Multi-parameter sensing-based barrack health monitoring method and system

Abstract

The invention relates to a barrack health monitoring method and system based on multi-parameter sensing. The method comprises the following steps: acquiring fluctuation monitoring data, gas monitoring data and barrack structure data of a target barrack; performing feature extraction on the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data; performing object hazard analysis according to the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barrack; performing hazard protection simulation according to the barrack structure data and the health hazard data to obtain hazard protection data of the target barrack; and adjusting the health hazard data according to the hazard protection data to obtain actual health data of the target barrack. By adopting the method, specific health data in the barrack can be accurately obtained, and effective barrack health maintenance is realized.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a barracks health monitoring method and system based on multi-parameter sensing. Background Art

[0002] In traditional technology, the radiation dose monitored by the radiation sensor or / and the concentration of pollutants such as PM2.5, CO2, and formaldehyde monitored by the air quality sensor will be recorded in real time and compared with the preset safety standards. When the data exceeds the set safety threshold, the system will automatically issue an alarm. However, traditional technology only alarms and records data for radiation doses and / or air pollution, and does not evaluate the health of the environment in the barracks based on the radiation dose and / or air pollution in the barracks, resulting in the inability to accurately obtain specific health data in the barracks and difficulty in achieving effective barracks health maintenance. Summary of the invention

[0003] Based on this, it is necessary to provide a barracks health monitoring method and system based on multi-parameter sensing that can accurately obtain specific health data in the barracks and achieve effective barracks health maintenance in response to the above technical problems.

[0004] In a first aspect, the present application provides a barracks health monitoring method based on multi-parameter sensing, comprising: Obtain the target barracks’ fluctuation monitoring data, gas monitoring data, and barracks structure data; Performing feature extraction on the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data; Performing object hazard analysis according to the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks; Performing a hazard protection simulation according to the barracks structure data and the health hazard data to obtain hazard protection data of the target barracks; The health hazard data is adjusted according to the hazard protection data to obtain actual health data of the target barracks.

[0005] In a second aspect, the present application further provides a barracks health monitoring system based on multi-parameter sensing, comprising: a computer device and a data monitoring terminal; the computer device comprises a barracks data acquisition module, a hazard data extraction module, a hazard data analysis module, a barracks protection simulation module and a barracks health analysis module; The barracks data acquisition module is used to acquire the fluctuation monitoring data, gas monitoring data and barracks structure data of the target barracks; The hazard data extraction module is used to extract features from the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data; The hazard data analysis module is used to perform object hazard analysis based on the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks; The barracks protection simulation module is used to perform hazard protection simulation according to the barracks structure data and the health hazard data to obtain the hazard protection data of the target barracks; The barracks health analysis module is used to adjust the health hazard data according to the hazard protection data to obtain actual health data of the target barracks.

[0006] The above-mentioned barracks health monitoring method and system based on multi-parameter sensing can accurately extract the fluctuation characteristic data and gas characteristic data by acquiring the fluctuation monitoring data, gas monitoring data and barracks structure data of the target barracks, and combining data analysis and feature extraction technology, so as to provide a reliable basis for the subsequent object hazard analysis; further, by analyzing the potential impact of fluctuation sources and gases on the health of personnel in the barracks, detailed health hazard data can be obtained, and hazard protection simulation can be performed according to the specific structural characteristics of the barracks and the health hazard data, so as to predict the impact of different protection measures on health risks and generate the optimal protection plan; finally, according to the hazard protection data obtained by simulation, the health hazard data can be adjusted to achieve accurate health risk assessment. It can accurately obtain specific health data in the barracks, realize effective barracks health maintenance, and further ensure the provision of more scientific and personalized protection plans in complex environments, reduce the occurrence of potential health hazards, and improve the quality of survival and work of personnel in special environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0008] Figure 1 A diagram of an application environment of a barracks health monitoring method based on multi-parameter sensing in one embodiment; Figure 2 A schematic diagram of a process of a barracks health monitoring method based on multi-parameter sensing in one embodiment; Figure 3 A schematic diagram of a flow chart of a method for obtaining health hazard data in one embodiment; Figure 4 A schematic diagram of a flow chart of a method for obtaining a target multi-source data analysis algorithm in one embodiment; Figure 5A schematic flow chart of a method for obtaining a target multi-source data analysis algorithm in another embodiment; Figure 6 A schematic diagram of a flow chart of a method for obtaining hazard protection data in one embodiment; Figure 7 A schematic diagram of a flow chart of a method for obtaining hazard processing simulation data in one embodiment; Figure 8 A schematic diagram of a process for obtaining biofeedback monitoring simulation data in one embodiment; Fig. 9 It is a structural block diagram of a computer device part of a barracks health monitoring system based on multi-parameter sensing in one embodiment; Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0010] The barracks health monitoring method based on multi-parameter sensing provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the server 104 can be implemented with an independent server or a server cluster composed of multiple servers.

