Cosmetic plastic vulnerability detection method based on dynamic and static analysis

Abstract

The invention discloses an aesthetic and plastic vulnerability detection method based on dynamic and static analysis, and relates to the technical field of medical cosmetology, and the method comprises the following steps: constructing a biological energy conversion model based on patient cell mitochondria, converting a steady state value into a gRNA sequence of light-operated CRISPR by encoding energy, delivering the gRNA sequence to a target tissue, and generating an anchoring site; on the basis of the anchoring points, a metabolic oscillation spectrum of a preoperative tissue area of the patient is collected in real time, a dynamic risk coefficient of operation energy equipment is calculated by adopting a quantum Monte Carlo algorithm, and a risk gradient instruction matrix is generated; according to the risk gradient instruction matrix, high-precision optical detection is carried out in combination with an anchoring point, metabolic activity indexes of an operation area are collected according to a dynamic regulation threshold value, and meanwhile, signal response characteristics of a biomarker are captured; according to the method, quantization migration analysis is carried out by utilizing metabolic activity indexes and a biological energy conversion model, and seamless mapping from a microscopic biological metabolic state to macroscopic equipment control is realized.

Description

Technical Field

[0001] This invention relates to the field of medical aesthetics technology, and in particular to a method for detecting flaws in cosmetic surgery based on dynamic and static analysis. Background Technology

[0002] With the rapid development of biomedical engineering and precision medicine technologies, cosmetic surgery has gradually evolved from traditional experience-based procedures towards intelligent and digital approaches. Especially in minimally invasive plastic surgery, laser therapy, and tissue regeneration, higher demands are placed on real-time monitoring and risk control during the surgical process. In recent years, multimodal fusion detection methods based on bioenergy metabolism analysis, optical imaging technology, and quantum computing principles have gradually become a research hotspot.

[0003] Existing technologies have two main shortcomings: First, they lack a precise mechanism for constructing individualized bioenergy models, resulting in poor model generalization ability and difficulty in effectively adapting to physiological differences in organelle function and tissue metabolic levels among different patients, thus affecting the accuracy and reliability of intraoperative risk assessment. Second, their real-time response capability is limited, especially when facing abnormal situations such as sudden metabolic fluctuations or local energy imbalances, making it difficult to achieve rapid dynamic adjustment of surgical energy parameters, and thus failing to avoid potential tissue damage risks in a timely manner. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for detecting defects in cosmetic surgery based on dynamic and static analysis, which solves the problems of inaccurate construction of individualized bioenergy models and insufficient real-time response capability during surgery in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for detecting vulnerabilities in cosmetic surgery based on dynamic and static analysis. This method includes: constructing a bioenergy conversion model based on the patient's mitochondria; generating anchor points by encoding a gRNA sequence with a steady-state energy conversion value that is light-controlled CRISPR and delivering it to the target tissue; based on the anchor points, real-time acquisition of metabolic oscillation spectra of the patient's preoperative tissue region; calculating the dynamic risk coefficient of the surgical energy device using a quantum Monte Carlo algorithm; generating a risk gradient command matrix; performing high-precision optical detection based on the risk gradient command matrix and the anchor points; acquiring metabolic activity indicators of the surgical area according to a dynamically controlled threshold, while simultaneously capturing the signal response characteristics of biomarkers; performing quantum offset analysis using the metabolic activity indicators and the bioenergy conversion model to generate a power control command set for the surgical device; performing nonlinear distortion identification on the signal response characteristics of the biomarkers to generate document verification results; activating an ultrafast phase transition cooling mechanism in response to the power control command set of the surgical device; constructing quantum encryption evidence storage based on the document verification results; and simultaneously optimizing the response characteristics of the biomarkers and updating the bioenergy conversion model through abnormal scenarios.

[0008] As a preferred embodiment of the cosmetic surgery vulnerability detection method based on dynamic and static analysis described in this invention, the specific steps for generating anchor points are as follows:

[0009] Mitochondria were isolated from specific tissues of the patient, and the enzyme activities of respiratory chain complexes I and IV were measured.

[0010] A bioenergy conversion model was constructed based on enzyme activity values, and the steady-state energy conversion values ​​in tissue regions were calculated using the transmembrane proton pump-ATP synthesis coupling differential equation.

[0011] The steady-state value of energy conversion is encoded into a light-controlled CRISPR gRNA sequence, which is then delivered to the tissue target site to form a covalent anchor and acquire a quantum dot binding domain;

[0012] CdSe quantum dots are assembled in situ in the quantum dot binding domain. Spatial coordinates are measured by exciting entangled photon pairs and then combined with SHA-256 hash values ​​to generate anchor points.

[0013] As a preferred embodiment of the cosmetic surgery vulnerability detection method based on dynamic and static analysis described in this invention, the specific steps for generating the risk gradient instruction matrix are as follows:

[0014] Based on the quantum entangled coordinates of the anchor point, the metabolic oscillation spectrum of the captured tissue region is triggered, generating a quantum oscillation spectrum matrix;

[0015] The quantum oscillation spectral matrix is ​​encoded into a sequence of qubits, and the path integral is performed using the quantum Monte Carlo algorithm to calculate the dynamic risk coefficient.

[0016] The dynamic risk coefficient is expanded into a higher-order tensor and then convolved with a spatiotemporal Gaussian kernel to generate a risk gradient instruction matrix.

[0017] As a preferred embodiment of the cosmetic surgery vulnerability detection method based on dynamic and static analysis described in this invention, the high-precision optical detection based on the risk gradient instruction matrix and anchor positioning points involves the following specific steps.

[0018] Based on the risk gradient instruction matrix, the spatial gradient vector is extracted at the anchor point.

[0019] Convert the spatial gradient vector into a nanosecond-level pulse signal with quantum annealing properties;

[0020] The spontaneous fluorescence oscillation signal generated by two-photon excitation light is captured in real time based on nanosecond-level pulse signals;

[0021] The spontaneous fluorescence oscillation signal is encoded into a sequence of qubits to generate a quantum oscillation dataset.

[0022] As a preferred embodiment of the cosmetic surgery defect detection method based on dynamic and static analysis described in this invention, the specific steps of collecting metabolic activity indicators of the surgical area according to dynamically adjusted thresholds and simultaneously capturing the signal response characteristics of biomarkers are as follows.

