System for assessing risk of coronary heart disease based on genetic polymorphisms in patients infected with helicobacter pylori

Abstract

The application relates to the field of medical data processing and discloses a coronary heart disease risk assessment system based on Helicobacter pylori infection patient gene polymorphism, which comprises an input load coupling module, a differential pathological damage penetration logic module, a state hysteresis feedback module and a nonlinear gain regulation module. The input load coupling module is connected with a transient physiological load signal representing pathogen invasion intensity and obtains an endogenous defense impedance threshold based on gene polymorphism. The differential pathological damage penetration logic module generates basic overflow pathological strain data when the signal amplitude is out of limit. The state hysteresis feedback module latches a historical cumulative inflammatory thermal stress index. The nonlinear gain regulation module calculates a structural brittleness gain parameter representing nonlinear increment of impedance sensitivity with cumulative stress. The system stability evaluation module generates a risk assessment index by using the gain parameter. The application maps the chronic cumulative risk into nonlinear impedance drift of a circuit system and a dynamic steady-state control process by introducing quantitative compensation for thermal hysteresis loss, thereby improving the representation integrity and stability of the system.

Description

Technical Field

[0001] This invention belongs to the field of medical data processing technology, and in particular relates to a coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients. Background Technology

[0002] Currently, in the technology system for chronic disease risk assessment and health data processing, mainstream engineering practices generally adopt multi-factor linear weighted analysis based on cross-sectional data. The design philosophy of this type of model is based on the independent event assumption, which assumes that the contributions of each risk factor to the system's terminal state are linear, parallel, and independent of each other. In actual operation, the system usually collects discrete detection data at a single time point, such as pathogen infection intensity values ​​and genotyping characteristic values, and performs weighted summation calculations using preset weight coefficients to output the current risk probability assessment result. This processing method has become the common standard in the industry for processing massive amounts of medical data due to its low computational complexity and strong compatibility with discrete data.

[0003] However, when the aforementioned general approach is applied to the long-term interaction scenario of the specific combination of Helicobacter pylori infection and host gene polymorphism, its inherent linear time-invariant logic assumption begins to reveal fundamental engineering limitations. In the actual biological pathological evolution process, the vascular endothelial system is not a static linear response body, but a nonlinear time-varying system with significant historical memory effects and structural fatigue sensitization characteristics. At different stages of disease development, the same pathogen invasion intensity as an input signal results in drastically different effective damage increments to the system. Existing memoryless linear models only focus on the current input intensity, ignoring the nonlinear modulation effect of historical cumulative load on the current system response function. This leads to the system's inability to distinguish between the initial high-intensity attack and the structural collapse under long-term low-intensity erosion when facing complex conditions of long-term chronic infection accompanied by trace inflammatory leakage. Consequently, in long-term wind... Systemic biases arise in risk assessment. For example, Chinese invention patent CN100457907C discloses a recombinant Helicobacter pylori outer membrane protein 15. The gene of outer membrane protein 15 is cloned and protein expression is achieved through PCR technology. The recombinant protein is used for mucosal immunotherapy and related drug development, which has value in the fields of biopharmaceutical preparation and basic immunotherapy. However, at the risk assessment level, its technical approach is limited to the static biological effect study of specific protein fragments. It fails to establish a dynamic quantitative correlation between pathogen invasion intensity and host defense threshold. This single-dimensional output based on biochemical experiments lacks a recursive calculation mechanism for the historical cumulative load of the system when facing the complex evolution of chronic infection accompanied by trace inflammatory leakage. This results in the inability to characterize the nonlinear amplification effect of historical damage on current vascular vulnerability, and there is a significant lack of accuracy in long-term risk prediction.

[0004] Therefore, the technical problem to be solved by this invention is how to establish a risk assessment model that can integrate the static defense threshold of genes and the dynamic invasion intensity of pathogens, and has the ability to remember historical states and perform nonlinear cumulative calculations, without adding new physical sensor hardware, through the reconstruction of data processing logic. Summary of the Invention

[0005] This invention provides a coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients, comprising:

[0006] The input load coupling module is configured to receive the transient physiological load signal characterizing the intensity of external Helicobacter pylori invasion and obtain the endogenous defense impedance threshold characterizing the patient's gene polymorphism characteristics, thereby defining the linear response dead zone of the system to the transient physiological load signal characterizing the intensity of pathogen invasion.

[0007] The differential pathological damage penetration logic module is cascaded with the input load coupling module and configured to perform differential logic operations between the signal and the threshold within a clock cycle, generating non-zero basic overflow pathological strain data only when the amplitude exceeds the limit.

[0008] The state hysteresis feedback module is configured to latch the historical cumulative inflammatory thermal stress index and construct the temporal state vector of vascular injury evolution;

[0009] The nonlinear gain control module is connected to the state hysteresis feedback module and is configured to calculate the structural brittle gain parameter based on the historical exponent using a preset impedance drift transfer function. This parameter characterizes the dynamic matching characteristic of the system's impedance sensitivity to new load requests increasing nonlinearly with historical accumulated stress.

[0010] The system stability assessment module is configured to dynamically weight and amplify the basic data using gain parameters to generate a risk assessment index characterizing the steady-state margin of the cardiovascular system. The system maintains the inertial decay response of the assessment index during the thermal damping cycle after the signal returns to zero through the negative feedback closed loop collaboration of the nonlinear gain control module and the state hysteresis feedback module, thereby achieving dynamic characterization of the vascular steady-state imbalance and physiological overload recovery process.

