Medical instrument visual simulation method and system based on data identification

Abstract

The invention discloses a medical instrument visual simulation method and system based on data recognition, and relates to the field of medical instruments.The method comprises the following steps that a minimally invasive surgical instrument three-dimensional model is established, an end effector is added to the minimally invasive surgical instrument three-dimensional model according to a use scene, and a final simulation three-dimensional model is obtained; designing simulation test data according to the use scene, and simulating the working state of the minimally invasive surgical instrument in actual operation on the basis of the simulation three-dimensional model and the simulation test data; acquiring an operation position point of the end effector in the analogue simulation process, and judging the satisfaction degree of the minimally invasive surgical instrument in each use scene by taking the operation position point as a data identification basis; and comparing the satisfaction with a preset standard, and optimizing the element layout in the minimally invasive surgical instrument according to a comparison result. According to the invention, the quality and performance of the product can be improved, and the product can meet or exceed the preset clinical standard.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to a medical device visualization simulation method and system based on data recognition. Background Art

[0002] Minimally invasive surgical instruments are a type of medical device designed to minimize surgical incisions and patient recovery time. They are usually used in minimally invasive surgery. Compared with traditional open surgery, this surgical method has the advantages of less trauma, faster recovery and lower risk of infection. Minimally invasive surgical instruments can make operations more precise and reduce uncertainty and risks during surgery. At the same time, their design takes into account minimizing damage to surrounding tissues, thereby improving the safety of surgery. In short, the development of minimally invasive surgical instruments continues to promote the advancement of medical technology, making many complex surgeries possible, while also greatly improving patients' treatment experience and quality of life.

[0003] Visual simulation of minimally invasive surgical instruments is an advanced technology that uses computer graphics and simulation technology to create three-dimensional visual models of surgical instruments and surgical procedures. This simulation technology is mainly used for surgical training, equipment design testing and surgical rehearsal, and plays an important role in improving surgical accuracy and safety.

[0004] However, the existing instrument visualization simulation technology cannot judge the use effect of minimally invasive surgical instruments in specific applications based on the simulation path of the instruments in specific applications, and thus cannot objectively evaluate the performance of the instruments in different surgical scenarios, making it impossible to optimize the layout of internal components of the instruments based on the evaluation results, thereby reducing the quality and performance of the product.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a medical device visualization simulation method and system based on data recognition, which can optimize the layout of internal components of the device according to the evaluation results and improve the quality and performance of the product.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a medical device visualization simulation method based on data recognition, the medical device visualization simulation method comprising the following steps: S1. Establish a three-dimensional model of minimally invasive surgical instruments, and add an end effector to the three-dimensional model of minimally invasive surgical instruments according to the usage scenario to obtain a final simulation three-dimensional model; S2. Design simulation test data according to the usage scenario, and simulate the working state of minimally invasive surgical instruments in actual operation based on the simulation three-dimensional model and simulation test data; S3, obtaining the operation position points of the end effector during the simulation process, and using the operation position points as the basis for data recognition to determine the satisfaction of the minimally invasive surgical instrument in each usage scenario; S4, comparing the satisfaction with the preset standard, optimizing the layout of components in the minimally invasive surgical instrument according to the comparison result, and repeating steps S1 to S3 based on the layout optimization result until the satisfaction reaches the preset standard and the optimization is stopped; Establishing a 3D model of minimally invasive surgical instruments and adding an end effector to the 3D model of minimally invasive surgical instruments according to the usage scenario to obtain the final simulation 3D model includes the following steps: S11. Define a component entity according to the characteristics of the minimally invasive surgical instrument, and define attributes of the component entity, wherein the component entity includes a driving component and a flexible cable component; S12, using markup language technology to expand the component semantics and attribute files of the component entity, associating the driving component with the flexible component according to the component semantics, and building a component aggregation relationship based on the association result; S13, defining the state information corresponding to the driving component and the flexible cable component according to the component attributes, and establishing a generalized relationship based on the state information to complete the component entity definition extension; S14, defining all points on the component entity as feature points, and calculating the covariance from any feature point to the center point of the modeling scene based on the feature variance calculation formula, and mapping the feature point to the modeling scene according to the covariance result to perform entity graphical coordinate configuration operation; S15, performing surface, line and contour combination operations according to the configuration results to obtain a three-dimensional model of the minimally invasive surgical instrument, and selecting a modeling structure of the end effector based on the usage scenario of the minimally invasive surgical instrument; S16. The end effector modeling structure is combined with the three-dimensional model of the minimally invasive surgical instrument to obtain a final simulation three-dimensional model. The end effector includes a cutting effector and a suturing effector.

[0008] Furthermore, the characteristic variance calculation formula is expressed as: ; In the formula, v Representing feature points l The covariance to the center point of the modeled scene, g Represents the diameter of the center point of the modeling scene, f Represents any other feature point to feature point l The distance u Representing feature points l The horizontal coordinate of the center point of the modeling scene, l Indicates feature points.

[0009] Furthermore, the simulation test data is designed according to the usage scenario, and the working state of the minimally invasive surgical instrument in actual operation is simulated based on the simulation three-dimensional model and the simulation test data, including the following steps: S21, based on the selection status of the end effector, define the operation scenes and action parameters of the minimally invasive surgical instrument to be cut and to be suturing, and divide the operation scenes into a non-operation area and an operation area. The action parameters include cutting depth and suturing pressure; S22, using the two sets of operation scenarios and motion parameters as simulation test data, importing the simulation three-dimensional model into the simulation software, and integrating the forward and reverse motions of the minimally invasive surgical instrument according to the driving characteristics of the end effector and the minimally invasive surgical instrument; S23. Set the displacement and speed of the end effector according to the forward and reverse motions, and use the simulation software to configure the simulation test data and then run the simulation, and obtain the working state of the minimally invasive surgical instrument during actual operation based on the simulation results.

[0010] Furthermore, obtaining the operation position points of the end effector during the simulation process and using the operation position points as the basis for data recognition to determine the satisfaction of the minimally invasive surgical instrument in each usage scenario includes the following steps: S31, obtaining the motion trajectory points of the end effector in each operation scene and step in the simulation as operation position points, and dividing the operation scene into a plurality of groups of grids; S32, using a spatial connection method to obtain the area information contained in each group of grids, connecting the operation position points with the grids, and connecting the number of operation position points in each grid; S33, extracting the trajectory profile curve of the end effector based on the connection result analysis and data analysis technology to obtain a fitting curve, and analyzing the motion profile shape based on the curvature of the fitting curve; S34. Acquire simulation features and standard features based on the motion contour shape and simulation test data, and compare the standard features with the simulation features to determine the use satisfaction of the minimally invasive surgical instrument.

