Dynamic coding-based surgical box and real-time calibration and parameter optimization method thereof

Abstract

The invention discloses a dynamic coding-based surgical box and a real-time calibration and parameter optimization method thereof. The dynamic coding-based surgical box comprises a programmable chip, a dynamic coding carrier, a multi-modal sensor group, a data processing core and a communication interface, a dynamic calibration database is arranged in the programmable chip and used for storing a pressure sensor reference curve, pump performance attenuation parameters and corresponding dynamic coding sequences and updating coding content in real time to reflect latest calibration data. The dynamic coding carrier is a rewritable dynamic bar code matrix or a radio frequency tag; the multi-modal sensor group is used for collecting mechanical parameters of instruments in the surgical box and physiological parameters of tissues in an operation in real time; the data processing core is configured to execute a parameter dynamic adjustment operation; the communication interface supports bidirectional interaction with a surgical console. The technical problems that in the using process of an existing surgical box, instrument aging compensation is insufficient, environment adaptability is poor, and emergency response lags are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical surgical instruments, and in particular to a surgical box based on dynamic coding and a real-time calibration and parameter optimization method thereof. Background Art

[0002] Traditional surgical cassettes typically use fixed identifiers (such as barcodes and RFID chips) to identify their type, but their coded information only contains static parameters such as batch and model number, and cannot dynamically reflect instrument performance degradation, environmental interference, or real-time intraoperative needs. For example, existing technologies (such as U.S. Patent US6059544) use fragile or embedded storage chips. Although they can identify the cassette type, they lack the ability to record and update dynamic parameters such as pressure sensor calibration data and fluid pump performance degradation. In addition, existing systems rely on preset parameters and are unable to integrate multimodal intraoperative data (such as tissue impedance and blood flow rate) for adaptive adjustment, leading to the following problems: (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 console output parameters and the actual performance of the instrument, affecting surgical accuracy; (2) Poor environmental adaptability: When tissue characteristics change during surgery (such as impedance fluctuations and sudden bleeding), the system cannot dynamically adjust parameters, which may cause tissue damage or decreased efficiency; (3) Delayed emergency response: Existing technologies lack a rapid recalibration mechanism for emergencies during surgery (such as massive bleeding) and rely on manual intervention, increasing surgical risks. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a surgical box based on dynamic coding and its real-time calibration and parameter optimization method, which is used to solve the technical problems of insufficient compensation for instrument aging, poor environmental adaptability and delayed emergency response in the use of existing surgical boxes.

[0004] In order to solve the above problems, the technical solutions adopted by the present invention are as follows: A surgical box based on dynamic coding, comprising: A programmable chip with a built-in dynamic calibration database that stores pressure sensor reference curves, pump performance attenuation parameters, and their corresponding dynamic coding sequences, and updates the coding content in real time to reflect the latest calibration data; Dynamic coding carrier, which is a rewritable dynamic barcode matrix or radio frequency tag; A multimodal sensor set, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter, and a bioimpedance detection electrode, is used to collect the mechanical parameters of the instruments in the surgical box and the physiological parameters of the tissue during surgery in real time; A data processing core configured to perform a parameter dynamic adjustment operation; a communication interface that supports two-way interaction with the surgical console; Among them, the dynamic parameter adjustment operation includes: establishing an instrument performance degradation compensation model, identifying environmental interference factors, and dynamically adjusting output parameters.

[0005] A method for real-time calibration and parameter optimization of a surgical cassette based on dynamic coding comprises the following steps: Scan the current identification of the dynamic coding carrier when the surgical instrument is connected, and extract the corresponding pressure-flow reference parameters and historical attenuation records in the programmable chip; The bioimpedance detection electrodes are used to obtain the tissue characteristic data of the surgical area, and the energy transfer efficiency correction coefficient is obtained by combining the three-dimensional organ model of the surgical console. Establish a state equation that includes device degradation factors and environmental interference factors; Solving the state equation using a model predictive control algorithm to generate an optimal operating parameter set under dynamic constraints; The optimal operating parameter set is written into the dynamic coding carrier and synchronized to the surgical console visualization interface, and the parameter emergency recalibration protocol is triggered when sudden bleeding is detected during surgery.

