17results about How to "Promote intelligent development" patented technology

This invention relates to a navigation method for underwater unmanned vehicles (UUVs), comprising the following steps: acquiring data from multiple sensors and performing spatiotemporal synchronization, feature extraction, and data fusion preprocessing; defining the underwater operating area as a directed graph, with edge weights determined based on terrain complexity, obstacle risk, and water flow influence; based on the directed graph, employing an improved A* algorithm for global path planning and a fast exploratory random tree algorithm for local path planning and dynamic obstacle avoidance; assessing the environmental state considering aquatic environmental parameters, water flow characteristics, and topographic changes; and adjusting the navigation strategy using a deep reinforcement learning algorithm, with the navigation strategy adjustment objective function as the optimization goal, based on the environmental assessment results and the UUV's own state. Compared with existing technologies, this invention offers advantages such as improved navigation accuracy, reliability, and adaptability of underwater UUVs in complex underwater environments.

The application discloses a lithium battery walking electric pallet truck and relates to the technical field of electric pallet trucks, which comprises an electric pallet truck body, wherein the electric pallet truck body comprises a frame, a power supply, an electrical system, a hydraulic system and a driving system, the electrical system comprises a main controller, an accelerator and an instrument panel, and the power supply adopts a lithium battery power supply; a wedge-shaped shell is arranged on the front side of the main controller, an electronic lock is arranged in the wedge-shaped shell, and the electronic lock is electrically connected with the main controller. The lithium battery is adopted as the power supply, the quick charging performance of the lithium battery is utilized, the defects of the lead-acid power supply electric pallet truck are improved, the influence of long-time charging on use is avoided, the electronic lock is used to lock the equipment, the problem that the equipment cannot be used due to the loss of keys is completely solved, and the intelligent development of the overall equipment is promoted.

The invention relates to the field of computer-aided diagnosis, discloses a system for judging and recognizing the intestinal tract cleanliness level of an enteroscope patient, and aims to solve the problems that existing intestinal tract cleanliness evaluation is high in subjectivity, low in evaluation efficiency and lack of quantitative standards. The method comprises the following steps: acquiring and preprocessing enteroscopy image sequence data, and extracting multi-scale features; and the intestinal tract cleanliness is identified through a deep learning model, and a quantitative scoring report is generated. The system comprises an image data acquisition module, a preprocessing module, a multi-scale feature extraction module, a cleanliness identification and classification module and a report generation module. According to the technical scheme, evaluation objectivity, accuracy and efficiency can be improved, automation, standardization and high-precision quantification are achieved, and dependence on doctor experience is reduced.

The application discloses an intelligent pipeline type self-propelled pesticide spraying vehicle, which is characterized by comprising a vehicle frame, a driving wheel set, a pipeline wheel set and a supporting wheel set, a pesticide spraying rod, a pesticide box, a control cabinet, a lithium battery, an air pump, a water pump, a driving motor, a speed reducer and an encoder which are installed on the vehicle frame, a T-shaped pesticide spraying pipe being bound on the pesticide spraying rod, a display system being installed on the control cabinet, and the speed reducer being connected with the driving wheel set.

The invention relates to a human body joint motion range measurement system based on artificial intelligence vision, and relates to the technical field of AI vision joint measurement, and the system comprises an image collection and contour generation module which is used for obtaining a time sequence image sequence, recognizing a human body contour, and generating a time sequence human body contour map sequence; the multi-scale time sequence feature fusion module is used for extracting multi-scale contour features and generating fusion time sequence contour features; the joint point identification and extraction module is used for identifying blind areas and visible joint points and extracting node coordinates; the constraint optimization function construction module is used for constructing an initial joint point set and establishing a kinematics constraint optimization function; and the coordinate optimization and angle calculation module is used for solving the kinematics constraint optimization function, carrying out collaborative optimization on all node coordinates, and outputting and calculating the movement angle of the target joint. According to the method, the problems of joint position judgment deviation or fuzzy estimation caused by the fact that traditional single-camera measurement is easily affected by shielding and key point recognition is inaccurate are solved.

The application discloses a sintering machine tail state monitoring method based on machine vision. The method comprises the following steps: S1, preprocessing of the input sintering machine tail image; S2, color gamut conversion of the original image to extract the combustion area in the image; S3, image processing after extracting the combustion area and extraction of the machine tail area; S4, feature value calculation of the extracted machine tail area; and S5, sintering state judgment and monitoring according to the feature value. The application constructs a sintering machine tail state monitoring system through machine vision and image processing related technologies, realizes real-time monitoring of the sintering machine, gives a prompt and a warning to the abnormal part, further realizes intelligent production of the sintering process section, improves the production efficiency of the steel enterprise, reduces the labor cost, and further promotes the intelligent manufacturing transformation and upgrading and intelligent development of the steel metallurgy field in China.

