High altitude work ability evaluation coordinate system and model construction method

Abstract

The application provides a high-altitude operation ability evaluation coordinate system and model construction method, comprising: S3, establishing a multi-dimensional coordinate system with physiological dimension S, environmental stress dimension E and cognitive function dimension C as coordinate axes, and weighting a plurality of indexes in each dimension according to correlation weight to obtain state coordinate point X(t)=[S(t), E(t), C(t)] at time t; S4, inputting X(t) into a multiple linear regression model Y(t)=b+W·X(t) and a feedforward neural network model Y'(t)=f(X(t); theta) respectively to obtain two evaluation values; S5, adopting a fusion strategy to obtain a final evaluation value Z(t), and judging Z(t) as a I-V level risk grade according to a grading threshold set. Through the fusion of physiological, environmental and cognitive data in a unified coordinate space and the introduction of linear regression and neural network models for joint calculation, real-time quantitative evaluation and dynamic risk warning of individual high-altitude operation ability can be realized.

Description

Technical Field

[0001] This invention belongs to the field of high-altitude medical care, specifically relating to a coordinate system and model construction method for assessing high-altitude work capacity. Background Technology

[0002] High-altitude environments are characterized by low air pressure, insufficient oxygen partial pressure, and harsh climate, producing complex physiological stress responses in the human respiratory, circulatory, and central nervous systems. Individuals performing tasks in high-altitude areas often experience decreased blood oxygen levels, increased heart rate, slowed reaction times, and decreased attention, leading to reduced work capacity and even increased safety risks. Currently, methods for assessing high-altitude work capacity mainly fall into three categories: first, real-time monitoring methods based on single physiological parameters such as blood oxygen saturation and heart rate, which are simple in structure but limited in dimensions; second, self-assessment methods relying on subjective symptom scores or questionnaires, which, while reflecting the trend of altitude sickness, lack objectivity and real-time accuracy; and third, multi-parameter integrated monitoring systems, which can simultaneously collect multiple signals but lack a unified mathematical model and algorithm system. In practical applications, these methods generally suffer from problems such as isolated information, simplistic models, and unstable assessment results, making it difficult to comprehensively reflect the dynamic changes in individual work capacity under high-altitude conditions.

[0003] However, existing technologies have significant limitations: First, most methods focus only on a single physiological indicator, failing to integrate key influencing factors such as environmental stress and cognitive load; second, the lack of standardized and unified mapping mechanisms for multi-dimensional data makes it difficult to collaboratively calculate different types of data within the same model; third, traditional threshold determination or linear weighting methods cannot accurately capture the non-linear relationship of high-altitude physiological stress, resulting in assessment results lacking individualized adaptability; fourth, existing systems are mostly one-way monitoring structures, failing to form a closed-loop control process of "collection-analysis-feedback-early warning," leading to delayed risk identification and untimely intervention. These technological bottlenecks severely restrict the intelligent and scientific progress of safety management in high-altitude operations.

[0004] Therefore, there is an urgent need for a high-altitude work capability assessment method that can integrate multi-source data and possess multi-dimensional mapping and intelligent modeling capabilities. By fusing physiological, environmental, and cognitive data in a unified coordinate space and introducing linear regression and neural network models for joint calculation, real-time quantitative assessment and dynamic risk warning of individual high-altitude work capabilities can be achieved. The high-altitude work capability assessment coordinate system and model construction method proposed in this invention are designed to address the problems of existing assessment systems, such as single-dimensionality, rigid algorithms, and lagging feedback, aiming to establish a standardized expression. Summary of the Invention

[0005] The purpose of this invention is to provide a coordinate system and model construction method for assessing high-altitude work capabilities. This invention integrates physiological, environmental and cognitive data in a unified coordinate space and introduces linear regression and neural network models for joint calculation, which can realize real-time quantitative assessment and dynamic risk warning of individual high-altitude work capabilities.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising: a coordinate system and model construction method for assessing high-altitude operation capabilities, including the following steps: S1, the physiological data acquisition module acquires physiological signals such as heart rate, blood oxygen saturation, blood pressure, and respiratory rate of the workers at a set sampling period; the environmental perception module simultaneously acquires environmental quantities such as altitude, atmospheric pressure, ambient temperature, and oxygen concentration; the cognitive testing module acquires cognitive quantities such as reaction time and accuracy according to a strategy; the central processing module timestamps the three types of data. S2. Perform dimensionless standardization on each aligned indicator and use linear normalization or Z-score standardization to convert data with different dimensions to a unified dimension range. S3. Establish a multidimensional coordinate system with physiological dimension S, environmental stress dimension E, and cognitive function dimension C as coordinate axes, and weight the standardized indicators in each dimension according to their correlation weights to obtain the state coordinate point X(t)=[S(t),E(t),C(t)] at time t; S4. Input the multiple linear regression model Y(t)=b+W·X(t) and the feedforward neural network model Y′(t)=f(X(t);θ) into X(t) respectively to obtain two evaluation values; S5. The final evaluation value Z(t) is obtained by adopting a fusion strategy, and Z(t) is determined to be a risk level I–V based on the set of graded thresholds. S6. When the risk reaches the warning threshold, the display and alarm module is triggered to issue an audible and visual warning. The central processing module increases the frequency of subsequent physiological / cognitive sampling and the trigger density of cognitive tests, forming a closed-loop control of data acquisition, analysis and calculation, risk output and sampling adaptation to solve the problems of difficulty in integrating multi-source data, difficulty in modeling nonlinearity and lack of real-time warning.

[0007] Furthermore, the standardization process in step S2 includes: performing time alignment and missing data interpolation on data from different modules within the same monitoring window; performing Z-score standardization on each indicator based on its historical mean and standard deviation, or performing [0,1] linear normalization based on its statistical upper and lower bounds, in order to eliminate dimensional differences and improve coordinate mapping stability. The standardization parameters are learned offline by the central processing module and can be fine-tuned online.

[0008] Furthermore, the multidimensional coordinate mapping in step S3 satisfies: (1) the coordinate values ​​of each dimension are a weighted combination of m standardized indicators within that dimension: and (2) Define the reference coordinate point Xref≈[0,0,0] for normal operation at sea level and define the dangerous coordinate point Xdng≈[1,1,1] for severe altitude sickness. (3) Use historical samples to calibrate the weights and reference points so that the coordinate values ​​correspond one-to-one with the actual changes in capabilities, in order to solve the problem of incomparability of data from different sources.

[0009] Furthermore, the parameters W and b of the multiple linear regression model in step D are solved on the training set using the least squares method, and the following are performed: (1) F test to verify the overall significance of the model; (2) t test of each regression coefficient to screen out insignificant variables; (3) residual normality and homoscedasticity test and cross-validation to evaluate the generalization error; after passing the test, the coefficients are solidified into the central processing module to achieve online fast calculation, thereby improving interpretability and reducing false alarm rate.

