58 results about "Relational system" patented technology

The invention discloses an emotion pressure recognition method and system based on an action form fusion graph neural network, and belongs to the field of image processing and machine learning. A double-graph data structure is constructed, wherein graph data with human body key points as nodes and graph data with image frames as nodes are processed through T-GCN and PCSN respectively. The T-GCN model fuses a GCN layer and a Mama encoder, the cohesion and coupling ability of emotion category characterization is enhanced through a graph partitioning strategy, and the spatial-temporal characteristic relation of human body actions is effectively captured. The PCSN adopts a three-branch parallel structure including one-dimensional global average pooling, 3 * 3 convolution and 5 * 5 convolution, efficient extraction of non-homogeneous features of the human body, the face and the hand is achieved, and the long and short distance dependence and local cross-channel interaction relation of the global features is established. According to the system, through combination of transfer learning and small sample learning, a public data set is utilized to initialize a network, and secondary training is performed on a small sample data set of campus emotion pressure, so that the recognition precision is remarkably improved.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

The invention discloses a teaching course recommendation method and system based on English learning data, particularly relates to the field of semantic processing, is used for solving the problem of poor pertinence of a traditional English learning course, and comprises the following steps: aiming at systematic difference of native language and English expression on a syntactic structure, constructing a language structure difference map and extracting a structure offset label; the method comprises the following steps: performing dependency syntactic analysis on semantic consistent sentence pairs of multi-language aligned corpora to generate a structural difference feature set; on the basis, a semantic-grammar dimension mapping graph is constructed in a classified mode, and a general structure expression vector is established for graph nodes. A teaching course is divided into knowledge point units in combination with a context label, and a mapping relation between a structure label and the course is established. The system performs structure analysis and semantic matching on sentences input by the learner, identifies structure migration type expression errors, and recommends accurate teaching content according to context and structure labels.

Techniques for multi-stage training of a machine learning model to extract key-value pairs from documents are disclosed. A system trains a machine learning model using a set of training data including unlabeled documents of various document categories. The initial stage identifies relationships among tokens, or words, numbers, and punctuation, in documents. The system re-trains the machine learning model using a set of training data which includes a particular category of documents while excluding other categories of documents. The second training stage is a supervised machine learning stage in which the training data is labeled to identify key-value pairs in the documents. In the initial training stage, the system sets parameters of the machine learning model to an initial state. In the second stage, the system modifies the parameters of the machine learning model based on the characteristics of the training data set including the documents of the particular category.

The invention discloses an authority centralized management system and method for applications in an enterprise, and the system comprises an application data management module, an application authorization management module, a system basic data management module and an interface module, the maintenance module is used for realizing maintenance of basic data such as an application system, role information, menu management, API (Application Program Interface) resources, users and organizations; the application authorization management module is used for realizing a binding relationship between roles and resources; the system basic data management module is used for supporting maintenance of fine-grained permission authorization data; and the interface module is used for realizing interaction among the modules. Unified management of internal application system resources and data permissions of multiple enterprises can be achieved, permission module integration of the application systems in the enterprises is completed through diversified permission integration modes, and centralized management and control of the application system permissions of the enterprises are achieved.

The invention relates to the technical field of diffusion model AI application, and discloses an AI aided design workflow method and system based on a diffusion model, and the method comprises the steps: receiving multi-modal data; vectorizing the sketch, and extracting key set features; analyzing the text into structured semantics; parameter constraints are converted into mathematical expressions or boundary conditions; mapping the multi-modal data to a unified space; selecting a generation path with the minimum loss function from the candidate paths; gaussian noise is gradually added into the submerged space, and noise distribution is predicted; injecting the parameter constraint into a denoising process to generate a path; and the optimized latent vector is converted into a vector graph through a decoder, and a geometric topological relation is reserved. The system comprises a unified multi-modal data module, a path generation module and a user interaction module. According to the method, the actual availability of a design result is improved; the design period is shortened, the process is simplified, and human resource investment is reduced.

