Method for adjusting the feel of an electrocoagulation surgical instrument and system therefor

Abstract

The application discloses a method and system for adjusting the feel of an electrocoagulation surgical instrument. The method includes: performing exponential moving average incremental updating based on historical pressure thresholds and robust pressure samples to obtain incrementally updated pressure samples; performing online statistics on the incrementally updated pressure samples to obtain a thermal center position coordinate, a holding pressure mean value, a holding pressure standard deviation, and a pressure rise rate; obtaining a reference threshold based on the holding pressure mean value and the holding pressure standard deviation, the reference threshold including a normal holding pressure interval and an overpressure threshold; and obtaining an operation trigger confirmation duration based on the pressure rise rate. The application learns individual holding habits online and derives the reference threshold and the confirmation duration, so that the instrument actively adapts to the operator, presents a fit feel, shortens the learning curve, reduces unnatural holding postures and hand fatigue, realizes online calibration with automatic convergence, is resistant to environmental drift, and ensures stable convergence of the calibration without divergence.

Description

Technical Field

[0001] This invention relates to the field of adaptive control technology, and in particular to a method and system for personalized adjustment of the feel of electrocoagulation surgical instruments. Background Technology

[0002] For electrocoagulation instruments with force feedback, the feel depends on how the instrument interprets the surgeon's grip. Existing instruments suffer from the following shortcomings in the individual adaptation of feel: First, the judgment thresholds of existing force feedback instruments are mostly factory-preset, leading to inaccurate feel depending on the individual, i.e., not responsive to the hand. Second, reliance on the surgeon to actively adapt to the instrument results in a long learning curve, and prolonged maintenance of an unnatural grip can easily lead to hand fatigue. Third, the lack of online, non-disruptive individual feel calibration methods leads to incomplete individual adaptation, or the need for model retraining, poor stability, and inability to be completed in real-time during surgery. Finally, online statistical samples are not robust enough and are susceptible to environmental drift contamination, causing calibration threshold deviations or even iterative divergence. Summary of the Invention

[0003] The technical problem to be solved by this invention is: to address the issue that surgeons' hands easily become fatigued when holding traditional electrocoagulation instruments during long surgeries, requiring them to actively adapt to instrument operation, and to provide a method and system for personalized adjustment of the feel of electrocoagulation surgical instruments.

[0004] To achieve the above objectives, the present invention provides a method for personalized adjustment of the feel of electrocoagulation surgical instruments, comprising the following steps:

[0005] Based on historical pressure threshold μ old and robust pressure sample μ sample Perform exponential moving average incremental updates to obtain the incremental update pressure sample μ. new The incremental update satisfies:

[0006] (1)

[0007] Where A is the weighted weight value, and 0 < A < 1;

[0008] For the incremental update pressure sample μ new Online statistics were performed to obtain the coordinates of the location of the usual thermodynamic center (x c0 y c0 Mean grip pressure μ p Grip pressure standard deviation σ p Rate of pressure rise ;

[0009] The baseline threshold Th is obtained based on the mean grip pressure and the standard deviation of grip pressure. base The reference threshold includes a normal grip pressure range and an overpressure threshold, wherein the normal range is [μp −1.5σ p μ p +1.5σ p The overvoltage threshold is μ. p +3σ p ;

[0010] The operation trigger confirmation duration is obtained based on the pressure rise rate, and the operation trigger confirmation duration is as follows:

[0011] (2)

[0012] Where K is 0.5 to 2.0 and C is 0.05 to 0.20 s.

[0013] Another aspect of this application provides a system for personalized adjustment of the feel of electrocoagulation surgical instruments, comprising:

[0014] The preprocessing module performs environmental compensation and noise reduction on the raw grip signal to obtain a robust pressure sample μ. sample The environmental compensation includes temperature drift compensation and humidity drift compensation;

[0015] Sliding window module: used to display the robust pressure sample μ sample The pressure data is organized into a continuous, overlapping sequence using a sliding window method.

