48 results about "Tissue deformation" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention belongs to the technical field of ablation puncture navigation, and particularly relates to an ablation puncture surgery navigation system under AI visual guidance. Through cooperative work of the phase monitoring module, the channel construction module, the needle moving control module, the displacement prediction module and the feedback control module, an intelligent surgical navigation system is constructed, breathing phase information and multi-modal medical image data of a patient can be obtained in real time, and therefore a safe and optimized needle moving path is accurately planned, and the operation accuracy is improved. Meanwhile, through real-time monitoring and quantitative evaluation of parameters such as tissue deformation and pressure distribution in the needle moving process, potential operation risks can be found and corrected in time, smooth operation is ensured, in addition, the system can dynamically adjust the needle moving path and the puncture angle according to the real-time breathing state of a patient, and the operation safety is improved. The accuracy and the safety of the operation are further improved, so that intelligent management and personalized treatment in the ablation puncture operation process are realized.

The invention provides a control method of an intestinal tumor resection robot combining preoperative images and intraoperative navigation, and relates to the technical field of medical instrument control. The method comprises the following steps: acquiring preoperative multi-modal image data of the intestinal tract of a patient, and performing three-dimensional reconstruction on the image data to generate an initial three-dimensional model; dividing a navigation area in the enteric cavity according to the initial three-dimensional model; based on the core area, collecting a dynamic image of the intestinal tract inner cavity in real time through intraoperative navigation equipment, extracting anatomical mark points in the dynamic image, registering the dynamic image with the initial three-dimensional model, updating tissue deformation parameters in the three-dimensional model, and obtaining an updated three-dimensional model; on the basis of the updated three-dimensional model, obstacles in the intestinal tract are recognized, and the minimum space distance between the obstacles and the robot tail end instrument is calculated; and according to the spatial distribution and the minimum spatial distance of the obstacles, an initial motion path of the robot tail end instrument is planned. According to the invention, the efficiency and accuracy of the intestinal tumor resection operation can be improved.

The invention discloses an intraoperative pulmonary nodule positioning method and system based on fusion of a CT image and an endoscope video. The method comprises the following steps: firstly, constructing a three-dimensional lung model containing a pulmonary nodule position based on preoperative high-resolution CT data; generating a real-time lung surface model by using an endoscope video stream through a three-dimensional reconstruction technology during the operation; a feature point extraction and matching algorithm is adopted, and a non-rigid registration technology is combined to realize accurate registration of the preoperative model and the intraoperative dynamic model; projection display of pulmonary nodules in a real-time endoscope visual field is established through space coordinate mapping, and a dynamic compensation algorithm is developed to solve the problem of tissue deformation caused by respiratory movement and instrument operation. Meanwhile, the virtual nodule position is overlaid to an endoscope video picture through the augmented reality technology, and visual intraoperative navigation is provided for surgeons. The method effectively solves the technical bottleneck that a traditional method depends on a metal marker and cannot adapt to intraoperative tissue deformation and the like, and has clinical practical value.

The invention relates to the technical field of medical image processing, and discloses a multi-mode ultrasonic mammary gland and multi-mode fusion method. The method comprises the following steps: S1, acquiring medical images of at least two modalities of a mammary gland area, wherein the medical images comprise a first modal image and a second modal image; s2, performing standardization preprocessing on the first modal image and the second modal image; and S3, constructing a physical modeling model of the breast tissue based on the second modal image, and generating a tissue deformation displacement field to obtain tissue response information. By adopting a multi-modal ultrasonic mammary gland image registration and fusion technology and combining feature fusion and deformation field optimization based on an image neural network, the image registration precision is remarkably improved. Compared with a traditional image registration method in the prior art, the multi-modal image registration method not only optimizes the alignment effect among the multi-modal images, but also effectively reduces registration errors caused by modal differences, and particularly provides more accurate alignment on detail structures of breast tissues.