The invention relates to the technical field of data processing and analysis, in particular to a knowledge graph entity accurate identification relation system for a service system, which comprises the following steps of: analyzing database table data, extracting an attribute change track and generating an attribute evolution sequence to provide a time sequence data basis for subsequent analysis and ensure the traceability of attribute evolution; meanwhile, the adaptability of the system to heterogeneous data is enhanced; clustering and dividing attribute clusters based on an attribute change mode, establishing a cross-cluster association index, revealing a potential association rule between attributes, and supporting rapid classification and dynamic adjustment of attribute relationships in a service scene; dynamic analysis of intra-cluster and cross-cluster attribute influence is realized by using a bidirectional attribute propagation model, and coupling strength between attributes is accurately quantified through iterative calculation and feedback calibration; by constructing a dynamic attribute topological graph and monitoring attribute value changes in real time, association reconstruction is triggered to generate an attribute association group set, dynamic changes of a service system are adapted, and association accuracy is improved.

The invention relates to an agricultural economy cross-border cooperation English negotiation auxiliary decision-making system, in particular to the field of data processing and analysis, and the system can capture multi-source policy data in real time, construct a policy chain reaction network through dynamic network modeling, effectively evaluate the trade dependency relationship between participating countries, and improve the accuracy of the trade dependency relationship between the participating countries. The system generates a multi-dimensional policy risk score vector based on historical policy data through a risk prediction engine, constructs a multi-objective optimization decision model in combination with a preset negotiation constraint condition, and can output accurate English negotiation policy scripts including clause revision suggestions and interaction sequential logic through a policy optimization generation module. Therefore, accurate decision support is provided for cross-border cooperation negotiation, the cross-border agricultural economic cooperation negotiation method has remarkable advantages in the aspects of improving the decision efficiency of cross-border agricultural economic cooperation, reducing policy risks and optimizing negotiation strategies, the negotiation effect and the agreement achievement rate can be remarkably improved, and smooth proceeding of international trade is promoted.

The invention discloses a water-turbine generator set digital twinning system for state monitoring and diagnosis, which comprises a water-turbine generator entity layer, the water-turbine generator entity layer is in communication connection with a sensor system, the sensor system is in communication connection with a digital twinning model layer, and the digital twinning model layer is in communication connection with a twinning data processing layer. The twinborn data processing layer is in communication connection with the digital twinborn decision-making layer; the hydro-generator entity layer, the sensor system, the digital twinborn model layer, the twinborn data processing layer and the digital twinborn decision-making layer are all in bidirectional communication connection with the hydro-generator set intelligent operation and maintenance platform and are in communication connection with the cloud computing cloud service module. The system is used for solving the problems that in the prior art, a control strategy cannot clearly reflect the corresponding relation between faults and symptoms, system data is too single, accuracy is insufficient, and efficiency is poor. The fault of the water-turbine generator set can be efficiently, accurately and quickly judged and predicted in the real-time operation process of the water-turbine generator set.

The invention relates to the technical field of automobile lightweight design and intelligent databases, and provides an automobile lightweight intelligent decision database system and method based on artificial intelligence. The invention aims to solve the technical problems of low design efficiency, low performance prediction and parameter optimization precision and difficulty in balancing multi-target requirements caused by data heterogeneous dispersion, difficulty in intercommunication and information splitting in the existing automobile lightweight design. According to the scheme, an intelligent decision database system is constructed through five core modules: a multi-source heterogeneous data acquisition module realizes data standardization processing and secure storage; the lightweight knowledge graph construction module establishes an association system and dynamically updates a standard; the intelligent algorithm module integrates a deep residual network, Bayesian optimization and a Transform architecture, and completes performance prediction, parameter optimization and scheme generation; the multi-objective optimization decision module screens an optimal scheme through multi-dimensional evaluation and constraint processing; the visual interaction module provides a visual interaction function and supports multi-mode deployment.

