templates are driven to complete adaptive adjustment of corresponding parts so as to realize intelligent updating. Manual plate-by-plate adjustment is not needed, the plate type updating efficiency is greatly improved, and size deviation and process defects are effectively reduced.

The invention discloses a four-dimensional index-based scientific and technical literature knowledge automatic labeling method and system, and relates to the technical field of natural language processing, and the method comprises the following steps: constructing a four-dimensional index architecture; the method comprises the following steps: receiving multi-format scientific and technical literatures through a data input layer, and performing index analysis to obtain a document layer semantic structure, a paragraph layer semantic relation graph, a sentence semantic relation network and an entity semantic relation; performing semantic density calculation and knowledge enrichment region recognition to obtain a knowledge enrichment region recognition result; and establishing a cross-hierarchy semantic association model, performing entity boundary prediction and multi-level labeling on a knowledge enrichment area recognition result to obtain a structured semantic labeling result, and performing visual output and display. The technical problem that in the prior art, scientific and technical literature knowledge recognition is not accurate, hierarchical labeling is insufficient, and consequently the literature processing efficiency and accuracy are low is solved, and the technical effects that precise recognition and multi-hierarchical labeling of scientific and technical literature knowledge are achieved, and the literature processing efficiency and accuracy are improved are achieved.

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to associate detected objects from sensor data with tracked objects in a tracking list stored in the memory by executing an association model, determine kinematic states of the tracked objects by executing a Bayesian filter, and update which of the tracked objects are stored in the tracking list by executing a track-management model. The association model includes association weights and outputs probabilities that the detected objects are the same as the tracked objects. An association-model gradient of the association model is computable. The association-model gradient is of the probabilities with respect to the association weights. The Bayesian filter includes Bayesian parameters. A Bayesian-filter gradient of the Bayesian filter is computable. The Bayesian-filter gradient is of the kinematic states with respect to the Bayesian parameters. A track-management gradient of the track-management model is computable.

This invention discloses a method and system for automatic annotation of scientific and technological literature knowledge based on a four-dimensional index, belonging to the field of natural language processing technology. The method includes: constructing a four-dimensional index architecture; receiving multi-format scientific and technological literature through a data input layer, performing index parsing to obtain document-level semantic structure, paragraph-level semantic relationship graph, sentence semantic relationship network, and entity semantic relationships; performing semantic density calculation and knowledge-rich region identification to obtain knowledge-rich region identification results; establishing a cross-level semantic association model, performing entity boundary prediction and multi-level annotation on the knowledge-rich region identification results to obtain structured semantic annotation results, and performing visualization output. This invention solves the technical problems of inaccurate scientific and technological literature knowledge identification and insufficient hierarchical annotation in existing technologies, leading to low efficiency and accuracy in literature processing. It achieves accurate identification and multi-level annotation of scientific and technological literature knowledge, improving the efficiency and accuracy of literature processing.

The invention relates to intelligent classification of coal mining materials. The method comprises the following steps: acquiring multi-source data associated with coal mining, and performing feature fusion on the multi-source data to obtain a three-dimensional association model; the three-dimensional association model is an association model for equipment information, material information and equipment maintenance information of coal mining, generating an association map by using the three-dimensional association model based on a pre-constructed unified data category model, and inputting the association map into the trained map neural network model to obtain a map neural network model; and obtaining a material classification result output by the graph neural network model. According to the scheme, the material management efficiency and the intelligent level are improved.

The present application relates to the technical field of geological disaster monitoring and early warning, in particular to a complex high-position landslide risk dynamic identification system based on multi-source InSAR cooperation, comprising: a data acquisition module for constructing a normalized feature vector representing the state of each monitoring node; an association modeling module for quantifying the physical influence degree between heterogeneous nodes; a state prediction module for iteratively updating the hidden state of each heterogeneous node using a spatio-temporal graph neural network model, and interpreting the hidden state as incremental displacement; a risk assessment module for calculating the risk precursor index of each heterogeneous node deviating from the normal evolution mode based on the hidden state, and determining and issuing graded early warning according to the preset threshold system; the present application effectively alleviates the overfitting problem of pure data-driven models when data is sparse, significantly improving the generalization ability and physical interpretability of the prediction results.