[0006] Preferably, when extracting the corresponding historical attenuation record in the programmable chip, the method includes: Retrieving the attenuation segment associated with the current surgical mode from the ring buffer of the programmable chip through a timestamp matching algorithm; Inputting the retrieved decay segments into a time-weighted aging model to calculate the current device aging rate, wherein the weight factors of the time-weighted aging model are dynamically adjusted according to the parameters; The annular buffer stores historical attenuation data of pressure-flow parameters by surgery type, and the data in each partition is indexed by the cumulative value of surgery energy; The pressure-flow reference parameters are verified through interaction with the surgical system of the surgical console, and after confirming that the parameters have not been tampered with by blockchain hash value comparison, they are loaded into the encrypted data area of the dynamic coding carrier.

[0007] Preferably, when calculating the current device aging rate, the time-weighted aging model outputs a device aging rate coefficient based on different types of dynamic weight factors, aging base rates, and time decay constants.

[0008] Preferably, when obtaining the energy transfer efficiency correction coefficient, it includes: The surgical console analyzes preoperative CT / MRI imaging data, reconstructs a 3D model of the target organ, and divides the tissue density gradient area. The 3D model is then loaded into the coprocessor of the programmable chip to generate an energy transfer topology grid. The energy absorption 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 cell is updated according to the intraoperative ultrasound Doppler blood flow data; Based on the energy absorption matrix and the thermal conductivity coefficient of each grid cell, the energy loss compensation factor is obtained; 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 attenuation compensation curve; After the instrument performance attenuation compensation curve is verified through fluid dynamics simulation, it is written into the PID control register of the programmable chip in real time, and a corresponding energy transfer efficiency correction coefficient is generated.

[0009] Preferably, when generating the optimal operating parameter set under dynamic constraints, the following steps are included: Construct a state-space equation that includes the device effectiveness attenuation compensation term and the tissue energy absorption term; Based on the state space equation, rolling optimization is performed in an embedded MPC module of a programmable chip, and the rolling optimization includes: prediction model construction, multi-objective constraint optimization and optimal parameter set generation.

[0010] Preferably, the prediction model construction includes: generating a time-varying prediction model based on each grid unit according to the organ tissue heat conduction simulation data provided by the surgical system; The multi-objective constrained optimization provides an objective function combined with hard constraints; The hard constraints include: physical limits of the device and physiological boundaries of the patient; The generation of the optimal parameter set includes: using a branch and bound algorithm to solve a constrained quadratic programming problem, outputting an optimal operating parameter set, and injecting it into the FPGA control core of the fluid pump for instruction update.

[0011] Preferably, when triggering the parameter emergency recalibration protocol, it includes: When the optical coherence tomography (OCT) unit detects a sudden change in the blood flow rate in the surgical field greater than a preset threshold and a decrease in the return pressure of the fluid pump, a three-level emergency response is triggered, including: dynamic model reconstruction, constraint strengthening, and control timing reconstruction; Among them, after the implementation of the third-level emergency response, if the blood flow rate in the surgical field suddenly changes within 5-7 consecutive control cycles If the pressure is less than 0.2-0.3 times the preset threshold and the fluid pump return pressure returns to ±10% of the baseline value, the system will automatically exit the third-level emergency response.

[0012] Preferably, the dynamic model reconstruction includes: correcting the energy loss compensation factor by loading the three-dimensional coordinates of the bleeding point in the surgical system; The control timing reconstruction includes: shortening the prediction time domain to , is the original prediction time domain, It is the prediction time domain for emergency response; the sampling frequency is increased to 1-2kHz, and the sliding window Kalman filter is used to estimate the heat conductivity coefficient of the bleeding point in real time ; Through FPGA dynamic local reconfiguration technology, the PID control core is switched to fuzzy adaptive control mode, giving priority to stabilizing fluid pressure fluctuations.