A system and method that generates and stores a multivariate time series of the user's conditions and activities, based on the interpretation of the user's natural language input through trained AI including large language models. This time series serves as a comprehensive record of the user's well-being and provides the foundation for the system's ability to identify the factors that most significantly impact the user's desired outcome. By tracking and analyzing the relationships between the various conditions and activities, the system can determine the key drivers of the outcome of interest to the user and provide insights into how to improve and maintain overall well-being.

The invention relates to a unit combination scheduling method and device, computer equipment, a medium and a product. The method comprises the following steps: acquiring topological connection information and system operation information corresponding to a target unit combination; the topological connection information is used for indicating a topological connection relationship in a target power network where the target unit combination is located; the system operation information is used for indicating system operation characteristics of the target power network; based on the topological connection information and the system operation information, performing spatial decoupling on the target power network to obtain a plurality of target decoupling sub-regions corresponding to the target power network; and determining a plurality of sub-scheduling results corresponding to the unit combinations in the plurality of target decoupling sub-regions, and determining a target scheduling result of the target unit combination in combination with the plurality of sub-scheduling results. By adopting the method, the scheduling processing efficiency can be improved.

This invention discloses a method for constructing a relationship-driven cybersecurity knowledge graph. This method, driven by relational semantics, sequentially executes the following steps: multi-source threat intelligence collection and preprocessing, relational semantic modeling, relationship-priority-driven knowledge extraction, entity alignment based on relational context, and knowledge graph construction. By constructing a relational system with entity type constraints and relational reasoning rules, it achieves complete attack chain modeling and implicit relational derivation; it employs a reverse extraction mechanism of relational identification → entity location to reduce error propagation; it introduces relational context to achieve accurate cross-source entity alignment; and finally, it establishes an indexed knowledge graph based on a graph database, supporting efficient querying and attack attribution.

Embodiments of the present disclosure relate to comparing network topologies, states, and configurations at different time instances. A network management system for orchestrating a network is provided. During operation, the system generates a graph representing the network. The respective vertices correspond to entities in the network, and the respective edges indicate a relationship between pairs of vertices. The system may determine first and second timestamps for respective edges. The first timestamp indicates a time instance when the relationship indicated by the edge is established. The second timestamp indicates a time instance when the relationship is terminated. A time range between the first and second timestamps indicates a valid time period of the edge. The system then receives instructions from an interface of the system to compare the topology, status, and configuration of the network. The system determines the topology, state, and configuration of the network at the target time instance indicated by the instruction by traversing the valid edges of the graph.

An intelligent cloud-based information distribution system for online payment is provided and includes: an information distribution module, a managing and controlling module, and a system management module. The information distribution module is configured to distributing and transmitting files. The managing and controlling module includes a central control sub-module and an indexing service sub-module. The central control sub-module is configured to control each component to operate and to display global information. The indexing service sub-module is configured to record correspondence between distribution nodes and resources. The system management module is configured to maintain normal operation of the system. Each region is configured with a bridgehead node to associate all distribution nodes within the region and to achieve resource interaction. The number of distribution nodes to be invoked is reduced. Information services are provided for operators optimally, a utilization rate of the bandwidth for data transmission is improved.

The invention provides a manufacturing decision system-oriented modular functional architecture and a collaborative mechanism thereof, and the structural organization and reusable collaboration of a manufacturing decision process are realized by constructing a multi-layer capability system, a functional module system and a standardized behavior interface. The architecture comprises a data interface module, a decision-making target module, a disturbance response module and a fusion decision-making module, and defines a data flow, a control flow and a trigger relationship among the modules. The system completes target expression, constraint analysis, disturbance identification and collaborative decision generation through standardized access of multi-source data such as orders, productivity, materials and processes, and forms an interpretable and executable decision link. According to the invention, modularization, interpretability and intelligent cooperation of the manufacturing decision-making system are realized, and multi-scene and multi-industry manufacturing decision-making application is supported.

