Double-pole current phase difference energy control method and system

Abstract

The invention discloses a double-rod current phase difference energy control method and system, and relates to the technical field of electrosurgery, and the method comprises the steps: collecting a tissue impedance signal in real time, carrying out the anti-interference processing of an improved conditioning circuit, and calculating the impedance change rate. And when the change rate exceeds a threshold value, acquiring basic information of the suborgan tissues to carry out phase difference matching so as to obtain an optimal phase difference, and abandoning a single fixed phase difference output mode in the prior art. The phase difference is dynamically adjusted according to the tissue impedance signals collected in real time and the basic information of the suborgan tissue, complex changes of tissue characteristics are accurately adapted, and the problem that phase difference decision-making precision is insufficient is effectively solved. Meanwhile, the thermal injury risk can be remarkably reduced through accurate phase difference control, the operation safety is improved, the operation efficiency is optimized, and more reliable technical support is provided for clinical operation.

Description

Technical Field

[0001] This invention relates to the field of electrosurgical technology, and in particular to a dual-bar current phase difference energy control method and system. Background Technology

[0002] In electrosurgery, bipolar electrodes are core tools for precise cutting and hemostasis, and the stability and accuracy of their energy output directly determine the safety and efficiency of the surgery. Dynamically adjusting the energy output by monitoring tissue impedance signals is key to achieving precise control. Changes in impedance signals reflect the physiological state of the tissue in real time, and adjusting the electrode output energy accordingly avoids excessive damage to normal tissue. Currently, bipolar electrode energy control systems are widely used in complex surgeries such as liver resection and gastrointestinal anastomosis.

[0003] In existing technologies, the phase difference selection of bipolar electrodes mostly adopts a single fixed mode, that is, regardless of changes in surgical type, tissue characteristics, or surgical stage, the phase difference always maintains a preset fixed value. However, this single fixed phase difference selection method has significant limitations. On the one hand, the electrical characteristics of different tissues vary greatly, and a fixed phase difference cannot adapt to their needs. For high-resistivity tissues, insufficient energy may lead to incomplete cutting / hemostasis, while for low-resistivity tissues, excessive energy may cause thermal damage to spread. On the other hand, the tissue state changes dynamically during surgery, and a fixed phase difference cannot respond to this real-time change, resulting in a continuous disconnect between energy output and tissue needs. This manifests as inconsistent tissue penetration depth and thermal damage exceeding expectations, seriously affecting the safety and efficiency of the surgery. Summary of the Invention

[0004] To address the technical problem that existing technologies employ a single, fixed phase difference output mode, which cannot dynamically adapt to complex changes in tissue characteristics, resulting in insufficient phase difference decision accuracy, mismatch between bipolar electrode output energy and tissue needs, uneven tissue penetration depth, and excessive thermal damage, thus affecting surgical safety and efficiency, this application provides a dual-bar current phase difference energy control method and system.

[0005] This application provides a dual-bar current phase difference energy control method, which adopts the following technical solution: Real-time acquisition of tissue impedance signals from the surgical subject; By improving the conditioning circuit, the tissue impedance signal is subjected to multi-channel anti-interference processing to obtain a processed impedance signal. The impedance change rate is obtained by calculating the impedance change rate using the processed impedance signal. When the impedance change rate is greater than a preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained; The optimal phase difference is obtained by using the basic information of the sub-organ tissue for phase difference matching. A dual-bar current with the optimal phase difference is generated and applied to the surgical subject with energy.

[0006] By adopting the above technical solution and improving the multi-channel anti-interference processing of the conditioning circuit, redundant signals such as electromagnetic interference and contact noise in the surgical environment are effectively filtered out, ensuring accurate acquisition and processing of tissue impedance signals and providing reliable data support for subsequent impedance change rate calculation. Using an impedance change rate exceeding a preset threshold as a trigger condition, dynamic changes in tissue characteristics can be keenly captured, avoiding the lag response to tissue changes in the traditional fixed phase difference mode, and achieving real-time perception of tissue needs. Phase difference matching based on sub-organ tissue basic information can overcome the limitations of single-parameter decision-making, enabling the optimal phase difference to accurately adapt to the fixed phase difference of different sub-organs. With its unique characteristics and dynamic changes, it significantly improves decision-making accuracy. By driving a dual-bar current with optimal phase difference to perform energy intervention, it can precisely control the energy intensity and distribution output by the bipolar electrodes, ensuring that the energy penetration depth is highly matched with the actual needs of the tissue. This avoids poor intervention results due to insufficient energy, while also preventing thermal damage or uneven penetration caused by excessive energy. It improves surgical efficiency while minimizing the risk of tissue damage. At the same time, it can dynamically adapt to different surgical types and diverse tissue characteristics, eliminating the need to preset cumbersome parameters for specific scenarios, reducing the complexity of clinical operations, and providing stable and reliable energy control support for various delicate surgeries.

[0007] Preferably, the improved conditioning circuit includes a series-connected input buffer module, a parallel processing module, a fusion module, and an output buffer module. The process of performing multi-channel anti-interference processing on the tissue impedance signal through the improved conditioning circuit to obtain a processed impedance signal includes: The input buffer module buffers the tissue impedance signal to obtain a buffered impedance signal. The buffer impedance signal is enhanced by multi-channel feature enhancement through the parallel processing module to obtain the enhanced impedance signal. The enhanced impedance signal is coupled with a preset channel weighting factor through the fusion module to obtain a coupled impedance signal; The output buffer module buffers the coupled impedance signal to obtain a processed impedance signal.

[0008] By adopting the above technical solutions, the improved conditioning circuit stabilizes the original signal through the input buffer module, the parallel processing module enhances the signal characteristics in multiple dimensions to suppress different types of interference, the fusion module accurately aggregates effective information by combining channel weights, and the output buffer module ensures the stable output of the processed signal. Overall, it achieves efficient anti-interference processing of tissue impedance signals, significantly improving signal quality and signal-to-noise ratio.

[0009] Preferably, the parallel processing module includes a low-frequency anti-interference enhancement unit, a high-frequency broadband fidelity unit, and an adaptive signal optimization unit connected in parallel. The parallel processing module performs multi-channel feature enhancement on the buffer impedance signal to obtain multiple enhanced impedance signals, including: The buffer impedance signal is subjected to anti-interference enhancement processing by the low-frequency anti-interference enhancement unit to obtain the first enhanced impedance signal. The buffer impedance signal is processed by the high-frequency broadband fidelity unit to obtain a second enhanced impedance signal; the buffer impedance signal is processed by the adaptive signal optimization unit to obtain a third enhanced impedance signal. The enhanced impedance signal includes the first enhanced impedance signal, the second enhanced impedance signal, and the third enhanced impedance signal.

[0010] By adopting the above technical solutions, the low-frequency anti-interference enhancement unit of the parallel processing module targets low-frequency interference for targeted suppression, the high-frequency broadband fidelity unit preserves the details and integrity of high-frequency signals, and the adaptive signal optimization unit dynamically adjusts the processing strategy to adapt to signal fluctuations. The three work together to enhance the signal from different frequency bands and dynamic characteristics, effectively improving the identification of effective features in impedance signals and further strengthening anti-interference capabilities.

[0011] Preferably, the anti-interference enhancement process specifically involves sequentially performing high-frequency noise filtering, power frequency interference suppression, weak signal amplification, and drift compensation on the buffer impedance signal. The broadband fidelity processing specifically involves sequentially performing low-frequency interference filtering, high-frequency noise suppression, broadband signal amplification, and gain adaptive adjustment on the buffer impedance signal. The signal optimization processing specifically involves sequentially performing dynamic filtering adjustment, gain calibration, and noise RMS quantization on the buffer impedance signal.

[0012] By adopting the above technical solutions, the anti-interference enhancement processing specifically filters out high-frequency noise, suppresses power frequency interference, and enhances the stability of weak signals. The broadband fidelity processing effectively eliminates low-frequency interference and ensures the integrity and gain adaptability of high-frequency signals. The signal optimization processing achieves noise quantification control through dynamic filtering and calibration. These three types of processing implement precise measures from different interference types and signal characteristics, synergistically improving the purity, stability, and feature retention of impedance signals, providing a highly reliable enhanced signal source for multi-channel fusion.

[0013] Preferably, the basic information of the sub-organ tissue includes tissue characteristic parameters and tissue temperature change rates associated with different types of sub-organ tissues. The step of using the basic information of the sub-organ tissue to perform phase difference matching to obtain the optimal phase difference includes: Based on the tissue characteristic parameters associated with different types of sub-organ tissues, first-stage candidate values ​​are determined; Based on the tissue temperature change rate associated with different types of sub-organ tissues, the first-stage candidate values ​​are corrected to obtain the second-stage candidate values; Based on the tissue characteristic parameters associated with different types of sub-organ tissues, the second-stage candidate values ​​are corrected to obtain the third-stage candidate values; The candidate values ​​of the third stage are compared with the preset standard phase difference range, and the optimal phase difference is determined based on the comparison results.