[0011] Preferably, the nonlinear gain control module is configured to execute variable gain control logic based on the logarithmic cascade amplification law, and the specific calculation rules for the structurally brittle gain parameters satisfy the following physical quantization relationship: ,in, The structural brittleness gain parameter for the nth clock cycle. The first provided by the state hysteresis feedback module The historical cumulative inflammatory thermal stress index at the end of each clock cycle The sensitization adjustment coefficient is a preset dimensionless coefficient that characterizes the ratio of thermal capacity to thermal resistance of the system's physical medium in relation to human physiological functions. It is used to define the impedance decay rate of the system's memory of historical loads.

[0012] Preferably, the state hysteresis feedback module integrates an RC damping network simulation unit. The RC damping network simulation unit is configured to prevent the historical cumulative inflammatory thermal stress index from being abruptly cleared to zero when the basic overflow pathological strain data is detected to be interrupted or returned to zero. The RC damping network simulation unit executes an energy release logic based on a capacitor discharge model. According to a preset discharge time constant, the historical cumulative inflammatory thermal stress index is smoothly dissipated over time, thereby introducing a time-series inertial component characterizing the system recovery delay characteristics into the risk assessment index.

[0013] Preferably, the input load coupling module includes an impedance characteristic lookup table mapping unit, which pre-stores a multi-dimensional physical impedance characteristic lookup table. The impedance characteristic lookup table mapping unit is configured to retrieve the corresponding physical quantization value from the physical impedance characteristic lookup table according to the input system architecture type code, and lock the value as the intrinsic defense impedance threshold based on genetic polymorphism. The intrinsic defense impedance threshold based on genetic polymorphism is configured to present a discrete impedance ladder distribution with different system architecture type codes, which is used to simulate the differentiated shielding and absorption capabilities of different physical architectures for the same external disturbance power flow.

[0014] Preferably, the differential pathological damage penetration logic module includes a unidirectional clamping logic unit configured to perform a threshold truncation operation. When the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity is less than or equal to the intrinsic defense impedance threshold based on gene polymorphism, the unidirectional clamping logic unit forcibly clamps the basic overflow pathological strain data to zero level to simulate the steady-state insulation characteristics of the system under subthreshold load conditions. Only when the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity causes an overload potential difference characterizing the pathological imbalance at the system input terminal is the unidirectional clamping logic unit allowed the power flow signal to penetrate and enter the next stage module.

[0015] Preferably, the system further includes an adaptive baseline drift correction module, which is connected in parallel to the signal output of the input load coupling module. The adaptive baseline drift correction module is configured to continuously monitor the background noise power spectrum of the transient physiological load signal characterizing the intensity of pathogen invasion, and update the zero-point reference level of the system in real time based on a moving average filtering algorithm to eliminate spurious load signals caused by sensor temperature drift or environmental electromagnetic interference, ensuring that the differential pathological damage penetration logic module only responds to valid load mutation signals.

[0016] Preferably, the system stability assessment module includes a multi-level overload protection trigger, which has multiple stability collapse warning thresholds set in a gradient distribution. The multi-level overload protection trigger is configured to compare the calculated risk assessment index with the stability collapse warning thresholds in real time: when the risk assessment index exceeds the first-level threshold, it outputs a primary warning level signal indicating that the system has entered a metastable state due to excessive load; when the risk assessment index exceeds the second-level threshold, it outputs a high-level emergency level signal indicating that the system is about to experience a functional interruption, and generates a circuit breaker control command to cut off external disturbance input.

[0017] Preferably, the system is constructed as a distributed pathological sensing and control architecture, including: a front-end load sensing node, used to perform analog-to-digital conversion and data packet encapsulation of transient physiological load signals characterizing the intensity of pathogen invasion; and a back-end central processing core, used to centrally perform complex logic operations of the state hysteresis feedback module and the nonlinear gain control module; the front-end load sensing node and the back-end central processing core are connected through an encrypted industrial fieldbus, which is configured to periodically synchronize the timing state vector at a frequency of not less than 100Hz to ensure the consistency of system state variables under the distributed architecture.

[0018] Preferably, the input load coupling module is configured to receive time-series signals from a multi-channel signal acquisition array; the time-series signals are processed by a fast Fourier transform unit and decomposed into high-frequency components characterizing transient impact loads and low-frequency components characterizing steady-state fundamental loads based on a preset cutoff frequency; the differential pathological damage penetration logic module is configured to apply different weighted impedance network coefficients to the high-frequency components and the low-frequency components respectively, wherein the coefficients applied to the high-frequency components are greater than the coefficients applied to the low-frequency components, so as to highlight the destructive effect of transient impacts on system power stability in the basic overflow pathological strain data.

[0019] Preferably, the nonlinear gain control module also includes a saturation limiting logic unit; the saturation limiting logic unit is configured to set a physical upper limit amplitude for the structural brittle gain parameter to prevent the output of the system stability assessment module from diverging when the historical cumulative inflammatory thermal stress index tends to infinity; the physical upper limit amplitude is a fixed constant pre-calibrated based on the physiological tolerance limit or maximum allowable thermal stress of the system physical medium characterizing human physiological function, ensuring that the risk assessment index is always constrained within the linear operating region or recoverable nonlinear region of the system.