[0011] In an exemplary embodiment, Figure 2 As shown in the figure, a barracks health monitoring method based on multi-parameter sensing is provided. Figure 1 The server in the example is used to illustrate, including the following steps 202 to 210. Among them: Step 202, obtaining the fluctuation monitoring data, gas monitoring data and barracks structure data of the target barracks.

[0012] Among them, the target barracks can be specific buildings or facilities that require health hazard assessment and protection simulation. It can be military barracks, scientific research laboratories, medical facilities, etc., which are usually located in specific working environments and face potential hazards such as radiation or harmful gases.

[0013] Among them, the fluctuation monitoring data can be the data information with fluctuation properties in the target barracks collected by special detection equipment (such as detectors, radiation instruments, etc.), including but not limited to radiation, water quality and earthquakes. Radiation includes radiation type (such as α, β, γ rays), radiation intensity, radiation source location, radiation dose rate, etc.; water quality includes pH value, conductivity, dissolved oxygen (DO), turbidity, heavy metal content (such as lead Pb, mercury Hg), organic pollutants (such as COD, BOD), pathogenic microorganism content, ammonia nitrogen (NH4-N), etc.; earthquakes include earthquake magnitude (expressed in Richter magnitude or moment magnitude Mw), focal depth (km), epicenter location (latitude and longitude), seismic wave propagation speed, pre-earthquake stress accumulation, aftershock activity, historical earthquake data, etc. Among them, gas monitoring data can be data related to gas transmission in the barracks obtained by video monitoring equipment or sensors, including but not limited to poisonous gas, virus, etc. Toxic gas includes the type, concentration, diffusion range, etc. of toxic gas in the air. For example, common toxic gas monitoring data involves the real-time concentration (ppm or mg / m³) of harmful gases such as carbon monoxide, sulfur dioxide, ammonia, and chlorine, as well as the location of the gas leakage source; viruses include virus type, transmission speed, virus concentration in the air or water, etc.

[0014] It is worth noting that in order to illustrate the data processing relationship between fluctuation monitoring data and gas detection data, in subsequent embodiments, fluctuation monitoring data is mainly introduced with radiation as the object, while other data with fluctuation properties (such as earthquakes and water) are processed using the same method.

[0015] Among them, the barracks structure data can be information related to the structure of the target barracks building, such as barracks structure, wall material, building thickness, window size, ventilation system layout, etc.

[0016] Specifically, radiation monitoring instruments and gas detection equipment are deployed in the target barracks to collect real-time data on radiation intensity and harmful gas concentration in the environment, and obtain fluctuation monitoring data and gas monitoring data of the target barracks. At the same time, the structural drawings and sensor data of the building are identified to obtain the structural information of the barracks and obtain the barracks structural data of the target barracks, such as wall thickness, window size, ventilation system, etc.

[0017] Step 204, extracting features from the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data.

[0018] Among them, the fluctuation characteristic data can be the key features extracted from the fluctuation monitoring data, usually including radiation type (α, β, γ rays), radiation intensity, dose rate, radiation source distribution, temporal and spatial changes and other information.

[0019] Among them, gas characteristic data can be important information extracted from gas monitoring data, usually including gas type (such as CO2, ammonia), concentration fluctuation, maximum concentration value, average concentration value, concentration change trend, etc.