[0014] By adopting the above technical solution, the initial phase difference candidate value is determined by the characteristic parameters of different types of sub-organ tissues, combined with dynamic correction based on the tissue temperature change rate, and further optimized based on the characteristic parameters. Finally, the optimal phase difference is determined by standard interval verification. This achieves a progressive optimization of the phase difference from basic matching to dynamic adaptation, accurately matching the characteristic differences and dynamic changes of different sub-organ tissues, significantly improving the matching degree between phase difference decision and tissue needs, and ensuring the accuracy and safety of energy intervention.

[0015] Preferably, determining the first-stage candidate value based on the tissue characteristic parameters associated with different types of the sub-organ tissues includes: When the sub-organ tissue is a densely vascularized area, the basic value associated with the densely vascularized area is obtained based on the comparison result between the preset blood vessel diameter threshold and the associated tissue feature parameters. When the sub-organ tissue is adipose tissue, the basic value associated with the adipose tissue is obtained based on the comparison result between the preset fat thickness threshold and the associated tissue feature parameters. When the sub-organ tissue is muscle tissue, the basic value associated with the muscle tissue is obtained based on the comparison result between the preset electromyographic signal intensity threshold and the associated tissue characteristic parameters. When the sub-organ tissue is mucosal tissue, the basic value associated with the mucosal tissue is obtained based on the comparison result between the preset mucosal change rate threshold and the associated tissue characteristic parameters. Based on a preset matching weight factor, the basic values ​​associated with different types of sub-organ tissues are weighted to obtain first-stage candidate values. By adopting the above technical solution, for different sub-organ tissues such as vascular dense areas, fat, muscle, and mucosa, corresponding basic values ​​are matched according to the characteristic parameters of blood vessel diameter, fat thickness, electromyographic signal intensity, and mucosal change rate, respectively. Then, the first-stage candidate values ​​are obtained through preset weighting calculation. This achieves accurate adaptation to the inherent characteristics of various tissues, avoids matching deviations caused by a single standard, provides a reasonable initial benchmark for subsequent phase difference correction, and improves the pertinence and reliability of phase difference decision-making.

[0016] Preferably, the step of correcting the first-stage candidate values ​​based on the tissue temperature change rate associated with different types of the sub-organ tissues to obtain the second-stage candidate values ​​includes: Based on the preset temperature change range in which the temperature change rate of each sub-organ tissue is associated with the tissue temperature change rate, a plurality of first correction values ​​are determined; Based on a preset temperature change weighting factor, each of the first correction values ​​is weighted to obtain a temperature factor correction value; the temperature factor correction value is then summed with the first stage candidate value to obtain a second stage candidate value.

[0017] By adopting the above technical solution, the correction value is determined according to the temperature change rate range of different sub-organ tissues. The comprehensive correction factor is obtained by temperature weighting and coupled with the first-stage candidate value to obtain the second-stage candidate value. This allows the phase difference decision to be integrated with the dynamic change factors of tissue temperature, effectively compensating for the impact of temperature fluctuations on energy demand and improving the adaptability of the phase difference to the tissue thermal state.

[0018] Preferably, the tissue characteristic parameters include tissue impedance, and the process of correcting the second-stage candidate values ​​to obtain third-stage candidate values ​​based on the tissue characteristic parameters associated with different types of sub-organ tissues includes: The tissue impedance associated with each of the sub-organ tissues is subjected to second-order differential operation to obtain multiple second-order differential values; Based on the preset trend determination interval where the second-order differential value associated with each of the sub-organ tissues is located, a number of second correction values ​​are determined. Based on a preset trend weighting factor, each of the second correction values ​​is weighted to obtain the impedance factor correction value. The third-stage candidate value is obtained by summing the impedance factor correction value with the second-stage candidate value.

[0019] By adopting the above technical solution, the impedance of different sub-organ tissues is subjected to second-order differential operation to obtain its changing trend. The correction value is determined by combining the preset interval and the impedance factor correction value is obtained by weighting the trend weight. The correction value is coupled with the second-stage candidate value to obtain the third-stage candidate value. This allows the phase difference decision to be integrated into the dynamic changing trend of tissue impedance, accurately captures the subtle changes in tissue characteristics, and further improves the matching accuracy between phase difference and real-time tissue status.

[0020] Preferably, the step of comparing the third-stage candidate value with a preset standard phase difference range and determining the optimal phase difference based on the comparison result includes: Compare the candidate values ​​in the third stage with the preset standard phase difference range; When the third-stage candidate value is within the preset standard phase difference range, the third-stage candidate value is taken as the optimal phase difference; When the candidate value of the third stage is greater than the upper limit of the preset standard phase difference range, the upper limit value is taken as the optimal phase difference; When the candidate value in the third stage is less than the lower limit of the preset standard phase difference interval, the lower limit value is taken as the optimal phase difference.

[0021] By adopting the above technical solution, the candidate value in the third stage is compared with the preset standard phase difference range. If it is within the range, it is directly adopted. If it exceeds the upper limit or is lower than the lower limit, the boundary value of the range is taken as the optimal phase difference. This ensures that the phase difference is always within a safe and effective range, avoids abnormal energy output caused by extreme values, further guarantees the stability and safety of energy intervention, and provides reliable parameter constraints for surgical results.

[0022] This application provides a dual-bar current phase difference energy control system, which adopts the following technical solution: The data acquisition module is used to acquire the tissue impedance signal of the surgical object in real time. The tissue impedance signal is processed by multi-channel anti-interference through an improved conditioning circuit to obtain the processed impedance signal. The phase difference calculation module is used to perform impedance change rate calculation using the processed impedance signal to obtain the impedance change rate. When the impedance change rate is greater than a preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained, and the phase difference is matched using the basic information of the sub-organ tissue to obtain the optimal phase difference. A current output module is used to generate a dual-bar current with the optimal phase difference and to perform energy intervention on the surgical object.

[0023] By adopting the above technical solution, the data acquisition module acquires tissue impedance signals in real time and obtains reliable processed impedance signals through multi-channel anti-interference processing of the improved conditioning circuit. The phase difference calculation module calculates the impedance change rate based on this, and when the change rate exceeds the threshold, it matches the optimal phase difference by combining the basic information of the sub-organ tissue. The current output module generates the corresponding dual-bar current to implement energy intervention. The modules work together to achieve precise linkage from signal processing to phase difference decision and energy output, dynamically adapt to changes in tissue characteristics, ensure that energy transfer is highly consistent with tissue needs, and improve the accuracy, stability and safety of surgical intervention.

[0024] In summary, this application includes at least one of the following beneficial technical effects: This invention acquires tissue impedance signals in real time and calculates the impedance change rate after anti-interference processing using an improved conditioning circuit. When this change rate exceeds a threshold, basic information about the sub-organ tissue is acquired for phase difference matching to obtain the optimal phase difference, abandoning the single, fixed phase difference output mode of existing technologies. This invention dynamically adjusts the phase difference based on real-time acquired tissue impedance signals and basic information about sub-organ tissues, accurately adapting to complex changes in tissue characteristics and effectively solving the problem of insufficient accuracy in phase difference decision-making. Simultaneously, precise phase difference control can significantly reduce the risk of thermal damage, improve surgical safety, optimize surgical efficiency, and provide more reliable technical support for clinical surgery. Furthermore, by controlling the phase difference of the dual-bar current, the bipolar electrode can dynamically adjust its energy output, ensuring a high degree of matching between the bipolar electrode output energy and the actual needs of the tissue, avoiding uneven tissue penetration depth and guaranteeing surgical outcomes. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the steps of a dual-bar current phase difference energy control method provided in an embodiment of the present invention. Figure 2 This is a structural block diagram of a dual-bar current phase difference energy control system provided in an embodiment of the present invention. Detailed Implementation

[0026] The present invention aims to provide a dual-bar current phase difference energy control method and system to solve the technical problems of existing technologies that use a single fixed phase difference output mode, which cannot dynamically adapt to the complex changes in tissue characteristics, resulting in insufficient phase difference decision accuracy, mismatch between the bipolar electrode output energy and tissue needs, uneven tissue penetration depth and excessive thermal damage, thus affecting the safety and efficiency of surgery.

[0027] Please see Figure 1 , Figure 1 A flowchart illustrating the steps of a dual-bar current phase difference energy control method provided in an embodiment of the present invention.

[0028] This invention provides a dual-bar current phase difference energy control method, comprising: Step 101: Real-time acquisition of tissue impedance signals from the surgical subject.

[0029] The surgical object refers to the human organ or tissue that is the direct target of medical intervention during surgical procedures. For example, in liver tumor resection surgery, the liver is the surgical object. The surgeon's operation revolves around the liver, planning the surgical path based on the liver's anatomical structure, the location and size of the tumor within the liver, and other factors, in order to achieve precise removal of the tumor while preserving as much normal liver tissue as possible.