[0020] Compared with existing technologies, the coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients has the following advantages:

[0021] 1. In coronary artery disease risk assessment, a recursive fatigue gain modulation mechanism is constructed in the cumulative load assessment stage. By introducing a state feedback loop into the processing logic of discrete time series, a nonlinear time-varying response model is established. This changes the existing technology's approach of treating risk signals within each time window as independent events and linearly superimposing them. Instead, the cumulative load state output from the previous time window is used as an active parameter and recursively mapped to the structural fragility gain coefficient of the current time window. This data processing architecture based on historical states logically realizes the mathematical representation of the system's damage sensitization effect. When processing long-span time series data, the system can dynamically adjust the amplification factor of the current input signal according to the accumulation of historical load. Without changing the sampling frequency of the original detection data, this processing method eliminates data distortion caused by ignoring the influence of historical fatigue on the current transfer function through logical reconstruction at the algorithm level, thereby constructing a cumulative load index that conforms to the evolution law of nonlinear systems.

[0022] 2. By leveraging the synergistic operation of the differential penetration calculation module and the physiological entropy increase attenuation logic, a signal filtering and dynamic calibration system based on dual-channel gating is established. On one hand, the system uses genotyping data to set an initial endogenous tolerance threshold and constructs a signal cutoff threshold for the intensity of pathogen invasion, extracting only the overflow portion exceeding this threshold as effective computational quantity. On the other hand, the system introduces an attenuation operator coupled with the time dimension. Based on the Boolean value characteristics of physiological age and basal metabolic state, it performs dynamic depreciation calculations on the tolerance threshold in accordance with the law of physical entropy increase. This processing logic parametrically couples static genetic characteristics with dynamic physiological evolution characteristics at the data operation level, enabling the system to automatically adapt to the baseline defense level at different life cycle stages. This mechanism solves the misjudgment problem caused by baseline drift in long-term evaluation of static threshold models from the underlying logic of data processing, ensuring the logical self-consistency and stability of the evaluation model across the entire time axis. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall logical architecture and module interaction principle of the system of the present invention;

[0024] Figure 2 This is the operation logic diagram of the structurally brittle gain parameter in the nonlinear gain control module of this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, 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.

[0026] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0027] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0028] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0029] A coronary artery disease risk assessment system based on Helicobacter pylori infection gene polymorphism includes:

[0030] The input load coupling module is configured to receive the transient physiological load signal characterizing the intensity of external Helicobacter pylori invasion and obtain the endogenous defense impedance threshold characterizing the patient's gene polymorphism characteristics, thereby defining the linear response dead zone of the system to the transient physiological load signal characterizing the intensity of pathogen invasion.

[0031] The differential pathological damage penetration logic module is cascaded with the input load coupling module and configured to perform differential logic operations between the signal and the threshold within a clock cycle, generating non-zero basic overflow pathological strain data only when the amplitude exceeds the limit.

[0032] The state hysteresis feedback module is configured to latch the historical cumulative inflammatory thermal stress index and construct the temporal state vector of vascular injury evolution;

[0033] The nonlinear gain control module is connected to the state hysteresis feedback module and is configured to calculate the structural brittle gain parameter based on the historical exponent using a preset impedance drift transfer function. This parameter characterizes the dynamic matching characteristic of the system's impedance sensitivity to new load requests increasing nonlinearly with historical accumulated stress.

[0034] The system stability assessment module is configured to dynamically weight and amplify the basic data using gain parameters to generate a risk assessment index characterizing the steady-state margin of the cardiovascular system. The system maintains the inertial decay response of the assessment index during the thermal damping cycle after the signal returns to zero through the negative feedback closed loop collaboration of the nonlinear gain control module and the state hysteresis feedback module, thereby achieving dynamic characterization of the vascular steady-state imbalance and physiological overload recovery process.

[0035] Preferably, the nonlinear gain control module is configured to execute variable gain control logic based on the logarithmic cascade amplification law, and the specific calculation rules for the structurally brittle gain parameters satisfy the following physical quantization relationship: ,in, The structural brittleness gain parameter for the nth clock cycle. The first provided by the state hysteresis feedback module The historical cumulative inflammatory thermal stress index at the end of each clock cycle The sensitization adjustment coefficient is a preset dimensionless coefficient that characterizes the ratio of thermal capacity to thermal resistance of the system's physical medium in relation to human physiological functions. It is used to define the impedance decay rate of the system's memory of historical loads.

[0036] Preferably, the state hysteresis feedback module integrates an RC damping network simulation unit. The RC damping network simulation unit is configured to prevent the historical cumulative inflammatory thermal stress index from being abruptly cleared to zero when the basic overflow pathological strain data is detected to be interrupted or returned to zero. The RC damping network simulation unit executes an energy release logic based on a capacitor discharge model. According to a preset discharge time constant, the historical cumulative inflammatory thermal stress index is smoothly dissipated over time, thereby introducing a time-series inertial component characterizing the system recovery delay characteristics into the risk assessment index.