[0020] Specifically, the collected fluctuation monitoring data and gas monitoring data are preprocessed to remove noise and outliers, and the preprocessed fluctuation monitoring data and preprocessed gas monitoring data are obtained. Then, the feature extraction algorithm is used to convert the preprocessed fluctuation monitoring data and preprocessed gas monitoring data into representative information. For example, for the processed fluctuation monitoring data, the radiation intensity, radiation wavelength and ray type (such as α, β, γ rays, etc.) in different time periods are extracted, and the distribution and change trend of the radiation dose are calculated; for the preprocessed gas monitoring data, the concentration changes of different gas types (such as CO2, ammonia, etc.) can be analyzed, and the gas concentration peak value, mean, maximum fluctuation range and other features can be extracted. In addition, the time series analysis method can be used to mine the periodic and sudden changes in the data and further identify potential danger points. After feature extraction, fluctuation feature data and gas feature data are obtained.

[0021] Step 206, performing object hazard analysis based on the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks.

[0022] Among them, object hazard analysis can be a process of combining fluctuation characteristic data and gas characteristic data for quantitative analysis. By simulating the health effects of radiation and harmful gases on humans and animals in the target barracks, the health hazards that may occur under different exposure conditions (such as radiation damage, respiratory diseases, etc.) are analyzed.

[0023] Among them, health hazard data can be potential risk information about human health based on object hazard analysis.

[0024] Specifically, the fluctuation characteristic data, combined with the type, intensity and duration of radiation, are used to evaluate the potential hazards of different radiation sources to humans and animals, such as radiation damage to cells and the risk of cancer. For gas characteristic data, the hazards of harmful gases (such as carbon monoxide, ammonia, etc.) to the respiratory system, nervous system, etc. of humans and animals are evaluated by analyzing the gas concentration, type and exposure time. At the same time, considering that the radiation and gas concentrations at different locations in the barracks may vary, the exposure degree of humans and animals is analyzed, and the health effects under different exposure conditions are simulated through human models. Combined with the specific health impact data of radiation and gas obtained above, health hazard data are finally obtained, including the probability of occurrence, severity and potential health problems of each hazard.

[0025] Step 208, performing hazard protection simulation based on the barracks structure data and the health hazard data to obtain hazard protection data of the target barracks.

[0026] Among them, hazard protection simulation can be achieved by establishing a protection model to simulate the inhibitory effects of different protection measures (such as radiation shielding materials, gas purification devices, ventilation optimization, etc.) on health hazards.

[0027] Among them, the hazard protection data can be the results of hazard protection simulation, which usually include the changes in radiation intensity and gas concentration in the barracks after the implementation of protection measures, and the inhibitory effect of these changes on health hazards.

[0028] Specifically, the barracks structural data of the target barracks (such as wall materials, window layout, ventilation system, pipe gallery structure, etc.) are combined to calculate its protective performance and shielding effect on radiation and gas. For example, thick walls may have a better shielding effect on radiation, and windows with poor sealing may increase the leakage of harmful gases. Then, the health hazard data is combined to simulate the suppression or obstruction effect of different protective measures on these hazards, or the weakening of these hazards through barracks structural data at different locations in the target barracks. Protective measures include radiation shielding materials below, starting air purification devices, optimizing ventilation systems, or planning safe evacuation passages. Through simulation calculations, the changes in radiation intensity and gas concentration in the barracks caused by these measures are predicted to change the health risks of humans and animals, and detailed hazard protection data is obtained.

[0029] Step 210, adjusting the health hazard data according to the hazard protection data to obtain the actual health data of the target barracks.

[0030] Among them, the actual health data can be the adjustment of health hazard data after hazard protection simulation, reflecting the actual health status of people and animals in the barracks after the protection measures.

[0031] Specifically, according to the hazard protection data obtained after the simulation, the radiation dose and gas concentration under the simulated actual protection conditions are adjusted to new values, and the health risks of the personnel are re-evaluated. Specifically, if the hazard protection data shows that the protection measures effectively reduce the concentration of radiation or harmful gases, the risk value in the health hazard data will be reduced accordingly, otherwise it will be adjusted accordingly; in addition, the side effects or other factors that may be caused by the protection measures, such as limited air circulation, movement of radiation sources, etc., must also be considered, and the actual health data of the target barracks will be obtained in the end.

