Military flight crew perianal health risk assessment and dynamic intervention management system

Abstract

The present application relates to the technical field of aviation medical monitoring and flight protection, in particular to a perianal health risk assessment and dynamic intervention management system for military flight personnel, comprising: a data sensing step: synchronously collecting environmental stress data and real-time physiological sensing data; a benchmark reconstruction step: calculating ideal physiological response data based on environmental stress; a pathological simulation step: injecting pathological flow parameters to generate pathological simulation data; a difference extraction step: constructing a double-track difference vector, and respectively calculating a real residual vector and a theoretical residual vector; a coupling judgment step: calculating vector similarity to determine the risk type; and a dynamic intervention step: generating a physical intervention strategy to adjust the equipment state in response to the risk type; the present application effectively distinguishes physiological congestion from pathological disorders by dynamically stripping environmental noise, thereby reducing the false positive rate in a high dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of aviation medical monitoring and flight protection technology, specifically to a management system for risk assessment and dynamic intervention of perianal health for military flight personnel. Background Technology

[0002] With the rapid development of high-performance fighter jet technology, the physiological tolerance limit of pilots under high overload conditions has become a key factor restricting the generation of combat effectiveness. In order to effectively guarantee the pilot's continuous combat capability, real-time monitoring and management of the pilot's physiological state, especially the assessment of the microcirculation health of soft tissues in the load-bearing area, has become particularly important.

[0003] While existing pilot health monitoring technologies have shown potential in acquiring physiological data using various sensors, the complex mechanical conditions of highly dynamic flight environments can induce severe physiological fluctuations. For example, increased pelvic hydrostatic pressure and congestion due to overload can be difficult to distinguish from early pathological microcirculatory disorders in terms of signal characteristics. Existing monitoring methods often rely on static statistical values, making it difficult to effectively isolate environmental noise in dynamic environments. This results in the system being unable to accurately determine whether the observed abnormalities originate from normal fluctuations derived from physical laws or spontaneous pathological changes, leading to high false alarm rates or potential missed alarms. Furthermore, the lack of closed-loop physical intervention mechanisms based on clear pathological characteristics makes it difficult to provide in-situ relief without interrupting the mission.

[0004] Therefore, how to utilize collected environmental stress data and real-time physiological sensor data to construct a dual-track dynamic model of health and pathology for differential analysis, thereby accurately identifying risk types based on dynamically offsetting environmental impacts, and establishing proactive physical intervention strategies to adjust the state of anti-G equipment, is crucial for ensuring the perianal health of military flight personnel and improving operational safety in high-G environments. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a risk assessment and dynamic intervention management system for perianal health of military flight personnel. Specifically, the technical solution of this invention includes:

[0006] The data sensing module is used to simultaneously collect environmental stress data and real-time physiological sensing data of the target area under flight conditions.

[0007] The baseline reconstruction module is used to drive a preset healthy human dynamics model based on environmental stress data to calculate the ideal physiological response data under the current working conditions.

[0008] The pathological simulation module is used to inject preset pathological rheological parameters into a healthy human dynamic model to generate a diseased simulation state, and combine it with environmental stress data to generate diseased simulation data.

[0009] The differential extraction module is used to construct a dual-track differential vector, which calculates the real residual vector between real-time physiological sensing data and ideal physiological response data, and the theoretical residual vector between disease-simulated data and ideal physiological response data.

[0010] The coupled decision module is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector, and to determine the risk type based on the vector similarity.

[0011] The dynamic intervention module is used to generate corresponding physical intervention strategies to adjust the status of anti-load equipment in response to a determined risk type.

[0012] Preferably, the method for acquiring environmental stress data and real-time physiological sensing data includes:

[0013] The overload value, flight duration and cabin pressure are read in real time through the flight telemetry interface as environmental stress data.

[0014] By integrating a non-invasive sensor array onto the surface of anti-G clothing or seats, multi-point pressure distribution values ​​and local thermal gradient values ​​in the perianal region are collected as real-time physiological sensing data.

[0015] Preferred methods for calculating ideal physiological response data include:

[0016] Based on fluid mechanics and biomechanics equations, a multibody dynamic coupling model of human body-seat-anti-G suit is constructed as a dynamic model of healthy human body.

[0017] Environmental stress data is input as boundary conditions into a healthy human dynamics model to calculate in real time the theoretical blood flow velocity, blood vessel wall shear force, and soft tissue deformation distribution data that should be presented in the target area under non-pathological conditions, and the calculation results are combined into ideal physiological response data.

[0018] Preferably, methods for generating simulation data with defects include:

[0019] Obtain pathological correction operators from a medical expert knowledge base, including varicose vein factors, inflammatory factors, and tissue damage factors;

[0020] Based on the pathological correction operator, the vascular elastic modulus parameter, flow resistance coefficient and soft tissue viscoelastic properties in the dynamic model of healthy human body are parametrically adjusted to construct the simulated state with disease.

[0021] In the simulated state with the disease, environmental stress data is loaded again for parallel simulation, and the corresponding pathological characteristic response is output as the simulated data with the disease.

[0022] Preferably, methods for constructing dual-track difference vectors include:

[0023] Subtract the ideal physiological response data from the real-time physiological sensing data to eliminate normal physiological fluctuations caused by environmental stress, and retain the real residual vector containing true pathological features and environmental noise.

[0024] By subtracting the ideal physiological response data from the simulated data with disease, the theoretical deviation features caused purely by specific pathologies under current environmental stress are extracted and used as the theoretical residual vector.

[0025] Preferred methods for determining risk types include:

[0026] The dynamic time warping algorithm or the cosine similarity algorithm is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector in the time domain and the spatial domain.