[0037] Preferably, the input load coupling module includes an impedance characteristic lookup table mapping unit, which pre-stores a multi-dimensional physical impedance characteristic lookup table. The impedance characteristic lookup table mapping unit is configured to retrieve the corresponding physical quantization value from the physical impedance characteristic lookup table according to the input system architecture type code, and lock the value as the intrinsic defense impedance threshold based on genetic polymorphism. The intrinsic defense impedance threshold based on genetic polymorphism is configured to present a discrete impedance ladder distribution with different system architecture type codes, which is used to simulate the differentiated shielding and absorption capabilities of different physical architectures for the same external disturbance power flow.

[0038] Preferably, the differential pathological damage penetration logic module includes a unidirectional clamping logic unit configured to perform a threshold truncation operation. When the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity is less than or equal to the intrinsic defense impedance threshold based on gene polymorphism, the unidirectional clamping logic unit forcibly clamps the basic overflow pathological strain data to zero level to simulate the steady-state insulation characteristics of the system under subthreshold load conditions. Only when the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity causes an overload potential difference characterizing the pathological imbalance at the system input terminal is the unidirectional clamping logic unit allowed the power flow signal to penetrate and enter the next stage module.

[0039] Preferably, the system further includes an adaptive baseline drift correction module, which is connected in parallel to the signal output of the input load coupling module. The adaptive baseline drift correction module is configured to continuously monitor the background noise power spectrum of the transient physiological load signal characterizing the intensity of pathogen invasion, and update the zero-point reference level of the system in real time based on a moving average filtering algorithm to eliminate spurious load signals caused by sensor temperature drift or environmental electromagnetic interference, ensuring that the differential pathological damage penetration logic module only responds to valid load mutation signals.

[0040] Preferably, the system stability assessment module includes a multi-level overload protection trigger, which has multiple stability collapse warning thresholds set in a gradient distribution. The multi-level overload protection trigger is configured to compare the calculated risk assessment index with the stability collapse warning thresholds in real time: when the risk assessment index exceeds the first-level threshold, it outputs a primary warning level signal indicating that the system has entered a metastable state due to excessive load; when the risk assessment index exceeds the second-level threshold, it outputs a high-level emergency level signal indicating that the system is about to experience a functional interruption, and generates a circuit breaker control command to cut off external disturbance input.

[0041] Preferably, the system is constructed as a distributed pathological sensing and control architecture, including: a front-end load sensing node, used to perform analog-to-digital conversion and data packet encapsulation of transient physiological load signals characterizing the intensity of pathogen invasion; and a back-end central processing core, used to centrally perform complex logic operations of the state hysteresis feedback module and the nonlinear gain control module; the front-end load sensing node and the back-end central processing core are connected through an encrypted industrial fieldbus, which is configured to periodically synchronize the timing state vector at a frequency of not less than 100Hz to ensure the consistency of system state variables under the distributed architecture.

[0042] Preferably, the input load coupling module is configured to receive time-series signals from a multi-channel signal acquisition array; the time-series signals are processed by a fast Fourier transform unit and decomposed into high-frequency components characterizing transient impact loads and low-frequency components characterizing steady-state fundamental loads based on a preset cutoff frequency; the differential pathological damage penetration logic module is configured to apply different weighted impedance network coefficients to the high-frequency components and the low-frequency components respectively, wherein the coefficients applied to the high-frequency components are greater than the coefficients applied to the low-frequency components, so as to highlight the destructive effect of transient impacts on system power stability in the basic overflow pathological strain data.

[0043] Preferably, the nonlinear gain control module also includes a saturation limiting logic unit; the saturation limiting logic unit is configured to set a physical upper limit amplitude for the structural brittle gain parameter to prevent the output of the system stability assessment module from diverging when the historical cumulative inflammatory thermal stress index tends to infinity; the physical upper limit amplitude is a fixed constant pre-calibrated based on the physiological tolerance limit or maximum allowable thermal stress of the system physical medium characterizing human physiological function, ensuring that the risk assessment index is always constrained within the linear operating region or recoverable nonlinear region of the system.

[0044] This embodiment, in conjunction with the accompanying drawings, describes the technical solution of the present invention: as follows Figure 1 As shown, the transient physiological load signal characterizing the intensity of pathogen invasion is input to the input load coupling module. This module defines the linear response dead zone of the system by acquiring the endogenous defense impedance threshold based on gene polymorphism, and cascades it to the differential pathological damage penetration logic module to perform signal-threshold differential operation and generate basic overflow pathological strain data. The generated data is directly input into the system stability assessment module as basic overflow power flow data. At the same time, the differential pathological damage penetration logic module establishes a feedback association with the state hysteresis feedback module through the stress accumulation path shown by the dashed line. The state hysteresis feedback module latches the historical accumulated inflammatory thermal stress index and constructs a time-series state vector. The output historical accumulated thermal stress index is then input into the nonlinear gain control module and the structural brittle gain parameter is calculated based on the impedance drift transfer function. Finally, the system stability assessment module receives the parameter and dynamically weights and amplifies the basic data to generate a risk assessment index characterizing the steady-state margin of the cardiovascular system.

[0045] The logical flow between the aforementioned system modules is reflected in clinical practice through specific business execution procedures, such as... Figure 2As shown, medical staff first perform sample collection and information entry. This step includes PCR amplification and sequencing after extracting genomic DNA, as well as collecting clinical baseline data. Then, the system runs a polymorphism risk assessment model and identifies CagA gene polymorphism sites to quantify the association strength of virulence factors. At the same time, multivariate logistic regression analysis is performed. After obtaining the risk assessment results, the system generates an individualized risk grading report and triggers an early warning mechanism and formulates a tiered intervention plan accordingly. Finally, the assessment logic is dynamically evolved by initiating model iteration updates.

