1830 results about "Human–robot interaction" patented technology

The embodiment of the invention provides an excavator handle control system for remote operation, a handle control method based on a Hall sensor is adopted, non-contact rocker data sensing is achieved, the influence of contact and movement abrasion on the service life of a handle is reduced, and the excavator handle control system has the advantages of being high in sensitivity and good in performance; the handle mechanism is more flexible to operate, and the service life of the handle is prolonged; a two-hand cooperative control mode is adopted, and two three-degree-of-freedom handles are used for simulating the driving environment of a real excavator, so that a good remote control effect is achieved, and the feeling of presence of an operator in the remote operation process is improved; the operation habits of a driver are fully considered, the walking and working of the excavator are respectively controlled through combination of different degrees of freedom of the left hand handle and the right hand handle, an operating lever of the real excavator is simulated in the aspects of structure, motion degree of freedom and working range, and the man-machine interaction environment of excavator driving is restored.

The invention relates to the technical field of multi-modal posture recognition, and relates to a multi-modal posture recognition system based on Transform fusion, which fully excavates the complementary advantages of visual information in spatial detail representation and inertial information in time sequence dynamic capture through space-time alignment of multi-modal data and deep fusion based on an attention mechanism. The accuracy and the stability of attitude estimation under the conditions of visual occlusion, rapid movement and complex illumination are obviously improved; posture optimization is carried out by introducing physical constraints such as bone length constancy and joint movement range limitation, and time domain smoothing and contact state correction are applied, so that the precision of the generated three-dimensional posture sequence is ensured, a solid technical foundation is laid for improving the naturalness, safety and intelligent level of man-machine interaction, and the method is suitable for popularization and application. And meanwhile, reliable application and deep development of the intelligent robot in key fields of service robots, virtual reality, remote cooperation and the like are powerfully promoted.

The invention discloses an Android system user interface interaction method and device based on a large language model, and relates to the technical field of intelligent man-machine interaction, and the method comprises the steps: receiving a natural language instruction of a user, and synchronously capturing a real-time interface state of a current Android screen to generate a structured context containing semantic information of interactive elements; inputting the instruction and the context into a large language model, and generating a hierarchical task plan formed by a plurality of atomic operations with a logic dependency relationship; based on a dynamic adaptation mechanism, converting the atomic operation sequence into an operation instruction which can be executed by the Android system; receiving interface change information after instruction execution, and feeding back the interface change information as a new round of context to the large language model to determine a task execution state; and finally generating natural language feedback information to the user according to the execution state. By means of the mode, complex cross-application tasks can be understood and executed, and interaction intelligence of the Android system is remarkably improved through real-time interface perception and dynamic planning adjustment.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention discloses a method, a system and equipment for voice interaction inside and outside a vehicle and a storage medium, and relates to the technical field of intelligent cabins and human-computer interaction. Identifying an interaction trigger type (in-vehicle active request, out-of-vehicle passive request, or system active trigger) based on the perceived data and / or the vehicle event; determining a corresponding response permission strategy according to the trigger type, and determining whether to allow the system to respond based on the response permission strategy; and if so, calling a large language model to generate a voice text, synthesizing voice by combining external perception characteristics, and broadcasting the voice through a loudspeaker outside the vehicle or a sound box in the vehicle. According to the scheme, three types of interaction intentions including the in-vehicle active request, the out-vehicle passive request or the system active triggering are recognized, the large language model is called to generate the scene-adaptive voice text, and personalized voice synthesis and broadcasting are performed in combination with the external perception characteristics, so that the response efficiency, the expression naturalness and the object adaptability of the in-vehicle and out-vehicle voice interaction are improved.

The invention relates to the technical field of man-machine cooperation, and aims to solve the problems that the traditional man-machine cooperation process mainly depends on a preset flow and timing rhythm control, and is difficult to dynamically capture personnel misoperation, machine response delay or cooperation rhythm imbalance, so that response strategy lagging and low regulation and control efficiency are caused in the face of complex and variable man-machine interaction situations. According to the enterprise intelligent man-machine cooperation method and system, through comprehensive analysis with behavior response offset, cooperation rhythm consistency and a personnel fatigue state as core indexes, a set of intelligent man-machine cooperation process which is distinct in hierarchy, logic closed-loop and sustainable iterative optimization is constructed; the real-time response capability of man-machine cooperation and the production takt time stability are improved, the cooperation efficiency fluctuation can be dynamically monitored, the precise regulation and control strategy is output in time, quantitative evaluation and actual improvement of man-machine cooperation efficiency are achieved, and efficient production of a production line and the quality level of products are guaranteed.