[0016] The incremental update and online statistics module is used to receive pressure data within a sliding window, first based on the historical pressure threshold μ. old Compared with the current robust stress sample μ sample Perform exponential moving average incremental updates to obtain the incremental update pressure sample μ. new Then for μ new The sequence is statistically analyzed online, and the mean grip pressure μ is output. p Grip pressure standard deviation σ p Pressure rise rate μ r and the coordinates of the location of the usual thermodynamic center (x c0 y c0 This module incorporates fixed-point shift operation logic to reduce power consumption;

[0017] The threshold derivation module is used to obtain a benchmark threshold Th based on the mean grip pressure and the standard deviation of grip pressure. base The reference threshold includes a normal grip pressure range and an overpressure threshold, wherein the normal range is [μ p −1.5σ p μ p +1.5σ p The overvoltage threshold is μ. p +3σ pAnd the operation trigger confirmation duration is obtained based on the pressure rise rate. ;

[0018] Spatial correction module, used for positioning based on the coordinates of the thermodynamic center (x) c0 y c0 ) and the current holding thermal center position coordinates (x c y c ), for the benchmark threshold Th base Spatial weight correction is performed to obtain the correction threshold Th. final ;

[0019] The parameter injection module is configured to use the correction threshold and / or the operation trigger confirmation duration as external parameters, and inject them into the feedback judgment process of the device without modifying the weights of the existing decision model within the device.

[0020] Compared with existing technologies, the present invention has the following beneficial effects: First, by learning individual grip habits online and deriving benchmark thresholds and confirmation time, the instrument actively adapts to the surgeon, presenting a comfortable feel, shortening the learning curve, and reducing unnatural grip postures and hand fatigue; Second, by using exponential moving average incremental updates to derive thresholds, online calibration can be achieved through on-demand learning and automatic convergence; Third, updates are based on robust pressure samples, resisting environmental drift and ensuring that calibration converges stably without diverging in real intraoperative environments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the calibration process for online learning and threshold injection provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram illustrating how the same gripping action is judged as different feel under different individual thresholds, as provided in an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram illustrating how the personalized threshold provided in this embodiment of the invention automatically converges as the surgeon uses it. Detailed Implementation

[0024] The following embodiments further illustrate the content of the present invention, but should not be construed as limiting the present invention. Any modifications or substitutions made to the methods, steps, or conditions of the present invention without departing from the spirit and essence of the invention are within the scope of the present invention.

[0025] This embodiment provides a method for personalized adjustment of the feel of electrocoagulation surgical instruments. This method primarily relies on a computing unit such as a microcontroller unit (MCU) or digital signal processor (DSP) integrated inside or outside the electrocoagulation surgical instrument.

[0026] At the hardware infrastructure level, the grip area of ​​the electrocoagulation surgical instrument in this embodiment is equipped with a fingertip sensing device (such as a pressure sensor array or a capacitance sensor array). Those skilled in the art should understand that the physical gripping force applied by the surgeon when operating the instrument is converted into an analog electrical signal by the aforementioned sensor array, then digitally sampled by an analog-to-digital converter (ADC), and finally transmitted to the MCU via an internal communication bus (such as an I2C or SPI bus) as the "raw gripping signal" for the algorithm of this invention.

[0027] like Figure 1 As shown, the method for personalized adjustment of the feel of this electrocoagulation surgical instrument specifically includes the following steps:

[0028] Step S0: Initialization of the cold start baseline state

[0029] Before the system establishes a personalized threshold for a particular surgeon and before historical statistics for that surgeon are available, it is necessary to establish the iteration starting point for incremental updates. Before the first execution of the exponential moving average incremental update, the default baseline value μd is used. efault Alternatively, the arithmetic mean of the first N grip samples can be used as the historical pressure threshold μ. old The initial value.

[0030] Specifically, if there is no record of the practitioner in the system, the arithmetic mean of the initial N holding samples is taken as the cold start estimate:

[0031]

[0032] In this embodiment, N is 5 to 20, corresponding to approximately the first 5 to 20 effective holding cycles. If there are prior records of similar practitioners in the system, the record value can also be directly retrieved as μ. default .

