5141 results about "Modularity" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

The invention discloses an intelligent analysis and remote control algorithm based on digital twinning. The intelligent analysis and remote control algorithm comprises a digital twinning model building and dynamic updating module, an intelligent analysis algorithm module based on digital twinning, a self-adaptive remote control algorithm module and an algorithm process and implementation module. Through multi-dimensional modeling and data fusion, the precision of the digital twin model is improved, and the system state can be predicted more accurately. In combination with deep learning and a multi-objective optimization algorithm, intelligence and optimization of a control strategy are realized, and the operation efficiency and safety of the system are improved. Based on model predictive control and a distributed architecture, real-time and accurate remote control of a physical system is realized, and the dynamic adaptability of the system is enhanced. The algorithm framework has good generalization ability, can be suitable for different types of complex systems, and supports to adapt to new application scenarios through modular extension.

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

The invention discloses an intelligent reclosing circuit breaker modular protection system, which relates to the technical field of circuit breaker intelligent management, and comprises an acquisition module, a judgment module, a reclosing control module and an optimization module. Voltage holes caused by fault tripping and non-fault disturbance are effectively distinguished, and false triggering of reclosing operation is avoided; meanwhile, the optimization module introduces a machine learning algorithm to carry out continuous filing and strategy updating on misjudgment events, and self-adaptive discrimination logic optimization is realized, so that reclosing judgment accuracy and power supply stability of the power grid system under a complex load condition are remarkably improved, and system impact and equipment damage are effectively prevented.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

The invention discloses an energy management multi-agent collaborative optimization method and system based on a large model agent architecture. The system comprises a multi-agent architecture module, a model processing module, a data acquisition and management module, a system integration and interaction module and a collaborative optimization control module. The model processing module comprises a large model and a plurality of small models and is used for realizing task processing and analysis; the data acquisition and management module acquires and processes multi-source data; the system integration and interaction module realizes multi-platform integration and visual interaction; and the collaborative optimization control module ensures collaborative work and system optimization of the intelligent agents. The system has the advantages of autonomy and decentralized control, isomerism and modularization, intelligent collaboration and the like, instrument faults can be accurately diagnosed, energy management is optimized, production is intelligently controlled, enterprise energy utilization efficiency, instrument operation stability and the overall operation management level are improved, enterprises are assisted to achieve green and low-carbon transformation, and the system is suitable for popularization and application. The method is suitable for energy management and energy equipment maintenance scenes of various industrial production enterprises.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention relates to the technical field of program control systems, in particular to an intelligent daily chemical production line online control system. The system comprises an equipment digital twin modeling module, a process parameter optimization module, a modular production unit adjustment module, an automatic scheduling and Internet of Things transmission module, an equipment monitoring and data analysis module and a fault prediction and remote operation and maintenance module. Acquiring equipment real-time operation state data of the intelligent daily chemical production line, and performing digital twinborn modeling based on the equipment real-time operation state data to obtain an equipment digital twinborn model; and performing simulation optimization calculation on each process parameter based on the digital twin model of the equipment to obtain stable-state process parameter set data of the equipment. Through technological parameter optimization, modular production unit adjustment, equipment monitoring and fault prediction, efficient dynamic optimization of the production process is realized, the flexibility, stability and fault prevention capability of the production line are improved, the production cost is reduced, and the production efficiency is ensured.

The invention relates to the technical field of modular assembly production lines, in particular to a modular flexible digital twin layout optimization method and system. Comprising the following steps: constructing multi-dimensional constraint modeling, constructing a multi-objective optimization model containing route length, equipment cost and human resource quantity, and generating an initial layout scheme based on three-dimensional geometric attributes and logistics paths of production line equipment; dynamic weight distribution is carried out, according to priority parameters input by a user, a route optimization weight # imgabs0 #, a cost optimization weight # imgabs1 # and a human resource weight # imgabs2 # are adjusted in real time, and a dynamic weight combination matrix is generated; digital twinborn simulation is carried out, the initial layout scheme is mapped to a digital twinborn platform, and the actual equipment utilization rate, the material carrying efficiency and the worker operation path are calculated through logistics path simulation; according to the scheme, dynamic balance multiple targets can be provided based on a simulation result, simulation and optimization real-time linkage can be realized, and a layout optimization technology of a multi-dimensional verifiable scheme is provided.