[0046] Example 1: In a coronary heart disease risk assessment system based on the genetic polymorphism of Helicobacter pylori-infected patients deployed in a medical data center, the input load coupling module accesses a data stream representing the current physiological state of the subject through an encrypted data interface. This stream includes a transient physiological load signal representing the intensity of pathogen invasion from a 52-year-old male subject. This signal is obtained by normalizing the DOB value from the C13 urea breath test, and its physical amplitude... With a standardized unit of 3.8, this module simultaneously extracts the IL-1B-511T genotyping code from the subject's electronic medical record and locks the initial endogenous defense impedance threshold based on gene polymorphism using a pre-set physical impedance characteristic lookup table. The standard unit is 3.0, and the data is combined with the subject's physiological age parameter of 52 years and the Boolean value of metabolic syndrome status, using a built-in exponential decay operator. Operations, where With an attenuation coefficient set to 0.05, the effective endogenous defense impedance threshold based on gene polymorphism at the current time was calculated. It is a standardized unit of 2.1.

[0047] The differential pathological damage penetration logic module performs a differential comparison operation on the transient physiological load signal characterizing the intensity of pathogen invasion and the effective endogenous defense impedance threshold based on gene polymorphism within the system clock cycle. Based on the current signal amplitude of 3.8 being greater than the threshold of 2.1, the module determines that the system's current impedance barrier has been breached and generates non-zero baseline overflow pathological strain data based on the difference between the two. The state hysteresis feedback module retrieves the subject's historical cumulative inflammatory thermal stress index from the storage unit within a sliding time window over the past 5 years. This value characterizes the cumulative inflammatory load on the vascular endothelial system over a long period. The nonlinear gain control module retrieves this historical index and applies it according to the formula. The operation is performed, where μ is a sensitization coefficient with a preset value of 0.2, resulting in an output structural brittleness gain parameter of 1.6. This parameter quantifies the nonlinear increase in the system's sensitivity to current disturbances caused by the accumulation of historical damage. The system stability assessment module receives basic overflow pathological strain data and structural fragility gain parameters, and couples the two through a multiplier unit to calculate the final risk assessment index. As a quantitative technical indicator characterizing the risk of coronary heart disease, it is output to the clinical decision terminal. If its value falls into the preset high-risk warning range, it triggers the corresponding graded warning signal.

[0048] Example 2: In a validation environment equipped with a high-performance medical data processing unit, this example constructs a controlled digital simulation and data playback experimental platform. The platform's input is connected to a standardized medical information database containing longitudinal tracking data from 2000 coronary heart disease patients. The data in this database has undergone desensitization and noise injection. Each sample contains a complete C13 urea breath test DOB value sequence, IL-1B-511T genotyping code, and corresponding long-term cardiovascular event (MACE) records. To simulate unavoidable signal perturbations in real clinical scenarios, the experimental platform superimposes Gaussian white noise with a signal-to-noise ratio of 20 dB onto the original DOB value sequence and introduces a baseline drift with an amplitude of 0.5 normalized units, thereby constructing... The experiment, designed to objectively verify the risk assessment system based on a nonlinear time-varying load impact model proposed in this invention, was designed to handle the specific hidden high-risk population with low bacterial count and high sensitivity, compared to the traditional linear weighted assessment model. To this end, two parallel treatment groups were designed: the sample group of this invention and the control group of the linear model. The sample group of this invention fully deployed the aforementioned input load coupling module, differential pathological damage penetration logic module, state hysteresis feedback module, nonlinear gain control module, and system stability assessment module, while the control group of the linear model adopted the traditional logic architecture, which linearly weighted and summed the DOB value and the gene risk factor, and lacked a nonlinear processing mechanism for the dynamic decay of the threshold and the historical cumulative effect.

[0049] During the trial phase, the system processed data from a subject with typical low bacterial load (DOB value fluctuating between 3.5 and 4.5) and carrying the IL-1B-511T mutant gene. For this subject, although their DOB value never exceeded the conventional pathogenicity threshold, it remained in a borderline fluctuation state. In the linear model control group, because the DOB value did not reach the threshold and the linear weight calculation could not generate sufficient risk scores, this subject was classified as low-risk. However, in the sample group of this invention, the input load coupling module, based on genotyping, initialized the endogenous defense impedance threshold based on gene polymorphism. The value was set at 3.0, and combined with the subject's biological age of 52, it was calculated using a decay formula. Calculate the current effective endogenous defense resistance threshold based on gene polymorphism. The threshold was lowered to 2.1. The differential pathological damage penetration logic module detected that the real-time DOB value (amplitude approximately 3.8) after noise injection was significantly higher than the reduced threshold of 2.1, thus determining that impedance breakdown had occurred and outputting non-zero baseline overflow pathological strain data. Subsequently, the state hysteresis feedback module read the subject's historical cumulative inflammatory thermal stress index over the past 5 years. (Value is 8.5), the nonlinear gain control module is based on the formula (in (Set to 0.2), calculate the structural brittleness gain parameter. The gain parameter, 1.45, directly affects the base overflow power flow, significantly amplifying the final risk assessment index and causing it to exceed the high-risk warning line. Statistical analysis of the comparative data revealed significant performance differences. In batch testing of the entire sample library, for the specific subgroup of low bacterial count + high sensitivity, the risk detection rate of the present invention's sample group reached 92.5%, while the linear model control group only reached 45.0%. More importantly, the experimental data showed a clear nonlinear performance inflection point: when the historical cumulative inflammatory thermal stress index... After exceeding a certain threshold (approximately 5.0), the risk index of the system of this invention showed an exponential upward trend, while the control group maintained a linear and slow growth. This confirmed the key role of the nonlinear gain control module in capturing long-term cumulative damage and proved that the present invention is not a simple parameter adjustment, but rather solves the signal deafness problem of traditional linear models when dealing with hidden cumulative risks by introducing a nonlinear operator that conforms to the biological cumulative law.