[0010] Furthermore, the feedforward neural network in step S4 has an input layer-single hidden layer-output layer structure. The hidden layer uses Sigmoid activation, and the output layer uses linear activation to output Y′(t) in regression form. During training, the mean squared error is used as the loss, and the weights and biases are updated iteratively using error backpropagation and gradient descent. This model is used to capture nonlinear features such as thresholds / inflection points caused by physiological and environmental interactions, so as to solve the complex coupling relationships that are difficult to cover by linear models.

[0011] Furthermore, the fusion strategy in step E includes: (a) weighted average fusion: Z(t) = α·Y(t) + β·Y′(t), where α + β = 1, and α and β are adaptively set based on the performance of the validation set; and / or (b) rule-priority triggering: with Y(t) as the main factor, when the neural network detects a nonlinear high-risk pattern that causes Y′(t) to be significantly lower than Y(t) and the difference exceeds the threshold Δ, the risk level is directly increased; and the threshold and α and β are finely adjusted online based on historical false alarm / missed alarm statistics during long-term operation to balance robustness and sensitivity.

[0012] Furthermore, a system for implementing the above method includes: a physiological data acquisition module for wearable data acquisition of heart rate, blood oxygen saturation, blood pressure, and respiratory rate; an environmental perception module for monitoring altitude, atmospheric pressure, temperature, and oxygen concentration; a cognitive testing module for conducting reaction time and accuracy tests based on a handheld or wearable terminal; a central processing module for performing time alignment, standardization, multi-dimensional coordinate mapping, regression and neural network evaluation and fusion, threshold grading, and early warning decision-making, and distributing control strategies to each module; and a display and alarm module for presenting the assessment values ​​and risk levels and implementing audible and visual alarms. The modules are connected via wired or wireless communication to form a closed-loop system from data acquisition to risk output and then to adaptive sampling scheduling, thereby achieving real-time quantification and early warning control of individual operational capabilities in complex plateau environments.

[0013] Furthermore, the central processing module is configured to: (1) automatically increase the physiological sampling frequency and cognitive test trigger density when Z(t) is detected to be continuously approaching the grading threshold or X(t) is rapidly drifting in the physiological / cognitive dimension; (2) reduce the sampling frequency to save energy when Z(t) is in the safe range for a long period of time. (3) After the warning is triggered, the event, the corresponding coordinate trajectory, the original signal fragment and the model output are packaged and archived for subsequent retraining and adaptive updating of the threshold / weight.

[0014] Furthermore, the system deployment includes two-level calibration: on-site and individual. (1) On-site calibration is used to calibrate environmental parameters such as altitude, temperature and oxygen concentration locally and update the environmental dimension mapping. (2) Individual calibration is used to determine the individual's physiological baseline and cognitive baseline through short-term online data collection and initialize weights and risk thresholds. (3) After the baseline is completed, the system enters an all-weather monitoring state and supports periodic correction of thresholds and fusion coefficients to solve the problem of assessment bias caused by population differences.

[0015] Furthermore, the display and alarm module simultaneously presents Z(t) and the risk level in three forms: numerical value, color, and text. When the warning threshold is reached, it issues an audible and visual alarm and reports to the remote monitoring terminal via the communication module. Based on this, the remote terminal automatically generates intervention suggestions such as evacuation, oxygen supply, or shift rest, and can send execution instructions back to the central processing module to complete the intervention in a closed loop.

[0016] The "Coordinate System and Model Construction Method for High-Altitude Operation Capability Assessment" proposed in this invention revolves around the complete chain of "multi-source acquisition—standardization—multi-dimensional coordinate mapping—dual-model assessment—fusion and classification—closed-loop early warning—adaptive control." Addressing the core pain points of existing technologies—"difficulty in unified modeling of multi-dimensional data, difficulty in characterizing nonlinear laws, and lack of real-time closed-loop early warning and individualized parameter tuning"—it achieves the following beneficial effects: The integrated representation of multi-source information solves the problem of data incomparability. By implementing time alignment and dimensionless standardization on three key indicators of physiology, environment and cognition, and performing weight mapping in a unified multidimensional coordinate system, heterogeneous data can be directly compared and operated on in the same mathematical space. This eliminates the evaluation bias caused by the difference in dimensions and provides a stable and interpretable input representation for subsequent algorithm evaluation.

[0017] An evaluation framework that emphasizes both interpretability and learnability significantly improves discrimination accuracy. It employs a dual-model structure of "multiple linear regression + feedforward neural network": the regression model provides clear linear weights and statistical significance tests, ensuring interpretability of the conclusions; the neural network captures nonlinear patterns such as thresholds and inflection points caused by physiological-environment interactions, significantly reducing the false negative rate of a single linear model. The complementary nature of these two approaches effectively enhances evaluation accuracy and robustness in complex scenarios.

[0018] The model fusion and hierarchical threshold system reduces false alarms and enhances risk resolution. Through a fusion strategy of weighted averaging or rule-based priority triggering, the regression output and the neural network output are synthesized into the final evaluation value on a unified scale and mapped to I–V level risk levels. The fusion coefficient and threshold are dynamically calibrated in combination with historical validation to achieve a better balance between sensitivity and specificity, thereby reducing false alarms and delayed reports.

[0019] Closed-loop early warning and strategy linkage enables the transition from "seeing risks" to "managing risks." When a risk reaches a threshold, the system immediately triggers an audible and visual alarm and remote reporting, and links with the central processing module to automatically adjust sampling and testing strategies (increasing sampling frequency and encrypting cognitive testing), thus closing the "monitoring-assessment-alarm-re-monitoring" loop and significantly shortening the time window from risk identification to intervention decision-making.

[0020] Adaptive sampling and online fine-tuning balance real-time performance and energy consumption; the sampling frequency and trigger density are adaptively increased or decreased based on the trend of the evaluation value and the coordinate drift speed; while ensuring timeliness, energy consumption and bandwidth usage are controlled, making it suitable for long-term continuous operation and field deployment. This mechanism reduces unnecessary data redundancy and avoids insufficient sampling at critical moments.

[0021] Dual calibration, both on-site and individual, enhances the generalization ability across altitudes and individuals. In the early stages of deployment, environmental parameters such as altitude, temperature, and oxygen concentration are calibrated on-site, and individual physiological and cognitive baselines are established through short-term online data collection. This allows for the initial customization of coordinate weights, thresholds, and fusion coefficients, significantly reducing evaluation bias caused by population differences and scene changes.

