High-frequency surgical equipment control system based on digital audio bus state

Abstract

The embodiment of the invention discloses a high-frequency surgical equipment control system based on a digital audio bus state, and the system comprises a physical layer signal obtaining unit which is configured to collect a physical layer signal on a single-line digital audio bus in real time; the bus perturbation feature extraction unit is configured to extract bus perturbation features used for representing physical channel degradation characteristics from the physical layer signals; a coupling model construction unit configured to construct a coupling model between the physical channel of the single-wire digital audio bus and the electrical characteristics of the surgical tissue; the inversion calculation execution unit is configured to perform inversion calculation on the bus perturbation characteristics to obtain organization state proxy parameters; the risk evolution judgment unit is configured to judge whether the high-frequency surgical equipment enters a tissue safety risk evolution interval or not; and the control parameter adjusting unit is configured to adaptively adjust energy output control parameters of the high-frequency surgical equipment. The operation safety and effectiveness are improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a high-frequency surgical equipment control system based on digital audio bus status. Background Technology

[0002] High-frequency surgical devices (high-frequency electrosurgical units) use high-frequency current to cut, stop bleeding, and coagulate human tissue, and are widely used electrosurgical tools in modern surgery. During the procedure, electrical parameters such as tissue impedance, local temperature, and dielectric properties change significantly with tissue heating, dehydration, and carbonization. If the energy output cannot be adaptively adjusted in time, it can easily lead to tissue overheating, carbonization, adhesion, electrode failure, and even safety risks such as arcing and burns.

[0003] In existing technologies, tissue condition monitoring and closed-loop control of high-frequency electrosurgical units mainly rely on dedicated impedance measurement circuits, voltage / current sensors, or additional temperature probes. These solutions require additional hardware modules, leading to increased system complexity and cost, and measurement accuracy is easily affected in environments with strong electromagnetic interference (the high-frequency output itself generates EMI). Furthermore, traditional methods are mostly based on direct impedance feedback, which makes it difficult to accurately capture the coordinated evolution of multiple time-series and multi-scale physical processes such as rapid changes in tissue impedance, delayed response of dielectric properties, and local temperature accumulation, resulting in limited timeliness of early warning and control precision.

[0004] Single-wire digital audio buses (such as I²S single-wire variants or custom protocols) are often used for auxiliary functions such as internal audio prompts and voice alarms. They are simple to wire and have strong anti-interference capabilities. However, they have traditionally been regarded only as communication channels and their physical layer characteristics (such as clock jitter, phase noise, amplitude modulation) and their sensitive response to high-frequency electromagnetic fields have not been fully utilized.

[0005] Therefore, there is an urgent need for a low-cost, robust method that does not require additional sensors and utilizes an existing single-wire digital audio bus to passively sense changes in tissue state and achieve adaptive and safe closed-loop control of high-frequency energy output. Summary of the Invention

[0006] This application provides a high-frequency surgical equipment control system based on digital audio bus status, which realizes non-invasive indirect sensing of tissue status and progressive adaptive adjustment of energy output, thereby improving surgical safety and effectiveness.

[0007] This application provides the following solution: According to a first aspect, a high-frequency surgical device control system based on the state of a digital audio bus is provided. The system includes: a physical layer signal acquisition unit configured to, during the operation of the high-frequency surgical device, use a single-wire digital audio bus as a physical channel affected by electromagnetic coupling between a high-frequency energy field and surgical tissue, and acquire physical layer signals on the single-wire digital audio bus in real time; a bus perturbation feature extraction unit configured to extract bus perturbation features from the physical layer signals to characterize the degradation properties of the physical channel, the bus perturbation features including at least jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features; and a coupling model construction unit configured to construct a single-wire digital audio bus physical channel based on the bus perturbation features. A coupling model between the electrical characteristics of the surgical tissue and the physical channel is included, which describes the influence of changes in surgical tissue impedance and local temperature on the degradation characteristics of the physical channel. An inversion calculation execution unit is configured to perform inversion calculations on the bus perturbation characteristics using the coupling model to obtain tissue state proxy parameters characterizing the trend of changes in the surgical tissue state. A risk evolution judgment unit is configured to determine whether the high-frequency surgical device has entered the tissue safety risk evolution range based on the tissue state proxy parameters. A control parameter adjustment unit is configured to adaptively adjust the energy output control parameters of the high-frequency surgical device according to the degree of change in the tissue state proxy parameters when it is determined that the high-frequency surgical device has entered the tissue safety risk evolution range. According to one achievable method in an embodiment of this application, the acquisition of the physical layer signal includes real-time high-resolution sampling of the alignment clock signal, data edge signal, and frame synchronization signal, with a sampling frequency not less than 8 times the nominal clock frequency of the single-wire digital audio bus.

[0008] According to one achievable method in an embodiment of this application, the extraction of the bus perturbation features includes: performing multi-scale averaging time analysis on the jitter spectrum features to separate the different contributions of white noise, flicker noise, and random walk noise; performing frequency domain integration on the phase noise spectrum features to quantify the difference in noise power distribution between the low-frequency and high-frequency bands; and performing envelope detection and sideband symmetry evaluation on the amplitude modulation sideband features to identify asymmetric modulation effects caused by organization.

[0009] According to one achievable method in this application embodiment, the construction of a coupling model between the physical channel of a single-line digital audio bus and the electrical characteristics of surgical tissue based on the bus perturbation features includes: during the energy output process of the high-frequency surgical equipment, performing causal order constraint modeling on the bus perturbation features according to the order of physical responses after energy acts on the surgical tissue; the causal order constraint modeling includes: firstly, determining the instantaneous electromagnetic coupling response caused by the rapid change of the equivalent impedance of the surgical tissue based on the change of the jitter spectrum features; then, determining the delayed electromagnetic response caused by the change of the dielectric properties of the surgical tissue based on the change of the phase noise spectrum features; and finally, determining the slow modulation response caused by the cumulative change of local temperature of the surgical tissue based on the change of the amplitude modulation sideband features; through the causal order constraint, the jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features are associated in a non-commutative response order to form a coupling model for describing the impact of changes in the electrical characteristics of the surgical tissue on the degradation of the physical channel of the single-line digital audio bus.

[0010] According to one achievable method in this application embodiment, the step of performing inversion calculation on the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes includes: mapping the bus perturbation features to a feasible domain of state changes corresponding to the physical response of the surgical tissue under the constraints of the coupling model; determining the dominant evolutionary trend of surgical tissue state changes within the feasible domain of state changes based on the evolution direction and rate of change of the bus perturbation features in a continuous time window; and outputting the dominant evolutionary trend as the tissue state proxy parameters, wherein the tissue state proxy parameters are used to characterize the direction and intensity of change of the surgical tissue state.

[0011] According to one achievable method in this application embodiment, determining whether the high-frequency surgical device has entered the tissue safety risk evolution interval based on the tissue state proxy parameters includes: calculating the change direction, change rate, and change continuity of each proxy parameter based on the evolution trajectory of the tissue state proxy parameters within a continuous time window; dividing the surgical tissue state into at least three evolution stages based on the multidimensional collaborative characteristics of the evolution trajectory, including a safe evolution stage, a risk warning evolution stage, and a high-risk evolution stage; determining that the high-frequency surgical device has entered the tissue safety risk evolution interval when the evolution trajectory of the tissue state proxy parameters meets the determination rules for the risk warning evolution stage or the high-risk evolution stage; wherein, the determination rules are established based on the temporal correlation of the tissue state proxy parameters and the causal coupling relationship between different proxy parameters.