The embodiment of the invention provides a method and equipment for dynamically adjusting a motion control mode based on an intraoperative visual field environment, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring an intraoperative view image and a preset basic force threshold value according to the tissue type of a to-be-operated site; calculating an intraoperative tissue real-time deformation rate based on the intraoperative view image, and calculating a real-time force safety threshold according to a preset basic force threshold and the tissue real-time deformation rate; receiving real-time contact force of the instrument and the tissue, comparing the real-time contact force with a real-time force safety threshold value, and if the real-time contact force is smaller than or equal to the real-time force safety threshold value, outputting an instruction of damping force in direct proportion to the real-time contact force; if the real-time contact force is larger than the real-time force safety threshold value, an emergency braking program instruction is output. According to the method, factors, such as tissue deformation and illumination change, which cause instability of the surgical field of view in the surgical environment are introduced, so that the environmental adaptability and dynamic adjustment of the field of view mechanism in the robot-assisted surgical operation process are improved.

The invention relates to the technical field of remote single-hole robot operation guidance and demonstration, and discloses a system and a method for remote single-hole robot operation guidance and demonstration, which realize efficient, stable and secure transmission of remote force feedback signals and control instructions through a low-delay secure communication link based on SRv6 + TSN and a national password SM9 encryption technology. The problem that transparency and real-time performance are difficult to consider in remote operation is effectively solved, experts can seamlessly perceive interaction force of local instruments and tissues and carry out accurate remote intervention, and through dynamic modeling of a cloud twinning service module and Transformer and graph neural network algorithms, the interaction force between the local instruments and the tissues can be accurately and remotely intervened. Real-time prediction of risks such as bleeding and perforation in the operation and accurate matching of tissue deformation are achieved, a visual and fused AR guidance interface is provided for experts in combination with a mixed reality guidance engine, and the accuracy of remote guidance and operation safety are remarkably improved.

The invention discloses a four-dimensional dynamic image guided dose optimization system for tumor radiotherapy, and belongs to the technical field of medical imaging and radiotherapy. The system comprises a real-time image acquisition module, a dynamic anatomical model construction module, an organ deformation prediction module, a motion compensation engine, a four-dimensional dose accumulation calculation module, a self-adaptive treatment plan generation module and a dose delivery control module. A patient-specific four-dimensional dynamic anatomical model is constructed based on a deep neural network, an autoregression algorithm is adopted to predict organ motion trails, motion compensation parameters are calculated in real time, cumulative dose distribution considering tissue deformation is accurately calculated, a treatment plan is automatically adjusted, and dose delivery with millimeter-level precision is achieved. According to the system, the target area coverage deviation is reduced by 62%, the normal tissue irradiation dose is reduced by 37%, and the precision and safety of thoracic and abdominal tumor radiotherapy are remarkably improved.

The invention discloses a sequence image relative pose estimation method, device and equipment and a medium, and relates to the technical field of image processing, the method accurately estimates inter-frame poses through deep learning, so that ultrasonic three-dimensional reconstruction can be realized under the condition of not using external tracking equipment such as electromagnetic tracking and the like; therefore, various possible complex medical environments can be adapted more flexibly, and medical workers can be helped to perform examination and diagnosis better. And meanwhile, the method eliminates the dependence of ultrasonic three-dimensional reconstruction on external equipment such as optical / magnetic tracking equipment, so that the system architecture is simpler and more compact, the hardware cost and complexity are remarkably reduced, and the method is favorable for adapting to various different clinical application scenes. Besides, through rotation isovariant convolution and multi-scale cross attention, the method can better adapt to factors such as angle change and tissue deformation in the acquisition process, and the robustness of the method is improved.

This invention relates to the field of biomedical technology, and more particularly to a real-time lesion AR annotation method for endoscopic systems based on augmented reality. The method first acquires and preprocesses a real-time video stream from the endoscope; then, a two-stage cascaded model is used to detect and segment lesions in the preprocessed video stream, identifying suspicious lesion areas, generating a segmentation mask, and removing interfering areas in the endoscopic scene; real-time pose data of the endoscope is acquired using an electromagnetic-visual fusion positioning method; based on the segmentation mask and real-time pose data, two-dimensional lesion information is mapped to three-dimensional space to construct a three-dimensional lesion model, which is then completed by combining anatomical structural features and adjusted according to tissue deformation; finally, the adjusted three-dimensional lesion model is back-projected onto the endoscopic field of view to generate AR annotation information, which is then fused with the real-time endoscopic image and output. This invention enables real-time and accurate annotation of endoscopic lesions, providing auxiliary support for endoscopic diagnosis and treatment.