[0050] Example 3: In an algorithm verification environment configured with a high-performance computing cluster, this example addresses the potential issues of opaque algorithm paths and unclear parameter sources in risk assessment systems based on nonlinear time-varying load impact models. It constructs a deeply transparent parameter calibration and adaptive evolution procedure, determining key system parameters (such as the sensitization adjustment coefficient μ and the attenuation coefficient) through mathematical derivation and data-driven simulation iteration. The optimal value logic of the nonlinear gain control module is determined, and its stability under extreme boundary conditions is verified. Addressing the issue of unclear parameter sources, this embodiment does not simply set empirical values, but instead introduces a parameter optimization subsystem based on a genetic algorithm to determine the sensitization adjustment coefficient to be calibrated. With attenuation coefficient Defined as gene loci on chromosomes, an initial population of 1000 virtual individuals was constructed, with each virtual individual representing a set of parameter combinations. The data is then input into a parallel risk assessment simulation unit.

[0051] Within the simulation unit, the system loads 5000 standardized test samples with different genotypes and pathogen load characteristics. For each sample, the system applies the current parameter combinations. Complete execution Calculation and A recursive update process generates a corresponding risk assessment index sequence, and a fitness function based on the area under the receiver operating characteristic curve (AUC) is introduced. By comparing the simulated risk index with the actual clinical outcomes of the samples, the classification performance of this parameter combination in distinguishing between hidden high-risk and non-high-risk populations was quantified. After 50 generations of genetic iteration and natural selection, the parameter combination in the population gradually converged. Simulation data showed that when the sensitization regulation coefficient... It converges to the interval [0.18, 0.22], and the decay coefficient... When the convergence reaches the interval [0.045, 0.055], the overall AUC of the system is maximized (>0.92). This convergence process not only determines the parameter values ​​( =0.2, The mathematical basis for (=0.05) is further revealed in principle that this parameter combination is the globally optimal solution that balances sensitivity and specificity, eliminating the arbitrariness of parameter setting.

[0052] To address the issue of opaque algorithm paths, particularly the dynamic behavior of nonlinear gain control modules over long periods, this embodiment constructs a full lifecycle state evolution tracking model. This model no longer... Instead of treating it as a transient value, it is viewed as a state trajectory evolving with time t. The system introduces discretized state transition equations: ,in This is the state transition function. This represents the baseline overflow pathological strain data generated by the differential pathological damage penetration logic module in the nth clock cycle. Over a 10-year simulation, the system tracked the state evolution trajectory of a low bacterial load + highly sensitive subject. Data shows that in the initial phase (first 3 years), despite the cumulative burden index... Slow accumulation, but It consistently remained in the linear region close to 1.0; however, when After exceeding a certain critical threshold (approximately 5.0), The algorithm exhibits a sharp, non-linear increase, rapidly jumping from 1.1 to over 1.6, triggering a high-risk warning. This trajectory clearly demonstrates how the algorithm smoothly transitions from a quiet period to an explosive period, verifying the system's ability to capture phase transitions when dealing with chronically accumulated risks. Ultimately, this embodiment, through the convergence curve of parameter optimization and the trajectory diagram of state evolution, concretizes the abstract mathematical model into an observable and verifiable physical process. It not only solves the problem of parameter source but also proves the logical completeness and engineering reproducibility of the technical solution by transparently displaying the dynamic behavior inside the algorithm.

[0053] Example 4: In a simulated real-world clinical deployment environment configured with a high-availability medical data gateway, this example addresses the potential issue of unclear sources for core lookup table values ​​by constructing a rigorous offline calibration and data filling procedure. Through a series of controlled in vitro physical model experiments, a quantitative mapping relationship between genotype encoding and endogenous defense impedance thresholds based on gene polymorphism is established, ensuring that the construction of the core database is based on reproducible physical facts. Upon procedure initiation, the system constructs a set of microfluidic chip physical models corresponding to different IL-1 gene cluster polymorphic sites. Each model simulates the monolayer structure of vascular endothelial cells of a specific genotype under standard culture conditions and is placed within a precisely controlled fluid shear loop. In this environment, the system applies a series of standardized inflammatory mediator pulses with progressively increasing gradients to these physical models and uses a high-sensitivity transmembrane impedance monitor to record the impedance changes of cell monolayers in real time. When the transmembrane impedance drops to 50% of the initial value, the system records the corresponding concentration of inflammatory mediators as the physical tolerance limit for that genotype. By performing repeatability tests (n=50) on physical models covering all target genotypes, the system obtains a statistically significant tolerance limit dataset. After normalization, these physical measurements are used as standardized endogenous defense impedance thresholds based on gene polymorphism and filled into the physical impedance characteristic lookup table, thus completing the physical anchoring and transparent construction of the core database.