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

A tree-of-thought (ToT) system is presented that improves problem-solving capabilities of machine learning models, such as auto-regressive large language models (LLMs). The TOT system can solve complex reasoning tasks through trial and error. In this process, the system explores the solution space through a tree-like thought process, allowing for backtracking when necessary. The system augments an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. These modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem-solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other solution paths. This new system can be applied to a blockchain and / or a distributed computing system.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention provides an intelligent meter abnormal reading filtering system based on an elastic time window. Self-adaptive recognition and filtering of abnormal reading are achieved through multi-stage modular processing. The system firstly forms an original reading sequence; calculating a fluctuation index of each sampling moment in the original reading sequence based on a preset statistical fluctuation algorithm, and generating a fluctuation sequence; generating a time window parameter set through an elastic adjustment function according to the fluctuation degree sequence; extracting a context subsequence according to the time window parameter set, extracting a time sequence feature and a frequency domain feature in the subsequence, and fusing to form a context fusion feature; calculating an abnormal confidence coefficient sequence based on the abnormal scoring function, performing abnormal filtering on the original reading sequence according to a preset confidence coefficient threshold value, and outputting a filtered reading sequence; according to the system, through fusion of the elastic time window and the multi-dimensional features, the abnormal reading recognition accuracy is effectively improved, and the system is suitable for real-time data quality control of the intelligent meter in a complex power application scene.

The invention discloses a financial branch mechanism-oriented modular knowledge graph and retrieval enhanced large model fusion interaction method and system. The method comprises the steps that a query vector of a target object is obtained, and the query vector comprises a problem vector used for expressing the query requirement of the target object and a post vector used for reflecting post information of the target object in a financial branch; determining a system module which can be accessed by the target object according to the post vector; determining a sub-graph region corresponding to the problem vector in the knowledge graph; and retrieving a document paragraph corresponding to the sub-graph region in a sub-index database corresponding to the system module, and inputting the document paragraph into the large model to generate a target answer corresponding to the query vector. According to the method and the device, the technical problem that a question-answering system adopted in the related technology is lack of identification of user post identities and cannot provide matched answers according to different post responsibilities and permissions of financial branches is solved.

The invention relates to the technical field of distributed computing and cloud computing, in particular to a multi-service and multi-tenant-oriented micro-service method and system, and the method comprises the steps: constructing a dynamic modular service framework based on a pluggable service component, a cross-tenant service module based on resource sharing, and a multi-service and multi-tenant service module based on resource sharing; modeling the calling relationship between the micro-services and calculating each resource demand weight coefficient of the micro-services; and scheduling the resource-shared cross-tenant service module by using the dynamic resource allocator. According to the method, the dynamic modular service framework based on the pluggable service component is constructed, so that accurate loading and unloading of each service module of the micro-service can be completed within hundreds of milliseconds, and the agility of the system is remarkably improved. According to the method, calling relation modeling is carried out on the cross-tenant service module, the resource demand weight coefficient is calculated, basic data is provided for resource scheduling, load changes are considered, optimal configuration of resources is achieved, and the overall performance and the response speed of the system are improved.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention belongs to the technical field of target classification, and particularly relates to a three-dimensional point cloud data analysis method and system based on artificial intelligence. Comprising the steps of data acquisition, data preprocessing, model construction, model training, model prediction and the like. The integrity and precision of point cloud data are effectively improved by collecting original point cloud and performing intelligent denoising and complementation operation; constructing a depth model by adopting an input layer, a region proposal layer and a classification and regression layer, and carrying out accurate classification and three-dimensional bounding box prediction on a target; wherein the region proposal layer is combined with a seed point feature extraction and voting mechanism to generate a candidate region, a classification branch outputs a category probability, and a regression branch predicts bounding box parameters. The system adopts a modular design, has the advantages of strong noise suppression, high complementation precision, high target detection accuracy, multi-scene applicability and the like, and is particularly suitable for efficient automatic identification of a complex three-dimensional structure.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

