Dynamic intelligent monitoring and early warning system based on endocrine gland function

Abstract

The invention discloses a dynamic intelligent monitoring and early warning system based on endocrine gland functions, and relates to the technical field of endocrine monitoring, and the dynamic intelligent monitoring and early warning system comprises the following steps: obtaining hypothalamus, pituitary and target gland three-layer hormone concentration data, and carrying out timestamp alignment; after wavelet denoising, the deviation degree of each parameter relative to the health baseline is quantified; establishing an original differential equation set to simulate a hormone axis regulation and control relationship, and calculating a hierarchical weight; a fourth-order Runge-Kutta method is adopted to discretely solve a dynamic weight value; fusing the weight and the abnormality to generate a risk index, and dynamically adjusting an early warning line in combination with a hyperbolic threshold function; according to the threshold breakthrough duration and trend, three-level response of data review, targeted monitoring and drug intervention is triggered. According to the method, a hierarchical coupling differential equation is constructed by simulating a multi-stage regulation mechanism of an endocrine system, so that the recognition sensitivity of early signals of endocrine disorder is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of endocrine monitoring, and particularly relates to a dynamic intelligent monitoring and early warning system based on endocrine gland function. BACKGROUND

[0002] In the field of modern medical health, the function monitoring of the endocrine system is of great significance for human health assessment and disease prevention. As the core component of the human endocrine system, the function of the endocrine gland directly affects a series of physiological processes such as human metabolism, growth and development, reproduction and aging.

[0003] The existing endocrine monitoring system adopts a fixed weight distribution strategy to comprehensively evaluate each level of hormone parameters. Since the multi-level dynamic regulation characteristics of the hypothalamus, pituitary and target gland axis are not considered, the abnormal signals between the levels are masked, it is difficult to effectively capture the early imbalance characteristics of the hormone secretion link, and the monitoring sensitivity and early warning ability of the system to endocrine disorders are reduced. In view of the above problems, the following scheme is proposed. SUMMARY

[0004] The purpose of the present application is to provide a dynamic intelligent monitoring and early warning system based on endocrine gland function, which simulates the multi-level regulation mechanism of the endocrine system, constructs a level-coupled differential equation, realizes dynamic allocation of weight parameters, and solves the problem that the existing technology cannot effectively capture the early imbalance characteristics of the hormone secretion link, and reduces the monitoring sensitivity and early warning ability of the system to endocrine disorders.

[0005] To solve the above technical problems, the present application is realized by the following technical scheme:

[0006] The present application is a dynamic intelligent monitoring and early warning system based on endocrine gland function, which comprises a multi-source signal acquisition module, a signal preprocessing module, a dynamic weight distribution algorithm module, a numerical calculation module, a risk assessment module and an early warning execution module.

[0007] The multi-source signal acquisition module comprises a hypothalamic hormone detection unit, a pituitary hormone detection unit and a target gland hormone detection unit.

[0008] The multi-source signal acquisition module, the signal preprocessing module, the dynamic weight distribution algorithm module, the numerical calculation module, the risk assessment module and the early warning execution module are connected in sequence, and the output end of the early warning execution module is unidirectionally connected with the input end of the multi-source signal acquisition module.

[0009] The working process of the early warning system is as follows:

[0010] Step S1, multi-source signal synchronous acquisition: obtain the hypothalamus, pituitary, and target gland three-layer endocrine hormone concentration data through the multi-source signal acquisition module, and perform time synchronization;

[0011] Step S2, dynamic abnormality calculation: through denoising and baseline calibration, the abnormality degree of each parameter deviating from the healthy state is quantified;

[0012] Step S3, hierarchical weight dynamic modeling: a system of differential equations is constructed to simulate the promotion and inhibition relationship between endocrine levels, and the weights are dynamically allocated;

[0013] Step S4, weight iterative solution: the fourth-order Runge-Kutta numerical method is used to update the weight parameters of each level every second;

[0014] Step S5, adaptive risk synthesis: the dynamic weight and abnormality index are fused to generate a risk index, and the hyperbolic function is used to adaptively adjust the warning threshold;

[0015] Step S6, hierarchical early warning triggering: according to the risk index duration and change rate, trigger data review, targeted sampling, and drug intervention for three-level control.

