A small needle knife control method and system based on motion sensing data

Abstract

The application provides a small needle knife control method and system based on motion sensing data, and relates to the technical field of small needle knife control. The method comprises the following steps: acquiring patient data, and constructing a digital patient model according to the patient data; determining an operation forbidden area according to the digital patient model; acquiring operator historical data; acquiring a three-dimensional space coordinate of a knife tip through an optical positioning camera arranged above a surgical area; acquiring handle motion sensing data; determining a first unit displacement tissue impedance change rate and a second unit displacement tissue impedance change rate; determining a safety boundary warning distance according to the operator historical data, the operation forbidden area and the three-dimensional space coordinate of the knife tip; determining an operation stability coefficient; and performing small needle knife control according to the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance and the operation stability coefficient. According to the application, the accuracy of small needle knife control can be improved.

Description

Technical Field

[0001] This invention relates to the field of small needle knife control technology, and in particular to a small needle knife control method and system based on motion sensing data. Background Technology

[0002] In related techniques, traditional operations rely entirely on the doctor's touch and experience. When cutting the tendon sheath and releasing adhesions, the surgeon cannot directly see the precise location of the subcutaneous tendons, blood vessels, and nerves. Furthermore, the control of the depth, force, and angle of operation lacks quantitative standards, resulting in a steep learning curve. Novice doctors are prone to causing collateral damage due to improper control. In other words, related techniques are difficult to improve the accuracy of small needle knife control.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a method and system for controlling a small needle knife based on motion sensing data, which can solve the technical problem that related technologies are unable to improve the accuracy of small needle knife control.

[0005] According to a first aspect of the present invention, a method for controlling a small needle knife based on motion sensing data is provided, comprising: Acquire patient data and construct a digital patient model based on the patient data; Based on the digital patient model, determine the no-go zones for operation; Obtain operator's historical data; At multiple moments during the control cycle, the three-dimensional spatial coordinates of the blade tip are acquired by an optical positioning camera positioned above the surgical area. At multiple points in the control cycle, motion sensing data of the handle is acquired through a combination of sensors installed in the grip handle; Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined. Based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip, the safety boundary warning distance is determined; Based on the handle motion sensing data, the operation stability coefficient is determined; The small needle knife is controlled based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

[0006] According to the present invention, determining the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle includes: Based on the handle motion sensing data, the real-time tissue reaction force and the angle between the needle knife handle and the normal direction of the tissue surface are obtained. The real-time tissue reaction force includes: real-time normal tissue reaction force and real-time tangential tissue reaction force. Based on the three-dimensional spatial coordinates of the blade tip, the instantaneous velocity magnitude of the needle knife tip at the current moment is obtained, wherein the instantaneous velocity magnitude includes: the normal instantaneous velocity magnitude and the tangential instantaneous velocity magnitude; Obtain a fixed sampling time interval; Obtain the instantaneous contact area between the needle tip and the tissue; Obtain the preset local friction coefficient of each tissue category; The first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined based on the real-time tissue reaction force, the included angle of the blade normal, the instantaneous speed, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient.

[0007] According to the present invention, the determination of the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the real-time tissue reaction force, the included angle of the blade holder normal, the instantaneous velocity magnitude, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient includes: according to the formula:

[0008] Determine the rate of change of tissue impedance per unit displacement at time i of the control period. Second unit displacement tissue impedance change rate ,in, , , and For preset coefficients, To control the real-time normal tissue reaction force at the i-th moment of the control cycle, To control the real-time tangential tissue reaction force at the i-th moment of the cycle, To control the real-time normal tissue reaction force at the (i-1)th moment of the control cycle, To control the real-time tangential tissue reaction force at the (i-1)th moment of the control cycle, To control the magnitude of the normal instantaneous velocity at the i-th moment of the cycle, To control the magnitude of the tangential instantaneous velocity at the i-th moment of the cycle, For a fixed sampling time interval, To control the instantaneous contact area at the i-th moment of the control cycle, To control the angle between the tool holder normals at the i-th moment of the control cycle, The preset local friction coefficient of the surgical tissue at the i-th moment of the control cycle.

[0009] According to the present invention, determining the safety boundary warning distance based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip includes: Determine the preset hardness correction coefficient and the preset motion trend sensitivity coefficient; Determine the preset tissue hardness level at the location of the needle tip; The instantaneous velocity vector of the needle knife tip is determined based on the three-dimensional spatial coordinates of the tip. Determine the three-dimensional spatial coordinates of the center of the restricted area; Determine the fixed safety radius of the restricted area; The first Euclidean distance is determined based on the three-dimensional spatial coordinates of the center of the restricted area and the three-dimensional spatial coordinates of the blade tip; Based on the operator's historical data, determine the operator's style aggression coefficient; The safety boundary warning distance is determined based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset movement trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance.

[0010] According to the present invention, determining the operator style aggression coefficient based on the operator's historical data includes: Based on the operator's historical data, determine the operator's historical average needle knife speed and historical average needle knife force at the most recent sampling points; The first ratio is determined based on the historical average needle knife speed and the preset needle knife speed threshold. The second ratio is determined based on the historical average needle knife force and the preset needle knife force threshold. The operator style aggression coefficient is determined based on the first ratio and the second ratio.

[0011] According to the present invention, the safety boundary warning distance is determined based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset movement trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance, including: according to the formula:

[0012] Determine the safety boundary warning distance at time i of the control cycle. ,in, For preset coefficients, To control the three-dimensional spatial coordinates of the blade tip at the i-th moment of the control cycle, To determine the three-dimensional spatial coordinates of the restricted area center at the i-th moment of the control cycle, Let be the first Euclidean distance at the i-th time step of the control period. To define the fixed safety radius of the nearest restricted area at the i-th moment of the control cycle. This is the preset hardness correction factor. To control the preset tissue hardness level at the location of the needle tip at the i-th moment of the cycle, Let be the instantaneous velocity vector at the i-th moment of the control cycle. To preset the motion trend sensitivity coefficient, The operator's aggressiveness factor.