The invention discloses a project component list management method and device, equipment and a storage medium, and relates to the technical field of data management. The method comprises the steps that a product specification pool of all parts is constructed in advance according to part specification values of the parts, when a project part list needs to be generated, a project party directly selects a needed target product specification from the product specification pool, project specification demand information is generated, and then the project part list is generated according to the corresponding relation between the pre-constructed product specification and an actual part. Therefore, the system can directly determine the target component conforming to the target product specification, so that the item component list is automatically generated based on the information of the target component, material confirmation of the component does not need to be manually executed, and the accuracy and efficiency of item component list management are improved.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

Profile Information is aggregated from online websites and services to correlate discovered identities to one another via computational analysis of Intra-Personal Relationships, Inter-Personal Relationships, and Profile Data. The information, relationships and content of identities which have been determined to share Intra-Personal relationships are aggregated into Meta-Profiles; the Meta-Profiles are used in place of component Intra-Related identities to optimize computer functions such as social graph operations, content customization, and audience operations (including: analysis, metrics, profiling and targeting). The system processes identities and relationships belonging to individuals registered with the system and those belonging to unregistered users. The system is provided with data from users, via third-party systems, or through automated discovery. These innovations solve problems of online identity disambiguation specific to internet computing, and improve computer operations by optimizing memory footprint and operation calls to minimize the number of nodes and edges traversed.

In the transfer of assets across multiple organizations, it has been difficult to consistently track the chain of proof (chain of proof) and the history of ownership and management (chain of custody) across mergers and separations, and to conduct reliable appraisals and certifications by comprehensively evaluating the authenticity of assets. [Solution] The system acquires data on assets, events, states, assertions, evidence, subjects, and locations from a recording infrastructure, and generates a Chain of Provenance and a Chain of Custody based on this data. Based on the continuity, consistency, and interrelationship of these two chains, it generates appraisal and certification result information including the authenticity level classification (high, medium, low) of the assets.

To construct a mechanism which makes a system recommend a sending destination considered to be appropriate on task regulation, and a mechanism which makes a system recommend a negotiation destination considered to be appropriate on request of a purchase applicant.SOLUTION: A mechanism makes a system recommend a sending destination considered to be appropriate on task regulation by performing machine learning with conversion of a paradigm which is a case of a transaction matter becoming a standard into data, expressing a relationship between a feature of the transaction matter and the sending destination through abstracted numerical processing, and automatically evaluating and calculating the relationship with respect to a new matter. Moreover, the mechanism makes the system recommend a negotiation destination considered to be appropriate on request of a purchase applicant by performing machine learning with conversion of a paradigm in which an appropriate negotiation destination is selected in performing negotiation of the transaction matter into data, expressing a relationship between a feature of dealt products and the request of the purchase applicant and the negotiation destination through abstracted numerical processing, and automatically evaluating and calculating the relationship with respect to the products and new request of the purchase applicant.SELECTED DRAWING: Figure 1

Computer workloads can be assigned to nodes of a distributed computing environment based on energy consumption modes employed by the nodes. In one example, a system can determine a first tag assigned to a workload. The first tag can indicate a compatibility of the workload with one or more energy consumption modes employable by one or more nodes of a distributed computing environment. The system can also determine a second tag assigned to a node of the distributed computing environment. The second tag can indicate an energy consumption mode employed by the node. The system can then determine a correspondence between the first tag and the second tag indicating that the workload is compatible with the energy consumption mode employed by the node. Based on determining the correspondence between the first tag and the second tag, the system can assign the workload to the node.

A system receives a plurality of knowledge objects (KOs). The system receives repository structure definition information, the repository structure definition information specifying one or more repository structure definitions that define respective structures for the one or more data repositories. The system groups the plurality of KOs based on the name, type, and tag attributes of the KOs, and storage paths of the underlying unit of structured, semi-structured, and unstructured data at the one or more data repositories corresponding to the KOs to generate a number of groups of KOs. The system determines a number of compliance categories (CCs), each CC corresponding to a standard on data privacy or data protection compliance mandates. The system determines matching relationships between the CC to each group of KOs. The system generates a first mapping structure that maps relationships between each group of KOs to the CC based on the matching relationship.

