37482 results about "Roboty" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention discloses a target recognition and grabbing posture planning system for a collaborative robot, and belongs to the technical field of mechanical arms. According to material posture information provided by a sensing module, the optimal grabbing posture is intelligently matched from a preset grabbing strategy library by fusing mechanical arm movement track parameters and target material physical characteristics; the initial accuracy of the grabbing scheme is ensured, in the grabbing process, the camera continuously collects the material poses in real time, and the offset is accurately calculated by comparing the real-time poses with the expected poses. Once material deviation is detected, a visual and force closed-loop adjusting mechanism is triggered immediately, a force-position hybrid control model is utilized, position deviation is synchronously corrected, and grabbing force parameters are dynamically adjusted, so that the grabbing stability and precision are remarkably improved, the grabbing failure rate is effectively reduced, manual intervention and reset time are greatly shortened, and the grabbing efficiency is improved. Therefore, the production efficiency is comprehensively improved.

The invention discloses a multi-door-lock cooperative processing method and system, and the method comprises the steps: generating an assistance request IGMP multicast message according to a preset triggering operation detected by a first door lock B and read RFID identification information, and transmitting the assistance request IGMP multicast message to a router; according to the assistance request IGMP multicast message, recording interface information and validity period associated with the RFID identification information in a multicast forwarding table entry, and caching the multicast message; according to the preset triggering operation detected by the second door lock A and the same read RFID identification information, an assisted request multicast message containing the RFID identification information is generated, and the assisted request multicast message is sent to the router; according to the RFID identification information and the multicast forwarding table item, after the validity period is verified to be not expired, the message is directionally forwarded to the first door lock B, the first door lock B sends an assistance confirmation message to the second door lock A, and a robot calling instruction is triggered. According to the embodiment of the invention, low-delay and high-reliability door lock cooperative control can be realized.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a rear shell, and a frontal shell coupled to the rear shell to define a head volume between the frontal shell and the rear shell, and an electronics assembly with a display located in the head volume between the frontal shell and the rear shell. The frontal shell of the head housing assembly includes: a first arc length at a first location below the display, a second arc length at a second location aligned with a portion of the display, and wherein said second arc length is greater than the first arc length, and a third arc length at a third location above the display, and wherein said third arc length is greater than both the first arc length and the second arc length.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

The invention relates to the technical field of robot path planning, and discloses a robot path planning method based on reinforcement learning. The method comprises the following steps: acquiring environment depth information and an obstacle movement track through a multi-sensor array, constructing a dynamic environment sensing network, and generating an environment state tensor under space-time constraint; building a hierarchical reinforcement learning framework, and optimizing the motion track of the robot in stages by adopting a strategy gradient algorithm to obtain an initial path strategy; designing a reward function calculation model based on an attention mechanism, and accounting an action value in real time according to an environment state tensor; deploying a distributed experience playback buffer pool, and performing priority sampling and track fragment recombination on historical decision data; and establishing a strategy iterative optimization mechanism, and searching and dynamically correcting an initial path strategy by utilizing a Monte Carlo tree. The method can accurately adapt to the dynamic environment, optimize the path decision efficiency, enhance the adaptability and reliability of robot path planning, and is suitable for various robot autonomous operation scenes.

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

A multimodal shared telerobotic system and method for a three-arm space robot, the system at least includes a local-site system, a communication module, and a remote-site system, where the local-site system includes two force-feedback haptic devices for left and right hands, a microphone array, and upper computer software; the remote-site system includes two robotic arms provided with end-effectors, an observation arm with a stereo camera installed at an end thereof, two force sensors, a vision unit and lower computer software; an operator can control the two robotic arms of the robot outside a cabin for performing operations, and control the observation arm to obtain a better local view; and a multimodal telerobotic control method of pose control, voice control, and force control is integrated with the robot's autonomous control through a shared control algorithm.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health and the field of financial transaction, discloses an action generation method and device based on multi-modal pre-training, a robot and a medium, and is applied to a high-frequency action generation scene of an intelligent surgical robot or an intelligent customer service and wealth management scene. The method comprises the following steps: acquiring a language instruction, a visual image and robot body sensing data; performing multi-modal feature alignment and cross-modal feature fusion on the language instruction, the visual image and the robot body sensing data through a pre-trained visual language model to generate a fused joint feature vector; generating a target continuous control instruction based on the fused joint features through a pre-trained target action model by adopting a flow matching technology; and generating continuous actions of the robot based on the target continuous control instruction. According to the method, the robot action generation efficiency and the cross-platform adaptability are improved.

A humanoid robot includes a torso, a left arm assembly coupled to the torso and having a first reference line, a left wrist coupled to the left arm assembly and including at least a rotational axis, and a left end effector coupled to the left wrist. The left end effector is configured to move about the rotational axis and includes a finger assembly with a second reference line and at least two degrees of freedom, and a thumb assembly with at least three degrees of freedom. A first angle is formed between the first and second reference lines when the left wrist is in a first configuration, and a second angle is formed when the left wrist is in a second configuration. Both the first and second angles are greater than 70 degrees, and the difference between the first and second angles is greater than 150 degrees.