[0023] Calculate the dynamic control threshold based on a quantum oscillation dataset;

[0024] When the autofluorescence oscillation signal exceeds the dynamic regulation threshold, metabolic activity indicators are collected, pulsed currents are generated simultaneously, and the response characteristics of biomarkers are captured.

[0025] As a preferred embodiment of the cosmetic surgery defect detection method based on dynamic and static analysis described in this invention, the step of using metabolic activity indicators and a bioenergy conversion model to perform quantum shift analysis and generate a power control instruction set for surgical equipment involves the following specific steps.

[0026] The response characteristics of metabolic activity indicators and biomarkers are fused through tensor product to generate a bioenergy conversion tensor;

[0027] Based on the bioenergy conversion tensor, eigenstate migration is induced in the quantum biological coupling field, and time-domain-frequency domain response signals are captured through coherent resonance to generate quantum evolution trajectory features;

[0028] Map the quantum evolution trajectory features to the quantum trace norm space and calculate the quantum trace norm offset;

[0029] When the quantum trace norm offset exceeds the preset damage threshold, a power control instruction set for the surgical device is generated through non-classical correlation verification.

[0030] As a preferred embodiment of the cosmetic surgery vulnerability detection method based on dynamic and static analysis described in this invention, the specific steps for performing nonlinear distortion recognition on the signal response characteristics of biomarkers to generate document verification results are as follows:

[0031] The signal response characteristics of biomarkers are projected onto a quantum fiber bundle manifold to generate manifold coordinates;

[0032] Quantum topological distortion index is calculated using dynamic Chern-Simons gauge fields based on manifold coordinates.

[0033] By mapping the quantum topological distortion index to the Clifford algebra space, a noncommutative geometric verification matrix is ​​constructed.

[0034] Based on the imaginary part of the eigenvalues ​​and the real part of the determinant of the non-commutative geometric verification matrix, a document verification result for the validity of the signal is generated.

[0035] As a preferred embodiment of the cosmetic surgery vulnerability detection method based on dynamic and static analysis described in this invention, the method involves: activating an ultrafast phase-change cooling mechanism in response to the power control instruction set of the surgical equipment; constructing quantum encryption evidence storage based on document verification results; and simultaneously optimizing the response characteristics of biomarkers and updating the bioenergy conversion model through abnormal scenarios. The specific steps are as follows:

[0036] It responds to the power control command set of the surgical equipment and performs ultra-fast phase change cooling to generate cooling parameters;

[0037] Based on the document verification results and cooling parameters, a GHZ entangled state is generated, and an immutable quantum encrypted evidence is constructed through quantum Fourier transform.

[0038] When the document verification result is invalid, quantum encryption is used to trigger continuous coherence analysis and optimize the response characteristics of biomarkers.

[0039] The bioenergy conversion model is dynamically updated based on the optimized response characteristics of biomarkers.

[0040] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in the first aspect of the present invention.

[0042] The beneficial effects of this invention are as follows: by generating anchor positioning points by exciting the spatial coordinates of the measurement through entangled photon pairs and fusing the hash value of medical documents, the problem of risk prediction bias caused by the lack of individualized modeling mechanism in the prior art is effectively solved; furthermore, by using metabolic activity indicators and bioenergy conversion models for quantum offset analysis, a seamless mapping from microscopic biological metabolic state to macroscopic device control is achieved, breaking through the limitations of traditional methods that rely on fixed thresholds and delayed feedback. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a method for detecting vulnerabilities in cosmetic surgery based on dynamic and static analysis.

[0045] Figure 2 This is a flowchart for generating anchor points.

[0046] Figure 3 A flowchart for generating the risk gradient instruction matrix.

[0047] Figure 4 A flowchart for generating a power control instruction set for surgical equipment. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for detecting vulnerabilities in cosmetic surgery based on dynamic and static analysis, including the following steps:

[0052] S1: A bioenergy conversion model is constructed based on the mitochondria of patient cells. An anchoring site is generated by encoding a gRNA sequence that encodes an energy conversion steady-state value of light-controlled CRISPR and delivering it to the target tissue.

[0053] S1.1: Isolate mitochondria from specific tissues of the patient and measure the enzyme activity values ​​of respiratory chain complexes I and IV.

[0054] The specific process includes isolating mitochondria from specific tissue cells of the patient, purifying the mitochondria using differential centrifugation, measuring the NADH dehydrogenase activity of respiratory chain complex I at a specific wavelength using spectrophotometry, and obtaining the enzyme activity value by monitoring the NADH oxidation rate; when measuring the cytochrome C oxidase activity of respiratory chain complex IV, reduced cytochrome C is used as a substrate, and the rate of change in absorbance is recorded and converted into an enzyme activity value, finally obtaining the enzyme activity values ​​of respiratory chain complexes I and IV.

[0055] S1.2: A bioenergy conversion model was constructed based on enzyme activity values, and the steady-state energy conversion value of the tissue region was calculated using the transmembrane proton pump-ATP synthesis coupling differential equation. The expression is as follows:

[0056]

[0057] Where, Δψ Ω V represents the steady-state energy conversion value of the tissue region Ω. Ω α represents the volume of the tissue region Ω, β represents the membrane capacitance constant, k1 represents the proton pump mass transfer coefficient, and α IV (r) represents the enzyme activity value of respiratory chain complex IV at spatial location r, k2 represents the catalytic constant of ATP synthase, [D] Ω [P] represents the average ADP concentration in tissue region Ω. Ω Ω represents the average concentration of inorganic phosphorus in the tissue region, A represents the enzyme activity diffusion coefficient, and dV represents the volume element in the three-dimensional integral.

[0058] in, The Laplace operator for respiratory chain complex IV is expressed as:

[0059]

[0060] It should be noted that r represents the spatial position coordinate, x represents the spatial horizontal axis coordinate, y represents the spatial vertical coordinate, and z represents the spatial vertical coordinate.