The invention relates to the technical field of port vertical large model systems, in particular to a port vertical large model system based on a general large language model and a construction method. Comprising the following modules: a data acquisition module, a pre-training corpus construction module, an incremental pre-training module, a fine adjustment module, a port adaptation and optimization module, a knowledge base management module, a reasoning and man-machine interaction module and a system monitoring and optimization module. The port vertical large model has the beneficial effects that the port vertical large model can dynamically adapt to emergencies in port operation through incremental pre-training, a fine tuning technology and a real-time knowledge base updating mechanism, and multi-source data is integrated and analyzed to form a whole-process business simulation and optimization scheme. The modularized knowledge management mechanism enables the port management system to have a real-time decision support function, and improves the overall efficiency and safety of the port in the aspects of equipment maintenance, cargo scheduling, environment monitoring and the like.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention discloses a data analysis method and device based on multi-agent cooperation and a storage medium. The method comprises the following steps: receiving a natural language request by a workflow arrangement agent, and generating an execution plan comprising a plurality of subtasks and corresponding task processing units by consulting a data exploration agent and a data engine understanding agent; in the execution stage, the professional agent matched with the subtask converts the subtask into an executable instruction, and the data engine dispatch agent calls and coordinates the corresponding task processing unit to execute the instruction. According to the method, through a layered collaborative architecture, the separation of planning, translation and execution is realized, and the automation level, modularization and expandability of data analysis are remarkably improved.

A hoisting posture control system and method, applicable to the technical field of modular construction. According to the hoisting posture control system, controllers corresponding to functional modules are called on the basis of data detected by sensors, to drive posture control of a hoisted object, thereby assisting a tower crane operator in making work decisions, so that the operator does not need to take risks to get close to a module to be installed hoisted in the air, ensuring the safety of workers and the accuracy and reliability of module installation. Thus, the labor cost in modular construction work can be reduced, and the assembly quality and efficiency of modules are improved.

The present invention discloses a modular AI-based system for knowledge instantiation and evidence synthesis, unifying five modules: Knowledge Instantiation, ingesting anchor knowledge via retrieval- augmented generation and neuromorphic prompting; Case-Based Agent Generation, orchestrating domain constraints, performance criteria, and sub-task decomposition; Secondary Epistemogenesis, spawning sub-agents with inherited heuristics; Workflow Orchestration and Evidence Synthesis, merging outputs, tracking metadata, and storing final artifacts; and Reinforcement Learning from Human Refinement, capturing feedback and edits. The method for output generation initiates by defining knowledge requirements, indexing public and proprietary knowledge, validating output structure, drafting content, and refining outputs via sub-agent spawning and human oversight. The system ensures domain alignment, concurrency control, and persistent versioning. By combining specialized knowledge sources, multi-agent concurrency, and iterative human feedback loops, it dynamically adapts to evolving knowledge requirements. This invention emphasizes reliability, traceability, and AI-driven outputs in regulated industries, delivering trustworthy outcomes anchored in expert knowledge.

The invention relates to a Kubernetes cluster automatic deployment system based on modularization, and the system comprises a modularized cluster mirror image system which is used for providing a mirror image containing all assemblies of cluster operation and the optimal practice configuration of each assembly, and is used for flexibly customizing the assembly combination of a cluster according to different demands, and rapidly generating a customized cluster mirror image, the standardization of the delivery of the Kubernetes cluster is realized; the automatic deployment engine is used for defining a topological structure and configuration parameters of the cluster by adopting a declarative API (Application Program Interface), completing full-life-cycle automatic management through an architecture mode of a controller, and providing a monitoring alarm function which is ready to use after box opening; the application management system is used for providing application store based on Web, templated application configuration management and application real-time monitoring management; and the multi-tenant management system is used for providing resource isolation management, tenant authority management and security isolation management. According to the invention, the complexity of cluster deployment and management is greatly reduced, and the application efficiency and reliability of the cloud native technology are improved.

The invention provides a robot brain body-equipped intelligent system based on a large model and a task execution method of the robot brain body-equipped intelligent system. According to the system, a modular distributed architecture is realized, the system is endowed with high expandability and flexibility, and multi-platform deployment is supported. The task planning based on the large model significantly improves the ability of the system to understand and execute complex instructions. Unified sensor data management realizes integration and efficient processing of multi-source data, and enhances the environment perception capability of the system. Meanwhile, a friendly real-time monitoring management interface simplifies monitoring and configuration management of the system. In the aspect of performance, completeness and consistency of task execution are ensured through close cooperation among the modules; the optimized algorithm and model improve the efficiency of task planning and action execution and reduce the system response delay.