[0012] According to one achievable method in this application embodiment, the adaptive adjustment of the energy output control parameters of the high-frequency surgical device based on the degree of change of the tissue state proxy parameters includes: within a continuous time window, determining the current risk evolution trend of the surgical tissue based on the evolution direction, rate of change, and continuity of change of the tissue state proxy parameters; adjusting the energy output control parameters of the high-frequency surgical device in a continuous and gradual manner according to the risk evolution trend, wherein the control parameters include at least one or more of output power, duty cycle, or energy modulation mode; wherein the priority matching mechanism includes: firstly determining whether the impedance change rate component represented by the tissue state proxy parameters exceeds a first threshold; if it exceeds, prioritizing a reduction in output power as the dominant adjustment; performing subsequent corrections based on this, wherein if a delayed response occurs due to changes in dielectric properties, the duty cycle is adjusted to optimize energy distribution; if the local temperature accumulation trend exceeds a second threshold and continues to rise, the energy modulation mode is changed to limit heat deposition; when multiple components evolve simultaneously, the dominant adjustment and subsequent corrections are fused by a preset weight.

[0013] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application innovatively uses a single-wire digital audio bus as a passive physical channel affected by the electromagnetic coupling between the high-frequency energy field and surgical tissue. It extracts the physical layer perturbation characteristics in real time, constructs a coupling model with causal order constraints, and achieves indirect sensing and accurate inversion of states such as rapid changes in tissue impedance, delayed response of dielectric properties, and local temperature accumulation without additional sensors, thereby obtaining reliable tissue state proxy parameters. This method eliminates the need for dedicated impedance or temperature measurement circuits, significantly reducing equipment complexity, manufacturing costs, and electromagnetic interference risks, while providing higher robustness and early warning capabilities. Risk classification is performed based on the multidimensional evolution trajectory of the proxy parameters, and an adaptive adjustment mechanism of priority matching and weight fusion is adopted to achieve continuous and progressive closed-loop control of energy output. This effectively suppresses safety hazards such as tissue carbonization, adhesion, and overheating, while maintaining the continuity and efficacy of surgical operations, thus comprehensively improving the safety, reliability, and clinical applicability of high-frequency surgical equipment.

[0014] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A structural block diagram of a high-frequency surgical equipment control system based on digital audio bus status provided in this application embodiment; Figure 2 A flowchart illustrating a high-frequency surgical device control method based on digital audio bus status, provided in an embodiment of this application; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0019] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0020] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0021] According to another embodiment, a high-frequency surgical device control system based on digital audio bus status is provided. Figure 1 A schematic block diagram of a high-frequency surgical device control system based on digital audio bus status is shown according to one embodiment. Figure 1 As shown, the system 100 includes: The physical layer signal acquisition unit 101 is configured to use the single-line digital audio bus as a physical channel affected by the electromagnetic coupling between the high-frequency energy field and the surgical tissue during the operation of the high-frequency surgical equipment, and to acquire the physical layer signal on the single-line digital audio bus in real time.

[0022] The bus perturbation feature extraction unit 102 is configured to extract bus perturbation features from the physical layer signal to characterize the physical channel degradation characteristics. The bus perturbation features include at least jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features.

[0023] The coupling model construction unit 103 is configured to construct a coupling model between the physical channel of the single-wire digital audio bus and the electrical characteristics of the surgical tissue based on the bus perturbation characteristics. The coupling model is used to describe the influence of changes in surgical tissue impedance and local temperature on the degradation characteristics of the physical channel.

[0024] The inversion calculation execution unit 104 is configured to perform inversion calculations on the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes.

[0025] The risk evolution judgment unit 105 is configured to determine whether the high-frequency surgical device has entered the tissue safety risk evolution range based on the tissue state proxy parameters.

[0026] The control parameter adjustment unit 106 is configured to adaptively adjust the energy output control parameters of the high-frequency surgical device according to the degree of change of the tissue state proxy parameters when it is determined that the high-frequency surgical device has entered the tissue safety risk evolution range.

[0027] As can be seen from the above, this application innovatively uses a single-wire digital audio bus as a passive physical channel affected by the electromagnetic coupling between the high-frequency energy field and surgical tissue. It extracts the physical layer perturbation characteristics in real time, constructs a coupling model with causal order constraints, and achieves indirect sensing and accurate inversion of states such as rapid changes in tissue impedance, delayed response of dielectric properties, and local temperature accumulation without additional sensors, thereby obtaining reliable tissue state proxy parameters. This method eliminates the need for dedicated impedance or temperature measurement circuits, significantly reducing equipment complexity, manufacturing costs, and electromagnetic interference risks, while providing higher robustness and early warning capabilities. Risk classification is performed based on the multidimensional evolution trajectory of the proxy parameters, and an adaptive adjustment mechanism of priority matching and weight fusion is adopted to achieve continuous and progressive closed-loop control of energy output. This effectively suppresses safety hazards such as tissue carbonization, adhesion, and overheating, while maintaining the continuity and efficacy of surgical operations, thus improving the overall safety, reliability, and clinical applicability of high-frequency surgical equipment.

[0028] The following describes in detail each unit in the above system and the effects that can be further produced, with reference to the embodiments. First, with reference to the embodiments, the "physical layer signal acquisition unit 101, configured to, during the operation of the high-frequency surgical equipment, use the single-line digital audio bus as a physical channel affected by the electromagnetic coupling between the high-frequency energy field and the surgical tissue, and acquire the physical layer signal on the single-line digital audio bus in real time" will be described in detail.

[0029] During the operation of high-frequency surgical equipment, high-frequency current acts on human tissue through electrodes, generating a strong electromagnetic field. This electromagnetic field not only directly heats the tissue but also produces parasitic coupling effects through the cables and the internal physical structure of the equipment, interfering with or even modulating surrounding electronic signals. A single-wire digital audio bus was originally a communication link used to transmit voice prompts, alarm tones, or other low-speed digital audio signals. Its physical layer signals include critical timing information such as bit clock, data edges, and frame synchronization. These signals are extremely sensitive to clock jitter, phase shifts, and amplitude fluctuations, and the high-frequency electromagnetic field can indirectly transmit electrical changes at the tissue end to the bus through the parasitic capacitance, inductance, and distributed resistance of the cable.

[0030] As an feasible approach, physical layer signal acquisition includes real-time high-resolution sampling of the bit clock signal, data edge signal, and frame synchronization signal, with a sampling frequency no less than eight times the nominal clock frequency of the single-wire digital audio bus. A sampling frequency no less than eight times the nominal clock frequency of the single-wire digital audio bus is a key requirement for achieving high resolution. For example, if the nominal clock frequency of a common audio bus is several megahertz, the sampling frequency needs to reach tens of megahertz or higher. This oversampling design ensures multiple sampling points per bit cycle, enabling accurate reconstruction of the edge transition process and differentiation of transient jitter or phase drift caused by high-frequency electromagnetic coupling. If the sampling frequency is too low, for example, only equal to or slightly higher than the nominal clock frequency, many minute perturbations will be averaged or lost, making it impossible to reliably separate the instantaneous response caused by rapid changes in tissue impedance from other slowly varying effects.

[0031] When high-frequency energy is applied to surgical tissue, the tissue's impedance changes rapidly with processes such as heating, dehydration, and carbonization. This impedance change causes transient fluctuations in the high-frequency current, which further affect the distributed parameters of the single-wire bus cable through electromagnetic near-field coupling. This results in minute jitter in the bit clock on the bus, increased phase noise, or weak modulation of the data signal amplitude. Although these changes are very small and are usually considered noise, they can be detected and quantified under precise acquisition. The invention transforms the single-wire digital audio bus from a simple communication channel into a passive physical channel, utilizing its inherent sensitivity to electromagnetic disturbances to indirectly sense changes in the electrical state of the tissue without the need for additional dedicated sensors or measurement circuits.

[0032] Real-time acquisition of physical layer signals on the single-wire digital audio bus refers to high-resolution sampling of bit clock edges, data transition moments, and frame synchronization pulses at a sampling rate several times higher than the bus's nominal clock frequency while the device is operating normally. This high sampling rate acquisition can capture transient disturbances at the nanosecond to microsecond level, thus completely recording the perturbation characteristics caused by high-frequency electromagnetic field coupling. These characteristics include the time series of jitter, the spectral distribution of phase noise, and the sideband information of amplitude modulation, which together constitute a "mirror image" of tissue state changes on the bus physical layer. Through subsequent feature extraction and model inversion, these perturbation signals can be transformed into meaningful surrogate parameters such as rapid changes in tissue impedance, delayed response of dielectric properties, and local temperature accumulation, achieving non-invasive, real-time tissue state perception.

