Sliding table motion control method and device for laparoscopic surgery robot

Abstract

The invention discloses a sliding table motion control method and device for a laparoscopic surgical robot, and relates to the technical field of surgical robos.The method comprises the steps that a lesion three-dimensional model is established based on a lesion CT image, a target area and boundary coordinates of the target area are obtained, and a coordinate transformation matrix is calculated for space registration to obtain a unified coordinate system; selecting a moving mode according to the user control instruction; recording operation data of users, classifying the users to obtain corresponding force speed curves, force displacement sequences and habitual force, and establishing a comprehensive control model and an error compensation model; when entering a fine adjustment mode, inputting a real-time distance, a user type and a force displacement sequence into a comprehensive control model, determining a regulation and control scheme, and reversely correcting output displacement according to a real-time positioning error, so that the auxiliary surgical instrument reaches a target position; high-precision motion control over the sliding table (2) of the laparoscopic surgery robot is achieved, the surgery time is remarkably shortened, the target position of an auxiliary instrument is accurately and rapidly positioned, and the surgery efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical robots, and in particular, to a method and device for controlling the sliding table movement of a laparoscopic surgical robot. Background Art

[0002] In laparoscopic surgery, a full-time medical staff is often required to support and control surgical auxiliary instruments to assist the doctor. To avoid fatigue and jitter of personnel, surgical robots are now used for auxiliary operations. The surgical robot can control the auxiliary instrument in a passive control manner. The surgeon sends control instructions to the surgical robot through a foot pedal, voice, handle buttons, etc. The surgical robot passively executes the corresponding control instructions until an ideal surgical field of view or an ideal target position is achieved.

[0003] Due to the stability of transmission and the degree of precise control, the doctor generally controls the robotic arm through manual buttons to perform auxiliary work. However, during the control process, the target position of the robot cannot be accurately and quickly located, resulting in an increase in the adjustment time and adjustment error. Chinese Patent Application No. 202110405332.8 provides a method and device for controlling the sliding table movement of a laparoscopic surgical robot, which obtains the force applied to the sliding table and determines the speed of the sliding table, and adjusts through speed limit to ensure the stability and accuracy of the surgeon's use of surgical instruments for surgical operations.

[0004] In the prior art, acceleration, uniform speed, and deceleration all rely on adjusting the button pressing force. However, due to the different habitual forces of different doctors, the deceleration sliding distance and time used will also be different, ultimately resulting in different degrees of deviation from the target position and an increase in the adjustment time. Summary of the Invention

[0005] The present application provides a method and device for controlling the sliding table movement of a laparoscopic surgical robot, which solves the problems of too long adjustment time and too large adjustment error in the prior art, and achieves the technical effect of precise and rapid adjustment.

[0006] The present application provides a method for controlling the sliding table movement of a laparoscopic surgical robot, which is applied to a surgical robot, including a main control arm 1, a sliding table 2, a sub-control arm 3, and an operation button, and includes: S1: Establish a three-dimensional model of the lesion based on the CT image of the lesion, obtain the target area and its boundary coordinates, and calculate the coordinate transformation matrix for spatial registration to obtain a unified coordinate system; S2: Select a movement mode according to the user control instruction. The movement mode includes a coarse adjustment mode and a fine adjustment mode; in the coarse adjustment mode, the sliding table 2 is adjusted according to the user control instruction, and the real-time distance between the sub-control arm 3 and the target area is monitored in real time based on the unified coordinate system. If the real-time distance is less than the safety threshold, stop moving and prompt to enter the fine adjustment mode; S21: Record the operation data of the user, classify the user to obtain the corresponding force-speed curve, force-displacement sequence, and habitual force, and establish a comprehensive control model and an error compensation model; S22: When entering the fine-tuning mode, input the real-time distance, user type, and force-displacement sequence into the comprehensive control model to determine the control scheme, and reverse-correct the output displacement according to the real-time positioning error, so that the auxiliary surgical instrument reaches the target position.

[0007] Furthermore, the operation data includes the pressing force of the button, the duration, the moving distance of the slide table 2, and the error record; based on the operation data, first analysis data and second analysis data are obtained. The first analysis data refers to the average force, pressing duration, and total moving distance in the rough-tuning stage; the second analysis data refers to the force distribution histogram and the final positioning error in the fine-tuning stage; The control instruction refers to the operation button manipulated by the user and the corresponding pressing force applied. The moving direction of the slide table 2 is controlled based on the operation button, and the moving speed of the slide table 2 is controlled based on the pressing force.

[0008] Furthermore, calculating the coordinate transformation matrix for spatial registration includes: Paste a number of optical marker points on the patient's body surface, obtain the positions of the marker points in the image coordinate system through CT scanning, and generate a marker point coordinate set; based on optical navigation to real-time track the marker points, calculate the transformation matrix between the image coordinates and the operating table coordinate system; The spatial registration is to map the transformation matrix to the main control arm 1, perform dynamic alignment of the image coordinate system and the operating table coordinate system, and perform zero calibration on the guide rail of the main control arm 1 and the slide table 2.

[0009] Furthermore, the force-speed curve is a curve corresponding to the pressing force and the moving speed of the slide table 2, recording the force input of different users in various operation scenarios and the corresponding slide table 2 speed output data, which is used to describe the mapping relationship between the user's operation force and the slide table 2 speed; Based on the force-displacement sequence, the user's habitual pressing force is obtained. The habitual force refers to the force generated when the user presses habitually; The comprehensive control model is used to receive the force-speed curve characteristics, force-displacement sequence characteristics, and habitual force characteristics, and output the ideal force value, the ideal displacement of the slide table 2, and the speed control instruction; The error compensation model is established based on the overshoot or undershoot data records generated when the user operates the slide table 2 in historical surgeries, and is used to reverse-correct the output displacement during the surgery.

[0010] Further, the method further includes: S3: Obtain an ultrasonic image and a fluorescence image, calculate a first centroid based on the ultrasonic image; extract a blood vessel region by threshold segmentation based on the fluorescence image, and calculate a second centroid; obtain a target point set based on real-time target position monitoring, and calculate a third centroid and a standard deviation of the target point set; Determine a target centroid according to the first centroid, the second centroid and the third centroid, and obtain a new regional boundary according to the target centroid and the standard deviation; Determine a dynamic change difference according to the new regional boundary, and adjust the force-speed curve according to the dynamic change value; Wherein, perform threshold segmentation on the collected fluorescence image, extract the blood vessel region, calculate the centroid coordinates of the blood vessel region, and map the centroid coordinates to a unified coordinate system to obtain the second centroid; The target point set at time t is obtained as The centroid coordinates are obtained as: , calculate the standard deviations of the target point set on three coordinate axes according to the standard deviation formula; the target point set includes all feature points of the target region; Fuse the obtained first centroid, second centroid and third centroid to obtain a target centroid; calculate the semi-axis length of the target region based on the standard deviation; determine a new regional boundary according to the target centroid and the semi-axis length; Calculate the displacement speed of the target blood vessel according to the optical flow method, use the norm value of the displacement speed of the target blood vessel as the target speed, obtain the average speed of the current slide 2, and use the ratio of the target speed to the average speed of the slide 2 as the dynamic change difference.