There is provided an accelerated coupled filtering method for tissue deformation analysis. The method includes initializing, by a first processing device, a plurality of first motion parameters; applying, by a second processing device, a first filter on a pre-deformation image of a tissue to obtain a filtered pre-deformation image, wherein the second processing device is different from the first processing device; applying, by the second processing device, a second filter on a post-deformation image of the tissue to obtain a filtered post-deformation image, wherein the filtered pre-deformation image and the filtered post-deformation image are correlated by a first motion matrix comprising the plurality of first motion parameters, and wherein the plurality of first motion parameters comprise at least three fundamental first motion parameters, each of the three fundamental first motion parameters represents movement of the tissue along an axial direction relative to the axial direction, an elevational direction and a lateral direction, respectively; estimating, by the second processing device, a respective value for each of the plurality of first motion parameters, wherein each of the estimated respective values represents a difference between the pre-deformation and post-deformation filtered images; and in response to determining that at least one of the estimated respective values meets at least one predefined criterion, updating the at least one of the estimated respective values as an optimal value for at least one corresponding first motion parameter of the plurality of first motion parameters.

A surgical robotic system for automatic port repositioning includes a robotic arm coupled to an access port inserted through an incision, the access port is equipped with sensors to detect tissue deformation. Using imaging or force sensors, the system monitors tension around the port and calculates motion vectors to perform micro-adjustments, or "port burping," to relieve strain without disrupting surgery. The robotic arm executes these adjustments gradually, ensuring smooth operation. Additionally, the system monitors task and workspace volumes to keep instruments within safe zones, using algorithms like SLAM and bump detection. If instruments approach workspace limits, the system autonomously adjusts the port's position to expand the task space, minimizing manual intervention and enhancing surgical efficiency.

The embodiment of the invention provides a dynamic tissue deformation-oriented real-time electrical impedance tomography optimization method and system. The method comprises the following steps of: obtaining dynamic deformation characteristics of a target tissue; based on the dynamic deformation characteristics, determining and adjusting a data sampling frequency; acquiring electrical impedance data by using the adjusted data sampling frequency; and performing real-time image reconstruction based on the electrical impedance data. Through the scheme of the embodiment of the invention, the problem of how to regulate and control the data sampling frequency according to the dynamic tissue deformation characteristics so as to solve the problem of insufficient real-time performance of image reconstruction can be solved.

The invention discloses a feedback force prediction method and device in a virtual operation process, and belongs to the technical field of feedback force prediction in the virtual operation process, and the feedback force prediction method comprises the steps: obtaining data needed by prediction and data needed by updating in response to collision detected in the virtual operation process; based on the data required for updating, a lightweight online learning strategy is adopted to dynamically update the trained TCN-Transform parallel enhanced time sequence fusion model; inputting data required by prediction into the model to obtain a tissue deformation prediction value and a feedback force prediction value; calculating a tissue deformation prediction error based on the tissue deformation prediction value and the tissue deformation calculation value; and if the tissue deformation prediction error is smaller than a preset decision threshold, taking the feedback force prediction value as a final prediction result. According to the method, feedback force output delay is remarkably reduced through prediction instead of calculation, sudden operation changes are adapted through dynamic updating, and prediction accuracy is maintained.

This invention provides an intraoperative residual organ volume estimation system for surgical planning assistance, relating to the field of minimally invasive surgery. By matching the preoperative and intraoperative tissue mesh models, this invention transforms the measurement of residual organ volume into a volume measurement of the corresponding region in the preoperative tissue mesh model, avoiding interference from complex tissue deformation and invisible areas on volume prediction. Furthermore, it considers intraoperative interaction with the surgeon, receiving simple annotations from the surgeon regarding regions of interest to obtain accurate volume measurement information, achieving active selectivity and high reference value during surgery. In addition, the introduced online self-supervised learning depth estimation method based on binocular endoscopy utilizes a binocular depth estimation network with rapid overlearning capabilities, continuously adapting to new scenarios using self-supervised information, thereby ensuring the accuracy of the intraoperative tissue mesh model.

