Robotic arm operation traction fixing system combined with drag hook module

Abstract

The invention discloses a robotic arm surgical traction fixing system combined with a drag hook module, and particularly relates to the field of surgical tractor operation control and state comparison unit analysis. Forming and outputting target state data of the current operation stage in combination with drag hook traction path parameters solved according to the target tissue area data; and the state acquisition module is in structural connection and signal connection with the traction unit through the drag hook module, the mechanical arm unit and the traction unit which are included in the state acquisition module. By constructing a cooperative working mechanism of the target state establishing module and the state obtaining module, real-time matching judgment between the actual combination state of the drag hook module and the operation target state is achieved, and the problem that whether the drag hook module is matched with the operation space requirement logic or not cannot be dynamically sensed in an existing system is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation control and state comparison unit analysis of surgical retractors. More specifically, the present invention relates to a robotic arm surgical traction and fixation system combined with a retractor module. Background Art

[0002] In existing surgical robotic arm systems combined with retractor modules, methods such as preset trajectories, single-module force or displacement feedback are usually adopted to assist doctors in achieving tissue retraction and traction fixation. In simple single-organ exposure surgeries, such systems can provide certain auxiliary effects and reduce the manual retraction burden on doctors; However, as clinical applications expand to high-difficulty multi-organ combined exposure, complex tissue layer dissection or ultra-minimally invasive space operation scenarios, the operation of the retractor module is no longer an isolated behavior, but forms a dynamic, collaborative and progressive overall system behavior with multiple robotic arms, different retractor module combinations, and changing surgical objectives; In such a multi-factor, multi-module and multi-stage surgical environment, existing systems have obvious deficiencies: they cannot perceive whether the current combination state of the retractor module matches the spatial requirement logic of the current or subsequent surgical steps, nor can they evaluate the priority relationship between the retractor and the robotic arm during dynamic operation. Moreover, there is a lack of stability judgment after the retractor group evolves to the limit state, and a systematic prediction of the risk of micro-abnormal chain accumulation inside the micro retractor module; These defects force doctors to continuously adjust or reinstall the retractor module relying on subjective experience, which will increase the surgical complexity and accidental risks; Therefore, this solution urgently needs to solve: how to construct a control mechanism that can analyze and compare the matching relationship between the combination state of the retractor module and the surgical target state in a complex dynamic surgical environment in real time, form an active judgment, optimization decision-making and adaptive collaborative adjustment, so as to improve the practicability and safety of the surgical traction and fixation system. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a robotic arm surgical traction and fixation system combined with a retractor module, which collaboratively analyzes the matching relationship between the current state of the retractor module and the surgical target state through a state acquisition module and a comparison unit, and realizes adaptive adjustment based on a traction optimizer recursive feedback mechanism to solve the problems raised in the above background art.

[0004] To achieve the above object, the present invention provides the following technical solution: A robotic arm surgical traction and fixation system combined with a retractor module, comprising a target state establishment module, a state acquisition module, a comparison unit, and a retractor optimization module; The target state establishment module is used for feature extraction and analysis of the multimodal imaging data and surgical planning data before the patient's surgery, and combines the retractor traction path parameters solved according to the target tissue region data to form and output the target state data of the current surgical stage; The state acquisition module obtains and fuses the installation position, spatial pose data and force application state data of the retractor module through structural connection and signal connection with the retractor module, robotic arm unit and traction unit it includes, and forms and outputs the state vector and state evolution sequence of the robotic arm surgical traction fixation operation combined with the retractor module; The comparison unit is used to perform difference analysis on the state vector of the current surgical traction fixation operation and the target state data and calculate the state error vector, and output a state matching degree index to judge whether there is a "state contradiction existence signal" or "maintain the current state vector"; The retractor optimization module is used to identify the subset of the retractor module that triggers the state contradiction existence signal, generate and screen the set of target alternative retractor modules, and output the retractor module replacement execution suggestion that meets the target state data of the current surgical stage.

[0005] In a preferred embodiment, the target state establishment module further includes performing feature extraction on the multimodal imaging data and surgical planning data before the patient's surgery to obtain a surgical data set, and hierarchically analyzing the surgical data set to form a target data group including organ type, organ spatial position, and target tissue region; Screen the target data group to extract the target tissue region data corresponding to the current surgical stage; Combine the target tissue region data with the retractor traction path parameters solved according to the target tissue region data to form a retractor target traction path set, perform spatial reconstruction on the retractor target traction path set, and output the target state data of the current surgical stage; Judge whether there is an unresolved spatial region in the target state data of the current surgical stage. If so, regenerate the target data group using the surgical data set; if not, retain the target state data of the current surgical stage.

[0006] In a preferred embodiment, the state acquisition module includes a retractor module, a robotic arm unit, and a traction unit. The retractor module includes a physical traction device for connecting to and pulling a target tissue area. The robotic arm unit includes a driving component and an end effector component for installing, spatially positioning, and adjusting the pose of the retractor module. The traction unit includes a force application component and a force-displacement detection component for providing traction force and synchronously detecting the mechanical state parameters of the retractor module. The state acquisition module achieves physical coupling through the structural connection between the retractor module and the robotic arm unit, and achieves synchronous interaction of control and data acquisition through the signal connection between the traction unit and the robotic arm unit, so as to form a state data basis for the robotic arm surgical traction and fixation operation combined with the retractor module. Synchronously collect the installation position and spatial pose data of the retractor module formed by the structural connection between the retractor module and the robotic arm unit in the state acquisition module, and the force application state data of the retractor module obtained through the signal connection between the traction unit and the robotic arm unit, and output the original operation state set. Normalize the data of the original operation state set to form the current operation state data including the retractor module parameters and the robotic arm pose parameters. After screening the current operation state data, extract the current valid retractor module state set. The retractor module state set includes retractor model, installation position, traction direction, and parameters of the applied force. Fuse the current valid retractor module state set obtained through the state acquisition module with the pose data of the end effector component obtained by the robotic arm unit, output the state vector of the current robotic arm surgical traction and fixation operation combined with the retractor module, and append the current state vector to the state record sequence to form the state evolution sequence of the surgical stage. Judge whether the state evolution sequence contains a state anomaly flag. If it contains, output a state anomaly prompt; if it does not contain, continue to output the state vector of the current surgical traction and fixation operation.

