Condensation and bipolar coagulation integrated control system for surgical operating instrument

Abstract

The invention discloses a coagulation and bipolar coagulation integrated control system for a surgical operating instrument, and relates to the field of medical equipment. The system comprises an interface module, an energy detection module, a storage module, a load model matching module and an energy output control module. According to the system, an energy detection signal is output to a surgical instrument before energy output, and a real-time biological impedance spectrum of a target object is obtained; determining a target model from pre-stored electrical load models according to the impedance spectrum, and calling a corresponding adaptive energy output strategy; and in the energy output process, the impedance change of the target object is monitored, and the energy parameters are dynamically adjusted, so that the energy output is automatically stopped after the impedance change conforms to the preset stable target state. The self-adaptive precise energy control based on the real-time state of the tissue is realized, the problems of tissue carbonization, adhesion or incomplete coagulation and the like are effectively avoided, and the operation safety and effect are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical equipment, and particularly relates to a coagulation and bipolar electrocoagulation integrated control system for surgical instruments. BACKGROUND

[0002] Coagulation and bipolar electrocoagulation are two key energy technologies used to control tissue bleeding and achieve tissue closure in modern surgical operations. Coagulation achieves permanent closure of blood vessels by applying continuous pressure and relatively low-temperature heat energy to denature and fuse the collagen in the blood vessel wall, thereby achieving permanent closure of the blood vessels; bipolar electrocoagulation uses high-frequency current to pass between the two jaws of the instrument to generate heat energy to quickly coagulate tissue to achieve the purpose of rapid hemostasis.

[0003] In traditional surgical systems, coagulation and bipolar electrocoagulation usually exist as two independent modes. Surgeons need to manually judge the type and state of the tissue according to visual observation and personal experience, and select the energy mode and set the output parameters accordingly. Throughout the energy application process, the surgeon continuously observes the tissue response and manually intervenes to adjust or stop the output.

[0004] This manual experience-dependent operation method has a significant technical problem: since the dynamic physical properties (such as impedance) of the biological tissue clamped by the jaws of the surgical instrument cannot be sensed in real time and quantitatively, the energy output of the system is open-loop and fixed, and cannot be adaptively adjusted according to the actual change process of the tissue under the action of energy. This leads to excessive dependence on the personal skills and experience of the surgeon during the energy application process, and the effect is difficult to standardize, and there is a risk of incomplete tissue closure or excessive thermal damage. SUMMARY

[0005] In view of the defects in the above background art, the purpose of the present application is to provide a coagulation and bipolar electrocoagulation integrated control system for surgical instruments, which can solve the problem that the related art cannot adaptively adjust according to the real-time dynamic changes of the tissue, thereby leading to dependence on the experience of the surgeon during the operation, difficulty in standardization, and safety risks.

[0006] To achieve the above object, the application provides a coagulation and bipolar electrocoagulation integrated control system for surgical instruments, comprising: an interface module connected with a surgical instrument integrated with coagulation function and bipolar electrocoagulation function; an energy detection module connected with the interface module, used to output an energy detection signal to the surgical instrument before the surgical instrument activates an energy output mode selected from coagulation or bipolar electrocoagulation, so that the target object held by the jaw of the surgical instrument generates an electrical characteristic feedback signal; a storage module storing an electrical load model and an adaptive energy output strategy; the electrical load model comprises a first electrical load model corresponding to the coagulation function and a second electrical load model corresponding to the bipolar electrocoagulation function; the adaptive energy output strategy comprises a first adaptive energy output strategy corresponding to the first electrical load model and a second adaptive energy output strategy corresponding to the second electrical load model; a load model matching module connected with the interface module and the storage module respectively, used to determine a real-time biological impedance spectrum of the target object according to the electrical characteristic feedback signal, and determine a target electrical load model from the electrical load model according to the real-time biological impedance spectrum; an energy output control module connected with the load model matching module and the storage module respectively, used to call a target adaptive energy output strategy from the adaptive energy output strategy according to the target electrical load model, monitor the impedance value change of the target object according to the target adaptive energy output strategy, and adjust the physical parameters of energy output according to the impedance value change, so that the impedance value change meets the preset stable target state, and the energy output is automatically stopped after the stable target state is reached.

[0007] The application realizes intelligent closed-loop control of energy surgical process by using the above system, thereby solving the technical problems in the background art that energy output depends on the subjective experience of doctors, open-loop control leads to difficulty in standardizing the effect, and there is a risk of incomplete tissue closure or excessive thermal damage. The application realizes automatic identification of the target object by using the energy detection module to obtain the real-time biological impedance spectrum of the target tissue before energy application, and comparing it with the preset electrical load model by means of the load model matching module. Subsequently, the energy output control module calls the optimal adaptive energy output strategy according to the identification result, monitors the impedance change in real time during energy output and dynamically adjusts the physical parameters until the preset stable target state is reached and the energy output is automatically stopped. This closed-loop mechanism of "detection-identification-decision-execution-feedback" changes the fuzzy and experiential manual operation into an accurate, automatic and standardized control process based on real-time and quantitative biophysical parameters, significantly improves the safety and effectiveness of the operation, reduces the dependence on the experience of the operator, and ensures the consistency and reliability of the coagulation and electrocoagulation effect.

[0008] In some embodiments, the system further comprises a post-effect evaluation module connected with the interface module and the energy output control module, configured to control the energy detection module to detect again after the energy output control module automatically stops the energy output according to the target adaptive energy output strategy, so as to obtain the final electrical characteristics of the target object after the energy action is completed. The energy output control module is further configured to switch the current energy output mode to another energy output mode and configure a set of energy parameters for correcting the deviation for the another energy output mode when the deviation between the final electrical characteristics and the standard characteristic value associated with the target electrical load model exceeds the preset range.

[0009] By the above technical solutions, a postoperative instant quality evaluation and correction mechanism is introduced. By detecting the final electrical characteristics of the tissue again after the energy output is completed and comparing with the ideal standard, the system can quantitatively evaluate the effect of the operation. When the deviation is found, the supplementary correction program can be automatically started to ensure that each tissue treatment can reach the optimal clinical standard, greatly improving the success rate and reliability of the operation and avoiding the risk of postoperative complications due to "false completion".

[0010] In some embodiments, the post-effect evaluation module determines whether the deviation exceeds the preset range in the following manner: curve similarity calculation is performed on the impedance spectrum curve corresponding to the final electrical characteristics and the reference curve corresponding to the standard characteristic value to obtain a dynamic time warping distance between the two curves; and when the dynamic time warping distance is greater than a preset distance threshold, it is determined that the deviation exceeds the preset range.

[0011] By the above technical solutions, a more robust and accurate post-effect evaluation algorithm is provided. The dynamic time warping distance is used to measure the similarity between the final impedance spectrum and the standard spectrum, which can evaluate the overall morphology and effectively overcome the shortcomings of the traditional point-by-point comparison method which is easily affected by noise and small frequency shift. This advanced comparison method based on morphology makes the evaluation of the treatment effect more accurate and reliable, and improves the accuracy of deviation judgment.

[0012] In some embodiments, the post-effect evaluation module is further configured to perform pattern matching on the final electrical characteristics and a series of preset non-ideal state feature libraries, and the non-ideal state feature libraries store impedance spectrum feature signatures respectively corresponding to different specific postoperative adverse states, and the specific postoperative adverse states at least include tissue carbonization, tissue adhesion or incomplete coagulation. The energy output control module configures energy parameters for the other energy output mode in the following manner: when a feature signature matching the specific adverse state is found, a set of customized energy parameters for remedying the specific adverse state is called from a correction strategy library associated with the feature signature.

[0013] The above technical solution endows the system with the ability to diagnose specific adverse states and perform targeted remediation. By matching the non-ideal state feature library, the system can not only determine that the effect is poor, but also specifically identify whether the problem is "carbonization" or "incomplete closure", and call the corresponding customized correction strategy. This "diagnostic" aftereffect processing method makes the correction operation more targeted and effective, avoiding secondary damage that may be caused by blind remediation.

[0014] In some embodiments, the preset stable target state and the standard characteristic value are defined based on a biological heat chemical reaction completion degree model of biological tissue; The biological heat chemical reaction completion degree model is established in the following manner: in an in vitro experiment, a plurality of types of biological tissue samples are subjected to the action of different energy parameters, and the energy action is terminated at different impedance stages, and then the biological tissue samples are subjected to puncture strength testing and histopathological analysis; when the puncture strength reaches a preset threshold and the histopathological section shows that the collagen melting and recasting reaches a preset area ratio, the corresponding impedance change curve feature is defined as the stable target state and the standard characteristic value.

[0015] The above technical solution provides a solid biomedical basis for the core control target (stable target state and standard characteristic value) of the system. By directly associating electrical parameters with physical strength and pathological changes of the tissue, it is ensured that the electrical endpoint pursued by the system is truly corresponding to the best tissue closure effect expected in clinical practice. This calibration method based on the biological heat chemical reaction completion degree model ensures the scientificity and clinical effectiveness of the system control logic.

[0016] In some embodiments, the energy detection module outputs the energy detection signal in the following manner: outputting a frequency-modulated signal with a continuously linearly varying frequency within a preset frequency range; The load model matching module determines the real-time biological impedance spectrum in the following manner: performing fast Fourier transform or wavelet transform on the electrical characteristic feedback signal generated by the frequency-modulated signal excitation to calculate the continuous complex impedance spectrum data within the preset frequency range.

[0017] The above technical solutions significantly improve the efficiency and data dimension of tissue detection. By using a frequency-modulated signal for wideband scanning and combining with advanced algorithms such as fast Fourier transform for spectrum analysis, a high-resolution continuous bioimpedance spectrum can be obtained instantaneously. Compared to discrete point measurement, this method can capture more detailed electrical characteristics of the tissue, providing a higher quality data basis for subsequent accurate identification and judgment, and the detection speed is faster.