[0011] Further, the method further includes: S31: Set a state vector and an observation vector according to the new regional boundary and the adjusted force-speed curve to obtain a prediction equation, predict the displacement amount at the next moment based on the actual displacement and the speed of the slide 2, and calculate a compensation amount based on the user's habitual force; Set a prediction time domain, a control time domain and constraint conditions, obtain an optimal control sequence according to the prediction equation and the compensation amount, and determine a regulation scheme based on the first-step control amount of the optimal control sequence.

[0012] A slide movement control device for a laparoscopic surgical robot, the surgical robot includes a main control arm 1 for performing surgery, a slide 2 and a sub-control arm 3; the main control arm 1 is located above or on one side of the patient, the slide 2 slides on the main control arm 1 under the control of a motor, and one end of the sub-control arm 3 is movably connected to the slide 2, and the other end is provided with a fixed gripper 4 for fixedly gripping an auxiliary instrument.

[0013] Further, the surgical robot further includes an operator 5 and a display screen 6, and operation buttons are provided on the operator 5; The operation buttons include an up button, a down button and a fine-tuning button group; The fine-tuning button group includes an up button and a down button; the user control instructions correspond to the operation buttons, the up and down buttons represent the coarse-tuning mode, and the fine-tuning button group represents the fine-tuning mode; the user issues control instructions by operating the buttons, obtains the force applied to the operation buttons, and determines the moving speed of the slide table 2 according to the corresponding relationship. The display screen 6 is used to receive the CT images of the lesion, the target area and its boundary coordinates, and display the distance heat map in real time.

[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the coarse-tuning and fine-tuning modes, high-precision motion control of the slide table of the laparoscopic surgical robot is achieved; by reasonably controlling the movement of the slide table, the auxiliary instrument is quickly moved to the vicinity of the target area, and precise fine-tuning is realized, significantly shortening the operation time, accurately providing the auxiliary surgical instrument, avoiding the shaking caused by the medical staff holding the surgical instrument by hand, more accurately positioning the auxiliary instrument, and improving the operation efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of the slide table motion control method of a laparoscopic surgical robot in an embodiment of the present invention; Figure 2 It is a schematic diagram of the slide table motion control device of a laparoscopic surgical robot in an embodiment of the present invention; Reference numerals: 1, main control arm; 2, slide table; 3, sub-control arm; 4, fixed gripper; 5, operator; 6, display screen. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant drawings; the preferred embodiments of the present invention are shown in the drawings, however, the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0018] Embodiment 1: As Figure 1 shown, a slide table motion control method of a laparoscopic surgical robot, applied to a surgical robot, includes a main control arm 1, a slide table 2, a sub-control arm 3 and operation buttons; the method includes: S1: Establish a three-dimensional model of the lesion based on the CT image of the lesion, obtain the target area and its boundary coordinates, and calculate the coordinate transformation matrix for spatial registration to obtain a unified coordinate system; Calculating the coordinate transformation matrix for spatial registration includes: pasting a number of optical marker points on the patient's body surface, obtaining the positions of the marker points in the image coordinate system through CT scanning, and generating a marker point coordinate set; based on optical navigation, the marker points are tracked in real time, and the transformation matrix between the image coordinates and the operating table coordinate system is calculated; The spatial registration is to map the transformation matrix to the master arm 1, perform dynamic alignment of the image coordinate system and the operating table coordinate system, and calibrate the zero points of the guide rail and the slide table 2 of the master arm 1.

[0019] Specifically, use CT scanning to obtain the CT image of the lesion, generate DICOM format data. DICOM (Digital Imaging and Communications in Medicine) format refers to digital imaging and communications in medicine, which solves the problem of incompatible image data formats generated by different medical devices (such as CT, MRI, X-ray machines), and realizes the standardized storage, transmission and sharing of medical images. Generate a three-dimensional model of the lesion through an image processing system and label the surgical target area. Obtain the target area circled by the doctor on the display screen 6, extract the boundary coordinates, and generate a virtual fence (safe operation boundary).

[0020] Paste 4-6 optical marker points (such as NDI Polaris special reflective balls) on the patient's body surface. The selected positions include the opening position, ribs, iliac crest, etc. Obtain the positions of the marker points in the image coordinate system through CT scanning, generate a marker point coordinate set, use an optical navigation system (NDI Polaris) to track the marker points in real time, calculate the transformation matrix between the image coordinate system and the operating table coordinate system, map the transformation matrix to the controller of the master arm 1, and then perform spatial registration; the spatial registration refers to dynamically aligning the image coordinate system and the operating table coordinate system, and then calibrating the zero points of the guide rail of the master arm 1 and the slide table 2 to ensure the triaxial motion accuracy.

[0021] S2: Select a movement mode according to the user control instruction, and the movement mode includes a coarse adjustment mode and a fine adjustment mode; In the coarse adjustment mode, the slide table 2 is adjusted according to the user control instruction. Specifically, the user control instruction refers to the operation button manipulated by the user and the corresponding pressing force applied. Based on the operation button, the moving direction of the slide table 2 is adjusted, and based on the pressing force, the moving speed of the slide table 2 is controlled. That is, the moving direction and moving speed of the slide table 2 are adjusted according to the user operation button and the corresponding pressing force. Based on a unified coordinate system, the real-time distance between the secondary control arm 3 and the target area is monitored in real time. If the real-time distance is less than the safety threshold, the movement is stopped and a prompt is given to enter the fine adjustment mode. Then, in the fine adjustment mode, the secondary control arm 3 is further adjusted to move the auxiliary surgical instrument to the target position.

[0022] In some embodiments, the main control arm 1 is located above or on one side of the patient. The main control arm 1 can move left, right, forward, and backward. The slide table 2 slides on the main control arm 1. One end of the secondary control arm 3 is fixedly connected to the slide table 2, and the other end is used to fixedly hold the auxiliary instrument. The operation buttons include an up button, a down button, and a fine adjustment button group. The fine adjustment button group includes an up adjustment button and a down adjustment button. The user control instruction corresponds to the operation button. The up button and the down button represent the coarse adjustment mode, and the fine adjustment button group represents the fine adjustment mode. After determining the operation mode, the motor is controlled according to the pressing force to drive the slide table 2 to move. In the coarse adjustment mode, the minimum moving distance and the maximum moving speed are set. The parameter setting principle is to give priority to safety, ensure the balance of efficiency and accuracy, and dynamic adaptability, and ensure that the slide table 2 will not approach dangerous areas (such as blood vessels and important organs) due to excessive speed or excessive moving distance during the coarse adjustment stage. On the premise of ensuring safety, the positioning time of the coarse adjustment stage is shortened by reasonably setting parameters, and the parameters are adjusted according to the surgical type (such as cholecystectomy, gastrointestinal anastomosis) and patient individual differences (such as obesity degree, tissue elasticity).