[0016] Further, the multi-source signal acquisition module integrates a hypothalamic hormone detection unit, a pituitary hormone detection unit, and a target gland hormone detection unit, which can capture the hormone concentration signals of the hypothalamus, pituitary, and target gland, and ensure the spatiotemporal consistency of multi-level physiological data through a timestamp synchronization controller, providing raw data input for the system;

[0017] The signal preprocessing module uses wavelet transform to eliminate signal noise, combines with baseline drift correction algorithm to restore true physiological fluctuations, and converts the original signal into a standardized abnormality index through a dynamic abnormality quantification formula, constructing a clean data source for algorithm processing;

[0018] The dynamic weight allocation algorithm module is based on the endocrine level feedback mechanism, and constructs a coupled differential equation system to dynamically calculate the weight of each physiological parameter through the promotion-inhibition relationship of H-P-T three layers, realizing the autonomous adjustment of parameter importance in a biomimetic manner;

[0019] The numerical calculation module uses the fourth-order Runge-Kutta method to numerically solve the nonlinear differential equation system, and through weight boundary constraint verification, ensures the physical rationality of the calculation results;

[0020] The risk assessment module is used to integrate the dynamic weight and abnormality index to synthesize a multi-dimensional risk index, and uses the hyperbolic tangent function to adaptively adjust the warning threshold, realizing the intelligent and flexible adjustment of risk evaluation standards according to the changes in physiological state;

[0021] The early warning execution module is used to activate a graded response mechanism based on the duration and rate of change of the risk index exceeding the threshold, and triggers data verification, sampling optimization by the multi-source signal acquisition module, or control by external intervention equipment through decision tree.

[0022] Furthermore, step S1, multi-source signal synchronous acquisition, specifically includes the following steps:

[0023] Step S11: Acquire the following three types of core parameters using the multi-source signal acquisition module:

[0024] Hypothalamic hierarchical parameter H: Corticotropin-releasing hormone (CRH) and thyrotropin-releasing hormone (TRH) concentrations;

[0025] Pituitary hierarchical parameters P: Adrenocorticotropic hormone (ACTH) and thyroid-stimulating hormone (TSH) concentrations;

[0026] Target gland level parameter T: concentrations of cortisol and thyroxine (T4);

[0027] Step S12: Use timestamp alignment technology to ensure that the synchronization accuracy of multi-source signals is ≤10ms;

[0028] This design uses a multi-source signal acquisition module to simultaneously acquire key hormone concentration data (such as CRH, ACTH, cortisol, etc.) from three layers of endocrine glands: the hypothalamus (H layer), the pituitary gland (P layer), and the target glands (T layer). It uses timestamp alignment technology to ensure the temporal consistency of multi-level signals (error ≤10ms), providing a high-precision, synchronized raw data foundation for subsequent dynamic analysis.

[0029] Furthermore, step S2, the dynamic anomaly calculation, specifically includes the following steps:

[0030] Step S21: Perform wavelet denoising (using Daubechies 5 basis functions) and baseline drift correction on the original signal;

[0031] Step S22: Define the dynamic anomaly index to characterize the standard deviation of a parameter from the healthy baseline, and calculate the dynamic anomaly of each parameter:

[0032]

[0033] In the formula, E i (t) represents the dynamic anomaly degree of the i-th level at time t, x i (t) represents the measured value of the i-th level parameter at time t, μ i (t base ) represents the parameter of level i during the healthy baseline period t. base The mean, σ i (t base) is the standard deviation of the i-th level parameter at the healthy baseline period, i ∈ {H, P, T};

[0034] This design performs wavelet denoising and baseline drift correction on the original signal, eliminates environmental noise and physiological rhythm interference, and then quantifies the dynamic abnormality index (E H ,E P ,E T ) of each layer signal by calculating the statistical deviation (standard deviation multiple) of each parameter value from the healthy baseline period, providing a quantitative basis for weight allocation.

[0035] Further, the step S3 of dynamically modeling the hierarchical weight specifically includes the following steps:

[0036] Step S31: defining three-layer weights W H (t), W P (t), and W T (t) and setting initial values, simulating the multi-level regulation characteristics of the endocrine axis, and establishing a weight dynamic evolution model;

[0037] Step S32: establishing a hierarchical feedback equation to depict the dynamic balance relationship between promotion and inhibition among layers through a differential equation:

[0038]

[0039] In the formula, W H (t), W P (t), and W T (t) are the dynamic weight values of the H, P, and T levels, respectively, is the rate of change of the weight with time, α is the self-enhancement coefficient of the H layer, β is the cross-layer inhibition coefficient of the H layer, γ is the driving coefficient of the P layer by the H layer, δ is the negative feedback coefficient of the T layer to the P layer, ε is the self-enhancement coefficient of the T layer, is the quadratic inhibition coefficient of the H layer to the T layer;

[0040] This design is based on the hierarchical regulation mechanism of the endocrine system (such as the hypothalamus driving the pituitary and the target gland inhibiting by negative feedback), and designs a system of differential equations to dynamically allocate the weights (W H ,W P ,W T ) of the three layers of parameters, simulates the promotion and inhibition relationship among hormones through self-enhancement terms (such as αE H ) and cross-layer inhibition terms (such as βW H W P E P ), and realizes the adaptive evolution of the weight with the physiological state.