[0011] Furthermore, based on the connection result analysis and data analysis technology, the trajectory profile curve of the end effector is extracted to obtain a fitting curve, and the motion profile shape is analyzed based on the curvature of the fitting curve, including the following steps: S331, using data analysis technology to obtain a two-dimensional plane edge contour point cloud of the end effector motion trajectory in the connection result, and characterizing the trajectory contour curve with the contour point cloud shape; S332, segmenting the trajectory contour curve to obtain a trajectory contour line point cloud, and fitting the trajectory contour curve into a bimial curve by using a fitting bimial; S333, verifying the accuracy of the binomial curve based on the fitting degree formula, and selecting a curve with a fitting degree greater than a preset value as the final fitting curve according to the verification result; S334, calculating the curvature of the fitting curve based on the fitting binomial and drawing a curvature change graph, and analyzing the motion profile shape according to the curvature change graph.

[0012] Furthermore, the expression of the fitting degree is: ; In the formula, R 2 represents the goodness-of-fit value, m represents the number of fittings, t i represents the original bimial curve, represents the mean of the original binomial curve, represents the fitted data values ​​of the bimial curve, i Represents the number of bimial curves.

[0013] Furthermore, obtaining simulation features and standard features based on the motion profile shape and the simulation test data, and comparing the standard features with the simulation features to determine the use satisfaction of the minimally invasive surgical instrument includes the following steps: S341, analyzing the motion trajectory that the end effector should execute according to the operation area and action parameters defined by the simulation test data, and extracting horizontal straight line segment data of the motion trajectory through feature filtering; S342, after obtaining the horizontal straight line segment data, the vertical deviation least square method is used to evaluate the straight line segment geometry to obtain the straight line equation, and the Lagrange multiplier method is used to solve the straight line expression parameters; S343, analyzing the unit vector direction of the average position of all points on the straight line based on the straight line expression parameter, connecting the horizontal straight line segments according to the unit vector direction to obtain a polygon, calculating the geometric features of the polygon, and using the geometric feature results as standard features; S344, judging the shape of the running profile based on the shape of the motion profile, if it is a straight line segment, executing steps S342 to S344 to perform simulation feature calculation, if it is a non-straight line segment, executing step S346 to perform simulation feature calculation; S345, extracting arc segment data by feature filtering based on the running profile shape, and performing simulation feature calculation after obtaining arc segment geometric feature evaluation by least squares circle fitting; S346. Compare the standard features with the simulation features to determine the error, and determine the satisfaction with the use of the minimally invasive surgical instrument based on the error result.

[0014] Furthermore, comparing the standard features with the simulation features to determine the error, and determining the use satisfaction of the minimally invasive surgical instrument based on the error result includes the following steps: S3461. Compare the standard features with the simulation features to determine the error, and determine the performance of the minimally invasive surgical instrument based on the error result; S3462. Perform prior data analysis of minimally invasive surgical instrument skill capability assessment based on performance results, and establish a big data analysis model for skill capability assessment using a feature fusion clustering analysis model based on the analysis results; S3463. Divide the skill ability assessment into several levels, and determine the satisfaction with the use of minimally invasive surgical instruments based on the level results.

[0015] Further, comparing the satisfaction with the preset standard, optimizing the layout of components in the minimally invasive surgical instrument according to the comparison result, and repeatedly performing steps S1 to S3 based on the layout optimization result until the satisfaction reaches the preset standard and stopping the optimization includes the following steps: S41, comparing the satisfaction level corresponding to the simulation result with the preset standard, and determining the deviation between the actual performance of the minimally invasive surgical instrument and the preset standard; S42. Optimize the layout of the flexible cable assembly in the minimally invasive surgical instrument based on the deviation results, design the transmission mode of the flexible cable assembly according to the layout results, and construct the forward and inverse kinematics model of the minimally invasive surgical instrument; S43. After verifying the forward and inverse kinematics models using simulation analysis software, analyze the working space and singular shapes of minimally invasive surgical instruments; S44, after performing dynamic simulation on the minimally invasive surgical instrument based on the analysis results, performing impact sensitivity analysis, and determining the minimally invasive surgical instrument after the component layout is adjusted according to the analysis results; S45. Repeat steps S1 to S3 based on the minimally invasive surgical instrument after the component layout adjustment until the satisfaction of the minimally invasive surgical instrument reaches a preset standard and then stop the optimization.

[0016] In a second aspect, the present invention further provides a medical device visualization simulation system based on data recognition, the medical device visualization simulation system comprising a simulation three-dimensional model building module, a visualization simulation module, a device use satisfaction judgment module, and a device component layout optimization module, wherein the simulation three-dimensional model building module, the visualization simulation module, the device use satisfaction judgment module, and the device component layout optimization module are sequentially connected; A simulation 3D model building module is used to build a 3D model of minimally invasive surgical instruments and add an end effector to the 3D model of minimally invasive surgical instruments according to the usage scenario to obtain a final simulation 3D model; The visual simulation module is used to design simulation test data according to the usage scenario, and simulate the working state of minimally invasive surgical instruments in actual operation based on the simulation 3D model and simulation test data; The instrument usage satisfaction judgment module is used to obtain the operation position points of the end effector during the simulation process, and use the operation position points as the basis for data recognition to judge the satisfaction of minimally invasive surgical instruments in various usage scenarios; The device component layout optimization module is used to compare the satisfaction with the preset standard, optimize the component layout in the minimally invasive surgical instrument according to the comparison results, and repeatedly perform visual simulation operations based on the layout optimization results until the satisfaction reaches the preset standard and stops the optimization.

[0017] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: 1. The present invention creates a detailed three-dimensional model of a minimally invasive surgical instrument and adds an end effector according to a specific usage scenario to ensure the accuracy and practicality of the model. At the same time, the operating position points of the end effector during the simulation process are obtained and the usage satisfaction is evaluated on this basis. The performance of the instrument in different surgical scenarios can be objectively evaluated, and finally the satisfaction is compared with the preset standard. The layout of the internal components of the instrument is continuously optimized, and the three-dimensional model is simulated and the satisfaction is evaluated again, thereby improving the quality and performance of the product and ensuring that the product can meet or exceed the preset clinical standards.