[0023] Specifically, the minimum moving distance refers to the minimum displacement of the slide table 2 each time it responds to the control instruction in the coarse adjustment mode. The calculation formula is as follows:

[0024] where is the minimum displacement; is the shortest distance from the initial position to the target area, measured through the preoperative three-dimensional model of the lesion, that is, the distance from the nearest boundary point of the target area to the initial position; is the distance of the safety threshold; is the estimated maximum number of steps in the coarse adjustment mode, an estimated number of steps (such as 5 - 12 steps) obtained by comparing and analyzing the current nearest distance and historical data. For example: during cholecystectomy, the distance from the target area to the initial position is 30 mm, the safety buffer is 2 mm, and the number of coarse adjustment steps is set to 6 steps. Then: , in practical applications, rounding up can be adopted, that is, 5 mm.

[0025] The maximum moving speed is the highest allowable speed of the sliding table 2 in the coarse adjustment mode, which needs to be dynamically adjusted in combination with the pressing force and the safety threshold. The calculation formula is:

[0026] where, is the highest allowable speed; is the physical speed limit of the motor and the guide rail; is the distance of the safety threshold; is the response time, which refers to the reaction moving time from when the system receives the user control instruction to the end of the movement; a safety margin is set in the calculation result to fully ensure safety. For example, if the calculated maximum moving speed is 50 and a safety margin of 20 is set, the actual highest allowable speed is 30 mm / s.

[0027] In some embodiments, a dynamic adjustment strategy is implemented, deceleration based on distance, that is, when the distance between the end of the secondary control arm 3 and the target area is ≤ 10 mm, the maximum speed limit is automatically reduced to 10 mm / s. When the distance is ≤ 5 mm, it switches to the fine adjustment mode. It also includes adjustments based on patient characteristics. For obese patients, an increase should be made on the standard of the minimum moving distance.

[0028] Specifically, fix the auxiliary instrument on the secondary control arm 3, use the up or down button to control the sliding table 2 to move the secondary control arm 3 to the initial position, that is, the position where fine adjustment is required, use the fine adjustment button group to control the secondary control arm 3, and place the auxiliary instrument at the target position. The target position is confirmed bidirectionally according to the preset coordinate position and the doctor.

[0029] Specifically, the user controls the moving direction of the sliding table 2 through the control buttons on the operator 5 (such as the operation panel or the handheld operator 5). The up button controls upward movement, and the down button controls downward movement. When it is detected that the up button in the control buttons is pressed, it is determined that the sliding table 2 moves towards the upper end of the main control arm 1. When it is detected that the down button in the control buttons is pressed, it is determined that the sliding table 2 moves towards the lower end of the main control arm 1. Detect the pressure of the control buttons at the previous moment and the next moment, and convert the pressure at the previous moment and the next moment into the force at the previous moment and the force at the next moment respectively; obtain the acceleration of the sliding table 2 according to the force at the previous moment and the force at the next moment, and obtain the current speed of the sliding table 2 according to the acceleration.

[0030] Obtain the force applied to the button and determine the moving speed of the slide table 2 according to the corresponding relationship, send the moving speed of the slide table 2 to the motor, and use the motor to drive the movement of the slide table 2; classify the pressing force (0 - 100%), regulate the moving speed (0.1 - 50 mm / s), and set the maximum speed limit to prevent excessive movement of the slide table 2. The end position of the secondary control arm 3 is obtained in real time by the optical navigation system, and the Euclidean distance from the nearest boundary of the target area is calculated.

[0031] Specifically, a reflective ball is installed at the end of the secondary control arm 3 and tracked by an infrared camera with an accuracy of ±0.2 mm. Output the end (position + attitude) data in real time, and use the IMU data (angular velocity / acceleration) to assist in compensating the positioning error when the optical signal is blocked.

[0032] The end position of the secondary control arm 3 is obtained in real time by the optical navigation system, and the Euclidean distance from the nearest boundary of the target area is calculated as the real-time distance. The display screen 6 displays the distance heat map in real time (red → yellow → green indicates far → near, for example, the red area indicates a distance greater than 10 mm from the target, yellow indicates between 5 - 10 mm, and green indicates ≤5 mm). Both the doctor and the system judge whether to enter the fine-tuning mode based on the distance. A safety threshold is preset, which is obtained by summarizing and analyzing historical data. Calculate the average pressing force and average moving distance based on the pressing force and the corresponding moving distance in the historical data, and add an error distance (0.5 - 1 mm) to the average moving distance, that is, set it as the safety threshold. For example, if the threshold is 5 mm, then when the distance ≤5 mm, the system automatically stops moving and prompts to enter the fine-tuning mode, and the doctor needs to confirm whether to enter the fine-tuning mode for the second time.

[0033] In the fine-tuning mode, based on the three-dimensional model of the preoperative lesion, the doctor circles the target area (such as the cystic triangle) on the display screen 6. The system automatically extracts the boundary coordinates and generates a virtual fence (the boundary is expanded by 2 mm as a safety buffer zone), and at the same time further reduces the moving speed and moving distance, and reduces the maximum moving speed and unit moving distance.

[0034] For example, in laparoscopic cholecystectomy, use a CT device with 64 rows or more to scan the patient's abdomen to generate DICOM format data of the gallbladder and surrounding tissues. Generate a three-dimensional model of the gallbladder area through the image processing system, and mark the cystic triangle as the surgical target area. The doctor circles the target area on the display screen 6, and the system automatically extracts the boundary coordinates and generates a virtual fence (the boundary is expanded by 2 mm as a safety buffer zone).

[0035] The sliding platform 2 moves on the main control arm 1. The doctor controls the sliding platform 2 through the buttons on the handheld operator 5 to move the auxiliary instrument as a whole to the gallbladder area. When the distance ≤ 5 mm, the system triggers a warning and prompts to switch to the fine-tuning mode for the doctor to confirm. After the confirmation is completed, the doctor continues to finely adjust and control the secondary control arm 3 through the fine-tuning button group on the buttons of the handheld operator 5 to adjust the surgical instrument to the target position.

[0036] Through optical navigation, fusion positioning, coarse and fine adjustment collaborative control, and target boundary technology, this application realizes high-precision motion control of the sliding platform 2 of the laparoscopic surgical robot, accurately provides auxiliary surgical instruments, avoids the shaking caused by medical staff holding surgical instruments by hand, can significantly shorten the operation time, reduce the risk of complications, and provides a safe and efficient solution for minimally invasive surgery.