[0018] In some embodiments, at least one of the frequency range, scanning rate, and signal amplitude of the frequency-modulated signal is dynamically adjusted based on the instrument type identification of the surgical instrument; The storage module also stores an optimal detection parameter table associated with the instrument type identification; The load model matching module is also used to calculate the signal-to-noise ratio or signal quality index of the electrical characteristic feedback signal when solving the real-time bioimpedance spectrum; The energy detection module is used to output a new frequency-modulated signal when the signal-to-noise ratio is below a threshold, and to adaptively adjust the parameters of the new frequency-modulated signal based on the signal quality index to ensure the reliability of the energy detection signal.

[0019] The above technical solutions further enhance the reliability and adaptability of the tissue detection process. The system can dynamically adjust the optimal detection signal parameters based on the type of connected instrument, achieving "suitability" of detection. At the same time, by monitoring the signal quality in real time and adjusting the feedback, it ensures that even in a complex electromagnetic environment, the system can obtain high-quality, high-signal-to-noise ratio impedance spectrum data, providing a fundamental guarantee for the accuracy of the entire closed-loop control system.

[0020] In some embodiments, the electrical load model is defined by a pre-trained machine learning classifier; The load model matching module is used to compare the real-time bioimpedance spectrum with multiple electrical load models, specifically including: The real-time bioimpedance spectrum is input as an input feature vector into the machine learning classifier, and the target electrical load model is determined based on the output of the machine learning classifier.

[0021] The above technical solutions introduce artificial intelligence technology into the tissue recognition process, greatly improving the accuracy and intelligence level of recognition. Using a pre-trained machine learning classifier instead of traditional template matching can more effectively handle complex nonlinear characteristics and individual differences in bioimpedance spectrum. This makes the tissue recognition faster and more generalizable, and can adapt to a wider range of tissue types and physiological states, improving the universality and reliability of the system.

[0022] In some embodiments, the system further comprises a security authentication module linked with the interface module and the storage module respectively; the interface module is further configured to acquire an identification of the surgical instrument when connected with the surgical instrument; the storage module further stores a list of authorized instrument identifications; and the security authentication module is configured to compare the identification with the list of authorized instrument identifications to perform security authentication. The energy detection module is further configured to, after the security authentication passes and before clamping the target object, perform an electrical characteristic baseline test on the surgical instrument itself to acquire baseline data for compensating and calibrating subsequent measurement results.

[0023] With the above technical solutions, a dual-protection system from instrument access to measurement accuracy is constructed. The security authentication mechanism ensures that only authorized and qualified instruments can be activated by the system, thereby eliminating potential safety hazards caused by incompatible or poor-quality instruments from the source. The electrical characteristic baseline test and compensation calibration of the instrument itself eliminate the interference of electrical parameters of the instrument itself on the measurement, thereby ensuring the purity and accuracy of the measured bioimpedance data.

[0024] In some embodiments, the security authentication module is specifically configured to: generate a random number and send the random number to the surgical instrument through the interface module; receive a response code returned by the surgical instrument through a preset encryption algorithm according to the identification and the random number through the interface module; perform the same encryption algorithm locally to obtain an encryption operation result; and determine that the security authentication passes when the encryption operation result matches the response code.

[0025] With the above technical solutions, a cryptography-level security authentication means is adopted to improve the security of the system to a new level. Through the "challenge-response" mechanism, the system not only verifies the legality of the instrument identification, but also verifies whether it masters the correct encryption algorithm and key, which can effectively resist attacks of fake and "cloned" instruments. This dynamic and encrypted authentication method ensures the integrity and security of the medical device ecosystem and protects the interests of patients and hospitals. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is an architecture schematic diagram of a coagulation and bipolar electrocoagulation integrated control system for a surgical instrument according to an embodiment of the present application; Figure 2 is a flowchart of coagulation and bipolar electrocoagulation integrated control according to an embodiment of the present application; Figure 3 is a schematic diagram of the overall architecture of a coagulation and bipolar electrocoagulation integrated control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terminology used in the following description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It also will be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0028] It should also be noted that, unless otherwise explicitly specified and limited, the terms "set", "connected", and "connection" used in the embodiments of the present application should be given a broad meaning. For example, "connected" can be fixedly connected, or detachably connected, or integrally connected; can be mechanically connected, or electrically connected; can be directly connected, or indirectly connected through an intermediate medium; can be internal connection of two elements; can be wired communication connection, or wireless communication connection. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. The embodiments of the present application will be specifically described below.

[0029] In the related art, the operation control mode of the coagulation and bipolar coagulation integrated device is usually based on a real-time impedance feedback loop. During the operation process, the doctor first selects the coagulation or coagulation mode according to the operation requirement, then clamps the target tissue with the surgical instrument forceps, and starts the energy output. The control system continuously monitors the impedance value of the biological tissue between the forceps while outputting energy. When the real-time impedance value rises and reaches a general target threshold value preset by the system, the system judges that the tissue treatment is completed, and automatically stops the energy output.

[0030] However, due to the inability to real-time and quantitatively perceive the dynamic physical characteristics (such as impedance) of the clamped tissue, the energy output of the system is open-loop and fixed, and cannot be adaptively adjusted according to the actual change process of the tissue under the action of energy. This leads to excessive dependence on the personal skills of the doctor during the energy application process, and the effect is difficult to standardize, and there is a risk of incomplete tissue closure or excessive thermal damage.

[0031] Therefore, embodiments of the present invention provide an integrated control system for coagulation and bipolar electrocoagulation of surgical instruments. By introducing load model matching based on real-time bioimpedance spectrum and adaptive energy output control, a fundamental shift is achieved from open-loop operation relying on subjective experience to closed-loop intelligent control driven by objective data. Embodiments of the present invention can automatically identify the individual characteristics of the tissue held by the clamps and dynamically adjust the energy output parameters to accurately match the real-time changes in the tissue, thereby significantly improving the reliability and consistency of coagulation or electrocoagulation effects, effectively reducing the risk of incomplete tissue closure or excessive thermal damage due to energy mismatch, and making surgical procedures safer and more standardized.

[0032] like Figure 1 As shown, this embodiment of an integrated control system for coagulation and bipolar electrocoagulation of surgical instruments includes an interface module 1, an energy detection module 2, a storage module 3, a load model matching module 4, and an energy output control module 5.

[0033] First, the key technical terms involved in this embodiment are defined and explained: Vessel Sealing: An energy surgical technique that uses relatively low-temperature heat and continuous mechanical pressure to denature and fuse collagen and elastin in the blood vessel wall, forming a translucent fusion zone, thereby achieving permanent closure of the blood vessel.

[0034] Bipolar coagulation is an energy surgical technique that uses a high-frequency current to pass between the two electrodes of a surgical instrument (usually the two lobes of the jaws), causing Joule heating in the tissue along the current path, which leads to coagulation of tissue proteins and evaporation of water, thereby achieving rapid hemostasis.

[0035] Bioimpedance spectroscopy refers to the curves or datasets showing the relationship between the impedance (including impedance value and phase angle) and frequency of biological tissues under alternating current excitation at different frequencies. Different types and states of biological tissues (such as blood content, water content, and temperature) have unique bioimpedance spectra, which are like the "electrical fingerprints" of tissues.

[0036] Stable Target State: This refers to the state in which the impedance value of biological tissue reaches a predetermined stable condition during energy application, indicating that tissue coagulation or closure is complete. This condition can be a specific impedance threshold or a rate of change of impedance value less than a predetermined minimum over a period of time, signifying that the physicochemical changes in the tissue are essentially complete.

[0037] Physical parameters of energy output: refers to the multiple key variables that the energy output control module 5 can adjust in real time and dynamically, which directly determine the thermal effect of high-frequency electric energy in the tissue. These physical parameters mainly include: the amplitude of the output voltage (Voltage), which determines the electric field strength and the initial heating rate; the size of the output current (Current), which reflects the intensity of energy flowing through the tissue; the output power (Power), which is the product of voltage and current, is a direct measure of energy transmission rate. More refined control also involves modulation of the high-frequency energy waveform itself, including: pulse duration (Pulse Duration), which is the duration of a single energy pulse; pulse interval (Pulse Interval), which is the rest time between two energy pulses, allowing heat to diffuse within the tissue; and the pulse duty cycle (Duty Cycle) determined by the pulse width and pulse interval, which directly controls the average energy output per unit time.

[0038] In this embodiment, the interface module 1 is connected with a surgical instrument integrated with coagulation and bipolar electrocoagulation functions.

[0039] Among them, the interface module 1 is the bridge for the physical and electrical connection between the control system and the surgical instrument. The surgical instrument is an integrated instrument integrated with coagulation and bipolar electrocoagulation functions, such as a surgical forceps or a grasping forceps that has both coagulation and electrocoagulation capabilities.

[0040] In some embodiments, the interface module 1 can include one or more high-reliability medical-grade connectors for connecting to the handle or cable of the surgical instrument. The design of the connector needs to meet the requirements of electrical isolation and resistance to high-temperature and high-pressure sterilization.

[0041] On the internal circuit, the interface module 1 integrates multiple independent electrical paths, including: High-power energy output path: used to transmit the high-power high-frequency energy (for coagulation or bipolar electrocoagulation) generated by the energy output control module 5 to the jaw electrodes of the surgical instrument.

[0042] Low-power detection signal path: used to transmit the low-energy, high-frequency energy detection signal generated by the energy detection module 2 to the jaw electrodes of the surgical instrument.

[0043] Signal feedback path: used to transmit the voltage and current signals (i.e. electrical characteristic feedback signals) collected by the jaw electrodes of the surgical instrument during detection or treatment back to the energy detection module 2 and the energy output control module 5 inside the system. In addition, the interface module 1 can also include a digital communication interface for identifying the type, serial number, and other information of the surgical instrument, such as by reading the RFID chip or EEPROM memory integrated on the instrument.

[0044] In this embodiment, the energy detection module 2 is connected to the interface module 1, and is configured to output an energy detection signal to the surgical instrument before the surgical instrument activates an energy output mode selected from coagulation or bipolar electrocoagulation, so that the target object held by the jaws of the surgical instrument generates an electrical characteristic feedback signal.