[0018] 2. The present invention enhances the manageability and modifiability of the model by defining each component entity and its attributes in detail, and at the same time utilizes markup language technology to expand component semantics and associations between components, thereby strengthening the interaction and collaboration between components, making it easier to understand and track dependencies between components, and defining the corresponding status information of components and establishing generalized relationships, thereby capturing the behaviors and attributes of components in different working states, providing the necessary data basis for simulating the dynamic behavior of the device, and increasing the realism and prediction accuracy of the model.

[0019] 3. The present invention can simulate the specific needs of real surgery by defining various surgical scenarios and action parameters in detail, including cutting depth and suturing pressure. By applying forward and inverse kinematics, the displacement and speed of the end effector in the simulated environment can be accurately controlled, thereby improving the accuracy of the simulation. At the same time, by obtaining the motion trajectory points of the end effector in each operation scenario, the dynamic performance of the instrument can be recorded in detail, and by analyzing the curvature, the accuracy and stability of the instrument can be evaluated to ensure the smoothness and accuracy of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 is a flow chart of a medical device visualization simulation method based on data recognition according to an embodiment of the present invention; Figure 2 It is a principle block diagram of a medical device visualization simulation system based on data recognition according to an embodiment of the present invention.

[0022] In the figure: 1. Simulation 3D model building module; 2. Visual simulation module; 3. Equipment usage satisfaction judgment module; 4. Device component layout optimization module. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0027] See also Figure 1 The present invention provides a medical device visualization simulation method based on data recognition, and the medical device visualization simulation method comprises the following steps: S1. Establish a three-dimensional model of minimally invasive surgical instruments, and add an end effector to the three-dimensional model of minimally invasive surgical instruments according to the usage scenario to obtain the final simulation three-dimensional model.

[0028] In this embodiment, establishing a 3D model of a minimally invasive surgical instrument, and adding an end effector to the 3D model of the minimally invasive surgical instrument according to the usage scenario to obtain a final simulated 3D model includes the following steps: S11. Define component entities according to the characteristics of minimally invasive surgical instruments, and define attributes of the component entities. The component entities include drive components and flexible cable components.

[0029] Specifically, the component entity is defined according to the characteristics of the minimally invasive surgical instrument, and the attribute definition of the component entity includes: Define the necessary physical properties for the drive component and the cable component, such as size, weight, material type, etc., and define the functional properties of the components, such as sensing accuracy, force output, response time, etc., and describe the interface characteristics between the components, such as electrical connection, data exchange format, mechanical connection method, etc.

[0030] S12. Use markup language technology to expand the component semantics and attribute files of the component entity, associate the driving component with the flexible component according to the component semantics, and build the component aggregation relationship based on the association result.

[0031] Specifically, the component semantics and attribute files of the component entity are expanded by using the markup language technology, the driving component and the flexible component are associated according to the component semantics, and the component aggregation relationship is constructed based on the association result, including: Choose a markup language, including XML or JSON, to describe data structures and properties. Create a property file for each component, detailing its physical and functional properties. Define clear semantic labels for each component, such as the driver component is ` <driver>`, the flexible cable component is` <flexcable>`; Associate each component through tags, and build component aggregation relationships based on the functional dependencies of the components. Use markup language to encode the component aggregation structure, which may include that a driver component can contain multiple rope components to increase its operating range; Check whether the structure and logic in the markup language file correctly represent the relationship between components.

[0032] S13. Define the state information corresponding to the driving component and the flexible cable component according to the component attributes, and establish a generalized relationship based on the state information to complete the component entity definition extension.

[0033] Specifically, the state information corresponding to the driving component and the flexible cable component is defined according to the component attributes, and a generalized relationship is established based on the state information to complete the component entity definition extension, including: Identify and define the key properties of the drive component and the cable component, including force output, speed, range of motion, etc.; set possible state values ​​for each property. The specific speed of the drive component may include "low speed", "medium speed" and "high speed", etc. Select an appropriate encoding method to record state information, such as using JSON or XML markup language to describe each state in detail, and create a detailed description file for each state; define generalization rules based on the functions and operational requirements of the components, use the rules to describe the common behaviors and properties of the components under different states, establish a mapping from specific states to generalized properties, and ensure the consistency of component behavior in different situations.

[0034] S14. Define all points on the component entity as feature points, and calculate the covariance from any feature point to the center point of the modeling scene based on the feature variance calculation formula. Map the feature point to the modeling scene according to the covariance result to perform entity graphical coordinate configuration operation.

[0035] Specifically, all points on the component entity are defined as feature points, and the covariance between any feature point and the center point of the modeling scene is calculated based on the feature variance calculation formula. The feature point is mapped to the modeling scene according to the covariance result. The entity graphical coordinate configuration operation includes: Determine all key points on the component entity and define them as feature points. Each point is represented by its coordinates in three-dimensional space. Calculate the average position of all feature points to determine the center point of the modeling scene, and calculate the covariance between any feature point and the center point. Map the feature points to new coordinates in the modeling scene based on the calculated covariance matrix. In the modeling software, use the new coordinate data to configure the graphical representation of the execution entity, including setting the position, size, and orientation of the graphic to ensure that it is visually consistent with the design intent. Figure 1 To.

[0036] S15. Perform surface, line and contour combination operations according to the configuration results to obtain a three-dimensional model of the minimally invasive surgical instrument, and select the modeling structure of the end effector based on the usage scenario of the minimally invasive surgical instrument.

[0037] Specifically, the surface, line and contour combination operations are performed according to the configuration results to obtain a three-dimensional model of the minimally invasive surgical instrument, and the modeling structure of the end effector is selected based on the usage scenario of the minimally invasive surgical instrument, including: Organize the configured feature point data, including the location and attributes of points, lines, and surfaces, generate the basic outline of the device based on the feature point and line data, and form a closed surface by connecting the contour lines. Use modeling software tools such as surface generation or stretching tools to construct three-dimensional surfaces; analyze different usage scenarios, such as cutting and suturing, to determine the specific functions that the end effector needs to adapt to.

[0038] S16. The end effector modeling structure is combined with the three-dimensional model of the minimally invasive surgical instrument to obtain a final simulation three-dimensional model. The end effector includes a cutting effector and a suturing effector.

