243 results about "Joints movement" patented technology

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

The invention belongs to the field of robot motion control, and discloses an online generation and control method and system for whole-body coordinated walking motion of a humanoid robot, a computer readable storage medium and a computer program product. According to the method, in walking motion generation, a whole-body walking motion track including upper body joint motion information is generated in real time by adopting a whole-body kinematics-centroid dynamics model; a model predictive control model is adopted to realize organic combination of motion trail generation and real-time tracking control; and solving the model prediction control model, and taking the optimal state x * and the corresponding control input u * as the input of whole body control. According to the method, in the planning stage, namely the whole-body walking motion trail including upper body joint motion information, the whole-body dynamic characteristics of the arms, the waist and the like are comprehensively considered, and a new method of long-time-domain trajectory optimization and online control is combined, so that real whole-body coordinated walking is fundamentally realized.

The invention discloses an organ chip capable of simulating a physiological microenvironment in a joint and application of the organ chip. The organ chip comprises a cell culture layer 1, an elastic film 2, an air pressure control layer 3, a cell culture channel 4, a cell culture fluid perfusion channel 5, a cell suspension inlet 6, a cell suspension outlet 7, a culture fluid perfusion inlet 8, a culture fluid perfusion outlet 9, a through hole 10, a through hole 11, a first air pressure groove 12, a second air pressure groove 13, a pneumatic chamber 14, a pneumatic chamber 15, a cell bearing platform 16 and a matrix 17. The organ chip can simulate physical factors such as dynamic compression and relative sliding in joint movement in vitro, and the physical factors are combined with chemical stimulation factors.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention discloses a muscle strength prediction method and device based on fusion of a physical model and deep learning, and the method comprises the steps: taking the physical information of neuromusculoskeletal as the soft constraint of a data-driven model, fully considering the dynamic time relation of muscle strength distribution, converting the time series input data into time slice data, and carrying out the prediction of the muscle strength. Kinematic data and EMG signals are used as input, advanced features are automatically extracted through a deep learning algorithm, the relation between the input signals and muscle force is established, muscle force prediction meeting a kinematic equation is achieved, and the defect that an existing model for muscle force calculation generally does not contain definite physical modeling for the potential neuromechanical process, and the calculation accuracy is poor is overcome. And the physical mechanism in the joint movement process is not reflected.

The invention provides an intelligent scoring method and system for joint movement. The method comprises the following steps: acquiring six-degree-of-freedom inertial motion data and surface electromyogram signals, and establishing a motion sequence topological structure containing a space-time reference based on a dynamic sampling strategy; secondly, decomposing inertial data into rigid body and physiological tremor components, and generating a three-dimensional biomechanical model containing joint rotation center drift, multi-plane coupling errors and trajectory curvature; secondly, constructing a time-varying mapping relation between the surface electromyogram signals and the exercise load, segmenting the signals based on phase mutation points, analyzing phase synchronism of an active muscle group and an antagonistic muscle group, and extracting a muscle cooperation mode; and finally, generating a motion feature tensor in a four-dimensional space-time coordinate system through technologies such as space-time coupling and a dynamic transfer function, analyzing an evolution mode of the motion feature tensor in real time to construct a scoring function cluster, and generating a composite scoring value. According to the technical scheme provided by the invention, the accuracy and reliability of joint movement quality evaluation are remarkably improved.

The invention relates to the technical field of motion monitoring, in particular to a running posture monitoring method based on an image recognition technology, which comprises the following steps: deploying cameras at different angles, synchronously recording whole body actions of a user during running, extracting a joint angle change sequence, and constructing a running three-dimensional action model. According to the method, the consistency and the difference of the actions are recognized by comparing the amplitude and the speed of the joint movement, so that the detection of the action defects is facilitated, and the stability and the deviation of the gait can be quantitatively evaluated by analyzing the foot landing angle and the stride change; analysis of the motion change frequency and amplitude allows coaches and athletes to adjust and optimize gaits in a targeted manner in the training process, the risk of sports injury is reduced, and through continuous simulation and motion symmetry evaluation, recognition of sports defects and adjustment of the camera angle and shooting speed, the training effect of the coaches and the athletes is improved. And the data capture and analysis process in the training process is more efficient and targeted.