[0045] The core function of the energy detection module 2 is to non-destructively detect the electrical characteristics of the target object before activating the formal energy output mode (coagulation mode or bipolar electrocoagulation mode). The target object refers to the biological tissue held by the jaws of the surgical instrument.

[0046] In some embodiments, the energy detection module 2 can be a hardware and software combined functional module. The hardware part of the energy detection module 2 mainly includes a precise programmable signal generator, such as a direct digital frequency synthesizer (DDS), and corresponding signal conditioning and amplification circuit. The signal generator can generate a series of specific frequency, low voltage, low current alternating signals, which are energy detection signals. The energy level of the energy detection signal is strictly controlled within a safe range that will not cause thermal effects or physiological stimulation to the biological tissue.

[0047] The software part of the energy detection module 2 is responsible for controlling the workflow of the hardware part. When the system detects that the surgical instrument has held the tissue and is ready to activate the energy output, the software logic is triggered to control the signal generator to quickly scan a series of frequency points (such as a plurality of discrete frequency points from 10 kHz to 1 MHz) according to the preset program, and output an energy detection signal at each frequency point. At the same time, the module synchronously collects the current flowing through the tissue and the voltage across the tissue at each frequency through the signal feedback path of the interface module 1, forming the original electrical characteristic feedback signal data. These data are transmitted to the load model matching module 4 for further processing.

[0048] In this embodiment, the storage module 3 stores an electrical load model and an adaptive energy output strategy; the electrical load model includes a first electrical load model corresponding to the coagulation function, and a second electrical load model corresponding to the bipolar electrocoagulation function; the adaptive energy output strategy includes a first adaptive energy output strategy corresponding to the first electrical load model, and a second adaptive energy output strategy corresponding to the second electrical load model.

[0049] The storage module 3 is the "knowledge base" of the system, which pre-stores the core data and algorithms required for intelligent control.

[0050] In some embodiments, the storage module 3 can be a functional module of hardware and software. The hardware part of the storage module 3 is usually a non-volatile memory such as a flash memory or an electrically erasable programmable read-only memory (EEPRO) to ensure that data is not lost after power failure. The contents stored therein, i.e. the software and data part, mainly include the electrical load model and the adaptive energy output strategy.

[0051] Electrical load model: It can be a structured database or lookup table. A large number of models of electrical properties of tissues are stored therein, which are established by experimental or clinical data. The electrical load model specifically includes: First electrical load model: Corresponding to the coagulation function, it mainly contains the standard biological impedance spectrum of target objects suitable for coagulation operation, such as various blood vessels (e.g. arteries, veins), tissue bundles containing blood vessels, etc. For example, model A can define the typical impedance values and phase angles of an artery with a diameter of 2-3 mm at frequencies of 10 kHz, 50 kHz, 200 kHz, 1 MHz, etc.

[0052] Second electrical load model: Corresponding to the bipolar coagulation function, it contains standard biological impedance spectrum of a wider range of tissue types, such as adipose tissue, muscle tissue, mucosal tissue, and wounds with diffuse bleeding, etc. The electrical properties of these tissues are significantly different from blood vessels.

[0053] Adaptive energy output strategy: It is a series of control algorithms or parameter sets corresponding to the electrical load model.

[0054] First adaptive energy output strategy: Corresponding to the first electrical load model, it designs a special energy output scheme for coagulation tasks of different types of blood vessels. For example, for an artery with high wall thickness and collagen content, the strategy can use a lower voltage, pulse output, and gradually increasing duty cycle to ensure that heat penetrates slowly and the inner and outer layers are uniformly fused, and set a higher impedance value as a stable target state for coagulation completion.

[0055] Second adaptive energy output strategy: Corresponding to the second electrical load model, it designs an energy output scheme for coagulation tasks of different tissues. For example, for adipose tissue, the strategy can use a higher initial voltage to quickly break through its impedance due to its high initial impedance; while for ordinary soft tissue, it uses a conventional constant power output mode and sets an impedance value that rises sharply due to tissue dehydration as a stable target state for coagulation completion.

[0056] Among them, the construction method of the electrical load model is based on a large number of in-vitro and in-vivo biological tissue experimental data.

[0057] Specifically, researchers select target object samples covering various types, sizes, and pathological states, such as pig arteries and veins of different diameters, adipose tissue, muscle tissue, etc. Using a high-precision impedance analyzer and electrode clamps simulating the jaws of surgical instruments, a wide-band sweep signal (e.g., from 1 KHz to 1 MHz) is applied to each tissue sample under controlled pressure and temperature conditions. By synchronously measuring the current flowing through the tissue and the voltage across the tissue, complex impedance data of the tissue at different frequencies are collected. Statistical analysis, filtering and noise reduction, and feature extraction are performed on a large number of similar sample data to form a "standard bioimpedance spectrum" representing this type of tissue with statistical significance. These spectrum graphs containing specific impedance-frequency characteristic curves are digitized and stored in storage module 3, constituting the first and second electrical load models used to identify different biological loads.

[0058] The adaptive energy output strategy is pre-prepared in an experimental optimization process parallel to the construction of the electrical load model, aiming to achieve the best clinical effect.

[0059] Specifically, for each specific tissue model in the electrical load model, such as "3mm artery", researchers conduct a series of energy output experiments. In the experiment, by systematically changing the physical parameter combination of energy output (such as different output power, voltage curve, pulse duty cycle, etc.), energy is applied to the corresponding tissue sample. During the entire energy application process, not only the impedance change curve of the tissue is recorded in real time, but also the treatment effect of the tissue is quantitatively evaluated after the energy output ends, such as burst pressure testing of coagulated blood vessels to measure the closure strength, and histopathological section analysis to evaluate the heat damage range and collagen fusion quality. Through repeated experiments and data analysis, a set of dynamic control algorithms and parameters that can make the tissue impedance reach the ideal stable target state (corresponding to the best clinical effect) with the least energy and the smallest side damage in the fastest and most reliable way are finally determined, which are solidified as the adaptive energy output strategy corresponding to the tissue model.

[0060] In this embodiment, the load model matching module 4 is connected to the interface module 1 and the storage module 3, respectively, for determining the real-time bioimpedance spectrum of the target object according to the electrical characteristic feedback signal, and determining the target electrical load model from the electrical load model according to the real-time bioimpedance spectrum.

[0061] The load model matching module 4 is one of the "decision brains" of the system, responsible for identifying the type of the target object held by the current surgical instrument.

[0062] In some embodiments, the load model matching module 4 can be a software module running on the main controller (e.g. a microcontroller MCU or a digital signal processor DSP) of the system. Its function is implemented in the following steps: 1) Receive data: receive the raw electrical characteristic feedback signals from the energy detection module 2, i.e. the voltage and current values at multiple detection frequencies.

[0063] 2) Calculate real-time bioimpedance spectrum: the software algorithm processes each set of received voltage and current data to calculate the complex impedance value (including magnitude and phase) at the corresponding frequency. Combining the impedance values at all frequency points, we get the real-time bioimpedance spectrum of the target object.

[0064] 3) Model matching: the software algorithm compares the calculated real-time bioimpedance spectrum with all the electrical load models (including the first and second electrical load models) stored in the storage module 3. The matching algorithm can use the least squares method, correlation analysis, or a machine learning-based classifier (such as a support vector machine, neural network, etc.) to find the most similar or best matching model to the real-time bioimpedance spectrum.

[0065] 4) Determine the target electrical load model: once the best match is found, the model is determined as the target electrical load model, and the identification result is passed to the energy output control module 5.

[0066] In this embodiment, the energy output control module 5 is connected to the load model matching module 4 and the storage module 3, respectively, for calling a target adaptive energy output strategy from the adaptive energy output strategies according to the target electrical load model, monitoring the impedance value change of the target object according to the target adaptive energy output strategy, and adjusting the physical parameters of energy output according to the impedance value change to control the energy output of the surgical instrument to make the impedance value change meet the preset stable target state, and automatically stop energy output after reaching the stable target state.

[0067] The energy output control module 5 is the "execution core" of the system, responsible for intelligently executing and regulating the entire energy output process according to the identification result of the load model matching module 4 until the task is completed.

[0068] In some embodiments, the energy output control module 5 is a highly integrated software and hardware module. The hardware part of the energy output control module 5 includes a high frequency energy generator (e.g. a radio frequency power amplifier capable of precisely controlling output voltage, current and power) and a real-time monitoring circuit. The real-time monitoring circuit is capable of continuously and accurately measuring the instantaneous voltage and current through the tissue between the jaws of the surgical instrument during high power output, using the interface module 1, and thus calculating the real-time impedance value.

[0069] Specifically, the real-time monitoring circuit generates a signal proportional to the main current by connecting a high frequency current sensor (e.g. a current transformer or a precision sampling resistor) in series in the energy output main loop, and generates a signal proportional to the output voltage by connecting a high impedance precision voltage divider network in parallel across the output. These two analog signals representing the instantaneous current and voltage respectively are simultaneously captured and digitized by a high speed, dual channel, synchronous sampling analog-to-digital converter (ADC), ensuring the time consistency of data acquisition. Finally, the software part of the energy output control module 5 calculates the instantaneous voltage, current and impedance of the tissue in real time based on these digital signals, providing accurate data basis for adaptive closed-loop control.

[0070] The software part of the energy output control module 5 is a closed-loop control algorithm running on the main controller, and its workflow is as follows: 1) Strategy calling: receiving the target electrical load model determined by the load model matching module 4. According to this model, the target adaptive energy output strategy corresponding to it is called from the storage module 3. For example, if the matching result is "artery", the first adaptive energy output strategy for artery is called.

[0071] 2) Output initialization: according to the initial parameters (such as initial voltage, power, output mode, etc.) defined in the called target adaptive energy output strategy, the hardware part is controlled to start outputting treatment energy to the surgical instrument.

[0072] 3) Real-time monitoring and adjustment: during the whole process of energy output, the software algorithm acquires the real-time impedance value of the tissue through the hardware monitoring circuit at a very high frequency (e.g. every millisecond), forming a time curve of impedance value changes. The algorithm compares this actual impedance value change with the ideal change curve or control logic preset in the target adaptive energy output strategy. If the actual impedance rises too fast, it may mean that the tissue is dehydrating or carbonizing too fast, and the software will immediately adjust the physical parameters of energy output, such as reducing the output power or voltage; if the impedance rises too slowly, it may mean that the energy is insufficient, and the software will appropriately increase the energy output. This continuous monitoring and adjustment ensures that the process of energy application is dynamically adaptive.