[0027] If the vector similarity is greater than the preset judgment threshold, the current state is judged as a true pathological risk, and the pathological type corresponding to the simulated data with disease is marked as the determined risk type.

[0028] If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is greater than the preset safety threshold, then the current state is judged as sensor artifact or transient environmental noise, and no risk type is generated.

[0029] If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is less than or equal to the safety threshold, then the current state is judged as normal physiological fluctuation, and no risk type is generated.

[0030] Preferably, the methods for generating corresponding physical intervention strategies include:

[0031] To identify the pathological remission mechanisms corresponding to a specific risk type;

[0032] Based on the pathological relief mechanism, the optimal inflation timing of the hip airbag of the anti-G suit or the pressure distribution adjustment parameters of the seat were calculated.

[0033] The parameters for optimizing inflation timing or pressure distribution are converted into control commands and sent to the anti-load equipment control unit as a physical intervention strategy.

[0034] Preferably, the system further includes an evolution analysis module, used for:

[0035] After determining a true pathological risk, continuously track the dynamic trajectory of the actual residual vector as it changes with environmental stress data;

[0036] The dynamic trajectory is compared with a pre-set disease progression model to predict the evolution trend of microcirculatory disturbances in the target area during the remaining flight time, and a higher level of intervention strategy is triggered when the evolution trend exceeds the safety boundary.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. This system constructs a dynamic zero-point reference reconstruction and dual-track differential extraction mechanism, which effectively solves the technical problem of distinguishing between normal physiological fluctuations and early pathological risks under high overload flight conditions. Unlike traditional monitoring methods that rely on static statistical values, this solution uses environmental stress data to drive a healthy human dynamic model, calculates the ideal physiological response under the current conditions in real time, and removes background noise caused by overload changes through differential calculation, ensuring that the system only responds to pathological features derived from physical laws, which greatly reduces the false alarm rate in complex mechanical environments.

[0039] 2. This system employs multiphysics coupling modeling and observation operator mapping technology to achieve accurate inversion from non-invasive surface data to deep tissue state. By integrating fluid mechanics, solid mechanics, and biological thermal diffusion equations, the system can deduce internal blood flow velocity and blood vessel wall shear force in digital space, and use observation operators to project the high-dimensional model state to sensor space, thereby ensuring a high degree of physical consistency between theoretical simulation data and real sensor data, overcoming the limitation that it is difficult to assess deep microcirculatory disorders based solely on surface signals.

[0040] 3. This system establishes a coupled decision and hierarchical threshold verification logic based on vector similarity, which significantly enhances the robustness and reliability of the algorithm under extreme working conditions. By performing dimensionless processing and weighted calculation on the residual vector, the interference caused by the difference in dimensions of pressure and temperature data is eliminated. The consistency of vector direction is used to qualitatively assess the risk. Combined with amplitude verification and zero vector fuse mechanism, sensor artifacts and transient environmental noise are effectively shielded, ensuring the accuracy of risk assessment.

[0041] 4. This system realizes closed-loop control and disease progression prediction from monitoring and early warning to physical intervention, giving the system the ability to proactively protect and manage. The system can not only generate physical intervention strategies such as pulsating pressurization or seat pressure redistribution based on the determined risk type, and relieve microcirculation disorders in a timely manner without interrupting the mission, but also quantify the cumulative damage through integral algorithms and extrapolate the evolution trend within the remaining flight window, providing a forward-looking physiological status basis for command and decision-making, and effectively preventing sudden disability of pilots. Attached Figure Description

[0042] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0043] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0045] Example 1:

[0046] Please see Figure 1 The military flight personnel's perianal health risk assessment and dynamic intervention management system includes:

[0047] The data sensing module is used to simultaneously collect environmental stress data and real-time physiological sensing data of the target area under flight conditions.

[0048] The baseline reconstruction module is used to drive a preset healthy human dynamics model based on environmental stress data to calculate the ideal physiological response data under the current working conditions.

[0049] The pathological simulation module is used to inject preset pathological rheological parameters into a healthy human dynamic model to generate a diseased simulation state, and combine it with environmental stress data to generate diseased simulation data.

[0050] The differential extraction module is used to construct a dual-track differential vector, which calculates the real residual vector between real-time physiological sensing data and ideal physiological response data, and the theoretical residual vector between disease-simulated data and ideal physiological response data.

[0051] The coupled decision module is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector, and to determine the risk type based on the vector similarity.

[0052] The dynamic intervention module is used to generate corresponding physical intervention strategies to adjust the status of anti-load equipment in response to a determined risk type.

[0053] This embodiment details the synthetic analysis architecture and inter-module collaborative logic of the system. The system aims to address the core technical challenge of distinguishing between physiological congestion and early pathological microcirculatory disorders caused by high-G flight conditions, leading to high false alarm rates or missed detections. The data perception module executes two parallel data stream acquisition tasks: on the one hand, it synchronously acquires flight status parameters at high frequency through the airborne bus interface; on the other hand, it acquires the physical field response of the perianal region through a non-invasive sensor array, providing the original basis for subsequent decoupling. The baseline reconstruction module establishes a dynamically changing zero-point baseline, which calculates the physiological state that a completely healthy pilot should exhibit under the current severe conditions. This module is based on a preset healthy human dynamics model driven by environmental stress data. This model does not rely on static statistical values ​​but is based on a real-time solver of fluid dynamics and biomechanics equations, outputting theoretical predictions assuming no pathological changes in human tissue.

