32913results about How to "Increase flexibility" patented technology

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

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 relates to a control method and device for an intelligent agent with a body and a readable storage medium. The method comprises the steps of obtaining an instruction input by a user; performing intention recognition on the instruction to generate a current task; calling a large language model, and generating a planned path conforming to the current task based on the large language model and the knowledge graph; decomposing the current task to generate an operation instruction comprising a series of executable action sequences; calling a rule base to ensure that the generated action sequence conforms to various preset rules and operation standards included in the rule base; in the task execution process, environment data fed back by different sensors in the multi-mode perception sensor are collected in real time; and inputting the environment data into the large language model, understanding and processing the environment data by adopting the large language model, and adjusting an operation instruction or planning a path in real time according to a current task and an analysis result of the environment data. The control method provided by the invention is more intelligent, safer and highly adaptive, and can adapt to a more complex task environment.

The invention provides a micro-grid energy-saving scheme generation method and system based on energy storage optimization scheduling, and relates to the technical field of energy storage optimization scheduling, and the method comprises the steps: collecting historical operation data, real-time operation data and external environment data of a micro-grid system, and carrying out the multi-scene prediction through a Monte Carlo simulation method, and generating a multi-scene prediction result; according to a multi-scene prediction result, a fuzzy decision theory and an analytic hierarchy process are adopted, and an optimal microgrid energy-saving scheme is screened out from the solution set; and converting the optimal energy storage scheduling scheme into an intelligent energy-saving operation instruction set, recording a microgrid operation state, an energy storage scheduling result and energy transaction information by using a block chain, calculating a comprehensive performance index based on data recorded by the block chain, and performing distributed evaluation by using a federated learning method according to the comprehensive performance index. And continuously optimizing the micro-grid energy-saving scheme according to the evaluation result of the comprehensive performance index.

The invention discloses an intelligent visualization and text association method for a multi-modal knowledge graph, and relates to the technical field of information processing. According to the intelligent visualization and text association method for the multi-modal knowledge graph, multi-modal data in an internal database of an enterprise is obtained, and a multi-modal feature vector is generated by using a data preprocessing and feature extraction technology; the feature vectors are mapped to a unified vector space through a cross-modal embedding algorithm, semantic alignment, association and fusion among multi-modal data are realized, and a complete multi-modal knowledge graph is constructed. Through bidirectional association and multi-modal joint query, the information acquisition efficiency is improved, and visual display of the knowledge graph is realized by using the graph neural network. Meanwhile, the knowledge graph is dynamically updated through a graph reasoning algorithm, and the timeliness and accuracy of the knowledge graph are ensured. A more efficient solution is provided for an enterprise in the aspects of management and application of multi-modal data, and the intelligent process of knowledge management is promoted.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention discloses a medical visual question-answering method and system based on multi-task modeling, and the method comprises the steps: carrying out the feature extraction of a to-be-analyzed medical image through a visual encoder, obtaining an image feature, inputting a preliminary question instruction and historical question-answering data into a dialogue large model to generate a question instruction, and obtaining a text feature through a text encoder; carrying out feature fusion on the obtained text features and image features, and carrying out self-attention image importance weighting processing and image-text alignment to obtain image-text fusion representation; and inputting a multi-target output projection layer, carrying out multi-task prediction, outputting question answers, image categories and important region masks, inputting the question answers and the image categories into a dialogue large model, and finally generating diagnosis opinions with detailed description in combination with dialogue context and multi-round interaction data and in combination with the important region masks. According to the invention, the interaction experience and the performance of the question and answer system are improved, and more accurate medical diagnosis in multi-modal complex information is facilitated.

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

The invention relates to the field of inductive control of illuminating lamps, in particular to an inductive control system of an inductive LED illuminating lamp. The method comprises the following steps: the lighting scene module is used for identifying all LED lighting lamps in an area; calculating node positions of all the LED lighting lamps in the area one by one, and identifying use scenes in the area to construct a plurality of LED lighting scene sub-areas; the ambient light feature module is used for acquiring ambient light parameters of a scene sub-region; performing light spatial distribution tracking on the ambient light parameters of the scene sub-regions, and performing ambient light state feature mining to generate ambient light comprehensive state features; and the multi-area lighting cooperation module is used for carrying out dynamic light source output parameter adjustment and multi-area lighting cooperation smoothing on the plurality of LED lighting scene sub-areas based on the environment light comprehensive state characteristics so as to generate multi-area lighting cooperation control parameters. According to the invention, rapid and accurate induction control of the LED illuminating lamp is realized.

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention belongs to the technical field of image classification neural networks, and discloses a lightweight image classification neural network architecture system based on knowledge distillation. The invention provides a lightweight image classification neural network architecture system based on knowledge distillation. The lightweight image classification neural network architecture system comprises a data set preprocessing module, a teacher model preprocessing module, a subnet model construction module, a personalized training module, a real loss calculation module, a distillation loss calculation module, a total loss calculation module and a student model construction module. The invention provides a lightweight neural network architecture system based on knowledge distillation, and designs a brand new training mode by using the superiority of knowledge distillation for teacher model weight extraction: the knowledge distillation of teacher and student models is combined with a deep compression method; the system for constructing the lightweight network can be used for training various complex image classification neural networks so as to improve the network performance.