[0013] According to the present invention, determining the operational stability coefficient based on the handle motion sensing data includes: Based on the handle motion sensing data, the needle knife spindle direction angle and needle knife operating force are determined; Based on the needle knife spindle direction angle and the needle knife operating force, determine the first standard deviation of multiple needle knife spindle direction angles and the second standard deviation of multiple needle knife operating forces within a preset time window; The operational stability coefficient is determined based on the first standard deviation and the second standard deviation.

[0014] According to a second aspect of the present invention, a small needle knife control system based on motion sensing data is provided, comprising: The patient model module is used to acquire patient data and construct a digital patient model based on the patient data. The restricted area module is used to determine the restricted areas based on the digital patient model; The historical data module is used to acquire historical data of the operator; The three-dimensional coordinate module is used to acquire the three-dimensional spatial coordinates of the blade tip at multiple moments in the control cycle through an optical positioning camera positioned above the surgical area. The sensing data module is used to acquire handle motion sensing data at multiple moments in the control cycle through a combination of sensors set in the grip handle; The tissue impedance module is used to determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle. The boundary warning module is used to determine the safety boundary warning distance based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip; The stability coefficient module is used to determine the operation stability coefficient based on the handle motion sensing data; The needle knife control module is used to control the small needle knife based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

[0015] According to a third aspect of the present invention, a small needle knife based on motion sensing data is provided, comprising: a processor, wherein the processor is configured to execute a small needle knife control method based on motion sensing data, and further comprising: a needle knife tip, a blade head, and an inner edge of the blade head, wherein the needle knife tip of the small needle knife is a hemisphere with a diameter of 1 mm, the blade head bending angle is 90° to 110°, the blade head length is 2.5 mm to 3.5 mm, and the inner edge length of the blade head is 1 mm to 2 mm.

[0016] Technical Effects: According to the present invention, the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle can be accurately acquired by a sensor combination and an optical positioning camera installed in the grip handle. Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the degree of change of normal and tangential forces can be accurately analyzed to determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate. Based on the operator's historical data, the operating restricted area and the three-dimensional spatial coordinates of the blade tip, a dynamic safety boundary warning distance can be set, and the operator's operational stability can be evaluated to determine the operational stability coefficient. Furthermore, based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance and the operational stability coefficient, the small needle knife can be controlled, thereby improving the accuracy of the small needle knife control. When determining the first and second unit displacement tissue impedance change rates, the rates can be determined based on real-time tissue reaction force, the angle between the tool holder normal and the blade, instantaneous velocity, fixed sampling time interval, instantaneous contact area, and preset tissue local friction coefficient. During the calculation process, the influence of contact area, friction coefficient, and normal angle on the drastic changes in normal and tangential forces can be fully assessed, improving the accuracy of the first and second unit displacement tissue impedance change rates. When determining the safety boundary warning distance, the distance can be determined based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's aggressiveness coefficient, preset hardness correction coefficient, preset motion trend sensitivity coefficient, preset tissue hardness grade, instantaneous velocity vector, fixed safety radius, and first Euclidean distance. During the calculation process, the influence of tissue hardness, motion trend, and operator's operating style on the safety distance can be fully considered, improving the accuracy of the safety boundary warning distance.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort. Figure 1 An exemplary flowchart of a small needle knife control method based on motion sensing data according to an embodiment of the present invention is shown. Figure 2A schematic diagram illustrating the determination of the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate according to an embodiment of the present invention is shown. Figure 3 An exemplary schematic diagram illustrating the determination of a safety boundary warning distance according to an embodiment of the present invention is shown; Figure 4 A schematic diagram illustrating the determination of operational stability coefficients according to an embodiment of the present invention is shown exemplarily; Figure 5 A block diagram of a small needle knife control system based on motion sensing data according to an embodiment of the present invention is shown as an example; Figure 6 An exemplary front view of a small needle knife according to an embodiment of the present invention is shown; Figure 7 An exemplary top view of a small needle knife according to an embodiment of the present invention is shown; Reference numerals: 101-needle body, 102-needle knife tip, 103-inner edge of the knife head, 105-needle handle. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0021] Figure 1 An exemplary flowchart illustrates a small needle knife control method based on motion sensing data according to an embodiment of the present invention, the method comprising: Step S1: Obtain patient data and construct a digital patient model based on the patient data; Step S2: Determine the no-go zones based on the digital patient model; Step S3: Obtain operator's historical data; Step S4: At multiple moments during the control cycle, the three-dimensional spatial coordinates of the blade tip are obtained using an optical positioning camera positioned above the surgical area. Step S5: At multiple moments in the control cycle, handle motion sensing data is acquired through a combination of sensors installed in the grip handle. Step S6: Determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle. Step S7: Determine the safety boundary warning distance based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip; Step S8: Determine the operation stability coefficient based on the handle motion sensing data; Step S9: Perform small needle knife control based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

[0022] According to an embodiment of the present invention, the small needle knife control method based on motion sensing data can accurately acquire the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle through a sensor combination and an optical positioning camera installed in the grip handle. Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the method accurately analyzes the degree of change of normal and tangential forces, determines the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate, sets a dynamic safety boundary warning distance based on the operator's historical data, the operating restricted area and the three-dimensional spatial coordinates of the blade tip, and evaluates the operator's operational stability to determine the operational stability coefficient. Furthermore, the method performs small needle knife control based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance and the operational stability coefficient, thereby improving the accuracy of small needle knife control.

[0023] According to one embodiment of the present invention, step S1 involves acquiring patient data and constructing a digital patient model based on the patient data.

[0024] For example, importing patient data, namely CT / MRI images of the patient's treatment site, allows for the 3D reconstruction of key tissues such as bones, tendons, blood vessels, and nerves, forming a digital patient model.

[0025] According to one embodiment of the present invention, in step S2, an operation no-go zone is determined based on the digital patient model.

