33results about How to "Achieve understanding" patented technology

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The invention discloses a vehicle trajectory prediction method, electronic equipment and a storage medium, and the method comprises the steps: obtaining vehicle trajectory data, and carrying out the classification processing and feature extraction of the vehicle trajectory data, so as to generate a standardized data set; based on the standardized data set, a vehicle trajectory prediction network model is trained, and the vehicle trajectory prediction network model comprises a time sequence feature extraction module, a space interaction feature extraction module and a decoder; the time sequence feature extraction module is realized based on an improved Mama architecture; the space interaction feature extraction module is used for processing by using a mixed framework of a Transform framework and a graph attention mechanism; the decoder processes the encoded features through a multi-head attention mechanism and considers the relevance between different time steps and different vehicles so as to generate a prediction result of the vehicle trajectory; and inputting the data of the scene to be predicted into the trained vehicle trajectory prediction network model to obtain a prediction result of the vehicle trajectory, and the method has significant advantages in the aspects of calculation efficiency, prediction accuracy and scene adaptability.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention relates to a big data and deep learning-based public opinion analysis method and apparatus, and a medium. The method comprises the steps of collecting text, image and video multi-modal public opinion data of multiple cities; performing cleaning, duplicate removal, word segmentation and stop word filtering preprocessing operation on the collected public opinion data; respectively extracting text features and image or video features by using a natural language processing technology and a computer vision technology, and fusing the features through a multi-modal fusion algorithm; by adopting a distributed system and a distributed task scheduling algorithm, cross-platform and cross-language efficient data acquisition is realized, resource allocation can be dynamically adjusted according to public opinion popularity of each city, the real-time performance and comprehensiveness of key city data are ensured, and meanwhile, through a method of combining a TF-IDF algorithm, an emotion dictionary and deep learning, the real-time performance of the city is improved. The data quality is effectively improved, and a reliable basis is provided for subsequent analysis.

The invention relates to the technical field of medical data processing, and particularly discloses an intelligent medical data processing system and method based on artificial intelligence, and the system employs an image processing technology based on deep learning to carry out the multi-level feature extraction of a reexamination CT scanning image of a chronic disease patient. Texture features and structural features of a bronchiectasis area in a CT scanning image are mined, and fine-grained semantic interactive fusion based on core association features is performed on the texture features and the structural features of the bronchiectasis state, so that comprehensive understanding of the bronchiectasis state is realized, and then the bronchiectasis type of bronchiectasis is intelligently identified. Therefore, the identification precision of the bronchiectasis type can be effectively improved, and a more accurate diagnosis basis is provided for doctors.

The invention provides a self-supervised medical image segmentation method and system based on structural relation modeling, and the method comprises the steps: carrying out the modeling of a complex relation among nodes, sub-graphs and a whole graph, thereby comprehensively capturing the structural information of the graph; by constructing and optimizing the structural relation model of the graph, the performance of graph representation learning is improved, the method is particularly suitable for graph data with a complex topological structure and non-uniform distribution, and the segmentation effect of the medical image is improved by modeling the organization structure relation in the medical image.

The invention provides a multi-core consistency test method and device, electronic equipment and a storage medium, and relates to the technical field of communication. Comprising the following steps: determining a plurality of test parameters and a value range of each test parameter according to a test requirement of tested equipment; generating a plurality of groups of test parameter combinations according to the value range of each test parameter; according to the multiple groups of test parameter combinations, creating a test program corresponding to each processing core in a plurality of processing cores participating in the test in the tested equipment, and deploying the test program corresponding to each processing core to each processing core; running the test program on each processing core, and controlling the corresponding processing core to perform consistency test verification by the test program according to each group of test parameter combination to obtain a test result corresponding to each group of test parameter combination; and according to the test result corresponding to each group of test parameter combination, analyzing to obtain a consistency test result between the processing cores. According to the method, the test parameter combination capable of comprehensively covering more test scenes can be generated, and the test comprehensiveness and accuracy are improved.

The invention belongs to the technical field of system simulation, and relates to a joint simulation model integration method and tool and a medium. The method comprises the following steps: firstly, analyzing semantics of a model, including meta-model data analysis, conceptual model description analysis, static semantic analysis and dynamic semantic analysis; then performing interface function event association mapping to obtain a mapping relationship between a model interface and a standard interface; next, configuring a model integration environment, including a running environment, model assembly and scheduling parameters, a generation directory file, an agent model / model scheduling file and the like; and finally, integrating and packaging the packaged proxy model / model scheduling file and the like, and sending the packaged file to a model resource library. According to the method, automatic integration of most types of models (including components, entities, semi-physical models and systems) in the technical field of system simulation can be realized, the integrated polymorphic models can be expanded and reused, the simulation efficiency is greatly improved, and various existing model resources are fully utilized to shorten the integration period.

