A Surgical Box Based on Dynamic Coding and Its Real-Time Calibration and Parameter Optimization Method

Abstract

This invention discloses a surgical box based on dynamic coding and its real-time calibration and parameter optimization method, comprising: a programmable chip, a dynamic coding carrier, a multimodal sensor group, a data processing core, and a communication interface; the programmable chip has a built-in dynamic calibration database for storing pressure sensor baseline curves, pump performance degradation parameters and their corresponding dynamic coding sequences, and for real-time updating of the coding content to reflect the latest calibration data; the dynamic coding carrier is a rewritable dynamic barcode matrix or RFID tag; the multimodal sensor group is used to collect the mechanical parameters of instruments within the surgical box and the physiological parameters of tissues during surgery in real time; the data processing core is configured to perform dynamic parameter adjustment operations; the communication interface supports bidirectional interaction with a surgical console. This invention solves the technical problems of insufficient instrument aging compensation, poor environmental adaptability, and delayed emergency response in existing surgical boxes.

Description

Technical Field

[0001] This invention relates to the field of medical surgical instrument technology, specifically to a surgical box based on dynamic coding and its real-time calibration and parameter optimization method. Background Technology

[0002] Traditional surgical boxes typically use fixed identifiers (such as barcodes or RFID chips) for type identification, but their encoded information only contains static parameters such as batch number and model number, failing to dynamically reflect instrument performance degradation, environmental interference, or real-time intraoperative needs. For example, existing technologies (such as US Patent US6059544) use fragmentable or embedded storage chips, which, while able to identify box type, lack the ability to record and update dynamic parameters such as pressure sensor calibration data and fluid pump performance degradation. Furthermore, existing systems rely on preset parameters and cannot integrate intraoperative multimodal data (such as tissue impedance and blood flow rate) for adaptive adjustments, leading to the following problems:

[0003] (1) Insufficient compensation for instrument aging: Traditional calibration data cannot be updated in real time with the number of uses, resulting in a mismatch between the output parameters of the console and the actual performance of the instrument, which affects the accuracy of the surgery;

[0004] (2) Poor environmental adaptability: When the tissue characteristics change during the operation (such as impedance fluctuations or sudden bleeding), the system cannot dynamically adjust the parameters, which may cause tissue damage or decreased efficiency.

[0005] (3) Delayed emergency response: Existing technologies lack a rapid recalibration mechanism for sudden intraoperative situations (such as massive bleeding), relying on manual intervention and increasing surgical risks. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the present invention aims to provide a surgical box based on dynamic coding and its real-time calibration and parameter optimization method, which solves the technical problems of insufficient compensation for instrument aging, poor environmental adaptability and delayed emergency response in existing surgical boxes during use.

[0007] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0008] A surgical box based on dynamic coding, comprising:

[0009] The programmable chip has a built-in dynamic calibration database for storing pressure sensor reference curves, pump performance degradation parameters and their corresponding dynamic coding sequences, as well as updating the coding content in real time to reflect the latest calibration data.

[0010] The dynamic encoding carrier is a rewritable dynamic barcode matrix or radio frequency tag;

[0011] The multimodal sensor array, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter, and a bioimpedance detection electrode, is used to acquire in real time the mechanical parameters of instruments in the surgical box and the physiological parameters of tissues during surgery.

[0012] The data processing core is configured to perform dynamic parameter adjustment operations.

[0013] A communication interface that supports two-way interaction with the surgical console;

[0014] The dynamic parameter adjustment operation includes: establishing a device performance degradation compensation model, identifying environmental interference factors, and dynamically adjusting output parameters.

[0015] A real-time calibration and parameter optimization method for a surgical box based on dynamic coding includes the following steps:

[0016] When surgical instruments are connected, the current identifier of the dynamic coding carrier is scanned, and the corresponding pressure-flow reference parameters and historical attenuation records in the programmable chip are extracted.

[0017] Tissue characteristic data of the surgical area are obtained by using bioimpedance detection electrodes, and the energy transfer efficiency correction coefficient is obtained by combining the three-dimensional organ model of the surgical console.

[0018] Establish a state equation that includes instrument degradation factors and environmental disturbance factors;

[0019] The state equations are solved using a model predictive control algorithm to generate the optimal set of operating parameters under dynamic constraints.