[0054] The pathology simulation module actively tests and errors in the digital space, injecting preset pathological rheological parameters into a healthy human dynamic model to generate a diseased simulation state, and outputting abnormal response data predicted by the model when a specific pathology is assumed to exist in the target area under the current environmental stress. Based on this, the differential extraction module constructs a dual-track differential vector to calculate the actual residual vector representing the observed anomaly and the theoretical residual vector representing the derived pathological features, respectively. Then, the coupled decision module qualitatively assesses the risk through pattern matching and calculates the vector similarity between the actual residual vector and the theoretical residual vector. The dynamic intervention module forms a closed-loop control, generating physical intervention strategies in response to the determined risk type, and performing in-situ intervention without interrupting the flight mission.

[0055] By reconstructing health benchmarks and pathological simulations in real time, this embodiment removes environmental noise in a dynamic environment. This mechanism can dynamically offset normal physiological fluctuations caused by overload changes, such as the natural increase in pelvic floor hydrostatic pressure due to an increase in Gz, thereby avoiding misjudging normal physiological stress as pathological risk. At the same time, if the two are highly similar, it indicates that the observed abnormality conforms to the pathological characteristics derived from physical laws, thus being judged as a true risk, greatly reducing the false alarm rate in highly dynamic environments.

[0056] Example 2:

[0057] Methods for acquiring environmental stress data and real-time physiological sensing data include:

[0058] The overload value, flight duration and cabin pressure are read in real time through the flight telemetry interface as environmental stress data.

[0059] By integrating a non-invasive sensor array onto the surface of anti-G clothing or seats, multi-point pressure distribution values ​​and local thermal gradient values ​​in the perianal region are collected as real-time physiological sensing data.

[0060] This embodiment further specifies the hardware implementation path for data acquisition by the data sensing module in Embodiment 1; the system reads key parameters in real time as environmental stress data vectors through flight telemetry interfaces of aviation bus protocols such as MIL-STD-1553B or ARINC429. This includes overload values, which reflect the inertial forces exerted by the human body and are the direct cause of increased hydrostatic pressure. and Flight duration used to assess the cumulative effects of fatigue And cabin pressure, which affects the expansion of soft tissues and microcirculatory blood oxygen saturation. ;

[0061] The system collects data through a non-invasive sensor array integrated into the surface of the anti-G suit or the seat surface; specifically, it uses a flexible piezoresistive sensor array to measure the pressure contour map of the contact surface between the buttocks and the seat. The temperature distribution in the perianal region was measured non-contactly using a miniature infrared thermopile sensor. The set of raw signals collected by the aforementioned sensors, after preprocessing, is defined as a real-time physiological sensing data vector. ;

[0062] By integrating environmental and physiological data, this embodiment constructs a complete causal chain of stimulus-response. The synchronous acquisition of this multimodal data lays a solid data foundation for the subsequent separation of passive changes caused by the environment from spontaneous changes caused by pathology, ensuring the completeness and spatiotemporal alignment of the data source in complex electromagnetic and mechanical environments.

[0063] Example 3:

[0064] Methods for calculating ideal physiological response data include:

[0065] Based on fluid mechanics and biomechanics equations, a multibody dynamic coupling model of human body-seat-anti-G suit is constructed as a dynamic model of healthy human body.

[0066] Environmental stress data is input as boundary conditions into a healthy human dynamics model to calculate in real time the theoretical blood flow velocity, blood vessel wall shear force, and soft tissue deformation distribution data that should be presented in the target area under non-pathological conditions, and the calculation results are combined into ideal physiological response data.

[0067] This embodiment further specifies the algorithm principle for solving ideal physiological response data in the benchmark reconstruction module of Embodiment 2; it constructs a multibody dynamic coupling model of human body-seat-anti-G suit based on the equations of continuum mechanics and biofluid mechanics; the geometric boundary construction method of the healthy human body dynamic model is as follows: based on the 50th percentile anthropometric data of Chinese adult males, the soft tissue of the buttocks is simplified to have semi-axis lengths of... , , The model is a semi-ellipsoid, with the pelvic bones set as rigid boundaries and the outer skin as flexible contact boundaries. To comprehensively describe the human body response under high overload, the model is specifically divided into a solid domain, a fluid domain, and a biological heat diffusion domain.

[0068] For the solid domain, namely the contact surface between the soft tissue of the buttocks and the seat, the Neo-Hookean hyperelastic constitutive model is used to describe the large deformation behavior, and the strain energy density function is... Defined as:

[0069]

[0070] in, Left Cauchy-Green deformation tensor The first invariant characterizes the isochoric shear deformation of the material; For the deformation gradient tensor The Jacobian determinant characterizes the local volume change rate of a material; Related to shear modulus The specific conversion relationship is as follows: The values ​​are determined according to the ISO 13481 standard for tensile testing of biological soft tissues. The specific test conditions are as follows: Uniaxial tensile test under constant temperature environment, tensile rate set to The specimen dimensions conform to the dumbbell type II standard specified in ISO 13481. Parameter values ​​were obtained through nonlinear least-squares fitting of the stress-strain curve; typical value ranges are... , Related to bulk modulus The specific conversion relationship is as follows: ,in The typical range of values ​​is Therefore, the parameters The dimensions are The numerical order of magnitude is The level was adjusted to correct the original dimensional discrepancy; to accurately characterize the mechanical coupling boundary between the human body, the seat, and the anti-G suit, the model was applied to the surface of the human skin. Dynamic pressure boundary conditions were applied, which were determined by the inflation control law of the anti-G suit:

[0071]

[0072] in, For example, the standard load-bearing pressure adjustment curve for anti-G suits when hour, The interfacial frictional shear stress is used; its calculation model adopts the regularized Coulomb friction law:

[0073]

