434 results about "Modular architecture" patented technology

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

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.

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.

System and techniques to facilitate the translation of a sign language into another language are described herein. A modular architecture may be used in which the output of different classifiers may be used to produce intermediate representations, or final translations, of the sign language. These classifiers may be trained on different types of signs to enhance accuracy while reduce training time and complexity.

The invention discloses a dialogue type GIS visual scene generation method and system based on an LLM large model. The method comprises the steps that S1, an AI intelligent dialogue processing module receives and analyzes a natural language instruction input by a user; s2, the AI capability service module carries out standardized parameter packaging on the key information through an LLM large model; s3, analyzing the input parameters by the GIS capability service module, judging whether the scene code is empty or not, if yes, executing a step S4, and if not, executing a step S5; s4, the GIS capability service module judges whether latitude and longitude coordinates are contained in the place or not; s5, the GIS capability service module implements corresponding operation according to the operation type; s6, the GIS capability service module stores the created or updated scene; and S7, the AI intelligent dialogue processing module answers the question and presents the map thumbnail of the created or updated scene. According to the invention, full-link intelligentization of natural language-geographic analysis-dynamic visualization is realized through a modular architecture driven by LLM.

The invention relates to the field of flood forecasting, and discloses a flood forecasting method and system, a storage medium and computing equipment, and the method comprises the steps: topological modeling: taking a river catchment area surrounded by a water diversion line and a catchment area outlet hydrometric station as nodes of a graph, constructing a connection relation of edges according to the actual flow direction of a river and the connection relation of branches, and carrying out the topological modeling; therefore, topological graph structure data adaptive to a hydrological mechanism is constructed; converting month number characteristics, generating a dynamic periodic timestamp vector, splicing the dynamic periodic timestamp vector with topological graph structure data, and inputting an attention neural network for training; the attention neural network architecture comprises two independent feature channels which are used for processing historical data and forecast data respectively, the network supports a variable modular architecture, and a flood forecast result can be output according to needs after a model is trained. According to the method, the flood prediction precision and interpretability are improved, and a high-precision and interpretable space-time joint flood prediction scheme is realized.

The present invention discloses a computer motherboard automated integrated test and intelligent diagnosis system and method, which relates to the field of computer motherboard test diagnosis technology, including a test platform building module, multi-interface adaptation and automatic motherboard identification, including a high-precision signal source and acquisition card, a modular architecture and high-speed bus interconnection; a test project execution module, which carries out multiple functional tests and simulates multiple scenarios to adjust the load; a data acquisition and storage module, which synchronously acquires multiple types of data through multiple channels, and has distributed storage and redundant backup; an intelligent diagnosis and analysis module, big data and machine learning algorithm diagnosis, a report generation and output module, a template engine generates reports and optimizes layout; a system self-check and calibration module, which starts each self-check module. The present invention realizes efficient and accurate testing of computer motherboards, reliable data acquisition and storage, modular design facilitates upgrading, and convenient remote functions, improves test and diagnosis efficiency and quality, helps motherboard production, maintenance and research and development, and enhances industry competitiveness.

The invention discloses a Chinese named entity recognition system, method and equipment based on multi-scale features and a medium, and the method comprises the steps: decomposing an original text into a character sequence through a preprocessing module, and preprocessing characters, including removing punctuation marks and uniformly converting the characters into lower letters; a RoBERTa-WWM sub-module of a feature extraction module is responsible for converting an input text into a high-dimensional feature vector, a CNN sub-module effectively extracts local features through a sliding window mechanism, and a BiLSTM sub-module finally utilizes the advantage of bidirectional processing to obtain complete context information; decoding the hidden state representation by using a dynamic conditional random field (CRF) through a sequence tagging module to determine an optimal tag sequence; according to the method, pre-training, multi-scale feature fusion and dynamic decoding technologies are integrated, and efficient and accurate Chinese entity recognition is realized; through a unique preprocessing rule, a modular architecture design and optimized hyper-parameter configuration, the performance and robustness of the system in a complex scene are ensured.