[0073] 4) Judgment and stop: the software algorithm continuously judges whether the current impedance value change meets the preset stable target state in the target adaptive energy output strategy. For example, the strategy stipulates that when the impedance value exceeds 1000 ohms and fluctuates less than 2% in the next 200 milliseconds, it is considered that the coagulation is completed. Once the condition is met, the energy output control module 5 will automatically stop the energy output immediately, and prompt the doctor to operate the completion through sound or visual signal.

[0074] The embodiment of the present application adopts the above-mentioned system, through the cooperative work of the interface module 1, the energy detection module 2, the storage module 3, the load model matching module 4 and the energy output control module 5, a complete "detection-identification-decision-execution-feedback" closed-loop control system is constructed. The system of the embodiment can automatically identify the tissue type in the moment of operation, match the best energy output scheme, and adaptively adjust according to the real-time dynamic feedback of the tissue during the energy application process, and finally automatically stop when the ideal treatment effect is achieved, thereby greatly improving the accuracy, safety and consistency of coagulation and bipolar electrocoagulation surgery, and reducing the dependence on the personal experience of the doctor.

[0075] The working process of the system will be described below in combination with Figure 2 Taking a laparoscopic cholecystectomy surgery as an example, the working process of the system will be described below.

[0076] In a laparoscopic cholecystectomy surgery, the surgeon uses a surgical instrument integrated with coagulation function and bipolar electrocoagulation function to process the tissue. The system can automatically select coagulation or bipolar electrocoagulation mode according to different tissue types, and realize intelligent energy output control.

[0077] Step one: the surgical instrument clamps the tissue. The surgeon operates the surgical instrument to clamp the tissue to be processed with the jaws. In this example, it is assumed that the doctor first clamps the gallbladder artery (a blood vessel with a diameter of about 2 mm). The surgical instrument establishes a connection with the control system through the interface module 1, and the interface module 1 detects the jaw closing signal.

[0078] Step two: Energy detection phase. When the interface module 1 detects that the jaws of the surgical instrument have clamped tissue and the surgeon has not yet pressed the energy activation button, the energy detection module 2 automatically starts working. The energy detection module 2 outputs a series of energy detection signals to the jaw electrodes of the surgical instrument through the interface module 1. These energy detection signals are low-power, multi-frequency alternating current signals, for example, the detection signals with frequencies of 10 kHz, 50 kHz, 100 kHz, 200 kHz, 500 kHz and 1 MHz are output in turn, and the signal voltage amplitude of each frequency is only 0.5 V and the current is less than 1 mA, which will not cause any damage to the tissue. When the energy detection module 2 outputs the detection signal of each frequency, it synchronously collects the current value flowing through the tissue and the voltage value across the tissue through the signal feedback channel of the interface module 1, thereby obtaining the electrical characteristic feedback signal.

[0079] Step three: Biological impedance spectrum calculation and model matching. The load model matching module 4 receives the electrical characteristic feedback signal from the energy detection module 2. The software algorithm in the load model matching module 4 processes these signals, and for each frequency point, calculates the impedance value and phase angle at that frequency according to the measured voltage value and current value. For example, the impedance is 150 ohms and the phase angle is -15 degrees at 10 kHz, the impedance is 180 ohms and the phase angle is -10 degrees at 50 kHz, and so on, and finally a complete set of impedance values corresponding to the frequencies is obtained, which is the real-time biological impedance spectrum of the target object, i.e. the gallbladder artery.

[0080] The load model matching module 4 then reads the pre-stored electrical load models, including the first electrical load model and the second electrical load model, from the storage module 3. The load model matching module 4 compares the calculated real-time biological impedance spectrum with these pre-stored models one by one. In this example, the characteristics of the real-time biological impedance spectrum are highly consistent with the "small artery blood vessel model A" under the first electrical load model in the storage module 3, and the matching similarity reaches 95%. Therefore, the load model matching module 4 determines that the target electrical load model is this "small artery blood vessel model A", and transmits this identification result to the energy output control module 5.

[0081] Step four: Tissue type identification result and mode selection. Since the target electrical load model determined by the load model matching module 4 belongs to the first electrical load model, it indicates that the clamped tissue is a blood vessel tissue, and the system automatically enters the coagulation mode branch.

[0082] Step five: Start the first adaptive energy output strategy. After receiving the identification result from the load model matching module 4, the energy output control module 5 calls the corresponding target adaptive energy output strategy from the storage module 3 according to the target electrical load model "arteriole blood vessel model A", which is the "arteriole coagulation strategy" in the first adaptive energy output strategy. The strategy specifies the specific parameters for coagulating the arteriole: the initial output voltage is set to 40V, the upper limit of the output power is 30W, the pulse output mode is used, the pulse duty cycle gradually increases from 30% to 80%, the expected impedance change curve is from the initial 180 ohms gradually rising to 800-1000 ohms, and the stable target state is defined as the impedance value exceeding 900 ohms and the change rate being less than 1% within 300 milliseconds.

[0083] Step six: closed-loop control phase - start energy output. When the doctor presses the energy activation button on the surgical instrument, the energy output control module 5 controls its hardware part, the high-frequency energy generator, to start outputting energy according to the initial parameters of the "arteriole coagulation strategy". The high-frequency energy generator delivers a 40V, 30% duty cycle pulse high-frequency current to the jaw electrodes of the surgical instrument through the interface module 1, and the current flows in the blood vessel wall tissue to generate Joule heat. At the same time, the mechanical pressure applied by the jaw cooperates with the thermal energy to denature the collagen in the blood vessel wall.

[0084] Step seven: real-time monitoring of impedance value. During the entire process of energy output, the real-time monitoring circuit of the energy output control module 5 continuously collects the instantaneous voltage and current flowing through the tissue at a frequency of once per millisecond and calculates the real-time impedance value. For example, 1 second after the start of energy output, the impedance value is monitored to be 220 ohms; 2 seconds later, it is 310 ohms; 3 seconds later, it is 450 ohms; and 4 seconds later, it is 620 ohms. These data form a real-time curve of the impedance value change.

[0085] Step eight: judge whether the stable target state is reached - if not, adjust the parameters. The software algorithm of the energy output control module 5 compares the real-time monitored impedance value change with the ideal impedance change curve preset in the "arteriole coagulation strategy". In the first 4 seconds of energy output, the impedance value continues to rise but has not yet reached the target threshold of 900 ohms, so it is judged that the stable target state has not been reached. The software algorithm dynamically adjusts the energy output physical parameters according to the actual impedance rise rate. For example, if the impedance rise rate meets the expectation, the system gradually increases the pulse duty cycle from 30% to 50% according to the strategy to maintain stable energy input; if the impedance rises too fast, the duty cycle or output power is temporarily reduced to prevent excessive carbonization of the tissue.

[0086] The system repeatedly executes steps seven and eight to continuously monitor the impedance value and dynamically adjust the energy output parameters.

[0087] Step nine: judge whether the stable target state is reached - if yes, stop automatically. After about 6 seconds of energy output, the energy output control module 5 monitors that the impedance value has risen to 920 ohms, and in the following 300 milliseconds, the impedance value remains fluctuating between 915 ohms and 925 ohms, with a change rate less than 1%. The software algorithm determines that the current impedance value change condition meets the preset stable target state, which means that the collagen of the gallbladder artery wall has been sufficiently fused, and the coagulation is completed. The energy output control module 5 immediately controls the high-frequency energy generator to stop energy output, and at the same time sends a beeping prompt sound to the surgical instrument through the interface module 1, informing the doctor that the coagulation operation has been automatically completed.

[0088] Step ten: treatment is completed. The doctor releases the jaws of the surgical instrument, and completes the coagulation treatment of the gallbladder artery.

[0089] Comparative example - bipolar electrocoagulation mode: Suppose that in the same operation, the doctor needs to treat the bleeding of the gallbladder bed (diffuse bleeding of the wound tissue) later. The doctor clamps the wound tissue with the jaws of the surgical instrument again. The system repeats steps one to three. In step three, the real-time biological impedance spectrum calculated by the load model matching module 4 shows that the impedance value and phase angle characteristics of the tissue at each frequency are highly matched with the "bleeding wound model B" in the second electrical load model. Therefore, the load model matching module 4 determines that the target electrical load model is "bleeding wound model B", and the system automatically enters the bipolar electrocoagulation mode branch.

[0090] In the above scenario example, in step five, the energy output control module 5 calls the target adaptive energy output strategy corresponding to "bleeding wound model B" from the storage module 3, that is, "wound electrocoagulation strategy" in the second adaptive energy output strategy. The strategy specifies that the initial output voltage is 50V, the output power is 40W, the continuous output mode is adopted, and the stable target state is defined as the impedance value rising sharply to more than 2000 ohms.

[0091] In steps six to nine, the system performs the same closed-loop control process as the coagulation mode, but according to the parameters of the second adaptive energy output strategy. After about 3 seconds of energy output, due to rapid dehydration and protein coagulation of the tissue, the impedance value rises sharply from the initial about 100 ohms to 2100 ohms, and the energy output control module 5 determines that the stable target state is reached, and automatically stops the energy output, completing hemostasis.

[0092] As can be seen from the above two comparative examples, the coagulation and bipolar electrocoagulation integrated control system of the embodiment can automatically identify and select the corresponding energy output mode (coagulation mode or bipolar electrocoagulation mode) for different types of tissues (vascular or non-vascular tissues) in the same surgical procedure, call the corresponding adaptive energy output strategy (the first adaptive energy output strategy or the second adaptive energy output strategy), and perform closed-loop feedback control through real-time monitoring of the impedance value change, and finally automatically stop the energy output when the respective stable target state is reached, thereby truly realizing integrated, intelligent, and automated surgical energy control.