[0074] in, The coefficient of friction at the fabric-skin interface is calibrated using a flat plate friction test. ; The relative tangential slip velocity; To prevent division by zero, the regularization parameter is set to a value of... The introduction of this boundary condition ensures that the model can correctly calculate the deformation of soft tissue caused by the mechanical compression of the anti-load garment. With contact pressure The main contribution is overcoming the model distortion caused by only considering the gravitational field;

[0075] For the fluid domain, the Navier-Stokes simplified equation is introduced to calculate the theoretical blood flow velocity. With blood vessel wall shear force :

[0076]

[0077] In the simplified model of this embodiment, it is assumed that blood exhibits Newtonian fluid properties under macroscopic flow, with viscosity... Take a constant; where, This refers to the dynamic viscosity of blood, to distinguish it from the shear modulus in the solid domain. to replace the original ; For including environmental overload The force vector; it is worth noting that, in order to achieve real-time solution, the model adopts a reduced-order model technique based on eigenorthogonal decomposition; specifically, the flow field velocity vector Expand as An empirical orthogonal basis function Linear combination: Substituting this expansion into the Navier-Stokes equations and performing the Galerkin projection reduces the partial differential equations to a time-dependent form. The system of ordinary differential equations:

[0078]

[0079] Among them, subscript This is the order index of the POD mode, with values ​​ranging from 0 to 1. to , corresponding to the previous Energy-dominated eigenorthogonal basis functions; tensor This is for offline pre-computation; specifically, the pre-computation is obtained by projecting the Navier-Stokes operator onto the POD basis space generated by SVD decomposition of the snapshot matrix using Galerkin projection; the snapshot matrix is ​​constructed as follows: high-precision transient calculations are performed in ANSYS Fluent using the finite volume method, with a tetrahedral unstructured mesh of approximately 2 million nodes and a boundary layer... To capture near-wall shear flow, the inlet boundary is set as a pulsating velocity inlet function based on the cardiac cycle. The outlet is set as a pressure outlet;

[0080] In advance Within the overload range Steady-state CFD calculations were performed using a step size, and the flow field velocity vectors under each working condition were extracted and assembled as column vectors. The initial condition for transient calculations when constructing the snapshot matrix was set to a completely static field. The wall boundary is set to a no-slip condition, and the vessel wall is assumed to be rigid to facilitate the extraction of the flow field substrate; its numerical calculation formula is defined as follows: linear operator tensor ,in, For blood dynamic viscosity, Blood density, For time-averaged flow field; second-order nonlinear tensor The above inner product operation The computational complexity is reduced by performing offline numerical integration at the grid nodes, generating a fixed coefficient matrix stored in the ROM solver. Reduce to ;

[0081] Furthermore, to support the comparison of thermal imaging gradient data in Example 2, this example adds a biological heat conduction equation to the model to specifically characterize the physical processes of the biological heat diffusion domain:

[0082]

[0083] in, To organize the temperature field; These represent the densities of biological tissues and blood, respectively, with values ​​of [value missing]. ; These are the specific heat capacities of tissue and blood, respectively, and their values ​​are all... ; The core temperature of the artery is taken as a constant value. ; For the thermal conductivity of the tissue, a value is taken. Based on Pennes biothermal data, For blood perfusion rate, take the value. and followed The increase decays exponentially, and its value increases with... The relationship of change is defined as follows:

[0084]

[0085] in, Basic perfusion rate, The physiological attenuation coefficient is taken as the value in this embodiment. The above parameters and The numerical values ​​are based on measured data of buttock skin blood perfusion as a function of overload collected in Gz centrifuge experiments, and are derived using an exponential decay model. The correlation coefficient was obtained through fitting. ;

[0086] Basal metabolic heat production rate; value is... This value is calculated based on the human resting metabolic rate formula and the proportion of soft tissue volume in the buttocks.

[0087] This equation establishes a balance between ambient temperature, heat carried away by blood flow, and metabolic heat production;

[0088] To resolve the issue of the model's internal state variables and surface sensor data, i.e., pressure... ,temperature To address the dimension mismatch issue, the system introduces an observation operator. Mapping the high-dimensional model state to the sensor manifold:

[0089]

[0090] in the formula The constraint operator refers to extracting the spatial coordinates of the sensor array within the computational domain. The matching node values, i.e. ;

[0091] Using this operator, the system obtains the calculated stress tensor Extracting normal contact pressure From the temperature field Extracting epidermal temperature These observable physical quantities are then combined into an ideal physiological response data vector. This ensures that subsequent and real-time sensor data are consistent. The physical meaning of performing differential calculations is consistent.

[0092] Example 4:

[0093] Methods for generating simulation data with defects include:

[0094] Obtain pathological correction operators from the medical expert knowledge base, including varicose vein factors, inflammatory factors, and tissue damage factors;

[0095] Based on the pathological correction operator, the vascular elastic modulus parameter, flow resistance coefficient and soft tissue viscoelastic properties in the dynamic model of healthy human body are parametrically adjusted to construct the simulated state with disease.

[0096] In the simulated state with the disease, environmental stress data is loaded again for parallel simulation, and the corresponding pathological characteristic response is output as the simulated data with the disease.