The invention particularly relates to an atomic business intelligent arrangement device and method. The atomic business intelligent arrangement device comprises an atomic business relation graph intelligent construction module, an atomic business fine management module, a business intelligent arrangement and visual arrangement module and a man-machine interaction module. According to the atomic service intelligent arrangement device and method, full-chain intelligentization from user demand identification, service scene analysis to process automatic arrangement is realized, the service handling efficiency and the service quality are improved, manual correction is supported, the error rate is reduced, the reusability of atomic services is improved, policy scenes can be helped to rapidly fall into the ground, and the service quality is improved. And diversified operation requirements of the user are met, so that the user satisfaction is improved.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention relates to the technical field of unmanned aerial vehicle cluster navigation, and discloses an unmanned aerial vehicle cluster navigation method based on man-machine interaction. The method comprises the steps that cluster task parameters and environment constraint conditions input by an operator are efficiently received through a multi-modal interaction interface, the cluster task parameters comprise a target point coordinate set and formation form identification, and the environment constraint conditions cover no-fly zone coordinates and meteorological interference levels. And the system calls a pre-stored topological relation template according to the formation form identifier to generate an initial track, completes space conflict detection in combination with the no-fly zone coordinates, and outputs a corrected track point sequence. And inputting the meteorological interference level into a dynamic anti-interference model to calculate a course correction amount, and superposing the course correction amount to a corrected track point sequence to generate an anti-interference navigation instruction set. And acquiring position data of the unmanned aerial vehicle in real time to calculate a trajectory deviation degree, triggering a manual intervention request if the trajectory deviation degree exceeds a dynamic threshold value, calling a correction instruction, fusing to generate a hybrid control strategy, and distributing the hybrid control strategy to each node of the cluster to realize reliable man-machine collaborative navigation.

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

The invention belongs to the field of man-machine interaction, and particularly discloses a man-machine interaction method for electronic screen control, which comprises the following steps: acquiring touch track data of a user according to a touch sensor, and performing feature extraction on the touch track data to obtain a touch feature vector; the method comprises the following steps: acquiring gesture action data of a user according to image acquisition equipment, and performing key point detection on the gesture action data to obtain a gesture feature vector; the method comprises the following steps: acquiring voice instruction data of a user according to an audio acquisition device, and performing acoustic feature extraction on the voice instruction data to obtain a voice feature vector; acquiring current environment parameters in real time through an environment detection module, wherein the environment parameters comprise illumination intensity, environment noise level and distance between a user and a screen; the objective of the invention is to solve the problem of insufficient reliability of a man-machine interaction mode in a complex environment in the prior art.

The invention provides a backplane video generation method for real-time interactive digital humans and a related device, and relates to the technical field of image generation, in particular to the technical field of artificial intelligence such as human-computer interaction, digital humans, end-cloud integration and large models. The method comprises the following steps: acquiring a plurality of original images presented by the same target person at different angles; generating a digital human image taking a pure color as a background based on the plurality of original images; generating prompt information based on a preset action expression demand and the digital human image; and inputting the prompt information into a preset video generation large model, and generating a digital person bottom plate video which enables a digital person corresponding to the target person to show a target action corresponding to the action expression demand. According to the method, efficient and low-cost generation of the digital human bottom plate video is realized.

The invention relates to the field of human-computer interaction, and discloses a human-computer interaction robot facial expression implementation control method, which comprises the following steps: dynamically capturing the face, sound and posture of a user in a human-computer interaction scene, and generating an initial emotion perception sequence; performing hierarchical cross mapping on the initial emotion perception sequence, and performing combinatorial analysis on facial expressions, voices and intonations and body movement features to form an emotion matching vector; based on the emotion matching vector, utilizing a priority regulation model to predict the emotion trend of the user at the next moment, and identifying an expression enhancement point and an emotion attenuation area; mapping the generated facial micro-expression adjusting instruction to a robot facial driving unit, and performing real-time correction and conflict resolution on an expression action sequence; and reversely fusing a user fixation point, facial muscle micro-motion and voice emotion which are acquired in real time into an emotion matching vector, and dynamically updating a perception weight and expression regulation and control parameters. The method has the advantage of improving the emotion matching degree of the robot expressions.