[0033] The following describes in detail, with reference to an embodiment, the "bus perturbation feature extraction unit 102, configured to extract bus perturbation features from the physical layer signal to characterize the physical channel degradation characteristics, wherein the bus perturbation features include at least jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features."

[0034] Extracting bus perturbation features from the physical layer signals is a crucial step in the entire method, transforming raw sampled data into data usable for tissue state sensing. These physical layer signals, after high-resolution acquisition, contain timing deviations in the bit clock, phase shifts in data edges, and minute fluctuations in the amplitude envelope. These subtle changes, which might otherwise be considered noise, actually carry indirect information about the tissue's electrical characteristics under the influence of high-frequency electromagnetic fields and tissue electromagnetic coupling. The extraction process aims to separate features directly related to physical channel degradation from these signals, thereby characterizing the degree of "degradation" of the bus as a passive sensing channel—a degradation resulting from the influence of high-frequency energy fields on the bus through the tissue.

[0035] Jitter spectrum characteristics are one of the primary aspects to extract. The edge positions of a bit clock signal can be randomly or periodically advanced or delayed due to electromagnetic coupling; this timing instability is called jitter. By performing spectral analysis on the acquired edge time series, the power distribution of jitter at different frequencies can be obtained, i.e., the jitter spectrum. Low-frequency jitter often corresponds to the instantaneous electromagnetic response caused by rapid changes in tissue impedance, while mid-to-high frequency jitter may reflect environmental interference or random noise. This spectral characteristic provides a sensitive indicator of dynamic impedance changes for subsequent inversion.

[0036] The frequency domain characteristics of phase noise focus on the phase stability of the signal. Phase noise refers to the random deviation of a bit clock or data signal from its ideal phase. The power spectral density distribution of phase noise can be obtained by performing a Fast Fourier Transform or phase demodulation on the acquired signal. Low-frequency phase noise drift is usually related to delayed electromagnetic responses caused by changes in the dielectric properties of tissue; for example, tissue heating causes a slow change in the dielectric constant, thus modulating the phase stability on the bus. High-frequency phase noise is more affected by external electromagnetic interference. By quantifying the difference in noise power between low-frequency and high-frequency bands, it is possible to effectively distinguish between slowly varying phase disturbances caused by tissue and transient noise, thus providing a reliable basis for characterizing the delayed response of dielectric properties.

[0037] Amplitude modulation sideband characteristics focus on subtle changes in signal amplitude. High-frequency electromagnetic fields, through impedance fluctuations in the cable structure, can induce parasitic modulation effects, causing periodic fluctuations in the envelope of data or clock signals. These amplitude variations manifest in the frequency domain as symmetrical or asymmetrical sidebands on either side of the carrier wave. Envelope detection and spectral analysis can extract the width, amplitude, and symmetry of these sidebands. Slow-varying thermal effects caused by localized temperature accumulation often result in asymmetrical sideband characteristics in amplitude modulation, as temperature gradients cause asymmetrical changes in local cable parameters. This characteristic is particularly sensitive to slow-varying temperature accumulation processes and can serve as an indirect indicator of early signs of carbonization or thermal deposition in the cable structure.

[0038] Furthermore, the extraction of the bus perturbation features includes: performing multi-scale averaging time analysis on the jitter spectrum features to separate the different contributions of white noise, flicker noise, and random walk noise; performing frequency domain integration on the phase noise spectrum features to quantify the difference in noise power distribution between the low-frequency and high-frequency bands; and performing envelope detection and sideband symmetry evaluation on the amplitude modulation sideband features to identify asymmetric modulation effects caused by the organization.

[0039] Multi-scale averaging time analysis of jitter spectral characteristics is a core step in extracting jitter information. Jitter manifests as random or systematic deviations in the position of bit clock edges, exhibiting different statistical characteristics at different time scales. By calculating the variance or deviation of jitter over multiple averaging time windows, such as gradually expanding from short to long time scales, different types of noise contributions can be effectively separated. White noise has a uniform power distribution across all scales, flicker noise is stronger at longer scales, and random walk noise shows a cumulative trend over long scales. This multi-scale analysis can highlight systematic jitter components caused by rapid changes in tissue impedance, as these jitters often produce significant power peaks at medium time scales, while random environmental interference tends to be more uniformly distributed. Through this separation, the system can more accurately map jitter spectral characteristics to the intensity and rate of the tissue's instantaneous electromagnetic response.

[0040] Frequency domain integration of the phase noise spectral characteristics is used to quantify the overall distributional differences in phase stability. First, the power spectral density of the phase noise is obtained from the signal through Fast Fourier Transform or phase demodulation. Then, integration is performed over a specific offset frequency range in the frequency domain. The integrated value in the low-frequency range reflects the slow accumulation of phase drift, typically corresponding to the delayed electromagnetic response caused by changes in tissue dielectric properties, such as the gradual change in dielectric constant due to tissue heating. The integrated value in the high-frequency range captures more transient noise and external interference. By comparing the differences between the integration results in the low-frequency and high-frequency ranges, the degree of noise power transfer from high to low frequencies can be quantified, thereby identifying phase noise enhancement dominated by slow tissue processes. This frequency domain integration method not only provides the sum of noise energy but also highlights the unevenness of frequency distribution, providing a reliable numerical indicator for characterizing the delayed response of dielectric properties.

[0041] Envelope detection and sideband symmetry assessment of amplitude modulation sideband characteristics are specialized processing methods for signal amplitude variations. Envelope detection extracts the instantaneous amplitude envelope of the data or clock signal through Hilbert transform or low-pass filtering, revealing subtle fluctuations in amplitude over time. These fluctuations manifest as sideband components on either side of the carrier wave in the frequency domain. The modulation effect of high-frequency electromagnetic fields on the bus through impedance fluctuations often leads to sideband asymmetry: one sideband has a larger amplitude, while the other side has a smaller amplitude. This asymmetry usually stems from asymmetrical changes in cable parameters caused by localized temperature accumulation, such as differences in local resistance or capacitance due to temperature gradients. By calculating symmetry indices of sideband amplitude, such as the power ratio or phase difference between the left and right sidebands, specific modulation signatures caused by the structure can be identified. This assessment method is particularly sensitive to slow-heating effects and can serve as an indirect indicator of localized temperature accumulation and early signs of carbonization.

[0042] By simultaneously extracting three types of features—jitter spectrum, phase noise spectrum, and amplitude modulation sidebands—the system constructs a multi-dimensional perturbation feature space. These features are causally related: rapid impedance changes first affect jitter, phase noise subsequently responds to dielectric changes, and amplitude sidebands finally reflect temperature accumulation. This multi-feature joint extraction method ensures a comprehensive characterization of the degradation characteristics of the bus physical channel, providing a rich and complementary information foundation for subsequent inversion calculations of the coupled model, thereby achieving high-precision, non-intrusive perception of organizational state change trends.

[0043] The following describes in detail, with reference to an embodiment, the "coupling model construction unit 103, configured to construct a coupling model between the physical channel of a single-wire digital audio bus and the electrical characteristics of surgical tissue based on the bus perturbation characteristics, wherein the coupling model is used to describe the influence relationship between the impedance changes and local temperature changes of surgical tissue on the degradation characteristics of the physical channel."