[0041] Further, the step S4 of iteratively solving the weight specifically includes the following steps:

[0042] Step S41: Discretize the continuous-time differential equation set constructed in step S3 with a fixed time step of 1 second, and convert it into a difference form suitable for iterative calculation in a digital system;

[0043] Step S42: Use the fourth-order Runge-Kutta numerical integration method to approximate the solution of the differential equation: in each time step, calculate the four intermediate slope variables of the weight of each level in the differential equation set in turn, which correspond to the slope estimates of the weight value at the current time, the half-step trial value and the full-step predicted value, respectively, and finally update the weight value at the next time through the weighted average formula:

[0044]

[0045] In the formula, W i n+1 is the weight value of the i-th level at time step n+1, W i n is the weight value of the i-th level at time step n, k1, k2, k3, k4 are all slope estimates of the differential equation at the intermediate points, is the fixed weight coefficient of the fourth-order method;

[0046] This design uses the fourth-order Runge-Kutta numerical integration algorithm to discretize and solve the differential equation set with a time step of 1 second, iteratively updates the weight value by calculating the intermediate variables k1-k4, ensures that the calculation process considers both precision (high-order convergence) and (low delay), and meets the timeliness requirements of dynamic monitoring.

[0047] Further, the step S5, the adaptive risk synthesis specifically includes the following steps:

[0048] Step S51: Comprehensive evaluation of the overall risk state of the endocrine system, coupling calculation of the dynamic weight of each level and the corresponding abnormality, and generating a dynamic risk index, specifically integrating the abnormal information of hypothalamus H, pituitary P and target gland T through a weighted summation formula, the formula is:

[0049] R(t) = W H (t)E H (t) + W P (t)E p (t) + W T (t)E T (t);

[0050] In the formula, R(t) is the dynamic risk index; W i (t) is the dynamic weight, which is updated by step S4 to ensure that the contribution proportion of the upper control signal and the end feedback is dynamically adjusted with the physiological state; E i (t) is the dynamic abnormality;

[0051] Step S52: Introduce a nonlinear adaptive mechanism to cope with the hysteresis of the fixed threshold to the sudden risk change, use the formula Adjust the early warning threshold, when the risk index change rate increases, the hyperbolic tangent function normalizes it to the interval [-1, 1], and the threshold is controlled by the adjustment factor to control the threshold drop, so that the system actively reduces the trigger threshold when the risk rises rapidly, thereby warning potential abnormalities in advance, while avoiding false positives caused by transient fluctuations;

[0052] In the formula, θ(t) is the dynamic early warning threshold at time t, θ base is the basic threshold, λ is the threshold adjustment factor, tanh(·) is the hyperbolic tangent function, dR / dt is the time derivative of the risk index R(t), R max is the preset maximum change rate of the risk index;

[0053] This design fuses the dynamic weight and abnormality index to generate a comprehensive risk index R(t), and designs an adaptive threshold adjustment mechanism (θ(t)) based on the hyperbolic tangent function, which dynamically adjusts the early warning threshold according to the risk change rate (dR / dt), avoiding false triggering caused by normal fluctuations in physiological parameters and improving the specificity of early warning.

[0054] Further, the specific early warning mechanism in the step S6 of hierarchical early warning triggering is:

[0055] When the dynamic risk index R(t) exceeds the adaptive threshold θ(t), the system starts a hierarchical response mechanism according to the risk duration and change rate:

[0056] When the exceeding state lasts for 3 seconds, the primary early warning is triggered, and the data review module is activated to cross-verify the abnormal parameters;

[0057] When it lasts for 10 seconds and the risk index change rate dR / dt>0, it is upgraded to the intermediate early warning, and the sampling frequency of the multi-source signal acquisition module corresponding to the abnormal level is increased to realize high-precision tracking;

[0058] When the exceeding state lasts for 30 seconds or R(t)>1.5θ(t), the advanced early warning is started, and the external microfluidic drug pump releases the cortisol suppressor or thyroid regulator according to the dose formula Q(t)=k[R(t)-θ(t)];

[0059] In the formula, k is the drug sensitivity coefficient;

[0060] This design triggers the early warning mechanism in three levels according to the comparison result of the risk index and the adaptive threshold: primary early warning (data review), intermediate early warning (enhanced sampling), and advanced early warning (drug intervention), and adjusts the sampling frequency of the multi-source signal acquisition module or the action of the external device through the feedback control link.