The invention belongs to the field of equipment fault diagnosis, and particularly discloses an equipment remote fault diagnosis method based on a large model. Through multi-modal data fusion, an attention mechanism reasoning model, man-machine cooperation verification and intelligent resource scheduling, the problems that maintenance excessively depends on expert experience and a mature remote scheme is lacked are solved. Specifically, according to the scheme, firstly, multi-source data are aligned and fused to form a unified feature vector, and the defect of information isolation is overcome. Afterwards, a model based on an attention mechanism can automatically focus key features, preliminary diagnosis is generated, and dependence on expert experience is reduced. And then, through a man-machine interaction verification mechanism, an expert can remotely check and correct a result, so that the diagnosis reliability is ensured. And finally, the system automatically schedules resources according to a verified result, so that rapid linkage from diagnosis to disposal is realized, remote guarantee is accurately executed, and equipment deterioration is effectively restrained.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention discloses a shield construction full-process management and control system and method based on intelligent scheduling, and relates to the technical field of tunnel construction, the system comprises a data acquisition layer, a data fusion and intelligent scheduling layer, an execution control layer and a man-machine interaction and visualization platform. The data acquisition layer acquires multi-source data of shield construction in real time; the data fusion and intelligent scheduling layer performs fusion processing on multi-source data, generates a comprehensive scheduling scheme based on an intelligent algorithm, and dynamically manages task priorities and security constraints; the execution control layer receives and executes the scheme, and cooperatively controls tunneling, transportation, assembly and vertical transportation equipment; the man-machine interaction and visualization platform realizes construction state visualization, scheduling display, abnormity alarm, remote control and multi-work-point data access and authority management. According to the invention, through a multi-layer collaborative architecture, full-process intelligent management and control of shield construction are realized, the data-driven decision-making capability, the equipment collaborative efficiency and the multi-project scheduling level are improved, and the construction efficiency is improved.

The invention discloses a three-dimensional vision measurement system error compensation method based on a spatial adaptive weighted RBF residual neural network, and particularly provides a spatial adaptive residual learning framework SAW-RFramework, in the first stage, global rigid body errors are eliminated based on a Kabsch algorithm, and point cloud rigid alignment is realized; in the second stage, a thin-plate spline kernel RBF interpolator is used for modeling a spatial smoothing error field, and position-related systematic deviation is captured; and in the third stage, a partition weighted residual neural network is adopted, and an improved residual compensation network is proposed for residual high-fluctuation nonlinear residual after RBF modeling: a regional importance coefficient is calculated based on a mean value and a variance of the residual in a self-adaptive three-dimensional grid, so that network training focuses on a high-error and high-fluctuation region. According to the method, the problems of insufficient global spatial modeling capability, low boundary region compensation precision, poor generalization capability and the like of an existing method can be solved; high-precision error compensation of the three-dimensional vision measurement system is realized, the measurement result reaches submillimeter precision, and the application reliability in robot control, man-machine interaction and precision manufacturing is improved.

The embodiment of the invention discloses a gesture generation model training method, a gesture control method and related equipment. According to the main technical scheme, the method comprises the steps that a robot gesture data set filtered through physical constraints is acquired; using a first training sample pair in a robot gesture data set to perform melody alignment training on the initial gesture generation model to obtain a pre-trained gesture generation model, the first training sample pair comprising an audio sample and a corresponding gesture sequence sample; using a second training sample pair in a robot gesture data set to perform semantic alignment training on the pre-trained gesture generation model to obtain a gesture generation model for performing gesture control on the robot, the second training sample pair comprising an audio sample and a semantic sample and a gesture sequence sample corresponding to the audio sample; based on the gesture generation model, accurate gesture control cooperating with robot voice broadcast in real time can be realized, so that the naturalness and reliability of robot gesture control in a man-machine interaction scene are improved.