[0013] Preferably, the constraint condition strengthening includes: setting the temperature safety threshold Dynamic shrinkage , is the temperature safety threshold after dynamic shrinkage, is the dynamic shrinkage coefficient, Sudden change of blood flow rate in the surgical field; A bleeding risk penalty term is added to the objective function combined with hard constraints.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) Dynamic parameter update: Real-time recording of instrument performance degradation data (such as pressure sensor calibration curves and pump performance attenuation parameters) through rewritable coding carriers (dynamic barcodes, RFID chips) to achieve adaptive parameter compensation of the surgical console; (2) Accurate performance compensation: By storing historical attenuation data through a dynamic coding 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 is achieved, thereby extending the service life of the instrument and improving surgical accuracy; (3) Multimodal data fusion: Integrate multimodal sensors such as contact pressure sensors and bioimpedance detection electrodes to collect intraoperative mechanical and physiological parameters in real time and build a dynamic constraint optimization model; (4) Environmental adaptive optimization: Integrate multimodal data (such as tissue impedance and thermal conductivity) with the three-dimensional model of the organ to dynamically generate energy loss compensation factors to ensure that energy output matches tissue characteristics and reduce the risk of thermal damage; (5) Intelligent feedback control: Generate the optimal operating parameter set through the model predictive control (MPC) algorithm, dynamically adjust the electrocoagulation power, irrigation flow rate and negative pressure threshold, and form a negative feedback relationship with environmental interference factors; (6) Real-time closed-loop control: Based on the state-space equation and rolling optimization algorithm, the output parameters are automatically adjusted to meet dynamic balance constraints (such as real-time matching of flushing flow and suction negative pressure), thereby improving surgical efficiency; (7) Emergency response mechanism: When an emergency situation such as sudden blood flow changes in the surgical field is detected, a three-level recalibration protocol (dynamic model reconstruction, constraint strengthening, and control timing reconstruction) is triggered to quickly stabilize the system state; (8) Data security: Blockchain hash values are used to verify dynamic coding parameters to prevent tampering, and key data is stored in encrypted partitions to ensure system reliability.

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a step diagram of a method for real-time calibration and parameter optimization of a surgical cassette based on dynamic coding according to an embodiment of the present invention; Figure 2 This is a flow chart of extracting historical attenuation records according to an embodiment of the present invention; Figure 3 This is a flow chart of obtaining the energy transfer efficiency correction coefficient according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0018] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0019] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0020] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0021] 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.

[0022] A programmable chip with a built-in dynamic calibration database is used to store pressure sensor reference curves, pump performance attenuation parameters and their corresponding dynamic coding sequences, and to update the coding content in real time to reflect the latest calibration data.

[0023] The dynamic coding carrier is a rewritable dynamic barcode matrix or radio frequency tag.

[0024] The multimodal sensor group, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter and a bioimpedance detection electrode, is used to collect the mechanical parameters of the instruments in the surgical box and the physiological parameters of the tissue during surgery in real time.

[0025] The data processing core is configured to perform a parameter dynamic adjustment operation.

[0026] A communications interface that supports two-way interaction with the surgical console.

[0027] The dynamic parameter adjustment operation includes: Analyze the calibration data in the dynamic coding carrier and establish a compensation model for device performance degradation; Fuse real-time data streams from multimodal sensor groups to identify environmental interference factors; An optimization algorithm based on surgical console feedback dynamically adjusts output parameters, including electrocoagulation power gradient, irrigation fluid flow rate, and negative pressure threshold, and creates a negative feedback relationship between the output parameters and environmental interference factors.

[0028] Example 2, see Figure 1 The present invention provides a real-time calibration and parameter optimization method step diagram. Figure 1 The method for real-time calibration and parameter optimization of a surgical box based on dynamic coding includes the following steps: Step S1: Scan the current identification of the dynamic coding carrier when the surgical instrument is connected, and extract the corresponding pressure-flow reference parameters and historical attenuation records in the programmable chip; Step S2: Obtaining tissue characteristic data of the surgical area through bioimpedance detection electrodes, and obtaining an energy transfer efficiency correction coefficient in combination with the three-dimensional organ model on the surgical console; Step S3: Establishing a state equation including the device degradation factor and the environmental interference factor; Step S4: Using the model predictive control algorithm to solve the state equation, generate the optimal operating parameter set under dynamic constraints, the dynamic constraints include: The coagulation power does not exceed the critical tissue carbonization threshold; the critical tissue carbonization threshold is a safety upper limit dynamically set by the surgical console algorithm based on the organ type (such as liver, muscle) and coagulation mode (monopolar / bipolar), and is stored in an encrypted partition of the programmable chip; The flushing fluid flow rate and suction negative pressure satisfy the dynamic balance equation; Step S5: writing the optimal operating parameter set into the dynamic coding carrier and synchronizing it to the visual interface of the surgical console, triggering an emergency parameter recalibration protocol when sudden bleeding is detected during surgery.