[0037] The technical solutions in the above embodiments of this application have at least the following technical effects or advantages: This application adopts the coarse adjustment and fine adjustment modes to realize high-precision motion control of the sliding platform 2 of the laparoscopic surgical robot; by reasonably controlling the movement of the sliding platform 2, quickly moves the auxiliary instrument to the vicinity of the target area, and realizes precise fine adjustment, significantly shortens the operation time, accurately provides the auxiliary surgical instrument, avoids the shaking caused by medical staff holding the surgical instrument by hand, more accurately locates the auxiliary instrument, and improves the operation efficiency and quality.

[0038] Embodiment 2: In Embodiment 1, two operation modes of coarse adjustment and fine adjustment are given, which further improves the control accuracy of the surgical robot and the accuracy of the doctor's regulation. In the fine-tuning mode, strict requirements are imposed on the user's force regulation, but the user's pressing force cannot be accurately adjusted, and the user cannot accurately apply his own force. Therefore, the number of regulation times and the regulation time are increased. This embodiment makes further improvements on the basis of the above content.

[0039] Step S2 also includes: S21: Record the operation data of the user, classify the user, and obtain the corresponding force-speed curve, force-displacement sequence, and habitual force, and establish a comprehensive control model and an error compensation model.

[0040] In some embodiments, the operation data includes historical data and operation data in the coarse adjustment mode during the current surgery. The operation data includes the pressing force of the button, the duration, the moving distance of the slide 2, and the positioning error. The first analysis data and the second analysis data are obtained based on the operation data. The first analysis data refers to the average force, the pressing duration, and the total moving distance during the coarse adjustment stage. The second analysis data refers to the force distribution histogram and the final positioning error during the fine adjustment stage. In practical applications, if there is no historical data of the current user, only the operation data during the coarse adjustment stage is analyzed, that is, the first analysis data is analyzed. The user is classified based on the first analysis data and the second analysis data of the user as input features. Through the clustering algorithm, the number of clustering categories is set to 3, and the optimal clustering center is found through multiple iterations. According to the clustering results, doctors are divided into three types: light pressing type, standard type, and heavy pressing type.

[0041] Specifically, the light pressing type means that the pressing force is relatively light, the positioning error is relatively large, and the number of operation regulations is relatively large. For example, the pressing force is less than 40%, and the error is greater than 0.4 mm. The standard type means that the pressing force is medium, and the situation analysis is in medium conditions. For example, the pressing force is between 40% - 60%, and the error is between 0.2 - 0.4 mm. The heavy pressing type means that the pressing force is relatively large, and the positioning error is relatively small. For example, the pressing force is greater than 60%, and the error is less than 0.2 mm.

[0042] In some embodiments, the force - speed curve is a curve corresponding to the pressing force and the moving speed of the slide 2, recording the force input and the corresponding slide 2 speed output data of different users in various operation scenarios (different distances, different target operations, etc.). It is used to describe the mapping relationship between the user's operation force and the speed of the slide 2, reflecting the speed that the user expects the slide 2 to reach when applying different forces. Under different user types and operation scenarios, the ideal force - speed curve will be different.

[0043] For light - pressing - type users, they usually hope that the slide 2 has a more sensitive speed response to a relatively small force input to achieve fine operation. Therefore, the slope of their force - speed curve is larger when the force is relatively small, that is, a relatively small force change can cause a relatively large speed change. Heavy - pressing - type users pay more attention to the smoothness of the operation and need a relatively large force to make the slide 2 reach a relatively high speed. Therefore, the slope of their force - speed curve is smaller when the force is relatively small, and the speed increases significantly only after the force reaches a certain level. For standard - type users, their force - speed curve is between that of the light - pressing type and the heavy - pressing type, showing a relatively balanced operation characteristic.

[0044] In some embodiments, corresponding force-speed curves are constructed for different types of users, and mathematical functions (such as linear functions, polynomial functions, etc.) are used to fit the relationship between force and speed. For example, a linear function is used for light-press users, and a quadratic function is used for heavy-press users for fitting. During the actual operation process, according to the actual situation, the force input and distance information are obtained in real time. According to the user type and real-time distance, the pre-set force-speed curve is dynamically adjusted according to the formulated strategy. For example, for light-press users, the slope of the curve in the low-force section is appropriately increased; for heavy-press users, the slope of the curve in the low-force section is decreased; according to the adjusted force-speed curve, the speed of the slide table 2 is controlled to match the doctor's operation intention.

[0045] For example, for light-press users, the basic force-speed curve is , the force F range is [0, 1], and the speed v range is [0, 1]; For standard users, the basic force-speed curve is ; For heavy-press users, the basic force-speed curve is ; During the actual operation process, for example, for heavy-press users, the real-time distance is monitored. When the real-time distance is less than the safety threshold (i.e., entering the fine-tuning mode), the force-speed curve is compressed to , reducing the response degree of speed to force. At the same force, the speed is reduced; furthermore, the force-speed curve is dynamically adjusted according to different user types and real-time distances to achieve more accurate and safe operation control.

[0046] In some embodiments, according to the operation habits and types of different users, personalized force-speed curves are provided, enabling the slide table 2 to better adapt to the doctor's operation style; combined with real-time distance information, the force-speed curve is dynamically adjusted so that the slide table 2 always maintains the best speed response characteristics during the operation process. It can control the slide table 2 more naturally and smoothly, reduce the operation discomfort caused by speed mismatch, and improve the fluency and comfort of the operation. When approaching the target position, by compressing the force-speed curve, the speed is reduced, effectively reducing overshoot phenomena and improving the positioning accuracy; at the same time, reducing safety risks such as accidental touch caused by too fast speed to ensure the safety of the operation.

[0047] In some embodiments, the force-displacement sequence is a sequence formed by arranging the number of presses, pressing force, and actual displacement distance according to time. Based on the force-displacement sequence, the habitual pressing force of the user is obtained. The habitual force refers to the force generated when the user habitually presses, which is the force characteristic that is least likely for the user to change and adjust.

[0048] In some embodiments, statistical analysis is performed on the collected force data to find the range of pressing forces frequently used by the user over a period of time. For example, the force distribution of the user's presses at different operation stages (such as coarse adjustment and fine adjustment) is statistically analyzed, the occurrence frequency of each force range is calculated, and the typical value within the force range with the highest occurrence frequency or the most frequently used by the user is used as the habitual pressing force. For instance, if the user's pressing force during most operations is concentrated between 30% and 40%, and the number of occurrences of the pressing force within this range is much higher than that in other ranges, then the median value of 35% in this range can be taken as the habitual pressing force. The habitual force is an important manifestation of the user's operation habit. As an important feature for user classification, personalized control can reduce the time for the user to adjust the operation force, improve the operation efficiency, and enable the user to control the slide table 2 more stably and reduce operation errors when operating at their habitual pressing force.