[0061] The present application has the following advantages:

[0062] 1、The present application simulates the multi-level regulation mechanism of the endocrine system, constructs a hierarchical coupled differential equation, and realizes dynamic allocation of weight parameters; can automatically adjust the contribution ratio of hypothalamus, pituitary and target gland parameters according to the abnormality change of physiological parameters at each level; such design can capture the early imbalance characteristics of hormone secretion link, avoid the problem of signal masking between levels caused by fixed weight; through adaptive attenuation and enhancement of weight, the system can distinguish the normal range of physiological fluctuations and pathological deviation, improve the recognition sensitivity of early signals of endocrine disorders, and maintain high tolerance to steady-state fluctuations.

[0063] 2、The present application realizes smooth dynamic adjustment of the early warning boundary based on the adaptive threshold mechanism of hyperbolic tangent function; by analyzing the change rate of risk index, the steepness and offset of the threshold curve are intelligently adjusted, overcoming the adaptability defects of fixed threshold in scenarios such as circadian rhythm and individual differences; the nonlinear regulation mechanism can avoid false triggering caused by normal fluctuations of physiological parameters while maintaining high early warning specificity, and ensure the continuous tracking ability for gradual pathological development.

[0064] Of course, implementing any product of the present application does not necessarily require all the advantages described above. DETAILED DESCRIPTION

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0066] Figure 1 The framework diagram of the dynamic intelligent monitoring and early warning system based on endocrine gland function of the present application;

[0067] Figure 2 The flowchart of the dynamic intelligent monitoring and early warning system based on endocrine gland function of the present application. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0069] Please refer to Figure 1As shown, the present application is a dynamic intelligent monitoring and early warning system based on the function of endocrine glands, which comprises a multi-source signal acquisition module, a signal preprocessing module, a dynamic weight distribution algorithm module, a numerical calculation module, a risk assessment module and an early warning execution module.

[0070] The multi-source signal acquisition module comprises a hypothalamus hormone detection unit, a pituitary hormone detection unit and a target gland hormone detection unit.

[0071] The multi-source signal acquisition module, the signal preprocessing module, the dynamic weight distribution algorithm module, the numerical calculation module, the risk assessment module and the early warning execution module are connected in sequence, and the output end of the early warning execution module is unidirectionally connected with the input end of the multi-source signal acquisition module.

[0072] The multi-source signal acquisition module integrates the hypothalamus hormone detection unit, the pituitary hormone detection unit and the target gland hormone detection unit, can capture the hormone concentration signals of the hypothalamus, pituitary and target glands, and ensure the spatio-temporal consistency of multi-level physiological data through a time stamp synchronization controller to provide raw data input for the system.

[0073] The signal preprocessing module uses wavelet transform to eliminate signal noise, combines with baseline drift correction algorithm to restore true physiological fluctuations, converts the original signal into a standardized abnormality index through a dynamic abnormality quantification formula, and constructs a clean data source that can be processed by the algorithm.

[0074] The dynamic weight distribution algorithm module is based on the endocrine level feedback mechanism, constructs a coupled differential equation set, dynamically calculates the weight of each physiological parameter through the promotion-inhibition relationship of H-P-T three layers, and realizes the autonomous adjustment of parameter importance in a biomimetic manner.

[0075] The numerical calculation module uses the fourth-order Runge-Kutta method to numerically solve the nonlinear differential equation set, and ensures the physical rationality of the calculation results through weight boundary constraint verification.

[0076] The risk assessment module is used for integrating the dynamic weight and abnormality index to synthesize a multi-dimensional risk index, using a hyperbolic tangent function to adaptively adjust the early warning threshold, and realizing the intelligent flexible adjustment of the risk evaluation standard with the change of physiological state.

[0077] The early warning execution module is used for activating a hierarchical response mechanism according to the risk index threshold duration and change rate, triggering data review, multi-source signal acquisition module sampling optimization or external intervention device control through a decision tree.

[0078] Please refer to Figure 2 As shown, the working process of the early warning system is as follows:

[0079] Step S1, multi-source signal synchronous acquisition: continuously capture the hypothalamus, pituitary and target gland level key hormone concentration signals through the multi-source signal acquisition module, and use timestamp alignment technology to ensure that the multi-level physiological parameters are synchronously transmitted under a unified time reference, providing time-consistent multi-dimensional data input for subsequent hierarchical analysis, specifically:

[0080] Step S11: the following three types of core parameters are acquired through the multi-source signal acquisition module:

[0081] Hypothalamic level parameters H: corticotropin-releasing hormone, thyrotropin-releasing hormone concentration;

[0082] Pituitary level parameters P: corticotropin, thyrotropin concentration;

[0083] Target gland level parameters T: cortisol, thyroxine concentration;

[0084] Step S12: use timestamp alignment technology to ensure that the multi-source signal synchronization accuracy is less than or equal to 10 ms.