[0020] The optimal set of operating parameters is written into the dynamic encoding carrier and synchronized to the surgical console visualization interface. When sudden intraoperative bleeding is detected, the parameter emergency recalibration protocol is triggered.

[0021] Preferably, extracting the corresponding historical decay record from the programmable chip includes:

[0022] The decay segment associated with the current surgical mode is retrieved from the circular buffer of the programmable chip using a timestamp matching algorithm.

[0023] The retrieved decay segments are input into the time-weighted aging model to calculate the current aging rate of the device. The weighting factors of the time-weighted aging model are dynamically adjusted according to the parameters.

[0024] The annular buffer is partitioned by surgical type to store historical attenuation data of pressure-flow parameters, and the data in each partition is indexed by the cumulative value of surgical energy.

[0025] The pressure-flow reference parameters are verified through interaction with the surgical system of the surgical console. After confirming that the parameters have not been tampered with by comparing blockchain hash values, they are loaded into the encrypted data area of ​​the dynamic encoding carrier.

[0026] Preferably, when calculating the current aging rate of the device, the time-weighted aging model outputs the device aging rate coefficient based on different types of dynamic weighting factors, the aging base rate, and the time decay constant.

[0027] Preferably, when obtaining the energy transfer efficiency correction coefficient, the following steps are included:

[0028] The surgical console analyzes preoperative CT / MRI image data, reconstructs a three-dimensional model of the target organ and divides tissue density gradient regions. The three-dimensional model is then loaded into the coprocessor of the programmable chip to generate an energy transfer topology mesh.

[0029] The energy absorption basis matrix corresponding to the instrument type is pre-stored in the non-volatile memory of the dynamic coding carrier, and the thermal conductivity coefficient of each grid unit is updated according to the intraoperative Doppler blood flow data.

[0030] Based on the energy absorption basis matrix and the thermal conductivity coefficient of each grid cell, the energy loss compensation factor is obtained.

[0031] The energy loss compensation factor is coupled with the device aging rate coefficient output by the time-weighted aging model to generate a device performance decay compensation curve.

[0032] After the device performance attenuation compensation curve is verified by fluid dynamics simulation, it is written into the PID control register of the programmable chip in real time, and the corresponding energy transfer efficiency correction coefficient is generated.

[0033] Preferably, when generating the optimal set of operating parameters under dynamic constraints, the following steps are included:

[0034] Construct a state-space equation that includes a device performance attenuation compensation term and a tissue energy absorption term;

[0035] Rolling optimization is performed in the embedded MPC module of the programmable chip based on the state-space equation. The rolling optimization includes: predictive model construction, multi-objective constraint optimization, and optimal parameter set generation.

[0036] Preferably, the prediction model construction includes: generating a time-varying prediction model based on each grid cell according to the organ and tissue heat conduction simulation data provided by the surgical system;

[0037] The multi-objective constrained optimization provides an objective function that incorporates hard constraints;

[0038] The hard constraints include: the physical limits of the instrument and the physiological boundaries of the patient;

[0039] The generation of the optimal parameter set includes: solving the constrained quadratic programming problem using a branch and bound algorithm, outputting the optimal operating parameter set, and injecting it into the FPGA control core of the fluid pump for instruction update.

[0040] Preferably, when the emergency recalibration protocol for parameters is triggered, it includes:

[0041] When the optical coherence tomography (OCT) unit detects a sudden change in the blood flow rate in the surgical field that exceeds a preset threshold and the fluid pump return pressure decreases, a three-level emergency response is triggered, including dynamic model reconstruction, constraint enhancement, and control time sequence reconstruction.

[0042] Among them, if the surgical field blood flow rate changes abruptly within 5-7 consecutive control cycles after the implementation of a Level III emergency response. If the pressure is less than 0.2-0.3 times the preset threshold and the fluid pump backflow pressure recovers to ±10% of the baseline value, the Level 3 emergency response will automatically exit.