The invention discloses an automatic integrated system from an asynchronous pipeline to an asynchronous circuit, which adopts a modular architecture, takes a high-level structure description file in a JSON / XML format as input, and gradually maps to generate a gate-level netlist and a standard delay format file so as to be in butt joint with a commercial back-end design process. According to the method, asynchronous design based on an asynchronous structure can be automatically mapped into an asynchronous circuit, the automatic mapping process can seamlessly process multi-level design representation, the mapping result is stable and cooperatively operated, asynchronous circuit time sequence detection and delay matching are automatically completed, the design efficiency is greatly improved, and the design cost is reduced. An automatic delay matching mechanism enables the circuit to have a good data ready detection and handshake mechanism, and the characteristics of low power consumption, high throughput and no global clock drift sensitivity are realized; in addition, automation of asynchronous design key links such as structure abstraction, time delay modeling and testability insertion is achieved while design readability, hierarchical consistency and time sequence controllability are guaranteed.

The invention discloses a hyperspectral data fusion analysis system, belongs to the technical field of remote sensing data processing and artificial intelligence, and provides a multi-level data fusion technology by integrating data management, preprocessing, fusion, modeling and visual function modules through a modular architecture aiming at the problems of an existing system. Machine learning algorithms such as a support vector machine and a random forest are integrated to realize dynamic parameter optimization and model evaluation, spectrum curve dynamic display, classification map rendering and customized analysis report generation functions are provided through an interactive visualization module, and system security and expansibility are enhanced based on hierarchical architecture and database standard design. And multi-source equipment access, user permission grading and data encryption storage are supported. The hyperspectral data processing efficiency, the analysis precision and the system stability are remarkably improved, and the method can be widely applied to the fields of environment monitoring, agricultural remote sensing, target recognition and the like.

An AI-and IoT-driven real-time construction management system continuously acquires and analyzes high-frequency sensor data, including temperature, GPS, and RFID inputs. Utilizing advanced predictive machine learning and reinforcement learning algorithms, the system forecasts schedule deviations and resource conflicts with an accuracy exceeding 90%. Upon identifying deviations, it autonomously triggers corrective actions—such as reallocating resources or adjusting task sequences—typically within five seconds. This integrated, closed-loop management approach seamlessly interfaces with external project management tools, achieving approximately 15-20% improved schedule adherence and notable cost reductions based on preliminary data. The system's modular architecture supports diverse sensor technologies and AI frameworks, ensuring adaptability and sustained performance in large-scale, dynamic construction environments.

The invention relates to the technical field of fire detection, prevention and control, and provides an intelligent fire prevention and control system based on multi-information fusion and Yolo detection, and an intelligent detection device adopts a modular architecture design and is composed of a basic device and an extended function module; the basic device integrates an environmental data acquisition module and a wireless communication module, and acquires environmental data such as temperature, smoke concentration and gas content in real time. The extended function module comprises a fire extinguishing bomb launching module, an alarm module, a monitoring camera module and a 4G communication module; the fire detection module is deployed at a server side, receives data and images uploaded by the intelligent detection device, performs fire feature recognition through an improved Yolov5 algorithm, and constructs a cooperative judgment mechanism of a Yolo algorithm recognition result and multi-source environment data; the matched client receives and displays the environment data and the fire detection result, sends a remote control instruction, and has the functions of real-time monitoring, intelligent early warning and remote control.

The invention discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

According to an embodiment of the present invention, a system may include a quantum memory module, a magic state preparation module, and a quantum computation module. The quantum computation module is coupled to either the quantum memory module or the quantum magic state preparation module using an 1-coupler. This may enable optimization of the hardware to more efficiently perform quantum computations. According to another embodiment of the present invention, a modular system may include a plurality of quantum systems, where the quantum systems are connected using 1-couplers according to a tree diagram. This may enable optimization of the hardware to more efficiently perform quantum computations.

Modular Rydberg architectures for fault tolerant quantum computing are provided. A first array and a second array of neutral atoms are provided. Each neutral atom has a first state and an excited Rydberg state. Each neutral atom is arranged to impose a Rydberg blockade on at least its nearest neighbors in its array when in the excited Rydberg state, thereby implementing a plurality of physical qubits. Each array comprises data qubits, and syndrome qubits. The syndrome qubits are configured to implement a quantum error correcting code with respect to the data qubits. Each array includes a subarray of communication qubits having a lower dimensionality than the array. Each communication qubit of the first subarray forms a Bell pair with one communication qubit of the second subarray. The first and second arrays of neutral atoms are configured to interact with each other only via the communication qubits.