[0049] According to the user's habitual pressing force, the parameters of the surgical robot are set personalized. In the fine adjustment mode, the response speed and accuracy of the slide table 2 to the habitual pressing force are optimized, so that the slide table 2 can move to the target position more accurately when the user applies the habitual pressing force.

[0050] In some embodiments, the comprehensive control model is a comprehensive regulation model obtained based on the user's historical operation data between the user's pressing force and the actual moving displacement of the slide table 2. Due to differences in operation habits and skills among different doctors, the actual moving displacement of the slide table 2 may be different under the same pressing force. Based on the comprehensive control model, it is possible to accurately predict the displacement that the slide table 2 should move under a specific force for different users, making the control of the slide table 2 more in line with the doctor's operation habits, improving the doctor's operation comfort and surgical efficiency. The accurate force-displacement mapping can reduce the positioning error caused by the mismatch between force and displacement, enabling the slide table 2 to reach the target position more accurately, thereby improving the positioning accuracy of the surgery.

[0051] For example, user A and user B respectively operate the slide table 2. In the historical data (or data in the previous coarse adjustment mode), when user A operates with the habitual force, the slide table 2 moves an average of 5 mm, while when user B operates with the habitual force, the slide table 2 moves an average of 3 mm. By learning this historical data, the comprehensive control model will predict that the slide table 2 should move 5 mm when user A presses again with the habitual force; when user B presses with the habitual force, it will predict that the slide table 2 should move 3 mm. According to the regulation mode and the actual displacement amount, displacement regulation is performed to ensure a stable corresponding displacement amount. In this way, the control of the slide table 2 can better adapt to the operation characteristics of different users.

[0052] In some embodiments, establishing the comprehensive control model includes extracting key features from the force-velocity curve, such as the slope of the curve, the inflection point position, the speed response change in different force intervals, etc.; extracting features from the force-displacement sequence, such as the average pressing force, the pressing frequency, the displacement change rate, etc.; and using the habitual force as an independent feature to represent the user's operation habit.

[0053] Construct a neural network model including an input layer, a hidden layer, and an output layer. The input layer of the comprehensive control model receives the force-velocity curve features, the force-displacement sequence features, and the habitual force features; the hidden layer is used to learn the complex relationships between the features; and the output layer outputs the ideal force value, the ideal displacement of the slide table 2, and the speed control instruction.

[0054] Divide the preprocessed data into a training set, a validation set, and a test set. Use the training set to train the model, and continuously adjust the weights and biases of the model through the backpropagation algorithm to minimize the prediction error. Use the validation set to monitor the training process of the model to prevent overfitting. According to the performance of the validation set, adjust the structure and hyperparameters of the model, such as the learning rate, the number of nodes in the hidden layer, etc., to improve the performance of the model.

[0055] In some embodiments, the error compensation model is established based on the overshoot or undershoot data records generated when the user operates the slide table 2 during historical surgeries (for users without historical data, rely on the regulation data corresponding to the coarse adjustment mode in the current surgery for analysis). Overshoot means that the actual moving distance of the slide table 2 exceeds the target distance, and undershoot means that the actual moving distance of the slide table 2 does not reach the target distance. Analyze these historical data to find the error patterns of different users in different operation scenarios.

[0056] In real-time surgical control, when the system detects that the current positioning situation has similar features to the historical data (such as a specific doctor at a specific target distance and operation force), it will perform a reverse correction on the output displacement according to the historical error value. Reverse correction means that if the historical data shows that the doctor overshot a certain distance in this situation, then in actual control, the corresponding displacement output will be reduced; if the historical data shows an undershoot of a certain distance, then the corresponding displacement output will be increased.

[0057] The error compensation model is used to perform a reverse correction on the moving error after using the comprehensive control model for adjustment. In real-time surgery, when the doctor operates, the system obtains the current target distance, pressing force, etc. in real-time, and determines whether the current operation scenario matches the similar situation in the historical data according to the error compensation model. If it matches, the output displacement of the slide table 2 is corrected according to the error value output by the model.

[0058] By compensating for historical errors, it is possible to reduce overshoot or undershoot phenomena during the actual movement of the sliding table 2, enabling the sliding table 2 to reach the target position more accurately, thereby improving the positioning accuracy of the surgery; avoiding repeated adjustment operations caused by inaccurate positioning, saving surgical time, and improving surgical efficiency; enabling the control system of the sliding table 2 to better adapt to the operating habits of different doctors and error changes in different surgical scenarios, enhancing the stability and reliability of the system.

[0059] For example, when user A applies a habitual force of 35%, the sliding table 2 moves an average of 5 mm. A comprehensive control model is established, and the formula for force and displacement is obtained by fitting the training data, i.e., d = 0.14F + 0.1. In actual operation, when user A applies a habitual force of 35%, the predicted displacement according to the model is d = 5 mm. However, due to some non-linear factors in the surgical robot, the actual displacement is 4.8 mm, and the error is 0.2 mm. Through error analysis, it is found that the error has a linear relationship with the pressing force, and an error compensation model is established to calculate the error as -0.005F. In subsequent operations, when user A applies a force of 35% again, first predict the displacement as 5 mm according to the comprehensive control model, then calculate the error as -0.175 mm according to the error compensation model, and finally control the displacement of the sliding table 2 to move as 5 - 0.175 = 4.825 mm, thereby improving the positioning accuracy.

[0060] S22: Input the real-time distance, user type, and force-displacement sequence into the comprehensive control model to determine the control scheme, and at the same time, reverse-correct the output displacement according to the real-time positioning error. The comprehensive control model outputs the ideal force value, the ideal displacement of the sliding table 2, and the speed control instruction. Control the movement of the secondary control arm 3 according to the corrected result to make the auxiliary surgical instrument reach the target position and perform subsequent surgical operations.

[0061] Briefly speaking, in the prior art, the methods of using voice control or direct positioning will have the problem of secondary adjustment and a greater data response delay; in practice, it is shown that directly controlling the movement of the auxiliary instrument by the doctor (user) is the method that can most quickly adjust to reach the target position, but human force is the most difficult to control. Especially, each person's habitual force will not change in a short time, and the problem solved by this application is how to achieve precise positioning of the target position based on the user's habitual force, improve accuracy, fluency, and comfort, and avoid problems such as repeated adjustments and inaccurate positioning caused by different habitual forces of users, that is, the problem that the pressing force of the user cannot be accurately controlled during the fine-tuning mode, resulting in an increase in the number of adjustments and adjustment time, improving the control accuracy of the surgical robot and the accuracy of doctor control.

