11results about How to "Fast perception" patented technology

The application discloses an enzymatic shrimp paste compound water product feeding attractant and application thereof, and belongs to the technical field of water animal feed additives, characterized in that the compound water product feeding attractant is composed of the following components in percentage by weight: 85% to 98% of enzymatic shrimp paste, 2% to 15% of food-grade eugenol, wherein the enzymatic shrimp paste is obtained by directional enzymolysis of shrimp head endocrine fluid through a four-component complex flavor protease containing endoprotease, aminopeptidase, carboxypeptidase and glutamine transaminase; the application further provides a preparation method of the enzymatic shrimp paste and application of the compound water product feeding attractant; and the advantages are that the enzymatic shrimp paste is rich in flavor peptides, after being compounded with eugenol, the eugenol can activate the olfactory receptor ion channel of water animals, amplify the sensing signal of flavor substances, and form a significant synergistic feeding effect, so that the feeding amount of the water animals can be effectively improved, and the feed coefficient can be reduced.

The invention discloses a vehicle active safety pre-detection method based on travel risk prediction, and relates to the technical field of vehicle active safety control, and the method comprises the steps: obtaining a historical travel data set of a vehicle, carrying out the division of road segments based on a road network topology, carrying out the risk feature extraction of the divided road segments, and constructing a road segment-risk feature mapping library; historical risk features of the road section are extracted from the mapping library, and the static features and the dynamic features of the road section are fused through a double-flow LSTM-attention mechanism model to obtain a road section risk probability; dividing the risk levels of the road sections according to the risk probability of the road sections, mapping the risk levels to an HSV color space, and transmitting the risk levels to a pre-check layer of the vehicle-mounted HUD; and calculating the triggering distance of the vehicle entering the predetermined road section, extracting risk level information from the pre-detection layer after the vehicle enters the triggering area, and triggering and loading the active safety pre-detection of the vehicle. According to the invention, the prediction capability and intervention capability of the vehicle on potential risks are improved, and the self-adaptability and intelligent level of a vehicle active safety system are enhanced.

The application relates to the technical field of automatic driving and provides an automatic driving assistance method and system, which comprises the following steps: acquiring point cloud data, wherein the point cloud data is data information acquired by a detection signal emitted by a laser radar installed on a first vehicle; generating a two-dimensional image according to the point cloud data, wherein the two-dimensional image is a two-dimensional image of the surrounding environment of the first vehicle perceived by the laser radar installed on the first vehicle; performing feature extraction on the two-dimensional image by using a neural network model to obtain feature information, wherein the feature information comprises feature information of each foreground point in the two-dimensional image; performing clustering on each foreground point in the two-dimensional image based on the feature information to obtain an instance segmented two-dimensional image; and assisting a user in taking over the first vehicle according to the instance segmented two-dimensional image, so that the automatic driving vehicle can accurately and quickly perceive the surrounding environment, the driving personnel can take over the vehicle in the automatic driving according to the perception result, and the safety of the automatic driving is improved.

A method and system for intelligent fault monitoring in distribution networks containing distributed energy resources are disclosed. The method first acquires multi-source information data of the distribution network containing distributed energy resources and extracts features to generate a node feature matrix. Then, based on the node feature matrix, a Markov decision model for fault detection in the distribution network containing distributed energy resources is constructed. The system's main body is used as the agent, and the agent is trained using an improved D4PG algorithm based on the Markov decision model. After training, the optimal fault detection strategy for the distribution network containing distributed energy resources is derived and deployed in the fault detection task for intelligent fault monitoring. This invention, through a deep reinforcement learning framework, transforms the complex fault detection task of the distribution network containing distributed energy resources into an optimal decision problem, successfully establishing a direct, fast, and reliable logical mapping relationship from the grid fault state to the optimal diagnostic action, providing a more robust and adaptive fault detection method.

This invention discloses a grouped point cloud-driven arm-hand operating system. The arm-hand operating system includes a VR-based teleoperation data acquisition module, an environmental perception and encoding unit, and a two-layer feature fusion diffusion denoising model. Specifically: the VR-based teleoperation data acquisition module consists of a posture recognizer, a posture mapper, and a data storage module; the environmental perception and encoding unit consists of a point cloud grouper, a point cloud encoding module, and a state integrator; and the two-layer feature fusion diffusion denoising model consists of a two-layer feature fusion module and a diffusion denoising network. This arm-hand operating system can respond to environmental changes in real time, achieving comprehensive scene perception while quickly formulating task execution plans. This data acquisition-algorithm deployment collaborative framework effectively solves the challenge of balancing cost, speed, and accuracy in intelligent arm-hand operation, providing a cost-effective solution for embodied intelligence, robotic operations, and other fields.