The invention discloses an explosive and powder safety information management system, and belongs to the technical field of explosive and powder management. The system comprises an information acquisition module, an explosive and powder state monitoring module, an explosive and powder safety evaluation module, a data processing module, an early warning and control module and an emergency plan storage module, the information acquisition module acquires safety information of explosives and powders; the explosive and powder state monitoring module predicts a potential explosion risk; the explosive and powder safety evaluation module monitors and analyzes stress distribution conditions of explosives and powders in storage and transportation processes; the data processing module is used for comprehensively analyzing potential explosion risks and stress distribution conditions of explosives and powders; the early warning and control module sends out an early warning signal; and the emergency plan storage module stores and calls a corresponding emergency plan. According to the invention, the monitoring precision and response speed of the system are improved, and the safety and reliability of explosive and powder management are ensured.

This invention discloses a method for generating poses of articulated objects based on physical perception and graph diffusion, relating to the field of computer vision. The method first reconstructs the componentized geometric and physical properties of objects from images using a bi-branch neural implicit network and initializes component connectivity relationships. Subsequently, it refines the component relationship graph through kinematic fitting and temporal consistency checks, and uses a physically enhanced graph diffusion process to infer the prior pose distribution of components on an SE(3) manifold. Finally, using this prior and reconstructed information as conditions, a conditional diffusion model is constructed on the SE(3) manifold, and backsampling is performed through a physically guided two-step backsampling framework to generate a diverse and physically plausible set of pose assumptions for articulated objects. This invention achieves efficient generation of diverse and highly physically plausible poses of articulated objects by tightly coupling physical laws with data-driven generation.

The invention discloses a sterile disinfectable mouse excrement classified collection device which comprises an experimental cage, a plurality of partition plates are arranged in the experimental cage to divide the experimental cage into a plurality of compartments to realize classified placement of a plurality of mice, and a mouse activity layer assembly is arranged in each compartment. Excrement collecting assemblies are arranged at the bottoms of the mouse activity layer assemblies, a first synchronous belt and a plurality of rotating discs are driven to rotate by utilizing timed starting of a motor, so that a second transmission belt and first transmission equipment are driven to continuously change an inclined state and a horizontal state, and the sterile mouse living experiment cage is realized in the process; regular work and rest of mice are achieved, mouse feces can be conveniently known and collected at regular time for collection experiments, the effects of classified collection, sterile collection and automatic timing collection are achieved, and the whole process is more controllable for experimenters.

The present invention discloses a data management method and system based on a knowledge graph, including: obtaining historical record data of various fields and standardizing it to obtain standardized data corresponding to each field; constructing a knowledge graph of the corresponding field based on the standardized data corresponding to each field to obtain a standard knowledge graph library; obtaining data to be processed and determining the field information of the data to be processed; querying the standard knowledge graph library based on the field information of the data to be processed to determine a target knowledge graph of the field corresponding to the data to be processed; managing the data to be processed based on the target knowledge graph; constructing knowledge graphs for different fields, and determining the target knowledge graph based on the field of the data to be processed, so as to more accurately classify and manage the data to be processed and improve the efficiency and accuracy of data management.

The invention discloses a work task prompting method and system, and relates to the technical field of task management, and the method comprises the steps: obtaining the work data of an operator in a work task management system, and constructing a task prediction model; analyzing task completion history and working habits of operators, predicting task requirements through a task prediction model, and performing priority setting of working tasks; and performing work task prompting, and comparing work task completion efficiency to adjust the task prediction model. According to the invention, the reasonability and accuracy of task allocation and the accuracy and efficiency of task allocation can be improved. It can be ensured that key tasks are processed preferentially, and the overall working efficiency and task completion quality are improved. The task demand is better predicted, more reasonable task prompt and priority setting are provided, and the efficiency and accuracy of the task management system are improved.