[0062] The technical solutions in the above embodiments of the present application at least have the following technical effects or advantages: This application provides personalized force-speed curves and parameter settings according to the operating habits and types of different users, enabling the slide table 2 to better adapt to the operating styles of doctors; dynamically adjusting the force-speed curve in combination with real-time distance information, enabling the slide table 2 to always maintain the best speed response characteristics during the operation, reducing the discomfort caused by speed mismatch, improving the fluency and comfort of the operation, and enhancing the control accuracy of the surgical robot and the accuracy of doctor regulation; When approaching the target position, the speed is reduced by compressing the force-speed curve, effectively reducing overshoot; at the same time, an error compensation model is used to reverse-correct the movement error, enabling the slide table 2 to reach the target position more accurately; Precise force-displacement mapping reduces the positioning error caused by the mismatch between force and displacement, avoids repeated adjustment operations caused by inaccurate positioning, saves surgical time; reduces safety risks such as accidental touch caused by too fast speed, and ensures the safety of the operation.

[0063] Embodiment 3: During the surgical process, the target area often undergoes dynamic position changes due to tissue peristalsis, respiratory movement, or surgical operations (such as cutting, suturing). The above content relies on the preoperative three-dimensional model of the lesion and virtual fence positioning, as well as the force-speed curve and error compensation model based on the static target area, resulting in a decrease in the positioning error accuracy for real-time response to the dynamic changes in the target area.

[0064] In laparoscopic surgery, the target tissues (such as tumors, blood vessels) often undergo dynamic displacement (typical fluctuation range ±3 mm) due to respiratory movement, organ peristalsis, or surgical operations (such as pulling, cutting), resulting in a decrease in the positioning accuracy of the above content.

[0065] The method further includes: S3: Obtain ultrasonic images and fluorescence images, calculate the first centroid based on the ultrasonic images; extract the blood vessel area through threshold segmentation based on the fluorescence images, and calculate the second centroid; obtain the target point set based on real-time target position monitoring, calculate the third centroid and standard deviation of the target point set; determine the target centroid according to the first centroid, the second centroid, and the third centroid, obtain the new boundary of the area according to the target centroid and the standard deviation; determine the dynamic change difference according to the new boundary of the area, and adjust the force-speed curve according to the dynamic change value.

[0066] In some embodiments, a high-frequency ultrasound probe and a near-infrared fluorescence imaging system are used to obtain corresponding ultrasound images and fluorescence images. A hardware trigger signal is adopted to ensure that the ultrasound probe and the NIR camera can collect the current frame images simultaneously. The accuracy of the trigger signal is controlled at the microsecond level to ensure the temporal synchronization of the two images. For example, the frame rate of the ultrasound imaging is set to 30 fps, and the gain, depth and other parameters of the ultrasound probe are adjusted according to the surgical site and the size of the tumor to obtain the best image quality. The frame rate of the near-infrared fluorescence imaging is set to 25 fps, and the exposure time and gain of the NIR camera are adjusted to ensure that the fluorescence signal of the blood vessels can be clearly captured.

[0067] Calculating the first centroid based on the ultrasound image includes: inputting the ultrasound image into the U-Net++ model for real-time inference, outputting a binary mask, calculating the centroid coordinates of the binary mask, and marking it as the first centroid.

[0068] Extracting the blood vessel region based on the fluorescence image by threshold segmentation and calculating the second centroid includes: performing threshold segmentation on the collected fluorescence image, extracting the blood vessel region, calculating the centroid coordinates of the blood vessel region, and mapping the centroid coordinates to a unified coordinate system to obtain the second centroid.

[0069] Obtaining a target point set based on real-time target position monitoring and calculating the third centroid and standard deviation of the target point set includes: obtaining the target point set at time t as The centroid coordinates obtained are: , and calculating the standard deviation of the target point set on the three coordinate axes according to the standard deviation formula. The target point set includes all the feature points of the target region.

[0070] Determining the target centroid according to the first centroid, the second centroid and the third centroid, and obtaining the new boundary of the region according to the target centroid and the standard deviation includes: fusing the obtained first centroid, second centroid and third centroid to obtain the target centroid; calculating the semi-axis lengths of the target region based on the standard deviation; determining the new boundary of the region according to the target centroid and the semi-axis lengths.

[0071] Specifically, mapping the target region to a unified coordinate system to form an ellipsoidal shape as the boundary data of the target region. The ellipsoid has three mutually perpendicular semi-axes, which are represented by a, b, and c respectively, and they determine the size and shape of the ellipsoid in three different directions. Calculating the semi-axis lengths of the target region based on the standard deviation, and multiplying the standard deviations of the target point set on the three coordinate axes by 1.5 respectively, and taking the three obtained values as the semi-axis lengths of the ellipsoid.

[0072] In some embodiments, determining the dynamic change difference according to the new regional boundary includes: calculating the displacement velocity of the target blood vessel according to the optical flow method, taking the norm value of the displacement velocity of the target blood vessel as the target velocity, obtaining the average velocity of the current stage 2, taking the ratio of the target velocity to the average velocity of stage 2 as the dynamic change difference, and adjusting the force-velocity curve according to the dynamic change value for scaling or expansion. The optical flow method is a technique used to estimate the motion information of objects in an image sequence. It is based on a basic assumption that the gray value of pixels in the image remains relatively stable between consecutive frames, that is, the gray value change of the same object in different frame images is small. By analyzing the change of pixel gray levels in adjacent frame images, the optical flow method can calculate the motion vector of each pixel point, and these motion vectors constitute the optical flow field, reflecting the motion of objects in the image.

[0073] The method further includes: S31: setting the state vector and the observation vector according to the new regional boundary and the adjusted force-velocity curve to obtain a prediction equation, predicting the displacement at the next moment based on the actual displacement and the velocity of stage 2, and calculating the compensation amount based on the user's habitual force; setting the prediction time domain, the control time domain, and the constraint conditions, obtaining the optimal control sequence according to the prediction equation and the compensation amount, and determining the regulation scheme based on the first-step control amount of the optimal control sequence.

[0074] In some embodiments, setting the state vector and the observation vector according to the new regional boundary and the adjusted force-velocity curve includes: the state vector is used to describe the motion state of the target object, and the state vector at time t is defined as

[0075] where respectively represent the displacement, velocity, and acceleration of the target at time t; The observation vector represents the position information and velocity information of the target object obtained from the sensor, provides the new regional boundary of the target object, and uses the boundary information to constrain the observation. The observation vector at time t is defined as

[0076] where is the actual displacement observation value, is the observation value of the velocity of stage 2; The prediction equation is:

[0077] where A is the state transition matrix, B is the control input matrix, is the input value at time t - 1; The update equation is:

[0078]

[0079] Among them, is the Kalman gain, is the predicted covariance matrix, is the observation matrix, is the observation noise covariance matrix. The Kalman filter receives the observed values, predicts the displacement at the next moment, and calculates the compensation amount of the displacement based on the user's habitual force for subsequent feedback control. During the prediction and update process, the motion range of the target object is restricted by boundary constraints. For example, after state prediction, it can be checked whether the predicted position exceeds the boundary. If it exceeds, the predicted position is adjusted to be within the boundary region. According to the new force-speed curve, the process noise covariance matrix and the observation noise covariance matrix can be adjusted to reflect the changes in the motion characteristics of the target object. For example, if the force-speed curve indicates that the motion speed of the target object increases, the speed-related components in the process noise covariance matrix can be increased to reflect greater uncertainty.