The invention provides a robot motion control method and device, electronic equipment and a storage medium. The motion control method is used for controlling a shank assembly of the robot, the shank assembly comprises a driving joint and an ankle joint, the driving joint drives the ankle joint to move, and the motion control method comprises the steps that target posture information of the ankle joint and target torque of the ankle joint are obtained; on the basis of the target attitude information of the ankle joint and the target torque of the ankle joint, determining expected attitude information of a driving joint and expected torque of the driving joint; acquiring current attitude information of the driving joint; based on the expected torque of the driving joint, the expected attitude information of the driving joint and the current attitude information of the driving joint, the control torque of the driving joint is determined; and driving joints are controlled to move based on the control torque. By means of the method, universal control over the parallel ankle structure is achieved, and the performance and stability of the robot adopting the parallel ankle structure are ensured.

The invention discloses a joint rehabilitation effect evaluation method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring video data of a target patient executing a preset rehabilitation action; identifying a joint position coordinate point from the video data; a joint movement track is constructed according to the time sequence of the joint position coordinate points, track parameters of the joint movement track are calculated, and the track parameters comprise the movement angle, the track curvature and the symmetry degree; acquiring a speed change sequence and an acceleration change sequence of joint movement in the movement track, and determining a joint rehabilitation index of the target patient based on the track parameter, the speed change sequence and the acceleration change sequence; and comparing the joint rehabilitation index with a reference index corresponding to the preset rehabilitation action, and determining the joint rehabilitation progress of the target patient. By implementing the technical scheme provided by the invention, the effect of improving the evaluation accuracy of the joint rehabilitation effect is achieved.

The invention relates to the technical field of robots, in particular to a dexterous hand and a robot, and solves the problems that the dexterous hand in the related technology is insufficient in rigidity and bearing capacity and cannot independently control joint movement. Two first driving assemblies of the dexterous hand are connected in parallel to drive two first swing arm assemblies to rotate, and the two first swing arm assemblies drive a thumb base to rotate around a first axis or drive a thumb knuckle assembly to rotate around a second axis through two first connecting rod assemblies. Therefore, the independent control of the movement of the thumb knuckle assembly in four directions, namely bidirectional side swaying, stretching and bending, is realized. Besides, the second axis and the first axis are perpendicular and not coplanar, so that the thumb knuckle assembly can resist lateral force borne by the thumb knuckle assembly in the stretching and bending process, the thumb knuckle assembly has higher rigidity and bearing capacity, the rigidity and bearing capacity of the dexterous hand are improved, and the flexibility of the dexterous hand is improved. And the requirements of fine operation and power output of the dexterous hand are met.

The invention provides a wearable motion capture method and system based on a flexible mechanical sensor, and relates to the technical field of motion capture. The wearable motion capture method based on the flexible mechanical sensor is realized by the flexible mechanical sensor, a signal acquisition and wireless transmission unit and an intelligent analysis and interaction unit. The method comprises the following steps: a flexible mechanical sensor monitors an individual joint movement signal in real time, and transmits the movement signal to a signal acquisition and wireless transmission unit to obtain monitoring data of the flexible mechanical sensor; the monitoring data are transmitted to the intelligent analysis and interaction unit, the corresponding relation between the individual motion signals and the motion amplitude and the motion direction is established by calibrating the motion amplitude and the motion direction of the joints, and a calibration result is obtained; and according to a calibration result, three-dimensional motion modeling and attitude restoration of the individual are realized by utilizing a computer technology. According to the invention, the capability of finely capturing the local motion of an individual can be improved, the wearable performance can be improved, and the anti-interference capability is enhanced.