[0007] In a preferred embodiment, the comparison unit is used to perform difference analysis on the state vector of the current surgical traction and fixation operation and the target state data of the current surgical stage to generate state difference data, perform a normalized error operation on the state difference data, and output a state error vector. Judge the state error vector based on a preset threshold rule, output the current state matching degree index, and judge whether the current state matching degree index is lower than the preset threshold. If it is lower than the preset threshold, output a signal indicating the existence of a state contradiction; if it is higher than the preset threshold, output an indication to continue maintaining the state vector of the current surgical traction and fixation operation.

[0008] In a preferred embodiment, the retractor optimization module is used to perform abnormal retractor module recognition on the state vector of the robotic arm surgical traction and fixation operation of the current combined retractor module, and extract the subset of retractor modules that cause the existence signal of state contradiction; Jointly analyze the subset of retractor modules and the target state data of the current surgical stage to generate conflict path data between the subset of retractor modules and the target state data; Match the conflict path data with the preset set of retractor module replacement rule data, and screen out the corresponding set of target alternative retractor modules; Jointly perform path simulation on the set of target alternative retractor modules and the end effector pose data of the robotic arm unit obtained by the state acquisition module to generate a set of replacement solutions; Based on the surgical safety, target tissue area protection, and operation target, comprehensively evaluate the set of replacement solutions, and output the target replacement solution; Judge whether the target replacement solution meets the target state data of the current surgical stage. If it does not meet, regenerate the set of replacement solutions based on the set of target alternative retractor modules. If it meets, output the retractor module replacement execution suggestion based on the target replacement solution.

[0009] In a preferred embodiment, it further includes an adaptive optimization module; The adaptive optimization module adjusts the end effector pose of the robotic arm unit and replaces the retractor module according to the retractor module replacement execution suggestion, forms the combined state of the updated retractor module and the robotic arm unit, and synchronously collects the combined state of the updated retractor module and the robotic arm unit through the state acquisition module to obtain the updated current operation state data; Process the updated current operation state data, output the state vector of the robotic arm surgical traction and fixation operation of the combined retractor module after update, and compare the state vector after update with the target state data of the current surgical stage to output a new state matching degree index; Judge whether the new state matching degree index reaches the preset threshold. If it does not reach, perform abnormal retractor module recognition on the updated state vector and repeat the execution of the retractor optimization module; if it reaches, output the updated state vector as the state record for the continuous surgical traction and fixation operation; Perform real-time update on the state record of the continuous surgical traction and fixation operation to form the dynamic state record of the retractor module and the robotic arm unit during the entire surgical process.

[0010] In a preferred embodiment, in the state acquisition module, define the state vector indicating the state of the current robotic arm surgical traction and fixation operation of the combined retractor module, and the state vector consists of the spatial coupling error state , Force application displacement response state , Dynamic target tracking error state It consists of three parts and is expressed as: Spatial coupling error state It is expressed as: Force application displacement response state It is expressed as: Dynamic target tracking error state It is expressed as: Among them, the spatial coupling error state represents the spatial coupling error state between the hook module and the robotic arm unit; the force application displacement response state represents the comprehensive response state of the force application displacement of the hook module; the dynamic target tracking error state represents the dynamic tracking error state between the actual path and the target path of the end effector assembly of the robotic arm unit; Among them represents the hook module installation position vector in the current valid hook module state set collected by the state acquisition module; represents the current position vector of the end effector assembly of the robotic arm unit collected by the state acquisition module; represents the axis spatial pose angle of the hook module in the current valid hook module state set obtained by the state acquisition module; represents the axis spatial pose of the end effector assembly of the robotic arm unit obtained by the state acquisition module; represents the geometric length of the hook module in the current valid hook module state set obtained by the state acquisition module; represents the current geometric length of the end effector assembly of the robotic arm unit obtained by the state acquisition module; Among them represents the traction force applied by the hook module in the current valid hook module state set obtained by the state acquisition module; represents the displacement change amount detected during the traction of the hook module in the current valid hook module state set obtained by the state acquisition module; represents the traction direction component force of the hook module in the current valid hook module state set obtained by the state acquisition module; Denotes the displacement change of the traction direction of the hook module in the set of current effective hook module states obtained by the state acquisition module; Denotes the actual contact area where the hook module in the set of current effective hook module states obtained by the state acquisition module contacts the target tissue area; Denotes the friction factor between the hook module in the set of current effective hook module states obtained by the state acquisition module and the target tissue area; Wherein Denotes the position of the j-th path point in the actual space path of the end effector assembly of the robotic arm unit at time t obtained by the state acquisition module; Denotes the position of the j-th path point of the target path in the target state data of the current surgical stage; Denotes the joint angle state of the k-th axis of the robotic arm unit obtained by the state acquisition module; Denotes the target joint angle state of the k-th axis in the target state data of the current surgical stage; Is the number of path points sampled for path tracking; Is the total number of joints of the robotic arm unit.