[0061] The specific process includes: when constructing a bioenergy conversion model based on the enzyme activity values ​​of respiratory chain complex I and respiratory chain complex IV, converting the enzyme activity values ​​into electron transport chain flux parameters, and combining them with the transmembrane proton pump-ATP synthesis coupling differential equation to describe the proton gradient formation and ATP synthesis process; the transmembrane proton pump-ATP synthesis coupling differential equation includes three terms: proton pumping rate, proton reflux rate, and ATP synthesis rate; by solving the transmembrane proton pump-ATP synthesis coupling differential equation, the steady-state value of energy conversion in the tissue region is obtained; the steady-state value of energy conversion reflects the energy conversion efficiency when ATP production and consumption reach equilibrium during mitochondrial oxidative phosphorylation in a specific tissue region.

[0062] Furthermore, constructing a bioenergy conversion model requires converting the enzyme activities of respiratory chain complex I and respiratory chain complex IV into electron transport rate parameters, which are directly related to the function of the electron transport chain on the inner mitochondrial membrane. Subsequently, a transmembrane proton pump-ATP synthesis coupling differential equation is established, containing three key variables: the proton pumping rate driven by electron transport chain activity, the reverse proton leakage rate determined by membrane potential difference, and the proton reflux and ATP synthesis rate regulated by ATP synthase activity. In the pump-ATP synthesis coupled differential equation, the proton pumping rate is proportional to the activity of the respiratory chain complex, the proton leakage rate follows a membrane potential-dependent exponential relationship, and the ATP synthesis rate is linearly related to the proton driving force and the density of ATP synthase. By solving the equilibrium conditions of these three rates simultaneously, the steady-state solution of mitochondrial energy conversion in the tissue region can be obtained. The steady-state solution is the maximum theoretical efficiency of ATP synthesis under specific physiological conditions. By comparing the differences in steady-state solutions of different tissue samples, the pathological changes in bioenergy conversion efficiency can be assessed, and ultimately a bioenergy conversion model can be generated.

[0063] Tissue region refers to the specific human tissue region targeted in cosmetic surgery.

[0064] S1.3: The steady-state value of energy conversion is encoded into a light-controlled CRISPR gRNA sequence, which is delivered to the tissue target site to form a covalent anchor and acquire the quantum dot binding domain.

[0065] The specific process includes: converting the energy conversion steady-state value into a specific nucleotide sequence using a binary encoding algorithm; this specific nucleotide sequence serves as the gRNA recognition region for light-controlled CRISPR; the synthesized gRNA sequence contains the energy conversion steady-state value coding region, the light-sensitive RNA aptamer, and a complementary sequence of the quantum dot binding domain; delivering the gRNA to the target tissue region using a liposome carrier; the gRNA binding to specific sites on the target cell genome through base complementarity pairing; under blue light irradiation, the conformational change of the light-sensitive RNA aptamer activates the Cas9 protein, prompting the gRNA coding region to form a covalent link with the genome; simultaneously, the complementary sequence of the quantum dot binding domain at the end of the gRNA unfolds, exposing specific secondary structures for subsequent quantum dot labeling; the covalent anchoring process ensures that the energy conversion steady-state value is stably integrated into the specific location of the target tissue genome in the form of gRNA, while maintaining the accessibility of the quantum dot binding domain.

[0066] S1.4: Assemble CdSe quantum dots in situ in the quantum dot binding domain, measure spatial coordinates by exciting entangled photon pairs, and generate anchor points by combining SHA-256 hash values.

[0067] The specific process includes: assembling CdSe quantum dots in situ on exposed quantum dot binding domains via thiol-metal coordination; forming amide bonds between carboxyl groups modified on the CdSe quantum dot surface and specific sites in the gRNA quantum dot binding domains; using laser excitation to generate fluorescence signals from the CdSe quantum dots while simultaneously emitting entangled photon pairs; measuring the coincidence count of the entangled photon pairs using a single-photon detector to determine the precise coordinates of the quantum dots in three-dimensional space; converting the determined precise coordinates into binary format and performing a hash operation with the energy conversion steady-state value to generate a unique SHA-256 hash value; using the SHA-256 hash value as a digital fingerprint for the anchor point, containing both the physical location information of the quantum dots and establishing a mathematical correlation with the energy conversion steady-state value, ultimately generating the anchor point.

[0068] S2: Based on anchor points, the metabolic oscillation spectrum of the patient's preoperative tissue area is collected in real time, and the dynamic risk coefficient of the surgical energy device is calculated using the quantum Monte Carlo algorithm to generate a risk gradient command matrix.

[0069] S2.1: Based on the quantum entanglement coordinates of the anchor point, the metabolic magnetic field oscillation of the captured tissue region is triggered, generating a quantum oscillation spectrum matrix.

[0070] The specific process includes: based on the quantum entanglement coordinates of the anchor points, a superconducting quantum interference device (SQU) is used to detect the weak magnetic field oscillations generated by metabolism in the tissue region; the quantum entanglement coordinates serve as a spatial positioning reference, enabling the SQU probe to be precisely aligned with the target tissue region; the magnetic field oscillation signals captured by the SQU are processed by a fast Fourier transform, exhibiting characteristic resonance peaks corresponding to different metabolites in the frequency domain; the frequency domain spectral lines corresponding to each anchor point are arranged according to spatial coordinates to construct a three-dimensional quantum oscillation spectrum matrix; each data point in the quantum oscillation spectrum matrix contains spatial location information and the corresponding metabolic magnetic field oscillation frequency, reflecting the spatial distribution characteristics of energy metabolism in the tissue region.

[0071] S2.2: Encode the quantum oscillation spectral matrix into a sequence of qubits, and calculate the dynamic risk coefficient by performing path integral using the quantum Monte Carlo algorithm. The expression is as follows:

[0072]

[0073] Where E(t) represents the dynamic risk coefficient at time t (0≤R(t)≤1), t represents the time variable, Ψ1 represents the conjugate transpose of the metabolic oscillation quantum state, U(t) represents the effective quantum evolution operator at time t, Ψ2 represents the preset safe quantum state, exp represents the natural exponential function, i represents the frequency offset sensitivity factor, τ represents the time integral variable, ω(τ) represents the real-time detected tissue metabolic oscillation frequency at the time integral variable τ, ω0 represents the reference value of the tissue metabolic oscillation frequency, and λ represents the spatial weighting coefficient. This represents the Laplace operator for the metabolic magnetic field at time t and spatial location r.