[0026] For example, in a digital patient model, the operator sets treatment targets (e.g., adhered tendon sheaths), safe routes (avoiding blood vessels / nerves), and no-go zones (e.g., more dangerous areas).

[0027] According to one embodiment of the present invention, step S3 involves acquiring historical data of the operator.

[0028] For example, the operator's operational data from past surgeries is stored in the database, and the operator's historical data can be retrieved through the database.

[0029] According to one embodiment of the present invention, in step S4, at multiple moments during the control cycle, the three-dimensional spatial coordinates of the blade tip are acquired by an optical positioning camera positioned above the surgical area.

[0030] For example, an optical positioning camera can be set up above the surgical area to track optical markers fixed on the handle and calculate the three-dimensional spatial coordinates of the needle knife tip in real time.

[0031] According to one embodiment of the present invention, in step S5, at multiple moments of the control cycle, motion sensing data of the handle is acquired by a combination of sensors disposed in the grip handle.

[0032] For example, motion sensing data of the handle can be acquired by a combination of sensors integrated inside the grip (such as, but not limited to, a miniature inertial measurement unit, a six-axis force / torque sensor).

[0033] According to an embodiment of the present invention, in step S6, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle.

[0034] Figure 2 A schematic diagram illustrating the determination of the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate according to an embodiment of the present invention is shown.

[0035] According to an embodiment of the present invention, step S6 includes: Step S61: Based on the handle motion sensing data, obtain the real-time tissue reaction force and the angle between the needle knife handle and the normal direction of the tissue surface, wherein the real-time tissue reaction force includes: real-time normal tissue reaction force and real-time tangential tissue reaction force; Step S62: Based on the three-dimensional spatial coordinates of the blade tip, obtain the instantaneous velocity magnitude of the needle knife tip at the current moment, wherein the instantaneous velocity magnitude includes: the normal instantaneous velocity magnitude and the tangential instantaneous velocity magnitude; Step S63: Obtain the fixed sampling time interval; Step S64: Obtain the instantaneous contact area between the needle tip and the tissue. Step S65: Obtain the preset local friction coefficient of each tissue type; Step S66: Determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the real-time tissue reaction force, the included angle of the blade normal, the instantaneous speed, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient.

[0036] For example, a strain gauge (e.g., a foil strain gauge) is attached to the surface of the metal rod of the needle knife. When the needle knife is subjected to tissue reaction force, the rod undergoes a slight deformation, and the resistance of the strain gauge changes accordingly. The resistance change is converted into a voltage signal through a Wheatstone bridge circuit. After amplification, filtering, and calibration, the magnitude of the force is obtained, i.e., the real-time tissue reaction force. The real-time tissue reaction force includes: real-time normal tissue reaction force (perpendicular to the tissue surface) and real-time tangential tissue reaction force (parallel to the tissue surface). The needle knife attitude angle is calculated using an optical spatial positioning system to determine the direction of the needle knife rod relative to the normal direction of the tissue surface. The normal angle is expressed in radians; based on the three-dimensional spatial coordinates of the blade tip, the instantaneous velocity of the needle knife tip at the current moment is obtained. For example, the instantaneous velocity of the needle knife tip at the current moment is obtained by differential calculation using the three-dimensional spatial coordinates of the blade tip. The instantaneous velocity includes both the normal instantaneous velocity and the tangential instantaneous velocity; a fixed sampling time interval is obtained, which is a fixed sensor sampling time interval, such as 0.01s; the contact projection area is calculated in real time through optical positioning combined with the needle knife geometric model to obtain the instantaneous contact area between the needle knife tip and the tissue, expressed in square millimeters. Because different tissues have different frictional characteristics and influence transmission efficiency, preset local friction coefficients are set for each tissue type. For example, tissue type (fat / muscle / fascia) is identified based on preoperative images and a preset friction coefficient is matched (e.g., fat = 0.05, fascia = 0.3). Based on real-time tissue reaction force, the angle between the scalpel and the normal, the instantaneous velocity, a fixed sampling time interval, instantaneous contact area, and the preset local friction coefficient, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined. The rate of change represents the intensity of the force change per unit displacement in directions perpendicular to and parallel to the tissue surface, respectively. If the intensity of the force change rate is too low, it means that the needle knife has not contacted tough tissue (e.g., tendon, fascia) or penetrated the tissue layer, and is in a smooth cutting / separation state. If the intensity of the force change rate is too high, it means that the needle knife has encountered a tissue interface with a sudden change in impedance (e.g., the boundary between fascia and muscle, the boundary between tendon and bone), or is cutting tough tissue (e.g., tendon). The system determines that it has encountered tough tissue and needs to trigger a warning (e.g., tactile vibration, visual cue) to prevent accidental damage to deep blood vessels / nerves.

[0037] According to an embodiment of the present invention, step S66 includes: controlling the rate of change of tissue impedance per unit displacement at the i-th moment of the control period according to formula (1). Second unit displacement tissue impedance change rate , (1) in, , , and For preset coefficients, To control the real-time normal tissue reaction force at the i-th moment of the control cycle, To control the real-time tangential tissue reaction force at the i-th moment of the cycle, To control the real-time normal tissue reaction force at the (i-1)th moment of the control cycle, To control the real-time tangential tissue reaction force at the (i-1)th moment of the control cycle, To control the magnitude of the normal instantaneous velocity at the i-th moment of the cycle, To control the magnitude of the tangential instantaneous velocity at the i-th moment of the cycle, For a fixed sampling time interval, To control the instantaneous contact area at the i-th moment of the control cycle, To control the angle between the tool holder normals at the i-th moment of the control cycle, The preset local friction coefficient of the surgical tissue at the i-th moment of the control cycle.