[0011] In a preferred embodiment, in the comparison unit, using the state vector output by the state acquisition module , and the target state data of the current surgical stage output by the target state establishment module As the input, through the state data weight adjustment function Perform data quality preprocessing, extract the differences in spatial pose parameters, force application state parameters and dynamic path parameters, and introduce the influence factors of force application and pose perturbation To realize the non-linear influence of force application on spatial error, and at the same time introduce the path perturbation force application correction coefficient To realize the real-time correction of the dynamic path error on the force application estimation, and finally recursively fuse to form the state error vector , expressed as: Define the difference in spatial pose parameters Expressed as: Define the difference in force application state parameters Expressed as: Define the difference in dynamic path parameters Expressed as: Where the spatial pose parameters include , Represents the current x, y, and z coordinates of the combination of the hook module output by the status acquisition module and the end effector component of the robotic arm unit; Are the target x, y, and z coordinates output by the target status establishment module; Is the current spatial pose angle of the i-th axis in the status acquisition module; Is the target spatial pose angle of the i-th axis in the target status establishment module; Is the target length of the hook module in the target status establishment module; Are the traction force and the target traction force exerted by the hook module in the status acquisition module and the target status data; Is the contact area between the hook module and the tissue in the target status establishment module; Is the current displacement corresponding to the force application process of the hook module; Is the target displacement corresponding to the force application process of the hook module; Is the current traction direction component force of the hook module; Is the target traction direction component force set for the hook module; Is the dynamic adjustment coefficient of the force application error; Is the force application adjustment coefficient of the spatial pose error; Where Is the number of discrete samples of the path tracking time; Is the spatial coordinate of the path point at time t in the status acquisition module; Is the target spatial coordinate of the path point at time t in the target status establishment module; Is the k-th joint angle state of the status acquisition module and the target status establishment module at time t; Is the total number of joints of the robotic arm unit; Is a positive number to prevent division by zero in the path error calculation.

[0012] In a preferred embodiment, the hook optimization module identifies the abnormal hook module through the state vector output by the status acquisition module and the target state data output by the target status establishment module, establishes a conflict topology relationship graph based on the subset of abnormal hook modules, dynamically generates a set of candidate replacement solutions conditional on the state of the abnormal hook module and the pose of the current end effector component of the robotic arm unit, embeds the set of candidate replacement solutions into the conflict topology recursive simulation, generates a simulation result, updates the conflict topology state in real time through the simulation result, recursively searches for the global conflict impact target replacement solution, and outputs a hook module replacement execution recommendation; Subset of abnormal hook modules Is expressed as: Based on the subset of abnormal hook modules Establish a conflict topology relationship graph of the abnormal module : Target replacement hook module set Expressed as: Define Indicate After jointly performing path simulation with the pose data of the end effector assembly of the robotic arm unit obtained by the status acquisition module, calculate the total energy of the conflict path of the generated replacement plan set, Expressed as: Target replacement plan Expressed as: Hook module replacement execution suggestion Expressed as: Wherein Indicates the i-th and j-th abnormal hook modules in ; Is an abnormality detection function, and the abnormality detection function is used to evaluate the current hook module Relative to State deviation degree; Is an abnormality determination threshold; edge set Indicates the mutual conflict influence between abnormal hook modules; Indicate And Conflict influence weight between; Is , Conflict relationship function between; Indicates a set of candidate replacement plans dynamically generated based on the abnormal hook module and the pose of the current end effector assembly; Is the th alternative hook module plan in the set of candidate replacement plans; Indicates the evaluation And And Adaptability function; Is the adaptability error threshold; Indicates the pose data of the end effector assembly of the robotic arm unit obtained by the status acquisition module; Is a single Local influence estimation of the conflict topology; Is the evaluation in the path simulation Influence factor on the stability of the surgical space path; Is Comprehensive score for the protection of the target tissue area; Suggestions for the replacement execution of the retractor module Generated by , and ; A function representing the comprehensive output of the replacement execution suggestions.

[0013] Technical effects and advantages of the present invention: 1. By constructing a collaborative working mechanism between the target state establishment module and the state acquisition module, the real-time matching judgment between the actual combined state of the retractor module and the surgical target state is realized, solving the problem in the existing system that it is impossible to dynamically perceive whether the retractor module matches the surgical space requirement logic.

[0014] 2. Through the joint analysis of multi-modal imaging data and surgical plan data by the target state establishment module, a surgical target space model is established in advance, combined with the dynamic calculation of the retractor traction path, providing a relatively accurate space reference for subsequent surgical operations, and improving the accuracy of surgical planning and the reliability of the overall system execution.

[0015] 3. Through the multi-source coupling design of the state acquisition module, the installation position, spatial pose and force application state data of the retractor module are comprehensively collected, realizing the real-time two-way synchronization of physics and signals between the robotic arm unit and the retractor module, and constructing a state evolution sequence throughout the surgical process, providing a data basis for subsequent anomaly judgment and optimization.

[0016] 4. By introducing a multi-dimensional state error analysis model in the comparison unit, fusing spatial pose error, force application state error and dynamic path error, a state matching degree index with recursive feedback is formed, improving the early recognition ability of the accumulation of micro-anomaly chains and the robustness of the surgical process.

[0017] 5. Through the retractor optimization module, combined with the identification of abnormal retractor modules, the construction of conflict topology relationship maps and path simulation, a recursive optimization process for the global conflict impact is established, which can automatically recommend the optimal target replacement plan without disturbing the surgical continuity, reducing the doctor's intervention frequency and operation risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] Refer to the attached instructions Figure 1 Figure 1 , a robotic arm surgical traction fixation system combined with a retractor module according to an embodiment of the present invention includes a target state establishment module, a state acquisition module, a comparison unit, and a retractor optimization module; The target state establishment module is used for feature extraction and analysis of preoperative multimodal imaging data and surgical planning data of the patient, and combines the retractor traction path parameters solved according to the target tissue region data to form and output the target state data of the current surgical stage; The state acquisition module obtains and fuses the installation position, spatial pose data, and force application state data of the retractor module through structural connection and signal connection with the retractor module, robotic arm unit, and traction unit it includes, and forms and outputs the state vector and state evolution sequence of the current robotic arm surgical traction fixation operation combined with the retractor module; The comparison unit is used to perform difference analysis on the state vector of the current surgical traction fixation operation and the target state data, calculate the state error vector, and output a state matching degree index to determine whether there is a "state contradiction existence signal" or "maintain the current state vector"; The retractor optimization module is used to identify the subset of the retractor module that triggers the state contradiction existence signal, generate and screen the target alternative retractor module set, and output the retractor module replacement execution suggestion that meets the target state data of the current surgical stage.