[0043] Preferably, the dynamic model reconstruction includes: correcting the energy loss compensation factor by loading the three-dimensional coordinates of the bleeding point into the surgical system;

[0044] The control timing reconstruction includes: shortening the prediction time domain to , For the original prediction time domain, For prediction in the time domain under emergency response; and by increasing the sampling frequency to 1-2kHz, a sliding window Kalman filter is used to estimate the heat conduction coefficient of the bleeding point in real time. ;

[0045] By using FPGA dynamic local reconfiguration technology, the PID control core is switched to fuzzy adaptive control mode to prioritize stabilizing fluid pressure fluctuations.

[0046] Preferably, the constraint strengthening includes: setting a temperature safety threshold. Dynamic contraction , The temperature safety threshold after dynamic contraction. The dynamic shrinkage coefficient, This is due to a sudden change in blood flow velocity in the surgical field;

[0047] Add a bleeding risk penalty term to the objective function that incorporates hard constraints.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] (1) Dynamic parameter update: Real-time recording of instrument performance degradation data (such as pressure sensor calibration curve and pump performance degradation parameters) is achieved through rewritable encoding carriers (dynamic barcodes, RFID chips) to realize adaptive parameter compensation of the surgical console;

[0050] (2) Precise performance compensation: By storing historical decay data through a dynamic encoding carrier and combining it with a time-weighted aging model to calculate the instrument degradation rate, real-time calibration of parameters such as electrocoagulation power and fluid pump pressure can be achieved, extending the service life of the instrument and improving surgical precision.

[0051] (3) Multimodal data fusion: Integrating multimodal sensors such as contact pressure sensors and bioimpedance detection electrodes, the mechanical and physiological parameters during surgery are collected in real time to construct a dynamic constraint optimization model;

[0052] (4) Environmental adaptive optimization: Integrating intraoperative multimodal data (such as tissue impedance and thermal conductivity coefficient) with organ three-dimensional models, dynamically generating energy loss compensation factors to ensure that energy output matches tissue characteristics and reduce the risk of thermal damage;

[0053] (5) Intelligent feedback control: The optimal set of operating parameters is generated through the model predictive control (MPC) algorithm, and the electrocoagulation power, flushing flow rate and negative pressure threshold are dynamically adjusted, and a negative feedback relationship is formed with environmental interference factors;

[0054] (6) Real-time closed-loop control: Based on state-space equations and rolling optimization algorithms, the output parameters are automatically adjusted to meet dynamic balance constraints (such as real-time matching of irrigation flow rate and suction negative pressure) and improve surgical efficiency;

[0055] (7) Emergency response mechanism: When an emergency such as a sudden change in blood flow in the surgical field is detected, a three-level recalibration protocol (dynamic model reconstruction, constraint enhancement, and control timing reconstruction) is triggered to quickly stabilize the system state;

[0056] (8) Data security: The system uses blockchain hash value to verify dynamic encoding parameters to prevent tampering, and stores key data in encrypted partitions to ensure system reliability.

[0057] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the real-time calibration and parameter optimization method for a surgical box based on dynamic coding, according to an embodiment of the present invention.

[0059] Figure 2 This is a flowchart illustrating the process of extracting historical attenuation records according to an embodiment of the present invention;

[0060] Figure 3 This is a flowchart of the process for obtaining the energy transfer efficiency correction coefficient according to an embodiment of the present invention. Detailed Implementation

[0061] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0062] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0063] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0064] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0065] Example 1: The surgical box based on dynamic coding provided by the present invention includes: a programmable chip, a dynamic coding carrier, a multimodal sensor group, a data processing core, and a communication interface.

[0066] The programmable chip has a built-in dynamic calibration database for storing pressure sensor reference curves, pump performance degradation parameters and their corresponding dynamic encoding sequences, as well as updating the encoding content in real time to reflect the latest calibration data.

[0067] The dynamic encoding carrier is a rewritable dynamic barcode matrix or radio frequency tag.

[0068] The multimodal sensor array, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter, and a bioimpedance detection electrode, is used to acquire in real time the mechanical parameters of instruments within the surgical box and the physiological parameters of tissues during surgery.

[0069] The data processing core is configured to perform dynamic parameter adjustment operations.

[0070] The communication interface supports bidirectional interaction with the surgical console.

[0071] The dynamic parameter adjustment operation includes:

[0072] Analyze the calibration data in the dynamic coding carrier and establish a device performance degradation compensation model;

[0073] By fusing real-time data streams from a multimodal sensor array, environmental interference factors can be identified.