This application provides a vehicle-mounted equipment cabin and system for dike hazard inspection based on multi-source sensing, belonging to the field of water conservancy engineering safety monitoring technology. The vehicle-mounted equipment cabin includes an operation command center, a drone and its nest module, an unmanned surface vessel and its cabin module, and a vehicle-mounted meteorological instrument module. The drone and its nest module is equipped with a lidar camera for aerial three-dimensional measurement and a thermal imager for dike thermal anomaly detection. The unmanned surface vessel and its cabin module is equipped with a multi-beam echo sounder for underwater three-dimensional measurement. The vehicle-mounted meteorological instrument module is fixed to the upper surface of the vehicle-mounted equipment cabin and is used to collect environmental meteorological parameters. The operation command center houses a water conservancy drone inspection cockpit system, which receives multi-source data from the drone and its nest module, the unmanned surface vessel and its cabin module, and the vehicle-mounted meteorological instrument module for multi-source data fusion. This solution achieves the location and identification of dike safety hazards through integrated air-ground-water collaborative detection.

The invention relates to an intelligent pressure bra and a control method thereof. Comprising a bra main body, a pressure sensing module, a posture sensing module, a pressure adjusting module and a central control module, the pressure sensing module, the posture sensing module and the pressure adjusting module are fixedly connected to the bra main body; the pressure sensing module is used for collecting original chest pressure signals of a patient; the posture sensing module is used for collecting an original posture signal of a patient; the pressure adjusting module and the pressure sensing module are correspondingly arranged; the central control module is connected with the pressure sensing module, the posture sensing module and the pressure adjusting module and used for processing data, communicating with remote terminal equipment and controlling the pressure adjusting module. Compared with the prior art, the method has the advantages that real-time closed-loop adjustment is realized; the compression strategy is dynamically optimized in combination with the posture of the patient, and the safety and accuracy of postoperative rehabilitation are improved.

The invention discloses a full-direct-current offshore wind power system active supporting method, and relates to the technical field of offshore wind power control. The invention aims to solve the problem of limited applicability caused by low degree of freedom of one-way DCT control in an offshore wind power system control mode. When an absolute value of frequency deviation of a shore power grid exceeds a first set threshold value, frequency-high-voltage direct-current voltage droop control is triggered to adjust a high-voltage direct-current voltage reference value, so that a frequency change signal of the shore power grid can be transmitted to an offshore booster station; when the absolute value of the high-voltage direct-current voltage deviation exceeds a second set threshold value, high-voltage direct-current voltage-medium-voltage direct-current voltage droop control is triggered to adjust the medium-voltage direct-current voltage deviation, so that a shore power grid frequency change signal can be transmitted to each direct-current fan; and when the direct-current fan detects that the absolute value of the medium-voltage direct-current voltage deviation exceeds a third set threshold value, medium-voltage direct-current voltage-power droop control is triggered to adjust the output power of the fan, and active frequency support for the shore power grid is achieved.

This invention discloses a six-dimensional force sensor with integrated temperature data acquisition function. The invention relates to the field of six-dimensional force sensor technology and includes a housing. A bottom cover plate is provided below the housing, and an upper cover plate is provided above the housing. A connecting assembly is provided between the housing, the bottom cover plate, and the upper cover plate for connecting and fixing the housing, the bottom cover plate, and the upper cover plate. An elastic body is provided inside the housing. Multiple strain beams are provided on one side of the elastic body. One end of each strain beam is fixedly connected to the side wall of the elastic body, and the other end of the elastic body is fixedly connected to the inner wall of the housing. Strain gauges are fixedly mounted on the surface of each strain beam, and an integrated circuit board is fixedly mounted on the surface of the elastic body. This device acquires temperature data generated during sensor use using a high-precision thermistor, then integrates and processes the data via a circuit board, and finally transmits the data to the cloud for further processing via a data transmission line and data transmission port.

The invention discloses a danger warning method and device, electronic equipment and a computer readable storage medium. The danger warning method comprises the following steps: acquiring a danger level corresponding to a dangerous environment object and theme style data corresponding to a human-computer interaction interface; based on the danger level and the theme style data, determining model rendering data corresponding to the dangerous environment object; based on the model rendering data, performing rendering processing on the initial three-dimensional model corresponding to the dangerous environment object to obtain a danger warning model corresponding to the dangerous environment object; and displaying the danger warning model in the human-computer interaction interface. According to the invention, the danger warning response efficiency of the driver can be improved.

The invention discloses an ROS2-based pose estimation method and system, and a guidance method and system, belongs to the technical field of intelligent robots and automatic control, and solves the problems that in the prior art, the three-dimensional pose and the physical volume of a battery cannot be sensed, physical marks are depended, the recognition rate is low in outdoor strong light and wear environments, and the reliability is high. Therefore, the technical problems that the four-degree-of-freedom mechanical arm is high in grabbing failure rate and poor in safety under the non-planar working condition, and an existing model is high in calculation complexity, large in memory occupation and remarkable in system processing delay are solved. Obtaining and synchronizing a depth image and an RGB image; carrying out identification by using a target detection model, screening an optimal bounding box of which the category is a battery, and obtaining a depth sub-matrix of the ROI region; and performing double filtering on the depth sub-matrix to obtain a smooth depth map, and calculating a projection angle of a unit normal vector of the battery surface in an X-Z plane based on the unit normal vector of the battery surface to obtain a battery rotation angle. The method is used for realizing safe and efficient unmanned aerial vehicle automatic battery replacement.