[0039] Specifically, the final simulation 3D model is obtained by merging the end effector modeling structure with the 3D model of the minimally invasive surgical instrument, including: Design and model the cutting actuator, including the blade, drive mechanism, etc., to ensure that it can accurately complete the cutting task; design and model the suturing actuator, including the needle, wire feeding mechanism, etc., to ensure that it can effectively perform suturing operations; check the interface between the end effector and the main model to ensure that the two can be correctly connected physically and functionally; confirm that all model parts have no geometric conflicts, and make necessary adjustments if necessary; import the model data of the end effector in the 3D modeling software, and merge it into the main model in the correct position; ensure that the details of the merged model are correct, such as docking gaps, interference checks of moving parts, etc.

[0040] Among them, the expression of the characteristic variance calculation formula is: ; In the formula, v Representing feature points l The covariance to the center point of the modeled scene, g Represents the diameter of the center point of the modeling scene, f Represents any other feature point to feature point l The distance u Representing feature points l The horizontal coordinate of the center point of the modeling scene, l Indicates feature points.

[0041] S2. Design simulation test data according to the usage scenario, and simulate the working state of minimally invasive surgical instruments during actual operation based on the simulation three-dimensional model and simulation test data.

[0042] In this embodiment, the simulation test data is designed according to the usage scenario, and the working state of the minimally invasive surgical instrument during actual operation is simulated based on the simulation three-dimensional model and the simulation test data, including the following steps: S21. Based on the selection status of the end effector, the operation scenes and action parameters of the minimally invasive surgical instrument to be cut and to be suturing are defined respectively, and the operation scenes are divided into a non-operation area and an operation area. The action parameters include cutting depth and suturing pressure.

[0043] S22. Use the two sets of operation scenarios and motion parameters as simulation test data, import the simulation three-dimensional model into the simulation software, and integrate the forward and reverse motions of the minimally invasive surgical instrument according to the driving characteristics of the end effector and the minimally invasive surgical instrument.

[0044] Specifically, two sets of operation scenarios and motion parameters are used as simulation test data, and the simulation 3D model is imported into the simulation software. The forward and reverse motion of the minimally invasive surgical instrument is integrated according to the driving characteristics of the end effector and the minimally invasive surgical instrument, including: Prepare the action parameters of two sets of operation scenarios (such as cutting and suturing) in detail, including cutting depth, suturing pressure, etc., confirm the integrity and compatibility of the 3D model, ensure that all file formats are suitable for the selected simulation software, import the 3D model file into the simulation software, perform necessary configuration and settings, and analyze the drive characteristics of the end effector and minimally invasive surgical instruments, such as power output, speed control, etc., and encode the drive characteristics into the simulation system to ensure that they can be correctly executed and simulated, define and program the forward and reverse motion logic of the instrument, which includes movement direction, angle adjustment and speed change, and ensure that the action of the end effector and the overall drive characteristics of the minimally invasive surgical instrument can work together in the simulation.

[0045] S23. Set the displacement and speed of the end effector according to the forward and reverse motions, and use the simulation software to configure the simulation test data and then run the simulation, and obtain the working state of the minimally invasive surgical instrument during actual operation based on the simulation results.

[0046] Specifically, the displacement and speed of the end effector are set according to the forward and reverse motions, and the simulation test data is configured using the simulation software and then the simulation is run. The working status of the minimally invasive surgical instrument during actual operation is obtained based on the simulation results, including: Define the displacement distance and speed parameters of the end effector in different operation scenarios (such as cutting and suturing), and program these parameters into the simulation software to ensure that the parameter settings are consistent with the actual surgical requirements; at the same time, input relevant operation scenario data, including the motion parameters and expected path of the end effector, and make necessary settings in the simulation software, such as physical environment parameters, instrument interaction characteristics, etc., start the simulation program, and observe whether the action of the end effector is performed correctly according to the preset parameters.

[0047] In order to facilitate understanding of the technical solution of the present invention, the simulation test of the present invention during actual operation is described in detail below.

[0048] Step 1: Establishment of 3D model of minimally invasive surgical instruments; (1) Component entity and attribute definition: Drive component attributes: dimensions are length = 150 mm, diameter 20 mm, weight 100 g, material type is stainless steel, functional attributes are maximum torque = 2 Nm, maximum speed is 5000 rpm, interface characteristics are electrical connection = 24 V DC, signal interface is RS-232; Flex cable component properties: length 500mm, diameter 5mm, material type silicone coated flexible steel cable, functional properties bendable radius = 50mm, interface feature mechanical connection method is quick plug-in interface.

[0049] (2) Component semantics and attribute files of component entities: Component semantic definition: The driver component label is <driver>; The cable component label is <flexcable>, the property file uses XML to describe the data structure.

[0050] The driver component XML file is as follows: <driver> <dimensions length="150" diameter="20" / > <weight> 100< / weight> <material> Stainless Steel< / material> <functional maxTorque="2" maxSpeed="5000" / > <interface voltage="24" signal="RS-232" / > < / driver> The XML file of the soft cable component is as follows: <flexcable> <length> 500< / length> <diameter> 5< / diameter> <material> Coated Flex Steel Cable< / material> <functional bendRadius="50" / > <interface type="quickCoupling" / > < / flexcable> (3) State information and generalization relationship: Drive component status information: speed is low speed = 1000rpm, medium speed is 3000rpm, high speed is 5000rpm; Status information of the flexible cable assembly: the bending status is no bending, slight bending (radius = 100mm), obvious bending (radius = 50mm); Generalization relationship definition: The generalization rule is that if the speed increases, the torque decreases accordingly to prevent damage.

[0051] (4) Feature point mapping: Center point calculation: Assuming that the coordinates of the feature points are distributed between (0, 0, 0) and (100, 100, 100), the coordinates of the center point are (50, 50, 50). After calculating the covariance, all feature points are adjusted to the new coordinate system according to their relative positions to the center point.

[0052] (5) 3D model combination and end effector selection: Basic outline: Create a basic shell model with dimensions of (200mm*50mm*50mm); Cutting actuator in the end effector: the blade length is 10mm and the cutting depth is set to 5mm; Suture actuator: needle length is 5mm, thread length is 20mm, and suture pressure is 5N; Integration approach: Designing exchangeable interfaces allows cutting and suturing actuators to be quickly replaced as needed, ensuring there are no geometric conflicts, and adjusting the model to accommodate the drive mechanism and electrical connections.

[0053] Step 2: Operation scene and action parameters; (1) Scene design; Cutting operation scenario: Operation area and action parameters: cutting depth is 5 mm (to ensure that the target tissue can be penetrated without damaging the underlying structure); Speed ​​is 2 mm / s (speed is set to ensure precise control and prevent tissue tearing); The cutting angle is right angle cutting (to ensure the clean and predictable cut); The operation path is a straight path with a length of 20 mm (simulating the straight line cutting commonly seen in surgery); Simulation model configuration: The model material is bionic tissue, simulating the physical properties of human soft tissue; The environment was set to room temperature 25°C to simulate a sterile surgical environment.