The invention discloses a knee bone joint state recognition method and system based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the steps: obtaining multi-source medical image data of a knee joint region of a patient, carrying out the spatial registration and standardization preprocessing, and constructing a three-dimensional structure model of a knee joint after unifying a coordinate system; establishing a mapping relation between joint movement and an anatomical structure by combining knee vibration and behavior characteristic data collected by a patient in a specific movement state, extracting joint key geometric and mechanical parameters from the three-dimensional structure model, and fusing the joint key geometric and mechanical parameters with synchronously collected vibration behavior characteristics; and performing state recognition on the fused multi-source features based on an intelligent recognition model, and outputting a health state label of the knee joint. According to the invention, more accurate and more comprehensive knee joint health state identification can be realized, the accuracy and reliability of an identification result are effectively improved, and the timeliness of disease discovery and the scientificity of intervention are enhanced.

The invention discloses a bionic joint with a locking mode and a one-dimensional spherical rotation motion mode, which comprises a base, and a first motion branch chain, a second motion branch chain and a third motion branch chain are connected to the base; the robot further comprises a fourth movement branch chain, and the first movement branch chain, the second movement branch chain and the third movement branch chain are all connected with the fourth movement branch chain. According to the bionic joint, the movement modes of the bionic joint can be changed through the driving motor, the output component is in the locked state, locking driving is not needed, and the output component can be kept in the locked state.

The invention discloses an integrated management system and method for postoperative care of orthopedic patients, and relates to the technical field of medical treatment. Comprising a bone mineral density dynamic monitoring value, six-dimensional mechanical parameters of a joint movement track, emotion spectrum characteristics in voice interaction, microenvironment temperature and humidity changes collected by wearable equipment and skin surface pressure distribution; inputting the recovery data into a multi-modal decision-making model based on orthopedic biomechanics improvement, and generating a nursing scheme including a skeleton stress safety interval, a virtual rehabilitation scene dynamic parameter and an interdisciplinary intervention priority; the scheme parameters are dynamically optimized through a layered reinforcement learning framework special for the orthopedics department, and the deformation logic of the 3D printing brace and the correction strategy of the nursing robot are synchronously controlled; based on the postoperative complication space-time prediction map, a multidisciplinary collaborative response instruction strictly matched with the rehabilitation stage of the patient is triggered.

The invention discloses a compensation method and device for a transmission gap of a surgical instrument and a slave end controller, and relates to the field of robot control. The method comprises the following steps: receiving an instrument joint instruction of at least one instrument joint transmitted by a master end of the master-slave surgical robot; for any instrument joint, the movement direction of the instrument joint is recognized; determining the movement speed of the instrument joint based on the instrument joint instruction of the current period and the instrument joint instruction of the previous period; based on the movement speed, the movement direction and the compensation proportionality coefficient of the instrument joint and the clearance compensation angle of the instrument joint in the previous period, the clearance compensation angle of the instrument joint in the current period is determined; and determining a target instrument joint instruction based on the instrument joint instruction of the current period and the clearance compensation angle of the current period, and sending the target instrument joint instruction to a joint motor corresponding to the instrument joint, so that the joint motor drives the instrument joint to move. The position precision of each instrument joint of the surgical instrument can be effectively improved.

The invention provides a humanoid robot joint life monitoring method, and belongs to the technical field of life monitoring. The method comprises the steps that 1, a multi-mode sensor array is constructed to collect original joint motion data of a target robot; 2, standardizing the original joint movement data according to feature types to obtain standard feature data of all parameter types; 3, establishing a joint degradation model of the target robot based on the standard feature data; 4, inputting real-time motion data of the target robot into the joint degradation model, and outputting a joint residual life index; and 5, when the residual life index of the joint is lower than a preset threshold value, a self-adaptive maintenance strategy is triggered. Through real-time monitoring of joint health, early warning and optimization maintenance, the reliability of the robot is improved.