The invention relates to an unmanned end-to-end sensing method and system for an unstructured road scene, and belongs to the technical field of automatic driving. A point cloud supervision view angle conversion method is adopted, point cloud densification is realized by constructing a local subgraph, and multi-view angle depth estimation is optimized in combination with an optical flow consistency constraint; according to the method, long and short focus image features are fused through a cross attention mechanism, camera internal reference information is embedded, image BEV features are enhanced, and the long-distance perception capability is improved; according to the method, dynamic interference such as flying dust is effectively recognized and filtered through a space-time attention mechanism, a unified deep learning framework is adopted, the problems of information loss and error accumulation in a traditional modular framework are avoided, and unstructured scene perception is effectively improved. The problems of long-distance sensing, blocking of dust raising and the like, inaccurate space conversion and the like in an unstructured road scene are solved.

The invention provides a clinical test data checking method and system based on a modular architecture, and the method comprises the steps: obtaining a target clinical test scheme document, and mapping the target clinical test scheme document into a term mapping table; on the basis of the protocol structure atlas, combined with test actual acquisition data, generating an execution acquisition atlas and path confidence; comparing the protocol atlas with the execution acquisition atlas, and identifying the structure deviation of the acquisition path through structure alignment check to generate a structure deviation matrix; obtaining an index basic fluctuation interval and a medical standard, and generating a consistency behavior score in combination with the generated structure deviation matrix to quantify a behavior trend anomaly score of the subject; and in combination with the protocol structure atlas, executing an acquisition atlas, a structure deviation matrix and a consistency behavior score, calculating a causal deviation score of each subject, and marking missing acquisition, wrong acquisition and overtime acquisition events in clinical test data according to the causal deviation scores.

The invention provides a big data integration and distribution unit component of a hierarchical architecture and a use method, relates to the technical field of data integration and processing, and solves the problems of real-time integration, unified processing and safe distribution of multi-source heterogeneous data. The device adopts a five-layer modular architecture; a data access layer supports multi-source data access and is buffered through Kafka; the cleaning conversion layer performs data cleaning and standardization by using a FlinkSQL, an ETL engine and a rule engine; the intelligent distribution layer judges a data distribution path in combination with a rule engine and a machine learning model; the storage calculation layer adopts a cold and hot separation storage strategy and supports parallel operation of Spark batch processing and Flink stream processing; and the service and security layer provides an interface through an API gateway, and guarantees data security in combination with AES-256 encryption and RBAC access control. Besides, dynamic resource scheduling is realized through Kubernetes, cross-domain model cooperative training is supported by utilizing a federated learning technology, the intelligent shunting decision-making capability is improved, various industrial protocols such as MQTT and Modbus are compatible, and VXLAN tunnel transmission is supported.

The invention belongs to the field of intelligent robots, and discloses an intelligent mobile grabbing robot control method and system based on a modular architecture, and the method comprises the steps: collecting the image information of a target object in real time, processing the image data in real time, recognizing the target object, outputting the position and category of the target object, and obtaining the depth information of the detected target object. The user side sends a natural language text, and the cloud server receives text information, performs natural language interaction by using a large language model to generate language reply information, and performs action decomposition to generate a decomposed action instruction; the information of the language reply is returned to the user side through the cloud server; sending the decomposed action instruction to an edge computing device, and combining the decomposed action instruction with the depth information of the target object to obtain a final action instruction sequence; and the IPC controls the mobile grabbing robot to complete movement and grabbing related actions. The robot interaction naturalness, decision-making intelligence and system adaptability are improved, and the flexible intelligent manufacturing requirement is met.

The invention relates to an overhead transmission line wire acceptance robot system and a control method. The overhead transmission line wire acceptance robot system has the functions of multi-dimensional adjustment, dynamic clamping, profiling obstacle crossing and visual recognition. Wire specification adaptation is achieved through self-adaptive spacing adjustment, obstacle crossing is completed based on cooperation of elastic clamping and turnover type obstacle crossing, and wire surface defect detection is implemented by adopting a multi-angle visual acquisition technology. According to the system, the motion stability is enhanced through composite driving control, the acceptance efficiency is improved by combining an intelligent control algorithm, the modular architecture design supports multi-split conductor scene application, and the characteristics of high-precision detection and full-process automatic operation are achieved.