[0054] Suture operation scenario: Operating area and action parameters: Suturing pressure is 5N (tight enough to ensure wound healing, but not to damage tissue); The speed is 1 mm / s (slowly to improve suturing accuracy and tissue adaptability); The suture length is 20 mm (simulating the general suture length in actual surgery); The needle entry angle is 45° (simulating the angle commonly used in actual suturing to ensure proper needle penetration and exit); (2) Simulation operation; Pre-execution check: Confirm that all parameters are entered correctly and ensure that the simulation environment is set up correctly; Simulation start: Start the simulation program to monitor and record the end effector's motion performance and tissue response in real time; Data recording: Record the displacement, speed, force output and other data of the end effector, as well as the tissue response data (such as the actual effect of cutting depth and suturing pressure).

[0055] S3. Obtain the operation position points of the end effector during the simulation process, and use the operation position points as the basis for data recognition to determine the satisfaction of minimally invasive surgical instruments in various usage scenarios.

[0056] In this embodiment, obtaining the operation position points of the end effector during the simulation process and using the operation position points as the basis for data recognition to determine the satisfaction of the minimally invasive surgical instrument in each usage scenario includes the following steps: S31, obtaining the motion trajectory points of the end effector in each operation scene and step in the simulation as operation position points, and dividing the operation scene into a plurality of groups of grids.

[0057] Specifically, the motion trajectory points of the end effector in each operation scene and step in the simulation are obtained as operation position points, and the operation scene is divided into several groups of grids including: The operation scene is divided into several groups of grids, each grid represents a small operation area. The size and number of grids can be controlled by setting a grid density parameter. The simulation is started, and the end effector moves in the surgical scene according to the preset action parameters. Each position point of the end effector is recorded in real time, and its motion trajectory during the operation is described using the position points.

[0058] S32, using a spatial connection method to obtain the area information contained in each group of grids, connecting the operation position points with the grids, and connecting the number of operation position points in each grid.

[0059] Specifically, the area information contained in each group of grids is obtained by using the spatial connection method, the operation position points are connected to the grids, and the number of operation position points in each grid is connected, including: A uniform grid system is defined in the operation scenario, where each grid represents a specific spatial area. All operation position points of the end effector are extracted from the simulation data, and a spatial index structure, such as an R-tree or a quadtree, is established to effectively manage spatial objects and points. The grids and operation position points are added to the spatial index. For each operation position point, the spatial index is used to query the grid where it is located, and the grid information mapped to each point is recorded, including the ID or coordinates of the grid. The number of points in each grid is counted, and the number of operation position points contained in each grid is calculated. The statistical data of the operation position points are connected with the corresponding grid data to form a comprehensive data set, which includes the spatial information and the number of points of the grid.

[0060] S33. Based on the connection result analysis and data analysis technology, the trajectory contour curve of the end effector is extracted to obtain a fitting curve, and the motion contour shape is analyzed based on the curvature of the fitting curve.

[0061] Specifically, extracting the trajectory profile curve of the end effector based on the connection result analysis and data analysis technology to obtain a fitting curve, and analyzing the motion profile shape based on the curvature of the fitting curve includes the following steps: S331, using data analysis technology to obtain a two-dimensional plane edge contour point cloud of the end effector motion trajectory in the connection result, and characterizing the trajectory contour curve with the contour point cloud shape; S332, segmenting the trajectory contour curve to obtain a trajectory contour line point cloud, and fitting the trajectory contour curve into a bimial curve by using a fitting bimial; S333, verifying the accuracy of the binomial curve based on the fitting degree formula, and selecting a curve with a fitting degree greater than a preset value as the final fitting curve according to the verification result; S334, calculating the curvature of the fitting curve based on the fitting binomial and drawing a curvature change graph, and analyzing the motion profile shape according to the curvature change graph.

[0062] The expression of the fitting degree is: ; In the formula, R 2 represents the goodness-of-fit value, m represents the number of fittings, t i represents the original bimial curve, represents the mean of the original binomial curve, represents the fitted data values ​​of the bimial curve, i Represents the number of bimial curves.

[0063] S34. Acquire simulation features and standard features based on the motion contour shape and simulation test data, and compare the standard features with the simulation features to determine the use satisfaction of the minimally invasive surgical instrument.

[0064] Specifically, obtaining simulation features and standard features based on motion contour shape and simulation test data, and comparing standard features with simulation features to determine the use satisfaction of minimally invasive surgical instruments includes the following steps: S341, analyzing the motion trajectory that the end effector should execute according to the operation area and action parameters defined by the simulation test data, and extracting horizontal straight line segment data of the motion trajectory through feature filtering; S342, after obtaining the horizontal straight line segment data, the vertical deviation least square method is used to evaluate the straight line segment geometry to obtain the straight line equation, and the Lagrange multiplier method is used to solve the straight line expression parameters; S343, analyzing the unit vector direction of the average position of all points on the straight line based on the straight line expression parameter, connecting the horizontal straight line segments according to the unit vector direction to obtain a polygon, calculating the geometric features of the polygon, and using the geometric feature results as standard features; S344, judging the shape of the running profile based on the shape of the motion profile, if it is a straight line segment, executing steps S342 to S344 to perform simulation feature calculation, if it is a non-straight line segment, executing step S346 to perform simulation feature calculation; S345, extracting arc segment data by feature filtering based on the running profile shape, and performing simulation feature calculation after obtaining arc segment geometric feature evaluation by least squares circle fitting; S346. Compare the standard features with the simulation features to determine the error, and determine the satisfaction with the use of the minimally invasive surgical instrument based on the error result.

[0065] Among them, comparing the standard features with the simulation features to determine the error, and determining the use satisfaction of the minimally invasive surgical instrument based on the error result includes the following steps: S3461. Compare the standard features with the simulation features to determine the error, and determine the performance of the minimally invasive surgical instrument based on the error result; S3462. Perform prior data analysis of minimally invasive surgical instrument skill capability assessment based on performance results, and establish a big data analysis model for skill capability assessment using a feature fusion clustering analysis model based on the analysis results; S3463. Divide the skill ability assessment into several levels, and determine the satisfaction with the use of minimally invasive surgical instruments based on the level results.