[0074] The specific process includes: each data point in the quantum oscillation spectrum matrix is ​​converted into a corresponding qubit sequence through quantum state encoding; the qubit sequence contains spatial location information and metabolic magnetic field oscillation frequency; a quantum Monte Carlo algorithm is run in a quantum processor, which constructs a Feynman path integral by randomly sampling the qubit sequence; the correlation of metabolic magnetic field oscillations at different spatial locations in the quantum oscillation spectrum matrix is ​​considered during the Feynman path integral calculation; the quantum Monte Carlo algorithm simulates the quantum evolution of metabolic processes in Hilbert space, and finally outputs a dynamic risk coefficient reflecting the risk of abnormal tissue metabolism; the dynamic risk coefficient quantitatively assesses the development trend and potential impact of abnormal metabolic patterns in the quantum oscillation spectrum matrix.

[0075] The preset safe quantum state is a steady-state solution obtained by solving the quantum master equation based on the reference value of the metabolic oscillation frequency of healthy tissue, and is pre-calibrated using quantum state tomography.

[0076] S2.3: Perform high-order tensor expansion on the dynamic risk coefficient and generate the risk gradient instruction matrix by using spatiotemporal Gaussian kernel convolution.

[0077] The specific process includes: unfolding the dynamic risk coefficient into a three-dimensional tensor according to the time, space, and metabolic dimensions to form a three-dimensional tensor structure containing multi-parameter variation characteristics; convolving the unfolded three-dimensional tensor with a spatiotemporal Gaussian kernel with anisotropic characteristics, the spatiotemporal Gaussian kernel adopts an exponential decay function in the time dimension and a radial basis function in the space dimension; during the convolution operation, the local extreme points of the dynamic risk coefficient interact with the smoothing characteristics of the spatiotemporal Gaussian kernel, highlighting the changing trend of the risk gradient; the convolution result after normalization is transformed into a risk gradient instruction matrix, in which each element represents the rate and direction of risk change under specific spatiotemporal coordinates; the risk gradient instruction matrix simultaneously retains the quantitative characteristics of the original dynamic risk coefficient and the smooth transition characteristics after processing by the spatiotemporal Gaussian kernel.

[0078] An exponential decay function is a mathematical function whose value decreases regularly with increasing time or spatial distance.

[0079] Radial basis functions are scalar functions centered at a point whose values ​​decrease symmetrically with increasing distance.

[0080] S3: Based on the risk gradient instruction matrix, combined with anchor positioning points, high-precision optical detection is performed, and metabolic activity indicators of the surgical area are collected according to the dynamic control threshold, while capturing the signal response characteristics of biomarkers.

[0081] S3.1: Extract the spatial gradient vector at the anchor point based on the risk gradient instruction matrix.

[0082] The specific process includes: the risk gradient instruction matrix stores the risk change information corresponding to each anchor point; a spatial gradient vector is obtained at the anchor point through three-dimensional spatial difference operation; the direction of the spatial gradient vector is determined by the risk value difference between adjacent anchor points in the risk gradient instruction matrix, pointing to the direction of the fastest risk growth; the magnitude of the spatial gradient vector is proportional to the intensity of local risk changes in the risk gradient instruction matrix; the spatial gradient vector generated at each anchor point constitutes a vector field, which continuously reflects the dynamic change characteristics of risk distribution within the tissue region; and the spatial gradient vector forms a spatial mapping relationship with the metabolic information in the quantum oscillation spectrum matrix.

[0083] S3.2: Convert the spatial gradient vector into a nanosecond-level pulse signal with quantum annealing properties.

[0084] The specific process includes: the magnitude and direction of the spatial gradient vector are converted into analog signals with corresponding amplitude and phase by a voltage-to-frequency conversion module; the analog signals are then converted into nanosecond-level pulse sequences by a high-speed digital-to-analog converter; the repetition frequency of the nanosecond-level pulse sequence is proportional to the magnitude of the spatial gradient vector, and the pulse phase difference corresponds to the direction angle of the spatial gradient vector; the nanosecond-level pulse sequence is loaded onto the coupling coil of the quantum annealing chip, and the pulse signal acquires quantum annealing characteristics by utilizing the superconducting quantum interference effect; the nanosecond-level pulse signal with quantum annealing characteristics maintains classical pulse characteristics in the time domain and achieves non-Markovian evolution at the quantum state superposition level; the final output nanosecond-level pulse signal simultaneously retains the geometric characteristics of the spatial gradient vector and the coherence required for quantum annealing.

[0085] Quantum annealing refers to the ability to efficiently escape local optima and achieve global optimization within nanoseconds by utilizing quantum tunneling and quantum fluctuations (rather than thermal fluctuations).

[0086] S3.3: Real-time capture of the spontaneous fluorescence oscillation signal generated by two-photon excitation light based on nanosecond-level pulse signals.

[0087] The specific process includes: a nanosecond-level pulse signal is converted into an electrical signal by a high-speed photodetector, and the timestamp is accurately recorded by a time-correlated single-photon counting device; the spontaneous fluorescence oscillation signal generated when two-photon excitation light acts on the fluorescent material is synchronously extracted by a lock-in amplifier; the phase and amplitude information of the spontaneous fluorescence oscillation signal is decomposed into frequency domain components by a fast Fourier transform; the timing characteristics of the nanosecond-level pulse signal and the decay period of the spontaneous fluorescence oscillation signal are matched by a cross-correlation algorithm; the matching result drives the quantum state control circuit to generate a feedback pulse sequence with quantum annealing characteristics; the quantum tunneling effect of the feedback pulse sequence compresses the random fluctuations of the spontaneous fluorescence oscillation signal to near the ground state energy level, and finally outputs a stable spontaneous fluorescence oscillation signal.

[0088] S3.4: Encode the spontaneous fluorescence oscillation signal into a sequence of qubits to generate a quantum oscillation dataset.