[0044] The construction process first considers the dynamic evolution sequence of tissue electrical properties. When high-frequency energy is applied to the tissue, its impedance changes rapidly, within milliseconds, primarily due to tissue dehydration, protein denaturation, and abrupt changes in conductivity. This immediate response, through electromagnetic near-field coupling, initially affects the distributed parameters of the bus cable, leading to a significant increase in low-to-mid-frequency power in the bit clock jitter spectrum. Subsequently, tissue heating causes a slow change in dielectric constant; this delayed response manifests in the hundreds of milliseconds to seconds, further modulating the phase stability of the bus and shifting the phase noise spectrum to lower frequencies. Finally, the continuous accumulation of local temperature causes tissue carbonization or thermal deposition, producing a slower amplitude modulation effect, manifested as an asymmetric sideband expansion of the bus signal envelope. The coupling model, based on this causal temporal sequence, correlates the three perturbation characteristics according to an irreversible response sequence, avoiding the causal confusion that might result from simple linear superposition.

[0045] The model can take the form of a hierarchical structure or a parameterized mapping function. At the bottom layer, the electromagnetic coupling layer characterizes the near-field influence of high-frequency current on the bus cable through parasitic parameters, such as the dynamic adjustment of equivalent parasitic capacitance with tissue impedance changes, and the contribution of parasitic inductance to phase drift. The middle layer introduces a noise separation mechanism, performing multi-scale decomposition of the jitter spectrum, frequency domain integration of phase noise, and asymmetric quantification of amplitude sidebands to extract the pure tissue coupling contribution. The upper state mapping layer establishes the correspondence between feature vectors and tissue electrical parameters, for example, mapping the jitter power peak to the impedance change rate, mapping the low-frequency phase noise integral value to the dielectric constant drift amplitude, and mapping the sideband asymmetry to the temperature gradient intensity. This mapping can be calibrated using pre-operative phantom experiments to obtain initial parameters, and then adaptively adjusted according to power levels or tissue types during actual surgery.

[0046] As an implementable approach, a coupling model between the physical channel of a single-line digital audio bus and the electrical characteristics of surgical tissue is constructed based on the bus perturbation characteristics. This includes: during the energy output process of a high-frequency surgical device, modeling the bus perturbation characteristics using causal order constraints according to the order of physical responses after energy acts on the surgical tissue. The causal order constraint modeling includes: first, determining the instantaneous electromagnetic coupling response caused by rapid changes in the equivalent impedance of the surgical tissue based on changes in jitter spectrum characteristics; then, determining the delayed electromagnetic response caused by changes in the dielectric properties of the surgical tissue based on changes in phase noise spectrum characteristics; and finally, determining the slow modulation response caused by the cumulative local temperature changes of the surgical tissue based on changes in amplitude modulation sideband characteristics. Through these causal order constraints, the jitter spectrum characteristics, phase noise spectrum characteristics, and amplitude modulation sideband characteristics are correlated in a non-commutative response order to form a coupling model describing the impact of changes in the electrical characteristics of the surgical tissue on the degradation of the physical channel of the single-line digital audio bus.

[0047] Specifically, during the energy output of high-frequency surgical equipment, tissue first experiences a rapid change in equivalent impedance. This change occurs in milliseconds and is mainly caused by tissue cell membrane rupture, electrolyte outflow, and dehydration, leading to a sudden and dramatic change in the conductivity of the current path. This instantaneous response is first transmitted to the single-wire digital audio bus cable via electromagnetic near-field coupling, manifesting as significant jitter at the bit clock edge. Therefore, the first step in causal sequence constraint modeling is to determine the instantaneous electromagnetic coupling response based on the change in jitter spectrum characteristics. By analyzing the power peak or spectral shape changes in the jitter spectrum in the low-to-mid frequency band, the amplitude and rate of the rapid rise or fall of impedance can be quantified. This feature is used as the highest priority input layer in the model to capture the forefront information of tissue impedance dynamics. The jitter spectrum characteristics are used as the main input. The system first calculates the deviation of jitter at multiple average time scales (e.g., through multi-scale Allan variance or similar statistics) to extract the power peak or spectral energy concentration in the low-to-mid frequency band. Then, through a linear or nonlinear mapping function (e.g., weighted summation or simple neuron form), the instantaneous impedance response is obtained. This quantity directly characterizes the rapid rate and magnitude of change in tissue impedance, and is the driving signal at the forefront of the model.

[0048] Subsequently, tissue heating causes changes in dielectric properties, a process that is relatively delayed, typically manifesting within hundreds of milliseconds to several seconds. The change in dielectric constant affects the tissue's polarization response to high-frequency electric fields, further modulating the phase propagation characteristics of the bus cable. The second step of causal sequence constraint modeling is to determine this delayed electromagnetic response based on changes in the phase noise spectrum characteristics. Low-frequency drift or power density increases in the phase noise spectrum often correspond to a gradual change in the dielectric constant. By extracting the integral value or spectral slope change of the phase noise within a specific offset frequency range, the strength and delay of this delay element can be characterized. This sequential design ensures that the model does not incorrectly attribute the phase noise response to instantaneous impedance changes, thus avoiding modeling errors caused by causal inversion. Inputs include phase noise spectrum characteristics, as well as the previous layer's... As a constraint, the system integrates the phase noise power spectrum in the low-frequency range (e.g., the offset frequency range of 0.1 Hz to 100 Hz) to obtain the accumulated phase drift energy; simultaneously, it combines... The delayed version (achieving a hysteresis of several hundred milliseconds through a digital delay filter) uses another mapping function to calculate the dielectric change response. This step ensures that the delayed response is activated or amplified only when an instantaneous impedance change has occurred and accumulated to a certain extent, reflecting the physical causal hysteresis.

[0049] Finally, the continuous accumulation of local temperature triggers slow-varying processes such as tissue carbonization, protein coagulation, or thermal deposition, with response timescales typically ranging from several seconds to tens of seconds. The third step of causal sequence constraint modeling determines the slow-varying modulation response based on changes in amplitude modulation sideband characteristics. Temperature gradients cause asymmetric changes in local cable parameters, resulting in an asymmetric modulation effect in the amplitude envelope. Sideband width expansion, symmetry reduction, or enhancement of specific frequency components obtained through envelope detection can reflect the long-term impact of temperature accumulation on the bus signal. This step serves as the final input to the model, ensuring that the slow-varying process does not interfere with the judgment of instantaneous and delayed responses. The inputs are amplitude modulation sideband characteristics (sideband width, asymmetry, envelope energy, etc.) and the outputs of the first two layers. and The cumulative version (achieving a time constant of over a second through low-pass filtering or integrators). The cumulative temperature response is obtained through a similar mapping function. This emphasizes the slow-varying characteristic: this layer only contributes significantly when the responses of the first two layers continuously exceed a certain time window threshold, thus avoiding transient interference that could misjudge the temperature process.

[0050] Ultimately, the output of the coupled model is a fused vector or scalar surrogate parameters. Weighted fusion is typically used.

[0051] Among them, weight (And the mapping coefficients within each layer) are obtained in advance through phantom experiments or animal experiments, and are adaptively adjusted online according to power level and tissue type (fat / muscle, etc.) during equipment initialization or surgical intervals (e.g., using lookup tables or simple regression updates).

[0052] By constraining the causal order, the three perturbation features are correlated according to a non-commutative response order, forming a hierarchical and progressive coupling model. This non-commutativity emphasizes the temporal dependence between features: the jitter response must come first, the phase noise response follows with a lag, and the amplitude sideband response appears last; any reversal of the order will destroy physical realism. The model ultimately describes how changes in surgical tissue impedance and local temperature progressively affect the degradation characteristics of the physical channel of a single-wire digital audio bus, providing a causally clear and traceable framework for subsequent inversion calculations.

[0053] The following describes in detail, with reference to the embodiments, the "inversion calculation execution unit 104, configured to perform inversion calculation on the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes".

[0054] The purpose of the evolution calculation is to infer the actual state changes within the tissue from the observable degradation behavior of the bus physical channel, thereby obtaining one or more surrogate parameters that can characterize the trend of the tissue state. These surrogate parameters are not directly measured impedance or temperature values, but rather comprehensive indices indirectly derived through models, used to reflect the rate of change of tissue impedance, the magnitude of dielectric property drift, and the direction, intensity, and evolution speed of local temperature accumulation trends.