The invention provides a novel vacuum evaporator which comprises a vacuumizing system, a carrier rotating system, a light-operated integrated system, a crystal oscillation system, a heating system, a water cooling system and an evaporation system, and the light-operated integrated system and the crystal oscillation system are arranged in a hollow cavity of a first rotating mechanism, so that cooperative work of rotary coating and real-time detection is realized; a detection light path is shortened, and the signal-to-noise ratio is improved; rotation axis detection ensures data representativeness; the top radiation heating and the side wall conduction heating have a synergistic effect, so that the surface temperature difference of the substrate is smaller than + / -1.5 DEG C, the cooling efficiency of the evaporation system is improved by 40% through the water cooling system, the equipment is highly integrated through the modular architecture design, and the space is saved by 30% through the coaxial design of the carrier rotating system and the detection system; a rotating system, a heating system and a detecting system are cooperatively controlled, a dynamic compensation algorithm automatically adjusts the evaporation rate and the carrier rotating speed by comparing light control data with crystal oscillator data in real time, the film thickness control precision is + / -2 nm, and the refractive index deviation is smaller than 0.5%.

The invention discloses an edge control device for a common air conditioner in an electric power machine room under the background of low carbon and energy conservation, and belongs to the technical field of air conditioner control, which comprises the following steps of: A1, designing a device architecture, constructing an integral frame of the device, determining functions and mutual connection modes of components, and ensuring that the device can work cooperatively; a2, an intelligent control strategy is adopted, intelligent control, including real-time regulation and control, intelligent start and stop, parameter adjustment and fault diagnosis, of the device is achieved, and the stability and the energy-saving effect of the machine room temperature control system are improved; and A3, temperature and humidity sensing and data analysis: sensing the temperature and humidity data of the machine room, performing deep analysis, providing data support for a control strategy, and realizing accurate temperature control. Through modular architecture design, all the assemblies achieve efficient cooperation, the real-time performance of data transmission and control instructions is ensured, the system failure rate is reduced, an intelligent control strategy based on edge calculation is combined with multi-point temperature and humidity monitoring and AI data analysis, dynamic adjustment of air conditioner operation parameters is achieved, and energy waste is avoided.

A system and method are provided for dynamically generating interactive multimedia storytelling experiences using integrated artificial intelligence models. The system comprises a generative language model for producing narrative content in response to user input, a generative video synthesis module for visualizing story segments, and a generative audio synthesis module for producing synchronized speech, effects, and music. In alternative embodiments, a single multimodal generative model may perform both video and audio synthesis. A user interaction module accepts free-form input to evolve the story in real time, and a content generation coordinator manages orchestration, timing, and latency optimization between components. The system supports modular architecture, lip synchronization with character visuals, predictive pre-generation to reduce delay, personalization based on user profiles, and deployment across various platforms including desktop, mobile, and extended reality environments. The invention enables open-ended, user-driven narrative generation with seamless and adaptive audiovisual synthesis.

The invention discloses an industrial PLC (programmable logic controller) equipment data acquisition and intelligent monitoring method, which comprises the following steps: connecting multiple types of PLC equipment through a standardized industrial communication protocol to acquire original data, analyzing and converting through a point attribute configuration table, and combining with a dynamic dictionary mapping table to generate readable information. And the system is dynamically associated with a work order management system to realize production monitoring and analysis. A layered modular architecture is adopted, cross-platform deployment is supported, the efficiency, accuracy and intelligent level of data acquisition and monitoring are effectively improved, and the complex scene requirements of industrial production are met.