[0089] The specific process includes: the intensity time series of the spontaneous fluorescence oscillation signal is converted into a digital signal by an analog-to-digital converter; the digital signal is then mapped into a corresponding qubit sequence by a quantum state encoder; each qubit in the qubit sequence represents the intensity value of the spontaneous fluorescence oscillation signal at a specific time point, and the entanglement relationship between qubits preserves the temporal correlation of the spontaneous fluorescence oscillation signal; during the encoding process, an amplitude encoding method is used, the amplitude of the spontaneous fluorescence oscillation signal is converted into the superposition probability amplitude of the qubit, and the oscillation frequency characteristics are converted into qubit rotation gate parameters; the generated qubit sequence is arranged according to the time dimension to form a quantum oscillation dataset, which simultaneously contains fluorescence intensity information and quantum state evolution characteristics.

[0090] S3.5: Based on the quantum oscillation dataset, calculate the dynamic control threshold, expressed as:

[0091]

[0092] Where θ(t) represents the dynamic control threshold that changes in real time with time t during the operation, and θ0 represents the preoperative calibrated basal metabolic signal acquisition threshold. denoted by β (0.1≤β≤0.3), d represents the differential symbol, M(t) represents the risk gradient instruction matrix updated in real time with time t during the operation, k represents the risk energy weighting factor, I(t) represents the autofluorescence intensity of biological tissue collected in real time by a high-precision optical detection device at time t, Δt represents the fixed time integration window in the calculation of the metabolic activity regulation threshold, and C represents the spatial risk gradient vector.

[0093] The specific process includes: converting the qubit sequence in the quantum oscillation dataset into a frequency domain representation through quantum Fourier transform, with the amplitude distribution in the frequency domain reflecting the real-time metabolic activity intensity; extracting the weight values ​​of the main frequency components of the real-time metabolic activity intensity through quantum amplitude estimation, with the weight values ​​being proportional to the metabolic reaction rate; determining the dynamic control threshold based on the frequency domain distribution characteristics of the real-time metabolic activity intensity, triggering a dynamic control threshold update when the proportion of high-frequency components exceeds a set ratio; employing a sliding window quantum mean filter in the dynamic control threshold update process, with the window width matching the time resolution of the quantum oscillation dataset; and finally generating a dynamic control threshold that reflects the changing trend of metabolic activity in the quantum oscillation dataset in real time.

[0094] S3.6: When the autofluorescence oscillation signal exceeds the dynamic regulation threshold, metabolic activity indicators are collected, pulse currents are generated synchronously, and the response characteristics of biomarkers are captured.

[0095] The specific process includes: when the autofluorescence oscillation signal exceeds the dynamic control threshold, the metabolic activity index is collected by the Raman spectrometer and transmitted to the signal processor; the pulse current is generated by the constant current source according to the gradient change of the metabolic activity index; the response characteristics of the biomarker are captured by the surface plasmon resonance sensor; the intensity of the pulse current and the response characteristics of the biomarker are fitted by the least squares method; the fitting result triggers the fluorescence lifetime imaging device to record the spatiotemporal correlation data of the autofluorescence oscillation signal and the response characteristics of the biomarker; and finally, the synchronously calibrated dynamic response spectrum of the metabolic activity index and the biomarker is output.

[0096] S4: Quantized offset analysis is performed using metabolic activity indicators and bioenergy conversion models to generate a power control instruction set for surgical equipment, and nonlinear distortion identification is performed on the signal response characteristics of biomarkers to generate document verification results.

[0097] S4.1: The response characteristics of metabolic activity indicators and biomarkers are fused through tensor product to generate a bioenergy conversion tensor.

[0098] The specific process includes: metabolic activity indicators containing multidimensional data such as adenosine triphosphate concentration, oxygen partial pressure, and pH value; and biomarker response characteristics including frequency domain feature spectra of motion trajectories and binding affinity. The metabolic activity indicators and biomarker response characteristics are fused using tensor products via Kronecker product operations to form a higher-order tensor structure. Each element of the higher-order tensor structure represents the correlation strength between a specific metabolic activity state and the behavior of the biomarker. The spatiotemporal correlation of the original data is preserved during the tensor product operation, and the time series of metabolic activity indicators and the spatial distribution of biomarker response characteristics form a cross-mapping. The generated bioenergy conversion tensor integrates metabolic processes and molecular recognition information.

[0099] S4.2: Based on the bioenergy conversion tensor, eigenstate migration is induced in the quantum biological coupling field, and time-domain-frequency response signals are captured through coherent resonance to generate quantum evolution trajectory features.

[0100] The specific process includes: a bioenergy conversion tensor is input through the radio frequency interface of a quantum biocoupled field; the bioenergy conversion tensor is a multidimensional data structure formed by fusing metabolic activity indicators and biomarker response characteristics; in the quantum biocoupled field, the metabolic-molecular correlation information contained in the bioenergy conversion tensor is converted into waveform parameters of a radio frequency pulse, including three basic characteristics: amplitude, frequency, and phase; the waveform parameters precisely regulate the spatiotemporal distribution characteristics of the oscillating electromagnetic field, enabling the electromagnetic field to interact with the dipole moment of biomolecules in a specific mode; the interaction induces stimulated Raman transitions, leading to controllable coherent migration of eigenstates between oscillatory and rotational energy levels; during the coherent migration, lock-in amplification technology is used to monitor the time-domain and frequency-domain response signals in real time, ensuring that the detection frequency always resonates with the eigenstate transition frequency; after the response signal is enhanced for quantum state preservation by a Josephson parametric amplifier, evolutionary features are extracted through quantum Fourier transform; the finally obtained quantum evolution trajectory features completely record key parameters such as Rabi oscillation frequency, decoherence time, and energy level splitting.

[0101] A quantum biocoupled field refers to a nanoscale interaction domain that exchanges energy between the vibrational modes of biomolecules and the electromagnetic field through quantum coherence effects.

[0102] S4.3: Map the quantum evolution trajectory features to the quantum trace norm space and calculate the quantum trace norm offset, expressed as:

[0103]

[0104] Where L represents the quantum trace norm offset (0≤α≤3), G represents the normalization factor, and Γ represents the topological manifold of the quantum evolution trajectory. Let P denote the covariant differential operator on the manifold, P denote the phase-curvature joint distribution function, φ denote the phase offset angle, c denote the curvature, and R denote the phase-curvature joint distribution function. c Let represent the radius of curvature of the quantum evolution trajectory, ∈ represent a minimum positive number, ζ represent the coherence decay coefficient (0.5≤ζ≤1.0), Φ0 represent the reference quantum state of healthy tissue, δ represent the biological oscillation weight, and J(R c ) represents the curvature distortion function, μ represents the quantum oscillation angular frequency, and R(μ) represents the quantum oscillation angular frequency synchronous resonance response function.