[0055] The inversion calculation is first performed within the constrained framework of the coupled model. The model has already correlated the jitter spectrum, phase noise spectrum, and amplitude modulation sideband features in a causal order; therefore, the inversion process strictly adheres to this order: tracing upwards layer by layer from the instantaneous response layer. The system inputs the perturbation feature vectors acquired and extracted in real time into the model. First, at the instantaneous response layer, the jitter spectrum features are inversely mapped to calculate the rapidly changing component of the tissue's equivalent impedance. This component serves as a known condition, constraining the calculations of subsequent layers and avoiding ambiguities or errors that might result from disordered inversion.

[0056] Next, the inversion proceeds to the delayed response layer. The model utilizes the low-frequency integral or spectral drift of the phase noise spectrum characteristics, combined with the time-delayed version of the instantaneous impedance change component, to perform an inverse solution, obtaining the response to the change in dielectric properties. This layer of calculation emphasizes the delay effect: the dielectric response is only significantly activated when the instantaneous impedance change has occurred and reached a certain threshold. Through this constraint, the inversion results can more accurately reflect the gradual change in the dielectric constant after tissue heating, without misinterpreting early transient disturbances as dielectric drift.

[0057] Finally, the inversion process reaches the slowly varying response layer. Here, the model uses the asymmetry and width expansion of the amplitude modulation sideband features as the main inputs, while simultaneously fusing the accumulated historical values ​​from the outputs of the first two layers to perform inverse calculations, deriving the response quantity of local temperature accumulation. This layer has the longest time scale, processing multi-cycle data through integration or low-pass filtering to capture the long-term modulation effect of the temperature gradient on the bus signal. The entire inversion employs an iterative or layer-by-layer recursive approach, ensuring that the output of each layer serves as a constraint condition for the next layer, forming a closed-loop feedback.

[0058] As an implementable approach, the inversion calculation of the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes includes: mapping the bus perturbation features to a feasible domain of state changes corresponding to the physical response of the surgical tissue under the constraints of the coupling model; determining the dominant evolutionary trend of surgical tissue state changes within the feasible domain of state changes based on the evolution direction and rate of change of the bus perturbation features in a continuous time window; and outputting the dominant evolutionary trend as the tissue state proxy parameters, which are used to characterize the direction and intensity of change of the surgical tissue state.

[0059] Specifically, the inversion calculation first maps the bus perturbation features to a feasible region of state change corresponding to the physical response of the surgical tissue, under the constraints of the coupled model. This feasible region is composed of a reasonable range predefined by the model, such as the impedance change rate not exceeding the physical limits of biological tissue, dielectric drift lagging behind the impedance response, and temperature accumulation showing a gradual trend. The mapping process uses jitter spectrum, phase noise spectrum, and amplitude modulation sideband features as input vectors, and projects them into this feasible region through the model's layer-by-layer inverse function or constraint optimization method. If certain feature combinations exceed the boundaries of the feasible region, such as excessively high jitter power without corresponding phase noise delay, they will be considered noise interference and suppressed or corrected. This constrained mapping ensures that the inversion results conform to the physical laws of tissue electromagnetic-thermal coupling, avoiding unphysical or unreasonable output values ​​that may be generated by unconstrained inversion.

[0060] Within the feasible region of state change, the inversion calculation further determines the dominant evolutionary trend of surgical tissue state changes based on the evolution direction and rate of change of bus perturbation features within continuous time windows. The system tracks the feature vector sequence within each time window and calculates its directional derivative or rate of change, such as the rising slope of jitter spectrum power, the cumulative increment of the low-frequency integral value of phase noise, and the trend direction of amplitude sideband asymmetry. These rate and direction indicators are weighted and fused to form a dominant trend vector. The dominant trend reflects the main driving force of the tissue state evolution from stable to risky, such as the cutting stage dominated by rapid impedance rise and the coagulation risk stage dominated by temperature accumulation. Through continuous window smoothing, the system can filter out transient noise interference and highlight the true tissue evolution path.

[0061] Ultimately, the dominant evolutionary trend is output as a surrogate parameter for the tissue state. This parameter can be a scalar trend score, used to simply represent the overall evolution of the tissue from safe to high-risk, or it can be a multi-dimensional vector, corresponding to components such as the direction and intensity of impedance change, dielectric drift direction and intensity, and temperature accumulation direction and intensity. The direction of change of the surrogate parameter indicates whether the tissue state is tending towards stability, slow deterioration, or rapid risk, while the intensity of the change quantifies the severity of the evolutionary process. For example, when the surrogate parameter shows a sharp positive increase and the intensity exceeds a threshold, it indicates that the tissue is rapidly entering the risk range of carbonization or adhesion; conversely, negative or slow changes correspond to normal cutting or tissue recovery processes.

[0062] Through the above inversion calculations, surrogate parameters for tissue state are finally output. These parameters can be a multi-dimensional vector, including components for impedance change rate, dielectric drift intensity, and temperature accumulation, or they can be fused into a single trend index to characterize the overall direction and intensity of the evolution of surgical tissue from normal to risk. For example, when the surrogate parameters show a sharp increase in impedance rate and a sustained increase in temperature accumulation, it indicates an increased risk of carbonization or adhesion. The changing trends of the surrogate parameters are tracked and filtered within a continuous time window to suppress transient noise interference and improve the stability of trend judgment.

[0063] The following describes in detail, with reference to the embodiments, the “risk evolution judgment unit 105, which is configured to determine whether the high-frequency surgical device has entered the tissue safety risk evolution range based on the tissue state proxy parameters”.

[0064] Determining whether a high-frequency surgical device has entered a tissue safety risk evolution range based on tissue state surrogate parameters is the core link in the risk identification and early warning system of the entire closed-loop control system. This judgment process no longer relies on a single threshold or isolated parameter, but instead uses the dynamic evolution trajectory of the surrogate parameter within a continuous time window as the main basis, thereby achieving a continuous and gradual assessment of the tissue state from normal to dangerous. The surrogate parameter contains multiple components, such as impedance change rate, dielectric drift intensity, and temperature accumulation trend. These components together constitute a multi-dimensional profile of the tissue's electrical characteristics. By tracking the temporal changes of these components, the system can capture early evolution signals of tissue risk, avoiding the lag or misjudgment of traditional single-indicator judgments.

[0065] The determination is first based on the evolution trajectory of the surrogate parameter within a continuous time window, calculating the direction, rate of change, and continuity of change for each component. The direction of change indicates whether the surrogate parameter is continuously rising, falling, or oscillating; for example, a positive increase in the rate of change in impedance indicates a rapid increase in tissue resistance. The rate of change is calculated using the slope or difference within the window, used to quantify the speed of evolution. Continuity of change examines the stability of the parameter over multiple continuous windows, such as whether it exhibits a monotonic trend, frequent reversals, or anomalous jumps. These indicators are extracted in real time using methods such as digital filtering, sliding window averaging, or trend fitting to ensure the calculation results are robust to transient noise. Only when these dynamic characteristics exhibit a systematic rather than random pattern are they considered evidence of the true evolution of tissue state.

[0066] Based on the multidimensional synergistic characteristics of the evolutionary trajectory, the system divides the surgical tissue state into at least three evolutionary stages: a safe evolutionary stage, a risk warning evolutionary stage, and a high-risk evolutionary stage. The safe evolutionary stage corresponds to stable surrogate parameter changes, low rates, and good continuity, typically occurring during normal tissue cutting or initial heating. The risk warning evolutionary stage is characterized by some components beginning to accelerate, such as impedance change rates exceeding a certain level and a significant delayed response in dielectric drift, but temperature accumulation is not yet significant, indicating that the tissue is entering a transitional period with potential risk. The high-risk evolutionary stage is characterized by distinct features, with all components deteriorating synergistically: a sharp increase in impedance rate, a strong lag in dielectric response, and a continuously positive trend in temperature accumulation, indicating that carbonization, adhesion, or overheating is imminent. This three-stage division, based on multidimensional synergy rather than a single parameter threshold, can more accurately reflect the overall evolutionary laws of tissue physical processes.