[0038] According to one embodiment of the present invention, , , and For preset coefficients, To quantify the inhibitory effect of increased contact area on the change in internal force per unit normal displacement (the larger the contact area, the smaller the impedance change per unit displacement under the same force change), the specific determination method is as follows: 1. Select a simulation material that closely resembles the mechanical properties of human tissue (e.g., silicone to simulate soft tissue, rubber to simulate fascia, foam to simulate fat); 2. Use different needle tip shapes (e.g., flat head, round head) and contact areas (achieved by changing needle tips of different diameters or controlling the pressing depth) to press the simulation material at a controllable speed, simultaneously collecting force sensor data and displacement data; 3. Calculate the rate of change of force per unit displacement under different contact areas (i.e., ), and the rate of change of unit displacement force after correction of contact area (i.e., 4. When the corrected unit displacement force change rate can stably reproduce the material's "true impedance mutation" (e.g., the force mutation when entering silicone from foam), the reverse calculation is performed. The value (e.g., if the contact area increases by 100%, the rate of change of unit displacement force decreases by 50%) =0.5, so that 1+ This perfectly offsets the effect of the contact area. and The effect of quantifying the tilt of the incision angle on the effective cutting resistance (when the needle knife is used for oblique cutting, the actual vertical resistance component is reduced, and the impedance calculation needs to be corrected). The specific calculation method is as follows: 1. Build an optical spatial positioning system (e.g., Vicon, OptiTrack) to perform high-precision attitude calibration of the needle knife (obtain the angle between the knife handle and the normal of the tissue surface); 2. On ex vivo anatomical specimens (e.g., porcine tendon, bovine fascia), perform cutting / dissection operations at different incision angles (0-degree vertical incision, 30-degree oblique incision, 60-degree oblique incision), and simultaneously collect normal force, tangential force, and attitude angle; 3. Calculate the deviation of the rate of change at different angles (e.g., the rate of change is the baseline value when cutting vertically, and the deviation is the amount when cutting obliquely); 4. Adjust... and make and It can correct deviations caused by angles. For example, when the cut is 30 degrees, the normal force component decreases by about 40%. Set it to 0.4, so that 1+ This perfectly compensates for the weakening of the normal resistance due to the angle of adjustment. The method for quantifying the corrective effect of preset local friction coefficients on force transmission efficiency (the greater the friction, the more significant the loss / amplification of force in displacement) is as follows: 1. Select ex vivo samples of different tissue types (fat, muscle, fascia) and determine their static and dynamic friction coefficients through friction experiments (e.g., sliding a needle on the sample surface and collecting sliding friction and normal pressure); 2. In a simulated surgical environment (e.g., moistened with physiological saline, temperature 37℃), cut / peel these tissues with a needle at different speeds, simultaneously collecting force signals and friction coefficients (preset according to tissue type, e.g., fat = 0.05, fascia = 0.3); 3. The remaining steps are... , and The determination steps are similar, both involving calculating the deviation of the rate of change under different friction coefficients and... Make adjustments so that It can correct the effect of friction on changes in tangential force (e.g., fascia friction is 6 times that of fat; if friction increases the rate of change in tangential force by 50%). =0.5, making This perfectly reflects the amplification effect of friction.

[0039] According to one embodiment of the present invention, To control the absolute change between the real-time normal tissue reaction force at time i and time i-1 of the cycle, this represents the degree of energy mutation. The greater the mutation in force, the more drastic the change in tissue resistance (e.g., when encountering hard tissue, fascia, periosteum, etc.). For example, when a needle knife cuts into a tendon or ligament from soft tissue, the normal force will suddenly increase. The larger, The larger the size, the more likely the system will generate an alarm. Let the product of the magnitude of the instantaneous normal velocity at the i-th moment of the control period and the fixed sampling time interval represent the instantaneous displacement in the normal direction. This represents the change in real-time normal tissue reaction force divided by the instantaneous displacement in the normal direction. It indicates the change in real-time normal tissue reaction force per unit displacement, equivalent to local stiffness or impedance gradient. This avoids overestimation of force due to rapid propulsion speed; for example, even with soft tissue, the force may increase instantaneously during rapid propulsion. For contact area correction term, when When it increases, even The forces per unit area change is smaller, indicating that the tissue is more resistant to the current operation and should not trigger a high alarm. As the denominator, it indicates that the larger the instantaneous contact area, The smaller the tip, the higher the system's tolerance. That is, when the needle knife makes blunt-tipped or large-area contact (e.g., pushing against fascia), the system will not easily trigger an alarm even with fluctuations in force; while when puncturing with a pointed tip or making small-area contact (e.g., separating tendons), the system will be more sensitive even with small changes in force, preventing accidental damage to blood vessels / nerves. This is a correction term for the angle between the normals, because the more the needle knife is tilted ( The larger the value, the more difficult it is for the normal force to be effectively transmitted; therefore, As the denominator, operators often use an oblique cutting angle when dissecting fascia. >0), at this point the normal force is already small, and the system will not alarm due to small fluctuations in the normal force, thus avoiding interference with normal operation. However, during vertical puncture ( (Approximately equal to 0), sudden changes in normal force are easily detected and require timely warning. This indicates the degree of drastic change in normal force per unit displacement after correction for contact area and normal angle; that is, the rate of change of tissue impedance per unit displacement.

[0040] According to one embodiment of the present invention, similarly, This represents the real-time change in tangential tissue reaction force per unit displacement. This is a friction correction term. The larger the coefficient of friction, the less likely the tangential force is to change abruptly (because friction is stable). The system should reduce its sensitivity to abrupt tangential changes, i.e., As the pressure increases, the tangential force becomes more stable (friction dominates), and even with changes in tissue hardness, the abrupt change in tangential force is smaller. Therefore, the system should lower its alarm level. As the denominator, that is, The larger, The smaller the coefficient of friction, the more tolerant the system is to high-friction tissues (such as fascia and ligaments). For example, during fascia dissection, the high coefficient of friction means the system will not trigger an alarm due to minor fluctuations in tangential force. Conversely, during fat layer sliding, the low coefficient of friction makes sudden changes in tangential force easily detectable, indicating that the system may have slipped out of the target area. As a correction term for the normal angle, when the needle knife is tilted, the tangential force component increases, and the sudden change in tangential force is more easily detected. Therefore, the system should be more sensitive. As a multiplier The larger, The larger the value, the more sensitive the system is to tangential forces during oblique cuts. For example, when a surgeon performs oblique dissection, the tangential force is already large, making the system more sensitive to sudden changes in tangential force and enabling it to identify tissue tears or adhesion ruptures earlier. Conversely, during vertical cuts, the tangential force is small, and the system has a higher tolerance, avoiding false alarms. This indicates the degree of drastic change in tangential force per unit displacement after correction for friction coefficient and normal angle; that is, the rate of change of tissue impedance per unit displacement.