[0080] The update equation is used to correct the predicted state estimate according to the observed data. Its core is to fuse the predicted value and the observed value through the Kalman gain to obtain a more accurate state estimate. The prediction equation, on the other hand, predicts the state at the current moment based on the system model and the state at the previous moment. The prediction equation provides a prior estimate, and the update equation combines the observed data to correct this estimate to form a posterior estimate.

[0081] In some embodiments, the displacement amount at the next moment is predicted based on the actual displacement and the speed of the slide table 2, and the compensation amount is calculated according to the displacement amount at the next moment and the actual displacement amount corresponding to the user's habitual force, that is, the compensation amount is obtained by making an advance prediction based on the predicted amount and the displacement corresponding to the user's habitual force, and then the optimal control sequence is further confirmed according to the compensation amount.

[0082] In some embodiments, a prediction horizon, a control horizon, and constraint conditions are set. The prediction horizon (PredictionHorizon) and the control horizon are used to describe the planning scope of the control algorithm for future behavior. The control horizon is usually less than or equal to the prediction horizon, indicating how many steps of control inputs the control algorithm will optimize within the prediction horizon. The control horizon determines the number of control inputs that the control algorithm can directly optimize. For a rapidly changing system, shorter prediction and control horizons are required to respond to system changes in a timely manner. For example, the prediction horizon N = 5 and the control horizon M = 3 are set to reduce the computational complexity while ensuring control accuracy. The constraint conditions are the maximum values set to ensure that the movement speed and acceleration of the surgical instrument are within a safe range, and are dynamically set according to the actual situation. The optimization objective is preset, specifically set according to the real-time distance (i.e., the distance from the end of the secondary control arm 3 to the boundary of the target area in Embodiment 1), the prediction equation, and the compensation amount.

[0083] In some embodiments, an optimal control sequence is obtained according to the prediction equation and the compensation amount, including: determining the optimization objective according to the prediction equation and the compensation amount, inputting the optimization objective and the constraint conditions into the OSQP solver, solving to obtain the optimal control sequence, and determining the regulation scheme based on the first-step control amount of the optimal control sequence.

[0084] In some embodiments, based on the prediction of the dynamic changes of the target area, prevention is carried out in advance to obtain the optimal control sequence, and then the first-step control amount is obtained. Based on the user's habitual force, the position of the target area can be accurately located, reducing the number of subsequent repeated adjustments. An individualized optimal sequence can be dynamically set according to the habitual forces of different users, ensuring the accuracy of fine adjustment and the comfort and fluency of operation.

[0085] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: Through multi-modal image fusion and centroid calculation, the present application more accurately determines the position of the target area, real-time monitors the change of the target position, dynamically adjusts the force-speed curve, and reduces the positioning error; uses a Kalman filter for state estimation, combines the observation data to correct the predicted value, improves the system's resistance to noise and uncertainty, and ensures that the movement speed and acceleration of the surgical instrument are within a safe range by setting constraint conditions; obtains the optimal control sequence according to the prediction equation and the compensation amount, realizes more precise control, conducts prevention in advance, reduces the number of subsequent repeated adjustments, and improves the surgical efficiency; An individualized optimal sequence is dynamically set according to the habitual forces of different users, and the regulation scheme is determined based on the first-step control amount of the optimal control sequence, eliminating the subsequent displacement error problem from the beginning of regulation, and further ensuring the accuracy of fine adjustment and the comfort and fluency of operation.

[0086] Embodiment 4: This embodiment also provides a slide movement control device for a laparoscopic surgical robot, including: The surgical robot includes a main control arm 1 for performing surgery, a slide 2, and a sub-control arm 3; The main control arm 1 is located above or on one side of the patient, and the slide 2 slides on the main control arm 1 under the control of a motor. The main control arm 1 can drive the whole to move forward, backward, left, and right. One end of the sub-control arm 3 is movably connected to the slide 2, and the other end is provided with a fixed gripper 4 for fixedly gripping an auxiliary instrument and installing a reflective ball at the same time, which is tracked by an infrared camera.

[0087] The surgical robot also includes an operator 5 and a display screen 6. The operator 5 is provided with operation buttons; The operation buttons include an up button, a down button, and a fine-tuning button group; The fine-tuning button group includes an up-tuning button and a down-tuning button; The user control instruction corresponds to the operation button. The up button and the down button represent the coarse-tuning mode, and the fine-tuning button group represents the fine-tuning mode; The user issues a control instruction through the operation button, obtains the force applied to the operation button, and determines the moving speed of the slide 2 according to the corresponding relationship. A pressure sensor is provided under each operation button to obtain the force, convert the force into a moving speed, and transmit it to the motor, so that the motor drives the slide 2 to slide; The display screen 6 is used to receive the lesion CT image, the target area, and its boundary coordinates, and display the distance heat map in real time.

[0088] The user uses the operation buttons on the operator 5 to control the surgical robot, moves the surgical auxiliary instrument to the target position, and realizes the effect of surgical assistance.

[0089] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, 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 method for controlling the sliding table movement of a laparoscopic surgical robot, which is applied to a surgical robot, including a main control arm (1), a sliding table (2), a sub-control arm (3) and an operation button, characterized in that, Including: S1: Establish a three-dimensional model of the lesion based on the CT image of the lesion, obtain the target area and its boundary coordinates, calculate the coordinate transformation matrix for spatial registration to obtain a unified coordinate system; S2: Select a movement mode according to the user control instruction. The movement mode includes a coarse adjustment mode and a fine adjustment mode; in the coarse adjustment mode, adjust the slide table (2) according to the user control instruction, and based on the unified coordinate system, monitor the real-time distance between the auxiliary control arm (3) and the target area in real time. If the real-time distance is less than the safety threshold, stop moving and prompt to enter the fine adjustment mode; S21: Record the operation data of the user, classify the user to obtain the corresponding force-speed curve, force-displacement sequence and habitual force, and establish a comprehensive control model and an error compensation model; S22: When entering the fine adjustment mode, input the real-time distance, user type and force-displacement sequence into the comprehensive control model, determine the adjustment plan, and reverse-correct the output displacement according to the real-time positioning error to make the auxiliary surgical instrument reach the target position.