The invention discloses a flexible pressure sensor based on ionized hydrogel and a preparation method thereof. The sensor adopts a sandwich layered structure of a flexible composite electrode, an ionized water gel dielectric layer and a flexible composite electrode, ionized water gel is used as a dielectric layer, and an upper flexible electrode and a lower flexible electrode are prepared from a multi-walled carbon nanotube / polydimethylsiloxane (MWCNTs / PDMS) composite material; the surface, making contact with the dielectric layer, of the electrode is copied through an abrasive paper template method to form a micron-sized rough structure. Under the action of external pressure, the effective contact area between the microstructure electrode and the ionized water gel dielectric layer and the equivalent electrode distance change, so that the interface electric double-layer capacitance is regulated and controlled, and high-sensitivity conversion from a pressure signal to a capacitance signal is realized. The flexible pressure sensor is simple in structure and excellent in flexibility, can be further integrated into a pressure sensing array, and is suitable for the fields of wearable electronics, electronic skin, man-machine interaction and the like.

The invention discloses a control lever parameter field configuration method and system based on a CAN bus, and belongs to the technical field of control lever parameter field configuration, and the control lever parameter field configuration method comprises the following steps: parameter receiving and analysis, parameter updating and temporary effectiveness, operation feedback, parameter storage, power-down protection and power-on loading. According to the field configuration method and system for the joystick CAN bus parameters, a user can safely and conveniently change communication parameters by himself without returning to a factory, it is ensured that the changed parameters are not lost after power failure, the user can firstly test whether new parameters are valid or not by adopting a two-step mechanism of temporarily taking effect and confirming storage, and then the user can conveniently and rapidly configure the joystick CAN bus parameters. The risk that the system cannot communicate due to misoperation is avoided, and meanwhile operation feedback is provided to enhance man-machine interaction and reliability.

The invention discloses an operation system, method and equipment of a live working robot with an intelligent distribution network, and a storage medium, and relates to the technical field of intelligent distribution networks of robots, and the system comprises a first man-machine interaction module which supports bidirectional interaction with an operator through voice, characters, images and the like; the second multi-mode sensing module senses the equipment state, the obstacle and the environment change in the working environment in real time through a visual sensor, an auditory sensor, a force sensor and the like; the big language model processes and analyzes the instruction through a natural language and generates a task execution step, and adjusts a task strategy according to the perception data; the decision planning module formulates a task path and resource scheduling in combination with a multi-objective optimization algorithm; the safety monitoring module monitors the safety state in the operation process in real time and triggers early warning; the anomaly detection and processing module receives the early warning information and starts an autonomous navigation and path planning technology; according to the system, autonomous and safe operation of the robot is achieved, and task execution efficiency, adaptability and flexibility are improved.

The invention discloses a mooring type low-altitude monitoring countering unmanned system based on deep learning target recognition. The mooring type low-altitude monitoring countering unmanned system comprises a mooring unmanned aerial vehicle platform module, a multi-source sensing and data fusion module, a deep learning target recognition and classification module, a self-adaptive countering decision and execution module, a dynamic tracking and collaborative aiming module and a ground command and control module. The mooring unmanned aerial vehicle platform module forms a stable air monitoring and countering base point; the multi-source sensing and data fusion module provides high-quality input for system identification; the deep learning target recognition and classification module recognizes and predicts a target behavior through deep learning; the self-adaptive countering decision and execution module realizes precise countering; the dynamic tracking and collaborative aiming module applies counter energy to a dynamic target; the ground command and control module provides a human-computer interaction interface and displays global information. The low-altitude active defense system has the advantages that technologies such as multi-source fusion perception, deep learning AI recognition and self-adaptive precise countering are deeply fused, and the low-altitude active defense system is formed.

The invention discloses a cooperative control simulation system for an underwater robot and an umbilical cable system, belongs to the technical field of ocean engineering simulation and automatic control, and is used for analyzing system dynamic characteristics in the research and development stage of deep sea operation equipment. Comprising an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, an instruction generation module, a man-machine interaction control module and a data acquisition and storage module. According to the method, an external interference source with physical authenticity is provided for a control algorithm by combining an energy density function and simulating a composite environment modeling method of a sea level height field and an LOD dynamic grid, and a distributed model prediction control algorithm for uniformly optimizing underwater robot trajectory tracking and umbilical cable tension safety maintenance is constructed; data such as the motion state of the underwater robot and the tension of the umbilical cable can be output in real time, algorithm performance testing is completed in a virtual environment, and dependence on high-cost sea testing is not needed.