The invention relates to a multivariate evidence correlation analysis method in the judicial field, and belongs to the technical field of intelligent processing of documents. The method comprises the following steps: loading judicial case file data and extracting an evidence content text; constructing an evidence rule template according to judicial domain knowledge and expert opinions; extracting evidence elements based on the rule template and constructing an evidence library; building a multivariate evidence information network, calculating the condition probability of association information among evidence elements based on a Bayesian network, and forming a multivariate evidence association analysis model; optimizing the multivariate evidence information network through a random strategy reinforcement learning algorithm; and calculating an information probability value of the multivariate evidence subnet, and selecting the most valuable multivariate evidence information network. According to the method, a multivariate evidence correlation analysis event of a judicial case is converted into an updating and optimizing task of a multivariate evidence information network, the complex correlation problem among multivariate evidences is simplified, a correlation model among the multivariate evidences is given, and certain auxiliary support is provided in a judicial decision support system.

Provided are a transaction data processing method and system. The method comprises: acquiring a target proportion vector of a target across different item categories, and constructing a target proportion vector model sample on the basis of the target proportion vector; constructing an association model on the basis of the target proportion vector model sample and an association probability of the target being associated from one item category to another item category; constructing a transition model on the basis of the target proportion vector model sample, the association probability, and a transition association probability of the target being transitioned from one item category to another item category; performing parameter fitting on parameters of the transition model on the basis of the target proportion vector model sample to obtain the transition association probability; and computing target-related decisions for the different item categories on the basis of the transition association probability. Therefore, changes in proportions of a target in different item categories caused by participation of an acting object can be efficiently and accurately identified, and the accurate identification of the changes in proportion allows for optimization of resource allocation and supports effective risk management.

This invention provides an intelligent method and system for extracting and analyzing invoice information using deep learning, relating to the field of deep learning technology. First, based on the field flow and association logic of invoice business scenarios, it generates semantic traceability tracing trajectories for fields, collects scenario-related features of the invoice carrier to generate scenario feature guidance signals, and uses these signals to locate the physical presentation areas of key fields and extract their original information to form a set. Next, it constructs a dynamic field association model, inputting the set of original information for key fields and scenario-related features into the model, and uses deep learning algorithms to model dynamic dependency relationships to generate a dynamic field association graph. Finally, based on this graph, it integrates information to generate scenario-based structured invoice analysis results, including core field content, scenario association logic, and dynamic association descriptions, enabling efficient and accurate processing of invoice information.

The invention relates to the technical field of data authority management, in particular to a fine-grained data authority searching method based on an authority atlas and a dynamic index, which comprises a multi-level knowledge association model, a dynamic incremental index mechanism and an authority atlas and index collaborative optimization method. The multi-level knowledge association model hierarchically defines a user role, a data attribute and a context environment; a dynamic incremental index mechanism reduces the maintenance cost through distributed task decomposition and adaptive optimization; according to the collaborative optimization method, the consistency of local refreshing and caching is realized, and the response efficiency is improved. According to the method, the authority control precision and the search efficiency in a large-scale data scene can be remarkably improved, and a brand new solution is provided for complex authority management.

The invention discloses a cross-domain preference prediction recommendation method and system based on personality characteristics, and the method comprises the steps: obtaining the personality original data and historical behavior data of a user, and converting the personality original data into personality characteristic representation; on the basis of personality feature representation, through a pre-constructed personality preference association model, predicting the preference score of the user for the non-contact items; generating a first recommendation list according to the preference score; generating a second recommendation list according to the historical behavior data; dynamically adjusting the weights of the first recommendation list and the second recommendation list according to the recommendation scene and user feedback; and performing deep fusion on the first recommendation list and the second recommendation list according to the weight, and generating a final recommendation list. According to the method, through the personality preference association model, accurate prediction and recommendation of people, information and articles which are never touched by the user but are likely to be preferred by the user are realized, and the problems of'information cocoon house 'and'cold start' caused by the fact that a traditional recommendation method only depends on historical behaviors of the user are solved.