The invention provides a configuration method and system for an overall electrical scheme of a machine room, and relates to the technical field of electrical planning, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out feature extraction and semantic mapping, constructing a high-dimensional semantic association network, carrying out semantic enhancement, semantic modeling and logic association, extracting an abnormal mode, and obtaining a machine room operation state portrait; determining a machine room energy consumption rule and high-energy-consumption equipment, carrying out composite characteristic analysis to obtain a machine room electrical load curve, carrying out hierarchical progressive prediction in combination with exogenous variables, constructing a target function, solving to obtain optimal configuration parameters, and generating a power distribution system design scheme; a self-learning controller is added in a power distribution unit, the electrical load of a machine room is optimized, a power distribution equipment degradation mechanism model is constructed, service life prediction is performed, a power distribution equipment service life management strategy is output, equipment running abnormally is identified, abnormal reasons are judged by referring to historical abnormal data, and an abnormal report is generated and sent to equipment maintenance personnel.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention relates to a data access method and system of an intelligent Internet of Things gateway. The method comprises the following steps: performing feature matching and protocol identification on a plurality of sensors and terminal equipment connected to the intelligent Internet of Things gateway by using an equipment feature library to obtain an equipment protocol type; analyzing the original data to obtain standardized data; performing encryption processing to obtain an encrypted security data packet and an encrypted session key; performing multi-factor transmission channel selection to obtain an optimal transmission channel combination; and based on the optimal transmission channel combination, sending the encrypted security data packet to the Internet of Things platform, verifying the received data confirmation information based on a consensus algorithm, and outputting a data access state report through a data visual interface and an AI voice assistant. The processing strategy can be dynamically adjusted according to the equipment characteristics, the data characteristics and the network environment, and the flexibility and adaptability of the system are improved.

The invention relates to the technical field of digital twinning, and discloses an AGV path optimization method and system based on digital twinning, and the method comprises the steps: carrying out the data standardization processing of AGV real-time coordinate data, environment obstacle distribution data and cargo storage position data, and constructing a storage environment state matrix; aGV task time parameters are calculated, and a task evaluation matrix containing task timeliness indexes and task priority coefficients is obtained; performing multi-target parameter calculation on the AGV motion energy consumption coefficient and the task weight coefficient, and establishing an AGV path optimization target function; performing path planning analysis by adopting a path exchange operator, a path insertion operator and a path inversion operator to generate an AGV initial path set; according to the AGV optimal path planning method and system, the flexibility and the optimization effect of path planning are improved, and the convergence speed and the quality of path optimization are remarkably improved.

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention discloses a hierarchical multi-agent game confrontation and collaborative decision-making algorithm based on federated learning, and the algorithm comprises the following steps: setting a federated learning framework containing a plurality of agents, enabling the agents to be located in different regions, and carrying out the training of a local model through the data of the regions; in the federated learning framework, deploying a layered reinforcement learning model in each intelligent body; performing confrontation training on a plurality of agents in a game environment by using a hierarchical reinforcement learning model; on the basis of game confrontation, a collaborative decision algorithm is introduced to coordinate behaviors of multiple agents; and when a preset training round is reached or a specific condition is satisfied, uploading local model parameters to a central server, and sharing the model parameters through a federal learning framework to carry out global optimization. According to the method, efficient collaboration and strategy optimization among multiple agents can be realized on the premise that privacy is protected, and high adaptability and robustness are kept in a complex and changeable environment.

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

The invention belongs to but not limited to the technical field of emotion recognition, and discloses a multi-modal classroom emotion recognition method and system based on modal adaptive learning, and the method comprises the steps: building a multi-modal data collection system, collecting the multi-modal data, such as texts, audios and videos, in a classroom teaching process in real time, and carrying out the real-time collection of the multi-modal data; and preprocessing and feature extraction are carried out on data of different modes. For emotion expression feature differences of different classroom scenes, a multi-modal data fusion method based on adaptive modal scoring (AMS) is provided, and a time sequence modeling and emotion classification model is constructed. The multi-modal fusion and time sequence modeling mechanism simulates the multi-dimensional perception of the teacher on the emotional states of the students in the class, and enhances the interpretability of the emotion recognition system. According to the system, weighted fusion is carried out on data features from texts, audios and videos through fusion of multi-modal data, and complete emotion feature representation is formed.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention relates to the field of robot attitude control, in particular to a robot attitude control method and system based on intelligent recognition. The method comprises the following steps: continuously acquiring a real-time sandbox scene video stream based on a high-definition camera; and carrying out time sequence frame segmentation on the real-time sandbox scene video stream, and carrying out global contrast enhancement processing to obtain a global contrast optimization image sequence. Carrying out dynamic object identification on the global contrast optimization image sequence, and carrying out continuous frame optical flow motion tracking to generate a dynamic object optical flow change track; optimizing the image sequence according to the global contrast to extract scene ground and scene obstacle data; performing dynamic trajectory rendering on the scene ground and scene obstacle data based on the dynamic object optical flow change trajectory so as to construct a dynamic scene twinborn model; robot position posture parameters and user operation instructions are recognized. According to the invention, timely attitude adjustment response and accurate movement position control are realized.