The invention discloses a pathology adaptive prediction control method for a lower limb rehabilitation exoskeleton robot, and belongs to the technical field of rehabilitation robot control. The method comprises the steps that the joint angle, the angular speed and the man-machine interaction torque of the lower limb rehabilitation exoskeleton robot are collected in real time, and a kinetic equation of the lower limb rehabilitation exoskeleton robot is constructed; on the basis of the kinetic equation of the lower limb rehabilitation exoskeleton robot, parameter vectors representing the current pathological features of the patient are estimated online, so that a prediction model in the lower limb rehabilitation exoskeleton robot is updated, and the optimal auxiliary torque is solved. The intelligent brain of the exoskeleton robot can be deeply fused with the real-time state of the body of the patient, and rehabilitation training which is really personalized and self-adaptive and can effectively promote neural function recovery is provided for the patient.

The invention relates to the field of man-machine interaction, and particularly provides a task processing method, device, equipment and system, a medium and a product. The method comprises the following steps: in response to a first session instruction of a user for a target type task, executing the target type task; the target type task belongs to a task type capable of receiving supplementary information input by a user during task execution; in the task execution period of the target type task, responding to a received second session instruction input by the user, and executing a corresponding task operation according to the intention of the second session instruction; wherein the intention is used for indicating to input supplementary information of the target type task or initiate a new task. Therefore, on the premise of not interrupting the original target type task, the new task instruction of the user can be responded in parallel or the supplementary input of the user to the target type task can be processed, and the problems of interaction blockage and context chaos caused by the exclusive session state under the existing architecture are solved.

The invention discloses a hydraulic servo control system and method for a mining guniting robot. Belongs to the technical field of servo control. Comprising a hydraulic driving unit, a sensing and feedback unit, an electrical control unit, an embedded host and a man-machine interaction unit, wherein the electric control unit adopts a fuzzy PID (Proportion Integration Differentiation) controller for controlling the joint position of the mechanical arm according to the information of the sensing and feedback unit; aiming at linear synchronous control of chassis walking, an anti-integral saturation speed feedforward PI controller is adopted to adjust the opening size and direction of a hydraulic proportional valve in real time, and closed-loop control of the position is achieved.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

According to the man-machine interaction-oriented multi-modal fine-grained emotion recognition method based on the large model provided by the invention, cross-modal alignment from coarse granularity to fine granularity is realized through an attention pairing interaction module (APIM) on the basis of an aspect-driven vision-text alignment and fusion network (AVTAF); emotion-related visual features (such as facial expressions and gestures) in a robot scene can be accurately captured, and environmental noise is inhibited; meanwhile, the RD-GAT is enhanced, and the reasoning ability of a large model on multi-modal emotion semantics is improved by integrating external emotion knowledge (such as SenticNet). The technology provides a new normal form for intelligent upgrading of robot emotion interaction and multi-modal understanding of a large model, and is expected to promote breakthrough application in the fields of family service robots, medical accompanying assistants, multi-modal content generation and the like.

The invention provides an intelligent control method and system for a body-equipped robot based on man-machine interaction data, and relates to the technical field of medical robots, and the method comprises the steps: building a 5G network two-way transmission channel between a control end and a surgical robot end; through a 5G network two-way transmission channel, synchronously transmitting two types of data streams between the control end and the surgical robot end, namely a control instruction data stream generated by the control end and a surgical image data stream collected by the surgical robot end; in the transmission process of the two types of data streams, real-time network time delay is continuously monitored, whether the current network time delay exceeds a preset time delay threshold value or not is judged, and the preset time delay threshold value can be dynamically corrected according to the network state; and when it is judged that the current network delay exceeds the preset delay threshold, dynamically deploying a plurality of network probes on the 5G network two-way transmission channel. According to the invention, the real-time performance, the accuracy and the safety of the remote operation can be ensured.