Embodiments of the present application provide a result simulation method and device based on a generative adversarial network, and an equipment, and relate to the field of image processing. The method comprises: inputting a CT image and a proton beam flow parameter into a trained generative adversarial network; after the CT image is encoded and decoded by a primary generator, a basic tissue deformation field is obtained; after the basic tissue deformation field and a Bragg peak distribution diagram obtained based on the proton beam flow parameter are subjected to feature fusion and attention weight mapping by a fine-tuning generator, an enhanced image and an output feature vector are obtained; after the output feature vector is subjected to spatial feature discrimination in a double-channel discriminator, an image authenticity result is obtained; after the output feature vector is subjected to dosimetric feature discrimination in the double-channel discriminator, a dosimetric evaluation result is obtained; and after the enhanced image is discriminated according to the image authenticity result and the dosimetric evaluation result, an output image of the trained generative adversarial network is obtained. The embodiments provided in the present application improve the prediction accuracy.

The invention discloses an endoscope monocular dense reconstruction and real-time positioning method based on deformation modeling. The method comprises the following steps: constructing a hybrid map model combining a sparse key point graph and three-dimensional Gaussian point representation; performing initial three-dimensional structure reconstruction and non-rigid camera attitude estimation through the sparse point map; fine pose adjustment and high-quality reconstruction are carried out on the Gaussian map in combination with micro-rendering optimization; efficient conversion from sparse geometry to dense geometry is realized through a sparse prior guided depth alignment mechanism, and tracking stability and reconstruction density are both considered. According to the method, three-dimensional mapping and tracking of the deformation scene can be realized in real time under the monocular camera, the method is suitable for an endoscopic surgery scene with complex tissue deformation, and the visualization quality and the navigation precision in the surgery are improved.

This invention provides a method and system for soft tissue collaborative surgery control based on a humanoid robot. It relates to the fields of medical robotics, computer vision, and remote teleoperation. The method includes: displaying, via a display terminal, image stream information acquired in real time by the humanoid robot for the surgical operation area, wherein the surgical operation area includes the area where a first instrument held by a first robotic hand of the humanoid robot is located; in response to detecting a grasping operation representing soft tissue grasped by the first instrument, obtaining the tissue deformation rate of the grasped soft tissue; generating, based on the tissue deformation rate of the grasped soft tissue, a prompt message for indicating the tissue stretching state on the display terminal; and sending the prompt message to the display terminal for display.

The invention relates to the technical field of wireless communication network resource management, in particular to an interactive resource allocation method for a virtual anatomical model in a wireless network, which comprises the following steps of: 1, acquiring virtual organization deformation data caused by user operation in real time, and dynamically dividing operation precision grades based on the deformation degree; 2, generating a dynamic space influence range according to the operation precision grade, detecting a space overlapping region of multi-user operation, and calculating a conflict risk value; 3, dynamically calculating the resource allocation quantity by combining the operation precision grade, the organization deformation data and the conflict risk value; and 4, triggering resource redistribution when tissue deformation suddenly changes or an operation tool is replaced. Through accurate operation precision division and dynamic adjustment of resource allocation, resource waste can be effectively avoided in a multi-user interaction environment, meanwhile, real-time response of key operation is ensured, and the misoperation rate caused by delay or resource insufficiency is greatly reduced.

This invention discloses an intelligent puncture path planning method for interventional surgical robots, belonging to the field of interventional robot path planning technology. The method acquires multimodal medical images of the patient's puncture site, completes organ tissue segmentation and three-dimensional reconstruction, generates a digital twin anatomical model, and calibrates basic puncture parameters; constructs a respiratory phase tracking model, combines the basic puncture parameters to lock the respiratory cycle and real-time respiratory phase, and outputs the three-dimensional predicted coordinates of the puncture target point for a pre-set duration; acquires needle insertion force data through a six-dimensional force sensor, calculates tissue compression deformation and needle body flexible bending offset in real time, and generates a feedforward compensation vector; fuses the three-dimensional predicted coordinates and the feedforward compensation vector to generate a dynamic puncture path, performing continuous needle insertion without interruption throughout the entire process. This invention improves upon the shortcomings of traditional static path planning, which cannot adapt to respiratory displacement and tissue deformation, effectively improving puncture positioning accuracy and is suitable for minimally invasive interventional puncture surgery scenarios.

