8results about How to "Short adaptation time" patented technology

The utility model discloses a three-dimensional cultivation container convenient for displaying aquatic plants, which comprises an aquatic plant cultivation container, a planting cylinder and a water storage cylinder are arranged on the aquatic plant cultivation container, the bottom of the lower portion of the planting cylinder is arranged in the water storage cylinder, and a water pump is arranged outside the aquatic plant cultivation container in a matched mode. According to the aquatic plant cultivation container, the planting cylinder body and the water storage cylinder body form an up-down split structure, so that the aquatic plant cultivation container is simple in structure, convenient to disassemble, transport and display and suitable for being used as a cultivation exhibition device of aquatic plants; the aquatic plant cultivation container can be used for cultivation and display, the adaptation time of the aquatic plants in the landscape arrangement process is shortened, and damage to the aquatic plants can be effectively reduced.

The invention discloses an intelligent management optimization system and method based on exploration big data, and the system constructs a lake-cabin integrated data base, and innovatively introduces a dynamic business object model to organize multi-source heterogeneous data. The core lies in that the multi-Agent is driven to work cooperatively through a cloud native scheduling engine, and automatic decomposition and execution of scientific research tasks are achieved. According to the method, a model training normal form of'federal learning + meta learning 'is adopted, the model generalization ability in a small sample scene is improved while data privacy is guaranteed, and a'risk-cost-benefit' multi-objective optimization model is constructed for decision making. According to the method, integrated closed loop of exploration data management, knowledge discovery and decision optimization is realized, and the intelligent level and comprehensive benefits of exploration and development are remarkably improved.

The present application relates to the technical field of air conditioner intelligent control, and especially relates to a multifunctional air conditioner parameter control system and method based on dual-mode communication and an intelligent socket, which comprises: stable switching is realized by 4G and infrared dual-mode communication in combination with signal strength threshold and duration; a dynamic code library learning module automatically matches air conditioner protocols through polling instructions and temperature and power feedback; an MPC algorithm is adopted, and weight coefficients are dynamically adjusted according to temperature change rate to balance temperature control and energy saving; the control method covers instruction receiving, dual-mode switching, protocol matching, energy saving instruction generation and execution feedback process; the intelligent socket integrates 4G and infrared dual-mode communication, supports 16A national standard and non-standard plug adaptation, and cuts off power supply through overcurrent, leakage and overtemperature triple protection. The present application realizes reliable communication in a weak network environment, improves the compatibility of multiple types of air conditioners, optimizes energy saving effect and power safety protection, and can improve the success rate of air conditioner parameter control and reduce the failure rate.

This application provides a method, apparatus, equipment, medium, and program product for detecting the installation of a heating boiler. The method involves acquiring image data of the heating boiler's installation components; preprocessing the image data to obtain standardized images; inputting the standardized images into a deep learning model for image analysis to obtain installation detection results; wherein the deep learning model is used to identify the current installation state of each component of the heating boiler based on the standardized images, and outputs the installation detection results based on the current installation state. This application uses a deep learning model to extract features and classify defects in the images of the heating boiler's installation components, transforming human experience into quantifiable mathematical rules, reducing subjective bias, and achieving accurate identification of installation defects. Furthermore, by preprocessing the acquired image data, the consistency of the model's input data is ensured, reducing the model's adaptation time to complex environments and improving detection efficiency.

This application provides a method and system for unstructured text parsing and question answering based on a large language model. The method includes: acquiring raw unstructured text data from a data source at preset intervals; performing deep semantic analysis on the raw unstructured text data using a large language model to extract key entities and data structures; dynamically generating or updating an existing JSON structure template based on the key entities and data structures; converting the raw unstructured text data into standard structured data according to the JSON structure template; storing the structured data in a time-series database and establishing a multi-dimensional index; obtaining time information and query intent from natural language query information input by the user; and generating question-answering results or trend analysis results using a large language model based on the query intent and the structured data obtained from the time-series database according to the time information.

This invention discloses a safety production analysis method oriented towards multimodal data and causal knowledge graphs, belonging to the field of industrial safety production monitoring and intelligent analysis technology. The method comprises the following steps: S1. Conducting multimodal sensing and acquisition with spatiotemporal synchronous preprocessing to output a standardized multimodal data stream; S2. Performing cross-modal self-supervised pre-training based on the results of S1 to obtain a pre-trained basic model; S3. Using a three-layer federated architecture combined with privacy protection, collaboratively training the pre-trained basic model to generate an aggregated and optimized global model; S4. Constructing and dynamically updating an industrial safety causal knowledge graph, combining it with the global model for causal reasoning, and outputting risk assessment results; S5. Calculating and classifying a comprehensive risk value based on the results of S4, matching and executing response strategies; S6. Collecting feedback data from risk responses to complete closed-loop self-optimization. Using this method, accurate early warning of industrial safety can be achieved, ensuring data security, reducing annotation costs, and enhancing system adaptability.

The application discloses a GaN device AC simulation method, device and equipment and a storage medium. The method comprises the following steps: determining a first equivalent capacitance, a second equivalent capacitance and a third equivalent capacitance according to a gate simulation voltage, a drain simulation voltage and a source simulation voltage of a target GaN device, and a conversion coefficient and a trained charge determination model; determining a target gate current, a target drain current and a target source current of the target GaN device according to the gate simulation voltage, the drain simulation voltage, the source simulation voltage, the first equivalent capacitance, the second equivalent capacitance and the third equivalent capacitance; updating an original device model corresponding to the target GaN device according to the target gate current, the target drain current and the target source current to obtain a target device model; and performing AC simulation on the target GaN device based on a preset test circuit according to the target device model.

The present application relates to embodied intelligent robot technical field, specifically to embodied intelligent robot skill runtime data analysis and scheduling method. The method comprises: a skill data receiving module receives skill data packets of external data sources and completes format verification and integrity check; a multi-level semantic analysis engine carries out three-level progressive analysis of syntax layer, semantic layer and intention layer on the skill data packets, and generates executable action sequences; a real-time scheduling controller combines the current state of the robot and environmental information to prioritize and time slice allocate the action sequences, and generates a scheduling scheme; an adaptive optimization module continuously iterates scheduling strategy parameters based on historical execution data and real-time feedback. Through the technical framework of multi-level semantic analysis and dynamic scheduling cooperation, the present application solves the problems of information loss, response delay and insufficient adaptability in skill transfer process, and significantly improves the precision and task completion efficiency of robot skill execution in complex dynamic environment.