[0041] In this way, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate can be determined based on the real-time tissue reaction force, the angle between the normal and the tool holder, the instantaneous speed, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient. During the calculation process, the influence of the contact area, friction coefficient, and angle between the normal and the tool holder on the degree of change of the normal force and the tangential force can be fully evaluated, thus improving the accuracy of the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate.

[0042] According to one embodiment of the present invention, in step S7, the safety boundary warning distance is determined based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip.

[0043] Figure 3 An exemplary schematic diagram illustrating the determination of a safety boundary warning distance according to an embodiment of the present invention is shown.

[0044] According to an embodiment of the present invention, step S7 includes: Step S71: Determine the preset hardness correction coefficient and the preset motion trend sensitivity coefficient; Step S72: Determine the preset tissue hardness level at the location of the needle tip; Step S73: Determine the instantaneous velocity vector of the needle knife tip based on the three-dimensional spatial coordinates of the blade tip; Step S74: Determine the three-dimensional spatial coordinates of the center of the restricted area; Step S75: Determine the fixed safety radius of the restricted area; Step S76: Determine the first Euclidean distance based on the three-dimensional spatial coordinates of the center of the restricted area and the three-dimensional spatial coordinates of the blade tip; Step S77: Determine the operator's style aggression coefficient based on the operator's historical data; Step S78: Determine the safety boundary warning distance based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset motion trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance.

[0045] For example, determining the preset tissue hardness level at the location of the needle tip can be done by dividing the area based on preoperative images and setting a preset tissue hardness level. For instance, the preset tissue hardness level for fat is 0.2, for muscle it is 0.6, and for fascia it is 1. The higher the preset tissue hardness level, the higher the hardness and the faster the risk transmission. When the needle is close to hard tissue (e.g., fascia, tendon), even at the same distance, the risk of collision is higher because hard tissue is not easily deformed, and even a small error can lead to puncture injury. In this case, a preset hardness correction coefficient is needed to convert the tissue hardness into a redundancy of the safety boundary. The specific method for determining the preset hardness correction coefficient is as follows: 1. In the laboratory, using a standard tissue model (e.g., silicone to simulate fat / muscle / fascia), set different preset tissue hardness levels (e.g., 0.2, 0.6, and 1.0); 2. Simulate puncture on different hardness models with the needle and record the safe distance at which just the injury occurs; 3. According to " "The fitting process is performed, and the experimental baseline safe distance is the safe radius when the hardness is 0 (theoretical value, approximated by fat in practice). For example, for adipose tissue, the preset tissue hardness level is 0.2, and the experimental safe distance is 1.5mm (experimentally measured: the safe distance when the needle knife just causes damage in the fat). For fascia tissue, the preset tissue hardness level is 1, and the experimental safe distance is 2mm. The preset hardness correction coefficient can be calculated to be approximately 0.45. During the operation, if the needle knife rushes towards the risk target (such as blood vessels or nerves) at high speed, even if the current distance is safe, a collision may occur in the next moment. Therefore, through preset motion..." The trend sensitivity coefficient introduces dynamic compression in the velocity direction to achieve a predictive safety boundary. The larger the preset motion trend sensitivity coefficient, the smaller the safety distance is compressed. Clinical experience shows that when rushing towards the risk area at high speed, an early warning should be triggered within 0.1 to 0.3 seconds. Therefore, the preset motion trend sensitivity coefficient can be set to 0.1, meaning that when the velocity direction is pointing towards the risk area and the velocity is 10 mm / s, the safety distance is compressed by 1 mm per second, and an early warning of 1 mm can be given within 1 second. Based on the three-dimensional spatial coordinates of the blade tip, the instantaneous velocity vector of the needle knife tip is determined by IMU acceleration integration. Based on the digital patient model... The coordinates of the center point of the nearest predefined no-go zone are used to determine the three-dimensional spatial coordinates of the center of the no-go zone. A fixed safety radius for the no-go zone is also determined. For example, the fixed safety radius is the minimum safe Euclidean distance (in mm) from the blade tip to a specific no-go zone (e.g., blood vessels, nerves, vital organs). This can be set based on clinical experience. For instance, for small arteries (e.g., cutaneous arteries of the face / limbs), the fixed safety radius can be set to 1.5 mm (i.e., extending 1.5 mm outward from the outer wall of the artery as a safety zone). For nerve trunks (e.g., branches of the median nerve and common peroneal nerve), the fixed safety radius can be set to... The safe zone is defined as 2mm (i.e., the epineurium extends 2.0mm outward as a safety zone); the Euclidean distance between the two points is calculated based on the three-dimensional spatial coordinates of the center of the restricted area and the three-dimensional spatial coordinates of the blade tip, and the first Euclidean distance is determined; the aggressiveness of the current operator's operating style is assessed based on the operator's historical data, and the operator's aggressiveness coefficient is determined; the safety boundary warning distance is determined based on the three-dimensional spatial coordinates of the center of the restricted area, the three-dimensional spatial coordinates of the blade tip, the operator's aggressiveness coefficient, the preset hardness correction coefficient, the preset motion trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance.

[0046] According to an embodiment of the present invention, step S77 includes: Step S771: Based on the operator's historical data, determine the operator's historical average needle knife speed and historical average needle knife force at the most recent sampling points; Step S772: Determine the first ratio based on the historical average needle knife speed and the preset needle knife speed threshold; Step S773: Determine the second ratio based on the historical average needle knife force and the preset needle knife force threshold. Step S774: Determine the operator style aggression coefficient based on the first ratio and the second ratio.