[0085] Step S2, dynamic abnormality calculation: based on the synchronous signals obtained in step S1, after removing noise interference through wavelet denoising and baseline correction, the dynamic deviation of each level hormone concentration from the healthy baseline is calculated, generating a quantitative index representing the functional abnormality of each level, providing initial abnormal state input for dynamic weight allocation, specifically:

[0086] Step S21: wavelet denoising and baseline drift correction are performed on the original signal;

[0087] Step S22: define the dynamic abnormality index, which represents the standard deviation multiple of the parameter deviation from the healthy baseline, and calculate the dynamic abnormality of each parameter:

[0088]

[0089] Ei(t) = (xi(t) - μi(t)) / σi(t) i Ei(t) is the dynamic abnormality of the ith level at time t, xi(t) is the measured value of the ith level parameter at time t, μi(t) is the mean value of the ith level parameter at time t in the healthy baseline period, σi(t) is the standard deviation of the ith level parameter in the healthy baseline period, and i ∈ {H, P, T}. i i base base i base

[0090] ​​​​​​Step S3, hierarchical weight dynamic modeling: using the abnormality index output in step S2, a differential equation model simulating the endocrine hierarchical regulation mechanism is established, through the dynamic balance relationship of self-enhancement and cross-layer inhibition, the change rule of the weight of hypothalamus, pituitary and target gland is defined, the fixed weight is converted into a dynamic coupled weight affected by the multi-level abnormal state linkage, specifically:

[0091] Step S31: define the three-layer weight W H (t), W P (t), W T (t) and set the initial value, simulate the multi-level regulation characteristics of the endocrine axis, and establish a weight dynamic evolution model;

[0092] Step S32: establish a hierarchical feedback equation to describe the dynamic balance relationship between promotion and inhibition among layers by differential equation:

[0093]

[0094] In the formula, W H (t), W P (t), W T (t) are the dynamic weight values of H, P and T layers respectively, is the change rate of weight with time, α is the self-enhancement coefficient of H layer, β is the cross-layer inhibition coefficient of H layer, γ is the P layer driven coefficient of H layer, δ is the negative feedback coefficient of T layer to P layer, ε is the self-enhancement coefficient of T layer, is the quadratic inhibition coefficient of H layer to T layer.

[0095] Step S4, weight iterative solution: for the differential equation model established in step S3, a numerical integral algorithm is used to iteratively calculate the weight value with high precision, and the instantaneous value of the three-layer weight is output with a time resolution of seconds, to ensure that the weight distribution process can quickly respond to the state change of the endocrine system, specifically:

[0096] Step S41: discretize the continuous-time differential equation group constructed in step S3 with a fixed time step of 1 second, and convert it into a difference form suitable for iterative calculation of digital system;

[0097] Step S42: use the fourth-order Runge-Kutta numerical integral method to approximate the solution of the differential equation: in each time step, calculate the four intermediate slope variables of the hierarchical weight in the differential equation group in turn, which respectively correspond to the slope estimates of the weight value, half-step trial value and full-step predicted value at the current time, and finally update the weight value at the next time through the weighted average formula, the weighted average formula is:

[0098]

[0099] In the formula, Wi n+1 W i n W k1, k2, k3, k4 are the slope estimates of the differential equation at the intermediate points,

[0100] Step S5, adaptive risk synthesis: based on the weight values generated in step S4 and the abnormality index in step S2, a comprehensive risk index is generated by weighted fusion, and the warning threshold is dynamically adjusted according to the risk change rate, so that the threshold has the adaptability of automatically shrinking or expanding with the system state, avoiding false alarm or misjudgment caused by fixed threshold, specifically:

[0101] Step S51: comprehensively evaluate the overall risk state of the endocrine system, couple the dynamic weight of each level with the corresponding abnormality, and generate a dynamic risk index, specifically by weighted sum formula to fuse the abnormal information of hypothalamus H, pituitary P and target gland T, the formula is:

[0102] R(t) = W H (t)E H (t) + W P (t)E p (t) + W T (t)E T (t);

[0103] In the formula, R(t) is a dynamic risk index; W i (t) is a dynamic weight, updated by step S4, to ensure that the contribution proportion of upper regulation signal and end feedback is dynamically adjusted with physiological state; E i (t) is a dynamic abnormality;

[0104] Step S52: introduce a nonlinear adaptive mechanism to cope with the hysteresis of the fixed threshold to sudden risk changes, use the formula to adjust the warning threshold, when the risk index change rate increases, the hyperbolic tangent function normalizes it to the [-1, 1] interval, and the threshold value is controlled by the adjustment factor to control the threshold value. The amplitude of decline makes the system actively reduce the trigger threshold when the risk is rising rapidly, so as to early warning potential abnormalities, while avoiding false alarms caused by instantaneous fluctuations;

[0105] In the formula, θ(t) is the dynamic warning threshold at time t, θ base is the basic threshold, λ is the threshold adjustment factor, tanh(·) is the hyperbolic tangent function, dR / dt is the time derivative of the risk index R(t), R max is the preset maximum change rate of the risk index.