[0105] The specific process includes: converting quantum evolution trajectory characteristics into a density matrix using quantum state tomography, with the density matrix describing the statistical properties of the quantum state in Hilbert space; projecting the density matrix onto the quantum trace norm space, preserving parameters such as Rabi oscillation frequency, decoherence time, and energy level splitting in the quantum evolution trajectory characteristics; obtaining the quantum trace norm reference value by calculating the sum of the singular values ​​of the density matrix in the quantum trace norm space; determining the quantum trace norm offset by the difference between the current quantum trace norm and the reference value, with the difference reflecting the degree of deviation of the quantum state from the reference state; evaluating the change in quantum state purity using the von Neumann entropy formula, with the change in quantum state purity being proportional to the coherence loss in the bioenergy conversion process; and finally, quantitatively characterizing the dynamic changes of the quantum evolution trajectory characteristics in the quantum trace norm space using the quantum trace norm offset.

[0106] The quantum trace norm space is a subset of Hilbert space that uses the trace norm (nuclear norm) of the density matrix as the distance metric and is used to describe the geometric structure of mixed quantum states.

[0107] S4.4: When the quantum trace norm offset exceeds the preset damage threshold, a power control instruction set for the surgical device is generated through non-classical correlation verification.

[0108] The specific process includes the following steps: when the quantum trace norm offset exceeds a preset damage threshold, the quantum correlation measurement device performs a Belli projection measurement to verify the nonclassical correlation. After the verification is successful, the quantum control unit constructs a power control instruction set for the surgical device based on the measurement results. The power control instruction set for the surgical device includes amplitude modulation parameters and phase compensation coefficients. The power control instruction set for the surgical device is transmitted to the surgical device actuator through a quantum communication channel. The surgical device actuator parses the power control instruction set for the surgical device and adjusts the output power spectral density of the radio frequency transmitter to achieve precise power control of the surgical device in a quantum interference environment.

[0109] The preset damage threshold is a dynamic safety boundary derived from quantum Monte Carlo simulation based on the quantum state decoherence rate and thermal damage critical value of the target tissue.

[0110] S4.5: Project the signal response characteristics of biomarkers onto the quantum fiber bundle manifold to generate manifold coordinates.

[0111] The specific process includes: after dimensionality reduction of the biomarker's signal response features through principal component analysis, the Riemannian geometric method preserves the differential structure through Levi-Civita connections, embedding the biomarker's signal response features equidistantly into the local coordinate system of the quantum fiber bundle manifold; the basis space of the quantum fiber bundle manifold is composed of quantum evolution trajectory features, and the fiber orientation of the quantum fiber bundle manifold corresponds to the dynamic response mode of the biomarker; the projection process utilizes the differential geometric properties of Levi-Civita connections to maintain the signal features, establishing a local coordinate system on the quantum fiber bundle manifold, and obtaining the manifold coordinates through exponential mapping, with the manifold coordinate components representing the position of the biomarker's response in the quantum fiber bundle manifold; during the manifold coordinate generation process, the parallel translation operator ensures that the topological structure of the signal features is not distorted; finally, the manifold coordinates completely preserve the quantum correlation information and dynamic evolution characteristics of the biomarker's signal response features.

[0112] Quantum fiber bundles are differential geometric structures based on gauge field theory, using quantum states as fibers and spacetime as the base manifold, to describe the topological coupling relationship between gauge potentials and matter fields.

[0113] S4.6: Based on manifold coordinates, the quantum topological distortion index is calculated using a dynamic Chern-Simons gauge field, expressed as:

[0114]

[0115] Where D represents the quantum topological distortion index (-1≤D≤1), N represents the total number of manifold partitions, n represents the manifold partition index variable, and γ n σ represents the quantum phase shift angle of the nth manifold partition. n T represents the curvature intensity of the nth manifold partition. n Let H represent the Euler eigengradation of the nth manifold partition, H represent the curvature sensitivity coefficient, j represent the metabolic oscillation weight, and F represent the weight of the metabolic oscillation. 1.7 This represents the 1.7th order fractional Fourier transform. γ represents the reciprocal of time covariance, g represents the quantum phase shift angle, g represents the instantaneous oscillation frequency, and g0 represents the quantum resonance frequency.

[0116] The specific process includes: importing manifold coordinates as input parameters into the dynamic Chern-Simons gauge field computation framework; establishing a connection form corresponding to the manifold coordinates within the dynamic Chern-Simons gauge field; deriving a curvature form from the connection form through exterior differential operations, which characterizes the local geometric features of the quantum fiber bundle manifold; performing integration operations along the closed path generated by the dynamic Chern-Simons gauge field; the integration result reflecting the overall topological properties of the manifold coordinates in the quantum fiber bundle; obtaining the quantum topological distortion index after normalizing the Chern-Simons integral value; the quantum topological distortion index directly characterizes the signal response characteristics of the biomarker described by the manifold coordinates, and the degree of topological distortion in the quantum fiber bundle; maintaining synchronous updates of the differential geometric properties of the manifold coordinates and the dynamic parameters of the gauge field throughout the computation process, ensuring that the quantum topological distortion index reflects the quantum state changes of the biomarker in real time.

[0117] S4.7: Map the quantum topological distortion index to the Clifford algebra space to construct a noncommutative geometric verification matrix.

[0118] The specific process includes: the quantum topological distortion index is embedded into the noncommutative space through the geometric product operation of Clifford algebra, forming a multivector structure with definite geometric meaning under the action of generators; the multivector structure is expanded according to the Grassmann product rule, and the coefficient matrix of the multivector structure is directly transformed into a noncommutative geometric verification matrix; the elements of the noncommutative geometric verification matrix strictly maintain the topological invariants of the original quantum topological distortion index, while reflecting the noncommutative relation unique to Clifford algebra; the eigenvalue distribution of the noncommutative geometric verification matrix reflects the integrability condition of quantum topological distortion under the noncommutative framework.