[0047] For example, based on the operator's historical data, the average historical needle knife speed and the average historical needle knife force of the operator in the most recent 10-20 sampling points are determined; based on the average historical needle knife speed and the preset needle knife speed threshold, a first ratio is determined; based on the average historical needle knife force and the preset needle knife force threshold, a second ratio is determined. The preset needle knife speed threshold and the preset needle knife force threshold are both set according to clinical guidelines. The larger the first ratio and the second ratio, the more aggressive the operation (e.g., fast speed and strong force); based on the average of the first ratio and the second ratio, the operator's style aggressiveness coefficient is determined.

[0048] According to an embodiment of the present invention, step S78 includes: determining the safety boundary warning distance at the i-th moment of the control cycle according to formula (2). , (2) in, For preset coefficients, To control the three-dimensional spatial coordinates of the blade tip at the i-th moment of the control cycle, To determine the three-dimensional spatial coordinates of the restricted area center at the i-th moment of the control cycle, Let be the first Euclidean distance at the i-th time step of the control period. To define the fixed safety radius of the nearest restricted area at the i-th moment of the control cycle. This is the preset hardness correction factor. To control the preset tissue hardness level at the location of the needle tip at the i-th moment of the cycle, Let be the instantaneous velocity vector at the i-th moment of the control cycle. To preset the motion trend sensitivity coefficient, The operator's aggressiveness factor.

[0049] According to one embodiment of the present invention, This is an operation style modification item. If the warning value is too high, it indicates that the operator is being too aggressive. The system will automatically increase the safety margin to avoid excessive warnings that could disrupt the smoothness of operation. The preset coefficient is a mapping coefficient between a preset operating style and a safety margin. It can be calibrated through expert consensus combined with small-scale experiments (e.g., inviting 10 doctors to simulate the operation and statistically analyzing the optimal safety margin under different styles). For example, when... When the value is 0.2, it means that for every 0.1 increase in the operator's aggressiveness factor, the safety distance is adjusted by an additional 20%.

[0050] According to one embodiment of the present invention, This represents the unit direction vector pointing from the center of the restricted area to the tip of the blade. Let the difference between the first Euclidean distance and the fixed safety radius at the i-th time of the control period represent the effective remaining safety distance. The effective remaining safety distance is in vector form.

[0051] According to one embodiment of the present invention, This is a tissue hardness correction item, used to adjust the sensitivity of the safety distance based on tissue hardness. The harder the tissue, the smaller the effective margin of the safety distance (because hard tissue is not easily deformed, and even small errors can cause damage, requiring a stricter safety boundary). As the denominator, The larger the distance, the greater the safety boundary warning distance. The smaller, For the movement trend correction term, It is a unit direction vector. The product of the instantaneous velocity vector and the unit direction vector at the i-th moment of the control cycle represents the projection of the velocity along the direction from the blade tip to the center of the restricted area. A value greater than 0 indicates that the blade tip is approaching the target of risk. A value less than 0 indicates that the blade tip is moving away from the target; if the blade tip is moving towards the target, then... Less than 1, The smaller the value, the further away the blade tip is from the target of risk. Greater than 1, The smaller.

[0052] According to one embodiment of the present invention, This indicates the safety boundary warning distance, which takes into account factors such as tissue stiffness, movement trends, and operator operating style.

[0053] In this way, the safety boundary warning distance can be determined based on the three-dimensional spatial coordinates of the center of the restricted area, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset motion trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance. During the calculation process, the influence of tissue hardness, motion trend, and operator's operating style on the safety distance can be fully considered, thus improving the accuracy of the safety boundary warning distance.

[0054] According to an embodiment of the present invention, in step S8, an operation stability coefficient is determined based on the handle motion sensing data.

[0055] Figure 4A schematic diagram illustrating the determination of operational stability coefficients according to an embodiment of the present invention is shown.

[0056] According to an embodiment of the present invention, step S8 includes: Step S81: Determine the spindle direction angle and operating force of the needle knife based on the handle motion sensing data; Step S82: Based on the needle knife spindle direction angle and the needle knife operating force, determine the first standard deviation of multiple needle knife spindle direction angles and the second standard deviation of multiple needle knife operating forces within a preset time window; Step S83: Determine the operational stability coefficient based on the first standard deviation and the second standard deviation.

[0057] For example, using sensors such as inertial force units, the spindle angle of the needle knife (the angle between the geometric axis of the needle knife body and the anatomical reference plane) and the needle knife operating force (the force applied to the needle knife by the operator) are obtained; a preset time window is set from 2 seconds before the current time to the expiration time, and the first standard deviation of multiple spindle angles of the needle knife and the second standard deviation of multiple needle knife operating forces within the preset time window are obtained. The first and second standard deviations represent the fluctuation amplitude of the angle and force within the time window; according to Determine the operational stability coefficient, where... The coefficient represents the cumulative negative impact of directional and force fluctuations. The larger the operational stability coefficient, the more stable the operation.

[0058] According to an embodiment of the present invention, in step S9, the small needle knife is controlled based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