The invention discloses a message pushing method, system and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: searching and acquiring a plurality of messages related to information to be concerned from a plurality of preset information source services according to the information to be concerned preset by a user; generating a summary text corresponding to each message through a large language model; obtaining a text vector corresponding to each message; sequentially aiming at the text vector corresponding to each message, searching in a vector database and judging the similarity between the text vector corresponding to the message and a database text vector; and determining a target message based on a similarity retrieval result, storing a text vector corresponding to the target message into a vector database, and pushing a summary text corresponding to the target message to the user. According to the method, the technical problems of high labor cost, incapability of realizing customization, incapability of automatically summarizing message contents and high possibility of repeated pushing in the prior art are solved.

The invention relates to a boxing quality on-line monitoring system and method based on vision AI, the system comprises a digital industrial camera, an industrial computer, a controller and a front light source which are connected in sequence, the controller is further connected with an execution mechanism on a production line, the digital industrial camera is used for shooting an image of an internal area of the packaging box directly seen from the top of the packaging box, and processing the image by using a preset visual AI model; the industrial computer is used for comparing and analyzing the processed image to determine a detection result and outputting a corresponding control signal to the controller according to the detection result; the controller is used for correspondingly controlling the working state of the executing mechanism and adjusting the working parameters of the front light source. Compared with the prior art, the digital industrial camera is combined with the front light source for image collection, the high-precision AI image recognition technology and the automatic control technology are combined, the detection efficiency can be improved, the labor cost can be saved, and the boxing integrity and accuracy can be ensured.

The present application belongs to the technical field of generator equipment monitoring, and particularly relates to a method for preventing the sparking of a generator excitation brush. First, a monitoring module is arranged at the rotor excitation brush of the generator to obtain a digital switching signal and an analog signal of sparking frequency when the brush sparks. After the digital switching signal and the analog signal are preprocessed, it is determined whether the signal threshold is exceeded. If so, a de-excitation signal is generated. According to the de-excitation signal result, a solution is matched and pushed to the user. The present application can solve the problem of poor monitoring effect of the generator excitation system in the prior art.

The invention discloses an ancient poetry-to-video generation method and device based on a large language model, and relates to the technical field of video generation. The method comprises the steps of constructing an ancient poetry knowledge base; according to the large language model, adopting a Soft Prompt training method to obtain a trained story learning vector; according to the to-be-generated ancient poetry data, the corresponding text knowledge, the trained story learning vector and the large language model, obtaining a story corresponding to the to-be-generated ancient poetry data; obtaining a script corresponding to the to-be-generated ancient poetry data according to a story corresponding to the to-be-generated ancient poetry data, a preset cue word and a large language model; according to the script, the picture knowledge and a picture generation model, generating a story picture corresponding to the to-be-generated ancient poetry data; and generating a video corresponding to the to-be-generated ancient poetry data according to the story picture and a video generation model. By adopting the method, the problem that ancient poetry video data sets are deficient can be solved, and the generation quality of the ancient poetry video is improved.

The invention relates to the technical field of commercial data analysis, in particular to an intelligent demand prediction analysis method and system for commodity auction. The method comprises the following steps: acquiring auction item ontology data; analyzing auction item ontology feature data according to the auction item ontology data; performing dynamic association network analysis on the auction item ontology based on the auction item ontology feature data to generate a dynamic auction item ontology association network; detecting user interaction data; performing user interaction behavior feature analysis processing according to the user interaction data to generate user interaction behavior feature data; performing instant user demand auction probability prediction based on the user interaction behavior feature data to generate instant user demand auction probability prediction data; and transmitting the instant user demand auction probability prediction data to a dynamic auction body association network to carry out instant user association demand auction intelligent analysis, and generating instant user association demand auction prediction data. According to the invention, the accuracy and efficiency of demand prediction in the commodity auction platform are improved.

The invention discloses a multi-target optimized multi-model comprehensive energy system configuration method. The method comprises the following steps: establishing a wind power generation mathematical model, a photovoltaic power generation mathematical model and an energy storage battery charging and discharging mathematical model; based on a load following strategy, a multi-target optimized multi-model integrated energy system operation model is established with the load missing rate as a first target, the minimum electricity cost as a second target and capacity limitation, power balance and energy storage battery output limitation as constraint conditions; and solving the model by adopting a network search algorithm to obtain an optimal configuration scheme of the integrated energy system, which meets the load missing rate and the lowest cost per kilowatt-hour. According to the method, on the premise of meeting the load power supply target, the load following control strategy and the network search algorithm are adopted, the optimal configuration scheme of the comprehensive energy system meeting the load missing rate and the minimum electricity cost is obtained, the power generation efficiency of the comprehensive energy system is improved, and the stability and economical efficiency of operation of the comprehensive energy system are guaranteed.