The application discloses a downhole information interpretation method and system based on drill bit vibration data, and the method comprises the following steps: integrating an intelligent sensing module in the water eye of an existing drill bit, collecting downhole vibration data and environmental parameters in the drilling process at a high sampling frequency; synchronously aligning the downhole vibration data and the ground drilling parameters; calibrating and compensating the synchronized vibration data by using the environmental parameters and extracting features to obtain a vibration feature parameter vector reflecting the interaction between the drill bit and the stratum; and identifying the stratum lithology or the drilling working condition based on the vector and in combination with the environmental parameters. The application solves the problems of information distortion and low resolution caused by the fact that the sensor is far away from the drill bit and the data bandwidth limitation of the traditional measurement-while-drilling system, provides an unprecedented high-precision data basis for post-drilling fine analysis, geological model calibration and future drilling decision optimization, and the good compatibility with the existing drill bit does not require redesign and manufacture of the drill bit, thereby significantly reducing the application cost.

The invention provides an innovative train-ground joint control system and method based on real-time data aiming at the requirements of high efficiency and high safety in the current railway transportation field. According to the system, comprehensive intelligent management and optimization of the railway transportation process are realized by fusing advanced big data analysis technology, artificial intelligence algorithm, Internet of Things technology and cloud computing platform. The system can efficiently process and analyze mass transportation data, and uses an artificial intelligence algorithm to automatically learn and adjust a scheduling strategy, thereby realizing more accurate route planning and risk early warning. Meanwhile, information exchange among locomotives, vehicles and rail facilities is more real-time and accurate due to application of the Internet of Things technology, powerful data processing capacity and flexible resource allocation are provided for the system due to introduction of the cloud computing platform, and efficient operation and easy expansion of the system are ensured. The implementation of the invention is expected to obviously improve the railway transportation automation level, reduce the operation cost and enhance the reliability and stability of the system.

The present application belongs to the technical field of dredging engineering, and discloses a method and system for calculating the multi-factor coupled excavation yield of a drag suction dredger. Specifically, the present application determines the corresponding tooth information of the drag suction dredger based on the position information of the current water area; substitutes the tooth information and the position information of the water area into a mechanical soil breaking amount calculation model to calculate the mechanical soil breaking amount per unit time and generate a corresponding tooth soil breaking parameter table; and substitutes the high-pressure water discharge characteristic information of the drag suction dredger into a hydraulic soil breaking amount calculation model to calculate the hydraulic soil breaking amount per unit time and generate a corresponding high-pressure water discharge characteristic table. The hydrological environment information is processed by a fuzzy function, and the dredging influence level is divided according to the dredging demand of the drag suction area. Under uncertain external environment, the multi-factor coupled excavation yield calculation model is established through parameter identification learning, which takes into account the environmental constraints of the dredging influence level on the mechanical soil breaking amount and the hydraulic soil breaking amount per unit time, and finally calculates the excavation yield of the drag suction dredger.

The invention discloses an SMT production line control method and system based on an Internet of Things chip, and relates to the technical field of SMT production line control of the Internet of Things chip, an initial requirement matrix is generated by extracting calculation requirements of each link, and an integrated calculation framework is obtained by integrating heterogeneous calculation frameworks of a multi-core processor and an accelerator module; generating a resource allocation scheme, determining a task dependency graph in combination with interaction data of chip mounter coordinate operation and temperature curve adjustment, processing the task dependency graph by using a graph neural network, and inserting a buffer queue to perform synchronous execution and optimize a task scheduling sequence; and obtaining execution feedback data generated in real-time operation based on the optimized task scheduling sequence, and dynamically adjusting a computing power distribution vector to realize cooperative control of the SMT production line control system. According to the invention, a continuously optimized cooperative control framework is realized, and computing resources can be dynamically allocated and multi-task efficient collaboration can be realized according to real-time requirements of process links.

This invention discloses an intelligent perception and proactive early warning system for safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion, relating to the fields of logistics transportation and intelligent transportation technology. It includes: a raw data input module; a business scenario intelligent perception module; a data collection and analysis module; a safety risk early warning module; an operation guidance module; and a network information receiving module. This invention also discloses a method for intelligent perception and proactive early warning of safety risks in multimodal transport of long and heavy cargo based on multi-source data fusion. This invention solves the complex safety challenges faced by long and heavy cargo in multimodal transport, improving the safety and efficiency of the transportation process.

This invention provides a vegetation filtering method for dangerous rock slopes based on multi-dimensional features, comprising: Step 1, obtaining an initial three-dimensional model of the dangerous rock slope; Step 2, generating a reference surface based on the initial three-dimensional model; Step 3, initially filtering the vegetation of the dangerous rock slope; Step 4, further filtering the vegetation of the dangerous rock slope. This invention employs a layered filtering strategy of "initial filtering and secondary filtering" to filter vegetation on dangerous rock slopes, significantly improving filtering accuracy and effectively avoiding the problems of "over-stripping" and "under-stripping" in traditional methods.