[0029] Background description: Existing surgical boxes have two major defects during use: (1) they cannot track the performance degradation of the instrument; (2) they lack environmental perception: they do not integrate dynamic data of tissue characteristics during surgery (such as changes in bioimpedance), resulting in a mismatch between energy output and real-time tissue status. Based on this: In a possible embodiment, in the above step S3, the state equation is as follows: ; Where, The actual value of the current surgical instrument output parameter (such as electrocoagulation power, irrigation fluid flow, and negative pressure threshold); The reference parameter values calibrated for the device at the factory are stored in the dynamic calibration database of the programmable chip (such as the power value corresponding to the initial pressure sensor reference curve); is the device aging rate coefficient, which is obtained by fitting the performance degradation data in the historical attenuation record; The accumulated usage time of the instrument is recorded by the timer in the storage module and weighted and corrected according to the type of surgery (for example, the usage time of the high-frequency electrosurgical unit is converted according to the accumulated energy value); The sensitivity coefficient of tissue impedance change is calculated by comparing the impedance data of the surgical area collected by the bioimpedance detection electrode with the three-dimensional model of the organ on the surgical console; Is the real-time impedance offset, defined as the current impedance value Compared with the pre-operative reference impedance value The absolute difference of .

[0030] It is necessary to further explain in the embodiment of the present invention that the above-mentioned state equation provides a dynamic compensation mechanism to compensate for the aging of the device ( ) and environmental interference ( ) are multiplied to achieve double real-time correction.

[0031] Background description: Existing surgical boxes have the following problems in flushing and suction control during use: (1) Static flow matching: relying on a preset fixed ratio of flushing fluid flow and suction negative pressure, it cannot adapt to sudden changes in tissue fluid exudation rate during surgery (such as sudden bleeding, changes in tissue fluid viscosity); (2) One-way control defects: the suction negative pressure is only adjusted by triggering the pressure threshold, and does not form a closed-loop relationship with the flushing flow, which can easily lead to residual fluid in the surgical field or excessive suction to damage the tissue. Based on this: In a possible embodiment, in the above step S4, the dynamic balance equation is as follows: ; Where, The real-time flow rate of the flushing fluid is measured by the fluid pump flow meter in the multimodal sensor group and calibrated by the dynamic encoding carrier; is the real-time suction flow of the fluid pump, which is measured jointly by the negative pressure sensor and the fluid pump flow meter; is the dynamic balance coefficient, which is adaptively adjusted according to the intraoperative tissue fluid exudation rate (the higher the exudation rate, the The larger the value); It is the real-time pressure difference between the inside and outside of the negative pressure cabin, which is collected by the pressure sensor array and filtered.

[0032] It is necessary to further explain in the embodiment of the present invention that the above dynamic balance equation establishes a dynamic coupling relationship between flushing and suction, wherein (Real-time pressure difference between inside and outside of negative pressure chamber) reflects the effect of tissue fluid exudation on fluid resistance. In addition, a dynamic balance coefficient is set , The value is dynamically adjusted according to the exudation rate (such as ultrasound Doppler blood flow data) collected in real time by the multimodal sensor group. The higher the exudation rate, Larger values enhance suction compensation.

[0033] See Figure 2 The flowchart of extracting historical attenuation records, in the above step S1, when extracting the corresponding historical attenuation records in the programmable chip, includes: The attenuation segment associated with the current surgical mode is retrieved from the ring buffer of the programmable chip through a timestamp matching algorithm; 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 based on the following parameters: the number of times surgical instruments are exposed to high-temperature sterilization cycles; The product of the peak pressure and duration of the fluid pump in the most recent N operations, where N is a preset value; Estimated tissue friction coefficient corresponding to the current surgical mode; The ring buffer stores historical attenuation data of pressure-flow parameters by surgery type, and the data in each partition is indexed by the cumulative value of surgery energy. The pressure-flow reference parameters are verified through interaction with the surgical system of the surgical console, and after blockchain hash value comparison is used to confirm that the parameters have not been tampered with, they are loaded into the encrypted data area of the dynamic coding carrier.

[0034] Background description: Traditional instrument aging assessment methods have the following defects: (1) Single aging benchmark: It only relies on the basic aging rate calibrated at the factory (such as a fixed life cycle), and does not consider the impact 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 on usage time), which cannot reflect the differentiated degradation patterns of instruments under different surgical modes (open / minimally invasive) or tissue types. Based on this: In one possible embodiment, when calculating the current device aging rate, the time-weighted aging model is as follows: ; ; Where, is the device aging rate coefficient, For the Class aging basic rate, is the category number, which is extracted from the preset table of the programmable chip according to the instrument type (such as electrocoagulation knife, ultrasonic knife); is the time decay constant, which is calculated by the accumulated usage time of the device. Rated lifespan Dynamic adjustment of the ratio ( ); For the A quasi-dynamic weight factor, which is composed of the weighted components of sterilization, mechanical fatigue, and tissue friction; The number of high-temperature sterilization cycles (recorded by the chip's built-in counter); The maximum number of sterilization times allowed for the device (stored in the read-only area of the chip); 、 is the material degradation coefficient (for example: ), calibrated according to the thermal deformation test data of the device shell; 、 is the fatigue damage index (e.g. ), fitted by accelerated life test; is the peak pressure of the fluid pump during the last N operations (recorded by the pressure sensor); is the cumulative maintenance time of peak pressure; 、 The pressure reference value and time reference value are factory calibrated (for example: ); is the friction weight coefficient, which is dynamically modified by the surgical console based on the real-time force feedback data during the operation; It is an estimate of the tissue friction coefficient, generated based on preoperative imaging data (such as CT value, tissue hardness) and surgical type (open / minimally invasive).