[0093] In some embodiments, the system further comprises a post-effect evaluation module 6 connected with the interface module 1 and the energy output control module 5, respectively, for controlling the energy detection module 2 to detect again after the energy output control module 5 automatically stops the energy output according to the target adaptive energy output strategy, so as to obtain the final electrical characteristics of the target object after the energy action is completed. The energy output control module 5 is further configured to switch the current energy output mode to another energy output mode and configure a set of energy parameters for correcting the deviation for the another energy output mode when the deviation between the final electrical characteristics and the standard characteristic value associated with the target electrical load model exceeds the preset range.

[0094] In the embodiment, the post-effect evaluation module 6 can be a software module, specifically a specific control logic program running on the main controller. The running logic of the post-effect evaluation module 6 interacts with the interface module 1 and the energy output control module 5. The working process is as follows: First, when the energy output control module 5 completes the energy output according to the preset target adaptive energy output strategy and automatically stops (for example, the impedance value of the tissue reaches the preset stable target state), the energy output control module 5 sends a trigger signal of “energy output completion” to the post-effect evaluation module 6. Then, the post-effect evaluation module 6 is activated, and the energy detection module 2 is controlled again by software instructions to perform the same detection operation as the initial detection process. That is, the energy detection module 2 outputs a series of low-energy energy detection signals to the surgical instrument again, and the interface module 1 collects the electrical characteristic feedback signal of the target object after the energy action is completed, so as to obtain a final electrical characteristic. The final electrical characteristic is usually a final bioimpedance spectrum, which reflects the final physical state of the coagulated or closed tissue.

[0095] Then, the energy output control module 5 performs a new comparison function. The energy output control module 5 will retrieve a standard characteristic value associated with the target electrical load model of the current operation from the storage module 3. The standard characteristic value is the final bioimpedance spectrum representing the ideal therapeutic effect, which is theoretically or experimentally proven. The energy output control module 5 compares the actual measured final electrical characteristic with this standard characteristic value by an algorithm, and calculates the deviation between them.

[0096] Finally, if the calculated deviation exceeds a preset, clinically acceptable range, indicating that the actual therapeutic effect may not be optimal. At this time, the energy output control module 5 will automatically execute a correction program: switch the current energy output mode to another energy output mode (for example, if the main mode is coagulation, it may be switched to a short, low-power bipolar coagulation mode), and configure a set of energy parameters for correcting the deviation for this new energy output mode, such as a short supplemental heating, to ensure the reliability and consistency of the final effect.

[0097] In some embodiments, the way the post-effect evaluation module 6 determines that the deviation exceeds the preset range includes: performing curve similarity calculation on the impedance spectrum curve corresponding to the final electrical characteristic and the reference curve corresponding to the standard characteristic value to obtain a dynamic time warping distance between the two curves; when the dynamic time warping distance is greater than a preset distance threshold, it is determined that the deviation exceeds the preset range.

[0098] Wherein, the dynamic time warping distance (DTW distance) is an algorithm for measuring the similarity between two time series, especially suitable for comparing two sequences of different lengths or with stretching and shifting on the time axis.

[0099] In some embodiments, the way the post-effect evaluation module 6 determines the deviation is a process implemented by a software algorithm: First, the software algorithm represents the obtained final electrical characteristic in the form of a bioimpedance spectrum curve, which is a data sequence with frequency as the horizontal axis and impedance value as the vertical axis. At the same time, the algorithm retrieves the reference curve corresponding to the standard characteristic value from the storage module 3.

[0100] Then, the algorithm calls the dynamic time warping (DTW) algorithm to calculate the dynamic time warping distance between the two curves. The algorithm finds the optimal matching path between the data points of the two curves by nonlinearly bending the time axis, and calculates the cumulative distance on this path. This distance value can very sensitively reflect the overall similarity of the two curves in shape, and is not affected by small deviations at local frequency points.

[0101] Finally, the algorithm compares the calculated dynamic time warping distance with a pre-set distance threshold. The distance threshold is calibrated by a large amount of experimental data, representing the boundary between ideal and acceptable results. When the calculated dynamic time warping distance is greater than the pre-set distance threshold, the system determines that the deviation exceeds the pre-set range, thereby triggering the subsequent correction operation. This comparison method based on curve morphology is more robust and accurate than simple point-by-point comparison.

[0102] In some embodiments, the post-effect evaluation module 6 is further configured to: perform pattern matching of the final electrical characteristic with a series of pre-set non-ideal state feature libraries, in which impedance spectrum feature signatures corresponding to different specific postoperative adverse states are stored, including at least tissue carbonization, tissue adhesion or incomplete coagulation; The energy output control module 5 configures energy parameters for the other energy output mode in the following manner: when the feature signature of the specific adverse state is matched, a set of customized energy parameters for remedying the specific adverse state is called from the mode correction strategy library associated with the feature signature.

[0103] Among them, the impedance spectrum feature signature refers to a set of key data points or mathematical descriptors extracted from a complete bioimpedance spectrum curve, which can highly summarize and represent the core features of the curve. The feature signature is not the complete curve data, but a dimension-reduced "fingerprint" information. For example, a feature signature can include impedance values at specific low, medium and high frequency bands, frequency points where impedance peaks appear, frequency points where phase angles cross from positive to negative, and slope or curvature of the curve in a certain frequency interval, etc. In this way, complex high-dimensional spectral data can be converted into a low-dimensional feature vector, greatly simplifying the computational complexity of the pattern matching algorithm and improving the robustness and efficiency of the identification.

[0104] The above design provides a more diagnostic and targeted post-effect evaluation and correction mechanism.

[0105] Firstly, the configuration of the aftereffect evaluation module 6 is enhanced. On the hardware side, it still relies on the storage module 3; on the software side, a pattern matching function is integrated into the algorithm of the aftereffect evaluation module 6. A non-ideal state feature library is pre-stored in the storage module 3. The non-ideal state feature library is a structured database, which stores the impedance spectrum characteristic signatures corresponding to a plurality of specific postoperative adverse states. For example, the typical impedance spectrum when the tissue is slightly carbonized (usually manifested as an abnormal increase in impedance value at all frequency bands), the impedance spectrum when the tissue is adhered to the instrument clamp (may be manifested as an abnormal phase angle change at a specific frequency band), or the impedance spectrum when the blood vessel is not fully closed (although the impedance value is increased, it does not reach the ideal height).

[0106] Secondly, the way the energy output control module 5 configures energy parameters is also more intelligent. The storage module 3 also stores a treatment mode correction strategy library corresponding to the non-ideal state feature library. When the aftereffect evaluation module 6 performs pattern matching on the measured final electrical characteristics with the non-ideal state feature library and successfully matches a characteristic signature of a specific adverse state (for example, identifies "incomplete closure"), the energy output control module 5 will receive this specific diagnosis result. Then, the energy output control module 5 will accurately call a set of customized energy parameters specifically designed to remedy the specific adverse state from the treatment mode correction strategy library. For example, for "incomplete closure", the called parameters may be a longer time and moderate power supplemental coagulation energy; while for "tissue adhesion", the called parameters may be a very short and low voltage pulse energy, aiming to non-destructively separate the tissue through a small oscillation effect.

[0107] In this embodiment, the construction of the non-ideal state feature library is completed through systematic "fault injection" ex vivo experiments. Researchers intentionally create various typical postoperative adverse states, for example, by applying far more than normal doses of energy parameters to cause different degrees of carbonization of the tissue sample; or not removing the instrument clamp immediately after energy output, simulating the tissue adhesion process; or intentionally setting a lower energy output target, causing incomplete blood vessel coagulation. During and after the occurrence of each adverse state, the corresponding bio-impedance spectrum data is accurately collected and recorded using a high-precision impedance analyzer. Through feature extraction and pattern recognition algorithm processing of these "fault data", stable and repeatable impedance spectrum characteristic signatures that can uniquely identify each adverse state are extracted, and these characteristic signatures are associated with the corresponding adverse state names (such as "carbonization", "adhesion") and stored as database entries, thereby constructing the non-ideal state feature library.

[0108] The construction of the corrective strategy library is further completed through "remedial" experiments on the basis of the non-ideal state feature library. For each specific undesirable state in the non-ideal state feature library, such as a confirmed partially coagulated blood vessel sample, the researchers will try to apply a series of "secondary" or "corrective" energy with different parameter combinations. The goal of the experiment is to find the energy parameters that can most effectively and safely correct the undesirable state. For example, by applying different power and duration of supplemental electrocoagulation and then performing a burst pressure test, the optimal supplemental energy parameters that can repair the partially coagulated blood vessel to the standard strength are found. Similarly, for tissue adhesion, a variety of microsecond-level short pulse energies are tried, and the parameters that can non-destructively separate the tissue and the instrument are found through microscopic observation. These verified "optimal solution" energy parameter combinations for specific undesirable states are systematically organized and associated with the corresponding non-ideal state feature signatures, stored in the database, and form the corrective strategy library of the method.

[0109] In some embodiments, the preset stable target state and the standard characteristic value are defined based on a biological thermal chemical reaction completion degree model of biological tissue; The biological thermal chemical reaction completion degree model is established by applying different energy parameters to a variety of types of biological tissue samples in an ex vivo experiment and terminating at different impedance stages, and then performing puncture strength testing and histopathological analysis on the biological tissue samples; when the puncture strength reaches a preset threshold and the histopathological section shows that the collagen fusion recasting reaches a preset area ratio, the corresponding impedance change curve feature is defined as the stable target state and the standard characteristic value.

[0110] This embodiment explains the scientific basis of the two key thresholds in the system, "stable target state" and "standard characteristic value". The definition of the stable target state and the standard characteristic value is based on a biological thermal chemical reaction completion degree model.