[0030] Tissue impedance signal refers to the signal obtained by applying electrical excitation to human tissue using bipolar electrodes, and then collecting the voltage across the tissue or the current flowing through the tissue using an impedance sensor.

[0031] In this embodiment of the invention, the tissue impedance signal of the surgical object is collected in real time by an impedance sensor. The impedance sensor can be a COE0001N impedance sensor, which is a disposable impedance sensor composed of medical conductive adhesive, backing, silver chloride electrode and connecting wire. As a disposable product, it can avoid the risk of cross-infection, meet the sterility requirements of the operating room, and has a thin and light overall design. It can flexibly fit with surgical instruments (such as electrosurgical heads and hemostatic forceps) or directly contact the surface of organ tissue. By contacting the surgical object (such as liver or stomach), the impedance signal is collected in real time without interfering with the surgical operation space.

[0032] Step 102: By improving the conditioning circuit, the tissue impedance signal is subjected to multi-channel anti-interference processing to obtain the processed impedance signal.

[0033] It should be noted that, in order to solve the problems of single-channel failure affecting the overall output and difficulty in adapting to multi-frequency noise simultaneously in traditional series-structured processing circuits, this invention provides an improved conditioning circuit. Through a multi-level parallel structure, it adopts a multi-channel parallel processing and fusion architecture. The three parallel channels are optimized for different noise characteristics and signal frequency bands, allowing each channel to focus on processing one type of interference or one signal characteristic. Finally, the optimal coupling impedance signal is output through a fusion algorithm, thereby outputting the processed impedance signal.

[0034] Furthermore, the improved conditioning circuit includes a series-connected input buffer module, a parallel processing module, a fusion module, and an output buffer module. Step 102 may include the following sub-steps: S11. The tissue impedance signal is buffered by the input buffer module to obtain the buffered impedance signal.

[0035] In this embodiment of the invention, the input buffer module employs a high input impedance voltage follower (such as the OPA2340 operational amplifier) ​​to buffer the tissue impedance signal. This voltage follower has extremely high input impedance and extremely low output impedance. When a tissue impedance signal is input, it minimizes the load on the signal source, preventing signal attenuation or distortion due to excessively low input impedance in subsequent circuits, thus ensuring the integrity of the original signal. Simultaneously, the voltage follower can synchronously distribute a single tissue impedance signal to the three sub-channels of the parallel processing module, and its internal isolation design prevents crosstalk between sub-channels, ultimately outputting a stable, distortion-free buffered impedance signal.

[0036] S12. The buffer impedance signal is enhanced by multi-channel feature enhancement through the parallel processing module to obtain the enhanced impedance signal.

[0037] It should be noted that the complexity of tissue impedance signals and the diversity of interference during surgery are significant. Different surgical stages and different tissue types will produce impedance signals with different frequency characteristics (such as low-frequency signals of muscle tissue and high-frequency pulsation signals of blood vessels). At the same time, there are various interferences in the surgical environment (such as power frequency and high-frequency noise). A single channel cannot simultaneously achieve "effective signal extraction" and "interference suppression". The three channels correspond to three typical signal characteristics and interference scenarios in the surgical process.

[0038] Furthermore, the parallel processing module includes a low-frequency anti-interference enhancement unit, a high-frequency broadband fidelity unit, and an adaptive signal optimization unit connected in parallel. The enhanced impedance signal includes a first enhanced impedance signal, a second enhanced impedance signal, and a third enhanced impedance signal. S12 may include the following sub-steps: S121. The buffer impedance signal is subjected to anti-interference enhancement processing by the low-frequency anti-interference enhancement unit to obtain the first enhanced impedance signal. The anti-interference enhancement process specifically involves sequentially performing high-frequency noise filtering, power frequency interference suppression, weak signal amplification, and drift compensation on the buffer impedance signal.

[0039] It should be noted that during surgery, the impedance changes of most normal sub-organ tissues (such as muscle and fat) are slow and low-frequency. For example, the impedance gradually decreases when the tissue is continuously heated, and the impedance slowly increases during hemostasis as blood vessels coagulate. These signals contain key information about the surgical outcome (such as whether coagulation is sufficient), but are susceptible to 50Hz power frequency interference and temperature drift. 50Hz power frequency interference: Operating room electrical grids, monitoring equipment, etc., can introduce 50Hz power frequency noise, the intensity of which may mask low-frequency impedance signals. Temperature drift: Slow temperature changes (0.1-1Hz) when electrodes are in contact with tissue can cause impedance baseline drift, affecting signal accuracy. The low-frequency anti-interference enhancement unit's channels use "low-pass filtering + notch filtering" to specifically suppress the above interferences, retaining effective low-frequency signals of 1-100Hz. For example, during liver hemostasis, it can accurately extract the slow impedance changes during blood vessel coagulation.

[0040] In this embodiment of the invention, the low-frequency anti-interference enhancement unit sequentially completes high-frequency noise filtering, power frequency interference suppression, weak signal amplification and drift compensation of the buffer impedance signal through a series of first-order RC low-pass filter, double-T notch filter, high-gain preamplifier and programmable gain amplifier, and miniature DAC, and finally obtains the first enhanced impedance signal.

[0041] S122. The buffer impedance signal is processed by a high-frequency broadband fidelity unit to obtain a second enhanced impedance signal. The broadband fidelity processing specifically involves sequentially filtering out low-frequency interference, suppressing high-frequency noise, amplifying the broadband signal, and adaptively adjusting the gain of the buffer impedance signal.

[0042] It should be noted that dynamic operations during surgery, such as cutting and instantaneous hemostasis, generate high-frequency impedance signals. For example, when electrodes rapidly cut tissue, cell rupture causes a momentary drop in impedance (high-frequency abrupt change); when blood vessels rupture suddenly, blood infiltration into the tissue causes a sudden change in impedance (high-frequency fluctuation). These signals are short in duration but crucial in information, yet easily masked by high-frequency electromagnetic interference. The high-frequency broadband fidelity unit's channels utilize "high-pass filtering + broadband amplification" to specifically retain effective high-frequency signals in the 100-1kHz range while suppressing electromagnetic noise >1kHz. For instance, during the mucosal cutting stage, it can capture the high-frequency impedance abrupt change at the moment of tissue rupture, ensuring a rapid response from the phase difference optimization unit.

[0043] In this embodiment of the invention, the high-frequency broadband fidelity unit sequentially performs low-frequency interference filtering, high-frequency noise suppression, broadband signal amplification, and gain adaptive adjustment on the buffer impedance signal through a Butterworth high-pass filter, a wideband low-pass filter, a broadband operational amplifier, and an automatic gain control module connected in series, ultimately obtaining the second enhanced impedance signal.

[0044] S123. The buffer impedance signal is optimized by the adaptive signal optimization unit to obtain the third enhanced impedance signal. The signal optimization processing specifically involves sequentially performing filtering dynamic adjustment, gain calibration, and noise RMS quantization on the buffer impedance signal.

[0045] It should be noted that during surgery, the frequency characteristics of tissue signals may dynamically switch across frequency bands. For example, the signal may suddenly switch from "slow hemostasis" (low-frequency signal) to "rapid cutting" (high-frequency signal); the impedance change rate of the same tissue may vary greatly at different stages (e.g., slow coagulation in the early stage and fast coagulation in the later stage). The filtering and amplification parameters of the first two channels are fixed (e.g., the cutoff frequency of the low-frequency anti-interference enhancement unit is fixed at 100Hz, and the channel of the high-frequency broadband fidelity unit is fixed at 1kHz), which cannot adapt to such dynamic changes (e.g., when a high-frequency signal suddenly appears, the channel of the low-frequency anti-interference enhancement unit may filter out key information; when the low-frequency signal amplifies, the channel of the high-frequency broadband fidelity unit may amplify noise). The adaptive signal optimization unit adjusts its parameters in real time through "programmable filtering + intelligent gain control": when the signal is biased towards low frequency, the cutoff frequency is lowered (closer to the function of the low-frequency anti-interference enhancement unit); when the signal turns to high frequency, the cutoff frequency is raised (closer to the function of the high-frequency broadband fidelity unit); when the impedance change rate increases sharply, the gain is automatically increased to capture details. For example, in tumor resection surgery, it can process both low-frequency signals from normal tissues surrounding the tumor and respond to high-frequency signals at the moment of resection, avoiding the limitations of a single channel.

[0046] In this embodiment of the invention, the adaptive signal optimization unit sequentially completes the dynamic adjustment of the buffer impedance signal, gain calibration and effective noise value quantization through a series of programmable filters, intelligent gain control modules and noise monitoring units, and finally obtains the third enhanced impedance signal.

[0047] S13. The enhanced impedance signal is coupled with the preset channel weighting factor through the fusion module to obtain the coupled impedance signal.