[0022] Statistical testing and residual analysis are used to ensure stable engineering implementation; the regression model is screened for redundant variables through F-test and coefficient t-test, and supplemented by residual normality and homoscedasticity checks and cross-validation to ensure that the model has reliable explanatory power and generalizability before going live, providing statistical quality assurance for engineering deployment.

[0023] Event logging and retraining mechanisms continuously improve system performance. When an alert is triggered, the original signal, coordinate trajectory, and model output are archived as events, serving as data assets for subsequent threshold recalibration and model retraining. This forms a continuous improvement loop of "learning by doing and improving by learning," enabling the system to continuously approach optimal performance in long-term operation.

[0024] The intuitive human-machine interface and remote linkage optimize the command and clinical intervention process; the display and alarm modules present the assessment value and risk level simultaneously through three channels: numerical value, color and text, to achieve clear on-site prompts; at the same time, it supports remote monitoring terminals to receive alarms and generate intervention suggestions such as evacuation, oxygen supply and rest rotation, connecting front-line operations and back-end command.

[0025] The general coordinate-model-policy framework is extensible and portable. The coordinate construction, dual-model evaluation and policy fusion of this method are extensible frameworks: dimensions and indicators can be added or removed according to task requirements, and other learning models (such as gradient boosting and time series models) can be replaced or connected in parallel. It has good modularity and portability, and is easy to adapt to different high-altitude jobs, different altitude zones and different equipment conditions.

[0026] In summary, this invention achieves a systematic breakthrough in four aspects: unified modeling, nonlinear identification, real-time early warning, and individualized adaptation. It solves both the problem of "accurate identification" (multi-dimensional fusion + dual models) and the problem of "fast response" (closed-loop alarm + adaptive sampling), and ensures "stable use and increasing accuracy with use" through statistical verification and continuous retraining, significantly improving operational safety and management efficiency in high-altitude scenarios. Attached Figure Description

[0027] Figure 1 This is a system overall structure block diagram of the present invention; Figure 2 This is the control flow and timing diagram of the present invention, wherein: data acquisition start → transmission and aggregation → standardization processing → coordinate mapping (S / E / C) → regression evaluation Y and neural network evaluation Y′ → fusion and classification to obtain Z → threshold judgment → (yes) alarm and reporting / (no) policy adaptive adjustment → enter the next cycle. The dashed line represents the control and policy loop, which is used to increase the sampling frequency or encrypt cognitive testing; Figure 3 This is a schematic diagram of the multidimensional coordinate system construction and trajectory of the present invention. A multidimensional coordinate system is established with physiological dimension S, environmental stress dimension E, and cognitive dimension C as axes; Xref is the sea level reference point, and Xdng is the danger reference point. The trajectory points are used to illustrate the evolution of an individual's state over time, visually depicting the dynamic changes in high-altitude adaptation. Figure 4 This invention relates to a weighted mapping of indicators to coordinates. Three types of indicators are mapped: physiological indicators (HR, SpO2, blood pressure, respiration) are weighted to obtain S(t); environmental indicators (altitude, oxygen concentration, air pressure, temperature) are weighted to obtain E(t); and cognitive indicators (reaction time, accuracy) are standardized / mapped to obtain C(t). Each coordinate dimension is obtained by combining the standardized values ​​of multiple indicators within the corresponding dimension according to their weights. Figure 5 This is a model evaluation and fusion block diagram of the present invention, wherein the coordinate vector X(t) is simultaneously input into the multiple linear regression model to obtain Y(t) and the feedforward neural network model to obtain Y′(t); the two results are weighted averaged or rule-based fusion in the fusion and grading unit to obtain the final evaluation value Z(t), which is then mapped to I–V levels; Figure 6This is a schematic diagram of the risk classification and alarm linkage of the present invention, in which the correspondence between risk levels I–V and Z(t) is displayed in the form of segmented strips. When Z(t) reaches the threshold range, the system triggers the display and alarm module to provide on-site audio-visual / text prompts, and reports to the remote monitoring center through the communication channel to assist in the formation of intervention suggestions such as evacuation, oxygen supply, and shift rest. Figure 7 This is a schematic diagram of the feedforward BP neural network structure of the present invention, showing the network topology of input layer—hidden layer—output layer. The input layer corresponds to the feature components after S / E / C expansion, the hidden layer uses Sigmoid activation to extract nonlinear features, and the output layer uses linear activation to regress the output Y′(t) to capture the threshold / inflection point effect of physiological and environmental interaction; Figure 8 This is a schematic diagram of the field deployment and communication topology of the present invention, wherein the wearable device integrates physiological data collection and cognitive testing; and the fixed-point deployment provides environmental perception. Multi-source data is transmitted wirelessly to the central processing module for data processing, evaluation, and classification; the results are sent to the field display and alarm module and the remote monitoring center, respectively, to achieve local alerts and backend linkage. Figure 9 This is a schematic diagram of the sampling adaptation and parameter update of the present invention. In this diagram, the sampling strategy management unit dynamically adjusts the sampling frequency and cognitive test density according to the trend of Z(t) and the drift speed in the S / C direction. Alarm events, trajectories and model outputs are imported into the event library / retraining module to update the threshold, weights and fusion coefficients, forming a long-term self-learning closed loop. Figure 10 This is a schematic diagram of the typical operating trajectory and event points of the present invention. It shows the Z(t) curve that changes over time and multiple threshold reference lines, and marks key event points such as "early warning triggered", "strategy adjustment", "danger" and "recovery in progress", demonstrating the entire process from risk identification, intervention execution to recovery.

[0028] In the diagram, 100 is the physiological data acquisition module; 110 is the environmental perception module; 120 is the cognitive testing module; 200 is the data transmission and aggregation unit; 210 is the standardization processing unit; 220 is the multidimensional coordinate mapping unit; 230 is the regression evaluation unit (Y); 240 is the neural network evaluation unit (Y′); 250 is the fusion and grading unit (Z→I–V); 260 is the display and alarm module; 270 is the reporting and communication unit; 280 is the sampling strategy management unit (adaptive); and 290 is the historical data and parameter library. Detailed Implementation

[0029] This invention provides a coordinate system and model construction method for assessing high-altitude work capability, including the following functional modules: Physiological data acquisition module: This module collects real-time physiological parameters of the operator, such as heart rate, blood oxygen saturation, blood pressure, and respiratory rate. It digitizes this physiological data and transmits it to the central processing module, providing basic input for subsequent assessments. The physiological data acquisition module is wearable on the operator, and the frequency and timing of data collection are controlled by the central processing module to ensure continuous and reliable data.

[0030] Environmental Sensing Module: This module monitors high-altitude environmental conditions, including altitude, atmospheric pressure, ambient temperature, and oxygen concentration. It senses key parameters of the working environment in real time and transmits them to the central processing module. The environmental sensing module ensures synchronized acquisition of external environmental data and physiological data, enabling the central processing module to incorporate environmental stress factors into its assessment.