[0097] This embodiment is a further specification of the method for generating disease-containing simulation data in the pathological simulation module of Embodiment 3; the system uses a medical expert knowledge base to obtain pathological correction operators; the medical expert knowledge base is constructed based on the clinical hemodynamic data of 500 patients with different degrees of stage I-III varicocele and perianal varicose veins, and extracts typical pathological parameter features through machine learning clustering to ensure that the correction operators have clear clinical statistical significance; in order to implement the above parameter adjustment, the finite element mesh of the healthy human dynamic model is pre-divided into a blood vessel wall subdomain and a surrounding soft tissue subdomain, so that different material property corrections can be applied to them respectively;

[0098] The specific parameter adjustment logic is as follows: Varicose vein factor : Elastic modulus acting on the subdomain of the blood vessel wall This simulates increased compliance due to vascular wall relaxation; regarding the flow resistance coefficient: this parameter is also affected by varicose vein factors. Regulation, acting on the equivalent hydraulic resistance term in the fluid domain, is defined by the correction formula as follows: ,in, The reference value for vascular fluid impedance under healthy conditions, in units of Its physical definition is determined based on Poiseuille's law, that is... ,in, For blood viscosity, The length of the blood vessel segment. The reference radius of the blood vessel is given; this correction formula is obtained based on nonlinear least squares fitting of in vitro blood vessel perfusion experimental data, with a goodness of fit of [missing information]. ,in The relative dilation ratio characterizing the diameter of blood vessels, the square term. This accurately reflects the nonlinear increase in blood return resistance caused by vascular tortuosity and valve failure. The squared term form originates from nonlinear regression analysis of isolated blood vessel pressurized perfusion experimental data. Experiments show that during pathological vascular dilation, due to eddy current generation and valve regurgitation, the flow resistance exhibits a variation with the dilation ratio. The quadratic growth characteristic;

[0099] Tissue damage factors : Viscous damping coefficient acting on the Kelvin-Voigt model ,in, The reference value for viscous damping of healthy soft tissue is set to [value]. The results were obtained through microindentation relaxation experiments on healthy volunteers; simulating damping decay caused by microfiber breakage; 4. Inflammatory factors. : It acts on the metabolic heat production term in the newly added Pennes biothermal equation in Example 3. It simulates the abnormal increase in local metabolic rate caused by inflammatory response, which is the physical root cause of redness, swelling, heat and pain.

[0100] In the simulated state with the disease present, environmental stress data is reloaded for parallel simulation; crucially, the internal pathological state output by the simulation must also be processed by the observation operator defined in Example 3. The process projects the anomalies in the internal flow and temperature fields caused by the lesions onto the surface sensor space:

[0101]

[0102] Final output of simulation data with defects It includes macroscopic features that can be captured by surface sensors, such as high temperatures caused by inflammation or local pressure waveform distortion caused by varicose veins, thus providing a mathematical basis that is isomorphic to real measurement data for subsequent calculation of theoretical residual vectors.

[0103] Example 5:

[0104] Methods for constructing dual-track difference vectors include:

[0105] Subtract the ideal physiological response data from the real-time physiological sensing data to eliminate normal physiological fluctuations caused by environmental stress, and retain the real residual vector containing true pathological features and environmental noise.

[0106] By subtracting the ideal physiological response data from the simulated data with disease, the theoretical deviation features caused purely by specific pathologies under current environmental stress are extracted and used as the theoretical residual vector.

[0107] This embodiment is a further concretization of the mathematical logic for constructing the dual-track difference vector in the difference extraction module of embodiment 4; the system calculates real-time physiological sensing data. Compared with ideal physiological response data The difference between them yields the actual residual vector. The formula is as follows:

[0108]

[0109] in, The real residual vector, derived from differential computation, physically represents a mixed signal containing real pathological features, sensor noise, and unmodeled environmental interference; the system calculates simulation data with the disease. Compared with ideal physiological response data The difference between them yields the theoretical residual vector. The formula is as follows:

[0110]

[0111] in, The theoretical residual vector, derived from differential calculation, physically represents the theoretical deviation characteristics caused purely by a specific pathology under current environmental stress. The dual-track differential method used in this embodiment is essentially a common-mode suppression technique. Since environmental stress simultaneously acts on real-time data, simulation data, and baseline data, high-amplitude environmental background signals are effectively eliminated through subtraction, leaving only tiny residual vectors. This allows weak pathological signals to emerge from severe environmental noise, greatly improving the system's detection sensitivity under extreme conditions.

[0112] Example 6:

[0113] Methods for determining risk types include:

[0114] The dynamic time warping algorithm or the cosine similarity algorithm is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector in the time domain and the spatial domain.

[0115] If the vector similarity is greater than the preset judgment threshold, the current state is judged as a true pathological risk, and the pathological type corresponding to the simulated data with disease is marked as the determined risk type.

[0116] If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is greater than the preset safety threshold, then the current state is judged as sensor artifact or transient environmental noise, and no risk type is generated.

[0117] If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is less than or equal to the safety threshold, then the current state is judged as normal physiological fluctuation, and no risk type is generated.

[0118] This embodiment is a further specification of the risk type determination logic and algorithm of the coupled decision module in Embodiment 5. To address the division-by-zero anomaly and logic failure issues that may arise from zero vector input in traditional similarity calculations, this embodiment employs a hierarchical threshold decision logic. In particular, to eliminate the heterogeneity in physical dimensions between pressure data (kPa) and temperature data (°C) in the residual vector, and to prevent large numerical dimensions from masking the characteristics of small numerical dimensions, the system performs weighted dimensionless processing before calculating the norm and similarity: constructing a normalized matrix. ,in These are preset pressure and temperature characteristic scales, for example Here, the feature scale and The value of is not set arbitrarily, but is based on statistical principles. Specifically, it is set to three times the standard deviation of the physiological signal in the target area at rest. The principle aims to normalize 99.7% of the normal physiological background noise to below the order of magnitude, thereby highlighting pathological features; and to normalize the original reality residual vector. With the theoretical residual vector Mapped to a dimensionless vector and All subsequent calculations are based on this dimensionless vector.