[0048] In this embodiment of the invention, preset channel weight factors are dynamically allocated. When the power frequency interference is >30mV, the channel weight of the low-frequency anti-interference enhancement unit is increased to 60%, and the channel weights of the high-frequency broadband fidelity unit and the adaptive signal optimization unit are both 20%. When the high-frequency signal change rate is >10% / ms, the channel weight of the high-frequency broadband fidelity unit is increased to 50%, and the channel weights of the low-frequency anti-interference enhancement unit and the adaptive signal optimization unit are both 25%. When the signal change rate is <1% / ms and the noise is low, the channel weight of the adaptive signal optimization unit is increased to 50%, and the channel weights of the low-frequency anti-interference enhancement unit and the high-frequency broadband fidelity unit are both 25%.

[0049] Weighted summation is achieved using an operational amplifier, specifically as follows: V out =ω1×V1+ω2×V2+ω3×V3 In the formula, V out V1, V2, and V3 represent the first enhanced impedance signal, the second enhanced impedance signal, and the third enhanced impedance signal, respectively. ω1, ω2, and ω3 represent the channel weights of the low-frequency anti-interference enhancement unit, the high-frequency broadband fidelity unit, and the adaptive signal optimization unit, respectively.

[0050] S14. The coupled impedance signal is buffered by the output buffer module to obtain the processed impedance signal.

[0051] In this embodiment of the invention, the output buffer module is a voltage follower, which buffers the coupled impedance signal. The voltage follower has extremely high input impedance and extremely low output impedance, which can effectively isolate signal interference and thus obtain a high-quality processed impedance signal.

[0052] Step 103: Perform impedance change rate calculation using the processed impedance signal to obtain the impedance change rate.

[0053] In this embodiment of the invention, firstly, the continuously acquired impedance signal is discretized to obtain a series of discrete impedance value data points. Then, the impedance change rate is calculated using the ratio of the difference between adjacent data points to the time interval. The calculation formula can be expressed as: Where r is the rate of change of impedance, Z n Z n-1 The processed impedance values ​​are the nth and (n-1)th sampling times, respectively, and Δt is the time interval between two adjacent sampling times. The impedance change rate is calculated in this way.

[0054] Step 104: When the impedance change rate is greater than the preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained.

[0055] In this embodiment of the invention, when the impedance change rate exceeds a preset change rate threshold, it indicates that the tissue state of the surgical object may have undergone a significant change. At this point, the operation of acquiring basic information about the sub-organ tissues of the surgical object will be triggered. Specifically, pre-stored or real-time detected basic information about the sub-organ tissues will be retrieved through detection devices or database interfaces related to the surgical object. This information includes, but is not limited to, tissue characteristic parameters associated with different types of sub-organ tissues and tissue temperature change rates. By acquiring this basic information, comprehensive and accurate data can be provided for subsequent phase difference matching based on the sub-organ tissue basic information to obtain the optimal phase difference, thereby better adapting to the dynamic changes in tissue state during surgery and ensuring the accuracy and safety of the surgery.

[0056] It should be noted that the inclusion relationship between the surgical object and the sub-organ tissue is shown in Table 1 below: Step 105: Use sub-organ tissue basic information to perform phase difference matching to obtain the optimal phase difference.

[0057] Furthermore, the basic information on sub-organ tissues includes tissue characteristic parameters and tissue temperature change rates associated with different types of sub-organ tissues. Step 105 may include the following sub-steps: S21. Determine the candidate values ​​for the first stage based on the tissue characteristic parameters associated with different types of sub-organ tissues.

[0058] It should be noted that, as shown in Table 1 above, the types of sub-organ tissues contained in different surgical subjects vary. Therefore, in this step S21, it is necessary to first identify the type of sub-organ tissue corresponding to the surgical subject, and then determine the candidate values ​​for the first stage based on the baseline values ​​associated with each sub-organ tissue type.

[0059] Furthermore, S21 may include the following sub-steps: S211. When the sub-organ tissue is a densely vascularized area, the baseline value associated with the densely vascularized area is determined based on the comparison results between the preset blood vessel diameter threshold and the associated tissue characteristic parameters.

[0060] In this embodiment of the invention, when the sub-organ tissue is a densely vascularized area, the tissue characteristic parameter is the blood vessel diameter. A blood vessel diameter threshold is preset, for example, 2 mm is set as a key threshold. In actual operation, the blood vessel diameter of the detected densely vascularized area is compared with this preset threshold. If the detected blood vessel diameter is greater than 2 mm, according to the preset rule, the baseline value associated with the densely vascularized area is determined to be 18°; if the detected blood vessel diameter is less than 2 mm, then the baseline value associated with the densely vascularized area is determined to be 15°. In this way, the baseline value corresponding to the densely vascularized area can be accurately determined based on the blood vessel diameter, a tissue characteristic parameter.

[0061] S212. When the sub-organ tissue is adipose tissue, the baseline value associated with adipose tissue is determined based on the comparison results between the preset fat thickness threshold and the associated tissue characteristic parameters.

[0062] In this embodiment of the invention, when the suborgan tissue is adipose tissue, the tissue characteristic parameter is fat thickness. Pre-set fat thickness thresholds, including key thresholds such as 5mm and 3mm, are used. In actual operation, the detected fat thickness of the adipose tissue is compared with these preset thresholds. If the detected fat thickness is greater than 5mm, according to preset rules, the baseline value associated with the adipose tissue is determined to be 30°; if the detected fat thickness is between 3mm and 5mm, the baseline value associated with the adipose tissue is determined to be 27°; if the detected fat thickness is less than 3mm, the baseline value associated with the adipose tissue is determined to be 25°. In this way, the baseline value corresponding to the adipose tissue can be accurately determined based on the tissue characteristic parameter of fat thickness.

[0063] S213. When the sub-organ tissue is muscle tissue, the baseline value associated with the muscle tissue is determined based on the comparison results between the preset electromyographic signal intensity threshold and the associated tissue characteristic parameters.

[0064] In this embodiment of the invention, when the sub-organ tissue is muscle tissue, the tissue characteristic parameter is the electromyographic (EMG) signal intensity. A preset EMG signal intensity threshold is used to distinguish EMG signals in different activity states. In actual operation, the detected EMG signal intensity of muscle tissue is compared with this preset threshold. If the detected EMG signal intensity is strong, it indicates that the muscle tissue is in a highly active state, and according to preset rules, the baseline value associated with the muscle tissue is determined to be 20°; if the detected EMG signal intensity is weak, it means that the muscle tissue is in a low-activity state, and the baseline value associated with the muscle tissue is determined to be 25°. In this way, the baseline value corresponding to the muscle tissue can be accurately determined based on the tissue characteristic parameter of EMG signal intensity.

[0065] S214. When the sub-organ tissue is mucosal tissue, the baseline value associated with the mucosal tissue is determined based on the comparison results between the preset mucosal change rate threshold and the associated tissue characteristic parameters.

[0066] In this embodiment of the invention, when the sub-organ tissue is mucosal tissue, the tissue characteristic parameter is the mucosal change rate. A mucosal change rate threshold is preset, for example, 10% / ms is set as a key threshold. In actual operation, the detected mucosal change rate of the mucosal tissue is compared with this preset threshold. If the detected mucosal change rate is greater than 10% / ms, according to preset rules, the baseline value associated with the mucosal tissue is determined to be 15°; if the detected mucosal change rate is less than or equal to 10% / ms, then the baseline value associated with the mucosal tissue is determined to be 18°. In this way, the baseline value corresponding to the mucosal tissue can be accurately determined based on the mucosal change rate, a tissue characteristic parameter.

[0067] S215. Based on the preset matching weight factor, perform weighted calculations on the basic values ​​associated with different types of sub-organ tissues to obtain the first-stage candidate values.

[0068] In this embodiment of the invention, firstly, corresponding preset matching weight factors are set for different types of sub-organ tissues. These weight factors reflect the importance of different sub-organ tissues in the overall assessment. Assume there are n different types of sub-organ tissues, denoted as T... i Let i = 1, 2, ..., n, and their corresponding base values ​​be B. i Let i = 1, 2, ..., n, and the preset matching weight factor be W. i Let i = 1, 2, ..., n. Then, weighted calculations are performed on the baseline values ​​associated with different types of sub-organ tissues according to the following formula to obtain the first-stage candidate values.

[0069] value In the formula, C1 represents the candidate value in the first stage, B i W represents the baseline value for sub-organ tissue association. i The matching weight factor represents the sub-organ tissue association, and n represents the number of different types of sub-organ tissues contained in the surgical object.

[0070] S22. Based on the tissue temperature change rate associated with different types of sub-organ tissues, the candidate values ​​of the first stage are corrected to obtain the candidate values ​​of the second stage.

[0071] Furthermore, S22 may include the following sub-steps: S221. Based on the preset temperature change range in which the tissue temperature change rate associated with each sub-organ tissue is located, determine multiple first correction values.