[0021] The target state establishment module further includes performing feature extraction on the preoperative multimodal imaging data and surgical planning data of the patient to obtain a surgical data set, hierarchically analyzing the surgical data set to form a target data group including organ types, organ spatial positions, and target tissue regions; Filter the target data group to extract the target tissue region data corresponding to the current surgical stage; Combine the target tissue region data with the retractor traction path parameters solved according to the target tissue region data to form a retractor target traction path set, perform spatial reconstruction on the retractor target traction path set, and output the target state data of the current surgical stage; Judge whether there is an unresolved spatial region in the target state data of the current surgical stage. If so, regenerate the target data group using the surgical data set; if not, retain the target state data of the current surgical stage.

[0022] The state acquisition module includes a retractor module, a robotic arm unit, and a traction unit. The retractor module includes physical traction devices of multiple types and specifications for connecting and pulling the target tissue area. The robotic arm unit includes a driving component and an end effector component with multiple degrees of freedom for installing, spatially positioning, and adjusting the pose of the retractor module. The traction unit includes a force application component and a force-displacement detection component for providing traction force application and synchronously detecting the mechanical state parameters of the retractor module. The state acquisition module achieves physical coupling through the structural connection between the retractor module and the robotic arm unit, and achieves synchronous interaction of control and data acquisition through the signal connection between the traction unit and the robotic arm unit, so as to form a state data basis for the robotic arm surgical traction and fixation operation combined with the retractor module. Synchronously collect the installation position and spatial pose data of the retractor module formed by the structural connection between the retractor module and the robotic arm unit in the state acquisition module, and the force application state data of the retractor module obtained through the signal connection between the traction unit and the robotic arm unit, and output the original operation state set. Normalize the data of the original operation state set to form the current operation state data including the retractor module parameters and the robotic arm pose parameters. After screening the current operation state data, extract the current valid retractor module state set. The retractor module state set includes retractor model, installation position, traction direction, and applied force parameters. Fuse the current valid retractor module state set obtained through the state acquisition module with the pose data of the end effector component obtained by the robotic arm unit, output the state vector of the current robotic arm surgical traction and fixation operation combined with the retractor module, and append the current state vector to the state record sequence to form the state evolution sequence of the surgical stage. Judge whether the state evolution sequence contains a state anomaly identifier. If it contains, output a state anomaly prompt. If it does not contain, continue to output the state vector of the current surgical traction and fixation operation.

[0023] The comparison unit is used to perform difference analysis on the state vector of the current surgical traction and fixation operation and the target state data of the current surgical stage to generate state difference data, perform a normalized error operation on the state difference data, and output a state error vector. Judge the state error vector based on a preset threshold rule, output the current state matching degree index, and judge whether the current state matching degree index is lower than the preset threshold. If it is lower than the preset threshold, output a signal indicating the existence of a state contradiction. If it is higher than the preset threshold, output an indication to continue maintaining the state vector of the current surgical traction and fixation operation.

[0024] The retractor optimization module is used to identify abnormal retractor modules in the state vector of the current robotic arm surgical traction and fixation operation combined with the retractor module, and extract the subset of retractor modules that cause the signal indicating the existence of a state contradiction. Jointly analyze the subset of the retractor module and the target state data of the current surgical stage to generate conflict path data between the subset of the retractor module and the target state data; Match the conflict path data with the preset set of retractor module replacement rule data to screen out the corresponding set of target replacement retractor modules; Jointly perform path simulation on the set of target replacement retractor modules and the end effector pose data of the robotic arm unit obtained by the state acquisition module to generate a set of replacement schemes; Based on the surgical safety, protection of the target tissue area, and operation target, comprehensively evaluate the set of replacement schemes and output the optimal target replacement scheme; Determine whether the optimal target replacement scheme meets the target state data of the current surgical stage. If it does not meet, regenerate the set of replacement schemes based on the set of target replacement retractor modules. If it meets, output the retractor module replacement execution suggestion based on the optimal target replacement scheme.

[0025] It also includes an adaptive optimization module; the adaptive optimization module adjusts the end effector pose of the robotic arm unit and replaces the retractor module according to the retractor module replacement execution suggestion, forms the combined state of the updated retractor module and the robotic arm unit, and synchronously collects the combined state of the updated retractor module and the robotic arm unit through the state acquisition module to obtain the updated current operation state data; Process the updated current operation state data, output the state vector of the robotic arm surgical traction and fixation operation combined with the retractor module, and compare the updated state vector with the target state data of the current surgical stage to output a new state matching degree index; Determine whether the new state matching degree index reaches the preset threshold. If it does not reach, identify the abnormal retractor module for the updated state vector and repeat the execution of the retractor optimization module. If it reaches, output the updated state vector as the state record for the continuous surgical traction and fixation operation; Update the state record of the continuous surgical traction and fixation operation in real time to form the dynamic state record of the retractor module and the robotic arm unit during the entire surgical process.