[0106] Step S6, hierarchical early warning trigger: According to the comparison result of the risk index output in step S5 and the adaptive threshold, multi-level early warning is triggered according to the duration and change trend, and the sampling strategy of the multi-source signal acquisition module is adjusted or the external intervention device is started according to the early warning level. The specific early warning mechanism is as follows:

[0107] When the dynamic risk index R(t) exceeds the adaptive threshold θ(t), the system starts the hierarchical response mechanism according to the risk duration and change rate:

[0108] When the exceeding state lasts for 3 seconds, the primary early warning is triggered, and the data review module is activated to cross-verify the abnormal parameters;

[0109] When it lasts for 10 seconds and the risk index change rate dR / dt>0, it is upgraded to the intermediate early warning, and the sampling frequency of the multi-source signal acquisition module corresponding to the abnormal level is directed to improve to realize high-precision tracking;

[0110] When the exceeding state lasts for 30 seconds or R(t)>1.5θ(t), the advanced early warning is started, and the external microfluidic drug pump releases the cortisol suppressor or thyroid regulator according to the dose formula Q(t)=k[R(t)-θ(t)];

[0111] In the formula, k is the drug sensitivity coefficient.

[0112] One specific application of the embodiment is:

[0113] I. Implementation object and device:

[0114] The subject is a 45-year-old male, and the health baseline data is collected in the previous 30 days (measured at 10:00 AM every day in a resting state);

[0115] Device configuration:

[0116] Hypothalamic activity substitute monitoring: functional near-infrared spectroscopy (fNIRS) headband (non-invasive, sampling rate 5Hz) is used to monitor prefrontal cortex hemodynamic signals, which indirectly reflect the hypothalamic activation level (studies have shown that the correlation between the two is r=0.72, p<0.01);

[0117] Baseline parameter: μ H = 0.52 HbO2 / HbR (oxygenated / deoxygenated hemoglobin ratio), σ H = 0.08;

[0118] Pituitary ACTH monitoring: micro-invasive subcutaneous tissue fluid sampling needle (Medtronic Minimed 7 series, sampling interval 5 minutes) combined with electrochemical immunosensor (detection limit 1 pg / mL);

[0119] Baseline parameter: μ P= 24.3 pg / mL, σ P = 2.9 pg / mL;

[0120] Target Cortisol monitoring: Salimetrics LLC, Model SLC-001 saliva microfluidic chip integrated with impedance spectroscopy analysis, 30 seconds delay detection;

[0121] Baseline parameters: μ T = 0.21 pg / dL, σ T = 0.05 pg / dL (Saliva-blood concentration conversion factor: 5.2:1);

[0122] Time window: 09:55:00-10:00:00 (stress experiment induction phase)

[0123] Step S1, multi-source signal fusion and calibration:

[0124] Data synchronization and compensation: sliding window mean interpolation for different sampling rates (fNIRS 5Hz vs. tissue fluid 5 minutes):

[0125]

[0126] Cortisol saliva detection delay compensation:

[0127]

[0128] t = 120 seconds data:

[0129]

[0130] Step S2, cross-modal anomaly degree unified quantification:

[0131] Signal normalization (eliminate dimensional differences):

[0132]

[0133] Dynamic anomaly degree mapping (introducing Sigmoid function constraint):

[0134]

[0135] Eliminate negative offset interference (such as cortisol abnormally low value without early warning significance)

[0136] For example:

[0137] Step S3, improved hierarchical coupling weight calculation:

[0138] Dynamic equation optimization (introducing saturation function to prevent weight overflow):

[0139]

[0140] Parameter adjustment: α = 0.7, β = 0.15, γ = 0.6, δ = 0.25, ε = 0.5,

[0141] Function description: σ(x) = 1 / (1+e -0.5x ) is the Sigmoid function, ReLU(x) = max(0,x);

[0142] t = 120 seconds iteration calculation:

[0143]

[0144] Step S4, variable step size Runge-Kutta method iteration:

[0145] Adaptive step size control (based on weight change rate ΔW):

[0146]

[0147] Iteration results (take Δt = 0.5 seconds as an example):

[0148]

[0149] Weight normalization:

[0150]

[0151] Step S5, dynamic synthesis and verification of risk index:

[0152] Index calculation (introducing time decay factor):

[0153]

[0154] Last second R(119) = 4.8:

[0155]

[0156] Adaptive threshold (sliding window standard deviation method):

[0157] θ(t) = μ R (t-60:t) + 2.5σ R (t-60:t);

[0158] Past 60 seconds μ R = 5.1, σ R = 1.2, then θ(120) = 5.1 + 3.0 = 8.1;

[0159] Step S6, multi-level early warning linkage control:

[0160] The tiering strategy is optimized as shown in the following table:

[0161]

[0162] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.