[0072] In this embodiment of the invention, firstly, for different types of sub-organ tissues, the system pre-sets multiple different preset temperature change ranges, each corresponding to a specific temperature change rate range. These preset temperature change ranges are determined based on the physiological characteristics and thermal response patterns of the sub-organ tissues. For example, for some sub-organ tissues that are more sensitive to temperature changes, the division of their preset temperature change ranges will be more refined.

[0073] After obtaining the tissue temperature change rate associated with each sub-organ tissue, it is compared with the corresponding preset temperature change range. Assume there are m preset temperature change ranges, denoted as R... j j = 1, 2, ..., m, each interval corresponds to a first correction value C 1j j = 1, 2, ..., m. For example, if the rate of change of tissue temperature of a certain sub-organ tissue is in the interval R1 (e.g., heating rate > 2℃ / s), then the corresponding first correction value is C. 11 (e.g., -3°C to -5°C); if it falls within the range R2 (e.g., heating rate between 0.5°C / s and 2°C / s), then the corresponding first correction value is C. 12 (e.g., 0°); if it is in the range R3 (e.g., heating rate < 0.5°C / s), then the corresponding first correction value is C. 13 (e.g., +2° to +3°, but not exceeding the upper limit of 30°). In this way, for each sub-organ tissue, a corresponding first correction value is determined based on the preset temperature change range in which its tissue temperature change rate falls.

[0074] S222. Based on the preset temperature change weighting factor, perform a weighted calculation on each first correction value to obtain the temperature factor correction value.

[0075] In this embodiment of the invention, a preset temperature change weighting factor is first set for the first correction value corresponding to different sub-organ tissues. This weighting factor reflects the importance of temperature changes in different sub-organ tissues in the overall correction. Assume there are m first correction values, denoted as C... 1j j = 1, 2, ..., m, and their corresponding preset temperature change weighting factors are W. tj j = 1, 2, ..., m. Then, the first correction values ​​are weighted according to the following formula to obtain the temperature factor correction value.

[0076] In the formula, C t The value represents the temperature factor correction, m represents the number of different sub-organ tissues contained in the surgical object, and W represents the temperature factor correction value. tj C represents the preset temperature change weighting factor corresponding to the j-th sub-organ tissue. 1j This represents the first correction value corresponding to the j-th suborgan tissue.

[0077] S223. The temperature factor correction value and the first-stage candidate value are summed to obtain the second-stage candidate value.

[0078] In this embodiment of the invention, the temperature factor correction value and the first-stage candidate value are summed to obtain the second-stage candidate value.

[0079] C2 = C t +C1 In the formula, C2 represents the candidate value for the second stage.

[0080] S23. Based on the tissue characteristic parameters associated with different types of sub-organ tissues, the candidate values ​​of the second stage are corrected to obtain the candidate values ​​of the third stage.

[0081] Furthermore, the tissue characteristic parameters include tissue impedance, and S23 may include the following sub-steps: S231. Perform second-order differential operations on the tissue impedance associated with each sub-organ tissue to obtain multiple second-order differential values.

[0082] In this embodiment of the invention, data on the time-varying tissue impedance associated with each sub-organ tissue are obtained. Then, second-order differential operations are performed on these tissue impedance data. Assuming the tissue impedance Z is a function Z(t) of time t, its second derivative is calculated using the finite difference method. For each suborgan tissue, this second-order differential operation is performed to obtain multiple second-order differential values. For example, for different suborgan tissues such as densely vascularized areas, adipose tissue, and muscle tissue, the second-order differential values ​​of their respective tissue impedances are calculated. These second-order differential values ​​can reflect the acceleration of changes in tissue impedance.

[0083] S232. Based on the preset trend determination interval where the second-order differential value of each sub-organ tissue is located, determine multiple second correction values.

[0084] In this embodiment of the invention, preset trend determination intervals corresponding to the second-order differential values ​​are pre-defined for different types of sub-organ tissues. These intervals are used to quantify the acceleration trend of tissue impedance changes (such as rapid increase, steady change, rapid decrease, etc.). Each interval corresponds to a specific second correction value, the magnitude and direction (positive or negative) of which are determined according to the degree of influence of the impedance change trend reflected by the interval on the candidate value.

[0085] If the second derivative value of a certain suborganism is in the "rapid enhancement region" (e.g., second derivative value > 0.5 Ω / s) 2 This indicates that the rate of impedance change of the tissue is accelerating. At this point, the corresponding second correction value is positive (e.g., +1.5°) to reinforce the influence of this trend on the candidate value.

[0086] If the second derivative value is in a "stationary range" (e.g., -0.2Ω / s) 2 ~0.5Ω / s 2 This indicates that the rate of impedance change tends to stabilize, and the corresponding second correction value is 0°, meaning that the current candidate value is not changed.

[0087] If the second derivative value is in the "rapidly decreasing region" (e.g., second derivative value < -0.2Ω / s) 2 The value indicates that the rate of impedance change is decreasing at an accelerating rate. The corresponding second correction value is negative (e.g., -1°) to weaken the influence of this trend on the candidate value.

[0088] By using the above method, a corresponding second correction value is determined for each sub-organ tissue, thereby transforming the dynamic trend of tissue impedance into a quantifiable correction parameter.

[0089] S233. Based on the preset trend weighting factor, perform a weighted calculation on each second correction value to obtain the impedance factor correction value.

[0090] In this embodiment of the invention, a preset trend weighting factor is set for the second correction value corresponding to different sub-organ tissues. This weighting factor reflects the importance of the impedance change trend of different sub-organ tissues in the overall correction. Assume there are k second correction values, denoted as C... 2l l = 1, 2, ..., k, and their corresponding preset trend weight factors are W. rl Let l = 1, 2, ..., k. Then, the second correction values ​​are weighted according to the following formula to obtain the impedance factor correction value.

[0091] In the formula, C r W represents the impedance factor correction value, k represents the number of different sub-organ tissues contained in the surgical object, and W represents the impedance factor correction value. rl C represents the preset trend weight factor corresponding to the l-th sub-organ tissue. 2l This represents the second correction value corresponding to the l-th suborgan tissue.

[0092] S234. The impedance factor correction value and the second-stage candidate value are summed to obtain the third-stage candidate value.

[0093] In this embodiment of the invention, the impedance factor correction value and the second-stage candidate value are summed to obtain the third-stage candidate value.

[0094] C3 = C r +C2 In the formula, C3 represents the candidate value for the third stage.

[0095] S24. Compare the candidate values ​​of the third stage with the preset standard phase difference range, and determine the optimal phase difference based on the comparison results.

[0096] Furthermore, S24 may include the following sub-steps: S241. Compare the candidate values ​​of the third stage with the preset standard phase difference range.

[0097] In this embodiment of the invention, a preset standard phase difference range suitable for the current surgical scenario is pre-defined. This range is a safe and effective range determined based on a large amount of clinical data and the physiological characteristics of sub-organ tissues, preferably 15° to 30°. The third-stage candidate value C3 calculated through the aforementioned steps is numerically compared with the preset standard phase difference range to clarify the positional relationship of C3 relative to the range (within the range, beyond the upper limit, or below the lower limit).

[0098] S242. When the candidate value of the third stage is within the preset standard phase difference range, the candidate value of the third stage is taken as the optimal phase difference.

[0099] In this embodiment of the invention, when the comparison result shows that the third-stage candidate value C3 is within the preset standard phase difference range (e.g., 15°≤C3≤30°), it indicates that the candidate value meets the dual requirements of clinical safety and precise operation, and can adapt to the actual state of the current sub-organ tissue. Therefore, the third-stage candidate value C3 can be directly determined as the optimal phase difference, and can be used to guide the parameter settings of the surgical equipment without additional adjustment.

[0100] S243. When the candidate value in the third stage is greater than the upper limit of the preset standard phase difference range, the upper limit value shall be taken as the optimal phase difference.

[0101] In this embodiment of the invention, when the candidate value C3 in the third stage is greater than the upper limit of the preset standard phase difference interval (e.g., C3 > 30°), it indicates that directly using this candidate value may exceed the safe operating range and pose a risk of damaging suborgan tissues. Therefore, the system automatically uses the upper limit of the preset standard phase difference interval (e.g., 30°) as the optimal phase difference to ensure the safety of the surgical procedure and avoid tissue damage caused by excessive phase difference.

[0102] S244. When the candidate value in the third stage is less than the lower limit of the preset standard phase difference range, the lower limit value shall be taken as the optimal phase difference.

[0103] In this embodiment of the invention, when the candidate value C3 in the third stage is less than the lower limit of the preset standard phase difference interval (e.g., C3 < 15°), it indicates that the candidate value may not meet the accuracy requirements of the surgical operation, and poor treatment or detection results are likely to occur. At this time, the system determines the lower limit of the preset standard phase difference interval (e.g., 15°) as the optimal phase difference, ensuring that the surgical operation achieves the expected results while ensuring safety.

