45 results about "Association model" patented technology

The invention discloses a mapping prediction method and system based on a hardware resource load and system operation relationship. The method comprises the following steps: collecting hardware index data in real time when an operating system operates; the method comprises the following steps: preprocessing hardware index data, performing association analysis on each index data by adopting an association rule mining algorithm, generating association rules, evaluating the association rules, screening out the association rules meeting conditions, and constructing a multivariable association model; based on a correlation analysis result, a load prediction model is constructed and trained, and the trained model is deployed in the system for load prediction; and dynamically adjusting a system hardware resource allocation strategy according to a prediction result, carrying out task migration and load balancing according to a predicted load condition, and automatically adjusting hardware resources through an automatic script or a scheduling tool. Through real-time monitoring and deep correlation analysis of indexes such as the CPU, the memory, the storage I / O and the network bandwidth, the resource utilization rate and the operation efficiency of the system are improved, and the stability and the reliability of the system are improved.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

The invention discloses a bearing life prediction method and system based on dynamic knowledge embedding, and the method comprises the following steps: S1, encoding bearing field data, expert experience and monitoring data into a structured triple, building a dynamic knowledge graph frame, and designing a sliding window confidence mechanism to achieve the online updating of a graph node relation; s2, extracting knowledge embedding vectors by using a relational graph convolutional network, and extracting vibration signal features by using a hierarchical convolutional network; s3, mapping the knowledge embedding vector and the vibration characteristics to a unified semantic space through a linear projection layer; s4, constructing a Transform encoder based on multi-head self-attention, and establishing a dynamic correlation model between vibration characteristics and knowledge embedding; s5, designing a full-connection network output life prediction result and feeding back the optimized knowledge graph; and S6, adaptively adjusting the size of the sliding window based on the change rate of the working condition, dynamically balancing the contribution weight of new and old knowledge in combination with a gating mechanism, and ensuring the adaptability of the model to the complex working condition.

The invention discloses a multi-target track association method and system based on interactive attention map matching and a storage medium, and the method comprises the steps: preprocessing track data, and obtaining an input track; utilizing a track historical feature extraction network to obtain embedded features of each track; a multi-graph structure is utilized to model tracks, nodes represent one track, edges represent message passing between the tracks, and the number of the edges is limited through graph cutting; features of neighbor nodes are aggregated by using a self-attention mechanism and a cross attention mechanism, and finally embedded representation of the nodes is obtained; and constructing a score matrix, and performing a Sinkhorn algorithm on the matrix to obtain an association probability matrix. And track association is carried out by using the multi-target track association model based on interaction attention map matching, and an association task is completed. According to the method, the spatial-temporal characteristics of the track can be fully mined, the track association accuracy is improved, and the track association error rate is reduced.

The invention provides a method, a device, equipment and a medium for generating an engineering project management digital model, and the method comprises the steps: collecting original data related to engineering project features, carrying out the preprocessing, forming a standardized project data set, constructing a space-time reference frame based on the data set, fusing a multi-dimensional feature tensor of multiple engineering features, and carrying out the construction of a space-time reference frame. The method comprises the steps of generating a project progress node and an integrated feature tensor, fusing the project progress node and the integrated feature tensor to generate a multi-source association graph containing entity relationships and feature semantics, generating a verified engineering project management digital model according to the project progress node, the integrated feature tensor and the association graph, and dynamically updating and optimizing the model in response to change data in project execution. According to the method, the problems of data dispersion, lack of association and static stiffness of the model in a traditional method are solved, automatic construction and dynamic evolution from multi-source heterogeneous data to an intelligent decision-making model are realized, and the accuracy, real-time performance and intelligent level of project management are remarkably improved.

The invention discloses a marketing strategy optimization method based on consumer group behavior analysis, and the method comprises the following steps: deeply integrating and analyzing multi-source heterogeneous data in real time, such as biological characteristic data, physical environment and spatial data, social contact and content interaction deep analysis data, and voice and dialogue analysis data; carrying out association modeling on the behavior data, psychological characteristics, physiological states and social environment factors, and constructing a dynamic holographic user portrait; a large language model and a multi-mode generation model are utilized, marketing content with high personalization and situation adaptation is generated in real time based on a user portrait and a real-time context, and an interactive co-creation mode of the user and AI is explored; and according to an evaluation result, performing online updating and optimization on each link by using a machine learning algorithm to form a continuously improved closed loop. According to the invention, a more accurate and personalized marketing strategy can be provided, the sense of participation of users is enhanced, and the marketing effect and enterprise competitiveness are improved.

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 a data processing method and system based on network security service, relates to the technical field of network data security processing, and realizes structured expression and behavior extraction of potential threats in encrypted communication by establishing a new data structure and behavior association model. By introducing a context causal chain modeling mechanism, associating the originally isolated security events into a complete attack path; by establishing a set of event grading mechanism based on comprehensive scoring of service criticality, response cost and attack propagation path, threat processing does not distribute resources blindly and averagely, but performs intelligent scheduling according to priority, so that the resource utilization efficiency is improved; a structured and executable defense instruction or blocking strategy can be generated according to an analysis result, man-machine cooperation or full-automatic response is supported, and response efficiency and strategy adaptability are remarkably improved. And a set of intelligent data processing solution which is oriented to a network security service practical application scene and has high availability and high expansibility is constructed.