[0031] Cognitive Testing Module: Used to conduct cognitive function tests on workers, such as simple calculation, memory tests, or reaction time tests. The central processing module can trigger the cognitive testing module to execute test tasks according to a predetermined strategy (such as at fixed intervals or when physiological indicators are abnormal). The test results are quantified into cognitive performance indicators (such as reaction time and accuracy) and sent back to the central processing module to assess the impact of high-altitude hypoxia on cognitive abilities.

[0032] Central Processing Module: The core data processing unit of the system, containing a built-in algorithm model for assessing high-altitude work capabilities. This module acquires multi-source data from the physiological data acquisition module, environmental perception module, and cognitive testing module, and performs the following functions: standardizing and preprocessing the data; constructing a multi-dimensional coordinate system to represent the current state; mapping each dimension of data to coordinate values; based on this, calling a pre-built assessment model (regression model and / or neural network model) to perform capability assessment calculations; and finally, making risk judgments and early warning decisions based on the assessment results. The central processing module is also responsible for the control logic of the entire system, such as setting the data acquisition frequency, triggering cognitive tests, and coordinating the work of various modules, so that the system forms a closed loop of data acquisition-processing-feedback.

[0033] Display and Alarm Module: Used for result display and risk alarm. This module receives assessment indicators and risk levels output from the central processing module and displays the personnel's current high-altitude work capability assessment results in real time on the human-machine interface (e.g., displaying the risk level in numerical or indicator light form). When the risk assessment result exceeds a preset threshold, this module issues a warning signal (such as an audible and visual alarm) to remind personnel or managers to take safety measures. The display and alarm module can also store historical assessment data for later query and analysis.

[0034] The above modules are closely interconnected, forming a continuous closed-loop path of "data acquisition → standardized processing → coordinate mapping → model evaluation → risk output". Among them, the physiological acquisition, environmental perception and cognitive testing modules provide raw data input, the central processing module processes and evaluates the data, and the display and alarm module outputs evaluation results and alarms, realizing a timely feedback loop of information.

[0035] The control flow of this system executes the following steps sequentially, with the central processing module at its core, to ensure a smooth flow of data from acquisition to risk output: Data Acquisition Initiation: The central processing module periodically issues commands to activate the physiological acquisition module and the environmental perception module to simultaneously acquire and standardize the current data on personnel status and environmental parameters. If personnel are detected entering a high-altitude area or abnormal fluctuations in physiological indicators are detected, the central processing module will also trigger the cognitive testing module to perform cognitive function tests. Through this mechanism, the system ensures comprehensive data collection at critical moments.

[0036] Data transmission and aggregation: Each acquisition module transmits the acquired data to the central processing module in real time via wired or wireless communication. The central processing module timestamps and fuses the data from different sources, aggregating them into a raw multivariate dataset reflecting the personnel status.

[0037] Data standardization: The central processing module preprocesses the aggregated raw data, standardizing indicators with different dimensions and units. For example, heart rate (measured in beats per minute) and blood oxygen saturation (measured as a percentage) need to be converted to a unified range of [0,1] using linear normalization or Z-score standardization; environmental indicators such as altitude are also converted to standard values ​​for relative hypoxia intensity. Standardization formulas are as follows: in For the j-th index collected in the i-th time, and These are the mean and standard deviation of the indicator, respectively. These are the standardized values. Through standardization, data from different sources are made dimensionless, eliminating dimensional differences and facilitating subsequent multidimensional coordinate mapping and model calculations.

[0038] Multidimensional Coordinate Mapping: The central processing module maps standardized data to a predefined multidimensional coordinate system. This system uses various key factors as axes, such as physiological state, environmental stress, and cognitive function. The coordinate values ​​for each dimension are calculated by comprehensively considering relevant indicators. For example, several physiological indicators, such as heart rate and blood oxygen, can be weighted to obtain "physiological state coordinate values," while altitude and oxygen concentration can be used to obtain "environmental stress coordinate values," and cognitive test scores can be directly used as "cognitive function coordinate values." After mapping, the system obtains a multidimensional coordinate point. It comprehensively depicts the overall condition of personnel in a high-altitude environment. Among them... These represent the coordinate values ​​for each dimension, derived from standardized data for that dimension. This coordinate representation method integrates multi-source information into a unified mathematical space, providing an intuitive input for model evaluation.

[0039] Model evaluation calculation: The central processing module calls the evaluation model to calculate the coordinate points. Analysis and calculations are performed to obtain the personnel's current high-altitude work capability score and risk level. The assessment model includes a statistical regression-based analytical model and a machine learning-based neural network model, which can be used in combination to improve the accuracy and robustness of the assessment. First, a preliminary capability assessment value is calculated using a multiple regression model, while a neural network model is used to supplement the assessment of non-linear factors. The central processing module combines the outputs of the multiple regression and neural network to determine the current risk level. The model assessment process will be described in detail below.

[0040] Risk Output and Feedback: The central processing module determines the risk level based on the model assessment results. If the assessed high-altitude operation capability index is below the safety threshold (indicating that personnel may be in a high-risk state), the central processing module immediately sends an early warning command to the display and alarm module. The display and alarm module then displays a red warning on the interface accompanied by an audible and visual alarm, prompting that intervention measures are needed; simultaneously, it stores the risk information and can report it to the remote monitoring center via the communication module. If the assessment result is within the safe range, the display and alarm module indicates a normal state, continuously recording data without triggering an alarm. After risk output, the system returns to wait for the data acquisition command for the next cycle, thus looping and achieving closed-loop control. Throughout the process, the central processing module can also adjust the acquisition frequency or model parameters based on historical data, such as increasing the sampling frequency and strengthening the control intensity when a gradual increase in risk is detected, to achieve early warning and dynamic response.

[0041] Through the above control steps, the various modules of this system work closely together: after data is collected from the sensors, it undergoes standardization and coordinate transformation in the central processing unit, enters the evaluation model for calculation, and finally feeds the results back to the user, achieving closed-loop integration of information flow and control flow. This ensures the real-time performance and reliability of the high-altitude operation capability assessment.

[0042] In this embodiment, a multidimensional coordinate system is used to characterize different aspects of personnel's working conditions at high altitudes, transforming complex multi-source data into an intuitive coordinate representation. The method for constructing the multidimensional coordinate system is as follows: 1. Determining Coordinate Axis Dimensions: Based on the factors influencing high-altitude work capacity, several key dimensions are selected as coordinate axes. For example, the physiological dimension mainly reflects the body's tolerance to high-altitude hypoxia, the environmental dimension reflects the severity of the external high-altitude environment, and the cognitive dimension reflects the degree to which brain function is affected by high altitude. Additionally, psychological state dimensions (such as subjective fatigue) can be added as needed. The number of dimensions is set to n, with each dimension corresponding to one coordinate axis.

