11results about How to "Self-directed learning" patented technology

The invention relates to a multi-mode-based family member relationship autonomous learning method and device. The method comprises the following steps: acquiring image data and voice data; the image data comprises a face image, and the voice data comprises voiceprint information and semantic information; acquiring attribute information corresponding to the face image based on the image data, predicting a family identity corresponding to the face image based on the face image in combination with the voiceprint information and the attribute information, and generating a family personnel knowledge graph; identity keywords related to family identities are monitored on the basis of continuously updated semantic information, and a family personnel knowledge graph is iteratively corrected on the basis of the identity keywords and continuously updated face images, attribute information and voiceprint information, so that autonomous learning of family personnel relationships is realized. According to the invention, through combination of various modes such as human faces and voiceprints and combination of a natural language processing technology, completely autonomous learning of family personnel relationships is realized, and naturalness and friendliness of man-machine interaction are improved.

The present invention discloses a pig farming intelligent decision-making management system based on multimodal information monitoring, which relates to the technical field of pig farming management. The intelligent decision-making management system includes: an information monitoring module, an abnormal warning module, an analysis and decision module, a management and control module, and an intelligent collaboration module, wherein the information monitoring module is used to divide the pig house into several enclosure areas through enclosure fences, and use the monitoring equipment configured in each enclosure area to monitor the multimodal information of the pig herd growth process in the enclosure area in real time. The present invention realizes the intelligent management of the whole process of pig farming by comprehensively applying information monitoring, abnormal warning, analysis and decision, management and control, and intelligent collaboration modules; comprehensively grasps the growth status of the pig herd by real-time collection of multimodal information in the enclosure area; analyzes information data in real time through edge computing equipment, quickly identifies and warns of abnormal situations, ensures timely handling of potential problems, and avoids the development of abnormal problems.

The invention belongs to the technical field of aircraft aircrew and ground crew aircraft system theory learning, and discloses an aircraft multi-mode fusion aviation theory teaching system and teaching method.The system comprises PC end equipment, server end equipment, PAD end equipment and aircraft aviation theory computer-aided teaching system software supporting system operation. The defects in a traditional air and ground service aircraft aviation theory training mode are overcome, training activities which are mainly explained by teachers and are matched with aviation theory auxiliary teaching software are achieved, and the training form courses are novel in display, visual in content and convenient to use; the learning and assessment requirements of different types of user trainees in different occasions are met in a multi-terminal form, and the learning efficiency and the training effect of the user are improved.

The invention relates to the technical field of advanced mathematics teaching systems, in particular to a multi-dimensional dynamic visualization advanced mathematics teaching system. The technical problem is that when an advanced mathematics teaching system is used, the cultivation of mathematics application ability is often neglected, and the dominant position of teachers is usually emphasized, so that teaching effects are often different due to individual differences of students, and teaching according to materials is difficult to realize; according to the technical scheme, the multi-dimensional dynamic visual advanced mathematics teaching system comprises a personalized learning path recommendation module; compared with a traditional advanced mathematics teaching system, the advanced mathematics teaching system often neglects cultivation of mathematics application ability, so that teaching effects are different due to individual differences of students, and teaching according to materials is difficult to realize, and the advanced mathematics teaching system has the advantages that advanced artificial intelligence technology and education theories are introduced, so that teaching efficiency is improved. Students can select proper learning contents and paths according to their own learning progress and ability level, thereby realizing autonomous learning and personalized development.

The present invention provides a method for extracting rapeseed from high-resolution images based on a deep learning algorithm. The model integrates DRN and an improved U-Net semantic segmentation model, and includes two core modules of Encoder and Decoder, which realize the autonomous learning of spatial and spectral features of GF-2 images and the automatic extraction of rapeseed at the pixel level. The model uses GF-2 images during the rapeseed flowering period (late February to mid-April) as the data source, is trained and tested through labeled rapeseed samples, and increases rapeseed samples through a data enhancement algorithm and obtains an optimized rapeseed extraction model by adjusting model parameters. When applying the model, a data input clipping strategy and a result output clipping strategy are proposed to realize the extraction of rapeseed from input images of any size. Compared with traditional classification analysis methods based on pixels and statistical theories and object-oriented classification algorithms, this method makes full use of the spatial and spectral features of GF-2 images, realizes the autonomous learning of semantic features used for rapeseed extraction, and improves the accuracy of rapeseed extraction and the generalization ability of the model.