[0119] The system prioritizes calculating the Euclidean norm of the dimensionless real residual vector. ;like That is, a preset safety threshold is set, which is the background noise level. Specifically, this is obtained by analyzing the sensor noise floor data of the pilot in a resting state for 30 minutes. This ensures that only significant anomalies pass the initial screening, indicating that the data fluctuation is within the normal range. The system directly determines the current state as a normal physiological fluctuation, skipping subsequent similarity calculations to save computing power. The system then enters a rigorous pre-calculation verification process: checking the magnitude of the theoretical residual vector. For the boundary case where the vector magnitude equals 0, which causes the algorithm to crash, the system sets a very small numerical tolerance. ,For example ;like At this point, the denominator of the cosine similarity formula is zero, indicating that the current environmental stress is theoretically insufficient to induce this pathological feature, and forced calculation will lead to a division by zero error.

[0120] Therefore, the system executes a circuit breaker in this branch, skipping the division operation and directly determining whether the current state is a sensor artifact or transient environmental noise; only when and The system is only allowed to perform the cosine similarity core calculation when both non-zero conditions are met simultaneously:

[0121]

[0122] in, Vector similarity, derived from algorithmic calculations, physically represents the consistency of two vectors across multiple spatial directions, with a range of values... The system is based on similarity. Execute final decision: in response to That is, a preset judgment threshold, for example The threshold The system's design is based on ROC curve analysis of 100 historical flight data samples, including 50 confirmed cases of microcirculatory disorders and 50 healthy samples. The Youden index (sensitivity + specificity - 1), representing the maximum similarity, was selected as the optimal operating point to balance the false negative and false positive rates. This indicates a high degree of consistency between the observed abnormalities and the theoretical pathological simulation, leading the system to determine the current state as a true pathological risk. The corresponding pathological type is marked as a defined risk type; in response to This is determined to be either sensor artifacts or transient environmental noise.

[0123] The judgment logic in this embodiment not only solves the drawback of traditional threshold monitoring methods that only consider size and not shape, but also enhances the code robustness of the algorithm under extreme conditions through pre-amplitude verification and zero vector circuit breaking mechanism.

[0124] Example 7:

[0125] Methods for generating corresponding physical intervention strategies include:

[0126] To identify the pathological remission mechanisms corresponding to a specific risk type;

[0127] Based on the pathological relief mechanism, the optimal inflation timing of the hip airbag of the anti-G suit or the pressure distribution adjustment parameters of the seat were calculated.

[0128] The parameters for optimizing inflation timing or pressure distribution are converted into control commands and sent to the anti-load equipment control unit as a physical intervention strategy.

[0129] This embodiment further specifies the process of generating physical intervention strategies by the dynamic intervention module in Embodiment 6; the system acquires pre-stored pathological relief mechanisms corresponding to various risk types, such as a pulsating pressure mechanism to promote venous return for the risk of perianal venous congestion; specific control parameters are calculated based on the determined relief mechanism; for the anti-G suit hip airbag, the airbag pressure is set. The waveform is used to generate a pulsating pressure wave with a frequency synchronized with the heart rate and a phase lag behind the diastolic phase of the heart. The specific pressure waveform function is defined as follows:

[0130]

[0131] in, Time variable, physically referring to the real-time time of the physical intervention process, in seconds. ; Base holding pressure, in physical terms, is the minimum pressure required to ensure a proper fit under load, and its unit is... ; Pulsation amplitude, in physical terms, is the dynamic pressure increase value that promotes blood return, and its unit is... ; Real-time heart rate, derived from physiological sensors, unit: ; Phase lag angle, physically meaning the time delay that ensures pressurization occurs during diastole, is measured in units of... For seats with active deformation capabilities, calculate the adjustment matrix. This matrix is ​​used to inflate and deflate the seat cushion airbags to transfer pressure in high-pressure areas. Dimensional pressure adjustment matrix:

[0132]

[0133] in, Indicates the row number of the seat cushion airbag unit array along the longitudinal direction, subscript physical space coordinates The mapping relationship is defined as follows: ,in, These represent the longitudinal and lateral spacing of a single airbag unit, respectively. This indicates the number of columns distributed along the horizontal direction; the specific values ​​for both depend on the hardware resolution configuration of the seat sensors, for example... Specifically, the computational logic employs a local peak suppression and neighborhood compensation algorithm: scanning the real-time pressure distribution map. It identifies pressure values ​​exceeding the capillary occlusion threshold. ,set up ,For example The system internally converts to a uniform value. Local high pressure points The threshold The settings are strictly based on human physiological parameters, referencing the Landis capillary blood pressure measurement experiment. The average hydrostatic pressure at the arterial end of the skin microcirculation is taken as the critical point; any external pressure exceeding this value is considered to block blood perfusion. The negative adjustment value at this point is calculated. ,in, To convert to The measured value afterwards For example, the dimensionless pressure feedback gain coefficient. The coefficient The value of is determined through a step response experiment of airbag charging and discharging under dummy load. In the experiment, a target pressure step signal is set and adjusted. Value, typical range Until the overshoot of the system response is less than And the adjustment time is less than To balance response speed and stability; to ensure calculation results The unit is To match the airbag controller instruction format.

[0134] Perform neighborhood pressure redistribution calculation: Define the high pressure point Centered The matrix region is the neighborhood. From this, we select those with pressure values ​​below the safety threshold. unit set As a receptor; here. The physical reference value is To maintain consistency with the system's main computing unit, this threshold was converted during the initialization phase. Storage; in this step, to prevent the set from being under high pressure due to all cells in the neighborhood being in a high-pressure state. It is an empty set, that is This can lead to program infinite loops or division by zero errors. The system introduces a dynamic radius search and a global circuit breaker mechanism.