The application relates to a multi-arm mobile robot control system, which comprises a robot, each joint position of the mechanical arm of which is configured with a joint controller for driving joint movement; a robot controller for realizing the operation of joint motors according to control quantities; a joint position hard analysis unit for calculating the coordinate positions of each joint or mechanical arm in a space coordinate system to output results; and a joint space point position trajectory constraint model for forming a correction quantity of a joint and superimposing the correction quantity on an original control quantity of the joint to become a final control quantity for controlling the joint, the final control quantity being used for output to the joint controller to realize joint movement. The application provides a multi-arm mobile robot control system, which effectively realizes the cooperative control among multi-arm robots, avoids the mutual collision among mechanical arms, improves the avoidance and dynamic response speed of the mechanical arms, and improves the agility of robot actions.

The invention discloses a mechanical arm state limited preset time point control method and system, and relates to the technical field of robot control, and the method comprises the steps: obtaining a joint position tracking error and a joint speed tracking error based on a joint motion state and an expected trajectory of a mechanical arm; based on constrained system states corresponding to the joint position tracking error and the joint speed tracking error, nonlinear transformation is carried out on the joint position tracking error and the joint speed tracking error through an unconstrained state function, and an equivalent unconstrained virtual error variable is constructed in combination with coordinate transformation; based on the unconstrained virtual error variable, designing a sectional sliding mode variable; and designing a second-order reaching law based on the sectional sliding mode variable to form a closed-loop dynamic state, and solving based on the closed-loop dynamic state to obtain a control torque so as to control the mechanical arm joint. According to the method, the trajectory tracking error of the mechanical arm can be converged at the moment specified by a user, the system state is ensured to meet strict physical constraints in the whole process, and the method can be applied to accurate trajectory control in a complex task scene.

The invention relates to the technical field of electric digital data processing, in particular to an intelligent early warning method and system for joint rehabilitation medical equipment, and the method comprises the steps: obtaining the heart rate and joint motion resistance of a user at a plurality of historical moments and in the rehabilitation training process at the current moment; fitting based on the joint movement resistance of the user at multiple historical moments to obtain a resistance safety baseline, and determining a resistance threshold value according to a mean value and a standard deviation of the resistance safety baseline within a set time; if the heart rate of the user at the current moment exceeds a set first threshold value or the joint movement resistance of the user at the current moment exceeds a resistance threshold value for a preset duration, triggering an alarm and stopping rehabilitation training. According to the invention, the problem that self-adaptive switching and intensity optimization adjustment of training modes cannot be realized is solved.

The invention provides a multi-joint control method and device, electronic equipment, a medium and a program product. The multi-joint control method comprises the following steps: constructing a joint control function; obtaining each piece of key motion data based on the obtained expected positions of the multiple joints and the joint control function; and controlling all joints to move to a desired position based on the motion data. The joint control function is constructed, the motion data of all the joints are obtained based on the expected positions of all the joints and the joint control function, all the joints are controlled to move at the same time according to the motion data, and the purpose of improving control efficiency and collaboration is achieved.

The present invention discloses a key joint motion estimation based method for estimating continuous human postures. A motion estimation block matching algorithm is applied to human key joint tracking, so as to obtain continuous human posture results. Meanwhile, the results are continuously corrected by using a deep neural network based human posture estimator. The present invention may estimate the continuous human postures in a video stream, where the human postures are specifically embodied as coordinate positions of human joints in a video frame. Compared with a posture estimation method completely relying on a deep neural network, the posture estimation method provided by the present invention has the advantages of high frame rate, low hardware requirements, and sequential continuity of recognition results; and compared with a posture estimation method completely relying on a motion estimation algorithm, the present invention may correct a cumulative error, to improve the estimation accuracy.

The invention provides a multi-scene body position conversion-based standing-up assisting control method and system for the elderly suffering from stroke. According to the method, firstly, the standing-assisting device is controlled to obtain a fusion sensing result composed of body pressure distribution data and joint movement data, multi-feature coupling analysis is carried out to generate a combined recognition conclusion containing a body position state and a scene type, and then dynamic mode mapping is carried out according to the combined recognition conclusion. Outputting a target control mode matched with the current scene type and the body position, and finally performing power instruction synthesis by utilizing the target control mode to generate a cooperative control signal for driving the standing assisting equipment to execute body position conversion; according to the technical scheme provided by the invention, accurate conversion from standardized control to personalized control is realized, the suitability and the user experience of the standing assisting process are improved, the uncertainty and the individual difference in actual operation are overcome, and the stability, the safety and the adaptability of the body position conversion process are improved.