[0026] It should be noted that in the formula structure involved in this solution, the dimensionless term can be used as a proportional or structural adjustment factor. When combined with a quantity with a unit, it only plays a role in numerical scaling and does not introduce a new physical dimension. Therefore, it will not change or confuse the overall unit system of the expression; such a combination of "dimensionless term and quantity with a unit" can be understood as the composite structure expression form commonly used in mathematical and physical modeling, which conforms to the principle of dimensional consistency and has a clear physical interpretation basis; Secondly, in the formula structure of this solution, if there are multiple variable terms with different physical units, including but not limited to time - related, mass - related, or energy - related variables, their combined appearance is for expressing the co - modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination, or normalization adjustment, with clear units and definite meanings. The overall expression conforms to the principle of dimensional consistency and the common paradigm of engineering modeling; In this solution, if constants, weights, adjustment factors, threshold parameters, proportionality coefficients, etc. are designed, they all belong to adjustable control parameters for different application environments. Their values depend on the target device configuration, data input characteristics, and performance optimization goals, and converge and are set within a reasonable range through methods such as model verification, performance constraints, or engineering calibration during the implementation stage. Although these parameters do not have a preset unique value, they have clear adjustment logics and calculation paths, belonging to a deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the solution has both general adaptability and reproducibility and operability, without affecting its technical clarity and implementability; In the state acquisition module, define the state vector to represent the state of the current robotic arm surgical traction fixation operation in combination with the retractor module. The state vector consists of the spatial coupling error state , the force - application displacement response state , and the dynamic target tracking error state , which is expressed as: The spatial coupling error state is expressed as: The force - application displacement response state is expressed as: The dynamic target tracking error state is expressed as: Among them, the spatial coupling error state represents the spatial coupling error state between the retractor module and the robotic arm unit; the force - application displacement response state represents the comprehensive response state of the force - application displacement of the retractor module; the dynamic target tracking error state represents the dynamic tracking error state between the actual path and the target path of the end - effector assembly of the robotic arm unit; Among them represents the retractor module installation position vector (x, y, z coordinates) in the current valid set of retractor module states collected through the state acquisition module; Represents the current position vector (x, y, z coordinates) of the end effector assembly of the robotic arm unit collected by the status acquisition module; Represents the axis spatial pose angle ( is the roll angle, is the pitch angle, is the yaw angle) in the set of current valid hook module states obtained by the status acquisition module; Represents the axis spatial pose ( is the roll angle, is the pitch angle, is the yaw angle) of the end effector assembly of the robotic arm unit obtained by the status acquisition module; Represents the geometric length of the hook module in the set of current valid hook module states obtained by the status acquisition module; Represents the current geometric length of the end effector assembly of the robotic arm unit obtained by the status acquisition module; where in the formula Represents the three degrees of freedom of the spatial pose of the hook module and the robotic arm unit, and the three degrees of freedom include roll angle, pitch angle, and yaw angle; Where Represents the traction force exerted by the hook module in the set of current valid hook module states obtained by the status acquisition module; Represents the micro displacement change amount detected during the traction process of the hook module in the set of current valid hook module states obtained by the status acquisition module; Represents the component of the traction direction of the hook module in the set of current valid hook module states obtained by the status acquisition module; Represents the displacement change amount in the traction direction of the hook module in the set of current valid hook module states obtained by the status acquisition module; Represents the actual contact area of the hook module in the set of current valid hook module states obtained by the status acquisition module contacting the target tissue area; Represents the friction factor between the hook module and the target tissue area in the set of current valid hook module states obtained by the status acquisition module; Where Represents the position of the j-th path point in the actual spatial path of the end effector assembly of the robotic arm unit at time t obtained by the status acquisition module; Represents the position of the j-th path point of the target path in the target state data of the current surgical stage; Represents the joint angle state of the current k-axis of the robotic arm unit obtained by the status acquisition module; Represents the target joint angle state of the k-axis in the target state data of the current surgical stage; The number of path points sampled for path tracing; is the total number of joints of the robotic arm unit.

[0027] In the comparison unit, using the state vector output by the state acquisition module , and the target state data of the current surgical stage output by the target state establishment module as the input, through the state data weight adjustment function perform data quality preprocessing, extract the differences in spatial pose parameters, force application state parameters and dynamic path parameters, and introduce the influence factors of force application and pose perturbation realize the non-linear influence of force application on spatial error, and at the same time introduce the path perturbation force application correction coefficient realize the real-time correction of force application estimation by dynamic path error, and finally recursively fuse to form a state error vector , expressed as: Define the difference in spatial pose parameters Expressed as: Define the difference in force application state parameters Expressed as: Define the difference in dynamic path parameters Expressed as: Among them, the influence factor of force application and pose perturbation represents the influence of force application on spatial error. The greater the force application, the stronger the pose error weighting in the above formula; the path perturbation force application correction coefficient represents the feedback of path perturbation on force application error. The greater the path error in the above formula, the stronger the force application error correction weight; the spatial pose parameters include , represents the current x, y, z coordinates combined by the retractor module output by the state acquisition module and the end effector assembly of the robotic arm unit; is the target x, y, z coordinates output by the target state establishment module; is the i-th axis in the state acquisition module ( is the roll angle, is the pitch angle, is the yaw angle) of the current spatial pose angle; is the target spatial pose angle of the i-th axis in the target state establishment module; is the target length of the retractor module in the target state establishment module; is the traction force and target traction force applied by the retractor module in the state acquisition module and the target state data; Establish the contact area between the hook module and the tissue in the target state establishment module; The current micro displacement corresponding to the force application process of the hook module; The target micro displacement corresponding to the force application process of the hook module; The current traction direction component force of the hook module; The target traction direction component force set for the hook module; The dynamic adjustment coefficient of the force application error; The force application adjustment coefficient of the spatial pose error; Where The number of discrete samples of the path tracking time; The spatial coordinates of the path point at time t in the state acquisition module; The target spatial coordinates of the path point at time t in the target state establishment module; The k-th joint angle state of the state acquisition module and the target state establishment module at time t; The total number of joints of the robotic arm unit; A very small positive number to prevent division by zero in path error calculation.