[0074] An optimization algorithm based on feedback from the surgical console dynamically adjusts the output parameters, including the electrocoagulation power gradient, irrigation fluid flow rate, and negative pressure threshold, and establishes a negative feedback relationship between the output parameters and environmental interference factors.

[0075] Example 2, see Figure 1 The present invention provides a real-time calibration and parameter optimization method with step-by-step diagrams. Figure 1 The method for real-time calibration and parameter optimization of a surgical box based on dynamic coding, as shown, includes the following steps:

[0076] Step S1: When the surgical instrument is connected, scan the current identifier of the dynamic coding carrier and extract the corresponding pressure-flow reference parameters and historical attenuation records from the programmable chip;

[0077] Step S2: Obtain tissue characteristic data of the surgical area through bioimpedance detection electrodes, and obtain the energy transfer efficiency correction coefficient by combining the three-dimensional organ model of the surgical console;

[0078] Step S3: Establish a state equation that includes instrument degradation factors and environmental disturbance factors;

[0079] Step S4: Solve the state equations using a model predictive control algorithm to generate the optimal set of operating parameters under dynamic constraints. The dynamic constraints include:

[0080] The electrocoagulation power does not exceed the critical tissue carbonization threshold; the critical tissue carbonization threshold is a safe upper limit value dynamically set by the surgical console algorithm based on organ type (such as liver, muscle) and electrocoagulation mode (monopolar / bipolar), and is stored in the encrypted partition of the programmable chip;

[0081] The flushing fluid flow rate and the suction negative pressure satisfy a dynamic equilibrium equation;

[0082] Step S5: Write the optimal set of operating parameters into the dynamic encoding carrier and synchronize it to the surgical console visualization interface. When sudden bleeding during the operation is detected, trigger the parameter emergency recalibration protocol.

[0083] Background Description: Existing surgical kits have two major drawbacks during use: (1) they cannot track instrument performance degradation; (2) they lack environmental awareness: they do not integrate dynamic data on intraoperative tissue characteristics (such as changes in bioimpedance), resulting in a mismatch between energy output and real-time tissue status. Based on this:

[0084] In one possible embodiment, in step S3 above, the state equation is as follows:

[0085] ;

[0086] In the formula, This refers to the actual values ​​of the current surgical instrument output parameters (such as electrocoagulation power, irrigation fluid flow rate, and negative pressure threshold). The reference parameter values ​​calibrated at the factory for the instrument are stored in the dynamic calibration database of the programmable chip (such as the power value corresponding to the initial pressure sensor reference curve). The aging rate coefficient of the device is obtained by fitting performance degradation data from historical decay records; The cumulative usage time of the instrument is recorded by a timer in the storage module and weighted and corrected according to the type of surgery (e.g., the usage time of high-frequency electrosurgical unit is calculated based on the cumulative energy value). The tissue impedance change sensitivity coefficient is calculated by comparing the surgical area impedance data collected by the bioimpedance detection electrode with the three-dimensional organ model on the surgical console. Real-time impedance offset, defined as the current impedance value. Compared with the pre-set reference impedance value The absolute difference, i.e. .

[0087] In this embodiment of the invention, it needs to be further explained that the above-mentioned state equation provides a dynamic compensation mechanism to account for device aging ( ) and environmental disturbances Multiply by , to achieve dual real-time correction.

[0088] Background Description: Existing surgical kits have the following problems in controlling irrigation and aspiration during use: (1) Static flow matching: They rely on a fixed ratio of preset irrigation fluid flow rate and aspiration negative pressure, which cannot adapt to sudden changes in the rate of tissue fluid exudation during surgery (such as sudden bleeding or changes in tissue fluid viscosity); (2) One-way control defects: The aspiration negative pressure is only adjusted by triggering a pressure threshold, and it does not form a closed loop with the irrigation flow rate, which can easily lead to residual fluid in the surgical field or excessive aspiration that damages tissue. Based on this:

[0089] In one possible embodiment, in step S4 above, the dynamic equilibrium equation is as follows:

[0090] ;

[0091] In the formula, The real-time flow rate of the flushing fluid is measured by a fluid pump flow meter in a multimodal sensor array and calibrated via a dynamic coding carrier; The real-time suction flow rate of the fluid pump is measured jointly by a negative pressure sensor and a fluid pump flow meter; This is a dynamic equilibrium coefficient, adaptively adjusted according to the intraoperative tissue fluid exudation rate (the higher the exudation rate, the better). (The larger the value) The real-time pressure difference between the inside and outside of the negative pressure chamber is obtained by collecting and filtering data through a pressure sensor array.