The application discloses a kind of construction engineering quality hidden danger positioning screening method and system, the method is by obtaining the structure, material, environment and so on parameter of construction engineering quality hidden danger and classification, data are fused using multidimensional heterogeneous building hidden danger data screening model;Based on the initial search population established by optimized bat positioning algorithm, set fitness function to evaluate individual, update population according to iteration rule until meet termination condition and output hidden danger positioning result.Introduce data fusion, fitness calculation and multiple models in the process, fully consider parameter weight, correlation and coupling relationship.System includes six units such as parameter acquisition, data fusion processing, and each unit cooperates with each other.The application effectively integrates multi-source data, accurately locates construction engineering quality hidden danger, overcomes the defects of insufficient data utilization and inaccurate positioning of traditional technology, realizes efficient and accurate screening of construction engineering quality hidden danger.

Non-mechanistic, differential-equation-free approaches are provided for predicting a particular structure-activity response of a system using a reduced scale and a reduced number of variables. These approaches provide one-to-one relationships between model parameters and model output to obtain a specificity of results between model parameters and model output to get a unique input-response relationship. The systems, methods, and devices (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods.

An asset relationship system and method store assets and related data for the asset so that it can be queried. In one embodiment, the asset and the related data may be stored in a graph database in which each node in the graph database stores a piece of asset data and each edge in the graph database encodes a relationship between the two nodes / piece of asset data to which it is connected.

The invention provides a cooperative adversarial iterative learning controller design method based on a communication protocol, and belongs to the technical field of communication information processing. The method comprises the following steps: S1, constructing a cooperation-confrontation system with n nodes, wherein the dynamic characteristics of each node are described by two time scales; s2, designing a communication network based on an RAP protocol so as to avoid data conflicts caused by simultaneous data transmission between nodes and adjacent nodes; and S3, designing an iterative learning controller with a cooperation-adversarial relationship, calculating an error according to the ideal trajectory to realize tracking, designing parameters of the iterative learning controller, and realizing a convergence condition. According to the method, the dynamic tracking problem of a cooperative-adversarial relation system can be effectively solved, data conflicts in signal transmission are reduced, and the target trajectory is tracked more accurately.

Embodiments rank entity importance in monitored networks by analyzing network traffic to collect metrics representing relationships between entities. Network monitoring engines construct network graphs where nodes represent entities and edges represent relationships based on collected metrics. The system employs graph clustering algorithms to partition entities into logical clusters based on interaction patterns. Within each cluster, entities may be ranked by topological importance using metrics to identify primary entities that represent their clusters. Monitoring resources may be allocated preferentially to primary entities from each cluster. The topology-based ranking approach distributes monitoring resources throughout the network.

Techniques for initiating system actions based on text content are disclosed. A system applies a semantic analysis model at run-time to human-understandable text to identify actionable content within the human-understandable text. The system analyzes metadata associated with the text to identify a mapping between one or more data objects associated and the semantic content in the text. The system identifies one or more contact lists associated, respectively, with the one or more data objects. Using a database schema, the system analyzes the contact lists to determine whether to modify the contact lists.

The application provides a display device and a switching method of a WiFi module. The method identifies a target use scenario according to an operation type corresponding to a user interaction action, and evaluates scores of each WiFi module in the scenario in real time based on performance parameters and weight coefficients recorded in a module state library. When the score of a current module is lower than a threshold value, the system automatically switches to a standby module with a higher score, and supports simultaneous operation of multiple modules. By constructing a module state library and a dynamic evaluation mechanism, the problem of poor flexibility of the WiFi module is solved. In addition, a user can customize the allocation relationship between the module and the scenario, and the system dynamically allocates resources according to priorities to prevent stuttering caused by scenario competition. The scheme considers static hardware parameters and dynamic operation indexes comprehensively, so that the system can flexibly adapt to different scenario requirements, significantly improves the use flexibility and resource utilization rate of the WiFi module, and further solves the problem of poor flexibility of the WiFi module.