[0067] When the evolution trajectory of the tissue state surrogate parameters meets the judgment rules for the risk warning evolution stage or the high-risk evolution stage, the high-frequency surgical device is determined to have entered the tissue safety risk evolution range. These judgment rules are pre-established based on the temporal correlation of the surrogate parameters and the causal coupling relationship between different surrogate parameters. For example, the rules may stipulate that if the impedance change rate continuously exceeds the first threshold and the change direction continues positively for at least three windows, while the dielectric drift intensity begins to follow positively, then the risk warning stage is entered; if the temperature accumulation component further exceeds the second threshold and shows strong temporal coupling with the first two items, then it is upgraded to the high-risk stage. This rule design makes full use of the causal order constraints embedded in the coupling model to ensure that the judgment logic is highly consistent with the tissue physical response process, avoiding false triggering without causal relationship.

[0068] This judgment mechanism is typically embedded in the real-time loop of the device's main control software. After updating the agent parameters at each time window, trajectory analysis, stage division, and rule matching are performed. The entire process is computationally efficient, mainly relying on vector operations, threshold comparisons, and simple correlation tests, making it suitable for the resource constraints of medical equipment. Through this judgment method based on multidimensional evolutionary trajectories and causal coupling, the system can issue early warnings when risks occur and provide accurate grading criteria for subsequent adaptive energy adjustment, thereby significantly improving the safety and operational controllability of high-frequency surgeries.

[0069] The following describes in detail, with reference to the embodiments, the "control parameter adjustment unit 106 is configured to adaptively adjust the energy output control parameters of the high-frequency surgical device according to the degree of change of the tissue state proxy parameters when it is determined that the high-frequency surgical device has entered the tissue safety risk evolution range".

[0070] Upon entering the risk evolution phase, the system first comprehensively assesses the changes in surrogate parameters within the current and recent consecutive time windows. If the surrogate parameters generally show a positive accelerating trend and high continuity, indicating that organizational risk is rapidly evolving, adaptive adjustment is triggered. The goal of the adjustment is to change energy output control parameters in a continuous and gradual manner, including at least one or more of the following: output power, duty cycle, and energy modulation mode. Continuous and gradual means that the adjustment is not an abrupt switch or a large jump, but rather a smooth transition, such as a gradual decrease in power with a ramp function, a slow decrease in duty cycle, or a gradual change in modulation mode from a continuous wave to a low duty cycle pulse. This gradual adjustment can effectively avoid secondary thermal shock to the tissue, while providing the tissue with a brief cooling gap to mitigate further risk deterioration.

[0071] Within a continuous time window, the system first determines the current risk evolution trend of the surgical tissue based on the evolution direction, rate of change, and continuity of change of the surrogate parameters. The evolution direction indicates whether the surrogate parameter is rising positively or falling negatively; for example, a continuously positive impedance change rate component indicates a rapid increase in tissue resistance. The rate of change is quantified by slope or difference calculation within the window to determine the speed of evolution. The continuity of change examines the stability of the parameter across multiple windows, such as whether it exhibits a monotonic trend or fluctuates. Through comprehensive analysis of these dynamic characteristics, the system derives the currently dominant risk type, such as the risk of cutting overheating dominated by a rapid increase in impedance, or the risk of thermal deposition during the coagulation stage dominated by temperature accumulation. This trend determination method makes the adjustment decisions more closely aligned with the actual physical processes of the tissue, avoiding the lag or overreaction of static threshold judgments.

[0072] Based on the determined risk evolution trend, the system adjusts the energy output control parameters of the high-frequency surgical equipment in a continuous and gradual manner. These control parameters include at least one or more of the following: output power, duty cycle, and energy modulation mode. Continuous and gradual adjustment means that the adjustment is not an abrupt switching action, but a smooth transition, such as gradually reducing the power from the current value, slowly decreasing the duty cycle, or gradually changing the modulation mode from a continuous wave to a pulse form. This gradualism is achieved through digital control algorithms, typically using PID-like or ramp functions, to ensure that changes in energy output are gradual, avoiding secondary shocks to tissues or surgical interruptions.

[0073] The priority matching mechanism is the core logic in the regulation process, used to handle decision priorities when multiple components co-evolve. First, it determines whether the impedance change rate component in the organizational state proxy parameters exceeds a first threshold. If it does, output power is preferentially reduced as the dominant regulation. This priority stems from the physical fact that rapid impedance changes directly affect power matching and arc risk, making it the most urgent response target. Reducing power can immediately decrease energy deposition and alleviate overheating or ineffective output caused by impedance mismatch. Further corrections are made based on this: if the dielectric property change shows a significant delayed response, such as a significant increase in the phase noise integral value after impedance change, the duty cycle is adjusted to optimize energy distribution, for example, by reducing the duty cycle to extend the cooling gap and improve heat diffusion; if the local temperature accumulation trend exceeds a second threshold and continues to rise, the energy modulation mode is further changed, for example, by switching from continuous wave to intermittent pulse or reducing the pulse frequency, to limit the heat deposition rate and prevent further carbonization.

[0074] When multiple components evolve simultaneously, the system uses preset weights to fuse the primary adjustment and subsequent corrections, avoiding overlooking other risks with a single adjustment. For example, if impedance velocity and temperature accumulation both exceed limits, the primary adjustment reduces power while simultaneously adjusting the duty cycle and switching modulation modes according to weighted proportions. This fusion typically employs a weighted summation or state machine approach, with weights pre-calibrated based on clinical trials or phantom validation and fine-tuned during equipment operation according to power levels or tissue types. Through this mechanism of priority matching and weighted fusion, the adjustment process ensures rapid response to the most urgent risks while achieving coordinated control of multiple risk factors. Ultimately, it maintains the continuity and clinical effectiveness of high-frequency surgical procedures while suppressing safety hazards such as tissue carbonization, adhesion, and overheating.

[0075] To further illustrate the technical effects of this application, a specific implementation method and the test results of this implementation method are given below.

[0076] In this embodiment, the high-frequency surgical device adopts a main unit + handpiece structure. The main unit has a built-in high-frequency power generator (operating frequency 400 kHz to 5 MHz, maximum output power 150 W) and a main control MCU (based on an ARM Cortex-M7 core, operating frequency 480 MHz). A single-wire digital audio bus uses a custom single-wire protocol (based on an I²S variant) with a nominal clock frequency of 12.288 MHz, used to transmit voice alarms and device status prompts. The main unit is equipped with a high-speed ADC module (sampling rate not less than 100 MSPS) to perform real-time high-resolution sampling of the bit clock signal, data edge signal, and frame synchronization signal on the bus. The sampling frequency is set to 10 times the nominal clock frequency, i.e., 122.88 MSPS. The sampled data is transferred to the MCU's buffer via DMA, and the feature extraction window is updated every 5 ms.

[0077] The feature extraction module is implemented in the MCU using digital signal processing algorithms. First, a multi-scale averaging time analysis is performed on the bit clock edge time series (averaging time scale from 10). (Up to 10 ms, 8 scales in total) Allan bias is calculated to separate white noise, flicker noise, and random walk noise, and the low-to-mid frequency power peaks of jitter spectrum characteristics are extracted. Next, the instantaneous phase sequence is obtained by Hilbert transform of the phase noise, followed by FFT calculation of the power spectral density. Frequency domain integration is then performed within the offset frequency range of 0.1 Hz to 1 kHz to quantize the low-frequency noise power distribution. Finally, envelope detection (low-pass filter cutoff frequency 100 kHz) is performed on the data signal amplitude to extract the sideband spectrum and calculate the left-to-right sideband power ratio as an asymmetry index.