[0092] In this embodiment of the invention, it needs to be further explained that the above dynamic equilibrium equation establishes a dynamic coupling relationship between flushing and suction, wherein... The real-time pressure difference between the inside and outside of the negative pressure chamber reflects the impact of tissue fluid exudation on fluid resistance. Additionally, a dynamic balance coefficient is set. , The value is dynamically adjusted based on the exudation rate (such as ultrasound Doppler blood flow data) collected in real time by the multimodal sensor array; the higher the exudation rate, the higher the value. A higher value enhances suction compensation.

[0093] See Figure 2 The flowchart for extracting historical attenuation records shows that, in step S1 above, when extracting the corresponding historical attenuation records from the programmable chip, the process includes:

[0094] The decay segment associated with the current surgical mode is retrieved from the circular buffer of the programmable chip using a timestamp matching algorithm.

[0095] The retrieved decay segments are input into the time-weighted aging model to calculate the current device aging rate. The weighting factors of the time-weighted aging model are dynamically adjusted according to the following parameters:

[0096] Number of times surgical instruments are exposed during high-temperature sterilization cycles;

[0097] The product of the peak pressure and duration of the fluid pump in the most recent N surgeries, where N is a preset value;

[0098] Estimated tissue friction coefficient corresponding to the current surgical procedure;

[0099] The ring buffer stores historical attenuation data of pressure-flow parameters in partitions according to surgical type, and the data in each partition is indexed by the cumulative value of surgical energy.

[0100] The pressure-flow baseline parameters are verified through interaction with the surgical system of the surgical console. After confirming that the parameters have not been tampered with by comparing the blockchain hash value, they are loaded into the encrypted data area of ​​the dynamic encoding carrier.

[0101] Background Description: Traditional medical device aging assessment methods have the following drawbacks: (1) Single aging benchmark: They rely solely on the factory-calibrated basic aging rate (such as a fixed lifespan), without considering the influence of dynamic factors such as sterilization, mechanical fatigue, and tissue friction during actual use; (2) Static weight allocation: The parameter weights in the aging compensation model are fixed (such as linear decay based solely on usage time), which cannot reflect the differentiated degradation patterns of the device under different surgical modes (open / minimally invasive) or tissue types. Based on this:

[0102] In one possible embodiment, the time-weighted aging model is as follows when calculating the current device aging rate:

[0103] ;

[0104] ;

[0105] In the formula, This is the aging rate coefficient of the device. For the first Base rate of aging The number of categories is extracted from a preset table of the programmable chip based on the type of device (such as electrocautery scalpel, ultrasonic scalpel); The time decay constant is the cumulative usage time of the device. Compared with nominal life value Dynamic adjustment of the ratio ( ); For the first The dynamic weighting factor is composed of three weighted components: sterilization, mechanical fatigue, and tissue friction. This represents the number of high-temperature sterilization cycles (recorded by a built-in counter in the chip). The maximum number of sterilization cycles allowed for the device (stored in the read-only area of ​​the chip). , Material degradation coefficient (e.g.: ), calibrated based on experimental data of thermal deformation of the instrument shell; , The fatigue damage index (e.g.: (), fitted through accelerated life testing; The peak pressure of the fluid pump during the most recent N surgeries (recorded by a pressure sensor); This refers to the cumulative duration of peak pressure. , The factory-calibrated pressure and time reference values ​​(e.g.: ); The friction weighting coefficient is dynamically corrected by the surgical console based on real-time force feedback data during the operation. The estimated coefficient of tissue friction is generated based on preoperative imaging data (such as CT values ​​and tissue stiffness) and the type of surgery (open / minimally invasive).