[0163] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all of the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations of the application can be made in light of the contents of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical application of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic intelligent monitoring and early warning system based on the function of endocrine glands, characterized in that, The early warning system comprises a multi-source signal acquisition module, a signal preprocessing module, a dynamic weight distribution algorithm module, a numerical calculation module, a risk assessment module and an early warning execution module. The multi-source signal acquisition module comprises a hypothalamus hormone detection unit, a pituitary hormone detection unit and a target gland hormone detection unit. The multi-source signal acquisition module, the signal preprocessing module, the dynamic weight distribution algorithm module, the numerical calculation module, the risk assessment module and the early warning execution module are sequentially connected, and the output end of the early warning execution module is unidirectionally connected with the input end of the multi-source signal acquisition module. The working process of the early warning system is as follows: Step S1, multi-source signal synchronous acquisition: the concentration data of the hypothalamus, pituitary and target gland three-layer endocrine hormones are acquired through the multi-source signal acquisition module, and time synchronization is performed; Step S2, dynamic abnormality calculation: through denoising and baseline calibration, the abnormality degree of each parameter deviating from the healthy state is quantified; Step S3, hierarchical weight dynamic modeling: a differential equation set is constructed to simulate the promotion and inhibition relationship among the endocrine levels, and the weight is dynamically distributed; Step S4, weight iterative solution: the fourth-order Runge-Kutta numerical method is adopted to update the weight parameters of each level every second; Step S5, adaptive risk synthesis: the dynamic weight and abnormality are fused to generate a risk index, and the early warning threshold is adaptively adjusted by using a hyperbolic function; Step S6, hierarchical early warning triggering: according to the risk index duration and change rate, three-level control of data review, targeted sampling and drug intervention is triggered.

2. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The multi-source signal acquisition module integrates the hypothalamus hormone detection unit, the pituitary hormone detection unit and the target gland hormone detection unit, can capture the hormone concentration signals of the hypothalamus, pituitary and target gland, ensures the space-time consistency of multi-level physiological data through a time stamp synchronization controller, and provides raw data input for the system; The signal preprocessing module adopts wavelet transform to eliminate signal noise, combines with baseline drift correction algorithm to restore true physiological fluctuations, converts the original signal into a standardized abnormality index through a dynamic abnormality quantification formula, and constructs a clean data source for algorithm processing; The dynamic weight distribution algorithm module is based on the endocrine level feedback mechanism, constructs a coupled differential equation set, dynamically calculates the weight of each physiological parameter through the promotion-inhibition relationship of the H-P-T three layers, and realizes the self-regulation of the importance of the parameters in the bionic way; The numerical calculation module uses the fourth-order Runge-Kutta method to numerically solve the nonlinear differential equation set, and ensures the physical rationality of the calculation results through weight boundary constraint verification; The risk assessment module is used for integrating the dynamic weight and abnormality index to synthesize a multi-dimensional risk index, adaptively adjusting the early warning threshold by using a hyperbolic tangent function, and realizing the intelligent flexible adjustment of the risk evaluation standard with the change of physiological state; The early warning execution module is used for activating the hierarchical response mechanism according to the risk index threshold duration and change rate, triggering data review, multi-source signal acquisition module sampling optimization or external intervention equipment control through a decision tree.

3. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The step S1, multi-source signal synchronous acquisition, specifically comprises the following steps: Step S11: the following three types of core parameters are acquired through the multi-source signal acquisition module: Hypothalamic hierarchy parameter H: concentration of corticotropin-releasing hormone, thyrotropin-releasing hormone; Pituitary hierarchy parameter P: concentration of adrenocorticotropic hormone, thyrotropic hormone; Target gland hierarchy parameter T: concentration of cortisol, thyroxine; Step S12: Adopt a timestamp alignment technique to ensure that the multi-source signals are synchronized with an accuracy of ≤10 ms.

4. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The step S2, the dynamic abnormality degree calculation specifically includes the following steps: Step S21: Wavelet denoising and baseline drift correction are performed on the original signal; Step S22: A dynamic abnormality degree index is defined to represent the standard deviation multiple of the parameter deviating from the healthy baseline, and the dynamic abnormality degrees of the parameters are calculated: where E i (t) is the dynamic abnormality of the i-th level at time t, x i (t) is the measured value of the i-th level parameter at time t, μ i (t base ) is the mean of the i-th level parameter at the healthy baseline period t base , σ i (t base ) is the standard deviation of the i-th level parameter at the healthy baseline period, i ∈ {H, P, T}.

5. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The step S3, the hierarchy weight dynamic modeling specifically includes the following steps: Step S31: define three-layer weight W H (t), W P (t), W T (t) and set initial values, simulate the multi-level regulation characteristics of the endocrine axis, and establish a weight dynamic evolution model; Step S32: A hierarchy feedback equation is established to depict the dynamic balance relationship between promotion and inhibition among hierarchies through a differential equation: In the formula, W H (t), W P (t), W T (t) are respectively the dynamic weight values of the H, P, and T layers, is the change rate of the weight with time, α is the H layer self-enhancement coefficient, β is the H layer cross-layer inhibition coefficient, γ is the P layer driven by the H layer coefficient, δ is the negative feedback coefficient of the T layer to the P layer, ε is the T layer self-enhancement coefficient, is the secondary inhibition coefficient of the H layer to the T layer.

6. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The step S4, the weight iterative solving specifically includes the following steps: Step S41: The continuous-time differential equation group constructed in step S3 is discretized with a fixed time step of 1 second, and is converted into a difference form suitable for iterative calculation of a digital system; Step S42: A fourth-order Runge-Kutta numerical integration method is adopted to approximate the solution of the differential equation with a fourth-order precision: In each time step, four intermediate slope variables of the hierarchy weights in the differential equation group are calculated in turn, which correspond to the slope estimates of the weight values, half-step trial values and full-step predicted values at the current time, respectively. Finally, the weight values at the next time are updated through a weighted average formula, and the weighted average formula is: where W i n+1 Wn+i is the weight value of the i-th level at time step n+1, Wn is the weight value of the i-th level at time step n, k1, k2, k3, k4 are the slope estimates of the differential equation at the intermediate points, i n Wn+i is the weight value of the i-th level at time step n+1, Wn is the weight value of the i-th level at time step n, k1, k2, k3, k4 are the slope estimates of the differential equation at the intermediate points, Wn+i is the weight value of the i-th level at time step n+1, Wn is the weight value of the i-th level at time step n, k1, k2, k3, k4 are the slope estimates of the differential equation at the 7. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The step S5, the adaptive risk synthesis specifically includes the following steps: Step S51: The overall risk state of the endocrine system is comprehensively evaluated, the dynamic weights of each hierarchy are coupled with the corresponding abnormality degrees for calculation, and a dynamic risk index is generated. The abnormal information of the hypothalamus H, the pituitary P and the target gland T is fused through a weighted summation formula, and the formula is: R(t) = W H (t) E H (t) + W P (t) E p (t) + W T (t) E T (t); In the formula, R(t) is a dynamic risk index; W i (t) is a dynamic weight, updated by step S4, to ensure that the contribution ratio of the upper regulatory signal and the end feedback is dynamically adjusted with the physiological state; E i (t) is a dynamic abnormality degree; Step S52: Introduce a nonlinear adaptive mechanism to cope with the hysteresis of the fixed threshold to the sudden risk changes, use the formula Adjust the early warning threshold, when the risk index change rate increases, the hyperbolic tangent function normalizes it to the interval [-1, 1], and the threshold drop amplitude is controlled by the adjustment factor, so that the system actively reduces the trigger threshold when the risk rises rapidly, thereby warning potential abnormalities in advance, while avoiding false positives caused by transient fluctuations; where θ(t) is a dynamic warning threshold at time t, θ base is a base threshold, λ is a threshold adjustment factor, tanh(·) is a hyperbolic tangent function, dR / dt is a time derivative of the risk index R(t), R max is a preset maximum rate of change of the risk index.

8. The dynamic intelligent monitoring and early warning system based on endocrine gland function according to claim 1, characterized in that, The specific early warning mechanism in the step S6, the hierarchical early warning triggering is: When the dynamic risk index R(t) exceeds the adaptive threshold θ(t), the system starts a hierarchical response mechanism according to the risk duration and the change rate: When the exceeding state lasts for 3 seconds, the primary early warning is triggered, and the data review module is activated to cross-verify the abnormal parameters; When it lasts for 10 seconds and the risk index change rate dR / dt>0, it is upgraded to the intermediate early warning, and the sampling frequency of the multi-source signal acquisition module corresponding to the abnormal hierarchy is directionally improved to realize high-precision tracking; When the exceeding state lasts for 30 seconds or R(t)>1.5θ(t), the advanced early warning is started, and the external microfluidic drug pump releases the corticosteroid inhibitor or the thyroid regulator according to the dose formula Q(t)=k[R(t)-θ(t)]; In the formula, k is a drug sensitivity coefficient.

Images