The invention relates to the technical field of data processing, in particular to a rehabilitation training control method and system based on a flexible hand joint rehabilitation glove, and the method comprises the steps: obtaining an individualized physiological parameter baseline of a patient; real-time data of a patient in the rehabilitation training process are collected through a multi-modal sensor array on the flexible glove, the active participation degree, the joint movement smoothness and the muscle fatigue degree of the patient are calculated based on the real-time data, the active movement intention of the patient is recognized, and a real-time rehabilitation state evaluation result of the patient is obtained; generating a control instruction through an adaptive control engine according to a preset personalized rehabilitation scheme and the real-time rehabilitation state evaluation result; the generated control instruction is sent to a driving control circuit so as to control a driving unit to execute corresponding bending or stretching actions and assist the patient in completing target training actions; according to the invention, personalized, self-adaptive and quantitative evaluation of rehabilitation training can be realized, and the efficiency and effect of hand rehabilitation training are improved.

The invention relates to the technical field of rehabilitation training, and particularly discloses a method, system and device for assisting walking training through visual and auditory stimulation. Visual and auditory data is generated by means of auxiliary training equipment to guide a user to walk and train, parameters are adjusted according to the body state of the user, limb coordination characteristics and muscle group coordination characteristics are obtained according to the multiple pieces of joint movement track information, gait parameters are calculated according to the limb coordination characteristics and the muscle group coordination characteristics, and the gait training accuracy is improved. The method comprises the following steps: acquiring accurate limb gait information, then judging whether the limb gait information meets a preset condition or not, if not, generating posture auxiliary information according to an analysis result, adjusting own parameters of auxiliary training equipment according to the posture auxiliary information, and re-acquiring joint movement track information; if yes, equipment parameters are fixed, a complete and scientific home rehabilitation training system is formed, and it is ensured that training is stable and coherent.

The invention relates to the technical field of medical simulation training, in particular to a simulation live wire treatment training method and system for high-risk orthopedic wounds, and the method comprises the following steps: calling preset explosion shock wave parameters, calculating the influence of shock waves on bones of multiple parts in combination with the stress-strain characteristics of bone tissues, and analyzing the mechanical response of the bone tissues, and simulating fracture injury conditions to obtain injury simulation data. According to the invention, through refined simulation of fracture injury and fracture complications, injury condition data are close to a real battlefield environment, students can visually understand dynamic evolution of wounds, and by collecting joint movement data of the students in real time and combining mechanical response of fracture parts, direct influence of treatment actions on the wounds is analyzed, so that the treatment effect of the wounds is improved. In the training process, wound state mapping is adjusted in real time, students are helped to understand operation feedback in real time, in combination with assessment of treatment skills, the skill level of the students is helped to be understood, a data basis is provided for adjusting training courses, and the skill training effect and efficiency are improved.

The invention relates to the technical field of robots, and particularly discloses a bionic humanoid mechanical arm which comprises a shoulder joint module, a big arm module, an elbow joint module, a small arm module and a wrist joint module which are connected in sequence. The wrist joint module realizes two-degree-of-freedom flexible movement through a regular octahedron tensegrity structure, and the rotation centers of the wrist joint module naturally intersect at one point and are connected with the rolling shaft in series to form three-degree-of-freedom concurrent rotation; the length-adjustable supporting rod in the flexible structure changes the joint rigidity in real time and dynamically adapts to the load change so as to enhance the man-machine interaction safety; an elbow joint adopts a virtual pitch circle rolling mechanism, all joint-crossing driving rope paths are forced to remain unchanged, and multi-joint motion coupling is eliminated from the mechanical intrinsic level; a driving motor and a rope wheel system are arranged on the large arm module in a centralized mode, the rope tensioning state is continuously optimized in cooperation with a ratchet wheel type self-locking tension mechanism, and the tail end inertia is reduced under the condition that an elbow and wrist joint motor is completely removed.