This invention discloses a method and apparatus for predicting feedback force during virtual surgery, belonging to the technical field of feedback force prediction during virtual surgery. The feedback force prediction method includes: in response to collision detection during virtual surgery, acquiring data required for prediction and data required for updating; based on the data required for updating, dynamically updating a trained TCN-Transformer parallel enhanced temporal fusion model using a lightweight online learning strategy; inputting the data required for prediction into the model to obtain predicted tissue deformation values ​​and predicted feedback force values; calculating the tissue deformation prediction error based on the predicted tissue deformation values ​​and the calculated tissue deformation values; if the tissue deformation prediction error is less than a preset decision threshold, then the predicted feedback force value is used as the final prediction result. This invention significantly reduces the feedback force output delay by replacing calculation with prediction, and maintains prediction accuracy by dynamically updating to adapt to sudden operational changes.

The invention relates to the technical field of robot control, and discloses a movement track planning system for a needle-knife robot, which comprises a sensor module, a kinematics modeling module, a path generation and correction module, an optimization module and an intelligent control module, by arranging the path generation and correction module and the optimization module, a basic trajectory can be obtained by dynamically correcting a path in combination with a tissue deformation prediction model, and by combining real-time force feedback and tissue deformation data, the dynamic adaptability is improved, trajectory deviation is effectively prevented, and the accuracy of the system is improved. Meanwhile, a smooth speed curve is generated by adopting a quintic polynomial interpolation method on the basis of the basic trajectory, the smoothness of the trajectory and the continuity of the acceleration are ensured, acceleration abrupt change is restrained through a dynamic compensation algorithm, impact control is conducted on the needle-knife robot, mechanical vibration caused by acceleration abrupt change of the needle-knife robot in the operation process is prevented, and the operation reliability is improved. The surgical risk is reduced, and the surgical safety is improved.

The invention discloses a single pencil beam dose simulation method for an LSTM model, and the method comprises the steps: collecting a dynamic image slice through a high-frequency imaging device, marking a timestamp, and synchronously generating a time sequence dose data set related to an organization form; fusing image deformation and dose characteristics by using a double-flow LSTM network, predicting high-precision dose distribution and outputting a deformation compensation factor; dynamically adjusting a dose threshold and a voxel weight according to the tissue deformation degree, and strengthening the prediction precision of a deformation sensitive area; and combining real-time image feedback to optimize pencil beam parameters, intercepting image slices in real time, and using a dynamic LSTM model to predict high-precision dose distribution online. The predicted dose is corrected in real time through the deformation compensation factor and compared with the actual monitoring dose, and an error is fed back to the model; and finally, the pencil beam dose distribution is superposed to generate dynamic Monte Carlo dose distribution, so that the dose prediction real-time performance and accuracy are improved, and the radiotherapy treatment safety and effectiveness are enhanced.

The utility model discloses a medical suture belongs to medical supplies technical field. The medical suture includes suture body and the tail end setting of its stop flab, and stop flab width is greater than suture body, and is equipped with at least two orifices that can pass through suture body along suture body extension direction. When the above -mentioned medical suture is used to carry out tissue suture, by the abutment of stop flab and the tissue to be sutured, suture body is connected with tissue again after setting aperture, can make suture force evenly dispersed on stop flab, thereby dispersing force to greater area, significantly reduce the stress of tissue at the suture, reduce tissue deformation and breakage risk, especially suitable for the suture of fragile tissue, provide good environment for tissue healing. Meanwhile, stop flab increases the contact area with tissue, provides more stable support and fixed.

This invention provides a digital intelligent MSE automatic design method based on biomechanical simulation, belonging to the field of biomechanical simulation technology. The method includes: acquiring three-dimensional morphological data and physiological load data of the target anatomical structure and mapping them to obtain a load distribution matrix; performing tensor operations on the load distribution matrix and tissue mechanical response characteristics to obtain a stress prediction field; identifying high-stress concentration areas and low-stiffness weak areas based on the stress prediction field; extracting contour curves and calculating the normal gradient to determine the expansion constraint direction; minimizing the stress distribution variance and the total tissue deformation energy and determining the spatial configuration parameters of the MSE device; using the spatial configuration parameters as boundary conditions to iteratively calculate the displacement field and strain field; correcting the spatial configuration parameters based on the difference between the cumulative deformation and a preset physiological threshold and the iterative deformation energy change rate; and outputting the corrected configuration parameters as design instructions when the convergence condition is met.