[0119] The Grassmann product rule is a mathematical rule that describes the antisymmetric and associative properties of the cross product operation of vectors in Clifford algebra.

[0120] S4.8: Generate file verification results for signal validity based on the imaginary part of the eigenvalues ​​and the real part of the determinant of the non-commutative geometric verification matrix.

[0121] The specific process includes: extracting the imaginary parts of all eigenvalues ​​of the non-commutative geometric verification matrix through eigenvalue decomposition to form an imaginary part set; simultaneously extracting the real part value of the determinant of the non-commutative geometric verification matrix; jointly evaluating the statistical distribution characteristics of the imaginary part set and the real part value of the determinant through preset logical rules; converting the evaluation result into a binary judgment flag; encoding the binary judgment flag according to a standard file format to generate a file verification result containing the signal validity conclusion; the file verification result simultaneously records the mean square error of the imaginary part of the eigenvalues ​​of the non-commutative geometric verification matrix and the absolute value of the real part of the determinant as the mathematical basis for validity determination.

[0122] The preset logic rules are established based on the mathematical and physical characteristics of the quantum topological distortion index in the Clifford algebraic space. They are set by analyzing the correlation between the imaginary part of the eigenvalues ​​and the real part of the determinant.

[0123] S5: Activates an ultrafast phase-change cooling mechanism in response to the power control command set of surgical equipment, constructs quantum encryption evidence storage based on document verification results, and optimizes the response characteristics of biomarkers and updates the bioenergy conversion model through abnormal scenarios.

[0124] S5.1: Responds to the power control command set of surgical equipment and performs ultra-fast phase change cooling to generate cooling parameters.

[0125] The specific process includes: the power control command set of the surgical equipment is converted into a current signal by a digital-to-analog converter, and the current signal is input to the electromagnetic drive unit of the liquid metal microfluidic platform; the electromagnetic drive unit adjusts the flow rate of the liquid metal in the microchannel according to the current frequency and amplitude, and the flow rate is positively correlated with the phase change cooling efficiency; the liquid metal undergoes an ultrafast phase change under specific current parameters, and the ultrafast phase change process absorbs a large amount of heat to achieve active cooling; the cooling parameters include the temperature change rate, the phase change duration, and the heat flux density, and the cooling parameters are acquired in real time through an embedded thermocouple array; the acquired data is encoded according to the format requirements of the power control command set to generate a standardized cooling parameter record file, and the content of the file is synchronized with the power control command of the surgical equipment.

[0126] S5.2: Generate GHZ entangled states based on file verification results and cooling parameters, and construct an immutable quantum encrypted evidence storage through quantum Fourier transform.

[0127] The specific process includes: converting the document verification result and cooling parameters into a two-mode squeezed state optical field through a quantum state preparation device; the two-mode squeezed state optical field strictly corresponds to the binary code of the document verification result and the numerical characteristics of the cooling parameters; generating GHZ entangled states in a nonlinear crystal through a parametric downconversion process; the three modes of the GHZ entangled state respectively encode the hash value of the document verification result, the timestamp of the cooling parameters, and the characteristic value of the quantum topological distortion index; processing the frequency correlation characteristics of the GHZ entangled state using quantum Fourier transform, and the transformed spectral components constitute a multidimensional orthogonal basis; the projection measurement results of the multidimensional orthogonal basis form the mathematical basis of quantum encryption and evidence storage; the quantum encryption and evidence storage content includes the quantum correlation characteristics of the document verification result and the cooling parameters; the immutability of the quantum encryption and evidence storage is guaranteed by the quantum nonlocality of the GHZ entangled state, and any tampering operation will destroy the entanglement correlation and be detected by quantum measurement.

[0128] S5.3: When the document verification result is invalid, use quantum encryption to trigger continuous coherence analysis and optimize the response characteristics of biomarkers.

[0129] The specific process includes: when the document verification result is deemed invalid by the quantum encrypted evidence, the GHZ entangled state measurement data stored in the quantum encrypted evidence automatically activates the continuous cohomology analysis process; the continuous cohomology analysis identifies topological feature changes in the GHZ entangled state measurement data through the Betti number persistence of the quantum Fourier transform spectrum in the quantum encrypted evidence; the analysis results are transmitted to the control terminal of the magneto-fluidic self-assembly unit, and the control terminal dynamically adjusts the magnetic field interaction time and fluid shear force intensity of the superparamagnetic nanoparticles according to the topological feature parameters output by the continuous cohomology analysis; the adjusted magneto-fluidic parameters optimize the binding dynamics of biomarkers and ligands, making the peak value of the frequency domain characteristic spectrum of the biomarker's trajectory more significant; the optimized biomarker response characteristics re-enter the quantum fiber bundle manifold projection calculation stage to generate manifold coordinate data with higher fidelity.

[0130] S5.4: Dynamically update the bioenergy conversion model based on the response characteristics of optimized biomarkers.

[0131] The specific process includes: optimizing biomarker response features, which include updated motion trajectory frequency domain feature spectra and binding affinity parameters, and re-integrating them with real-time metabolic activity indicators through Kronecker product operations; replacing the original biomarker response feature portion in the bioenergy conversion tensor with the fused real-time metabolic activity indicators to form updated bioenergy; inputting the updated bioenergy conversion tensor into the quantum biological coupling field to recalculate eigenstate migration paths and quantum evolution trajectory features; reconstructing the quantum evolution trajectory features through a density matrix to reflect the impact of optimized biomarker behavior on the energy conversion process; and finally generating an updated bioenergy conversion model that includes the correlation parameters between optimized biomarker response features and metabolic activity indicators.

[0132] This embodiment also provides a computer device applicable to the cosmetic surgery vulnerability detection method based on dynamic and static analysis, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the cosmetic surgery vulnerability detection method based on dynamic and static analysis as proposed in the above embodiment.