[0028] The hook optimization module identifies the abnormal hook module through the state vector output by the state acquisition module and the target state data output by the target state establishment module, establishes a conflict topology relationship graph based on the subset of the abnormal hook module, dynamically generates a set of candidate replacement solutions based on the state of the abnormal hook module and the pose of the end effector component of the current robotic arm unit, embeds the set of candidate replacement solutions into the conflict topology recursive simulation, generates a simulation result, updates the conflict topology state in real time through the simulation result, recursively searches for the optimal target replacement solution with the global conflict impact, and outputs a hook module replacement execution suggestion; Subset of abnormal hook modules Is expressed as: Based on the subset of abnormal hook modules Establish the conflict topology relationship graph of the abnormal module : Set of target alternative hook modules Is expressed as: Define Denote After jointly performing path simulation with the pose data of the end effector component of the robotic arm unit obtained through the state acquisition module, calculate the total energy of the conflict path impact of the generated set of replacement solutions, Is expressed as: Optimal target replacement scheme Expressed as: Suggestions for the execution of the retractor module replacement Expressed as: Wherein Indicates the i-th and j-th abnormal retractor modules in ; Is an anomaly detection function, and the anomaly detection function is used to evaluate the current retractor module Relative to State deviation degree; Is the anomaly determination threshold; the edge set Indicates the mutual conflict influence between abnormal retractor modules; Indicates And The conflict influence weight between; Is , The conflict relationship function between, , The conflict relationship function between is evaluated based on physical characteristics such as spatial distance and force application interference; Indicates a set of candidate replacement schemes dynamically generated based on the poses of the abnormal retractor module and the current end effector assembly; Is the th alternative retractor module scheme in the set of candidate replacement schemes; Indicates the evaluation And And Fitness function; Is the acceptable threshold for the adaptation error; Indicates the pose data of the end effector assembly of the robotic arm unit obtained by the state acquisition module; Is a single Local influence estimation of the conflict topology; Is the influence factor for evaluating the stability of the surgical space path during path simulation ; Is Comprehensive score for the protection of the target tissue area; Suggestions for the execution of the retractor module replacement Generated by , And ; Indicates the function for comprehensively outputting the replacement execution suggestions.

[0029] For further overall description: The present invention provides a robotic arm surgical traction and fixation system combined with a retractor module. Aiming at the problems in the prior art that real-time adaptation of the surgical space cannot be achieved, and the functions of abnormal state recognition and self-optimization are lacking in multi-organ combined exposure and complex tissue dissection surgeries, a technical system including a target state establishment module, a state acquisition module, a comparison unit, a retractor optimization module, and an adaptive optimization module is established; through the cooperation of the modules, dynamic real-time comparison and adaptive adjustment between the current state and the surgical target state are realized, ensuring the continuity and safety of the surgical traction operation; The formation logic of the system stems from the essential requirements of the surgical process: in the pre-operative stage, the target state establishment module extracts multi-modal imaging data and surgical plan data of the patient, establishes digital models of organ types, organ spatial positions, and target tissue regions, and determines the target tissue region and the retractor traction path according to the current surgical stage, providing an accurate benchmark for subsequent dynamic monitoring and feedback; During the surgical operation, the state acquisition module, through the combined work of the retractor module, the robotic arm unit, and the traction unit, connects the retractor module and the robotic arm unit structure to obtain the installation position and spatial pose of the retractor module, and obtains the force application state data through the traction unit; the two data are synchronized to form the state vector of the current robotic arm surgical traction and fixation operation combined with the retractor module, ensuring real-time and complete recording of the actual operation state; Based on the state vector and the target state data, the comparison unit calculates the spatial pose error, the force application state error, and the dynamic path error, and establishes a multi-dimensional state difference analysis model; through a recursive feedback mechanism, the comparison unit determines whether the current state is consistent with the target state. If there is a deviation, it timely triggers the intervention of the subsequent optimization module; After identifying the retractor module that causes the state deviation, the retractor optimization module establishes a conflict topology relationship, and comprehensively analyzes the influences caused by spatial interference and force application interference between abnormal retractor modules; based on the topology result, the module dynamically generates a set of target alternative retractor modules, combines with the pose of the current robotic arm end effector for simulation, recursively optimizes in real time and finally screens out the globally optimal target replacement scheme, and outputs the retractor module replacement execution suggestion; According to the replacement suggestion, the adaptive optimization module controls the robotic arm unit to adjust the pose of the end effector, completes the retractor module replacement, and synchronously collects the updated state vector again through the state acquisition module; the updated state vector enters the comparison unit again to perform the comparison with the target state data; if the coincidence degree does not reach the threshold, the system starts the abnormal detection and retractor optimization process again; if the coincidence degree meets the standard, the current state vector is recorded as the continuous state of the surgical traction and fixation operation, and the whole process dynamic state record is formed in real time by appending; Based on a modular structure and recursive feedback logic, this solution constructs an adaptive control system to continuously perceive the spatial state and force application state during the surgical process, automatically identify abnormalities, output adjustment suggestions and execute optimizations; this solution has achieved a technological leap from passive assistance to active dynamic decision-making, improving the reliability and safety of the robotic arm surgical traction and fixation operation combined with the retractor module, and is particularly suitable for surgical scenarios with dynamically changing spatial requirements and high operation requirements, having practicality and clinical application value.

[0030] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A robotic arm surgical traction and fixation system combined with a retractor module, comprising a target state establishment module, a state acquisition module, a comparison unit, and a retractor optimization module, characterized in that: The target state establishment module is used for feature extraction and analysis of preoperative multimodal imaging data and surgical plan data of a patient, and combines the retractor traction path parameters solved according to the target tissue region data to form and output the target state data of the current surgical stage; The state acquisition module obtains and fuses the installation position, spatial pose data, and force application state data of the retractor module through structural connection and signal connection with the retractor module, robotic arm unit, and traction unit it includes, and forms and outputs the state vector and state evolution sequence of the current robotic arm surgical traction and fixation operation combined with the retractor module; The comparison unit is used for performing difference analysis on the state vector of the current surgical traction and fixation operation and the target state data, calculating the state error vector, and outputting a state matching degree index to determine whether there is a "state contradiction existence signal" or "maintain the current state vector"; The retractor optimization module is used to identify the subset of retractor modules that trigger the state contradiction existence signal, generate and screen the target alternative retractor module set, and output the retractor module replacement execution suggestion that meets the target state data of the current surgical stage.