The embodiment of the invention provides an unhooking robot control method based on multi-source visual fusion, which is applied to the technical field of robot control and comprises the following steps: acquiring an RGB image, a depth image, an infrared image and IMU data through a multi-source sensing system mounted at the tail end of a robot; carrying out feature fusion identification by adopting a double-branch neural network, and outputting the boundary contour of the lifting hook and the three-dimensional coordinates of the optimal grabbing point; the visual coordinates are unified to a robot base coordinate system through a registration correction mechanism; a Transform prediction model is constructed based on the visual and inertial signals, and future pose changes of the lifting hook are estimated; a feedforward control track is generated to counteract swing of the lifting hook, and track correction is carried out in combination with visual servo feedback; and a joint instruction is generated through path planning and inverse kinematics solution, and the mechanical arm is driven to complete precise unhooking operation. According to the method, the recognition precision, the anti-interference capability and the operation success rate of unhooking operation in complex illumination and dynamic environments are effectively improved.

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The present disclosure provides a humanoid robot with an arrangement of components that allows the robot to mimic the movements, functionality and capabilities of a human being. The robot includes a torso coupled to a waist, an arm assembly, and a head assembly. A pelvis is coupled to the waist and has left and right actuator mounts. Left and right hip assemblies are coupled to the respective actuator mounts. Each hip assembly includes a hip pitch actuator assembly, a hip roll actuator assembly, and a leg twist actuator assembly. The hip pitch actuator assembly has a portion positioned within the pelvis and is coupled to the actuator mount. The hip roll actuator assembly is coupled to the hip pitch actuator assembly, with a non-90 degree angle formed between their axes. The leg twist actuator assembly is coupled to the hip roll actuator assembly and positioned below extents of both the hip pitch and hip roll actuator assemblies.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention discloses a mechanical arm motion control method based on multi-agent cooperation, and the method comprises the steps: firstly, receiving an RGB image through a sub-task generation agent, and generating a structured sub-task sequence according to a natural language task instruction of the RGB image; secondly, performing joint modeling on a task text and a scene image through a 3D sensing intelligent body, positioning specific coordinates of a target object in a three-dimensional space, reasoning dynamic characteristics of a current environment based on historical state information of a robot by combining an environment sensor, and generating an environment sensing vector; and finally, the action generation agent performs fusion modeling according to the subtask text, the subtask target coordinates, the current state of the robot and the environment perception vector, generates a continuous action vector, drives a mechanical arm to complete each subtask action, and constructs closed-loop feedback by a controller and a discriminator to realize task execution state judgment and automatic circulation. The precise action control instruction can be effectively generated, and the task execution fineness of the mechanical arm is remarkably improved.

The invention discloses a sparse reward environment-oriented collaborative knowledge fusion reinforcement learning method, and relates to the field of collaborative knowledge fusion reinforcement learning methods. By constructing a lightweight collaborative knowledge fusion model and a dynamic reward remodeling mechanism, the problems of low intelligent agent exploration efficiency and difficulty in strategy convergence in a sparse reward environment are solved. The method comprises the following steps: constructing a reinforcement learning framework comprising a policy network and a value network; designing an action space mutation supervision mechanism and a lightweight collaborative knowledge fusion model, and generating a smooth substitution action when a strategy is detected to be unstable; and a reward function is designed in combination with the task target and the dynamic constraint, and reward remodeling is realized by activating rewards through sub-target potential energy difference and knowledge fusion. According to the method, effective intermediate feedback can be provided for the agents in the sparse reward environment, the exploration efficiency is improved, the convergence time is shortened, the stability and cross-scene migration ability of the strategy are enhanced, and an effective solution is provided for sparse reward scenes such as robot control and multi-agent game.

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

The invention relates to the technical field of robot control, and discloses a robot control method, system, equipment and medium based on a multi-modal large model, and the method comprises the steps: collecting the multi-source modal data of a scene where an operation task is located, and carrying out the processing through a machine learning model, obtaining a multi-modal feature, and carrying out the position coding and Transform fusion processing, multi-modal fusion features are obtained, the multi-modal fusion features and the constructed job task knowledge base are input into a large language model to decompose a target job task, a human-in-the-loop mechanism is introduced to optimize a decomposition result, and a sub-task sequence is obtained; according to a subtask type in the subtask sequence, processing the subtask sequence through a visual language action model or a reinforcement learning model, and generating a motion instruction to enable the robot to start an execution process of the target operation task; live-line work tasks are processed through the multi-modal large models LLM, VLA and the like, and the work efficiency of the autonomous distribution network live-line work robot is improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of robots, and discloses a multi-robot cooperation control method and system, and the system comprises an environment sensing module, a robot state monitoring module, a task cooperation center, a real-time communication network, a cooperation efficiency evaluation module, and a dynamic optimization execution module. A time and energy consumption dual-target optimization model is constructed through a distributed task allocation mechanism, environment obstacle distribution and robot state parameters are fused in real time, the matching degree of task requirements and robot execution capacity can be verified in the initial planning stage, the risk of task interruption caused by sudden abnormity is reduced, and the task planning efficiency is improved. Meanwhile, the task allocation relation is automatically adjusted based on a dynamic priority strategy, and the system resource scheduling efficiency and the energy consumption balance are improved; when a robot moving path is generated, coupling strength analysis is carried out on a path crossing area through a space-time conflict prediction model, and the dynamic obstacle avoidance capability and response real-time performance of the system are enhanced.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.