[0059] For example, when the rate of change of tissue impedance per unit displacement exceeds the warning threshold (which can be set to 0.3 N / mm), immediately stop the needle advance (cut off the power or air supply to the feed motor), trigger reverse needle retraction, and prompt the operator "Sudden change in normal resistance, suspected contact with hard tissue, please confirm the operation path." When the rate of change of tissue impedance per unit displacement suddenly decreases, it may be that the blade tip has penetrated soft tissue (e.g., from skin to fat layer), or excessive force has caused tissue collapse. Pause the needle advance, maintain the current position for 1 to 2 seconds, and observe whether the rate of change of tissue impedance per unit displacement is stable. If the rate of change of tissue impedance per unit displacement continues to decrease, prompt the operator "Sudden drop in normal resistance, soft tissue has been penetrated, please pay attention to depth control." If the rate of change of tissue impedance per second unit displacement exceeds the warning threshold (which can be set to 0.5 N / mm), it may be that the blade tip has cut into high-resistance tissue (e.g., tendon, dense fascia). If the operation target is "cutting," maintain the current tangential speed and monitor the rate of change of tissue impedance per second unit displacement. If it continues to exceed the warning threshold, The system displays the message "Cutting resistance is too high, it is recommended to reduce the tangential speed or adjust the angle." If the operation target is "abrasion," the tangential feed is immediately stopped, the needle is withdrawn in the reverse direction, and the message "Suspected cut to tendon, abrasion failed, please adjust the operation direction" is displayed. When the rate of change of tissue impedance per second unit displacement is less than the efficiency threshold (which can be set to 0.1 N / cmdotps / mm to prevent insufficient feed), the tangential feed is paused, the needle angle is slightly adjusted (by IMU feedback to change the tangential direction), and the tangential feed is retried. When the safety boundary warning distance is greater than the warning threshold (e.g., 2 mm), the system does not display a warning. When the safety boundary warning distance is greater than 0 but less than or equal to the warning threshold, the system linearly increases the warning audio frequency according to the distance value ratio, providing an intuitive sense of approach. When the safety boundary warning distance is less than or equal to 0, it means that the blade tip has invaded the safety boundary, and the system immediately issues a strong audible and visual alarm and can lock the drive motor of the handle via optional configuration to prevent further movement in the dangerous direction. When the operation stability coefficient is less than 0.8, the operator is reminded to pay attention to operation stability.

[0060] According to an embodiment of the present invention, the small needle knife control method based on motion sensing data can accurately acquire the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle through a sensor combination and an optical positioning camera installed in the grip handle. Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the method accurately analyzes the degree of change of normal and tangential forces, determines the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate, sets a dynamic safety boundary warning distance based on the operator's historical data, the operating restricted area and the three-dimensional spatial coordinates of the blade tip, and evaluates the operator's operational stability to determine the operational stability coefficient. Furthermore, the method performs small needle knife control based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance and the operational stability coefficient, thereby improving the accuracy of small needle knife control. When determining the first and second unit displacement tissue impedance change rates, the rates can be determined based on real-time tissue reaction force, the angle between the tool holder normal and the blade, instantaneous velocity, fixed sampling time interval, instantaneous contact area, and preset tissue local friction coefficient. During the calculation process, the influence of contact area, friction coefficient, and normal angle on the drastic changes in normal and tangential forces can be fully assessed, improving the accuracy of the first and second unit displacement tissue impedance change rates. When determining the safety boundary warning distance, the distance can be determined based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's aggressiveness coefficient, preset hardness correction coefficient, preset motion trend sensitivity coefficient, preset tissue hardness grade, instantaneous velocity vector, fixed safety radius, and first Euclidean distance. During the calculation process, the influence of tissue hardness, motion trend, and operator's operating style on the safety distance can be fully considered, improving the accuracy of the safety boundary warning distance.

[0061] Figure 5 An exemplary block diagram of a small needle knife control system based on motion sensing data according to an embodiment of the present invention is shown, the system comprising: The patient model module is used to acquire patient data and construct a digital patient model based on the patient data. The restricted area module is used to determine the restricted areas based on the digital patient model; The historical data module is used to acquire historical data of the operator; The three-dimensional coordinate module is used to acquire the three-dimensional spatial coordinates of the blade tip at multiple moments in the control cycle through an optical positioning camera positioned above the surgical area. The sensing data module is used to acquire handle motion sensing data at multiple moments in the control cycle through a combination of sensors set in the grip handle; The tissue impedance module is used to determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle. The boundary warning module is used to determine the safety boundary warning distance based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip; The stability coefficient module is used to determine the operation stability coefficient based on the handle motion sensing data; The needle knife control module is used to control the small needle knife based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

[0062] Figure 6 An exemplary front view of a small needle knife according to an embodiment of the present invention is shown, comprising: a processor, wherein the processor is configured to execute a small needle knife control method based on motion sensing data, and further comprising: a needle knife tip 102, a blade head, and an inner edge 103, wherein the needle knife tip 102, the blade head, and the inner edge 103 are disposed on a needle body 101, the needle knife tip is a 1mm diameter hemispherical shape, the blade head bending angle is 90° to 110°, the blade head length is 2.5mm to 3.5mm, the inner edge of the blade head is "V" shaped, and the length is 1mm to 2mm, the needle body 101 is made of stainless steel, and the needle handle 105 is made of plastic. Figure 6 The length units in all text are mm.

[0063] Figure 7 An exemplary top view of a small needle knife according to an embodiment of the present invention is shown, wherein, Figure 7 This indicates that the thickness of the needle shank 105 is 3mm.

[0064] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0065] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A method for controlling a small needle knife based on motion sensing data, characterized in that, include: Acquire patient data and construct a digital patient model based on the patient data; Based on the digital patient model, determine the no-go zones for operation; Obtain operator's historical data; At multiple moments during the control cycle, the three-dimensional spatial coordinates of the blade tip are acquired by an optical positioning camera positioned above the surgical area. At multiple points in the control cycle, motion sensing data of the handle is acquired through a combination of sensors installed in the grip handle; Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined. Based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip, the safety boundary warning distance is determined; Based on the handle motion sensing data, the operation stability coefficient is determined; The small needle knife is controlled based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

2. The small needle knife control method based on motion sensing data according to claim 1, characterized in that, Based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined, including: Based on the handle motion sensing data, the real-time tissue reaction force and the angle between the needle knife handle and the normal direction of the tissue surface are obtained. The real-time tissue reaction force includes: real-time normal tissue reaction force and real-time tangential tissue reaction force. Based on the three-dimensional spatial coordinates of the blade tip, the instantaneous velocity magnitude of the needle knife tip at the current moment is obtained, wherein the instantaneous velocity magnitude includes: the normal instantaneous velocity magnitude and the tangential instantaneous velocity magnitude; Obtain a fixed sampling time interval; Obtain the instantaneous contact area between the needle tip and the tissue; Obtain the preset local friction coefficient of each tissue category; The first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined based on the real-time tissue reaction force, the included angle of the blade normal, the instantaneous speed, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient.