The invention relates to the technical field of air traffic control, in particular to an air-ground conversation intelligent semantic understanding method based on a multi-task hierarchical network, which comprises the following steps: encoding air-ground conversation corpus data to obtain word level representation, sentence level representation and word level mask representation; performing knowledge distillation processing to obtain slot position representation; performing role attention processing and intention attention processing on the sentence level representation, and performing role-intention fusion to obtain role representation and intention representation; performing semantic level bidirectional enhancement interaction processing on the slot position representation, the role representation and the intention representation to obtain word-level features and sentence-level features; a multi-task decoder is adopted to decode the word-level features and the sentence-level features, and a predicted slot position, a predicted role and a predicted intention of the air-ground communication corpus data are obtained; according to the invention, comprehensive and meticulous intelligent semantic understanding of air-ground communication can be realized.

The invention relates to an intelligent management method and system for diabetics based on artificial intelligence. The method comprises the following steps: constructing a text data set and an image data set at a specified time point; carrying out text feature extraction and image feature extraction, and carrying out feature conversion on image feature data to obtain corresponding text features; performing feature fusion processing on the text features based on the text data and the text features based on the image data at the same time point according to a specified time point; creating and training a disease development prediction model, and inputting feature fusion data to predict the disease at discrete time points; acquiring the disease development condition of the whole time period and the disease development condition of each time period, and performing planning and diagnosis and treatment planning; and obtaining examination data after a specified period, predicting illness state data at the same time point by the illness state development prediction model, and if the illness state is more serious than the predicted illness state, adjusting diet and diagnosis and treatment plans. According to the process, the individuation of the diabetics and the intelligent management of the whole life cycle are realized.

The invention provides an intention recognition method and system for programming technical issues, and belongs to the technical field of software engineering.The method comprises the steps that the programming technical issues of a target platform are collected and screened; performing intention annotation on the programming technical problem by adopting an iterative statistical sampling mode, and constructing a multi-level intention classification system to obtain a basic data set; performing data enhancement on samples in the basic data set based on a large language model to obtain an enhanced data set; and training an intention recognizer through the enhanced data set, and carrying out intention recognition on the to-be-tested programming technical problem through the trained intention recognizer. According to the method, the multi-level intention classification system is constructed and the intention recognizer is trained, so that understanding of deep semantics of programming technical problems is realized, the problem of category imbalance in a data set can be relieved through a data enhancement mechanism, and the intention recognition accuracy is improved.

The invention relates to a video recall method and device, a storage medium and electronic equipment. The method comprises the following steps: when a video uploading request is received, acquiring a first video requested to be uploaded by the video uploading request and a video tag of the first video; when the first video comprises an associated video, constructing a label relation tree according to a video label of the first video and a video label of the associated video; when a video search request of a target user is received, determining a searched target video according to the video search request; under the condition that the video tag of the target video is located in the tag relation tree, obtaining all videos corresponding to all tags in the tag relation tree; and pushing the video identifiers of all videos to the target user. The technical problems that a series of intentions of a user cannot be understood and videos in all related series cannot be effectively recalled are solved.

This invention belongs to the field of intelligent transportation and artificial intelligence technology, specifically disclosing a method for traffic flow clustering and prediction based on semantic similarity of road nodes. The steps include: road traffic data modeling, node semantic vector construction, semantic similarity matrix calculation, semantic mask matrix construction, a spatial attention mechanism based on the semantic mask, and clustering and prediction output. This method analyzes the semantic similarity between road nodes to cluster and model traffic flow data, thereby more accurately capturing local and global spatiotemporal dependencies and improving the accuracy and robustness of traffic flow prediction. This technology can be widely applied in urban traffic management, route planning, congestion early warning, and other fields, providing core decision support for intelligent transportation systems.

The invention provides a bionic long video understanding method and a related device, and the method comprises the steps: obtaining long video content data, carrying out the video preprocessing of the long video content data, obtaining a plurality of short video segments, and enabling the short video segments to at least comprise video segment sizes, video subtitles and open vocabularies; storing the plurality of short video clips in a vector database, and meanwhile, receiving a dynamic task problem sent by a user side by the vector database; and finally, according to the plurality of short video clips, calling a multi-modal large model to perform task evaluation, decomposition, planning and tool calling on the dynamic task problem so as to realize long video understanding. According to the technical scheme, the purposes of reducing the calculation amount, improving the calculation efficiency and quickly adapting to downstream tasks are achieved by observing the process of searching and understanding the long video by human beings.