The invention provides a virtual reality-based joint movement injury rehabilitation mode generation method and a virtual reality-based joint movement injury rehabilitation mode generation system, and relates to the technical field of virtual reality, and the method comprises the following steps: analyzing a rehabilitation path according to movement joint injury data of a user; performing rehabilitation limb posture decomposition based on the rehabilitation analysis path to generate a rehabilitation analysis view; a multi-modal sensor unit is used for collecting rehabilitation feedback actions of a user, and matching recognition of physiological parameters and rehabilitation constraints is carried out in a rehabilitation analysis view; and according to the posture deviation feedback, performing amplitude and muscle power guidance feedback of the rehabilitation limb posture, and synchronously performing virtual reality visual display. The technical problem that in the prior art, most VR rehabilitation systems mainly pay attention to visual feedback, so that only the motion appearance can be evaluated, and the compensatory motion mode cannot be recognized is solved, real-time monitoring and guidance on the force exerting correctness are achieved through combination of posture decomposition and physiological parameter projection, and the effect that the correctness of force exerting is improved is achieved. Therefore, the compensatory motion mode is effectively blocked.

The invention provides a control method and device of a mechanical arm, a robot and a storage medium, and relates to the technical field of robotics.The method comprises the steps that according to the actual reachable range of at least one mechanism joint of the mechanical arm and the target pose expected to be reached by the tail end of the mechanical arm, the target pose is determined; determining an expected joint position of at least one mechanism joint under the target pose; and motion control is conducted on the mechanical arm, so that at least one mechanism joint moves to the corresponding expected joint position. Therefore, when the inverse kinematics of the mechanical arm is calculated, the expected joint position of each mechanism joint is determined according to the actual reachable range of at least one mechanism joint and the expected target pose of the tail end, so that the expected joint position of each mechanism joint can be accurately calculated; the deviation between the calculation result and the actual movement condition is greatly reduced, the movement potential of each mechanism joint can be fully utilized, the movement range of the tail end of the mechanical arm is effectively expanded, and the mechanical arm can reach more poses.

The invention discloses a human body action recognition method and system based on a skeleton spatial-temporal feature fusion graph convolutional network, and relates to the field of computer vision, the method comprises the following steps: inputting a to-be-detected video to a posture estimation algorithm to obtain a human body skeleton data set of the to-be-detected video; performing data preprocessing on the human skeleton data set to obtain skeleton joint information flow, skeleton joint movement information flow, skeleton information flow and skeleton movement information flow, and fusing the information flows to form a double-flow network branch; respectively inputting skeleton joint information flow-skeleton joint motion information flow branches and skeleton information flow-skeleton motion information flow branches in the double-flow network branches into a skeleton spatial-temporal feature fusion graph convolutional network model to obtain double-flow network branch output; and on the basis of double-flow network branch output, obtaining a human body action prediction result of the video to be detected by adopting a weighted fusion method. According to the invention, the recognition precision and robustness of complex actions can be effectively improved.

The invention relates to the technical field of path optimization, in particular to a multi-axis robot path planning optimization method and system, and the method comprises the following steps: collecting the current position and the start-stop interval of a multi-motion-axis joint, calculating the motion distance of each joint, converting the motion distance into an interval occupation ratio, and generating an initial path point through the interpolation of the current position; the method comprises the following steps of: selecting a plurality of path point joint combinations, performing occupation proportion comparison on the plurality of path point joint combinations to determine a dominant constraint direction, performing mapping cutting on other joint motion ranges according to a residual interval proportion, screening path points meeting constraint conditions, and verifying path sequence consistency and joint displacement continuity to form an optimized motion path of the robot. Through multi-motion-axis joint interval proportional description, an interval occupation state is introduced to participate in path screening, a dominant constraint direction is determined, and other joint intervals are mapped and cut, so that multi-axis motion proportions are coordinated, invalid displacement is reduced, and collaboration and motion smoothness are improved.