[0054] Furthermore, to address the potential lack of on-site adaptability procedures, this embodiment establishes a pre-deployment calibration procedure. Before the system is formally connected to the clinical data stream, a standardized zero-point drift and background noise calibration process is executed. The process requires the system to continuously collect environmental background signals for no less than 24 hours under no-load conditions, and to calculate the specific noise power spectral density in that environment using an adaptive baseline drift correction module. Based on this spectral density, the system automatically adjusts the dead zone threshold parameter in the differential pathological lesion penetration logic module to ensure that the signal-to-noise ratio of the system remains within the preset compliance range under different electromagnetic environments. This pre-calibration process ensures that the system can automatically adapt and maintain consistent detection accuracy when migrating from a laboratory environment to a complex clinical environment.

[0055] Example 5: In a pre-deployment testing environment for a system integrating intelligent fault diagnosis and fault-tolerant control units, this example addresses potential issues such as missing boundary anomaly handling strategies and hardware consistency deviations. It establishes standardized system adaptive parameter initialization and boundary condition stress testing procedures to ensure the system can automatically adapt to performance differences between different batches of sensors before clinical use and possesses stability under extreme or abnormal input conditions. This mitigates the risk of system crashes or false alarms caused by hardware consistency deviations or sporadic signal anomalies. Regarding the issue of initial condition variations due to sensor performance dispersion, the procedure defines a power-on self-test and parameter adaptive initialization process. After system power-on, the adaptive calibration module... The system automatically executes a micro-current excitation scanning program, applying a set of standardized test signals covering frequencies from 0.1Hz to 100Hz to the input load coupling module. The system acquires the module's response curve in real time and calculates its amplitude-frequency and phase-frequency characteristics. The system compares the measured characteristic curve with the standard golden reference model stored in the read-only memory and calculates the deviation vector. Based on this deviation vector, the system automatically adjusts the gain compensation coefficient and filter cutoff frequency at the input end until the root mean square error (RMSE) between the measured curve and the reference model is less than 0.05. This adaptive initialization process ensures that regardless of the fluctuations in sensor batches, the system's input and output characteristics during actual operation are always locked above the preset design baseline.

[0056] In response to abnormal operating conditions such as signal loss or extreme value impacts that may occur in the clinical environment, the procedure constructs an online fault-tolerant and anomaly handling logic based on a state machine. The system has a built-in real-time monitoring sentinel process that continuously monitors the integrity and rationality of the input signal. When the input signal is detected to be at zero level for five consecutive sampling cycles or the amplitude instantaneously exceeds 120% of the range, the sentinel process triggers a safety clamping mode. In this mode, the differential pathological damage penetration logic module is forcibly locked, the output is clamped to the valid value of the previous moment, and a specific fault code is sent to the user interface. When the signal returns to the normal dynamic range within three consecutive cycles, the system automatically unlocks and resumes normal risk assessment calculation. This anomaly handling mechanism not only prevents invalid data from polluting the historical cumulative load index, but also ensures the continuous availability of the system and the confidence of the output results under harsh operating conditions.

[0057] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients, characterized in that, include: The input load coupling module is configured to receive the transient physiological load signal characterizing the intensity of external Helicobacter pylori invasion and obtain the endogenous defense impedance threshold characterizing the patient's gene polymorphism characteristics, thereby defining the linear response dead zone of the system to the transient physiological load signal characterizing the intensity of pathogen invasion. The differential pathological damage penetration logic module is cascaded with the input load coupling module and configured to perform differential logic operations between the signal and the threshold within a clock cycle, generating non-zero basic overflow pathological strain data only when the amplitude exceeds the limit. The state hysteresis feedback module is configured to latch the historical cumulative inflammatory thermal stress index and construct the temporal state vector of vascular injury evolution; The nonlinear gain control module, connected to the state hysteresis feedback module, is configured to calculate the structural brittle gain parameter based on the historical cumulative inflammatory thermal stress index using a preset impedance drift transfer function. This parameter characterizes the dynamic matching characteristic of the system's impedance sensitivity to new load requests increasing nonlinearly with historical cumulative inflammatory thermal stress. The system stability assessment module is configured to dynamically weight and amplify the baseline overflow pathological strain data using structural fragility gain parameters to generate a risk assessment index characterizing the steady-state margin of the cardiovascular system. The system maintains the inertial decay response of the risk assessment index within the thermal damping cycle after the signal returns to zero through the negative feedback closed-loop collaboration of the nonlinear gain control module and the state hysteresis feedback module, thereby achieving dynamic characterization of the vascular steady-state imbalance and physiological overload recovery process.

2. The coronary heart disease risk assessment system based on Helicobacter pylori infection gene polymorphism according to claim 1, characterized in that, The nonlinear gain control module is configured to execute variable gain control logic based on the logarithmic cascade amplification law. The specific calculation rules for the structurally brittle gain parameters satisfy the following physical quantization relationship: ,in, The structural brittleness gain parameter for the nth clock cycle. The historical cumulative inflammatory thermal stress index is provided by the state hysteresis feedback module at the end of the (n-1)th clock cycle. μ is a preset dimensionless sensitization adjustment coefficient, which characterizes the ratio of thermal capacity to thermal resistance of the system's physical medium in terms of human physiological function and is used to define the impedance decay rate of the system's memory of historical loads.