[0066] S4. Compare the satisfaction level with the preset standard, optimize the layout of components in the minimally invasive surgical instrument according to the comparison result, and repeat steps S1 to S3 based on the layout optimization result until the satisfaction level reaches the preset standard and the optimization is stopped.

[0067] In this embodiment, the satisfaction level is compared with a preset standard, the layout of components in the minimally invasive surgical instrument is optimized according to the comparison result, and steps S1 to S3 are repeatedly performed based on the layout optimization result until the satisfaction level reaches the preset standard and the optimization is stopped, which includes the following steps: S41. Compare the satisfaction level corresponding to the simulation result with the preset standard to determine the deviation between the actual performance of the minimally invasive surgical instrument and the preset standard.

[0068] Specifically, the satisfaction level corresponding to the simulation results is compared with the preset standard to determine the deviation between the actual performance of the minimally invasive surgical instrument and the preset standard, including: Clearly define the performance indicators that minimally invasive surgical instruments should achieve, such as cutting accuracy, suture strength, operation time, safety, etc., set satisfaction evaluation standards, including user convenience of operation, surgical success rate, instrument reliability, etc., collect all relevant data about the performance of the end effector from the simulation software, compare the actual performance data with the preset standards, and identify any significant deviations.

[0069] S42. Optimize the layout of the flexible cable assembly in the minimally invasive surgical instrument based on the deviation results, design the transmission method of the flexible cable assembly according to the layout results, and construct the forward and inverse kinematics model of the minimally invasive surgical instrument.

[0070] Specifically, the layout of the flexible cable assembly in the minimally invasive surgical instrument is optimized based on the deviation results, the transmission mode of the flexible cable assembly is designed according to the layout results, and the forward and inverse kinematics model of the minimally invasive surgical instrument is constructed, including: Evaluate the performance deviation caused by the current cable assembly layout, such as insufficient flexibility and low force transmission efficiency, determine the optimization goals, such as improving flexibility, enhancing force transmission efficiency or reducing wear, and design a new cable layout scheme based on the deviation results, including changing the path, length or configuration of the cable, and use simulation software to simulate the cable layout and check the theoretical performance of the new layout. And select a suitable transmission method, such as using gears, belts or chains to drive, according to the layout and performance requirements of the cable, design the specific parameters of the transmission system, including gear ratio, belt length, chain type, etc., and conduct mechanical analysis to ensure that the selected transmission method can effectively transmit the required power and speed; based on the optimized cable layout and transmission method, establish the forward and inverse kinematics model of minimally invasive surgical instruments, define the kinematic equations, and describe the mathematical relationship between the end effector position and the transmission system input.

[0071] S43. After verifying the forward and inverse kinematics models using simulation analysis software, the working space and singular shapes of minimally invasive surgical instruments are analyzed.

[0072] Specifically, after verifying the forward and inverse kinematics model using simulation analysis software, the workspace and singular shapes of minimally invasive surgical instruments are analyzed, including: The optimized forward and inverse kinematics model is input into the simulation analysis software, and all relevant parameters are set, including joint limits, driving capabilities, speed and force ranges. The simulation is started, and the performance of the model in operation is monitored to ensure that the model reflects the real physical properties and motion capabilities. The workspace is defined according to the set of all spatial points that the end effector of the surgical instrument can reach. Through simulation runs, the extreme positions that the end effector can reach under various configurations are collected to map out the entire workspace, and the shape and range of the workspace are displayed using three-dimensional graphics to help analyze its applicability to surgical operations, determine the state in which the surgical instrument may lose a certain degree of freedom in a specific position or posture, and analyze the singular shape.

[0073] S44. After implementing dynamic simulation on the minimally invasive surgical instrument based on the analysis results, an impact sensitivity analysis is performed, and the minimally invasive surgical instrument after the component layout is adjusted is determined according to the analysis results.

[0074] Specifically, after dynamic simulation of the minimally invasive surgical instrument is performed based on the analysis results, an impact sensitivity analysis is performed, and the minimally invasive surgical instrument after the component layout is adjusted is determined according to the analysis results, including: Based on the analysis results, the initial conditions of the simulation are set, including dynamic parameters such as load, speed, and acceleration. The simulation software is used to perform dynamic simulation to observe the dynamic response of the surgical instrument under different operating conditions. The key variables that may have a significant impact on the performance of the surgical instrument, such as driving torque, joint stiffness, etc., are identified, and the key variables are adjusted to observe the impact on the dynamic performance of the surgical instrument. According to the results of the sensitivity analysis, the layout of components that need to be adjusted is determined, for example, reconfiguring the cable path or changing the angles of certain joints to improve power transmission or reduce wear. The adjusted design is re-dynamically simulated to ensure that the modified design meets all operational requirements.

[0075] S45. Repeat steps S1 to S3 based on the minimally invasive surgical instrument after the component layout adjustment until the satisfaction of the minimally invasive surgical instrument reaches a preset standard and then stop the optimization.

[0076] In order to facilitate understanding of the technical solution of the present invention, the simulation layout optimization during the actual operation of the present invention is described in detail below.

[0077] Embodiment overview: Objective: To evaluate the performance of minimally invasive surgical instruments in different surgical scenarios through high-precision simulation and optimize the layout and transmission mode of the instrument's flexible cable components based on the analysis results.

[0078] Tools: Advanced simulation software such as ANSYS or MATLAB, as well as CAD design tools such as SolidWorks, can be used.

[0079] Step 1: Simulation setting; The simulation environment is ANSYS Workbench; the simulation model is the CAD model of minimally invasive surgical instruments; the operation scene is straight line cutting and curve suturing; the grid density is 1mm*1mm for each scene area; Get the operation position point: the simulation speed is the end effector speed set to 5mm / s; the recording frequency is to record the position point every 0.1s; Spatial data structure and connection: Build an R-tree index to perform spatial indexing, and analyze the number of recorded operation points in each grid to count the number of operation location points.

[0080] Step 2: Trajectory and shape analysis; Trajectory fitting and shape analysis MATLAB was used to collect the two-dimensional plane edge contour point cloud, and the trajectory curve was fitted using a quadratic polynomial. The fitting threshold was set to 0.95, and the curvature was calculated and graphed. Finally, MATLAB was used for numerical analysis and graphical display.