The invention relates to the technical field of power system automation, discloses a thermal power generation-based multi-source node association model construction method and an association anomaly analysis system, and aims to solve the problems of inaccurate description of a complex interaction relationship and low anomaly recognition and fault tracing accuracy of a thermal power generation system in the prior art. The method comprises the steps of executing whole-process data granulation modeling, establishing a dynamic cause and effect graph model, implementing an entropy weight dynamic correction algorithm, integrating high-relevance additional technical means, executing relevance anomaly analysis, and regularly performing model efficiency evaluation and online learning. The system comprises a data acquisition and preprocessing module, a multi-source node association model construction module, an association anomaly analysis engine module and a performance evaluation and continuous optimization module. According to the technical scheme, accurate description of the thermal power generation complex system can be realized, the abnormity identification timeliness and the fault tracing accuracy can be improved, the system deviation caused by working condition disturbance and equipment aging can be overcome, the non-planned shutdown frequency can be reduced, and the system can be ensured to continuously adapt to the change of the operating environment.

The invention relates to the technical field of data communication, and discloses a data real-time pushing method and system based on association of producers and consumers. The method comprises the following steps: constructing a system comprising an association registration module, a data acquisition module, an association matching module, a push scheduling module, a fault-tolerant processing module and a monitoring operation and maintenance module; the producer and the consumer respectively register and submit attribute, demand and terminal information; the association matching module establishes a dynamic association model through semantic analysis and feature extraction, and generates an association relation table; the data acquisition module acquires and preprocesses producer data in real time; the push scheduling module pushes the data to the target terminal in real time by adopting a distributed scheduling strategy according to the association relation table and the data attribute label; the fault-tolerant processing module monitors and processes pushing abnormity; and the monitoring operation and maintenance module performs full-link monitoring and generates a report. According to the method, dynamic and accurate association matching and data pushing are realized, and the pushing real-time performance, the reliability and the system resource utilization rate are remarkably improved.

The analysis method of the storage system and the electronic equipment of the application relate to the technical field of storage systems, and through a pre-constructed resource association model containing logical association relationships of all resource objects in a service link, log information analysis in an event dimension and time delay index analysis in a performance dimension are performed on a target object and its associated objects, the problems of low efficiency and poor accuracy of traditional troubleshooting methods are overcome, fast, comprehensive and associated analysis of the performance of the storage system is realized, and the efficiency and accuracy of performance analysis are significantly improved. Therefore, the technical problem of how to quickly and accurately analyze the performance of the storage system can be solved, and the technical effect of improving the efficiency and accuracy of performance analysis of the storage system is achieved.

The application discloses a method and related device for answering questions in an insurance business. In the method, a question to be answered by a user is obtained; a target knowledge base associated with the question to be answered is obtained based on the question to be answered through a knowledge base association model; a plurality of pieces of knowledge in the target knowledge base are respectively subjected to similarity calculation with the question to be answered to determine K pieces of candidate knowledge with the highest similarity to the question to be answered; K>0; and an answer to the question to be answered is obtained based on the K pieces of candidate knowledge and the question to be answered. In this way, the pre-constructed knowledge base is fully utilized, the semantics of the question to be answered by the user is fully mined in combination with vector similarity, K pieces of candidate knowledge with strong semantic association can be determined for the question to be answered, and therefore, a more accurate answer to the question to be answered can be obtained based on the K pieces of candidate knowledge, which is beneficial to further improving user experience.

This invention relates to the field of power asset management technology, and in particular to a power asset management method and system based on an event-equipment correlation model. It collects multi-source heterogeneous data from the power asset operating environment, generates a standardized event-equipment interaction dataset, constructs a dynamic causal correlation graph of events and equipment, employs a temporal causal hypergraph attention network combined with counterfactual interference minutiae, outputs a pure causal correlation weight matrix, and generates an event-equipment correlation model. This model deduces the equipment performance degradation trajectory and remaining useful life probability distribution under target event-driven conditions, quantifies the marginal benefits and risk exposure values ​​of assets at different operation and maintenance intervention nodes, solves for the optimal asset scheduling sequence through deep reinforcement learning, generates power asset management decision schemes, and achieves collaborative closed-loop evolution of the model and decisions based on online meta-learning. This invention effectively improves the intelligence level of power asset management and ensures the safe, stable, and economical operation of the power grid.