[0078] The coupling model employs a three-layer causal constraint structure, implemented in the MCU as C language functions. The first layer is the immediate response layer: ,in This represents the power integral value in the low-frequency band of the jitter spectrum. This refers to the quantitative metric of "peak jitter" or "maximum jitter amplitude" obtained after analyzing the bit clock jitter sequence during the jitter spectrum feature extraction process. Second layer delay response layer: (Delay 200 ms). Third layer, slow-varying response layer: The accumulated values ​​are obtained through a first-order low-pass filter (time constant 5 s). Final organizational state proxy parameters. .

[0079] The risk assessment module executes every 100 ms, analyzing the evolution trajectory of the surrogate parameter P over the past 2 s window. It calculates the direction of change (sign), rate of change (slope), and continuity (monotonic trend count). If P continuously increases and the rate exceeds 0.15 / s, it enters the risk warning stage; if P further exceeds 0.7 and the temperature component... If the impedance component rate is greater than 0.2 / s, the output power is gradually reduced by 5% per cycle (dominant adjustment). If the dielectric component delay response is significant, the duty cycle is reduced by 10%–30%. If the temperature component continues to rise, the modulation mode is switched from continuous wave to pulse mode (20% duty cycle, 500 Hz frequency). When multiple components evolve simultaneously, the adjustment amount is fused according to the above weights.

[0080] In a pig liver phantom experiment, the high-frequency electrosurgical prototype of this embodiment was used for cutting and coagulation tests (power set at 80 W, duration 60 s). Compared with the traditional direct impedance measurement closed-loop control scheme, this method can detect a significant increase in the surrogate parameter P in the early stage of tissue impedance surge (approximately 8–12 s before carbonization), triggering power reduction and mode switching in advance, reducing the carbonization rate from 42% in the control group to 18%. Under the interference conditions of simulated cable aging (increased parasitic capacitance by 20%), the accuracy of this method in judging tissue state trends remained above 92%, while the accuracy of the traditional scheme decreased to 65%. Clinical simulated surgery (pig model, n=15) showed that this method effectively reduced electrode adhesion events (incidence rate reduced to 7%), and the average surgical continuity score (operator's subjective evaluation) improved by 18%. The above test results indicate that this invention has higher early warning capability and safety control performance under strong electromagnetic interference and complex tissue conditions.

[0081] The methods provided in this application can be applied to various scenarios, including but not limited to: First, in laparoscopic or open hepatobiliary surgery, when cutting liver tumors or stopping bleeding, rapid changes in tissue impedance can easily lead to electrode adhesion or local carbonization. This method passively senses early perturbation characteristics through a single-wire digital audio bus, which can trigger a gradual reduction in power and pulse mode switching in advance, effectively reducing adhesion events and protecting normal liver tissue. Second, in gynecological myomectomy or endometrial ablation surgery, the process of tissue dehydration and temperature accumulation is complex. Traditional methods often have delayed warnings, which can easily cause uterine perforation or excessive thermal damage. This invention utilizes a causal sequence-constrained inversion model to accurately track dielectric delay response and slow temperature change trends, adaptively adjust the duty cycle and modulation mode, maintain uniformity of coagulation depth, and reduce the risk of complications. Third, in delicate tissue cutting procedures in otolaryngology or plastic surgery (such as sinus or skin growth removal), where the surgical area is small and cable interference is severe, this method eliminates the need for additional sensors. It utilizes existing audio buses to provide highly robust tissue state sensing even in strong electromagnetic environments, ensuring precise and controllable energy output and avoiding damage to adjacent nerves or blood vessels. This approach is particularly suitable for minimally invasive or day surgery scenarios with extremely high safety requirements, comprehensively improving the intelligence level and clinical applicability of high-frequency electrosurgical units.

[0082] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0083] Figure 2 A flowchart illustrating a high-frequency surgical device control method based on digital audio bus status, provided in an embodiment of this application. Figure 2 As shown, the method may include the following steps: Step 201: During the operation of the high-frequency surgical equipment, the single-line digital audio bus is used as a physical channel affected by the electromagnetic coupling between the high-frequency energy field and the surgical tissue, and the physical layer signal on the single-line digital audio bus is collected in real time.

[0084] Step 202: Extract bus perturbation features from the physical layer signal to characterize the physical channel degradation characteristics. The bus perturbation features include at least jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features.

[0085] Step 203: Based on the bus perturbation characteristics, construct a coupling model between the physical channel of the single-wire digital audio bus and the electrical characteristics of the surgical tissue. The coupling model is used to describe the influence of changes in surgical tissue impedance and local temperature on the degradation characteristics of the physical channel.

[0086] Step 204: Using the coupling model, perform inversion calculation on the bus perturbation features to obtain tissue state proxy parameters that characterize the trend of changes in surgical tissue state.

[0087] Step 205: Based on the tissue state proxy parameters, determine whether the high-frequency surgical device has entered the tissue safety risk evolution range.

[0088] Step 206: When it is determined that the high-frequency surgical device has entered the tissue safety risk evolution range, the energy output control parameters of the high-frequency surgical device are adaptively adjusted according to the degree of change of the tissue state proxy parameters.

[0089] As one feasible approach, the acquisition of the physical layer signals includes real-time high-resolution sampling of the bit clock signal, data edge signal, and frame synchronization signal, with a sampling frequency not less than 8 times the nominal clock frequency of the single-wire digital audio bus.

[0090] As an implementable approach, the extraction of bus perturbation features includes: performing multi-scale averaging time analysis on the jitter spectrum features to separate the different contributions of white noise, flicker noise, and random walk noise; performing frequency domain integration on the phase noise spectrum features to quantify the difference in noise power distribution between low-frequency and high-frequency bands; and performing envelope detection and sideband symmetry assessment on the amplitude modulation sideband features to identify asymmetric modulation effects caused by organization.

[0091] As an implementable approach, when constructing a coupling model between the physical channel of a single-line digital audio bus and the electrical characteristics of surgical tissue based on the bus perturbation characteristics, it can be configured as follows: during the energy output process of the high-frequency surgical equipment, the bus perturbation characteristics are modeled with causal order constraints according to the order of physical responses after energy acts on the surgical tissue; the causal order constraint modeling includes: firstly, determining the instantaneous electromagnetic coupling response caused by the rapid change of the equivalent impedance of the surgical tissue based on the change of the jitter spectrum characteristics; then, determining the delayed electromagnetic response caused by the change of the dielectric properties of the surgical tissue based on the change of the phase noise spectrum characteristics; and finally, determining the slow modulation response caused by the cumulative change of local temperature of the surgical tissue based on the change of the amplitude modulation sideband characteristics; through the causal order constraints, the jitter spectrum characteristics, phase noise spectrum characteristics, and amplitude modulation sideband characteristics are associated in a non-commutative response order to form a coupling model for describing the impact of changes in the electrical characteristics of the surgical tissue on the degradation of the physical channel of the single-line digital audio bus.

[0092] As an implementable approach, when performing inversion calculations on the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes, the configuration can be as follows: under the constraints of the coupling model, the bus perturbation features are mapped to a feasible domain of state changes corresponding to the physical response of the surgical tissue; within the feasible domain of state changes, the dominant evolutionary trend of surgical tissue state changes is determined based on the evolution direction and rate of change of the bus perturbation features within a continuous time window; the dominant evolutionary trend is output as the tissue state proxy parameters, which are used to characterize the direction and intensity of change of the surgical tissue state.

[0093] As an implementable approach, when determining whether a high-frequency surgical device has entered the tissue safety risk evolution interval based on the tissue state proxy parameters, the method can be configured as follows: based on the evolution trajectory of the tissue state proxy parameters within a continuous time window, calculate the direction of change, rate of change, and continuity of change of each proxy parameter; according to the multidimensional collaborative characteristics of the evolution trajectory, divide the surgical tissue state into at least three evolution stages, including a safe evolution stage, a risk warning evolution stage, and a high-risk evolution stage; when the evolution trajectory of the tissue state proxy parameters meets the determination rules for the risk warning evolution stage or the high-risk evolution stage, determine that the high-frequency surgical device has entered the tissue safety risk evolution interval; wherein, the determination rules are established based on the temporal correlation of the tissue state proxy parameters and the causal coupling relationship between different proxy parameters.