[0035] It is necessary to further explain in the embodiment of the present invention that the time decay constant in the above time-weighted aging model is Accumulated usage time by device Rated lifespan The ratio of is dynamically adjusted, so that the aging rate increases nonlinearly with the life consumption, and adaptive time decay is achieved. The above time-weighted aging model introduces the number of high-temperature sterilization cycles ( )、Mechanical fatigue( ) and estimated tissue friction coefficient ( ) Three types of dynamic weight factors to quantify the contribution of different scenarios to aging.

[0036] See Figure 3 The flow chart of obtaining the energy transfer efficiency correction coefficient is as follows: in the above step S2, when obtaining the energy transfer efficiency correction coefficient, it includes: The surgical console analyzes preoperative CT / MRI imaging data, reconstructs a 3D model of the target organ, and divides the tissue density gradient area. The 3D model is then loaded into the coprocessor of the programmable chip to generate an energy transfer topology grid. The energy absorption 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 cell is updated according to the intraoperative ultrasound Doppler blood flow data; Based on the energy absorption matrix and the thermal conductivity coefficient of each grid unit, the energy loss compensation factor is obtained ; Energy loss compensation factor The device aging rate coefficient output by the time-weighted aging model Perform coupling to generate a device performance attenuation compensation curve; After the instrument's efficiency attenuation compensation curve is verified through fluid dynamics simulation, it is written into the PID control register of the programmable chip in real time, and a corresponding energy transfer efficiency correction coefficient is generated.

[0037] Background description: Traditional methods have the following problems when obtaining energy loss compensation items: (1) Static energy model: It relies on preset tissue energy absorption parameters (such as homogenized thermal conductivity coefficient) and does not consider the impact of intraoperative tissue density gradient and blood flow dynamic changes on energy transfer; (2) Intraoperative dynamic mismatch: Energy output does not match real-time tissue characteristics, which can easily lead to energy excess or deficiency. Based on this: In one possible embodiment, the energy loss compensation factor As shown in the following formula: ; Where, is the energy loss compensation factor, is the energy absorption basis matrix, is the number of high-density tissues, From the end of the instrument to the The Euclidean distance of a high-density tissue, is the tissue attenuation constant, For the The thermal conductivity of each grid cell in a high-density tissue; are the coordinates of each grid cell; is the gradient operator, is the norm.

[0038] It is necessary to further explain in the embodiment of the present invention that the energy loss compensation factor is obtained by matching the energy absorption matrix with the device type. , real-time update of the heat transfer coefficient of each grid cell ( ) to achieve dynamic heat conduction update; by introducing the Euclidean distance from the end of the instrument to the high-density tissue ( ), tissue attenuation constant ( ), which quantifies the energy loss gradient in heterogeneous tissue. Among them, the energy absorption basis matrix Reflects the inherent energy absorption characteristics of different tissues, thermal conductivity coefficient ( ) Dynamically correct the energy diffusion path, Euclidean distance ( ) is farther away or the tissue attenuation is stronger ( The higher the value), the energy loss compensation factor The smaller it is, the higher the output power will be.

[0039] Background description: Traditional methods have the following defects when compensating for instrument performance degradation: (1) Static aging compensation: It only relies on the basic attenuation 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 ) has an indirect impact on device performance. Based on this: In a possible embodiment, the instrument performance attenuation compensation curve is shown as follows: ; Where, is the estimated value of the tissue friction coefficient, For time The partial derivative operation of is the device aging rate coefficient, is the energy loss compensation factor, is the basic value of device performance, It is the compensation item for the attenuation of device performance.