The present application relates to the technical field of criminal case data analysis and early warning, in particular to a multi-technology coordinated criminal case intelligent early warning system and method fusing pre-training large model customization adaptation, dynamic weighted clustering and semantic vector retrieval, belonging to the cross technical field of judicial big data and artificial intelligence, through the coordinated technical path of "large model label extraction-weighted clustering-vector deduplication", a full-process intelligent system of "data input-intelligent analysis-accurate early warning-feedback iteration" applicable to judicial case analysis and processing scenes is constructed, not only effectively solving the core problems of low efficiency and early warning redundancy under the traditional manual analysis mode, but also completely solving the many common problems existing in the current technical system of criminal case analysis and early warning.

The application relates to a classification sampling tool and method suitable for a low-visibility seabed environment, which comprises a sampler for collecting samples, the structure of the sampler comprising a sampling cylinder, one end of the sampling cylinder being closed by a sealing plate, and a matching rod being connected to the sealing plate; a sample barrel provided with a barrel body for containing the collecting sampler; a cover capable of moving along the axial direction of the sample barrel and controllably closing the barrel body; a limiting structure arranged on the sample barrel and capable of controllably limiting the axial movement of the cover on the sample barrel; and a matching hole corresponding to the matching rod being arranged on the cover. Through the above operation, in-situ classification sampling in a low-visibility environment can be completed, the sound and light pollution of the sampling process to the seabed environment is effectively reduced, the cross contamination between different types of sampling samples is effectively reduced, the classification sampling of samples with different requirements for sample treatment is facilitated, the structure is simple, the sampling operation process is simplified, intelligent sampling operation is facilitated, and the application has strong practicability.

The invention relates to the technical field of integrated navigation, in particular to a crowdsourcing data driven model and parameter self-learning navigation method and device.The method comprises the steps that crowdsourcing map data of a target navigation system is obtained, and at least one feature factor is extracted; updating the crowdsourcing map data, and generating crowdsourcing map label data; based on the at least one characteristic factor and the crowdsourcing map label data, constructing a preset variance self-learning model, and optimizing position variance information of the target navigation system to obtain a model prediction position variance; and constructing a scaling factor according to the model prediction position variance, and generating a final positioning result of the target navigation system through the scaled model prediction position variance and a preset semi-tight combination architecture. According to the method, the position variance information can be autonomously learned, manual intervention and high-precision reference truth value equipment are not needed, the system model can be continuously optimized through crowdsourcing data updating while high-precision navigation positioning is performed, and the generalization ability and the intelligent level of the system are continuously improved.

The invention discloses a crown block operation control method and crown block equipment, and the crown block operation control method comprises the steps: obtaining an image collected by visual detection equipment on a crown block in real time in the moving process of the crown block; determining a traffic environment in a certain range in front of the crown block based on an image acquired by visual inspection equipment on the crown block and acquired in real time and information acquired from a scheduling system; whether the crown block where the visual detection equipment is located needs to enter a protection operation mode or not is determined at least according to the passing environment within a certain range in front of the crown block; and controlling the moving speed of the visual inspection equipment based on the real-time distance between the crown block on which the visual inspection equipment is located and another crown block moving in the same direction in front of the crown block determined by the real-time acquired image acquired by the visual inspection equipment when entering a protection operation mode. Compared with traditional radar or laser positioning, the method has higher precision and reliability through a visual scheme.

The invention relates to the technical field of robot intelligent control and machine vision, and discloses a robot binocular vision motion mapping method and system, and the method comprises the steps: generating scene perception data containing the three-dimensional information of a target object and joint points of two arms of a human body based on the obtained binocular vision input information and a current operation task instruction; extracting space-time motion features of joint points of two arms of the human body from the scene perception data; and the task-related visual features in the scene perception data are fused with the space-time motion features to obtain fused features, and the fused features are mapped into corresponding robot joint motion tracks through a sequence mapping model. Therefore, the robot motion mapping accuracy and flexibility can be improved.

The application discloses a kind of based on embodiment intelligent large model driving's inspection autonomous learning method, device, equipment and medium, it is related to artificial intelligence technical field, method includes: control multi-sensor acquisition current inspection scene original data and extract feature data, original data includes scene data and / or equipment state data;Feature data and the priori knowledge matching of energy inspection association of light model;When similarity is less than preset threshold, trigger autonomous learning, generate new inspection operation rule for unknown scene, or generate new fault judgment basis for unknown fault;Based on matching result or autonomous learning result, output action instruction;Inspection robot executes instruction and collects entity interaction feedback data;Calibrate action according to feedback data, simultaneously feedback data, new rule or new criterion are uploaded to cloud, and incremental update is carried out to large model, and learning closed loop is formed.The application improves the flexibility and intelligent level of inspection.