[0106] In this embodiment of the invention, it is necessary to further explain the time decay constant in the above time-weighted aging model. Cumulative usage time of equipment Compared with the nominal life value The ratio is dynamically adjusted to ensure that the aging rate increases non-linearly with the depletion of lifespan, achieving adaptive time decay. The aforementioned time-weighted aging model incorporates the number of high-temperature sterilization cycles (…). Mechanical fatigue ) and estimated coefficient of friction of the organization ( Three types of dynamic weighting factors are used to quantify the contribution of different scenarios to aging.

[0107] See Figure 3 The flowchart for obtaining the energy transfer efficiency correction coefficient is as follows: In step S2 above, obtaining the energy transfer efficiency correction coefficient includes:

[0108] The surgical console analyzes preoperative CT / MRI image data, reconstructs a three-dimensional model of the target organ and divides tissue density gradient regions. The three-dimensional model is then loaded into the coprocessor of the programmable chip to generate an energy transfer topology mesh.

[0109] The energy absorption basis matrix corresponding to the instrument type is pre-stored in the non-volatile memory of the dynamic coding carrier, and the thermal conductivity coefficient of each grid unit is updated according to the intraoperative Doppler blood flow data.

[0110] Based on the energy absorption basis matrix and the thermal conductivity coefficient of each grid cell, the energy loss compensation factor is obtained. ;

[0111] Energy loss compensation factor Device aging rate coefficients output by the time-weighted aging model Couple the components to generate a device performance attenuation compensation curve.

[0112] After the device performance degradation compensation curve is verified by fluid dynamics simulation, it is written into the PID control register of the programmable chip in real time, and the corresponding energy transfer efficiency correction coefficient is generated.

[0113] Background Description: Traditional methods have the following problems when obtaining energy loss compensation terms: (1) Static energy model: It relies on preset tissue energy absorption parameters (such as homogenized thermal conductivity coefficient) and does not consider the influence of intraoperative tissue density gradient and blood flow dynamic changes on energy transfer; (2) Intraoperative dynamic mismatch: The energy output does not match the real-time tissue characteristics, which can easily lead to energy excess or deficiency. Based on this:

[0114] In one possible embodiment, the energy loss compensation factor As shown in the following formula:

[0115] ;

[0116] In the formula, As an energy loss compensation factor, For energy absorption basis matrix, The number of high-density tissues, For the instrument tip to the first Euclidean distance of a high-density organization The tissue attenuation constant, For the first Thermal conductivity coefficient of each grid cell in a high-density structure; These are the coordinates of each grid cell; For gradient operators, It is a norm.

[0117] In this embodiment of the invention, it needs to be further explained that the above-mentioned energy loss compensation factor is based on an energy absorption basis matrix that matches the device type. Real-time updates of the thermal conductivity coefficient of each grid cell ( Dynamic heat conduction and renewal are achieved by introducing the Euclidean distance from the instrument tip to high-density tissue. ), tissue attenuation constant ( This quantifies the energy loss gradient in heterogeneous tissues. The energy absorption basis matrix is ​​used to quantify this gradient. Reflecting the inherent energy absorption characteristics of different tissues, thermal conductivity ( Dynamically correct energy diffusion paths, Euclidean distance ( The farther away or the stronger the tissue attenuation () (The higher the value), the better the energy loss compensation factor. The smaller the value, the higher the output power.

[0118] Background Description: Traditional methods for compensating for instrument performance degradation have the following drawbacks: (1) Static aging compensation: It only relies on the basic degradation parameters calibrated at the factory (such as using a time-linear model) and does not integrate intraoperative environmental interference (such as dynamic changes in tissue friction coefficient); (2) Single-dimensional correction: Aging compensation only considers the performance degradation of the instrument itself and does not combine the intraoperative tissue energy absorption efficiency (such as energy loss compensation factor). The indirect impact on instrument performance. Based on this:

[0119] In one possible embodiment, the device performance attenuation compensation curve is shown in the following equation:

[0120] ;

[0121] In the formula, To estimate the coefficient of friction of the organization, For time The partial derivative operation, This is the aging rate coefficient of the device. As an energy loss compensation factor, This is the baseline value for device effectiveness. This is a compensation item for the decline in device performance.

