8results about How to "Guaranteed intelligence" patented technology

The present application belongs to the technical field of resource allocation, and particularly relates to a space resource dynamic allocation method based on an AI model and BIM operation and maintenance, comprising: obtaining a to-be-processed task and a spatial position identifier thereof, and constructing a task feature vector set; inputting the task feature vector set into a modal recognition network model to obtain a plurality of primary scheduling modes containing spatial coupling components and resource demand components through mapping; iteratively updating coupling weights between the primary scheduling modes and high-level scheduling modes until convergence, determining a current resource allocation mode based on the converged high-level scheduling modes, and determining the association strength of each task relative to the current resource allocation mode based on the coupling weights; generating a scheduling instruction set containing joint scheduling paths and independent scheduling instructions according to the association strength and in combination with the spatial position identifier, and converting the scheduling instruction set into executable control instructions; and the present application realizes fine and adaptive dynamic resource allocation based on BIM and real-time situation.

This utility model relates to an automatic cleaning, recycling, and conveying structure for concrete mixing plant waste, belonging to the technical field of concrete conveying equipment. This automatic cleaning, recycling, and conveying structure for concrete mixing plant waste includes a storage and circulation component. The storage and circulation component includes a storage bin, on one side of the top of the storage bin, an extension of a mortar conveying mechanism. A pressing component is installed on the top of the storage and circulation component near the mortar conveying mechanism. Through the structural design of the storage and circulation component and the spiral conveying component, this utility model can achieve washing during the conveying of concrete waste and facilitate the recycling of water resources, increasing environmental performance and reducing emissions. Only a small amount of water lost through evaporation needs to be replenished later, reducing water waste and avoiding environmental pollution caused by direct wastewater discharge, thus reducing the environmental treatment costs and energy consumption of the mixing plant.

The invention relates to the technical field of sediment cutting, and discloses an intelligent sediment cutting method and system based on an automatic cutter, and the method comprises the steps: recognizing a target layer of a to-be-cut sediment, and obtaining the sediment physical state information and layer shape information of the target layer based on a preset time period; determining a cutting evaluation coefficient of the target horizon based on the physical state information and the horizon shape information; presetting a preset cutting evaluation coefficient, and judging whether the to-be-cut sediment meets a cutting condition or not according to a relationship between the cutting evaluation coefficient and the preset cutting evaluation coefficient; when the to-be-cut sediment meets the cutting condition, the to-be-cut sediment is intelligently cut based on the cutting evaluation coefficient, whether the sediment is in the cuttable condition or not is accurately judged through the real-time change information of the sediment, forced cutting and damage to the sediment structure are avoided, and meanwhile the intelligence and accuracy of the sediment cutting process are guaranteed.

The application relates to the technical field of electromagnetic wave absorbing materials and devices, and particularly discloses a dynamic regulation and control system of a high-frequency electromagnetic protection carbon-based wave-absorbing material. The system comprises an environment sensing layer, an intelligent decision-making layer and a dynamic execution layer. The environment sensing layer captures and analyzes the frequency spectrum characteristics of incident electromagnetic waves in real time; the intelligent decision-making layer calculates optimal regulation and control parameters according to the environment characteristics based on deep reinforcement learning and impedance matching optimization; and the dynamic execution layer converts the optimal parameters into a physical regulation and control field and applies the field to the material to dynamically adjust the electromagnetic parameters of the material. The application constructs a complete closed-loop feedback control chain from environment sensing, intelligent decision-making to dynamic execution. The environment sensing layer captures the accurate frequency spectrum characteristics of incident electromagnetic waves in real time, the intelligent decision-making layer calculates optimal regulation and control parameters in real time based on deep reinforcement learning and impedance matching theory, and the dynamic execution layer accurately generates a regulation and control field to act on the material.

The present application relates to a zero-carbon park energy intelligent control method and system based on power prediction, comprising the following steps: obtaining park multi-source data, the multi-source data including historical power load data, weather forecast data, renewable energy power generation data and carbon emission monitoring data; based on the multi-source data, performing multi-time scale power prediction through a pre-constructed hybrid prediction model to generate ultra-short-term power prediction sequence, short-term power prediction sequence and medium and long-term power prediction sequence; according to the ultra-short-term power prediction sequence, the short-term power prediction sequence and the medium and long-term power prediction sequence, a park energy system optimization scheduling model considering carbon flow coupling characteristics is constructed; the optimization scheduling model is solved to generate a day-ahead scheduling plan and a real-time correction plan containing source-grid-load-storage multi-end cooperation; the present application can realize more intelligent zero-carbon park energy intelligent control.

The application relates to the field of artificial intelligence and robot technology, and provides a lightweight embodied intelligent system based on a big-small brain collaborative architecture and a construction method thereof.The system comprises a global planning large model module, a real-time embodied reasoning module, a task dynamic division and scheduling module, and a joint representation learning module.The global planning large model module is used for realizing a global task planning function; the real-time embodied reasoning module is used for realizing a real-time perception control function; the task dynamic division and scheduling module is used for receiving and responding to external task instructions, and splitting the external task instructions to obtain global planning type tasks and real-time control type tasks; the joint representation learning module is used for collecting multi-modal perception data and robot body state data, and training a joint representation model of a physical space and a robot body state; and the layered perception decision control module is used for sequentially executing real-time perception processing, local action decision and motion control calculation based on the joint representation model, and generating a bottom layer control instruction.

The invention discloses an intelligent operation and maintenance method and system for meteorological observation equipment, relates to the technical field of meteorological equipment operation and maintenance, and focuses on deployment of an outdoor integrated unit and intelligent operation and maintenance of the meteorological observation equipment under a low-temperature condition through an integrated unit integrating functions of multi-port fusion, equipment fingerprint identification and automatic command analysis. A sealed test bin is constructed for testing, an integrated unit deployment scheme which is low in outdoor exposure risk of operators and stable in operation under different low-temperature gradients is selected, an operation and maintenance scheme which is simple in remote control and reduces the outdoor exposure risk of the operators is selected, an integrated operation and maintenance database is constructed, and then the integrated operation and maintenance database is applied to an actual low-temperature environment. The method achieves the simplicity and stability of the whole process of connection, transmission and the like of meteorological observation equipment, guarantees the reliability of meteorological observation, improves the intelligence of operation and maintenance, reduces the outdoor exposure risk of personnel, and effectively adapts to and meets the operation and maintenance demands in a low-temperature scene in actual winter.

The application discloses a local boar remote semen collection training method based on human simulation interaction, and particularly relates to the field of artificial reproductive technology, which comprises obtaining environmental parameters to calculate a situation interaction adaptation index, executing a human simulation behavior path and collecting boar response data to construct a behavior cognition connection index when the situation interaction adaptation index meets preset conditions, inputting the two indexes into a training model to output a cognition transition prediction score, reconstructing the behavior path and reevaluating if the score is lower than a threshold, triggering a real person intervention process if the score is higher than the threshold for m consecutive times and the connection index fluctuation is less than a set range, and the like. The application realizes quantitative evaluation of pig house environmental adaptability, dynamic perception of target boar behavior response, and training decision closed-loop control based on double indexes and a machine learning model, ensures efficient promotion of semen collection training under the condition of strong environmental adaptability, sufficient individual response and stable cognitive trust, and improves training success rate and intelligence level.