[0033] Step S1: Input Sample Robustness Preprocessing

[0034] Before incorporating the original grip signal into the online statistics, the MCU first performs environmental drift and noise robustness processing to eliminate baseline drift and interference caused by changes in operating room temperature and humidity and the operation of high-frequency electrosurgical equipment, ensuring consistency of the samples sent for subsequent incremental updates.

[0035] Specifically, the original grip signal (denoted as the original pressure signal μ) is obtained. raw The robust pressure sample μ is obtained by performing robustness preprocessing according to the following formula. sample :

[0036] (5)

[0037] Where ΔT and ΔH are the temperature drift and humidity drift relative to the calibration reference, respectively; α and β are the corresponding temperature compensation coefficients and humidity compensation coefficients, the values ​​of which are determined by the no-load drift calibration during the instrument's power-on self-test; γ noise The high-frequency interference estimation term is obtained from the baseline estimate during the high-frequency electrosurgical excitation period. This step effectively prevents environmental drift and high-frequency interference from contaminating the statistics, ensuring stable convergence of subsequent calibrations.

[0038] Step S2: Sliding window data acquisition

[0039] The robust pressure sample μ output from step S1 sample Organized in a sliding window manner, it provides a continuous and overlapping stress data sequence for subsequent incremental updates and online statistics.

[0040] Let the system sampling rate be f. s The number of window samples in the sliding window is N. w (Corresponding window duration T) w = N w / f s The overlap rate of adjacent windows is 50% (i.e., per T). w / 2 Time interval: Scroll once, refresh window data once.

[0041] The pressure data set within each sliding window is denoted as {μ sample The result is then sent to step S3 for exponential moving average incremental update and subsequent statistics.

[0042] Step S3: Exponential Moving Average (EMA) Incremental Update

[0043] Receive the pressure data in each sliding window transmitted in step S2, and perform incremental updates and online statistics in sequence.

[0044] Incremental update: based on historical pressure threshold μ old and robust pressure sample μ sample Perform exponential moving average incremental updates to obtain the incremental update pressure sample μ. new The update process continues every 30 to 60 seconds, allowing the judgment threshold to learn as needed and converge automatically.

[0045] Incremental updates satisfy the following formula: (1)

[0046] Where A is the weighted weight value, and 0 <A<1。

[0047] Fixed-point approximation: To achieve a balance between smoothness and following speed, this embodiment takes A=0.95, then (1−A)=0.05, and equation (1) can be specifically written as:

[0048] (6)

[0049] On low-cost MCUs (microcontrollers) with low clock speeds, floating-point multiplication and division operations consume a lot of computing power and power. To improve computational efficiency, this embodiment converts the floating-point operations in equation (1) into pure integer operations and uses a shift algorithm of "multiplying by a constant and then shifting to the right" for high-precision approximation.

[0050] Executing integer division instructions (such as direct division by 20) by the MCU is still costly, while a right shift operation is essentially a division by 2. n (where n is the number of bits to shift), the hardware implementation requires only a single cycle, making it extremely fast. If "dividing by 20" could be transformed into "dividing by 2", it would be incredibly fast. n The performance can be significantly optimized by using the form "". To achieve this, an amplification constant is introduced, so that multiplying by this constant and then shifting it right by n bits has an effect close to dividing by 20.

[0051] Choosing n=16 means shifting right by 16 bits, which is equivalent to dividing by 2. 16 =65536. To ensure the transformed result remains unchanged, the following must be satisfied:

[0052]

[0053] Right now:

[0054]

[0055] Substituting this relationship into equation (6), we obtain an approximate formula consisting entirely of integer multiplication, addition, and shifting:

[0056] (7)

[0057] in" " indicates that the result of the 32-bit product is logically shifted right by 16 bits. The operation process of equation (7) is as follows:

[0058] 1. Calculate the integer value of the numerator: M = 19. μ old +μ sample ;

[0059] 2. Multiply by the amplification constant: M×3277;

[0060] 3. Shift the product 16 bits to the right, which is equivalent to dividing by 20.

[0061] Therefore, the EMA update can be completed with only pure integer multiplication and addition and a single 16-bit right shift instruction, which greatly reduces the computational overhead.