[0043] 2. Indicator Mapping and Coordinate Calculation: For each dimension, select one or more relevant monitoring indicators and define the mapping relationship from these indicators to the coordinate values ​​of that dimension. The mapping process typically includes steps such as normalization and weighted summation. For example, for the physiological dimension coordinates, two indicators can be used: heart rate (HR) and blood oxygen saturation (SpO2): first, HR and SpO2 are standardized to dimensionless values ​​between 0 and 1, and then weights are assigned based on their correlation with work capacity. and The physiological dimension coordinates were calculated as follows: in, These are the standardized values ​​of the corresponding indicators. Similarly, environmental dimension coordinates can be obtained by standardizing and weighting altitude and oxygen concentration, while cognitive dimension coordinates can be directly represented by standardized values ​​of cognitive test scores. Through this multi-indicator comprehensive mapping, the coordinate values ​​of each dimension are between 0 and 1. The larger the value, the stronger the adverse effect of that dimension on work ability (for example, a high physiological dimension value may indicate a state of excessively fast heart rate and low blood oxygen).

[0044] 3. Multidimensional coordinate point formation: Combining coordinate values ​​from various dimensions to form multidimensional coordinate points. For example, in the case of three-dimensional coordinates. These represent the relative intensity of an individual's current physiological state, environmental stress, and cognitive function. This coordinate point preserves the independent contribution of each type of information while unifying different types of data in mathematical space. By monitoring the changes in the position of this coordinate point within the coordinate system, the evolution of an individual's state over time can be intuitively determined; for example, proximity to extreme values ​​on a certain axis of the coordinate system indicates excessive stress in that corresponding aspect.

[0045] 4. Coordinate System Reference and Calibration: To give coordinate values ​​practical meaning, it is necessary to calibrate the multi-dimensional coordinate system. For example, the reference coordinate point can be defined as the sea level under normal operating conditions. (Values ​​close to zero in each dimension indicate no additional load), and dangerous coordinate points are defined based on the known occurrence of severe altitude sickness. (A value of 1 for each dimension indicates that the limit for that dimension has been reached). Coordinate points between these values ​​represent different degrees of capability decline or risk levels. By collecting data from a large number of personnel at different altitudes and under different conditions, the coordinate mapping relationship is calibrated to ensure that the coordinate values ​​correspond to actual physiological and operational capability changes. In this way, once the system calculates the real-time coordinate points, operational capability can be assessed based on their relative positions within the coordinate system.

[0046] In summary, the multidimensional coordinate system constructed using the above method integrates physiological, environmental, and cognitive data into a single coordinate vector, laying the foundation for model evaluation. Once the coordinate points are determined, they can be further input into the evaluation model to calculate quantitative capability assessment results and risk levels.

[0047] Derivation and Testing of Regression Model The multiple regression model in the central processing module takes multidimensional coordinate data as input and outputs an assessment value of high-altitude operation capability. The model establishment and derivation process is as follows: 1. Regression Model Specification: A multiple linear regression model is used, assuming the following assumptions regarding the high-altitude work capability assessment value. Input coordinates in each dimension The relationship between them is linear. The basic regression equation is in the form of: in, This is an assessment value for high-altitude work capability (for example, it can be defined as a work capability index, with a lower value indicating a more severe decline in capability). Input the standardized coordinates for each dimension constructed above. This is the intercept (constant term) of the regression equation. The regression coefficients for each dimension represent the weights of each factor on the evaluation value. The random error term (following a normal distribution with a mean of 0, representing unmodeled factors or measurement noise) is used. The mathematical meaning of this regression model is to approximate the prediction of an employee's work ability using a linear combination. The symbols are defined as above. The input data comes from multidimensional coordinates aggregated by the central processing module, and the output... It will be used for subsequent risk assessment and early warning triggering.

[0048] 2. Least squares estimation to solve for regression coefficients: The least squares method is used to estimate the regression coefficients. .

[0049] Specifically, this involves collecting a large amount of sample data from different individuals or the same individual under different conditions. Construct a normal equation and solve for the coefficients that minimize the sum of squared residuals. The residuals are defined as follows: ,in Let be the model's ability to predict the i-th sample. The sum of squared residuals is... Taking the partial derivative of each regression coefficient and setting it to 0 yields the normal equation system; its matrix form is: ;where the matrix It is a design matrix (each row corresponds to a sample, and each column corresponds to the intercept term and the coordinate values ​​of each dimension). It is a vector of observation capability assessment values. Let be the column vector of coefficients to be determined. Solving this system of equations yields the optimal estimate of the coefficients: The above formula is the analytical solution for the least squares estimate. [Note: matrix] [The invertible assumption is that the data is full rank]. Using this, we obtain a set of coefficients that minimize the sum of squared prediction errors of the model on historical data. Meaning of each symbol: It is the coefficient matrix of the least squares solution, which acts on the observation vector. Obtain the estimated coefficients The input data consists of historical training samples, and the output is... It will be used for real-time computing in the system. These coefficients, obtained through offline training, are loaded into the central processing module, which calculates the evaluation results for new inputs during actual runtime, thus enabling the model parameters to be learned from historical data.

[0050] 3. Model Significance Test (F-test): After establishing the regression model, it is necessary to test the overall effectiveness of the model, that is, whether the linear relationship between each input dimension and the output is significant. The F-test is used to test the overall significance. First, the regression sum of squares is calculated. and residual sum of squares Total sum of squared deviations ,in The mean of the observed values ​​is used to measure overall fluctuation.

[0051] Sum of squares of regression , representing the fluctuation component explained by the model.

[0052] Sum of Squares of Residuals , representing the fluctuation portion that the model failed to explain (i.e., the aforementioned residual sum of squares).

[0053] The degrees of freedom are as follows: (Regression section, number of independent variables n) (For the error part, the degrees of freedom are the sample size minus the number of estimated parameters). Construct the F-statistic: Its mathematical meaning is the ratio of the unit regression variance to the unit residual variance. When the regression model is invalid, the coefficients of each independent variable are actually zero, and the expected value is zero. Compared to It will not be significantly large, and the F-value will approach 1; if the model is effective, Will be significantly greater than The F-value increases. Given a significance level... (e.g., 0.05), find the critical value of the F-distribution. If the calculated If the value is greater than the critical value, then the null hypothesis is rejected (all). The model is considered to be significantly valid overall (i.e., there exists at least one pair of independent variables). [Linear Influence Exists]. During model construction, we performed an F-test on the collected training data. The results showed that the F-statistic was much larger than the critical value, and the p-value was much smaller than 0.01, indicating that the established linear regression model is highly significant overall. This test result ensures that the model has statistically significant explanatory power for assessing high-altitude work capability.