[0122] In this embodiment of the invention, it needs to be further explained that the above-mentioned device performance decay compensation curve realizes the device aging rate coefficient. Energy loss compensation factor Dynamic changes in friction coefficient Multi-source parameter coupling is achieved, and dynamic compensation is realized through the following mechanism:

[0123] The basic decay term (obtained from the time-weighted aging model) ): Corrects inherent performance degradation caused by instrument aging;

[0124] Environmental enhancement items ( ): Superimposed intraoperative tissue friction changes ( ) and energy loss compensation factor ( ), amplify or reduce the compensation range to match real-time environmental interference.

[0125] In step S4 above, generating the optimal set of operating parameters under dynamic constraints includes:

[0126] Construct a state-space equation that includes a device performance attenuation compensation term and a tissue energy absorption term;

[0127] Rolling optimization is performed in the embedded MPC module of a programmable chip based on state-space equations. Rolling optimization includes: predictive model construction, multi-objective constraint optimization, and optimal parameter set generation.

[0128] Background Description: Traditional methods have the following limitations when performing control optimization: (1) Linear control model: It relies on a linear transfer function to describe the device output and does not consider the nonlinear coupling effect between device aging and tissue energy absorption. Based on this:

[0129] In one possible embodiment, the state-space equation is as follows:

[0130] ;

[0131] In the formula, This is a compensation item for the decline in device performance. As an energy loss compensation factor, For coupled tensors, To control the input vector; Let be the system state vector. This is the pump pressure state vector. For flow state vectors, This is the temperature state vector at the end of the instrument. For transpose, The system matrix describes the dynamic coupling relationships between system state variables; The input matrix describes the control input vector. The impact on state changes This represents the system state vector under dynamic constraints.

[0132] In this embodiment of the invention, it needs to be further explained that the above state-space equation includes the device effectiveness attenuation compensation term ( ) and tissue energy absorption ( The product of () The tensor product (representing the external perturbation term) characterizes the dynamic impact of the "device-tissue" interaction. Furthermore, through... The project incorporates the coupling effect of device aging and tissue energy absorption into the system state equation, and corrects the control input vector in real time. This avoids the static deviation of traditional open-loop control.

[0133] Furthermore, the prediction model construction includes: generating a time-varying prediction model based on each grid cell, based on the organ and tissue heat conduction simulation data provided by the surgical system.

[0134] Background Description: Traditional methods face the following problems in target optimization: (1) Limitations of single-target optimization: Optimization is only performed on a single indicator, without simultaneously balancing the needs of multiple targets such as device efficacy, tissue safety, and operational efficiency; (2) Soft constraint failure: Relying on penalty functions to indirectly constrain the system state cannot guarantee the physiological safety boundary. Based on this:

[0135] In one possible embodiment, multi-objective constraint optimization provides an objective function that incorporates hard constraints, as shown below:

[0136] ;

[0137] In the formula, Let be the objective function. To predict the time domain, For a moment The system state vector, This is a vector of state reference values, representing the desired system state; This is the weight matrix for the system state variables, used to weight the deviations of different states in the objective function; The weight matrix of the system state variables Defined weighted norm; For a moment The increment of the control variable, The weight matrix is ​​used to control the increment and limit drastic changes in the control quantity; Weight matrix for controlling increment Defined weighted norm;

[0138] Hard constraints include:

[0139] Physical limits of machinery: , This refers to the fluid pump power, subject to the upper limit of pump power. constraint, This is the upper limit of pump power. This is the aging rate coefficient of the device.

[0140] Patient physiological boundaries: , For temperature safety threshold, As an energy loss compensation factor, Temperature at the end of the instrument This is the compensation constant;

[0141] Among them, the weight matrix of the system state variables and the weight matrix for controlling the increment Spatial weighting was performed based on the intraoperative real-time conductivity distribution map.

[0142] In this embodiment of the invention, it needs to be further explained that the above objective function minimizes the deviation between the system state and the reference value ( Limit the increment of control input to avoid sudden parameter changes that could cause instrument oscillation or tissue damage. In addition, the upper limit of fluid pump power needs to be adjusted according to the instrument aging rate coefficient (). Dynamic contraction prevents overload failures due to performance degradation; instrument end temperature ( It needs to be lower than the dynamic security threshold. ).

[0143] Furthermore, the optimal parameter set generation includes: solving the constrained quadratic programming problem using a branch and bound algorithm, and outputting the optimal set of operation parameters. , To set pressure, To set the flow rate, The FPGA control core of the fluid pump is updated with instructions based on the pulse time interval.