[0111] The biological thermal chemical reaction completion degree model itself is not an online running module, but a set of foundational model established during the development and calibration stage of the system. The establishment process of the model is as follows: In in-vitro laboratory experiments, researchers select a large number of biological tissue samples covering various types. Using the system of this invention or a similar experimental apparatus, different combinations of energy parameters are applied to these samples. Crucially, the energy application is not a one-time event, but is actively terminated when the tissue impedance changes to different stages (e.g., impedance rises to 2, 5, 10 times, etc., of the initial value). At each termination point, two key quantitative assessments are immediately performed on the biological tissue sample: first, physical property testing, such as puncture strength testing or burst pressure testing on clogged blood vessels, to quantify the strength of their mechanical closure; second, biological analysis, namely, performing histopathological analysis, preparing paraffin sections and observing them under a microscope to quantitatively assess the area proportion and uniformity of collagen denaturation and renaturation within the blood vessel wall.

[0112] By correlating a large amount of impedance change data with corresponding physical and biological assessment results, a mapping model from electrical characteristics to the degree of biological effect completion is established. Ultimately, when the puncture intensity reaches a preset clinically required threshold (e.g., able to withstand pressure several times higher than normal systolic blood pressure) and pathological sections show that collagen melting and recasting reaches an ideal area ratio (e.g., exceeding 80% of the tube wall thickness), the endpoint characteristic of the corresponding impedance change curve (such as reaching a specific impedance value and remaining stable for a period of time) is defined as the stable target state during energy output, and the standard characteristic value after energy output is completed. This ensures that the system's control objectives are directly related to optimal clinical outcomes.

[0113] In some embodiments, the energy detection module 2 outputs the energy detection signal by outputting a frequency-modulated signal whose frequency changes linearly within a preset frequency range; The load model matching module 4 determines the real-time bioimpedance spectrum by performing a fast Fourier transform or wavelet transform on the electrical characteristic feedback signal generated by the frequency modulation signal to calculate the continuous complex impedance spectrum data within the preset frequency range.

[0114] This embodiment optimizes the specific technical solutions for energy detection and impedance spectrum determination.

[0115] Firstly, the energy detection module 2 outputs the energy detection signal in a more efficient way. In the present embodiment, the hardware part of the energy detection module 2, especially its signal generator, is designed to output a Chirp Signal. The frequency of the Chirp Signal is continuously linearly varied within a preset frequency range (e.g. from 10 KHz to 1 MHz), instead of the multiple discrete frequency points mentioned in the previous embodiments.

[0116] Correspondingly, the way the load model matching module 4 determines the real-time bioimpedance spectrum has also changed. The load model matching module 4 integrates one or more advanced digital signal processing algorithms at the software level. When the electrical characteristic feedback signal (a complex time-domain waveform) generated by the above-mentioned Chirp Signal excitation is collected, the software algorithm of the load model matching module 4 will perform a Fast Fourier Transform (FFT) or Wavelet Transform on this time-domain signal. These transformations can convert the time-domain signal into the frequency domain at one time, directly solving the continuous complex impedance spectrum data (including amplitude spectrum and phase spectrum) within the entire preset frequency range. Compared to point-by-point measurement, this "sweeping-transform" method can obtain a high-resolution, high-information-density bioimpedance spectrum in a very short time, greatly improving the detection speed and the accuracy of subsequent tissue identification.

[0117] In some embodiments, at least one parameter of the frequency range, the scanning rate and the signal amplitude of the Chirp Signal is dynamically adjusted based on the instrument type identification of the surgical instrument; The storage module 3 also stores an optimal detection parameter table associated with the instrument type identification; The load model matching module 4, when solving the real-time bioimpedance spectrum, is also used to calculate the signal-to-noise ratio or signal quality index of the electrical characteristic feedback signal; The energy detection module 2 is used to re-output a new Chirp Signal when the signal-to-noise ratio is lower than a threshold value, and to adaptively adjust the parameters of the new Chirp Signal according to the signal quality index, so as to ensure the reliability of the energy detection signal.

[0118] With the above-mentioned embodiments, adaptive adjustment and quality control mechanisms are further introduced for the energy detection process, making it more robust and reliable.

[0119] Firstly, the system adds the function of instrument recognition and dynamic adjustment of parameters. The recognition circuit is integrated in the hardware of interface module 1, which can read the instrument type identification (for example, through an RFID chip or a simple resistance code) carried on the connected surgical instrument. The optimal detection parameter table is pre-stored in the storage module 3, which establishes the corresponding relationship between different instrument type identifications and a set of optimal detection parameters (including the start / stop frequency range of the frequency modulation signal, the scanning rate and the signal amplitude). Because instruments of different structures and sizes (such as fine micro forceps and bulky tissue forceps) have different electrode characteristics, the optimal detection signal is also different. The system will identify the instrument type before detection, and call the corresponding optimal parameters to configure the energy detection module 2, realizing the "different for different instruments" of detection.

[0120] Secondly, the system adds the function of real-time evaluation and feedback adjustment of detection signal quality. The software algorithm of load model matching module 4 will additionally calculate the signal-to-noise ratio (SNR) or more complex signal quality index (SQI) of the electrical characteristic feedback signal while transforming and solving the signal.

[0121] Finally, the energy detection module 2 performs adaptive adjustment based on the above quality evaluation results. The control software logic of the module sets a signal-to-noise ratio threshold. If the calculated signal-to-noise ratio is lower than the threshold, it means that the current detection is seriously disturbed and the result is not reliable, so the energy detection module 2 will automatically trigger a new detection, that is, output a new frequency modulation signal. Further, the energy detection module 2 can also adjust the parameters of the new frequency modulation signal used in the next detection according to the specific value of the signal quality index, for example, if the signal quality index shows that the signal amplitude is too weak, the signal amplitude will be appropriately increased in the re-detection. This closed-loop quality control process ensures that the real-time bioimpedance spectrum obtained by the system is of high quality and high reliability, laying a solid foundation for the accuracy of all subsequent decisions.

[0122] In this embodiment, the optimal detection parameter table is constructed by systematic experimental calibration. In the research and development stage, for each type of expected compatible surgical instrument, the researchers connect it to a high-precision test platform. Under simulated load conditions (for example, clamping standardized biological tissue simulators), by programming the energy detection module 2, the system systematically scans and changes the parameter combinations of the energy detection signal, including the frequency range of the frequency modulation signal, the scanning rate and the signal amplitude. Under each parameter combination, the quality of the returned electrical characteristic feedback signal is evaluated, and the signal-to-noise ratio (SNR) or signal quality index (SQI) is calculated. Finally, the parameter combination that can obtain the highest signal-to-noise ratio and the most stable reading for a specific instrument type is defined as the optimal detection parameter of the instrument, and is stored in the table together with the type identification of the instrument, forming the optimal detection parameter table.

[0123] In some embodiments, the electrical load model is defined by a pre-trained machine learning classifier; The load model matching module 4 is used to compare the real-time biological impedance spectrum with a plurality of electrical load models, specifically including: The real-time biological impedance spectrum is input as an input feature vector into the machine learning classifier, and the target electrical load model is determined according to the output of the machine learning classifier.

[0124] With this embodiment, an artificial intelligence-based tissue identification method is provided, making the load model matching process more efficient and intelligent.

[0125] In some embodiments, the electrical load model can no longer be a static database or lookup table stored in the storage module 3, but is implicitly defined by a pre-trained machine learning classifier. The machine learning classifier, in a specific implementation, is a software algorithm model such as a support vector machine (SVM), a random forest (Random Forest), or a deep neural network (DNN) that is embedded in the firmware of the system main controller. This classifier uses the large amount of biological impedance spectrum data collected during the construction of the electrical load model described above, with clear tissue type labels, for supervised learning training during the system development stage, so that the model learns the complex nonlinear mapping relationship between different tissue types (such as arteries, veins, fat, etc.) and their impedance spectrum. The weights and parameters of the model are stored in the storage module 3.

[0126] Accordingly, the function implementation of the load model matching module 4 also changes accordingly. Its comparison process is as follows: first, when the load model matching module 4 obtains the real-time bioimpedance spectrum, instead of performing curve similarity calculation, the continuous or discrete impedance spectrum data (including amplitude and phase information) is first converted into a fixed-dimension input feature vector through a preprocessing program. The feature vector is a numerical representation that can be directly processed by a machine learning classifier. Then, the load model matching module 4 inputs the input feature vector into the preloaded machine learning classifier. The classifier software model will perform high-speed operation and inference on the input feature vector according to its internally trained complex decision boundary.

[0127] Finally, the machine learning classifier outputs a classification result, which is usually a label representing a specific tissue category (for example, label "1" corresponds to "3mm artery", and label "2" corresponds to "fat tissue"). The load model matching module 4 can directly determine the current target electrical load model according to the output label, and pass this result to the energy output control module 5. This method has stronger generalization ability, higher recognition accuracy and faster processing speed than traditional template matching.

[0128] In this embodiment, the training process of the machine learning classifier is a supervised learning. First, researchers need to build a large-scale, diverse and accurately labeled training dataset. The dataset contains thousands or even tens of thousands of bioimpedance spectrum data collected from different types and different states of known biological tissue samples (for example, arteries, veins, fat tissues confirmed by pathology, etc.). Each spectrum data corresponds to an explicit tissue type label. During training, these impedance spectrum data are used as input features, and their corresponding tissue labels are used as expected outputs, which are fed into the selected machine learning model (such as support vector machine or neural network). The training algorithm automatically adjusts the weights and parameters inside the classifier through iterative optimization, so that it learns and establishes a complex mapping relationship from the input impedance spectrum features to the correct tissue category labels, until the classification accuracy of the model on the validation set reaches the preset performance standard. The final set of optimized model parameters is the pre-trained machine learning classifier.

[0129] In some embodiments, the system further comprises a security authentication module 7 linked with the interface module 1 and the storage module 3, respectively; the interface module 1 is further configured to obtain an identification of the surgical instrument when connected with the surgical instrument; the storage module 3 further stores a list of authorized instrument identifications; and the security authentication module 7 is configured to compare the identification with the list of authorized instrument identifications to perform security authentication. The energy detection module 2 is further configured to, after the security authentication passes and before clamping the target object, perform an electrical characteristic baseline test on the surgical instrument itself to obtain baseline data for compensation calibration of subsequent measurement results.

[0130] This embodiment adds a security and precision calibration mechanism in the system to ensure the safety of the system and the accuracy of the measurement.