The invention provides an automatic bait casting system for training and breeding juvenile plectropomus leopardus, which comprises a basic data module connected with an automatic bait training device and used for acquiring original data of a breeding pond and extracting feeding basic data; the dynamic association module is used for carrying out multi-level cross association modeling, causal inference association, sub-mode mining and classification adaptation to obtain an ingestion-turbidity dynamic association model and outputting an ingestion-turbidity relationship; the hunger index module is used for constructing a hunger index model and outputting the hunger index of each culture pond; the feeding strategy module is used for calculating the next feeding interval, the strip-shaped bait-live bait proportion, the actual time point of next feeding and the bait amount per hour and judging whether extra meal or increase of the next feeding interval is needed or not to obtain a self-adaptive feeding strategy; and the feeding driving module is used for controlling the running action of the automatic bait training device. Scientific feeding can be achieved, the feeding survival rate, the growth rate and the bait training effect are remarkably improved, feed waste is reduced, and intelligent and automatic management of the whole process is achieved.

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.

The present invention belongs to the field of equipment state deduction and prediction, and discloses a collaborative fusion digital twin deduction and evaluation method for an integrated industrial system. The specific steps of the deduction and evaluation method include the following: establishing a unit association model of four relationships between devices, further considering the mutual influence between devices in the model, and calculating the specific influencing parameters of the association model, establishing a correlation coefficient matrix, and constructing a deduction and evaluation model of the industrial system based on the matrix. Through a comprehensive evaluation of the entire industrial system, the operating status of the industrial system can be fully understood and grasped, potential problems can be discovered and solved in a timely manner, and the normal operation of the industrial system can be guaranteed.

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.

The invention relates to the field of data processing, in particular to an intelligent detection method and system for the impact performance of a forging blank, and the method comprises the steps: firstly, carrying out micro impact and thrust experiments on a sample forging, obtaining a basic data set containing parameters such as geometric dimensions, a force displacement curve and impact energy, and carrying out grouping storage according to material types and shapes; secondly, constructing an inflection point size correlation model and a target fracture energy and impact energy correlation model based on each group of data, and respectively representing correlation relations between inflection point coordinates and geometric dimensions and between fracture energy and impact energy; the model and the identity label of the forge piece group are associated and stored in a database. And when a detection request is received, a micro-impact experiment is executed on a requested forge piece, and impact performance parameters are predicted through the correlation model in the database. According to the method, efficient and accurate detection of the impact performance of the forge piece is achieved through combination of experimental data and an intelligent model, and the method is suitable for forge piece blanks of different material types and shapes.

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 dynamic nursing scheme generation method and system oriented to AIDS disease course evolution, and belongs to the technical field of public health. The AIDS patient disease course characteristic data is classified and analyzed, key information is accurately extracted, the nonlinear mapping model is constructed by means of the recurrent neural network, the influence of each characteristic on disease course evolution is quantified, the individual difference of the patient is comprehensively and accurately reflected, a data basis is provided for disease course stage division, nursing strategy making and scheme generation, and the accuracy and the reliability of the system are improved. In the whole process from disease course stage division to nursing scheme adjustment, a dynamic monitoring and feedback mechanism is introduced, disease course stages are scientifically divided according to multi-dimensional information, a specific nursing strategy is formulated for each stage and continuously optimized, and meanwhile, based on patient data collected in real time, the nursing intervention is accurately matched with the disease condition development requirement of a patient. The disease course change is evaluated in time through the correlation model, the nursing scheme is dynamically adjusted, and the nursing service can respond to the dynamic evolution of the condition of the patient in real time.

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 discloses an intelligent correlation analysis and visual display method for production data of a thermal power plant, and particularly relates to the technical field of thermal power plants, and the method comprises the following steps: S1, carrying out the collection and preprocessing of multi-modal data; s2, constructing a dynamic association model; s3, intelligent visual interaction design is carried out; s4, performing predictive analysis and health degree evaluation; and S5, closed-loop feedback control and optimization are carried out. According to the method, efficient utilization and intelligent decision-making of the production data of the thermal power plant are realized through technical means such as multi-modal data fusion, dynamic association graph construction, intelligent visual interaction, predictive analysis decision-making and edge calculation, so that the data utilization efficiency is remarkably improved; the reliability of fault prediction and energy consumption optimization is improved, the interactive experience is improved, the information expression limitation of a traditional two-dimensional chart is solved, and non-planned shutdown can be reduced.

The invention relates to the technical field of social media site analysis, and discloses a regional site characterization method and system, and the method comprises the steps: extracting a user group from a social media user group; performing entity feature word and camp feature word analysis on texts published by users in the sampled user group to obtain a first vertical distribution prediction result; carrying out double labeling of the standing and personality characteristics on the users whose activeness is higher than a set condition, training a personality-standing association model by using labeled data, analyzing the personality characteristics of the users whose activeness is low, deducing the standing, and carrying out statistics to obtain a second standing distribution prediction result; and carrying out weighted fusion on the first vertical field distribution prediction result and the second vertical field distribution prediction result to generate a final regional vertical field representation. According to the method, the quantitative mapping relationship among the user behaviors, the personality characteristics and the potential site is established, and accurate sampling is performed according to the population distribution in the real world, so that accurate and full-quantitative insight of the regional site is realized.

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.

The invention discloses an analysis method of a storage system and electronic equipment, and relates to the technical field of storage systems, and the method comprises the steps: carrying out the log information analysis of an event dimension and the time delay index analysis of a performance dimension on a target object and an associated object thereof through a pre-constructed resource association model containing the logic association relationship of all resource objects in a service link, and obtaining the log information of the target object and the associated object. The problems that a traditional troubleshooting mode is low in efficiency and poor in accuracy are solved, rapid and comprehensive correlation analysis of the performance of the storage system is achieved, and the efficiency and accuracy of performance analysis are remarkably 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 the performance analysis of the storage system is achieved.