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to associate detected objects from sensor data with tracked objects in a tracking list stored in the memory by executing an association model, determine kinematic states of the tracked objects by executing a Bayesian filter, and update which of the tracked objects are stored in the tracking list by executing a track-management model. The association model includes association weights and outputs probabilities that the detected objects are the same as the tracked objects. An association-model gradient of the association model is computable. The association-model gradient is of the probabilities with respect to the association weights. The Bayesian filter includes Bayesian parameters. A Bayesian-filter gradient of the Bayesian filter is computable. The Bayesian-filter gradient is of the kinematic states with respect to the Bayesian parameters. A track-management gradient of the track-management model is computable.

The invention relates to a large-model-driven dynamic and static target correlation modeling, value analysis and intention deduction method and a large-model-driven dynamic and static target correlation modeling, value analysis and intention deduction system. The method comprises the following steps: acquiring dynamic target data and static environment data; performing association modeling on the dynamic target data and the static environment data by using the large model to form a time-space coupled knowledge graph; the knowledge graph is utilized to drive the large model to carry out double-path analysis, one path drives the large model to execute incremental graph updating on newly added data, the other path takes dynamic and static association in the knowledge graph as constraint conditions to be embedded into a reasoning link of the large model, and the large model is utilized to carry out system value evaluation and intention deduction; and carrying out fusion presentation on a system value evaluation result and an intention deduction result on a visual platform. According to the method, deep collaboration of the knowledge graph and the large model can be realized, the intelligent analysis capability on the value and behavior intention of the target system is enhanced on the basis of associated modeling, and the accuracy and systematicness of situation cognition and decision support in a complex environment are comprehensively improved.

The application discloses a kind of association processing method, device, electronic equipment and storage medium of data model.Therein, the method comprises: determining at least one target model from multiple models;Determine the associated model associated with the target model from multiple models, wherein the mapping relationship exists between the association attribute of the target model and the association primary key in the associated model;According to the association primary key, the association attribute and the association mode between the target model and the associated model, generate target table data, wherein at least the same to-be-processed information between the first field information corresponding to the association attribute and the second field information corresponding to the association primary key in target table data, information related to to-be-processed information in the target model and information related to to-be-processed information in the associated model are included.The application solves the technical problem of low data association efficiency when data association is performed on data of different table files in the prior art.

The invention relates to the technical field of data linkage updating, in particular to a big-data-driven smart city data linkage updating method and system, and the method specifically comprises the steps: determining the attribute association degree between different attributes through the frequency of occurrence difference between data items of different attributes, so as to determine the association attribute of each attribute; and determining an inhibition factor of the result item set in each association rule by combining the attribute association degree on the basis of the occurrence frequency of the data items in the association attributes of the attributes of the result item set of the association rules in the transaction. Based on the attribute association degree and the inhibition factor, association rule confidence obtained by a traditional Apriori algorithm is adjusted, and an association rule base, namely a data association model, is constructed by using the adjusted confidence; the accuracy and the reliability of association rule mining are remarkably improved, and the real-time performance, the automation level and the cooperative capability between systems of smart city data updating are effectively improved.

The application provides an intelligent updating method for a master and sub-plate linkage database, and belongs to the technical field of database updating, comprising the following steps: based on a multi-dimensional parameterized association model, different key elements of a garment plate type are converted into quantitative association parameters, and a real-time binding relationship between a master plate and a sub-plate is established based on a mapping rule of a database layer; a multi-modal garment process knowledge graph is constructed, multi-modal data are fused, garment plate type data and key elements are deeply associated to obtain a plate type adjustment vector set, a multi-dimensional change type intelligent recognition and priority determination engine is used to automatically recognize the master plate change type and determine the adjustment priority based on the real-time binding relationship, and the preset AI self-optimization garment structure mathematical formula library and the plate type adjustment vector set are combined to drive the sub-plate to complete the adaptive adjustment of the corresponding parts and realize intelligent updating. Without manual adjustment of each plate, the plate type updating efficiency is greatly improved, and the size deviation and process defects are effectively reduced.