[0040] In the embodiment of the present invention, it is necessary to further explain that the above-mentioned instrument performance attenuation compensation curve realizes the instrument aging rate coefficient Energy loss compensation factor , Dynamic changes in friction coefficient Multi-source parameter coupling and dynamic compensation are achieved through the following mechanisms: Basic decay term (obtained from the time-weighted aging model ): Corrects for inherent performance degradation due to device aging; Environmental Enhancement ): Superimpose the changes in tissue friction during surgery ( ) and the energy loss compensation factor ( ), enlarging or reducing the compensation amplitude to match the real-time environmental interference.

[0041] In the above step S4, when generating the optimal operating parameter set under the dynamic constraint conditions, it includes: Construct a state-space equation that includes the device effectiveness attenuation compensation term and the tissue energy absorption term; Rolling optimization is performed in the embedded MPC module of the programmable chip based on the state-space equation. The rolling optimization includes: prediction model construction, multi-objective constraint optimization and optimal parameter set generation.

[0042] Background description: Traditional methods have the following limitations when performing control optimization: (1) Linear control model: It relies on linear transfer functions to describe device output and does not consider the nonlinear coupling effects of device aging and tissue energy absorption. Based on this: In one possible embodiment, the state space equation is as follows: ; Where, is the compensation term for device performance attenuation, is the energy loss compensation factor, is the coupling tensor, is the control input vector; is the system state vector, is the pump pressure state vector, is the flow state vector, is the temperature state vector of the instrument terminal, is the transpose, is the system matrix, describing the dynamic coupling relationship between system state variables; is the input matrix, describing the control input vector The impact of state changes, is the system state vector under dynamic constraints; It is necessary to further explain in the embodiment of the present invention that the above state-space equation converts the instrument performance attenuation compensation term ( ) and tissue energy absorption term ( ) multiplied by ( represents the tensor product) as the external perturbation term to characterize the dynamic impact of the “device-tissue” interaction. In addition, The coupling effect of device aging and tissue energy absorption is incorporated into the system state equation, and the control input vector is modified in real time ( ), avoiding the static deviation of traditional open-loop control.

[0043] Furthermore, the prediction model is constructed, including: generating a time-varying prediction model based on each grid unit according to the organ tissue heat conduction simulation data provided by the surgical system.

[0044] Background description: Traditional methods face the following problems in target optimization: (1) Single-objective optimization limitation: only optimizing a single indicator, without balancing multiple objectives such as device effectiveness, tissue safety, and operational efficiency; (2) Soft constraint failure: relying on penalty functions to indirectly constrain system states, unable to guarantee physiological safety boundaries. Based on this: In one possible embodiment, the multi-objective constrained optimization provides an objective function combined with hard constraints, as shown in the following formula: ; Where, is the objective function, For the prediction time domain, For the moment The system state vector, is the state reference value vector, which represents the desired system state; is the weight matrix of the system state variables, which is used to weight the deviations of different states in the objective function; is the weight matrix of the system state variables The weighted norm defined; For the moment The control variable increment, The weight matrix of the control increment is used to limit the drastic changes of the control amount; is the weight matrix for controlling the increment The weighted norm defined; Among them, hard constraints include: Physical limits of the device: , is the fluid pump power, subject to the upper limit of pump power constraint, is the upper limit of pump power, is the device aging rate coefficient, Patient's physiological boundaries: , is the temperature safety threshold, is the energy loss compensation factor, is the temperature at the end of the instrument, is the compensation constant; Among them, the weight matrix of the system state variables is and the weight matrix that controls the increment Spatial weighting was performed based on the intraoperative real-time conductivity distribution map.

[0045] It is necessary to further explain in the embodiment of the present invention that the objective function minimizes the deviation between the system state and the reference value ( ), limit the control input increment to avoid parameter mutations that may cause instrument oscillation or tissue damage ( In addition, the upper limit of the fluid pump power needs to be adjusted according to the device aging rate coefficient ( ) dynamic contraction to prevent overload failure caused by performance degradation; the end temperature of the instrument ( ) must be lower than the dynamic safety threshold ( ).

[0046] Furthermore, the optimal parameter set is generated, including: using a branch and bound algorithm to solve the constrained quadratic programming problem and outputting the optimal operating parameter set , To set the pressure, To set the flow rate, The pulse time interval is injected into the FPGA control core of the fluid pump to update the instructions.