[0054] 4. Significance test of regression coefficients (t-test): Assuming the model is significant overall, the t-test is used to further examine the significance of each regression coefficient. ( The significance of ) to determine which dimensions are important. The impact is significant. Assuming the null hypothesis... Construct the t-statistic for the j-th coefficient:

[0055] in For the estimated regression coefficients, for The standard error (which can be calculated from the covariance matrix estimated by least squares, generally...) , (For residual variance estimation).

[0056] At the significance level (e.g., below 0.05) if Greater than the critical value of t Then we reject the null hypothesis and assume that the coefficient Significantly non-zero. This means that the corresponding input dimension has a significant impact on the output dimension. There is a significant linear contribution. Through t-tests, we identified the main factors influencing high-altitude work capacity during the model development phase. For example, assuming that the absolute values ​​of the t-values ​​for the physiological and environmental dimensions are large and the p-values ​​are <0.01, it indicates that they significantly affect... If a secondary dimension is not significant in the t-test, it may be removed from the model to simplify it. This simplified model ensures the effectiveness of the main factors while avoiding noise from irrelevant variables.

[0057] 5. Residual Analysis and Model Validation: After establishing the regression model, the residuals are analyzed to validate the model's applicability. Residuals The residuals should satisfy an approximately normal, independent distribution with consistent variance. We plotted the normal probability of the residuals and a scatter plot of the residuals against the fitted values. The results show that the residuals are roughly distributed along a straight line with no obvious pattern, indicating that the assumptions of normality and homoscedasticity are reasonable. Furthermore, we calculated the coefficient of determination. To measure the goodness of fit of the model, the results A value close to 1 (e.g., above 0.85) indicates that the model can explain more than 85% of the total variance; adjusted Even after adding appropriate penalties, the accuracy remains high, indicating that the model has not overfitted due to too many independent variables. Through cross-validation, we divided the data into training and validation sets, trained the model, predicted the validation set, and calculated metrics such as mean squared error (MSE). The model also showed high prediction accuracy on the validation set. Residual analysis and validation results together demonstrate that the regression model can fit and predict the plateau operation capability indicators well under the selected main dimensions, and has the reliability for real-time evaluation by the central processing module.

[0058] Based on the derivation and statistical test results of the regression model above, we finally determined the regression equation and its coefficients, and embedded them in the central processing module. During actual operation, the central processing module will use the real-time collected coordinate values... Substitute into the regression equation The high-altitude work capability assessment value was calculated. This assessment value will serve as the basis for judging the personnel's condition: when When the threshold is lower than preset, it indicates a significant decline in operational capability, triggering an early warning from the system. Through a regression model, we achieved rapid linear combination calculations of multidimensional inputs, thereby obtaining quantitative risk assessment indicators, providing an intuitive basis for safety management in high-altitude operations.

[0059] Neural Network Model Structure and Algorithm Considering the complex nonlinear characteristics of altitude-related physiological responses, this system also introduces an artificial neural network model to evaluate operational capabilities. As a supplement to linear regression, the neural network model can fit the nonlinear mapping relationship between multidimensional inputs and evaluation results, improving evaluation accuracy and robustness. The structure and working principle of the neural network model are explained in detail below: 1. Network Structure: This embodiment employs a three-layer feedforward BP neural network, specifically a multilayer perceptron model comprising an input layer, a hidden layer, and an output layer. The input layer has n nodes, corresponding to the n input components of the multidimensional coordinate system (as used in the regression model). (Consistent); the hidden layer determines an appropriate number of nodes H (e.g., H=10) based on experience and experimentation, with each hidden node fully connected to all input nodes; the output layer has 1 node and is used to output the high-altitude operation capability assessment value. The network's topology is a directed acyclic graph with fully connected layers, where the input layer is mapped to the output layer via hidden layers. We describe its structure mathematically: for any time-varying, standardized input vector... Hidden layer The inputs received by each neuron are weighted and summed, then a bias is added, and the result is passed through an activation function to produce the output. in, Let be the weights from the i-th node in the input layer to the j-th node in the hidden layer. This represents the bias of the j-th node in the hidden layer. This is the activation function for the hidden layer. In this embodiment, the sigmoid function (a type of sigmoid function) is selected as the activation function. Its output range is (0,1), suitable for representing the activation level of each hidden feature. The hidden layer performs a nonlinear transformation on the linear combination of the input to extract the intermediate feature signals that affect the assessment of plateau ability. Subsequently, the output layer performs a weighted summation of the hidden layer outputs and obtains the final evaluation value through the output activation function: in, Let be the weight from the j-th node in the hidden layer to the output node. For output layer bias, This is the activation function for the output layer. Depending on the needs of the evaluation task, the activation function for the output layer can be chosen as an identity mapping. (Used for regression outputting continuous values) or the Sigmoid function (used to output risk probabilities in the range of 0 to 1). In this system, to obtain the results of linear regression... For the same scale of operational capability index, the output layer directly uses linear activation. ,make This represents the continuous evaluation value of the network output. At this point, the network output... A lower value also indicates a lower performance capability. Through the above two layers of nonlinear mapping, the neural network can approximate arbitrarily complex multivariate nonlinear relationships, realizing the mapping from input coordinates to evaluation results. Definitions of symbols: All of these are adjustable network parameters (weights or biases), learned through network training. and These are the system's inputs and outputs, respectively. These are the internal features extracted from the hidden layer. The input data originates from standardized multidimensional coordinates, and the output results are used by the system for risk assessment.

[0060] 2. Model Training: The aforementioned neural network model needs to learn the optimal parameters from historical data through a training algorithm. During training, a large number of samples with known job capabilities are collected (e.g., capability indicators obtained through actual job performance or expert ratings are used as the expected output). Multidimensional coordinate values ​​are used as input, and the backpropagation (BP) algorithm is employed to adjust the network weights. Specifically, the loss function is defined as the network output... Compared with the true ability value of the sample Mean square error of the difference To minimize the error using gradient descent, calculate the partial derivative of each weight parameter and update it according to the gradient direction: for example, the output layer weight update formula. Hidden layer weight update ,in The learning rate is used. Through iterative training, the network gradually approaches the optimal parameter combination, minimizing the prediction error of the training samples. After training, the determined network structure and parameters are obtained, thus completing model construction. The training results are evaluated on a validation set, revealing that the neural network model can effectively capture nonlinear relationships that linear regression fails to cover: for example, when the combination of physiological and environmental indicators exceeds a certain range, a threshold effect occurs in the decline of work ability, and this nonlinear inflection point is successfully captured by the neural network and reflected in the output.