3. The coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients according to claim 1, characterized in that, The state hysteresis feedback module integrates an RC damping network simulation unit. The RC damping network simulation unit is configured to prevent the historical cumulative inflammatory thermal stress index from being abruptly cleared to zero when the basic overflow pathological strain data is detected to be interrupted or returned to zero. The RC damping network simulation unit executes an energy release logic based on a capacitor discharge model. According to the preset discharge time constant, the historical cumulative inflammatory thermal stress index is smoothly dissipated over time, thereby introducing a time-series inertial component that characterizes the system recovery delay in the risk assessment index.

4. The coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients according to claim 1, characterized in that, The input load coupling module includes an impedance characteristic lookup table mapping unit, which pre-stores a multi-dimensional physical impedance characteristic lookup table. The impedance characteristic lookup table mapping unit is configured to retrieve the corresponding physical quantization value from the physical impedance characteristic lookup table based on the input genotyping code, and lock this value as the endogenous defense impedance threshold based on genomorphism. The endogenous defense impedance threshold based on genomorphism is configured to present a discrete impedance ladder distribution with different genotyping codes, which is used to simulate the differentiated shielding and absorption capabilities of different physical architectures against the same external disturbance power flow.

5. The coronary heart disease risk assessment system based on Helicobacter pylori infection patient gene polymorphism according to claim 1, characterized in that, The differential pathological damage penetration logic module includes a one-way clamping logic unit configured to perform a threshold truncation operation. When the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity is less than or equal to the intrinsic defense impedance threshold based on gene polymorphism, the one-way clamping logic unit forcibly clamps the basic overflow pathological strain data to zero level to simulate the steady-state insulation characteristics of the system under subthreshold load conditions. Only when the amplitude of the transient physiological load signal characterizing the pathogen invasion intensity causes an overload potential difference characterizing the pathological imbalance at the system input is the one-way clamping logic unit allowed the power flow signal to penetrate and enter the next stage module.

6. The coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients according to claim 1, characterized in that, The system also includes an adaptive baseline drift correction module, which is connected in parallel to the signal output of the input load coupling module. The adaptive baseline drift correction module is configured to continuously monitor the background noise power spectrum of the transient physiological load signal characterizing the intensity of pathogen invasion, and update the zero-point reference level of the system in real time based on the moving average filtering algorithm to eliminate spurious load signals caused by sensor temperature drift or environmental electromagnetic interference, and ensure that the differential pathological damage penetration logic module only responds to valid load mutation signals.

7. The coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients according to claim 1, characterized in that, The system stability assessment module includes a multi-level overload protection trigger, which has multiple stability collapse warning thresholds set in a gradient distribution. The multi-level overload protection trigger is configured to compare the calculated risk assessment index with the stability collapse warning thresholds in real time: when the risk assessment index exceeds the first level threshold, it outputs a primary warning level signal indicating that the system has entered a metastable state due to excessive load. When the risk assessment index exceeds the second-level threshold, the system outputs a high-level emergency signal indicating an impending functional interruption and generates a circuit breaker control command to cut off external disturbance inputs.

8. The coronary heart disease risk assessment system based on the gene polymorphism of Helicobacter pylori-infected patients according to claim 1, characterized in that, The system is constructed as a distributed pathological sensing and control architecture, which includes: a front-end load sensing node, used to perform analog-to-digital conversion and data packet encapsulation of transient physiological load signals characterizing the intensity of pathogen invasion; and a back-end central processing core, used to centrally perform complex logic operations of the state hysteresis feedback module and the nonlinear gain control module; the front-end load sensing node and the back-end central processing core are connected through an encrypted industrial fieldbus, which is configured to periodically synchronize the timing state vector at a frequency of not less than 100Hz to ensure the consistency of system state variables under the distributed architecture.

9. A coronary heart disease risk assessment system based on Helicobacter pylori infection patient gene polymorphism according to claim 1, characterized in that, The input load coupling module is configured to receive time-series signals from a multi-channel signal acquisition array; the time-series signals are processed by a fast Fourier transform unit and decomposed into high-frequency components characterizing transient impact loads and low-frequency components characterizing steady-state fundamental loads based on a preset cutoff frequency. The differential pathological damage penetration logic module is configured to apply different weighted impedance network coefficients to the high-frequency and low-frequency components, respectively, with the coefficients applied to the high-frequency components being greater than those applied to the low-frequency components, in order to highlight the destructive effect of transient shocks on system power stability in the basic overflow pathological strain data.

10. A coronary heart disease risk assessment system based on Helicobacter pylori infection gene polymorphism according to claim 1, characterized in that, The nonlinear gain control module also includes a saturation limiting logic unit; the saturation limiting logic unit is configured to set a physical upper limit value for the structural brittle gain parameter to prevent the output of the system stability assessment module from diverging when the historical cumulative inflammatory thermal stress index tends to infinity; the physical upper limit value is a fixed constant pre-calibrated based on the physiological tolerance limit or maximum allowable thermal stress of the system physical medium characterizing human physiological function, ensuring that the risk assessment index is always constrained within the linear operating region or recoverable nonlinear region of the system.

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