[0133] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0134] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for detecting vulnerabilities in cosmetic surgery based on dynamic and static analysis as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0135] In summary, this invention effectively solves the risk prediction bias problem caused by the lack of individualized modeling mechanism in the prior art by generating anchor positioning points through the excitation of spatial coordinates by entangled photon pairs and the fusion of medical document hash values. Furthermore, by using metabolic activity indicators and bioenergy conversion models for quantum offset analysis, it achieves a seamless mapping from microscopic biological metabolic state to macroscopic device control, breaking through the limitations of traditional methods that rely on fixed thresholds and delayed feedback.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting vulnerabilities in cosmetic surgery based on dynamic and static analysis, characterized in that: include, A bioenergy conversion model was constructed based on the mitochondria of patient cells. An anchoring site was generated by encoding a gRNA sequence that encodes an energy conversion homeostasis value for light-controlled CRISPR and delivering it to the target tissue. Based on anchor points, the metabolic oscillation spectrum of the patient's preoperative tissue area is collected in real time. The dynamic risk coefficient of the surgical energy device is calculated using the quantum Monte Carlo algorithm, and a risk gradient command matrix is ​​generated. Based on the risk gradient instruction matrix, high-precision optical detection is performed using anchor positioning points. Metabolic activity indicators of the surgical area are collected according to dynamically adjusted thresholds, while simultaneously capturing the signal response characteristics of biomarkers. Quantum offset analysis was performed using metabolic activity indicators and a bioenergy conversion model to generate a power control instruction set for surgical equipment. Nonlinear distortion identification was performed on the signal response characteristics of biomarkers to generate document verification results. The system activates an ultrafast phase-change cooling mechanism in response to the power control command set of the surgical equipment, constructs quantum encryption for evidence storage based on document verification results, and optimizes the response characteristics of biomarkers and updates the bioenergy conversion model through abnormal scenarios.

2. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 1, characterized in that: The specific steps for generating anchor points are as follows. Mitochondria were isolated from specific tissues of the patient, and the enzyme activities of respiratory chain complexes I and IV were measured. A bioenergy conversion model was constructed based on enzyme activity values, and the steady-state energy conversion values ​​in tissue regions were calculated using the transmembrane proton pump-ATP synthesis coupling differential equation. The steady-state value of energy conversion is encoded into a light-controlled CRISPR gRNA sequence, which is then delivered to the tissue target site to form a covalent anchor and acquire a quantum dot binding domain; CdSe quantum dots are assembled in situ in the quantum dot binding domain. Spatial coordinates are measured by exciting entangled photon pairs and then combined with SHA-256 hash values ​​to generate anchor points.

3. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 1, characterized in that: The specific steps for generating the risk gradient instruction matrix are as follows: Based on the quantum entangled coordinates of the anchor point, the metabolic oscillation spectrum of the captured tissue region is triggered, generating a quantum oscillation spectrum matrix; The quantum oscillation spectral matrix is ​​encoded into a sequence of qubits, and the path integral is performed using the quantum Monte Carlo algorithm to calculate the dynamic risk coefficient. The dynamic risk coefficient is expanded into a higher-order tensor and then convolved with a spatiotemporal Gaussian kernel to generate a risk gradient instruction matrix.

4. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 3, characterized in that: The high-precision optical detection based on the risk gradient command matrix and anchor positioning points involves the following specific steps. Based on the risk gradient instruction matrix, the spatial gradient vector is extracted at the anchor point. Convert the spatial gradient vector into a nanosecond-level pulse signal with quantum annealing properties; The spontaneous fluorescence oscillation signal generated by two-photon excitation light is captured in real time based on nanosecond-level pulse signals; The spontaneous fluorescence oscillation signal is encoded into a sequence of qubits to generate a quantum oscillation dataset.

5. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 4, characterized in that: The specific steps for collecting metabolic activity indicators of the surgical area according to dynamically adjusted thresholds and simultaneously capturing the signal response characteristics of biomarkers are as follows. Calculate the dynamic control threshold based on a quantum oscillation dataset; When the autofluorescence oscillation signal exceeds the dynamic regulation threshold, metabolic activity indicators are collected, pulsed currents are generated simultaneously, and the response characteristics of biomarkers are captured.

6. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 5, characterized in that: The process of generating a power control instruction set for surgical equipment by performing quantum shift analysis using metabolic activity indicators and a bioenergy conversion model involves the following specific steps. The response characteristics of metabolic activity indicators and biomarkers are fused through tensor product to generate a bioenergy conversion tensor; Based on the bioenergy conversion tensor, eigenstate migration is induced in the quantum biological coupling field, and time-domain-frequency domain response signals are captured through coherent resonance to generate quantum evolution trajectory features; Map the quantum evolution trajectory features to the quantum trace norm space and calculate the quantum trace norm offset; When the quantum trace norm offset exceeds the preset damage threshold, a power control instruction set for the surgical device is generated through non-classical correlation verification.

7. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 6, characterized in that: The specific steps for performing nonlinear distortion recognition on the signal response characteristics of biomarkers to generate document verification results are as follows. The signal response characteristics of biomarkers are projected onto a quantum fiber bundle manifold to generate manifold coordinates; Quantum topological distortion index is calculated using dynamic Chern-Simons gauge fields based on manifold coordinates. By mapping the quantum topological distortion index to the Clifford algebra space, a noncommutative geometric verification matrix is ​​constructed. Based on the imaginary part of the eigenvalues ​​and the real part of the determinant of the non-commutative geometric verification matrix, a document verification result for the validity of the signal is generated.

8. The cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in claim 7, characterized in that: The response surgical device power control command set activates an ultrafast phase-change cooling mechanism, constructs quantum encrypted evidence storage based on document verification results, and simultaneously optimizes the response characteristics of biomarkers and updates the bioenergy conversion model through abnormal scenarios. The specific steps are as follows. It responds to the power control command set of the surgical equipment and performs ultra-fast phase change cooling to generate cooling parameters; Based on the document verification results and cooling parameters, a GHZ entangled state is generated, and an immutable quantum encrypted evidence is constructed through quantum Fourier transform. When the document verification result is invalid, quantum encryption is used to trigger continuous coherence analysis and optimize the response characteristics of biomarkers. The bioenergy conversion model is dynamically updated based on the optimized response characteristics of biomarkers.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the cosmetic surgery vulnerability detection method based on dynamic and static analysis as described in any one of claims 1 to 8.

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