[0061] 3. Online Assessment and Model Fusion: The trained neural network model is deployed to the central processing module to achieve online assessment. Real-time data, after standardization and multi-dimensional coordinate mapping, is fed into the neural network model as input x, and matrix operations are performed to obtain the output Y′. This output, along with the regression model output Y, serves as the assessment basis. In practical applications, there are two fusion methods: First, model fusion and averaging, where the linear regression result Y and the neural network result Y′ are averaged with certain weights to obtain the final assessment value Yfinal = αY + 1 − αY′ (α is a coefficient determined based on the validation set performance, such as 0.5); Second, rule-based priority triggering, where the regression model Y is usually the primary assessment, and the risk level is increased when the neural network detects a risk signal caused by a special nonlinear pattern (e.g., Y′ is much lower than Y). Regardless of the method, the fusion result is converted into a clear risk level output in the central processing module and then sent to the display and alarm module. For example, this system sets five risk levels, from level I (safe) to level V (dangerous). The central processing module determines the level based on the numerical range of Yfinal and outputs the corresponding color signal and alarm strategy.

[0062] By introducing a neural network model, this system enhances its adaptability to complex situations while maintaining the interpretability of the linear regression model. The neural network structure formula and training process, as described above, ensure that the establishment of each input-output relationship is clearly defined. In actual operation, the neural network model, in conjunction with the regression model, provides a dual assessment of high-altitude work capability. When both indicate risk, the alarm reliability is increased; when inconsistencies arise, the central processing module handles the situation according to empirical rules, thereby improving the robustness of the assessment results. The capability index output by the neural network is also used for risk judgment and early warning triggering, particularly suitable for handling scenarios where nonlinear risk increases are caused by the interaction of physiological indicators and environmental factors. In summary, this model provides the system with intelligent analysis tools, making the assessment of high-altitude work capability more comprehensive and accurate.

[0063] Example Testing and System Deployment Instructions Example 1: The system of this invention was deployed at a construction site at an altitude of 4500 meters on the Qinghai-Tibet Plateau to conduct real-time capability assessment tests on on-site workers. A physiological data acquisition module was worn by a participant, continuously collecting data such as heart rate and blood oxygen saturation every minute. An environmental perception module was installed in the construction area, recording environmental data such as altitude (approximately 4500m), temperature (5℃), and oxygen concentration (approximately 15%) in real time. A cognitive testing module was set to initiate a simple reaction test every 2 hours via a handheld terminal (e.g., pressing a button to respond to a signal light, measuring reaction time). The central processing module ran a pre-trained model to analyze the above data. During the on-site testing, in the initial few hours, the participants' indicators were normal: heart rate remained at 80 beats / min, blood oxygen saturation was approximately 93%, and the average reaction time for the cognitive reaction test was 0.8 seconds. The central processing module standardizes these data and maps them to coordinate points, such as a physiological dimension of 0.4, an environmental dimension of 0.8, and a cognitive dimension of 0.3 (slightly higher than the baseline but not reaching the risk threshold). The evaluation model calculates the operational capability index Y to be approximately 85 (out of 100, with a threshold of 60), and the risk level is determined to be Level II (mild risk). The display and alarm module indicates normal operation with a green indicator.

[0064] As work time increased and fatigue accumulated, the worker's heart rate rose to 110 beats per minute, blood oxygen levels dropped to 88%, and reaction time in a cognitive test increased to 1.5 seconds. The central processing module detected a significant increase in physiological and cognitive dimension coordinates (e.g., reaching 0.7 and 0.6 respectively), while the environmental dimension remained at 0.8, indicating a bias towards a high-risk area. The linear regression model's calculated capability index Y dropped to 55, while the neural network model, due to detecting the non-linear threshold effect of heart rate and blood oxygen, output a capability index Y' of 50. The central processing module's fusion result assessment determined that the current work capability had severely declined. At this point, the system determined the risk level to be raised to Level IV (high risk) and immediately issued a red alarm with sound and light signals through the display and alarm modules, displaying "High Risk, Evacuate Immediately or Provide Oxygen" on the screen. Upon receiving the alarm, on-site management personnel immediately arranged for the worker to stop working, rest, and receive oxygen intervention. After intervention, the worker's physiological indicators gradually returned to normal, the risk level dropped back to Level II, and the alarm stopped. Throughout the entire implementation process, the system successfully completed the closed-loop operation of data acquisition, evaluation, and early warning, promptly identified the decline in high-altitude operation capabilities and triggered safety measures, thus verifying the effectiveness and reliability of the present invention.

[0065] System Deployment Instructions: The high-altitude work capability assessment system of this invention can be deployed in conjunction with wearable devices and fixed monitoring devices. The physiological data acquisition module and cognitive testing module should ideally be integrated into the smart terminal worn by the workers (such as a wristband monitor and a handheld tablet), while the environmental perception module can be placed at fixed points in the work area. The central processing module can be handled by a field laptop workstation or a remote server, receiving data from each module and issuing control commands via a wireless network. In practical applications, the number of modules can be adjusted according to needs: for example, when multiple personnel are working simultaneously, each person can be equipped with a set of physiological and cognitive modules, while the environmental module provides a shared data reference. The software implementation of the central processing module includes functional modules such as data processing, model calculation, and alarm strategies, and can be written in C / C++ or Python and optimized for real-time performance. The display and alarm modules can be on-site alarm lights and buzzers, or a computer interface in a remote monitoring center. Upon initial deployment, the system requires calibration: this includes adjusting coordinate mapping parameters according to the local altitude and individual differences among personnel, and setting risk thresholds and alarm level classification standards according to specific safety requirements. After calibration, the system can monitor the status of workers 24 hours a day without interruption. Once a high-risk situation is detected, the system will issue an early warning through various means (on-site audible and visual alarms, SMS notifications to the command center, etc.) to help take timely medical rescue or rotation rest measures.

[0066] Through the above deployment and operation, the method and system of this invention can provide an objective assessment of the physiological and cognitive state of workers in the harsh environment of high altitudes, achieving quantitative monitoring and risk warning of high-altitude work capabilities. The tight integration of data and control between various functional modules ensures a smooth and reliable process from data acquisition to model evaluation and alarm feedback, demonstrating good stability in field tests. This specific implementation fully demonstrates the application value of this invention in the field of high-altitude work safety assurance.