[0144] In step S5 above, when the emergency recalibration protocol for parameters is triggered, the following is included:

[0145] When the optical coherence tomography (OCT) unit detects a sudden change in blood flow velocity in the surgical field Greater than the preset threshold And the rate of decrease in fluid pump backflow pressure ( This represents the drop in backflow pressure of the fluid pump. This refers to the interval between pressure drops in the fluid pump return flow. When the efficiency is critical, a three-level emergency response is triggered, including dynamic model reconstruction, constraint strengthening, and control timing reconstruction.

[0146] Among them, if the surgical field blood flow rate changes abruptly within 5-7 consecutive control cycles after the implementation of a Level III emergency response. Less than 0.2-0.3 times the preset threshold And the fluid pump backflow pressure If the value recovers to ±10% of the baseline value, the Level 3 emergency response will automatically exit.

[0147] Background Description: Traditional methods have the following problems when dealing with sudden bleeding: (1) Static energy compensation: The energy output parameters depend on a preset global compensation factor and are not dynamically adjusted according to the local tissue characteristics of the bleeding point, resulting in low hemostasis efficiency or energy overload damaging surrounding tissues; (2) Lack of spatial positioning: There is a lack of precise loading capability of the three-dimensional coordinates of the bleeding point in real time during the operation, and it is impossible to quantify the interference of the bleeding area on the energy transfer path. Based on this:

[0148] In one possible embodiment, dynamic model reconstruction includes:

[0149] By loading the three-dimensional coordinates of the bleeding point into the surgical system Energy loss compensation factor Make the correction as shown in the following formula:

[0150] ;

[0151] In the formula, This is the corrected energy loss compensation factor. The distance from the instrument tip to the bleeding point is the Euclidean distance. The radius of bleeding spread (extracted by edge detection of OCT image). As an energy loss compensation factor, It is a clotting factor.

[0152] In this embodiment of the invention, it needs to be further explained that the above correction process loads the three-dimensional coordinates of the bleeding point. The bleeding spread radius is extracted using an edge detection algorithm. ), defining the local interference area for energy transfer. Additionally, the Euclidean distance from the instrument tip to the bleeding point ( The closer the distance, the greater the correction of the compensation factor (increased weight of the exponential term); the radius of hemorrhage spread ( The larger the value, the higher the corrected energy loss compensation factor ( ). The wider the adjustment range for the global energy distribution, the better. The above correction process also dynamically adjusts based on real-time intraoperative coagulation data. Values ​​were used to match differences in patients' coagulation function.

[0153] Furthermore, the constraints are strengthened, including: setting a temperature safety threshold. Dynamic contraction , The temperature safety threshold after dynamic contraction. The dynamic shrinkage coefficient, This is due to a sudden change in blood flow velocity in the surgical field.

[0154] Add a bleeding risk penalty term to the objective function that incorporates hard constraints.

[0155] Furthermore, controlling the temporal reconstruction includes: shortening the prediction time domain to... , For the original prediction time domain, For prediction in the time domain under emergency response; and by increasing the sampling frequency to 1-2kHz, a sliding window Kalman filter is used to estimate the heat conduction coefficient of the bleeding point in real time. ;

[0156] By using FPGA dynamic local reconfiguration technology, the PID control core is switched to fuzzy adaptive control mode to prioritize stabilizing fluid pressure fluctuations.

[0157] In conclusion, the above description is only a preferred embodiment of the present invention and is 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 surgical box based on dynamic coding, characterized in that, include: The programmable chip has a built-in dynamic calibration database for storing pressure sensor reference curves, pump performance degradation parameters and their corresponding dynamic coding sequences, as well as updating the coding content in real time to reflect the latest calibration data. The dynamic encoding carrier is a rewritable dynamic barcode matrix or radio frequency tag; The multimodal sensor array, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter, and a bioimpedance detection electrode, is used to acquire in real time the mechanical parameters of instruments in the surgical box and the physiological parameters of tissues during surgery. The data processing core is configured to perform dynamic parameter adjustment operations. A communication interface that supports two-way interaction with the surgical console; The dynamic parameter adjustment operation includes: establishing a device performance degradation compensation model, identifying environmental interference factors, and dynamically adjusting output parameters.

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