2. The robotic arm surgical traction and fixation system combined with a retractor module according to claim 1, characterized in that: The target state establishment module further includes performing feature extraction on the preoperative multimodal imaging data and surgical plan data of the patient to obtain a surgical data set, and hierarchically analyzing the surgical data set to form a target data group including organ type, organ spatial position, and target tissue region; Screen the target data group to extract the target tissue region data corresponding to the current surgical stage; Combine the target tissue region data with the retractor traction path parameters solved according to the target tissue region data to form a retractor target traction path set, perform spatial reconstruction on the retractor target traction path set, and output the target state data of the current surgical stage; Judge whether there is an unresolved spatial region in the target state data of the current surgical stage. If so, use the surgical data set to regenerate the target data group; if not, retain the target state data of the current surgical stage.

3. The robotic arm surgical traction and fixation system combined with a retractor module according to claim 2, characterized in that: The state acquisition module includes a retractor module, a robotic arm unit, and a traction unit. The retractor module includes a physical traction device for connecting and traction the target tissue region. The robotic arm unit includes a driving component and an end effector component for realizing the installation, spatial positioning, and pose adjustment of the retractor module. The traction unit includes a force application component and a force-displacement detection component for providing traction force application and synchronously detecting the mechanical state parameters of the retractor module. The state acquisition module realizes physical coupling through the structural connection between the retractor module and the robotic arm unit, and realizes synchronous interaction of control and data acquisition through the signal connection between the traction unit and the robotic arm unit, so as to form the state data basis of the robotic arm surgical traction and fixation operation combined with the retractor module; Synchronously collect the installation position and spatial pose data of the hook module formed by the structural connection between the state acquisition module and the robotic arm unit through the hook module, and the force application state data of the hook module obtained through the signal connection between the traction unit and the robotic arm unit, and output the original operation state set; Normalize the data of the original operation state set to form the current operation state data including the hook module parameters and the robotic arm pose parameters. Screen the current operation state data to extract the current valid hook module state set; The hook module state set includes hook model, installation position, traction direction, and parameters of the applied force; Fuse the current valid hook module state set obtained through the state acquisition module with the pose data of the end effector assembly obtained by the robotic arm unit, output the state vector of the robotic arm surgical traction and fixation operation with the current hook module combined, and append the current state vector to the state record sequence to form the state evolution sequence of the surgical stage; Judge whether the state evolution sequence contains a state anomaly identifier. If it contains, output a state anomaly prompt; If not, continue to output the state vector of the current surgical traction and fixation operation.

4. The robotic arm surgical traction and fixation system combined with a hook module according to claim 3, characterized in that: The comparison unit is used to perform difference analysis on the state vector of the current surgical traction and fixation operation and the target state data of the current surgical stage to generate state difference data, perform a normalized error operation on the state difference data, and output a state error vector; Judge the state error vector based on a preset threshold rule, output the current state matching degree index, and judge whether the current state matching degree index is lower than the preset threshold. If it is lower than the preset threshold, output a signal indicating the existence of a state contradiction; If it is higher than the preset threshold, output an indication to continue to maintain the state vector of the current surgical traction and fixation operation.

5. The robotic arm surgical traction and fixation system combined with a hook module according to claim 4, characterized in that: The hook optimization module is used to identify abnormal hook modules in the state vector of the current robotic arm surgical traction and fixation operation combined with the hook module, and extract the subset of hook modules that cause the signal indicating the existence of a state contradiction; Jointly analyze the subset of hook modules and the target state data of the current surgical stage to generate conflict path data between the subset of hook modules and the target state data; Match the conflict path data with the preset set of hook module replacement rule data, and screen out the corresponding set of target replacement hook modules; Jointly perform path simulation on the set of target replacement hook modules and the pose data of the end effector of the robotic arm unit obtained through the state acquisition module to generate a set of replacement schemes; Perform a comprehensive evaluation on the set of replacement schemes based on surgical safety, protection of the target tissue area, and operation objectives, and output the target replacement scheme; Judge whether the target replacement scheme meets the target state data of the current surgical stage. If it does not meet, regenerate the set of replacement schemes based on the set of target replacement hook modules. If it meets, output a hook module replacement execution suggestion based on the target replacement scheme.

6. The robotic arm surgical traction and fixation system incorporating a retractor module according to claim 5, wherein: It further includes an adaptive optimization module; The adaptive optimization module adjusts the pose of the end effector of the robotic arm unit and replaces the retractor module according to the retractor module replacement execution suggestion, forming an updated combined state of the retractor module and the robotic arm unit. The updated combined state of the retractor module and the robotic arm unit is synchronously collected through the state acquisition module to obtain the updated current operation state data; The updated current operation state data is processed, and a state vector of the robotic arm surgical traction fixation operation combined with the retractor module is output. The updated state vector is compared with the target state data of the current surgical stage, and a new state matching degree index is output; It is judged whether the new state matching degree index reaches a preset threshold. If it does not reach, the updated state vector is used to identify the abnormal retractor module, and the retractor optimization module is repeatedly executed; if it reaches, the updated state vector is output as the state record for continuous surgical traction fixation operation; The state record of the continuous surgical traction fixation operation is updated in real time to form a dynamic state record of the retractor module and the robotic arm unit during the entire surgical process.