[0062] Online statistics on individual characteristics: For the above incremental update pressure sample μ newOnline statistics were performed to extract and calculate the following individual grip habits:

[0063] 1. Average grip pressure μ within the window new The arithmetic mean of such quantities is typically measured in Newtons (N) or kilopascals (kPa).

[0064] 2. Standard deviation of grip pressure μ within the window new The degree of dispersion of the sample.

[0065] 3. Rate of pressure rise μ within the window new The derivative or mean difference of the sequence over time, typically in N / s or kPa / s, characterizes the decisiveness or hesitation of the surgeon's pressure.

[0066] 4. Current coordinates of the thermal center The coordinates of the center of mass of the force on the current window are obtained by resolving the sensor array. These coordinates are output as multiple (position, pressure) pairs at each sampling time by the pressure sensor array and the capacitance sensor array. Each pressure sensor and capacitance sensor constitutes a sensing unit, where the position of the i-th sensing unit is (x...). i y i The measured pressure value is p i The instantaneous force centroid at each sampling moment is defined as the spatially weighted average coordinate, weighted by the pressure values ​​at each point:

[0067] (8)

[0068] (9)

[0069] The coordinate is in millimeters (mm) and represents the contact position of the surgeon's finger within the grip area at that sampling moment.

[0070] The window contains multiple sampling times. The arithmetic mean of the centroid coordinates of all sampling times within the window is calculated with respect to both the x and y components. This yields the current thermodynamic center position coordinates as statistically output for this window. .

[0071] (10)

[0072] (11)

[0073] Where N is the number of sampling points within the window, x c k y c k Let be the instantaneous coordinates of the centroid of the force at the k-th sampling moment.

[0074] The above online statistical output shows the current coordinates of the thermal center. As the update cycle progresses, iterative convergence occurs via EMA, where the EMA convergence effect applies to the thermodynamic center coordinates of the statistical output of each window. It gradually approximates the surgeon's long-term habitual thermal center coordinates in an exponential decay manner, eventually forming the habitual thermal center coordinates (x... c0 ,y c0 This represents the surgeon's long-term, stable, and habitual holding position, which is used for subsequent spatial correction steps.

[0075] Step S4: Derive the personalized feel judgment threshold

[0076] The average grip pressure obtained from the above statistics and grip pressure standard deviation Obtain the baseline threshold The baseline thresholds include the normal grip pressure range and the overpressure threshold:

[0077] Normal grip pressure range: set to Grip strength falling within this range is considered "normal" by the instrument.

[0078] Overvoltage threshold: set to Grip force exceeding this value is considered "overpressure".

[0079] Meanwhile, based on the pressure rise rate μ r Receive operation trigger confirmation time T confirm This embodiment employs a monotonically decreasing mapping, meaning that the faster the ascent rate (decisive and rapid pressure), the shorter the confirmation time; conversely, the slower the ascent rate (hesitant and gradual pressure), the longer the confirmation time, thereby suppressing false triggering. Specifically, an inverse proportional mapping is used:

[0080] (2)

[0081] Where K ranges from 0.5 to 2.0, and the dimension of K is pressure (such as N or kPa), so that K / μ r The unit of measurement is seconds (e.g., μ). r =20 N / s, K=1.0 N, therefore K / μ r =0.05 s), C is taken as 0.05~0.20s (C is used as a hard floor for the lower limit of the confirmation time to prevent the confirmation time from approaching zero when the rise rate is too large).

[0082] In actual code implementation, to avoid When the value approaches or equals zero, division operations may cause overflow or result in abnormally long confirmation times. In this embodiment, when calculating K / Former Perform lower limit protection:

[0083]

[0084] Where ε is a preset, extremely small positive number (e.g., ε = 0.01 N / s or 0.01 kPa / s), and the actual sampled data is calculated as follows. When it is less than ε, replace it with ε. The division operation of equation (2) is performed. Therefore, when the practitioner is in a static holding or applying pressure very slowly ( When ≈ 0), It will be clamped to a preset maximum value T. max = K / ε + C, to prevent the confirmation time from increasing indefinitely or the operation from overflowing, and to ensure that the system operates stably under various boundary conditions.