The invention relates to the technical field of semiconductor manufacturing, and discloses an integrated vacuum system test method of a mechanical arm and a vacuum test platform. A vacuum test platform which is provided with a two-stage vacuum system and a multi-channel proportional valve and is designed in a modular architecture mode is adopted for sequentially carrying out pre-installation calibration, vacuum environment simulation, positioning precision testing, vacuum adaptability testing and durability testing. The vacuum test platform is provided with a fine tuning clamping groove which is matched with the altimeter to ensure that the mounting position of the mechanical arm to be tested is accurate and the actual condition of the photoetching machine is reetched; a plurality of general standardized interfaces are also arranged, so that quick switching of to-be-tested mechanical arms of different models is realized; in the analysis stage, the three test results are analyzed, whether the performance of the mechanical arm to be tested reaches the standard or not is evaluated, the capability of integrally testing indexes such as positioning precision, vacuum adaptability and durability is achieved, external pre-debugging of the mechanical arm under the working environment of the simulated photoetching machine is achieved, and the debugging efficiency and precision are improved.

The invention discloses a high-efficiency current amplification circuit based on H-bridge structural design, the current amplification circuit comprises a power supply module, two control modules, an H-bridge amplification module, a logic control module, a protection module and a load module, the power supply module is used for providing a direct current input voltage, each control module comprises a half-bridge controller, two resistors and two capacitors, and the logic control module is connected with the H-bridge amplification module. Pins of each half-bridge controller are set to be a VCC end, an IN end, an SD end, a COM end, a VB end, an HO end, a VS end and an LO end, and the two resistors are used for limiting current and adjusting voltage. The H-bridge structure is cooperated with the IRF540N MOS tube, high voltage, large current output, multiple protection and redundancy design are realized, long-term stable operation in an industrial environment is ensured, general elements and a modular architecture are adopted, the purchase and maintenance cost is reduced, and by adjusting the capacitance value of a capacitor, the resistance value of a resistor or the model of the MOS tube, the device can be expanded to scenes with higher power or different voltages.

The invention belongs to the technical field of mobile power supplies, particularly relates to an assembled energy storage power supply, and aims to solve the problems that an existing modular energy storage power supply is unstable in assembly, poor in heat dissipation, easy to loosen, fast in temperature rise and the like, the assembled energy storage power supply comprises a plurality of assembly units, and the assembly units are sequentially stacked up and down to complete assembly and combination. Every two adjacent assembly units are electrically matched, each assembly unit is composed of a shell, two fixing parts and a heat dissipation base, according to the assembly type energy storage power source, flexible configuration of capacity and power is achieved through a modular framework, and the stacked assembly units meet diversified scene requirements; a mechanical interlocking structure ensures stable connection between the modules and effectively resists external force interference; the integrated heat dissipation system adopts a forced air cooling and automatic filter screen cleaning technology, so that the heat management efficiency is remarkably improved; and the regular layout of internal cables is combined with the design of the quick-release maintenance panel, so that the operation and maintenance process is simplified and the maintenance cost is reduced.

A present invention embodiment provides a Life Cycle Assessment (LCA) system that addresses the limitations of existing systems. The system offers an intuitive, user-friendly interface that simplifies the complexity of LCA methodologies, making environmental impact assessment accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date assessments. Additionally, the system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse items including products, services, and processes.

Multi-stage hybrid network integrates relationship regularization links and explainable elements to improve alignment with human values, explainability, robustness, and efficiency. The network comprises neural components, event prediction elements, and probability models across multiple stages, with relationship constraints enforcing structured knowledge representation. Explainable elements provide interpretable rationales for decisions, enhancing transparency. Training incorporates supervised learning, human-guided refinement, semi-automated knowledge engineering, and adversarial robustness techniques. A Socratic reasoning module detects contradictions and refines outputs for logical consistency. Indexed model elements enable dynamic memory optimization for improved efficiency. Candidate outputs may be scored, verified, or selected using neural and symbolic criteria. The invention supports retry loops and configurable subsystem pipelines to improve output quality. Applications include text generation, speech recognition, translation, and decision support. By combining structured constraints, human oversight, and modular architectures, the system improves the trustworthiness, safety, and adaptability of AI systems across diverse modalities and tasks.

The present invention embodiments provide a forecasting system that derives carbon intensity, emissions, and environmental impact forecasts from a life cycle assessment (LCA). The forecasting system offers an intuitive, user-friendly interface that simplifies the complexity of forecasting methodologies, making generating forecasts of carbon intensity, emissions, and environmental impacts accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date forecasts. Additionally, the forecasting system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse products and processes.