[0104] Through step-by-step processing from S241 to S244, the rationality of the candidate values ​​in the third stage can be verified and corrected by combining the preset standard phase difference range. The final optimal phase difference not only meets the clinical safety standards, but also adapts to the actual state of the sub-organ tissue, providing reliable parameter support for the precise implementation of the surgery.

[0105] Step 106: Generate a dual-bar current with optimal phase difference and apply energy intervention to the surgical subject.

[0106] Energy intervention refers to the technical means of applying specific forms of energy to biological tissues to achieve purposes such as diagnosis, treatment, or regulation of physiological functions. In this invention, energy intervention specifically manifests as the use of a dual-bar current with an optimal phase difference to precisely target sub-organ tissues of the surgical subject. This intervention method can achieve effects such as tissue ablation, hemostasis, nerve stimulation, and tumor treatment according to different clinical needs. Its core lies in precisely controlling energy parameters (such as phase difference, frequency, amplitude, etc.) to selectively target the target tissue while minimizing damage to surrounding normal tissues, thereby improving the safety and effectiveness of treatment.

[0107] In this embodiment of the invention, a dual-bar current with a determined optimal phase difference is generated using a high-precision current generator. This current generator has a precise phase control module capable of accurately adjusting the phase relationship of the dual-bar current according to the input optimal phase difference parameters. The generated dual-bar current has specific frequency, amplitude, and phase difference, which are set according to the sub-organ tissue characteristics and treatment requirements of the surgical subject.

[0108] When performing energy intervention on a surgical subject, a dual-bar current is applied to the surgical area through specialized surgical electrodes. The dual-bar current creates a specific electric field distribution within the surgical area, enabling selective energy application to the target sub-organ tissue. For example, for lesions requiring ablation, the energy generated by the dual-bar current can raise the temperature of the lesion to a specific value, thereby achieving ablation; for tissues requiring stimulation, the dual-bar current can activate the physiological activity of tissue cells.

[0109] During energy intervention, the system also monitors physiological parameters of the surgical area in real time, such as temperature and impedance, and dynamically adjusts the parameters of the dual-bar current based on the monitoring results to ensure the safety and effectiveness of the energy intervention. In this way, generating a dual-bar current with optimal phase difference and applying it to the surgical subject enables precise surgical treatment, improving surgical outcomes and patient prognosis.

[0110] Please see Figure 2 , Figure 2 This is a structural block diagram of a dual-bar current phase difference energy control system provided in an embodiment of the present invention.

[0111] This invention provides a dual-bar current phase difference energy control system, comprising: The data acquisition module is used to acquire the tissue impedance signal of the surgical object in real time. By improving the conditioning circuit, the tissue impedance signal is processed through multi-channel anti-interference processing to obtain the processed impedance signal. The phase difference calculation module is used to calculate the impedance change rate by processing the impedance signal to obtain the impedance change rate. When the impedance change rate is greater than the preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained, and the phase difference is matched using the basic information of the sub-organ tissue to obtain the optimal phase difference. The current output module is used to generate a dual-bar current with optimal phase difference and to apply energy intervention to the surgical subject.

[0112] Furthermore, the data acquisition module includes an impedance sensor and an improved signal conditioning circuit: Impedance sensor, used to acquire tissue impedance signals of surgical subjects in real time; An improved signal conditioning circuit is used to perform multi-channel anti-interference processing on tissue impedance signals to obtain processed impedance signals. Furthermore, the improved signal conditioning circuit includes a series-connected input buffer module, parallel processing module, fusion module, and output buffer module; The input buffer module is used to buffer the tissue impedance signal to obtain a buffered impedance signal. The parallel processing module is used to perform multi-channel feature enhancement on the buffered impedance signal to obtain the enhanced impedance signal. The fusion module is used to couple the enhanced impedance signal with a preset channel weighting factor to obtain a coupled impedance signal. The output buffer module is used to buffer the coupled impedance signal to obtain the processed impedance signal.

[0113] Input buffer module: The input buffer module employs a high input impedance voltage follower (such as OPA2340, Operational Amplifier 2340). The voltage follower has extremely high input impedance and extremely low output impedance, enabling it to buffer tissue impedance signals, providing isolation and preventing signal distortion caused by subsequent circuitry. Simultaneously, it distributes the sensor signal to three parallel channels, avoiding load interference between channels and obtaining a buffered impedance signal.

[0114] Parallel processing module: The parallel processing module contains 3 parallel processing channels, which are used to process "low frequency + power frequency interference", "high frequency noise" and "wide dynamic range signal" respectively. It performs multi-channel feature enhancement on the buffer impedance signal to obtain the enhanced impedance signal.

[0115] Fusion Module: Based on the signal-to-noise ratio (SNR) and signal integrity of each channel signal, the fusion module dynamically allocates weights and synthesizes the output. It couples the enhanced impedance signal with preset channel weight factors, and through a certain algorithm (such as weighted averaging), comprehensively considers the quality and reliability of each channel signal, reasonably allocates weights, and obtains the coupled impedance signal, thereby improving the overall signal quality and effectiveness.

[0116] Output buffer module: The output buffer module uses a buffer circuit composed of operational amplifiers to buffer the coupled impedance signal. It can isolate the fusion unit from the back-end ADC (Analog-to-Digital Converter), prevent the load effect of the back-end circuit from affecting the output of the fusion unit, ensure stable signal transmission, and finally obtain the processed impedance signal for subsequent analog-to-digital conversion and other operations.

[0117] Furthermore, the enhanced impedance signal includes a first enhanced impedance signal, a second enhanced impedance signal, and a third enhanced impedance signal, and the parallel processing module includes a low-frequency anti-interference enhancement unit, a high-frequency broadband fidelity unit, and an adaptive signal optimization unit connected in parallel. The low-frequency anti-interference enhancement unit is used to perform anti-interference enhancement processing on the buffer impedance signal to obtain the first enhanced impedance signal; the high-frequency broadband fidelity unit is used to perform broadband fidelity processing on the buffer impedance signal to obtain the second enhanced impedance signal. An adaptive signal optimization unit is used to perform signal optimization processing on the buffer impedance signal to obtain a third enhanced impedance signal.

[0118] Furthermore, the anti-interference enhancement process specifically involves sequentially performing high-frequency noise filtering, power frequency interference suppression, weak signal amplification, and drift compensation on the buffer impedance signal. The low-frequency anti-interference enhancement unit includes a series-connected first-order RC (Resistor-Capacitor) low-pass filter, a dual-T notch filter, a high-gain preamplifier and a programmable gain amplifier, and a miniature DAC (Digital-to-Analog Converter). A first-order RC low-pass filter (cutoff frequency 100Hz) is used to filter out high-frequency noise from the buffered impedance signal, resulting in a pre-denoised impedance signal. A dual-T notch filter (center frequency 50Hz, notch depth >60dB) is used to suppress power frequency interference in the pre-denoised impedance signal, resulting in an impedance signal resistant to power frequency interference. A high-gain preamplifier (AD8221, gain 20x) and a programmable gain amplifier (PGA204, gain 1-10x) are used to amplify the weak signal of the impedance signal resistant to power frequency interference, resulting in an enhanced impedance signal. A miniature DAC (AD5620) is used to compensate for drift in the enhanced impedance signal, resulting in the first enhanced impedance signal.

[0119] First-order RC low-pass filter (cutoff frequency 100Hz): An RC low-pass filter is a simple filter circuit composed of resistors and capacitors. It utilizes the capacitive reactance of capacitors to signals of different frequencies to allow signals below the cutoff frequency (100Hz in this case) to pass through, while attenuating high-frequency noise signals above the cutoff frequency. This achieves high-frequency noise filtering of the buffer impedance signal, resulting in a preliminary noise-reduced impedance signal.

[0120] Dual-T notch filter (center frequency 50Hz, notch depth >60dB): A dual-T notch filter is a special filter circuit capable of deep attenuation of signals at a specific frequency (50Hz in this case). In power systems, 50Hz is the main frequency of power frequency interference. This filter is specifically designed to suppress this frequency, effectively removing power frequency interference components from the impedance signal after initial noise reduction, resulting in an impedance signal resistant to power frequency interference.

[0121] High-gain preamplifier (AD8221, gain 20x) and programmable gain amplifier (PGA204, gain 1-10x): The high-gain preamplifier AD8221 features low noise and high common-mode rejection ratio, enabling it to initially amplify weak impedance signals after power frequency interference suppression, thus increasing signal strength. The programmable gain amplifier PGA204, on the other hand, allows for flexible adjustment of the amplification factor (1-10x) according to actual needs, further enhancing the signal to obtain an enhanced impedance signal for subsequent processing.

[0122] Miniature DAC (AD5620): The AD5620 digital-to-analog converter converts digital signals into analog voltage signals. In this circuit, it is used to compensate for drift in the enhanced impedance signal. Since factors such as ambient temperature can cause baseline drift in the signal, the miniature DAC injects compensation voltage in real time to counteract this drift, thereby obtaining the first enhanced impedance signal and ensuring the accuracy and stability of the signal.