[0047] In the above step S5, when the parameter emergency recalibration protocol is triggered, it includes: When the optical coherence tomography (OCT) unit detects a sudden change in blood flow rate in the surgical field Greater than the preset threshold , and the fluid pump return pressure drop rate ( is the return pressure drop value of the fluid pump, The interval time for the return pressure of the fluid pump to drop, When the efficiency is critical, a three-level emergency response is triggered, including dynamic model reconstruction, constraint strengthening, and control sequence reconstruction. Among them, after the implementation of the third-level emergency response, if the blood flow rate in the surgical field suddenly changes within 5-7 consecutive control cycles Less than 0.2-0.3 times the preset threshold , and the fluid pump return pressure If the value recovers to ±10% of the baseline value, the system will automatically exit the Level 3 emergency response.

[0048] Background description: Traditional methods have the following problems when dealing with sudden bleeding: (1) Static energy compensation: The energy output parameters rely on the 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 and damage to surrounding tissues; (2) Lack of spatial positioning: The lack of accurate loading capability of the three-dimensional coordinates of the real-time bleeding point during surgery makes it impossible to quantify the interference of the bleeding area on the energy transfer path. Based on this: In a possible embodiment, dynamic model reconstruction includes: By loading the three-dimensional coordinates of the bleeding point into the surgical system , energy loss compensation factor Make the correction as shown below: ; Where, is the corrected energy loss compensation factor, is the Euclidean distance from the end of the instrument to the bleeding point, is the bleeding diffusion radius (extracted by OCT image edge detection), is the energy loss compensation factor, For the coagulation factor.

[0049] In the embodiment of the present invention, it is necessary to further explain that the above correction process loads the three-dimensional coordinates of the bleeding point. , extract the bleeding diffusion radius by edge detection algorithm ( ), defines the local interference area of energy delivery. In addition, the Euclidean distance from the end of the instrument to the bleeding point ( ) is closer, the greater the compensation factor correction (the index item weight is enhanced); the bleeding spread radius ( ) is larger, the corrected energy loss compensation factor ( ) The wider the adjustment range of the global energy distribution. The above correction process is also dynamically adjusted by real-time coagulation data during surgery. Values, matching patients' coagulation function differences.

[0050] Furthermore, the constraints are strengthened, including: setting the temperature safety threshold Dynamic shrinkage , is the temperature safety threshold after dynamic shrinkage, is the dynamic shrinkage coefficient, Sudden change of blood flow rate in the surgical field; A bleeding risk penalty term is added to the objective function combined with hard constraints.

[0051] Furthermore, the control time series reconstruction includes: shortening the prediction time domain to , is the original prediction time domain, It is the prediction time domain for emergency response; the sampling frequency is increased to 1-2kHz, and the sliding window Kalman filter is used to estimate the heat conductivity coefficient of the bleeding point in real time ; Through FPGA dynamic local reconfiguration technology, the PID control core is switched to fuzzy adaptive control mode, giving priority to stabilizing fluid pressure fluctuations.

[0052] Finally: 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 in the scope of protection of the present invention.

Claims

1. A surgical box based on dynamic coding, characterized in that: include: A programmable chip with a built-in dynamic calibration database that stores pressure sensor reference curves, pump performance attenuation parameters, and their corresponding dynamic coding sequences, and updates the coding content in real time to reflect the latest calibration data; Dynamic coding carrier, which is a rewritable dynamic barcode matrix or radio frequency tag; A multimodal sensor set, including a contact pressure sensor, a negative pressure sensor, a fluid pump flow meter, and a bioimpedance detection electrode, is used to collect the mechanical parameters of the instruments in the surgical box and the physiological parameters of the tissue during surgery in real time; A data processing core configured to perform a parameter dynamic adjustment operation; a communication interface that supports two-way interaction with the surgical console; Among them, the dynamic parameter adjustment operation includes: establishing an instrument performance degradation compensation model, identifying environmental interference factors, and dynamically adjusting output parameters.

2. A method for real-time calibration and parameter optimization of a surgical cassette as claimed in claim 1, characterized in that: The following steps are involved: Scan the current identification of the dynamic coding carrier when the surgical instrument is connected, and extract the corresponding pressure-flow reference parameters and historical attenuation records in the programmable chip; The bioimpedance detection electrodes are used to obtain the tissue characteristic data of the surgical area, and the energy transfer efficiency correction coefficient is obtained by combining the three-dimensional organ model of the surgical console. Establish a state equation that includes device degradation factors and environmental interference factors; Solving the state equation using a model predictive control algorithm to generate an optimal operating parameter set under dynamic constraints; The optimal operating parameter set is written into the dynamic coding carrier and synchronized to the surgical console visualization interface, and the parameter emergency recalibration protocol is triggered when sudden bleeding is detected during surgery.