[0135] If the initial neighborhood is within If empty, the algorithm will automatically adjust the search radius. Gradually expand, that is If the maximum search radius is reached back If the value is still empty, the system triggers a global pressure relief circuit breaker strategy, generating a unified pressure reduction command across the entire matrix, such as for all units. This value is determined based on the maximum pressure relief rate when the anti-G suit's exhaust valve is fully open and the allowable pressure drop gradient for the human body. At this point, the previous local calculation results are directly overridden, and the adjustment matrix is ​​forced. all elements For all And immediately terminate the current iteration cycle, no longer perform subsequent neighborhood allocation calculations; prioritize microcirculation infusion at the expense of local support stiffness; if the set If not empty, calculate the total pressure to be transferred. And based on the remaining pressure margin Weighted allocation to set Each point in, any point Positive compensation value The calculation formula is:

[0136]

[0137] in, To and unit A unified pressure safety threshold; To prevent the regularization parameter from being introduced when the denominator is zero, its value is set to a very small positive number, for example... It is particularly important to note that, in order to satisfy the principle of dimensional consistency in physical formulas, this parameter... It must have the same properties as the denominator. and Same pressure dimensions The use of dimensionless scalars is strictly prohibited to ensure the accuracy of calculation results. It has a clear physical meaning;

[0138] This algorithm ensures This means eliminating the risk of pressure sores while maintaining the overall support stiffness of the seat for the human body, and avoiding the creation of new secondary high-pressure points in the vicinity due to compensation. To address the potential for superposition and overflow issues caused by multi-point pressure transfer, the system performs a global superposition check before generating the final instruction: calculating the algebraic sum matrix of all local adjustments. Construct a predictive stress map If there exist arbitrary coordinates Make Then calculate the global attenuation coefficient:

[0139]

[0140] For all predicted stress In case of risk, the total amount of positive inflation is forcibly constrained, and all positive inflation amounts are corrected to... This ensures that the pressure distribution across the entire field remains absolutely within the safety boundaries; among which, Corresponding coordinates The target pressure adjustment value for the airbag unit, physically meaning the increase or decrease in pressure at that point, is expressed in units of... Positive values ​​represent inflation and pressurization, while negative values ​​represent deflation and depressurization. The above calculation results are converted into CAN bus control commands that can be recognized by the underlying hardware and sent to the anti-G equipment control unit to execute physical actions.

[0141] This embodiment realizes closed-loop control from monitoring to treatment; the system is not just an alarm, but actively intervenes in the pilot's equipment environment, and relieves microcirculation disorders in real time through physical means, extending the pilot's continuous combat endurance time without interfering with the pilot's operation, reflecting the deep integration of human-machine ergonomics.

[0142] Example 8:

[0143] The system also includes an evolution analysis module, used for:

[0144] After determining a true pathological risk, continuously track the dynamic trajectory of the actual residual vector as it changes with environmental stress data;

[0145] The dynamic trajectory is compared with a pre-set disease progression model to predict the evolution trend of microcirculatory disturbances in the target area during the remaining flight time, and a higher level of intervention strategy is triggered when the evolution trend exceeds the safety boundary.

[0146] This embodiment further specifies the prediction function of the evolution analysis module in Embodiment 7; after determining a true pathological risk, the system initiates dynamic trajectory tracking and records the actual residual vector in phase space. Over time and environmental stress The path of change; the system compares the recorded dynamic trajectory with a preset disease progression model; in order to correct stress With temperature The inconsistency in physical dimensions leads to distortion in norm calculations, and the system forcibly invokes the normalized matrix defined in Example 6. Calculate the dimensionless real residual vector The evolution of microcirculation; introduction of a microcirculation deterioration index The quantification is performed using the following formula:

[0147]

[0148] in, The current moment, in physical terms, refers to the specific point in time at which the assessment occurred, and is measured in seconds. ; The integral variable, in physical terms, represents the time history, with the unit being seconds. ; The risk is first identified from system records; : Weighting coefficients, where To accumulate damage weights, the physical unit is set to... ,For example , The degradation rate weight is set to a dimensionless constant, for example... Weighting coefficients The specific numerical values ​​were obtained through optimization using a multi-objective genetic algorithm, with the objective function set to make the calculated values... The correlation coefficient between the curve and the timing of clinically observed microcirculatory disturbances was maximized.

[0149] In the preferred configuration of this embodiment, to ensure the reproducibility of the algorithm, the calibrated cumulative damage weight is... Fixed value Deterioration rate weight Fixed value This dimensional definition ensures that the integrand... It has a unified physical dimension of damage rate. Thus, the integral result It becomes a dimensionless damage index with a clearly defined physical meaning; The residual amplitude change rate, derived from differential calculation, physically represents the rapidity of disease progression. For discrete time-series data obtained from actual sampling, the system employs numerical difference and numerical integration algorithms to calculate the above formula in real time: for sampling intervals of... ,For example discrete time The differential term is approximated using a first-order backward difference: ;

[0150] The integral term is discretized and accumulated using the trapezoidal rule: Let the integrand value be... The deterioration index at the current moment Through this iterative recursive algorithm, the system can update the damage index in real time without storing all historical data; the system executes trend prediction and safety boundary determination logic; the system reads the preset total flight time for this mission. Calculate the remaining flight window duration To eliminate transient sensor noise, such as that caused by electromagnetic interference. Peaks can mislead long-term trend predictions; the system uses a sliding window smoothing algorithm to calculate the current average rate of deterioration. Instead of directly using the instantaneous rate of change:

[0151]