[0123] Furthermore, the broadband fidelity processing specifically involves sequentially filtering out low-frequency interference, suppressing high-frequency noise, amplifying the broadband signal, and adaptively adjusting the gain of the buffer impedance signal; the high-frequency broadband fidelity unit includes a Butterworth high-pass filter, a wideband low-pass filter, a broadband operational amplifier (OPA657), and an automatic gain control (AGC) module connected in series. A Butterworth high-pass filter (cutoff frequency 10Hz) is used to filter out low-frequency interference from the buffered impedance signal, resulting in an impedance signal free of DC and extremely low-frequency interference. A wideband low-pass filter (cutoff frequency 1kHz) is used to suppress high-frequency noise from the impedance signal free of DC and extremely low-frequency interference, retaining the effective signal from 10-1000Hz, resulting in an impedance signal resistant to high-frequency noise. A wideband operational amplifier (OPA657, gain-bandwidth product 1.6GHz) is used to amplify the impedance signal resistant to high-frequency noise, ensuring distortion-free amplification of the high-frequency signal, resulting in a wideband amplified impedance signal. An automatic gain control (AGC) module is used to adaptively adjust the gain of the wideband amplified impedance signal, automatically reducing the gain (response time <100μs) when the signal amplitude >3V to avoid saturation, resulting in a second enhanced impedance signal.

[0124] Butterworth high-pass filter (cutoff frequency 10Hz): The Butterworth high-pass filter is a high-pass filter with the largest flat amplitude response. Through a specific circuit structure design, it allows signals above the cutoff frequency (10Hz in this case) to pass through, while effectively attenuating DC and extremely low-frequency interference signals below the cutoff frequency. This achieves low-frequency interference filtering of the buffer impedance signal, resulting in an impedance signal free of DC and extremely low-frequency interference.

[0125] Wideband low-pass filter (cutoff frequency 1kHz): A wideband low-pass filter is used to allow signals below the cutoff frequency (1kHz in this case) to pass through while suppressing high-frequency noise signals above the cutoff frequency. In this circuit, it can retain effective signals from 10-1000Hz while filtering out high-frequency noise above 1kHz, resulting in an impedance signal resistant to high-frequency noise.

[0126] Wideband Operational Amplifier (OPA657, Gain-Bandwidth Product 1.6GHz): The OPA657 is a high-speed wideband operational amplifier with an extremely high gain-bandwidth product (1.6GHz). This means it can amplify signals over a wide frequency range and maintain good performance at high frequencies, ensuring distortion-free amplification of high-frequency signals and thus obtaining a wideband amplified impedance signal.

[0127] Automatic Gain Control (AGC) Module: The AGC module automatically adjusts the amplifier gain based on the amplitude of the input signal. When the amplitude of the broadband amplified impedance signal exceeds 3V, the AGC module automatically reduces the gain (response time <100μs) to prevent saturation distortion due to excessive amplitude, ensuring signal quality and ultimately obtaining a second enhanced impedance signal.

[0128] Furthermore, the signal optimization processing specifically involves sequentially performing dynamic filtering adjustment, gain calibration, and noise RMS quantization on the buffered impedance signal. The adaptive signal optimization unit includes a series-connected programmable filter (MAX262), an intelligent gain control module, and a noise monitoring unit. The programmable filter (MAX262) is used to dynamically adjust the buffered impedance signal, adjusting the cutoff frequency in real time (10-1000Hz continuously adjustable) via a microcontroller (MSP430) to obtain the dynamically filtered impedance signal. The intelligent gain control module is used to perform gain calibration on the dynamically filtered impedance signal, combining impedance change rate analysis (calculated every millisecond). When the change rate > 5%, the gain is automatically increased (up to 40 times) to capture rapidly changing signals, obtaining the gain-calibrated impedance signal. The noise monitoring unit is used to quantize the noise RMS value of the gain-calibrated impedance signal, using a built-in RMS detector (AD637) to calculate the noise amplitude within the channel in real time, obtaining the third enhanced impedance signal.

[0129] Programmable Filter (MAX262): The MAX262 is a programmable continuous-time filter that can be programmed and controlled by an external microcontroller (such as the MSP430) to adjust its cutoff frequency in real time (continuously adjustable within the range of 10-1000Hz). This feature allows it to dynamically adjust the filtering parameters according to the characteristics of the input signal, dynamically adjusting the buffer impedance signal to obtain a dynamically filtered impedance signal that is better adapted to different signal environments.

[0130] Intelligent Gain Control Module: The intelligent gain control module is mainly used for gain calibration of the dynamically filtered impedance signal. It combines impedance change rate analysis (calculated every millisecond) and automatically increases the gain (up to 40 times) when the detected impedance change rate exceeds 5%, in order to more clearly capture rapidly changing signals and obtain a gain-calibrated impedance signal, thus improving signal detectability and accuracy.

[0131] Noise Monitoring Unit: The noise monitoring unit is used to quantize the effective noise value of the impedance signal after gain calibration. It has a built-in effective value detector (AD637), a high-precision effective value / DC converter that can calculate the noise amplitude within a channel in real time, obtaining the third enhanced impedance signal. This noise amplitude information provides a weighting basis for the subsequent fusion unit, helping it to rationally allocate weights according to the noise status of each channel when processing multi-channel signals, thereby improving the overall signal processing quality.

[0132] Furthermore, the phase difference calculation module includes a series-connected impedance change rate analysis unit, a basic information acquisition unit, a phase difference optimization unit, and a timing generation unit; The impedance change rate analysis unit is used to calculate the impedance change rate by processing the impedance signal and obtain the impedance change rate. The basic information acquisition unit is used to acquire basic information of the sub-organ tissue of the surgical object when the impedance change rate is greater than a preset change rate threshold. The phase difference optimization unit is used to perform phase difference matching using basic information of sub-organ tissues to obtain the optimal phase difference.

[0133] Furthermore, the basic information on sub-organ tissues includes tissue characteristic parameters and tissue temperature change rates associated with different types of sub-organ tissues, and the phase difference optimization unit includes: The first-stage candidate value unit is used to determine the first-stage candidate value based on the tissue characteristic parameters associated with different types of sub-organ tissues; The second-stage candidate value unit is used to correct the first-stage candidate value based on the tissue temperature change rate associated with different types of sub-organ tissues, and obtain the second-stage candidate value. The third-stage candidate value unit is used to correct the second-stage candidate values ​​based on the tissue feature parameters associated with different types of sub-organ tissues, and obtain the third-stage candidate values. The optimal phase difference unit is used to compare the candidate value in the third stage with the preset standard phase difference range, and to determine the optimal phase difference based on the comparison result.

[0134] Furthermore, the first-stage candidate value units include: The first comparison subunit is used to determine the basic value associated with the densely vascularized area based on the comparison result between the preset blood vessel diameter threshold and the associated tissue feature parameters when the sub-organ tissue is a densely vascularized area. The second comparison subunit is used to determine the baseline value associated with adipose tissue based on the comparison results of the preset fat thickness threshold and the associated tissue characteristic parameters when the sub-organ tissue is adipose tissue. The third comparison subunit is used to determine the basic value associated with muscle tissue based on the comparison results of the preset electromyographic signal intensity threshold and the associated tissue characteristic parameters when the sub-organ tissue is muscle tissue. The fourth comparison subunit is used to determine the basic value associated with the mucosal tissue based on the comparison results of the preset mucosal change rate threshold and the associated tissue characteristic parameters when the sub-organ tissue is mucosal tissue. The weighted operation subunit is used to perform weighted operations on the basic values ​​associated with different types of sub-organ tissues based on preset matching weight factors to obtain the first-stage candidate values.

[0135] Furthermore, the second-stage candidate value units include: The first correction value subunit is used to determine multiple first correction values ​​based on the preset temperature change range in which the tissue temperature change rate associated with each sub-organ tissue is located. The temperature factor correction value subunit is used to perform a weighted calculation on each first correction value based on a preset temperature change weighting factor to obtain the temperature factor correction value. The first correction subunit is used to perform a summation operation between the temperature factor correction value and the first-stage candidate value to obtain the second-stage candidate value.

[0136] Furthermore, the third-stage candidate value units include: The second-order differential value subunit is used to perform second-order differential operations on the tissue impedance associated with each sub-organ tissue to obtain multiple second-order differential values. The second correction value subunit is used to determine multiple second correction values ​​based on the preset trend determination interval where the second derivative value of each sub-organ tissue is located. The impedance factor correction value sub-unit is used to perform weighted calculations on each second correction value based on a preset trend weight factor to obtain the impedance factor correction value. The second correction sub-unit is used to perform a summation operation between the impedance factor correction value and the second-stage candidate value to obtain the third-stage candidate value.