[0081] Step 3: Performance evaluation and optimization; Performance and satisfaction analysis: The standard features set the operating accuracy to ≤0.5mm, the operating time to ≤30s, and high safety. The statistical deviation analysis method is used to compare the simulation features with the standard features. After the comparison is completed, the minimally invasive surgical instrument optimization is performed: Cable assembly layout: optimize the path length to minimize the possible length and reduce the curvature; Transmission mode: Design gear ratio 1:2, belt length 100mm; Kinematic model: forward and inverse kinematic equations are established based on theoretical analysis and dynamics simulation; Workspace and singular shape analysis: Use SolidWorks Motion for dynamic analysis.

[0082] Step 4: Dynamic simulation and sensitivity analysis; Dynamic simulation conditions: load 5N, speed 5mm / s, acceleration 10mm / s s ²; Sensitivity analysis: The driving torque was adjusted to 10-50Nm and the joint stiffness was adjusted to 500-1000Nm / rad.

[0083] Step 5: Final verification and adjustment; Final design: Perform dynamic simulation verification on the optimized design again; Prototype Testing: Prototypes are manufactured and tested in a laboratory environment.

[0084] Through the steps of the above embodiments, the performance of minimally invasive surgical instruments can be evaluated and optimized to ensure their efficiency and safety in a real surgical environment.

[0085] See also Figure 2 The present invention also provides a medical device visualization simulation system based on data recognition, the medical device visualization simulation system comprises a simulation three-dimensional model building module 1, a visualization simulation module 2, a device use satisfaction judgment module 3, and a device component layout optimization module 4, wherein the simulation three-dimensional model building module 1, the visualization simulation module 2, the device use satisfaction judgment module 3, and the device component layout optimization module 4 are sequentially connected; The simulation three-dimensional model building module 1 is used to build a three-dimensional model of a minimally invasive surgical instrument, and add an end effector to the three-dimensional model of the minimally invasive surgical instrument according to the usage scenario to obtain a final simulation three-dimensional model; Visual simulation module 2, used to design simulation test data according to the usage scenario, and simulate the working state of minimally invasive surgical instruments in actual operation based on the simulation three-dimensional model and simulation test data; The instrument usage satisfaction judgment module 3 is used to obtain the operation position points of the end effector during the simulation process, and use the operation position points as the basis for data recognition to judge the satisfaction of minimally invasive surgical instruments in various usage scenarios; The device component layout optimization module 4 is used to compare the satisfaction with the preset standard, optimize the component layout in the minimally invasive surgical instrument according to the comparison result, and repeatedly perform the visual simulation operation based on the layout optimization result until the satisfaction reaches the preset standard and stops the optimization.

[0086] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.< / flexcable> < / driver> < / flexcable> < / driver>

Claims

1. A medical device visualization simulation method based on data recognition, characterized in that: The medical device visualization simulation method comprises the following steps: S1. Establish a three-dimensional model of minimally invasive surgical instruments, and add an end effector to the three-dimensional model of minimally invasive surgical instruments according to the usage scenario to obtain a final simulation three-dimensional model; S2. Design simulation test data according to the usage scenario, and simulate the working state of minimally invasive surgical instruments in actual operation based on the simulation three-dimensional model and simulation test data; S3, obtaining the operation position points of the end effector during the simulation process, and using the operation position points as the basis for data recognition to determine the satisfaction of the minimally invasive surgical instrument in each usage scenario; S4, comparing the satisfaction with the preset standard, optimizing the layout of components in the minimally invasive surgical instrument according to the comparison result, and repeating steps S1 to S3 based on the layout optimization result until the satisfaction reaches the preset standard and the optimization is stopped; The method of establishing a three-dimensional model of a minimally invasive surgical instrument and adding an end effector to the three-dimensional model of the minimally invasive surgical instrument according to the usage scenario to obtain a final simulated three-dimensional model includes the following steps: S11. defining a component entity according to the characteristics of the minimally invasive surgical instrument, and defining attributes of the component entity, wherein the component entity includes a driving component and a flexible cable component; S12, using markup language technology to expand the component semantics and attribute files of the component entity, associating the driving component with the flexible component according to the component semantics, and building a component aggregation relationship based on the association result; S13, defining the state information corresponding to the driving component and the flexible cable component according to the component attributes, and establishing a generalized relationship based on the state information to complete the component entity definition extension; S14, defining all points on the component entity as feature points, and calculating the covariance from any feature point to the center point of the modeling scene based on the feature variance calculation formula, and mapping the feature point to the modeling scene according to the covariance result to perform entity graphical coordinate configuration operation; S15, performing surface, line and contour combination operations according to the configuration results to obtain a three-dimensional model of the minimally invasive surgical instrument, and selecting a modeling structure of the end effector based on the usage scenario of the minimally invasive surgical instrument; S16. Combining the end effector modeling structure with the minimally invasive surgical instrument three-dimensional model to obtain a final simulation three-dimensional model, wherein the end effector includes a cutting effector and a suturing effector.

2. A medical device visualization simulation method based on data recognition according to claim 1, characterized in that: The characteristic variance calculation formula is expressed as: ； In the formula, v Representing feature points l The covariance to the center point of the modeled scene, g Represents the diameter of the center point of the modeling scene, f Represents any other feature point to feature point l The distance u Representing feature points l The horizontal coordinate of the center point of the modeling scene, l Indicates feature points.

3. The medical device visualization simulation method based on data recognition according to claim 1, characterized in that: The design of simulation test data according to the usage scenario and the simulation of the working state of the minimally invasive surgical instrument during actual operation based on the simulation three-dimensional model and the simulation test data include the following steps: S21, based on the selection status of the end effector, define the operation scenes and action parameters of the minimally invasive surgical instrument to be cut and to be suturing, respectively, and divide the operation scenes into a non-operation area and an operation area, wherein the action parameters include cutting depth and suturing pressure; S22, using the two sets of operation scenarios and motion parameters as simulation test data, importing the simulation three-dimensional model into the simulation software, and integrating the forward and reverse motions of the minimally invasive surgical instrument according to the driving characteristics of the end effector and the minimally invasive surgical instrument; S23. Set the displacement and speed of the end effector according to the forward and reverse motions, and use the simulation software to configure the simulation test data and then run the simulation, and obtain the working state of the minimally invasive surgical instrument during actual operation based on the simulation results.