[0085] In some embodiments, in addition to the inverse ratio described above, the monotonically decreasing mapping may also employ a "piecewise linear mapping" (e.g., setting...). hour ; It decreases linearly over time; hour Alternatively, a "lookup table method" can be used (a table showing the correspondence between speed and duration is pre-programmed into the MCU's Flash memory, and the output is obtained by interpolating the values ​​from the lookup table during runtime).

[0086] Step S5: Spatial Weight Correction

[0087] When the surgeon's actual contact position deviates from the center of their usual grip area, it indicates that the grip posture is not their normal state, and the confidence level of the threshold should be lowered accordingly.

[0088] Based on the coordinates of the inertial thermodynamic center of position converged by the EMA Coordinates of the current holding thermal center position For the benchmark threshold Spatial weight correction is performed to obtain the correction threshold. :

[0089] (3)

[0090] The spatial weight decay function is a Gaussian function:

[0091] f(x c ,y c )=exp{−[(x c -x c0 ) 2 +(y c -y c0 ) 2 ] / (2·d0 2 )} (4)

[0092] In the formula, This is the symbol for an exponential function; This is the tolerance radius for the contact position. In this embodiment, to reasonably define the area of ​​an individual's habitual contact center, [the following is used]. Take 10% to 30% of the equivalent diameter of the gripping area, where the equivalent diameter is the diameter of a circle with an area equal to that covered by the pressure sensor array. This applies when the contact position is precisely at the center of inertia. The threshold is not corrected;

[0093] Step S6: Injection Adaptation

[0094] The correction threshold derived above (Including the corrected normal range and overvoltage threshold) and operation trigger confirmation time. As an external parameter, it is injected into the feedback judgment process of the device.

[0095] Hardware and software implementation: In the MCU's software architecture, the feedback decision-making stage (i.e., the existing decision model that determines whether to power on and output force feedback) runs as an independent high-priority RTOS task. The "injection" action in this step is specifically manifested as follows: the online calibration algorithm task overwrites the calculated threshold parameters to the shared memory area of ​​the feedback control task through internal data bus or global variable assignment. In the next control cycle, the feedback decision-making stage directly reads the latest threshold from this memory address for logical comparison. The greater the deviation... The smaller the value, the lower the threshold confidence level should be.

[0096] The advantage of this injection-based calibration is that it does not modify the internal weights of the existing decision model (such as neural network weights or complex decision trees) within the instrument, and achieves real-time individual adaptation during the operation while maintaining the absolute stability of the existing model.

[0097] like Figure 2 As shown, using the above method, for the same input grip waveform P(t), the personalized overpressure threshold for surgeon A, who is accustomed to a light grip, is... If the pressure is too low, the action will exceed the threshold and be judged as "overpressure"; however, for practitioner B who habitually uses a heavy grip, their individual overpressure threshold is... The value is relatively high; the action did not reach the threshold and was therefore judged as "normal." For example... Figure 3 As shown, the decision threshold Th exhibits an asymptotically exponential convergence characteristic with the number of updates n (or the usage duration t). It automatically tends to the practitioner's true habits.

[0098] Example 2

[0099] This embodiment provides a system for personalized adjustment of the feel of electrocoagulation surgical instruments. This system is a variant of the hardware and software functional modules of the method described in Embodiment 1. The system is integrated into the main control circuit board of the electrocoagulation surgical instrument and includes:

[0100] 1. Preprocessing module: Used to perform environmental compensation and noise reduction on the raw grip signal to obtain robust pressure samples. The environmental compensation includes temperature drift compensation and humidity drift compensation.

[0101] 2. Sliding window module: used to display robust stress samples μ sample The stress data is organized into a continuous, overlapping sequence using a sliding window method and then passed to the incremental update and online statistics module.

[0102] 3. Incremental Update and Online Statistics Module: Used to receive pressure data within a sliding window, first based on historical pressure threshold μ. old Compared with the current robust stress sample μ sample Perform an exponential moving average incremental update to obtain μ. new Then for μ new The sequence is statistically analyzed online, and the mean grip pressure μ is output. p Grip pressure standard deviation σ p Pressure rise rate μ r and the coordinates of the thermal center (x c ,y c The coordinates of the location of the thermal center (x) c ,y c The coordinates of the centroid of the force are calculated based on the multi-point pressure weighted average of the pressure sensor array, and the calculation method is the same as that defined in step S3 of Example 1. This module incorporates fixed-point shift operation logic to reduce power consumption.