[0137] Furthermore, the optimal phase difference unit includes: The interval comparison sub-unit is used to compare the candidate value in the third stage with the preset standard phase difference interval; The first processing subunit is used to take the third-stage candidate value as the optimal phase difference when the third-stage candidate value is within the preset standard phase difference range. The second processing subunit is used to take the upper limit value as the optimal phase difference when the candidate value of the third stage is greater than the upper limit value of the preset standard phase difference range. The third processing subunit is used to take the lower limit value as the optimal phase difference when the candidate value of the third stage is less than the lower limit value of the preset standard phase difference interval.

[0138] Furthermore, the current output module includes a timing generation unit and an H-bridge drive circuit; The timing generation unit is used to generate control waveform commands based on the optimal phase difference. The H-bridge drive circuit is used to switch the polarity of the electrode current according to the control waveform command, generate a dual-bar current with optimal phase difference, and perform energy intervention on the surgical subject.

[0139] Timing Generation Unit: A timing generation unit is a control circuit based on a microcontroller or programmable logic device (such as an FPGA, Field-Programmable Gate Array). It can generate precise control waveform commands based on the optimal phase difference. These commands determine the on and off timing of the switching devices in the subsequent H-bridge drive circuit, thereby controlling parameters such as the output phase and frequency of the current.

[0140] H-bridge driver circuit: The H-bridge driver circuit is a commonly used power electronic circuit topology, consisting of four switching devices (such as MOSFETs, Metal-Oxide-Semiconductor Field-Effect Transistors) forming an H-shaped structure. It can flexibly switch the current polarity of the electrodes according to the control waveform commands issued by the timing generation unit, realizing bidirectional current flow, generating a dual-bar current with optimal phase difference, and applying this current to the surgical object for energy intervention. For example, in electrophysiological therapy, it can adjust the phase of the current to achieve better therapeutic effects.

[0141] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A dual-bar current phase difference energy control method, characterized in that, include: Real-time acquisition of tissue impedance signals from the surgical subject; By improving the conditioning circuit, the tissue impedance signal is subjected to multi-channel anti-interference processing to obtain a processed impedance signal. The impedance change rate is obtained by calculating the impedance change rate using the processed impedance signal. When the impedance change rate is greater than a preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained; The optimal phase difference is obtained by using the basic information of the sub-organ tissue for phase difference matching. A dual-bar current with the optimal phase difference is generated and applied to the surgical subject with energy.

2. The dual-bar current phase difference energy control method according to claim 1, characterized in that, The improved conditioning circuit includes a series-connected input buffer module, a parallel processing module, a fusion module, and an output buffer module. The improved conditioning circuit performs multi-channel anti-interference processing on the tissue impedance signal to obtain a processed impedance signal, including: The input buffer module buffers the tissue impedance signal to obtain a buffered impedance signal. The buffer impedance signal is enhanced by multi-channel feature enhancement through the parallel processing module to obtain an enhanced impedance signal. The enhanced impedance signal is coupled with a preset channel weighting factor through the fusion module to obtain a coupled impedance signal; The output buffer module buffers the coupled impedance signal to obtain a processed impedance signal.

3. The dual-bar current phase difference energy control method according to claim 2, characterized in that, The parallel processing module includes a low-frequency anti-interference enhancement unit, a high-frequency broadband fidelity unit, and an adaptive signal optimization unit connected in parallel. The parallel processing module performs multi-channel feature enhancement on the buffer impedance signal to obtain multiple enhanced impedance signals, including: The buffer impedance signal is subjected to anti-interference enhancement processing by the low-frequency anti-interference enhancement unit to obtain the first enhanced impedance signal. The buffer impedance signal is processed by the high-frequency broadband fidelity unit to obtain a second enhanced impedance signal. The adaptive signal optimization unit performs signal optimization processing on the buffer impedance signal to obtain a third enhanced impedance signal. The enhanced impedance signal includes the first enhanced impedance signal, the second enhanced impedance signal, and the third enhanced impedance signal.

4. The dual-bar current phase difference energy control method according to claim 3, characterized in that, The anti-interference enhancement process specifically involves sequentially performing high-frequency noise filtering, power frequency interference suppression, weak signal amplification, and drift compensation on the buffer impedance signal. The broadband fidelity processing specifically involves sequentially performing low-frequency interference filtering, high-frequency noise suppression, broadband signal amplification, and gain adaptive adjustment on the buffer impedance signal. The signal optimization processing specifically involves sequentially performing dynamic filtering adjustment, gain calibration, and noise RMS quantization on the buffer impedance signal.

5. The dual-bar current phase difference energy control method according to claim 1, characterized in that, The basic information of the sub-organ tissue includes tissue characteristic parameters and tissue temperature change rates associated with different types of sub-organ tissues. The step of using the basic information of the sub-organ tissue to perform phase difference matching to obtain the optimal phase difference includes: Based on the tissue characteristic parameters associated with different types of sub-organ tissues, first-stage candidate values ​​are determined; Based on the tissue temperature change rate associated with different types of sub-organ tissues, the first-stage candidate values ​​are corrected to obtain the second-stage candidate values; Based on the tissue characteristic parameters associated with different types of sub-organ tissues, the second-stage candidate values ​​are corrected to obtain the third-stage candidate values; The candidate values ​​of the third stage are compared with the preset standard phase difference range, and the optimal phase difference is determined based on the comparison results.

6. The dual-bar current phase difference energy control method according to claim 5, characterized in that, The step of determining first-stage candidate values ​​based on the tissue characteristic parameters associated with different types of sub-organ tissues includes: When the sub-organ tissue is a densely vascularized area, the basic value associated with the densely vascularized area is determined based on the comparison result between the preset blood vessel diameter threshold and the associated tissue feature parameters. When the sub-organ tissue is adipose tissue, the baseline value associated with the adipose tissue is determined based on the comparison result between the preset fat thickness threshold and the associated tissue characteristic parameters. When the sub-organ tissue is muscle tissue, the baseline value associated with the muscle tissue is determined based on the comparison result between the preset electromyographic signal intensity threshold and the associated tissue characteristic parameters. When the sub-organ tissue is mucosal tissue, the basic value associated with the mucosal tissue is determined based on the comparison result between the preset mucosal change rate threshold and the associated tissue characteristic parameters. Based on a preset matching weight factor, the basic values ​​associated with different types of sub-organ tissues are weighted to obtain the first-stage candidate values.

7. The dual-bar current phase difference energy control method according to claim 5, characterized in that, The process of revising the first-stage candidate values ​​based on the tissue temperature change rate associated with different types of sub-organ tissues to obtain second-stage candidate values ​​includes: Based on the preset temperature change range in which the temperature change rate of each sub-organ tissue is associated with the tissue temperature change rate, a plurality of first correction values ​​are determined; Based on a preset temperature change weighting factor, each of the first correction values ​​is weighted to obtain the temperature factor correction value. The second-stage candidate value is obtained by summing the temperature factor correction value with the first-stage candidate value.

8. The dual-bar current phase difference energy control method according to claim 5, characterized in that, The tissue characteristic parameters include tissue impedance. The third-stage candidate values ​​are obtained by correcting the tissue characteristic parameters associated with different types of sub-organ tissues, including: The tissue impedance associated with each of the sub-organ tissues is subjected to second-order differential operation to obtain multiple second-order differential values; Based on the preset trend determination interval where the second-order differential value associated with each of the sub-organ tissues is located, a number of second correction values ​​are determined. Based on a preset trend weighting factor, each of the second correction values ​​is weighted to obtain the impedance factor correction value. The third-stage candidate value is obtained by summing the impedance factor correction value with the second-stage candidate value.

9. A dual-bar current phase difference energy control method according to any one of claims 5-8, characterized in that, The step of comparing the candidate values ​​of the third stage with the preset standard phase difference range, and determining the optimal phase difference based on the comparison results, includes: Compare the candidate values ​​in the third stage with the preset standard phase difference range; When the third-stage candidate value is within the preset standard phase difference range, the third-stage candidate value is taken as the optimal phase difference; When the candidate value of the third stage is greater than the upper limit of the preset standard phase difference range, the upper limit value is taken as the optimal phase difference; When the candidate value in the third stage is less than the lower limit of the preset standard phase difference interval, the lower limit value is taken as the optimal phase difference.

10. A dual-bar current phase difference energy control system, characterized in that, include: The data acquisition module is used to acquire the tissue impedance signal of the surgical object in real time. The tissue impedance signal is processed by multi-channel anti-interference through an improved conditioning circuit to obtain the processed impedance signal. The phase difference calculation module is used to perform impedance change rate calculation using the processed impedance signal to obtain the impedance change rate. When the impedance change rate is greater than a preset change rate threshold, the basic information of the sub-organ tissue of the surgical object is obtained, and the phase difference is matched using the basic information of the sub-organ tissue to obtain the optimal phase difference. A current output module is used to generate a dual-bar current with the optimal phase difference and to perform energy intervention on the surgical object.

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