7. A robotic arm surgical traction fixation system combined with a retractor module according to claim 6, characterized in that: In the state acquisition module, a state vector is defined indicating the state of the robotic arm surgical traction fixation operation currently combined with the retractor module. The state vector consists of a spatial coupling error state , a force application displacement response state , and a dynamic target tracking error state and is composed of three parts, expressed as: Spatial coupling error state Expressed as: Force application displacement response state Expressed as: Dynamic target tracking error state It is expressed as: Among them, the spatial coupling error state represents the spatial coupling error state between the hook module and the robotic arm unit; the force application displacement response state represents the comprehensive response state of the force application displacement of the hook module; the dynamic target tracking error state represents the dynamic tracking error state between the actual path and the target path of the end effector assembly of the robotic arm unit; wherein represents the hook module installation position vector in the currently valid hook module state set collected by the status acquisition module; represents the current position vector of the end effector assembly of the robotic arm unit collected by the status acquisition module; represents the axis spatial pose angle of the hook module in the currently valid hook module state set obtained by the status acquisition module; represents the axis spatial pose of the end effector assembly of the robotic arm unit obtained by the status acquisition module; represents the geometric length of the hook module in the currently valid hook module state set obtained by the status acquisition module; represents the current geometric length of the end effector assembly of the robotic arm unit obtained by the status acquisition module; Among them represents the traction force exerted by the hook module in the currently valid set of hook module states obtained by the status acquisition module; represents the displacement change detected during the traction of the hook module in the currently valid set of hook module states obtained by the status acquisition module; represents the component force in the traction direction of the hook module in the currently valid set of hook module states obtained by the status acquisition module; represents the displacement change in the traction direction of the hook module in the currently valid set of hook module states obtained by the status acquisition module; represents the actual contact area where the hook module in the currently valid set of hook module states obtained by the status acquisition module contacts the target tissue area; represents the friction factor between the hook module in the currently valid set of hook module states obtained by the status acquisition module and the target tissue area; wherein represents the position of the j-th path point in the actual spatial path of the end effector assembly of the robotic arm unit obtained by the status acquisition module at time t; represents the position of the j-th path point in the target path in the target state data of the current surgical stage; represents the joint angle state of the k-th axis of the robotic arm unit obtained by the status acquisition module; represents the target joint angle state of the k-th axis in the target state data of the current surgical stage; is the number of path points sampled for path tracking; is the total number of joints of the robotic arm unit.

8. A robotic arm surgical traction fixation system combined with a retractor module according to claim 7, characterized in that: In the comparison unit, the state vector output by the state acquisition module , and the target state data of the current surgical stage output by the target state establishment module are used as inputs. Through the state data weight adjustment function , data quality preprocessing is performed to extract the differences in spatial pose parameters, force application state parameters, and dynamic path parameters, and the influence factors of force application and pose perturbation are introduced to realize the non-linear influence of force application on spatial error. At the same time, a path perturbation force application correction coefficient is introduced to realize the real-time correction of force application estimation by dynamic path error. Finally, a state error vector is recursively fused and formed , which is expressed as: Define the difference in spatial pose parameters It is expressed as: Define the difference in the force application state parameter It is expressed as: Define dynamic path parameter differences Expressed as: Among them, the spatial pose parameters include , representing the current x, y, and z coordinates of the combination of the hook module output by the state acquisition module and the end effector assembly of the robotic arm unit; being the target x, y, and z coordinates output by the target state establishment module; being the current spatial pose angle of the i-th axis in the state acquisition module; being the target spatial pose angle of the i-th axis in the target state establishment module; being the target length of the hook module in the target state establishment module; being the traction force and target traction force applied by the hook module in the state acquisition module and the target state data; being the contact area between the hook module and the tissue in the target state establishment module; being the current displacement corresponding to the force application process of the hook module; being the target displacement corresponding to the force application process of the hook module; being the current component force of the traction direction of the hook module; being the target set component force of the traction direction of the hook module; being the dynamic adjustment coefficient of the force application error; being the force application adjustment coefficient of the spatial pose error; wherein is the number of discrete samples of the path tracking time; is the spatial coordinate of the path point at time t in the state acquisition module; is the target spatial coordinate of the path point at time t in the target state establishment module; is the k-th joint angle state at time t of the state acquisition module and the target state establishment module; is the total number of joints of the robotic arm unit; is a positive number to prevent division by zero in path error calculation.

9. A robotic arm surgical traction fixation system combined with a retractor module according to claim 8, characterized in that: The retractor optimization module identifies the abnormal retractor module by using the state vector output by the state acquisition module and the target state data output by the target state establishment module, establishes a conflict topology relationship graph according to the abnormal retractor module subset, dynamically generates a set of candidate replacement schemes based on the abnormal retractor module state and the pose of the end effector component of the current robotic arm unit, embeds the set of candidate replacement schemes into the conflict topology recursive simulation, generates a simulation result, updates the conflict topology state in real time through the simulation result, recursively searches for the global conflict impact target replacement scheme, and outputs the retractor module replacement execution suggestion; Abnormal retractor module subset Expressed as: Based on the abnormal hook module subset Establish a conflict topology graph of abnormal modules : Target replacement retractor module set Expressed as: Definition Indicates After jointly performing path simulation with the pose data of the end effector assembly of the robotic arm unit obtained by the passing state acquisition module, it represents the total energy of the conflict path of the generated replacement scheme set calculated and generated, Expressed as: Target replacement solution Expressed as: Execution suggestions for the replacement of the hook module Expressed as: Among them indicates the i-th and j-th abnormal retractor modules in ; is an abnormal detection function, which is used to evaluate the state deviation of the current retractor module relative to ; is an abnormal determination threshold; the edge set represents the mutual conflict influence between abnormal retractor modules; represents and the conflict influence weight between; is , the conflict relationship function between; represents the set of candidate replacement solutions dynamically generated based on the poses of abnormal retractor modules and the current end effector component; is the -th alternative retractor module solution in the set of candidate replacement solutions; represents the function for evaluating the compatibility of with and ; is the compatibility error threshold; represents the pose data of the end effector component of the robotic arm unit obtained through the state acquisition module; is the local influence estimation of a single on the conflict topology; is the influence factor for evaluating the stability of the surgical space path of in the path simulation; is the comprehensive score for the protection of the target tissue area by ; the retractor module replacement execution recommendation is generated by , and ; represents the function for comprehensively outputting the replacement execution recommendation.

Images