4. The medical device visualization simulation method based on data recognition according to claim 1, characterized in that: The step of obtaining the operation position points of the end effector during the simulation process and using the operation position points as a basis for data recognition to determine the satisfaction level of the minimally invasive surgical instrument in each usage scenario includes the following steps: S31, obtaining the motion trajectory points of the end effector in each operation scene and step in the simulation as operation position points, and dividing the operation scene into a plurality of groups of grids; S32, using a spatial connection method to obtain the area information contained in each group of grids, connecting the operation position points with the grids, and connecting the number of operation position points in each grid; S33, extracting the trajectory profile curve of the end effector based on the connection result analysis and data analysis technology to obtain a fitting curve, and analyzing the motion profile shape based on the curvature of the fitting curve; S34. Acquire simulation features and standard features based on the motion contour shape and simulation test data, and compare the standard features with the simulation features to determine the use satisfaction of the minimally invasive surgical instrument.

5. A medical device visualization simulation method based on data recognition according to claim 4, characterized in that: The method of extracting the trajectory profile curve of the end effector based on the connection result analysis and data analysis technology to obtain a fitting curve, and analyzing the motion profile shape based on the curvature of the fitting curve includes the following steps: S331, using data analysis technology to obtain a two-dimensional plane edge contour point cloud of the end effector motion trajectory in the connection result, and characterizing the trajectory contour curve with the contour point cloud shape; S332, segmenting the trajectory contour curve to obtain a trajectory contour line point cloud, and fitting the trajectory contour curve into a bimial curve by using a fitting bimial; S333, verifying the accuracy of the binomial curve based on the fitting degree formula, and selecting a curve with a fitting degree greater than a preset value as the final fitting curve according to the verification result; S334, calculating the curvature of the fitting curve based on the fitting binomial and drawing a curvature change graph, and analyzing the motion profile shape according to the curvature change graph.

6. A medical device visualization simulation method based on data recognition according to claim 5, characterized in that: The expression of the fitting formula is: ； In the formula, R 2 represents the goodness-of-fit value, m represents the number of fittings, t i represents the original bimial curve, represents the mean of the original binomial curve, represents the fitted data values ​​of the bimial curve, i Represents the number of bimial curves.

7. A medical device visualization simulation method based on data recognition according to claim 6, characterized in that: The method of obtaining the simulation features and the standard features based on the motion profile shape and the simulation test data, and comparing the standard features with the simulation features to judge the use satisfaction of the minimally invasive surgical instrument comprises the following steps: S341, analyzing the motion trajectory that the end effector should execute according to the operation area and action parameters defined by the simulation test data, and extracting horizontal straight line segment data of the motion trajectory through feature filtering; S342, after obtaining the horizontal straight line segment data, the vertical deviation least square method is used to evaluate the straight line segment geometry to obtain the straight line equation, and the Lagrange multiplier method is used to solve the straight line expression parameters; S343, analyzing the unit vector direction of the average position of all points on the straight line based on the straight line expression parameter, connecting the horizontal straight line segments according to the unit vector direction to obtain a polygon, calculating the geometric features of the polygon, and using the geometric feature results as standard features; S344, judging the shape of the running profile based on the shape of the motion profile, if it is a straight line segment, executing steps S342 to S344 to perform simulation feature calculation, if it is a non-straight line segment, executing step S346 to perform simulation feature calculation; S345, extracting arc segment data by feature filtering based on the running profile shape, and performing simulation feature calculation after obtaining arc segment geometric feature evaluation by least squares circle fitting; S346. Compare the standard features with the simulation features to determine the error, and determine the satisfaction with the use of the minimally invasive surgical instrument based on the error result.

8. The medical device visualization simulation method based on data recognition according to claim 7, characterized in that: The method of comparing the standard features with the simulation features to determine the error, and determining the use satisfaction of the minimally invasive surgical instrument based on the error result includes the following steps: S3461. Compare the standard features with the simulation features to determine the error, and determine the performance of the minimally invasive surgical instrument based on the error result; S3462. Perform prior data analysis for minimally invasive surgical instrument skill capability assessment based on performance results, and establish a big data analysis model for skill capability assessment using a feature fusion clustering analysis model based on the analysis results; S3463. Divide the skill ability assessment into several levels, and determine the satisfaction with the use of minimally invasive surgical instruments based on the level results.

9. A medical device visualization simulation method based on data recognition according to claim 8, characterized in that: The step of comparing the satisfaction level with the preset standard, optimizing the layout of components in the minimally invasive surgical instrument according to the comparison result, and repeatedly performing steps S1 to S3 based on the layout optimization result until the satisfaction level reaches the preset standard and stopping the optimization includes the following steps: S41, comparing the satisfaction level corresponding to the simulation result with the preset standard, and determining the deviation between the actual performance of the minimally invasive surgical instrument and the preset standard; S42. Optimize the layout of the flexible cable assembly in the minimally invasive surgical instrument based on the deviation results, design the transmission mode of the flexible cable assembly according to the layout results, and construct the forward and inverse kinematics model of the minimally invasive surgical instrument; S43. After verifying the forward and inverse kinematics models using simulation analysis software, analyze the working space and singular shapes of minimally invasive surgical instruments; S44, after performing dynamic simulation on the minimally invasive surgical instrument based on the analysis results, performing impact sensitivity analysis, and determining the minimally invasive surgical instrument after the component layout is adjusted according to the analysis results; S45. Repeat steps S1 to S3 based on the minimally invasive surgical instrument after the component layout adjustment until the satisfaction of the minimally invasive surgical instrument reaches a preset standard and then stop the optimization.

10. A medical device visualization simulation system based on data identification, used to implement the medical device visualization simulation method based on data identification according to any one of claims 1 to 9, characterized in that: The medical device visualization simulation system comprises a simulation three-dimensional model building module, a visualization simulation module, a device use satisfaction judgment module, and a device component layout optimization module, wherein the simulation three-dimensional model building module, the visualization simulation module, the device use satisfaction judgment module, and the device component layout optimization module are connected in sequence; The simulation three-dimensional model building module is used to build a three-dimensional model of the minimally invasive surgical instrument, and add an end effector to the three-dimensional model of the minimally invasive surgical instrument according to the usage scenario to obtain a final simulation three-dimensional model; The visual simulation module is used to design simulation test data according to the usage scenario, and simulate the working state of the minimally invasive surgical instrument during actual operation based on the simulation three-dimensional model and the simulation test data; The instrument usage satisfaction judgment module is used to obtain the operation position points of the end effector during the simulation process, and use the operation position points as the basis for data recognition to judge the satisfaction of the minimally invasive surgical instrument in each usage scenario; The device element layout optimization module is used to compare the satisfaction with the preset standard, optimize the element layout in the minimally invasive surgical instrument according to the comparison result, and repeatedly perform the visual simulation operation based on the layout optimization result until the satisfaction reaches the preset standard and stops the optimization.

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