[0103] 4. Threshold derivation module: used to obtain a baseline threshold based on the mean grip pressure and the standard deviation of grip pressure. The reference threshold includes a normal grip pressure range and an overpressure threshold, wherein the normal range is: The overvoltage threshold is ; and the operation trigger confirmation duration is obtained based on the pressure rise rate. .

[0104] 5. Spatial Correction Module: Used for coordinates based on the inertial thermodynamic center position. Coordinates of the current holding thermal center position For the benchmark threshold Spatial weight correction is performed to obtain the correction threshold. .

[0105] 6. Parameter injection module: configured to inject the correction threshold and / or the operation trigger confirmation duration as external parameters into the feedback determination link of the device by means of shared memory overwrite without modifying the weight of the existing decision model in the device.

[0106] Embodiment 3

[0107] Based on Embodiments 1 and 2, this embodiment provides a production line consistency quantitative quality inspection scheme for verifying whether the above personalized calibration link works as designed. Since "handling feel" is subjective, this embodiment establishes objective criteria (DFM / QC) through automated quality inspection.

[0108] Specific quality inspection steps are as follows:

[0109] 1. Standard excitation: On the ex-factory quality inspection platform, an automated mechanical arm is used to apply a standard ramp force with a known slope to the holding area of the device, and the excitation waveform is , which lasts for a complete update cycle (e.g., 30~60s). The slope corresponds to a known standard pressure rise rate.

[0110] 2. Statistics conversion: The quality inspection platform uses the same sampling rate as the device and sliding window parameters to sample and sample the ramp force to obtain a pressure sample sequence isomorphic to that in surgery. Then, according to the statistics and derivation process of Embodiment 1, the theoretical standard pressure mean value is calculated and the standard pressure standard deviation . The standard part together with the window parameters determine the theoretical expected value, which serves as the gold reference for quality inspection.

[0111] 3. Objective determination: Read the overpressure threshold actually output by the device , and compare it with the theoretical overpressure threshold. If it satisfies

[0112]

[0113] wherein, is the tolerance allowed by the production line, such as within of the theoretical value, it is determined as qualified (Pass); otherwise, it is determined as unqualified (Fail). This scheme effectively eliminates the subjectivity of handling feel and ensures the consistency of the calibration link of mass-produced devices.

[0114] As an extension of Embodiment 1, this embodiment illustrates the modified application of the present invention in different scenarios.

[0115] On the one hand, in the robustness preprocessing stage of input samples, environmental compensation terms can be flexibly added or removed according to the deployment environment. For example, in outpatient surgical environments with less interference, only temperature compensation can be performed, omitting the high-frequency interference removal term; in complex electromagnetic environments, an adaptive electromagnetic interference estimation module can be added.

[0116] On the other hand, the online adaptive calibration method of the present invention can be extended to surgical training scenarios. In training mode, the system not only learns the individual grip characteristics of the trainee, but also compares them with pre-stored expert benchmark characteristics (such as those of senior surgeons). , The statistical distribution is compared to generate a tactile assessment report, which guides trainees to correct unnatural grip postures and shorten the learning curve.

[0117] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for personalized adjustment of the feel of electrocoagulation surgical instruments, characterized in that, Includes the following steps: Based on historical pressure threshold μ old and robust pressure sample μ sample Perform exponential moving average incremental updates to obtain the incremental update pressure sample μ. new The incremental update satisfies: （1） Where A is the weighted weight value, and 0 < A < 1; For the incremental update pressure sample μ new Online statistics were performed to obtain the coordinates of the location of the usual thermodynamic center (x c0 y c0 Mean grip pressure μ p Grip pressure standard deviation σ p Rate of pressure rise ; The baseline threshold Th is obtained based on the mean grip pressure and the standard deviation of grip pressure. base The reference threshold includes a normal grip pressure range and an overpressure threshold, wherein the normal range is [μ p −1.5σ p μ p +1.5σ p The overvoltage threshold is μ. p +3σ p ; The operation trigger confirmation duration is obtained based on the pressure rise rate, and the operation trigger confirmation duration is as follows: ； （2） Where K is 0.5 to 2.0 and C is 0.05 to 0.20 s.

2. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, Based on the coordinates of the inertial thermodynamic center (x c0 y c0 ) and the current holding thermal center position coordinates (x c y c ), for the benchmark threshold Th base Spatial weight correction is performed to obtain the correction threshold Th. final ; （3） f(x c ,y c )=exp{−[(x c −x c0 ) 2 +(y c −y c0 ) 2 ] / (2·d0 2 )} (4) in, d0 is the baseline threshold, d0 is the contact position tolerance radius, and exp is the exponential function symbol.

3. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, In the incremental update, the weighted weight value A is set to 0.95, and the incremental pressure sample μ new It is obtained through approximate equivalence using fixed-point numbers.

4. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, Prior to the exponential moving average increment update, the following is also included: For the original grip signal The robust pressure sample is obtained by performing a robustness preprocessing. The robustness preprocessing satisfies: （5） in, The original pressure signal is given, ΔT and ΔH represent the temperature drift and humidity drift, respectively, and α and β are the corresponding temperature compensation coefficients and humidity compensation coefficients. This is a term removed for high-frequency interference estimation.

5. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, Before the first execution of the exponential moving average incremental update, the historical stress threshold μ is used as either the default baseline value or the arithmetic mean of the previous N holding samples. old The initial value of N is 5 to 20.

6. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, The online statistics are performed using a sliding window, where the number of window samples in the sliding window is N, and the overlap rate between adjacent windows is 50%.

7. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 2, characterized in that, The contact position tolerance radius d0 is taken as 10% to 30% of the equivalent diameter of the gripping area, and the equivalent diameter is the diameter of a circle with the same area as the area covered by the pressure sensor array.

8. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 1, characterized in that, The update period for the exponential moving average incremental update is 30-60 seconds.

9. The method for personalized adjustment of the feel of electrocoagulation surgical instruments according to claim 2, characterized in that, The corrected threshold Th final And / or the operation trigger confirmation duration is used as an external parameter in the feedback decision-making process of the injection device, and the injection does not modify the weights of the existing decision model in the feedback decision-making process.

10. A system for personalized adjustment of the feel of electrocoagulation surgical instruments, characterized in that, include: The preprocessing module performs environmental compensation and noise reduction on the raw grip signal to obtain a robust pressure sample μ. sample The environmental compensation includes temperature drift compensation and humidity drift compensation; Sliding window module: used to display the robust pressure sample μ sample The pressure data is organized into a continuous, overlapping sequence using a sliding window method. The incremental update and online statistics module is used to receive pressure data within a sliding window, first based on the historical pressure threshold μ. old Compared with the current robust stress sample μ sample Perform exponential moving average incremental updates to obtain the incremental update pressure sample μ. new Then for μ new The sequence is statistically analyzed online, and the mean grip pressure μ is output. p Grip pressure standard deviation σ p Pressure rise rate μ r and the coordinates of the location of the usual thermodynamic center (x c0 y c0 This module incorporates fixed-point shift operation logic to reduce power consumption; The threshold derivation module is used to obtain a benchmark threshold Th based on the mean grip pressure and the standard deviation of grip pressure. base The reference threshold includes a normal grip pressure range and an overpressure threshold, wherein the normal range is [μ p −1.5σ p μ p +1.5σ p The overvoltage threshold is μ. p +3σ p And the operation trigger confirmation duration is obtained based on the pressure rise rate. ; Spatial correction module, used for inertial thermodynamic center position coordinates (x c0 y c0 ) and the current holding thermal center position coordinates (x c y c ), for the benchmark threshold Th base Spatial weight correction is performed to obtain the correction threshold Th. final ; The parameter injection module is configured to use the correction threshold and / or the operation trigger confirmation duration as external parameters, and inject them into the feedback judgment process of the device without modifying the weights of the existing decision model within the device.

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