Claims

1. A coordinate system and model construction method for assessing high-altitude work capabilities, characterized in that, It includes the following steps: S1. The physiological acquisition module acquires the physiological signals of the workers at a set sampling period, including heart rate, blood oxygen saturation, blood pressure, and respiratory rate; the environmental perception module simultaneously acquires environmental parameters, including altitude, atmospheric pressure, ambient temperature, and oxygen concentration; the cognitive testing module acquires cognitive parameters, including reaction time and accuracy, triggered by a strategy; the central processing module timestamps the three types of data. S2. Dimensionless standardization is applied to the aligned physiological signals, environmental quantities and cognitive quantities. Linear normalization or Z-score standardization is used to convert data of different dimensions to a unified dimension range. S3. Establish a multidimensional coordinate system with physiological dimension S, environmental stress dimension E, and cognitive function dimension C as coordinate axes, and weight the standardized indicators in each dimension according to their correlation weights to obtain the state coordinate point X(t)=[S(t),E(t),C(t)] at time t; S4. Input the multiple linear regression model Y(t)=b+W·X(t) and the feedforward neural network model Y′(t)=f(X(t);θ) into X(t) respectively to obtain two evaluation values; S5. The final evaluation value Z(t) is obtained by adopting a fusion strategy, and Z(t) is determined to be a risk level I–V based on the set of graded thresholds. S6. When the risk reaches the warning threshold, the display and alarm module is triggered to issue an audible and visual warning. The central processing module increases the frequency of subsequent physiological / cognitive sampling and the trigger density of cognitive tests, forming a closed-loop control of data acquisition, analysis and calculation, risk output and sampling adaptation to solve the problems of difficulty in integrating multi-source data, difficulty in modeling nonlinearity and lack of real-time warning.

2. The coordinate system and model construction method for assessing high-altitude operation capability as described in claim 1, characterized in that, The standardization process of S2 includes: time alignment and missing data interpolation for data from different modules within the same monitoring window; Z-score standardization for each indicator based on its historical mean and standard deviation, or linear normalization based on its statistical upper and lower bounds [0,1] to eliminate dimensional differences and improve coordinate mapping stability. The standardization parameters are learned offline by the central processing module and can be fine-tuned online.

3. The coordinate system and model construction method for assessing high-altitude operation capability as described in claim 1, characterized in that, The multidimensional coordinate mapping of S3 satisfies: (1) The coordinate values ​​of each dimension are a weighted combination of m standardized indicators within that dimension: and ; (2) Define the reference coordinate point Xref≈[0,0,0] for normal operation at sea level, and define the danger coordinate point Xdng≈[1,1,1] for severe altitude sickness. (3) By calibrating the weights and reference points through historical samples, the coordinate values ​​correspond one-to-one with the actual changes in capabilities, so as to solve the problem of incomparability of data from different sources.

4. The coordinate system and model construction method for assessing high-altitude operation capability as described in claim 1, characterized in that, The parameters W and b of the multiple linear regression model of S4 are solved on the training set using the least squares method, and the following are performed: (1) F test to verify the overall significance of the model; (2) t-tests of each regression coefficient are used to screen out insignificant variables; (3) Residual normality and homoscedasticity tests and cross-validation are used to assess generalization error; After passing the test, the coefficients are solidified into the central processing module to achieve online rapid calculation, thereby improving interpretability and reducing false alarm rate.

5. The coordinate system and model construction method for assessing high-altitude operation capability as described in claim 1, characterized in that, The feedforward neural network in step S4 has an input layer-single hidden layer-output layer structure. The hidden layer uses Sigmoid activation, and the output layer uses linear activation to output Y′(t) in regression form. During training, the mean squared error is used as the loss, and the weights and biases are updated iteratively using error backpropagation and gradient descent. This model is used to capture nonlinear features such as thresholds / inflection points caused by physiological and environmental interactions, so as to solve the complex coupling relationships that are difficult to cover by linear models.

6. The coordinate system and model construction method for assessing high-altitude operation capability as described in claim 1, characterized in that, The fusion strategy in step E includes: (a) weighted average fusion: Z(t) = α·Y(t) + β·Y′(t), where α + β = 1, and α and β are adaptively set according to the performance of the validation set; and / or (b) rule-priority triggering: with Y(t) as the main factor, when the neural network detects a nonlinear high-risk pattern that causes Y′(t) to be significantly lower than Y(t) and the difference exceeds the threshold Δ, the risk level is directly increased; and the threshold and α and β are finely adjusted online based on historical false alarm / false alarm statistics during long-term operation to balance robustness and sensitivity.

7. A coordinate system and model building system for assessing high-altitude operational capabilities as described in claims 1-6, characterized in that, include: Physiological data acquisition module, used for wearable data acquisition of heart rate, blood oxygen saturation, blood pressure, and respiratory rate; The environmental sensing module is used to monitor altitude, atmospheric pressure, temperature, and oxygen concentration. The cognitive testing module is used to conduct reaction time and accuracy tests based on handheld or wearable devices. The central processing module is used to perform time alignment, standardization, multidimensional coordinate mapping, regression and neural network evaluation and fusion, threshold classification and early warning decision-making, and to distribute control strategies to each module. The display and alarm module is used to present the assessment value and risk level and implement audible and visual alarms. The modules are connected by wired or wireless communication to form a closed-loop system from data acquisition to risk output and then to sampling adaptive scheduling, so as to realize real-time quantification and early warning control of individual operation capabilities in complex plateau environments.

8. The coordinate system and model construction system for assessing high-altitude operation capabilities as described in claim 7, characterized in that, The central processing module is configured as follows: When Z(t) is detected to be continuously approaching the grading threshold or X(t) is detected to be rapidly drifting in the physiological / cognitive dimension, the physiological sampling frequency and cognitive test trigger density are automatically increased. (2) When Z(t) is in the safe range for a long period of time, reduce the sampling frequency to save energy; (3) After the warning is triggered, the event, the corresponding coordinate trajectory, the original signal fragment and the model output are packaged and archived for subsequent retraining and adaptive updating of the threshold / weight.

9. The coordinate system and model construction system for assessing high-altitude operation capabilities as described in claim 7, characterized in that, System deployment includes two levels of calibration: on-site and individual. (1) On-site calibration is performed to locally calibrate environmental parameters such as altitude, temperature, and oxygen concentration, and update the environmental dimension mapping; (2) Individual calibration determines individual physiological and cognitive baselines through short-term online data collection and initializes weights and risk thresholds; (3) After the baseline is completed, the system enters the all-weather monitoring state and supports the periodic correction of threshold and fusion coefficient to solve the problem of evaluation bias caused by population differences.

10. The coordinate system and model construction system for assessing high-altitude operation capabilities as described in claim 7, characterized in that, The display and alarm module simultaneously presents Z(t) and the risk level in three forms: numerical, color, and text. When the warning threshold is reached, it issues an audible and visual alarm and reports to the remote monitoring terminal via the communication module. The remote terminal automatically generates intervention suggestions such as evacuation, oxygen supply, or shift rest based on this information, and can send execution instructions back to the central processing module to complete the intervention in a closed loop.

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