The invention relates to a dynamic security resource pool elastic scheduling method, system and device and a medium. The method comprises the steps of performing association analysis of security threats and resource demands according to real-time state data and real-time threat intelligence data, and generating a threat and resource association model; according to security level requirements in the threat and resource association model, performing fine-grained resource division on calculation, storage and network resources in a security resource pool, and generating scheduling atoms with security label attributes; based on the real-time threat intelligence data, dynamically binding the scheduling atom to a security service chain node according to a security policy, and generating a service chain topology adaptive to a current threat environment; and according to the resource type and the security level required by each node in the service chain topology, matching the resource units meeting the requirements from the scheduling atoms, and generating a resource allocation scheme. By adopting the method, resource scheduling and safety protection can be dynamically and accurately carried out, and efficient utilization of resources and continuous guarantee of services in a constantly changing threat environment are ensured.

The invention relates to a form construction method and device based on an entity association model, equipment and a medium, the method comprises the steps that attribute information of to-be-processed entities is acquired, the to-be-processed entities comprise a first entity, a second entity and a third entity which have the same attribute information, and the second entity and the third entity are applied to the first entity; according to the attribute information of the to-be-processed entity, an entity association model is constructed, and a first logic relation between the first entity and other entities and a second logic relation between all the entities and the attribute information of all the entities are described in the entity association model; and generating a form according to the entity association model and the business demand. According to the method provided by the invention, the form is generated according to the entity association model and the business demand, so that the automation and intelligence of form construction are realized, the efficiency of form construction is greatly improved, and the time and error rate of manual operation are reduced.

The invention discloses an equipment dynamic association importance evaluation method considering multi-factor fusion. The method comprises the steps of problem description, dynamic association model of association indexes, dynamic weight calculation and association importance calculation. The problem description comprises parameter definition and mathematical expression; the dynamic association model of the association indexes comprises a function complementarity association model, an information interactivity association model, a task process association model and a guarantee support association model; the dynamic weight calculation comprises semantic weight calculation, objective weight calculation and combined weight calculation; the correlation importance calculation comprises the step of calculating the correlation importance of the equipment pair; the invention relates to the technical field of equipment importance evaluation. According to the method, the dynamic association importance degree of the equipment pair is evaluated, the rule that the dynamic association importance degree of the equipment changes along with time is shown, the method is suitable for multi-dimensional related scenes with features and labels, components and environments and the like in a complex system, and the problem that multi-dimensional factors are not deeply fused is solved.

The invention discloses a multi-modal intelligent osteoarthritis prediction algorithm based on a KAN network, a storage medium and electronic equipment. The multi-modal intelligent osteoarthritis prediction algorithm comprises the following steps: modal data acquisition and preprocessing; performing multi-modal feature explicit fusion based on the KAN; carrying out association modeling on the knowledge graph; and performing multi-modal fusion based osteoarthritis prediction reasoning. And evaluating and optimizing the multi-modal feature fusion model. According to the method, a traditional black box type feature merging process is converted into a group of learnable explicit mathematical functions, so that a reliable interpretable basis is provided for a prediction result of osteoarthritis. Furthermore, according to the method, a correlation modeling module based on a knowledge graph is introduced, deep fusion of multi-modal data and medical priori knowledge is achieved, semantic correlation and pathology consistency among different modals are enhanced, and therefore accuracy and robustness of early prediction of osteoarthritis are effectively improved.

The invention discloses an information pushing method and device, computer equipment and a storage medium. The method comprises the following steps: constructing an integrated database based on unstructured data and structured data; constructing a material association model based on the integrated database; triggering a recommendation process according to the monitored target material information in the client interaction record; a customized recommendation report generated according to the recommendation process is pushed to a specific user; and collecting feedback data of the specific user to the customized recommendation report, and optimizing the material association model based on the feedback data. According to the method, the integrated database is constructed by integrating unstructured and structured data, a comprehensive data foundation is laid for accurate recommendation, the material association model is constructed based on an association rule mining algorithm in combination with personalized purchase characteristics of specific users, a feedback optimization mechanism is introduced, personalized recommendation adjustment is realized, and the recommendation efficiency is improved. The correlation matching precision and the model stability are effectively improved, and the defects of feedback iteration stiffness and lack of personalized adaptation in the prior art are overcome.