3. The small needle knife control method based on motion sensing data according to claim 2, characterized in that, Based on the real-time tissue reaction force, the included angle of the blade holder normal, the instantaneous velocity magnitude, the fixed sampling time interval, the instantaneous contact area, and the preset tissue local friction coefficient, the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate are determined, including: according to the formula: Determine the rate of change of tissue impedance per unit displacement at time i of the control period. Second unit displacement tissue impedance change rate ,in, , , and For preset coefficients, To control the real-time normal tissue reaction force at the i-th moment of the control cycle, To control the real-time tangential tissue reaction force at the i-th moment of the cycle, To control the real-time normal tissue reaction force at time i-1 of the control cycle, To control the real-time tangential tissue reaction force at the (i-1)th moment of the control cycle, To control the magnitude of the normal instantaneous velocity at the i-th moment of the cycle, To control the magnitude of the tangential instantaneous velocity at the i-th moment of the cycle, For a fixed sampling time interval, To control the instantaneous contact area at the i-th moment of the control cycle, To control the angle between the tool holder normals at the i-th moment of the control cycle, The preset local friction coefficient of the surgical tissue at the i-th moment of the control cycle.

4. The small needle knife control method based on motion sensing data according to claim 1, characterized in that, Based on the operator's historical data, the restricted operating area, and the three-dimensional spatial coordinates of the blade tip, the safety boundary warning distance is determined, including: Determine the preset hardness correction coefficient and the preset motion trend sensitivity coefficient; Determine the preset tissue hardness level at the location of the needle tip; The instantaneous velocity vector of the needle knife tip is determined based on the three-dimensional spatial coordinates of the tip. Determine the three-dimensional spatial coordinates of the center of the restricted area; Determine the fixed safety radius of the restricted area; The first Euclidean distance is determined based on the three-dimensional spatial coordinates of the center of the restricted area and the three-dimensional spatial coordinates of the blade tip; Based on the operator's historical data, determine the operator's style aggression coefficient; The safety boundary warning distance is determined based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset movement trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance.

5. The small needle knife control method based on motion sensing data according to claim 4, characterized in that, Based on the operator's historical data, an operator style aggression coefficient is determined, including: Based on the operator's historical data, determine the operator's historical average needle knife speed and historical average needle knife force at the most recent sampling points; The first ratio is determined based on the historical average needle knife speed and the preset needle knife speed threshold. The second ratio is determined based on the historical average needle knife force and the preset needle knife force threshold. The operator style aggression coefficient is determined based on the first ratio and the second ratio.

6. The small needle knife control method based on motion sensing data according to claim 4, characterized in that, Based on the three-dimensional spatial coordinates of the restricted area center, the three-dimensional spatial coordinates of the blade tip, the operator's style aggression coefficient, the preset hardness correction coefficient, the preset movement trend sensitivity coefficient, the preset tissue hardness level, the instantaneous velocity vector, the fixed safety radius, and the first Euclidean distance, the safety boundary warning distance is determined, including: according to the formula: Determine the safety boundary warning distance at time i of the control cycle. ,in, For preset coefficients, To control the three-dimensional spatial coordinates of the blade tip at the i-th moment of the control cycle, To determine the three-dimensional spatial coordinates of the restricted area center at the i-th moment of the control cycle, Let be the first Euclidean distance at the i-th time step of the control period. To define the fixed safety radius of the nearest restricted area at the i-th moment of the control cycle. This is the preset hardness correction factor. To control the preset tissue hardness level at the location of the needle tip at the i-th moment of the cycle. Let be the instantaneous velocity vector at the i-th moment of the control cycle. To preset the motion trend sensitivity coefficient, The operator's aggressiveness factor.

7. The small needle knife control method based on motion sensing data according to claim 1, characterized in that, Based on the handle motion sensing data, the operational stability coefficient is determined, including: Based on the handle motion sensing data, the needle knife spindle direction angle and needle knife operating force are determined; Based on the needle knife spindle direction angle and the needle knife operating force, determine the first standard deviation of multiple needle knife spindle direction angles and the second standard deviation of multiple needle knife operating forces within a preset time window; The operational stability coefficient is determined based on the first standard deviation and the second standard deviation.

8. A small needle knife control system based on motion sensing data, characterized in that, For performing the method of any one of claims 1-7, comprising: The patient model module is used to acquire patient data and construct a digital patient model based on the patient data. The restricted area module is used to determine the restricted areas based on the digital patient model; The historical data module is used to acquire historical data of the operator; The three-dimensional coordinate module is used to acquire the three-dimensional spatial coordinates of the blade tip at multiple moments in the control cycle through an optical positioning camera positioned above the surgical area. The sensing data module is used to acquire handle motion sensing data at multiple moments in the control cycle through a combination of sensors set in the grip handle; The tissue impedance module is used to determine the first unit displacement tissue impedance change rate and the second unit displacement tissue impedance change rate based on the three-dimensional spatial coordinates of the blade tip and the motion sensing data of the handle. The boundary warning module is used to determine the safety boundary warning distance based on the operator's historical data, the operating restricted area, and the three-dimensional spatial coordinates of the blade tip; The stability coefficient module is used to determine the operation stability coefficient based on the handle motion sensing data; The needle knife control module is used to control the small needle knife based on the first unit displacement tissue impedance change rate, the second unit displacement tissue impedance change rate, the safety boundary warning distance, and the operation stability coefficient.

9. A small needle knife based on motion sensing data, characterized in that, include: The processor, wherein the processor is used to perform the method according to any one of claims 1-7, further includes: a needle knife tip, a blade head, and an inner edge of the blade head, wherein the needle knife tip of the small needle knife is a 1mm diameter hemisphere, the blade head bending angle is 90° to 110°, the blade head length is 2.5mm to 3.5mm, and the inner edge length of the blade head is 1mm to 2mm.

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