[0094] As an implementable approach, the energy output control parameters of a high-frequency surgical device can be adaptively adjusted based on the degree of change of the tissue state proxy parameters as follows: Within a continuous time window, the current risk evolution trend of the surgical tissue is determined based on the evolution direction, rate of change, and continuity of change of the tissue state proxy parameters; according to the risk evolution trend, the energy output control parameters of the high-frequency surgical device are adjusted in a continuous and gradual manner according to a priority matching mechanism, wherein the control parameters include at least one or more of output power, duty cycle, or energy modulation mode; wherein the priority matching mechanism includes: firstly determining whether the impedance change rate component represented by the tissue state proxy parameters exceeds a first threshold, and if so, prioritizing a reduction in output power as the dominant adjustment; based on this, subsequent corrections are performed, including superimposing an adjustment of the duty cycle to optimize energy distribution if a delayed response occurs due to changes in dielectric properties; changing the energy modulation mode to limit heat deposition if the local temperature accumulation trend exceeds a second threshold and continues to rise; and fusing the dominant adjustment and subsequent corrections through preset weights when multiple components evolve simultaneously.

[0095] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. Components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0097] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0098] And an electronic device, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0099] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0100] in, Figure 3 The architecture of an electronic device is illustrated, which may include a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, and a memory 320. The processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320 can communicate with each other via a communication bus 330.

[0101] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0102] The memory 320 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 320 can store the operating system 321 for controlling the operation of the electronic device 300, and the basic input / output system (BIOS) 322 for controlling the low-level operations of the electronic device 300. Additionally, it can store a web browser 323, a data storage management system 324, and a high-frequency surgical equipment control system 325 based on a digital audio bus state, etc. The aforementioned high-frequency surgical equipment control system 325 based on a digital audio bus state can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310.

[0103] Input / output interface 313 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0104] Network interface 314 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0105] Bus 330 includes a pathway for transmitting information between various components of the device, such as processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320.

[0106] It should be noted that although the above-described device only shows the processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, memory 320, bus 330, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0107] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0108] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A high-frequency surgical equipment control system based on digital audio bus status, characterized in that, The system includes: The physical layer signal acquisition unit is configured to use the single-line digital audio bus as a physical channel affected by the electromagnetic coupling between the high-frequency energy field and the surgical tissue during the operation of the high-frequency surgical equipment, and to acquire the physical layer signal on the single-line digital audio bus in real time. The bus perturbation feature extraction unit is configured to extract bus perturbation features from the physical layer signal to characterize the physical channel degradation characteristics. The bus perturbation features include at least jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features. The coupling model construction unit is configured to construct a coupling model between the physical channel of the single-wire digital audio bus and the electrical characteristics of the surgical tissue based on the bus perturbation characteristics. The coupling model is used to describe the influence of changes in the impedance of the surgical tissue and changes in local temperature on the degradation characteristics of the physical channel. The inversion calculation execution unit is configured to perform inversion calculation on the bus perturbation features through the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes. The risk evolution judgment unit is configured to determine whether the high-frequency surgical device has entered the tissue safety risk evolution range based on the tissue state proxy parameters. The control parameter adjustment unit is configured to adaptively adjust the energy output control parameters of the high-frequency surgical device according to the degree of change of the tissue state proxy parameters when it is determined that the high-frequency surgical device has entered the tissue safety risk evolution range.

2. The system according to claim 1, characterized in that, The acquisition of the physical layer signals includes real-time high-resolution sampling of the alignment clock signal, data edge signal, and frame synchronization signal, with a sampling frequency not less than 8 times the nominal clock frequency of the single-wire digital audio bus.

3. The system according to claim 1, characterized in that, The extraction of the bus perturbation features includes: performing multi-scale averaging time analysis on the jitter spectrum features to separate the different contributions of white noise, flicker noise, and random walk noise; performing frequency domain integration on the phase noise spectrum features to quantify the difference in noise power distribution between the low-frequency and high-frequency bands; and performing envelope detection and sideband symmetry evaluation on the amplitude modulation sideband features to identify asymmetric modulation effects caused by the organization.

4. The system according to claim 1, characterized in that, The process of constructing a coupling model between the physical channel of a single-wire digital audio bus and the electrical characteristics of surgical tissue based on the bus perturbation characteristics includes: During the energy output process of high-frequency surgical equipment, the causal order constraint model of the bus perturbation features is performed according to the order of physical response after energy acts on surgical tissue. The causal sequence constraint modeling includes: first, determining the instantaneous electromagnetic coupling response caused by the rapid change in the equivalent impedance of the surgical tissue based on the change in jitter spectrum characteristics; then, determining the delayed electromagnetic response caused by the change in the dielectric properties of the surgical tissue based on the change in phase noise spectrum characteristics; and finally, determining the slow modulation response caused by the cumulative change in local temperature of the surgical tissue based on the change in amplitude modulation sideband characteristics. By using the causal order constraint, the jitter spectrum features, phase noise spectrum features, and amplitude modulation sideband features are associated in a non-commutative response order to form a coupled model that describes the impact of changes in the electrical characteristics of surgical tissue on the degradation of the physical channel of a single-wire digital audio bus.

5. The system according to claim 1, characterized in that, The step of inverting the bus perturbation features using the coupling model to obtain tissue state proxy parameters characterizing the trend of surgical tissue state changes includes: Under the constraints of the coupling model, the bus perturbation features are mapped to the feasible domain of state changes corresponding to the physical response of surgical tissue; Within the feasible domain of state change, the dominant evolutionary trend of surgical tissue state change is determined based on the evolution direction and rate of change of the bus perturbation features within a continuous time window. The principal-induced trend is output as the tissue state proxy parameter, which is used to characterize the direction and intensity of change in the surgical tissue state.

6. The system according to claim 1, characterized in that, The step of determining whether a high-frequency surgical device has entered a tissue safety risk evolution range based on the tissue state proxy parameters includes: Based on the evolution trajectory of the organizational state proxy parameters within a continuous time window, the direction of change, rate of change, and continuity of change of each proxy parameter are calculated. Based on the multidimensional collaborative characteristics of the evolutionary trajectory, the surgical tissue state is divided into at least three evolutionary stages, including a safe evolutionary stage, a risk warning evolutionary stage, and a high-risk evolutionary stage. When the evolution trajectory of the tissue state proxy parameters meets the judgment rules for the risk warning evolution stage or the high-risk evolution stage, the high-frequency surgical device is determined to have entered the tissue safety risk evolution range; wherein, the judgment rules are established based on the temporal correlation of the tissue state proxy parameters and the causal coupling relationship between different proxy parameters.

7. The system according to claim 1, characterized in that, The adaptive adjustment of the energy output control parameters of the high-frequency surgical device based on the degree of change of the tissue state proxy parameters includes: Within a continuous time window, the current risk evolution trend of the surgical tissue is determined based on the evolution direction, rate of change, and continuity of change of the tissue state proxy parameters. According to the risk evolution trend, the energy output control parameters of the high-frequency surgical equipment are adjusted in a continuous and progressive manner according to the priority matching mechanism. The control parameters include at least one or more of the following: output power, duty cycle, or energy modulation mode. The priority matching mechanism includes: firstly determining whether the impedance change rate component represented by the tissue state proxy parameter exceeds a first threshold; if it does, then prioritizing the reduction of output power as the dominant adjustment. Based on this, further modifications are made, including: if a delayed response occurs due to changes in dielectric properties, the duty cycle is adjusted to optimize energy distribution; if the local temperature accumulation trend exceeds the second threshold and continues to rise, the energy modulation mode is changed to limit thermal deposition. When multiple components evolve simultaneously, the dominant adjustment and subsequent correction are fused together by pre-set weights.

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