[0131] On the basis of the previous embodiment, the system adds a security authentication module 7. In specific implementation, the security authentication module 7 can be a software module, which is essentially a security check program running on the main controller, which interacts with the interface module 1 and the storage module 3.

[0132] First, the hardware design of the interface module 1 is added with the function of being able to read the physical identification on the surgical instrument, for example, an RFID reader or a serial communication interface for reading an EEPROM chip is integrated. The surgical instrument has an electronic tag with a unique identification (such as a serial number) built-in its handle or connector part. When the instrument is connected to the interface module 1, the interface module 1 will automatically obtain the identification. At the same time, the storage module 3 has a pre-burned or authorized updated authorized instrument identification list, which contains the identification of all legal surgical instruments authorized to use in the system.

[0133] The software logic of the security authentication module 7 is triggered, and the identification obtained from the interface module 1 is compared with the authorized instrument identification list in the storage module 3 one by one. Only when the identification exists in the list, the security authentication is passed, and the system will enter the standby state; otherwise, the system will lock the energy output function and prompt an error message to prevent the use of unauthorized or incompatible instruments.

[0134] After the security authentication passes, the energy detection module 2 is further configured to perform an additional key step: before the jaws of the surgical instrument clamp any target object (i.e. the jaws are in a suspended closed state), the energy detection module 2 will perform an electrical characteristic baseline test on the surgical instrument itself. The test will output a series of detection signals, measuring and recording the inherent impedance, capacitance and inductance of the instrument itself, including the jaws and the connecting cable. These parameters are temporarily stored as baseline data. When measuring the target object later, the system software will automatically deduct the influence of this baseline data from the total electrical characteristics measured, so as to realize the compensation calibration of the measurement results, greatly improving the accuracy of the measurement of the real impedance of the biological tissue.

[0135] In some embodiments, the security authentication module 7 is specifically configured to: generate a random number and send the random number to the surgical instrument through the interface module 1; receive, through the interface module 1, a response code returned by the surgical instrument according to the identification and the random number through a preset encryption algorithm; perform the same encryption algorithm locally to obtain an encryption operation result; and determine that the security authentication is passed when the encryption operation result matches the response code.

[0136] This embodiment describes a more secure and reliable "challenge-response" security authentication mechanism to prevent simple identification copying and cracking. In this implementation, the surgical instrument itself is a "smart instrument" that integrates a microcontroller (MCU) and a preset encryption algorithm program in addition to the chip storing the identification. The software function of the security authentication module 7 becomes more complex, and the specific authentication process is as follows: 1) Generate challenge: when the instrument is inserted, the security authentication module 7 first calls a random number generator algorithm to generate a random number with sufficient complexity, which is a one-time "challenge code".

[0137] 2) Send challenge: the security authentication module 7 sends the generated random number to the microcontroller of the surgical instrument through the digital communication channel of the interface module 1.

[0138] 3) Instrument-side operation and response: after receiving the random number, the microcontroller of the surgical instrument uses the same algorithm as the preset encryption algorithm (such as AES or SHA-256 algorithm) of the host system stored in itself to perform encryption operation using the unique identification and the received random number as inputs, to generate a unique and unpredictable response code. Then, the instrument returns the response code to the system through the interface module 1.

[0139] 4) Local verification: at the same time, the security authentication module 7 also performs the same encryption algorithm operation locally. It uses the identification read from the instrument and the random number it just generated as inputs to obtain a local encryption operation result.

[0140] 5) Matching confirmation: finally, the security authentication module 7 accurately compares the local encryption operation result with the response code received from the surgical instrument. Only when the two completely match, it can be proved that the instrument not only has a legal identification, but also has the correct encryption key and algorithm, thereby determining that the security authentication is passed.

[0141] This dynamic challenge-response mechanism can effectively prevent illegal behavior of manufacturing counterfeit instruments by simply copying the identification, greatly enhancing the security of the system.

[0142] In some embodiments, the energy output control module 5 adjusts the physical parameters of the energy output according to the impedance value change, and further comprises: comparing the real-time change sequence of the impedance value with a standard impedance evolution curve defined in the target adaptive energy output strategy and representing an ideal physical and chemical reaction process, and dynamically adjusting the power, voltage waveform and duty cycle of the energy output based on a predicted control deviation between the real-time change sequence and the standard impedance evolution curve, so as to actively lead the actual impedance change trajectory to the standard impedance evolution curve or within a preset error band thereof.

[0143] Specifically, first, in the target adaptive energy output strategy, in addition to defining the initial energy parameters and the final stable target state, a key curve data, i.e., a standard impedance evolution curve, is also defined in advance. The curve is a two-dimensional data sequence with time as the horizontal axis and impedance value as the vertical axis. The curve is obtained through a biological thermal chemical reaction completion model and a large number of experimental data statistics and optimization, and represents the optimal path of the impedance value of the target tissue changing with time from the initial state to complete coagulation / closure in an ideal coagulation or electrocoagulation operation. The curve represents the ideal physical and chemical reaction process with the highest efficiency and the lowest side damage.

[0144] Secondly, the software control logic inside the energy output control module 5 follows the following steps when executing the energy output: 1) Start and synchronize: at the moment when the energy output starts (t=0), the controller not only starts the energy output, but also starts a high-precision timer and loads the "standard impedance evolution curve" matching the current task from the memory.

[0145] 2) Real-time sampling and comparison: during the entire process of the energy output, the controller collects the real-time impedance value of the target object once every high frequency (for example, every millisecond), forming a real-time change sequence. At each sampling time point t, the controller compares the actual impedance value Z_actual(t) at this time with the reference impedance value Z_ref(t) at the same time t on the "standard impedance evolution curve".

[0146] 3) Predictive control deviation calculation: the control algorithm not only calculates the current deviation (Z_actual(t)-Z_ref(t)), but more importantly, it calculates a predictive control deviation based on the current deviation and the impedance change rate (i.e., the slope of the curve) in the recent period of time through a prediction model. The deviation predicts the trend and degree of the actual trajectory deviating from the ideal trajectory in a very short time in the future under the current energy output parameters.

[0147] 4) Dynamic parameter adjustment: the controller takes the prediction error as input into an algorithmic module such as PID (proportional-integral-derivative) or model predictive control (MPC). The algorithm calculates the exact amount of adjustment needed to the physical parameters of the energy output according to the size and direction of the error. For example, if the actual impedance rises slower than the "standard impedance evolution curve" (negative error), the controller will actively increase the output power or raise the pulse duty cycle; conversely, if it rises too fast (positive error, with the risk of carbonization), it will reduce the power or introduce longer pulse intervals to allow heat to diffuse.

[0148] 5) Active traction: through the above-mentioned high-frequency "sampling-comparison-prediction-adjustment" loop, the controller aims to actively and continuously pull the actual impedance change trajectory onto the "standard impedance evolution curve" or keep it within an extremely narrow preset error band around the curve.

[0149] In this way, the system realizes fine path management of the entire energy action process, ensuring that the organization not only finally reaches the ideal closed state, but also optimizes the entire thermochemical reaction process it experiences, thereby fundamentally ensuring the high consistency and safety of the surgical effect.

[0150] In some embodiments, the storage module 3 also stores a time-varying instrument heat accumulation model, which is used to estimate the real-time temperature of the surgical instrument jaw end according to historical energy output data; the energy output control module 5 is further configured to: when adjusting the physical parameters of the energy output, taking the real-time temperature estimated by the instrument heat accumulation model as a control feedback, and when the real-time temperature exceeds a safety threshold, forcibly introducing a cooling period or limiting the upper limit of the output power, even if the impedance value change has not reached the stable target state at this time.

[0151] Among them, the instrument heat accumulation model can be a mathematical algorithm or a lookup table in specific implementation. The instrument heat accumulation model is specifically used to describe the relationship between the temperature of the jaw end of a specific type of surgical instrument and its experienced energy output history. This model is established through a large number of experiments during the system development stage: by sticking micro temperature sensors on the instrument jaw, recording the actual temperature rise rate and natural cooling rate of the jaw under various different energy output modes (power, duration, frequency), and finally forming a mathematical function that can estimate (or "soft measurement") the real-time temperature of the current jaw end in real time according to the input historical energy output data (i.e. the integral of power with respect to time).

[0152] Accordingly, a parallel, higher-priority safety monitoring loop is added to the software control logic of the energy output control module 5. Its specific working mode is as follows: 1) Parallel monitoring: while the energy output control module 5 is performing its main, impedance-organizing feedback-based control task, a separate monitoring thread is continuously running. This thread obtains the power output data of the recent period of time from the energy output record at each control cycle.

[0153] 2) Real-time temperature estimation: the monitoring thread takes the obtained historical energy output data as input and calculates it in the "instrument heat accumulation model" to obtain an estimated value representing the real-time temperature of the surgical instrument jaw end.

[0154] 3) Threshold comparison and forced intervention: the monitoring thread compares the estimated real-time temperature with a safety threshold (for example, 80 degrees Celsius, which may cause accidental thermal burns to the surrounding tissue) preset in the storage module 3. When the estimated real-time temperature exceeds the safety threshold, the safety monitoring thread immediately triggers a high-priority forced intervention instruction.

[0155] 4) Execute safety measures: after receiving the forced intervention instruction, the energy output control module 5 will unconditionally and immediately execute the preset safety measures, even if the energy output task targeting the tissue impedance has not been completed, that is, the impedance value change has not reached the stable target state. Specific safety measures can include: 4.1) Forced cooling cycle: immediately suspend all energy output and maintain the suspended state for a preset time (for example, 2 seconds) to allow the instrument jaw to cool down through natural convection and conduction.

[0156] 4.2) Limit output power upper limit: instead of completely stopping output, the energy output power upper limit is forcibly limited to a lower, safe level, so that the instrument's heat generation rate is lower than or equal to its heat dissipation rate, preventing further temperature rise.

[0157] By introducing this feedback loop based on the instrument's own thermal state, the system builds a dual safety guarantee mechanism. It not only ensures that the target tissue is effectively treated (based on impedance feedback), but also ensures that the entire operation process is safe for the instrument and surrounding non-target tissue (based on temperature estimation feedback), greatly improving the overall safety of the system, especially in complex surgical scenarios that require long-term, high-intensity continuous work.