The application provides a pathological section and MRI image registration method and device based on biomechanical elastic deformation, which comprises the following steps: obtaining a three-dimensional digital twin of a tumor region, obtaining a digital resection region in the three-dimensional digital twin based on a preset surgical margin; performing ex vivo dynamics simulation on the digital resection region to obtain a tissue deformation result; obtaining an optimal deformation section with maximum mutual information in the tissue deformation result and a pathological section; obtaining an inverse transformation deformation field between the optimal deformation section and the three-dimensional digital twin, and projecting a tumor region edge of the pathological section to the three-dimensional digital twin based on the inverse transformation deformation field. The scheme realizes fine local nonlinear adaptation of the section by constructing an ex vivo dynamics finite element simulation technology of a volume shrinkage function and performing local deformation with the pathological section edge information as a constraint, quantitatively restoring the global non-uniform shrinkage of the ex vivo tissue and the differential local deformation of the tumor and normal tissue.

The invention discloses a tongue body edge dynamic detection and feature separation method and system, relates to the technical field of biomedical engineering, directly captures tissue deformation and vibration response under sound wave excitation through a flexible electronic patch, and breaks through reflection interference and dynamic fuzzy limitation of a traditional optical method in a humid environment. Based on a dynamic feature extraction mechanism of a mechanical state tensor, the elastic deformation gradient and the viscous damping phase of the tongue body edge can be sensed synchronously, and the stability and the anti-interference capability of dynamic detection are remarkably improved. The frequency domain feature inversion and sparse constraint pathological separation algorithm is combined to accurately distinguish the easily confused edema, fibrosis and indentation features of the traditional method. In addition, biomechanical characteristics of the tongue body are converted into grading of clinical syndromes such as liver depression and qi stagnation, spleen deficiency and dampness stagnation, and traditional Chinese medicine tongue diagnosis is promoted to span from experience judgment to objective quantification.

The invention relates to the technical field of neurosurgery, and provides a tissue dynamic grid updating method in neurosurgery simulation, which comprises the following steps: S1, initializing grids and physical parameters, creating an independent copy of original vertex coordinates as deformation reference data, and initializing an instantaneous movement speed array of each vertex; s2, multi-mode deformation parameter input and processing are carried out, deformation action point coordinates, force values and action modes input externally are received, and deformation action force is applied to a single vertex, a predefined vertex set and all vertexes according to action mode selection; s3, carrying out vertex motion real-time calculation, and calculating displacement vertex by vertex based on a spring-damping physical model; s4, dynamic scaling self-adaptive correction is carried out, a space scaling coefficient of the nervous tissue is obtained in real time, and normalization processing is carried out on displacement and speed in tissue deformation calculation; and S5, carrying out grid data synchronization and physical updating. And efficiency and precision collaborative optimization is realized, and sub-millisecond response is realized.

This invention discloses a modular VR / AR surgical teaching system and method based on a local large-scale model. The local large-scale model training module is deployed in a local computing environment, storing structured medical knowledge and dynamically generating teaching scripts and assessment question banks. A lightweight VR / AR hardware group collects multi-dimensional user operation data and provides multi-sensory feedback. A full-process training control module, with a built-in physical simulation engine and evaluation logic, constructs a virtual surgical scene, simulates tissue mechanical changes in real time, compares user operations with standard surgical procedure models and generates error correction guidance, and generates a quantitative ability assessment report based on the entire process data. A multi-scene data mapping interface adopts a modular architecture based on a standardized intermediate communication protocol, achieving low-coupling logical isolation and dynamic integration between functional modules through a unified data exchange format and abstract interface specifications. This application adopts an edge-side deployment strategy to protect patient privacy and ensure real-time force feedback and tissue deformation rendering in the VR / AR scene.