This invention relates to the field of medical emergency and telemedicine technology, and in particular to an online diagnosis and treatment system. Addressing the problems of lagging information transmission and lack of intelligent data analysis in existing traditional pre-hospital emergency care models, this invention proposes the following solution: a portable emergency intelligent terminal, a cloud-based big data and artificial intelligence analysis platform, a hospital-side command and preparation system, and a high-speed mobile communication network. The portable emergency intelligent terminal is deployed at the emergency site to collect and preprocess patient physiological and environmental data and transmit it in real time. After receiving the data, the cloud platform retrieves the patient's historical health data, diagnoses and evaluates the patient using an AI model, and generates a personalized dynamic emergency care plan based on a knowledge graph. This invention achieves real-time, multi-dimensional collection and intelligent analysis of pre-hospital emergency care data, breaks down information barriers between pre-hospital and in-hospital care, provides dynamic optimization solutions for on-site emergency care, guides precise in-hospital preparation, significantly improves emergency care efficiency and accuracy, and reduces the prognostic risks for critically ill patients.

The invention specifically relates to a fruit tree trunk identification and positioning method based on binocular machine vision, and relates to the technical field of autonomous operation of intelligent agricultural equipment, and the method comprises the steps: 1, creating and preprocessing a data set; step 2, carrying out optimization construction on a YOLOv7-tiny trunk identification model; 3, training a trunk identification model; 4, evaluating the trunk identification model; 5, detecting a trunk identification model; and 6, constructing a positioning model and solving the position of the trunk. According to the method, the tree trunk recognition performance is remarkably improved through multi-dimensional optimization, and the core advantage is reflected in the balance of recognition precision and operation efficiency; data set construction covers single independent, high and dwarf, complex illumination background and other multi-element scenes, the data robustness is high after HSV enhancement, overturning and other processing, and a foundation is laid for accurate recognition; in terms of model improvement, a PConv module replaces conventional convolution to reduce calculation redundancy, and a SimAM attention mechanism strengthens tree trunk key features, so that the improved YOLOv7-tiny model is optimal in an ablation experiment.

The invention provides an artificial intelligence interpretation method for multi-arm hole diameter logging data. The artificial intelligence interpretation method for the multi-arm hole diameter logging data comprises the steps that 1, imaging processing is conducted on logging interpretation data; 2, building a working condition interpretation model based on the convolutional neural network; step 3, training the built working condition interpretation model; 4, the well trained working condition interpretation model is utilized, and according to the collected actual multi-arm hole diameter logging data, the multi-arm hole diameter logging working condition is judged. According to the artificial intelligence interpretation method for the multi-arm hole diameter logging data, by combining the high-precision data acquisition capability of multi-arm hole diameter logging and the efficient intelligent analysis advantage of artificial intelligence, the interpretation efficiency and interpretation precision of multi-arm hole diameter logging interpretation work are effectively improved, and the labor intensity of workers is reduced.

This application provides a method, apparatus, electronic device, and storage medium for location reliability processing. The method includes: first, acquiring first point cloud data; then, filtering the first point cloud data to reduce the number of points while maintaining shape features, to obtain second point cloud data; smoothing a first grid map to eliminate noise, to obtain a second grid map; then, calculating the matching degree of each current point based on the second point cloud data and the second grid map; obtaining the actual matching degree of each current point by multiplying by a preset coefficient, and calculating the root mean square error to obtain a second laser matching degree; finally, using the second laser matching degree as the robot's location reliability. This application can obtain more accurate location reliability.

The present application belongs to the technical field of continuous pipe in coal mine, and relates to a coal mine drilling continuous pipe lowering anti-blocking device and method, which utilizes the anti-blocking device to lower the continuous pipe in the open hole drilling in the coal mine, and specifically comprises the following steps: selecting the corresponding specification of the anti-blocking device according to the inner diameter of the drilling and the outer diameter of the continuous pipe, and calculating the specified distance between the installation of the adjacent two anti-blocking devices; when lowering the continuous pipe into the drilling, the anti-blocking device is installed on the continuous pipe at the specified distance in sequence. The present application establishes a mathematical model for the reasonable arrangement of the anti-blocking device, controls the clearance coefficient of the anti-blocking device in combination with the inner diameter of the drilling, the outer diameter of the continuous pipe and the stress characteristics in the coal mine, and provides a method for selecting the appropriate specification of the anti-blocking device and the distance between the two anti-blocking devices, thereby avoiding the serious eccentricity caused by the excessively large distance or the structural redundancy caused by the excessively small distance.