[0152] in, For example, the duration of the sliding window. Based on this smoothing rate, the system constructs a robust first-order linear extrapolation model to predict the damage state at the end of the flight. :

[0153]

[0154] If the calculated predicted value Greater than the preset safety boundary threshold For example, the critical integral value for irreversible tissue necrosis determined based on animal experiments is set as follows: This security boundary The setting is based on pathological statistics from high-G centrifuge animal experiments, corresponding to the integral critical value when irreversible ischemic necrosis of the soft tissue in the buttocks of experimental animals occurs, i.e., pressure ulcer stage I. On this basis, a safety margin coefficient of 1.5 times is introduced; if the trend of microcirculatory disturbance exceeds the safety boundary, the system immediately triggers a higher level of intervention strategy, specifically including sending a decision-making assistance signal to the flight control computer to suggest stopping continuous high-G maneuvers, and forcibly adjusting the base pressure of the anti-G suit to the maximum safe value.

[0155] This embodiment endows the system with prognostic management capabilities; by quantifying the cumulative damage and deterioration rate through an integral algorithm and combining it with the remaining flight time for forward extrapolation, the system extends flight safety assurance from the transient to the entire flight, providing forward-looking physiological status basis for command and decision-making, and effectively preventing pilots from suffering sudden incapacitation without their knowledge.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A risk assessment and dynamic intervention management system for perianal health of military flight personnel, characterized in that, include: The data sensing module is used to simultaneously collect environmental stress data and real-time physiological sensing data of the target area under flight conditions. The baseline reconstruction module is used to drive a preset healthy human dynamics model based on environmental stress data to calculate the ideal physiological response data under the current working conditions. The pathological simulation module is used to inject preset pathological rheological parameters into a healthy human dynamic model to generate a diseased simulation state, and combine it with environmental stress data to generate diseased simulation data. The differential extraction module is used to construct a dual-track differential vector, which calculates the real residual vector between real-time physiological sensing data and ideal physiological response data, and the theoretical residual vector between disease-simulated data and ideal physiological response data. The coupled decision module is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector, and to determine the risk type based on the vector similarity. The dynamic intervention module is used to generate corresponding physical intervention strategies to adjust the status of anti-load equipment in response to a determined risk type. Methods for constructing dual-track difference vectors include: Subtract the ideal physiological response data from the real-time physiological sensing data to eliminate normal physiological fluctuations caused by environmental stress, and retain the real residual vector containing true pathological features and environmental noise. By subtracting the ideal physiological response data from the simulated data with disease, the theoretical deviation features caused purely by specific pathologies under current environmental stress are extracted and used as the theoretical residual vector.

2. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 1, characterized in that, Methods for acquiring environmental stress data and real-time physiological sensing data include: The overload value, flight duration and cabin pressure are read in real time through the flight telemetry interface as environmental stress data. By integrating a non-invasive sensor array onto the surface of anti-G clothing or seats, multi-point pressure distribution values ​​and local thermal gradient values ​​in the perianal region are collected as real-time physiological sensing data.

3. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 2, characterized in that, Methods for calculating ideal physiological response data include: Based on fluid mechanics and biomechanics equations, a multibody dynamic coupling model of human body-seat-anti-G suit is constructed as a dynamic model of healthy human body. Environmental stress data is input as boundary conditions into a healthy human dynamics model to calculate in real time the theoretical blood flow velocity, blood vessel wall shear force, and soft tissue deformation distribution data that should be presented in the target area under non-pathological conditions, and the calculation results are combined into ideal physiological response data.

4. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 3, characterized in that, Methods for generating simulation data with defects include: Obtain pathological correction operators from a medical expert knowledge base, including varicose vein factors, inflammatory factors, and tissue damage factors; Based on the pathological correction operator, the vascular elastic modulus parameter, flow resistance coefficient and soft tissue viscoelastic properties in the dynamic model of healthy human body are parametrically adjusted to construct the simulated state with disease. In the simulated state with the disease, environmental stress data is loaded again for parallel simulation, and the corresponding pathological characteristic response is output as the simulated data with the disease.

5. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 4, characterized in that, Methods for determining risk types include: The dynamic time warping algorithm or the cosine similarity algorithm is used to calculate the vector similarity between the actual residual vector and the theoretical residual vector in the time domain and the spatial domain. If the vector similarity is greater than the preset judgment threshold, the current state is judged as a true pathological risk, and the pathological type corresponding to the simulated data with disease is marked as the determined risk type. If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is greater than the preset safety threshold, then the current state is judged as sensor artifact or transient environmental noise, and no risk type is generated. If the vector similarity is less than or equal to the judgment threshold, and the magnitude of the actual residual vector is less than or equal to the safety threshold, then the current state is judged as normal physiological fluctuation, and no risk type is generated.

6. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 5, characterized in that, Methods for generating corresponding physical intervention strategies include: To identify the pathological remission mechanisms corresponding to a specific risk type; Based on the pathological relief mechanism, the optimal inflation timing of the hip airbag of the anti-G suit or the pressure distribution adjustment parameters of the seat were calculated. The parameters for optimizing inflation timing or pressure distribution are converted into control commands and sent to the anti-load equipment control unit as a physical intervention strategy.

7. The military flight personnel perianal health risk assessment and dynamic intervention management system according to claim 6, characterized in that, The system also includes an evolution analysis module for: After determining a true pathological risk, continuously track the dynamic trajectory of the actual residual vector as it changes with environmental stress data; The dynamic trajectory is compared with a pre-set disease progression model to predict the evolution trend of microcirculatory disturbances in the target area during the remaining flight time, and a higher level of intervention strategy is triggered when the evolution trend exceeds the safety boundary.

Images