[0158] The overall system architecture of the embodiment is as follows: Figure 3The data flow and control flow of the system of the embodiment follow a closed-loop path of "detection-identification-decision-execution-evaluation-correction", which can be simplified into four core stages: The first stage: after the safe access and the connection of the organization detection instrument, the safety authentication module verifies its legitimacy and triggers the baseline test. Subsequently, the energy detection module non-invasively detects the clamped tissue, outputs a multi-frequency detection signal, and collects the electrical characteristic feedback signal of the tissue, forming the original data stream, which is transmitted to the next stage.

[0159] The second stage: intelligent identification and strategy decision The load model matching module receives the original data and calculates the real-time bioimpedance spectrum. By comparing with the preset electrical load model (or machine learning classifier) in the storage module, the organization type (such as blood vessels or fat) is intelligently identified, and it is determined whether to enter the coagulation mode or the bipolar electrocoagulation mode. The decision result is transmitted to the energy output control module together with the matched target electrical load model.

[0160] The third stage: adaptive energy output and closed-loop control The energy output control module calls the corresponding adaptive energy output strategy (first or second strategy) from the storage module according to the decision result. During the energy output process, it continuously monitors the real-time impedance change of the tissue and compares it with the preset ideal change curve in the strategy, dynamically adjusts the energy parameters (such as power, duty cycle), so that the actual impedance trajectory closely follows the ideal trajectory. When the preset stable target state is reached, the energy output is automatically stopped, marking the end of the main treatment process.

[0161] The fourth stage: aftereffect evaluation and intelligent correction After the treatment, the aftereffect evaluation module starts, and the energy detection module performs secondary detection to obtain the final electrical characteristics of the tissue. By comparing with the standard characteristic values in the storage module or matching with the non-ideal state feature library, the system can judge whether the treatment effect meets the requirements and diagnose the specific problems (such as incomplete coagulation). If deviations are found, the system will call the correction strategy, switch to the appropriate energy mode and configure the correction parameters for supplementary treatment until the evaluation results meet the requirements.

[0162] The embodiment realizes the high integration of coagulation and bipolar electrocoagulation functions through the following six core designs, and breaks away from the limitations of independent modes in traditional systems: Unified hardware platform and interface: The system uses the same hardware host and the same integrated surgical instrument, and connects through a unified interface module. Whether it is coagulation or electrocoagulation, energy transmission and signal feedback are completed through the same physical channel, realizing true integration at the hardware level.

[0163] Unified tissue detection and recognition mechanism: The system does not rely on the doctor to manually select the mode, but through the energy detection module to perform a unified, standardized bioimpedance spectrum detection on any tissue. The load model matching module automatically identifies the tissue type based on this objective data, intelligently determines whether to use coagulation or bipolar electrocoagulation, which is the core of the decision-making of integrated control.

[0164] Parallel knowledge base and strategy base: The storage module stores an electrical load model and a first adaptive energy output strategy for coagulation, and a second electrical load model and a second adaptive energy output strategy for bipolar electrocoagulation in parallel. This constitutes the "double-track" knowledge base for the system to carry out differentiated and precise control.

[0165] Unified closed-loop control framework: Whether it is coagulation or bipolar electrocoagulation, the energy output process follows the same closed-loop control logic of "monitoring-comparison-adjustment-judgment-stop". The difference between the two modes is only in the called strategy parameters and targets, but the control framework and algorithm are unified, reflecting the high integration at the software architecture level.

[0166] Cross-mode intelligent correction capability: The aftereffect evaluation module breaks down the barriers between the two modes. For example, if the effect is not good after the coagulation mode is completed (such as incomplete coagulation), the system can automatically switch to a short-time, low-power bipolar electrocoagulation mode for supplementary correction. This cross-mode intelligent linkage and remedy is the highest embodiment of deep functional integration.

[0167] Integrated safety and quality assurance: The system's safety certification, baseline calibration, real-time monitoring, and aftereffect evaluation mechanisms cover the entire operation process of both coagulation and bipolar electrocoagulation modes, providing unified and seamless safety and quality assurance for both functions.

[0168] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions described in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A coagulation and bipolar electrocoagulation integrated control system for a surgical instrument, characterized in that, The application relates to a surgical instrument energy output control method and device. The application comprises: an interface module connected with a surgical instrument integrated with a coagulation function and a bipolar electrocoagulation function; an energy detection module connected with the interface module, used for outputting an energy detection signal to the surgical instrument before the surgical instrument activates an energy output mode selected from coagulation or bipolar electrocoagulation, so that a target object held by the surgical instrument generates an electrical characteristic feedback signal; a storage module storing an electrical load model and an adaptive energy output strategy; the electrical load model comprises a first electrical load model corresponding to the coagulation function and a second electrical load model corresponding to the bipolar electrocoagulation function; the adaptive energy output strategy comprises a first adaptive energy output strategy corresponding to the first electrical load model and a second adaptive energy output strategy corresponding to the second electrical load model; a load model matching module connected with the interface module and the storage module respectively, used for determining a real-time biological impedance spectrum of the target object according to the electrical characteristic feedback signal, and determining a target electrical load model from the electrical load model according to the real-time biological impedance spectrum; 2. The system of claim 1, wherein, an energy output control module connected with the load model matching module and the storage module respectively, used for calling a target adaptive energy output strategy from the adaptive energy output strategy according to the target electrical load model, monitoring impedance value change of the target object according to the target adaptive energy output strategy, and adjusting physical parameters of energy output according to the impedance value change, so that the impedance value change meets a preset stable target state, and energy output is automatically stopped after the stable target state is reached. The application further comprises an aftereffect evaluation module connected with the interface module and the energy output control module, used for controlling the energy detection module to detect again after the energy output control module automatically stops energy output according to the target adaptive energy output strategy, so as to obtain a final electrical characteristic of the target object after energy action is completed. The energy output control module is further used for:

3. The system of claim 2, wherein, switching a current energy output mode to another energy output mode when a deviation between the final electrical characteristic and a standard characteristic value related to the target electrical load model exceeds a preset range, and configuring a set of energy parameters for correcting the deviation for the another energy output mode. The aftereffect evaluation module determines that the deviation exceeds the preset range in the following way: performing curve similarity calculation on an impedance spectrum curve corresponding to the final electrical characteristic and a reference curve corresponding to the standard characteristic value, to obtain a dynamic time warping distance between the two curves; determining that the deviation exceeds the preset range when the dynamic time warping distance is greater than a preset distance threshold.

4. The system of claim 2, wherein, The post-effect evaluation module is further configured to: perform pattern matching on the final electrical characteristic with a preset non-ideal state feature library, in which impedance spectrum feature signatures corresponding to different specific postoperative adverse states are stored, the specific postoperative adverse states at least including tissue carbonization, tissue adhesion or incomplete coagulation; The energy output control module is configured to adjust the energy parameters for the other energy output mode in the following manner: when the feature signature of the specific adverse state is matched, a set of customized energy parameters for remedying the specific adverse state is called from a mode correction strategy library associated with the feature signature.

5. The system of claim 2, wherein, The preset stable target state and the standard characteristic value are defined based on a biological tissue bio-thermal chemical reaction completion degree model; The bio-thermal chemical reaction completion degree model is established in the following manner: in an ex vivo experiment, different types of biological tissue samples are subjected to the action of different energy parameters, and the action is terminated when reaching different impedance stages, and then the biological tissue samples are subjected to puncture strength testing and histopathological analysis; when the puncture strength reaches a preset threshold and the pathological section shows that the collagen melting recasting reaches a preset area ratio, the corresponding impedance change curve feature is defined as the stable target state and the standard characteristic value.

6. The system of claim 1, wherein, The energy detection module outputs the energy detection signal in the following manner: outputting a frequency-modulated signal with a continuously linearly changing frequency within a preset frequency range; The load model matching module determines the real-time biological impedance spectrum in the following manner: performing fast Fourier transform or wavelet transform on the electrical characteristic feedback signal generated by the frequency-modulated signal excitation to calculate continuous complex impedance spectrum data within the preset frequency range.

7. The system of claim 6, wherein, At least one of the frequency range, the scanning rate and the signal amplitude of the frequency-modulated signal is dynamically adjusted based on the instrument type identification of the surgical instrument; The storage module further stores an optimal detection parameter table associated with the instrument type identification; The load model matching module, when calculating the real-time biological impedance spectrum, is further configured to calculate a signal-to-noise ratio or a signal quality index of the electrical characteristic feedback signal; The energy detection module is configured to re-output a new frequency-modulated signal when the signal-to-noise ratio is lower than a threshold, and to adaptively adjust parameters of the new frequency-modulated signal according to the signal quality index, so as to ensure the reliability of the energy detection signal.

8. The system of claim 1, wherein, The electrical load model is defined by a pre-trained machine learning classifier; The load model matching module is configured to compare the real-time biological impedance spectrum with a plurality of electrical load models, specifically including: inputting the real-time biological impedance spectrum as an input feature vector into the machine learning classifier, and determining the target electrical load model according to the output of the machine learning classifier.

9. The system of claim 1, wherein, The security authentication module is further configured to: generate a random number and send the random number to the surgical instrument via the interface module; 10. The system of claim 9, wherein, receive, via the interface module, a response code returned by the surgical instrument according to the identification mark and the random number through a preset encryption algorithm; perform the same encryption algorithm operation locally to obtain an encryption operation result; determine that the security authentication is passed when the encryption operation result matches the response code. The energy detection module is further configured to: perform an electrical characteristic baseline test on the surgical instrument itself before clamping the target object after the security authentication is passed, to obtain baseline data for compensating and calibrating subsequent measurement results. The security authentication module is specifically configured to: generate a random number and send the random number to the surgical instrument via the interface module; receive, via the interface module, a response code returned by the surgical instrument according to the identification mark and the random number through a preset encryption algorithm; perform the same encryption algorithm operation locally to obtain an encryption operation result; determine that the security authentication is passed when the encryption operation result matches the response code.

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