The visual position recognition method and device, computer device and storage medium provided in the application relate to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target image, and performing multi-scale feature extraction on the target image to obtain a plurality of initial feature maps; performing channel dimension unification on each initial feature map to obtain a target feature map; taking each target feature map as a current feature map in turn, and performing attention fusion based on the current feature map and all target feature maps to obtain a current attention feature map; updating a historical position memory matrix according to the current attention feature map to obtain a current position memory matrix, and then performing position enhancement to obtain a position enhancement feature map; performing feature fusion on a plurality of position enhancement feature maps to obtain a position embedding vector, and then performing position detection to obtain a target position. The application is suitable for the financial field and the medical field, and can solve the technical problem of decreased visual position recognition accuracy in a dynamically changing environment.

The present invention discloses a method and system for generating virtual character interactive content based on behavior analysis, and belongs to the field of artificial intelligence technology. The present invention first realizes real-time capture and understanding of user behavior by efficiently acquiring and converting user interaction behavior logs into data sets that can be processed by AI models. Secondly, multimodal behavior semantic mining technology is used to deeply explore the potential needs behind user behavior, so that the response of virtual characters is more accurate and personalized. Further, the behavioral response features are decoded based on the mined potential demand features, and a rich and diverse virtual character response behavior data set is generated, which greatly enriches the interactive content. Finally, these response contents are seamlessly converted back to the visual interface state, so that users can instantly feel the intelligent feedback of the virtual character. In this way, not only the timeliness of user interaction is improved and delays are reduced, but also the fluency of interaction and personalized experience are greatly enhanced.

The utility model relates to the technical field of environment monitoring, and discloses an equipment operation environment data monitoring integrated tower which comprises a tower body. The atmosphere grading monitoring module is fixed on the tower body; the lower layer monitoring module is arranged at the lower part of the tower body and is used for monitoring soil and vegetation parameters; the data acquisition case is arranged on the tower body, and the data acquisition case is electrically connected with the atmosphere grading monitoring module and the lower layer monitoring module; and the upper computer is electrically connected with the data acquisition case. According to the utility model, the atmosphere grading monitoring module is arranged to monitor parameters of multiple levels in the atmosphere in real time, the lower layer monitoring module is adopted to monitor parameters of soil and vegetation in real time, and the atmosphere grading monitoring module and the lower layer monitoring module are arranged on the integrated tower, so that the parameter acquisition of the integrated tower is wide; and the environmental data are transmitted to the upper computer through the data acquisition case, so that the environmental data can be comprehensively monitored, and the overall environmental state can be known and mastered.

The present invention relates to the field of commercial data analysis technology, and in particular to an intelligent demand prediction and analysis method and system for commodity auctions. The method comprises the following steps: acquiring auction item data; analyzing auction item feature data according to the auction item data; performing dynamic association network analysis of the auction item based on the auction item feature data to generate a dynamic auction item association network; detecting user interaction data; performing user interaction behavior feature analysis and processing according to the user interaction data to generate user interaction behavior feature data; performing instant user demand auction item probability prediction based on the user interaction behavior feature data to generate instant user demand auction item probability prediction data; transmitting the instant user demand auction item probability prediction data to the dynamic auction item association network to perform instant user associated demand auction item intelligent analysis to generate instant user associated demand auction item prediction data. The present invention achieves the improvement of the accuracy and efficiency of demand prediction in the commodity auction platform.

The invention provides an index query method and device for a cloud product, equipment, a storage medium and a product, and relates to the technical field of cloud computation.The method comprises the steps that an index query request of the cloud product is obtained, and an index storage model and an index computing mode are obtained according to the index query request; based on the index storage model and the index calculation mode, determining a query result corresponding to the index query request; the query result represents a cloud resource use state of the cloud product, and the cloud resource use state is used for cloud resource optimization; wherein the index storage model is determined according to the common attribute of the index data corresponding to the plurality of cloud products; the index calculation mode is determined according to the common calculation logic of the index data corresponding to the plurality of cloud products. In the embodiment of the invention, the query result is determined according to the unified index storage model and the index calculation model, so that a user can know the use state of the cloud resources conveniently, and the user can make a decision according to the use state of the cloud resources conveniently.