3. The real-time calibration and parameter optimization method according to claim 2, characterized in that: When extracting the corresponding historical attenuation record in the programmable chip, it includes: Retrieving the attenuation segment associated with the current surgical mode from the ring buffer of the programmable chip through a timestamp matching algorithm; Inputting the retrieved decay segments into a time-weighted aging model to calculate the current device aging rate, wherein the weight factors of the time-weighted aging model are dynamically adjusted according to the parameters; The annular buffer stores historical attenuation data of pressure-flow parameters by surgery type, and the data in each partition is indexed by the cumulative value of surgery energy; The pressure-flow reference parameters are verified through interaction with the surgical system of the surgical console, and after confirming that the parameters have not been tampered with by blockchain hash value comparison, they are loaded into the encrypted data area of the dynamic coding carrier.

4. The real-time calibration and parameter optimization method according to claim 3, characterized in that: When calculating the current device aging rate, the time-weighted aging model outputs a device aging rate coefficient based on different types of dynamic weight factors, aging base rates, and time decay constants.

5. The real-time calibration and parameter optimization method according to claim 4, characterized in that: When obtaining the energy transfer efficiency correction factor, include: The surgical console analyzes preoperative CT / MRI imaging data, reconstructs a 3D model of the target organ, and divides the tissue density gradient area. The 3D model is then loaded into the coprocessor of the programmable chip to generate an energy transfer topology grid. The energy absorption 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 cell is updated according to the intraoperative ultrasound Doppler blood flow data; Based on the energy absorption matrix and the thermal conductivity coefficient of each grid cell, the energy loss compensation factor is obtained; 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 attenuation compensation curve; After the instrument performance attenuation compensation curve is verified through fluid dynamics simulation, it is written into the PID control register of the programmable chip in real time, and a corresponding energy transfer efficiency correction coefficient is generated.

6. The real-time calibration and parameter optimization method according to claim 5, characterized in that: When generating the optimal set of operating parameters under dynamic constraints, include: Construct a state-space equation that includes the device effectiveness attenuation compensation term and the tissue energy absorption term; Based on the state space equation, rolling optimization is performed in an embedded MPC module of a programmable chip, and the rolling optimization includes: prediction model construction, multi-objective constraint optimization and optimal parameter set generation.

7. The real-time calibration and parameter optimization method according to claim 6, characterized in that: The prediction model construction includes: generating a time-varying prediction model based on each grid unit according to the organ tissue heat conduction simulation data provided by the surgical system; The multi-objective constrained optimization provides an objective function combined with hard constraints; The hard constraints include: physical limits of the device and physiological boundaries of the patient; The generation of the optimal parameter set includes: using a branch and bound algorithm to solve a constrained quadratic programming problem, outputting an optimal operating parameter set, and injecting it into the FPGA control core of the fluid pump for instruction update.

8. The real-time calibration and parameter optimization method according to claim 7, characterized in that: When the parameter emergency recalibration protocol is triggered, it includes: When the optical coherence tomography (OCT) unit detects a sudden change in the blood flow rate in the surgical field greater than a preset threshold and a decrease in the return pressure of the fluid pump, a three-level emergency response is triggered, including: dynamic model reconstruction, constraint strengthening, and control timing reconstruction; Among them, after the implementation of the third-level emergency response, if the blood flow rate in the surgical field suddenly changes within 5-7 consecutive control cycles If the pressure is less than 0.2-0.3 times the preset threshold and the fluid pump return pressure returns to the baseline value ±10%, the system will automatically exit the third-level emergency response.

9. The real-time calibration and parameter optimization method according to claim 8, characterized in that: The dynamic model reconstruction includes: modifying the energy loss compensation factor by loading the three-dimensional coordinates of the bleeding point into the surgical system; The control timing reconstruction includes: shortening the prediction time domain to , is the original prediction time domain, It is the prediction time domain for emergency response; the sampling frequency is increased to 1-2kHz, and the sliding window Kalman filter is used to estimate the heat conductivity coefficient of the bleeding point in real time ; Through FPGA dynamic local reconfiguration technology, the PID control core is switched to fuzzy adaptive control mode, giving priority to stabilizing fluid pressure fluctuations.

10. The real-time calibration and parameter optimization method according to claim 8 or 9, characterized in that: The constraint conditions are strengthened, including: setting the temperature safety threshold Dynamic shrinkage , is the temperature safety threshold after dynamic shrinkage, is the dynamic shrinkage coefficient, Sudden change of blood flow rate in the surgical field; A bleeding risk penalty term is added to the objective function combined with hard constraints.

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