2. The slide table motion control method of a laparoscopic surgical robot according to claim 1, wherein, The operation data includes the pressing force of the button, the duration, the moving distance of the slide table (2) and the error record; the first analysis data and the second analysis data are obtained based on the operation data. The first analysis data refers to the average force, the pressing duration and the total moving distance in the coarse adjustment stage; the second analysis data refers to the force distribution histogram and the final positioning error in the fine adjustment stage; The control instruction refers to the operation button manipulated by the user and the corresponding pressing force applied. The moving direction of the slide table (2) is adjusted based on the operation button, and the moving speed of the slide table (2) is controlled based on the pressing force.

3. The slide table motion control method of a laparoscopic surgical robot according to claim 1, characterized in that, Calculating the coordinate transformation matrix for spatial registration includes: Paste several optical marker points on the patient's body surface, obtain the positions of the marker points in the image coordinate system through CT scanning, and generate a marker point coordinate set; based on optical navigation, track the marker points in real time and calculate the transformation matrix between the image coordinate system and the operating table coordinate system; The spatial registration is to map the transformation matrix to the main control arm (1), perform dynamic alignment of the image coordinate system and the operating table coordinate system, and perform zero calibration on the guide rail of the main control arm (1) and the slide table (2).

4. The slide table motion control method of a laparoscopic surgical robot according to claim 1, characterized in that, The method further includes: in the coarse adjustment mode, setting a minimum moving distance and a maximum moving speed, where the minimum moving distance refers to the minimum displacement of the slide table (2) each time it responds to a control instruction in the coarse adjustment mode, and the formula is as follows: ; Among them, is the minimum displacement; is the shortest distance from the initial position to the target area, which is the distance from the nearest boundary point of the target area measured through the three-dimensional model of the pre-operative lesion to the initial position; is the distance of the safety threshold; is the estimated maximum number of steps in the coarse adjustment mode; The maximum moving speed is the highest allowable speed of the slide table (2) in the coarse adjustment mode, and the formula is: ； Among them, is the maximum allowable speed; is the physical speed limit of the motor and the guide rail; is the response time, which refers to the reaction movement time from when the system receives the user's control command to the end of the movement.

5. The slide table motion control method of a laparoscopic surgical robot according to claim 1, characterized in that, The force-speed curve is a curve corresponding to the pressing force and the moving speed of the slide table (2). Record the force input of different users in various operation scenarios and the corresponding slide table (2) speed output data, which is used to describe the mapping relationship between the user operation force and the slide table (2) speed; Based on the force-displacement sequence, obtain the user's habitual pressing force. The habitual force refers to the force generated when the user presses habitually; The comprehensive control model is used to receive the force-speed curve characteristics, force-displacement sequence characteristics and habitual force characteristics, and output the ideal force value, the ideal displacement and the speed control instruction of the slide table (2); The error compensation model is established based on the overshoot or undershoot data records generated when the user operates the slide table (2) in historical surgeries, and is used to reverse-correct the output displacement during the surgery.

6. A method for controlling the sliding table movement of a laparoscopic surgical robot according to claim 1, characterized in that, The method further includes: S3: Obtain an ultrasonic image and a fluorescence image, calculate a first centroid based on the ultrasonic image; extract a blood vessel region by threshold segmentation based on the fluorescence image, and calculate a second centroid; obtain a set of target points based on real-time target position monitoring, and calculate a third centroid and a standard deviation of the set of target points; Determine a target centroid according to the first centroid, the second centroid, and the third centroid, and obtain a new regional boundary according to the target centroid and the standard deviation; Determine a dynamic change difference according to the new regional boundary, and adjust the force-speed curve according to the dynamic change value; Among them, perform threshold segmentation on the collected fluorescence image, extract the blood vessel region, calculate the centroid coordinates of the blood vessel region, and map the centroid coordinates to a unified coordinate system to obtain the second centroid; The target point set at time t is The centroid coordinates obtained are: , and the standard deviations of the target point set on the three coordinate axes are calculated according to the standard deviation formula; the target point set includes all feature points of the target area; Fuse the obtained first centroid, second centroid, and third centroid to obtain a target centroid; calculate the semi-axis length of the target region based on the standard deviation; determine a new regional boundary according to the target centroid and the semi-axis length; Calculate the displacement speed of the target blood vessel according to the optical flow method, use the norm value of the displacement speed of the target blood vessel as the target speed, obtain the average speed of the current slide table (2), and use the ratio of the target speed to the average speed of the slide table (2) as the dynamic change difference.

7. A method for controlling the movement of a sliding table of a laparoscopic surgical robot according to claim 6, characterized in that, The method further includes: S31: Set a state vector and an observation vector according to the new regional boundary and the adjusted force-speed curve to obtain a prediction equation, predict the displacement amount at the next moment based on the actual displacement and the speed of the slide table (2), and calculate a compensation amount based on the user's habitual force; Set a prediction time domain, a control time domain, and constraint conditions, obtain an optimal control sequence according to the prediction equation and the compensation amount, and determine a regulation scheme based on the first-step control amount of the optimal control sequence.

8. The method for controlling the sliding table movement of a laparoscopic surgical robot according to claim 7, characterized in that, The state vector is used to describe the motion state of the target object, and the state vector at time t is defined as ； wherein, respectively represent the displacement, velocity, and acceleration of the target at time t; The observation vector represents the position information and speed information of the acquired target object, and the observation vector at time t is defined as ； Among them, is the actual displacement observation value, is the speed observation value of the sliding table (2).

9. A sliding table motion control device for a laparoscopic surgical robot, which is applied to a sliding table motion control method for a laparoscopic surgical robot according to any one of claims 1 to 8, characterized in that, The surgical robot includes a main control arm (1) for performing surgery, a slide table (2), and a sub-control arm (3); the main control arm (1) is located above or on one side of the patient, the slide table (2) slides on the main control arm (1) under the control of a motor, and one end of the sub-control arm (3) is movably connected to the slide table (2), and the other end is provided with a fixed gripper (4) for fixedly gripping an auxiliary instrument.

10. The sliding table motion control device of a laparoscopic surgical robot according to claim 9, characterized in that, The surgical robot further includes an operator (5) and a display screen (6), and operation buttons are provided on the operator (5); The operation buttons include an up button, a down button, and a fine-tuning button group; The fine-tuning button group includes an up-tuning button and a down-tuning button; the user control instruction corresponds to the operation button, the up button and the down button represent the coarse-tuning mode, and the fine-tuning button group represents the fine-tuning mode; the user issues a control instruction through the operation button, obtains the force applied to the operation button, and determines the moving speed of the slide table (2) according to the corresponding relationship; The display screen (6) is used to receive the lesion CT image, the target region and its boundary coordinates, and display the distance heat map in real time.

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