[0032] In the above-mentioned barracks health monitoring method based on multi-parameter sensing, by obtaining the fluctuation monitoring data, gas monitoring data and barracks structure data of the target barracks, combined with data analysis and feature extraction technology, the fluctuation feature data and gas feature data can be accurately extracted, providing a reliable basis for the subsequent object hazard analysis; further, by analyzing the potential impact of the fluctuation source and gas on the health of the personnel in the barracks, detailed health hazard data can be obtained, and at the same time, according to the specific structural characteristics of the barracks and the health hazard data, the hazard protection simulation can be carried out, which can predict the impact of different protection measures on health risks and generate the optimal protection plan; finally, according to the hazard protection data obtained by simulation, the health hazard data is adjusted to achieve accurate health risk assessment. It can accurately obtain specific health data in the barracks, realize effective barracks health maintenance, and further ensure the provision of more scientific and personalized protection plans in complex environments, reduce the occurrence of potential health hazards, and improve the survival and work quality of personnel in special environments.

[0033] In an exemplary embodiment, Figure 3 As shown, object hazard analysis is performed based on the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks, including steps 302 to 306. Among them: Step 302: Analyze the correlation between the fluctuation characteristic data and the gas characteristic data to obtain multi-source hazard correlation information.

[0034] Among them, multi-source hazard correlation information can be obtained by analyzing the relationship between fluctuation characteristic data and gas characteristic data, and obtaining the connection and impact between different potential hazard factors.

[0035] Specifically, the fluctuation characteristic data and gas characteristic data are preprocessed to remove noise and perform standardization, and then statistical analysis methods such as correlation analysis, regression analysis or joint distribution analysis are used to analyze the potential relationship between the fluctuation characteristic data and the gas characteristic data, such as their common fluctuations in the same period or at the same location. Through correlation analysis, regression analysis or joint distribution analysis, it is possible to identify whether there is a synergistic effect or superposition effect between the fluctuation characteristic data and the gas characteristic data, and how they jointly affect health risks, and obtain multi-source hazard association information.

[0036] Step 304, according to the multi-source hazard association information, set the parameters of the initial multi-source data analysis algorithm of the target barracks to obtain the target multi-source data analysis algorithm.

[0037] The initial multi-source data analysis algorithm can be a preliminary algorithm model designed based on multi-source hazard association information to process fluctuation characteristic data and gas characteristic data. The core purpose of the algorithm is to integrate data from different sources and conduct a comprehensive analysis of the data through the algorithm to evaluate the impact of various hazard factors on health risks.

[0038] Among them, the target multi-source data analysis algorithm can be a final algorithm after optimization and adjustment, which is used to implement comprehensive analysis of radiation and gas data in the target barracks environment.

[0039] Specifically, according to the multi-source hazard association information between the fluctuation characteristic data and the gas characteristic data, the key factors affecting the health risk assessment, such as radiation intensity, gas concentration, exposure time, etc., are determined, and appropriate weight values ​​are assigned to these factors. Combined with historical data and research data in related fields, the initial parameters of the initial multi-source data analysis algorithm are set, including the weight coefficient of the data, the convergence speed of the algorithm, the error tolerance, etc., to ensure that the initial multi-source data analysis algorithm can reasonably handle the interaction effects of different types of data. Using the Pareto frontier optimization algorithm, these parameters of the initial multi-source data analysis algorithm are multi-objective optimized, so that the algorithm can more accurately handle the complex relationship between radiation and gas data, and obtain the target multi-source data analysis algorithm.

[0040] Step 306, inputting the fluctuation characteristic data and the gas characteristic data into the target multi-source data analysis algorithm to obtain health hazard data.

[0041] Specifically, the fluctuation characteristic data and the gas characteristic data are input into the target multi-source data analysis algorithm constructed in the previous step. The algorithm will comprehensively consider the interaction between the fluctuation characteristic data and the gas characteristic data, evaluate the health hazards that people and animals in the target barracks may face through calculation and model prediction, and output health hazard data. These data include but are not limited to potential health risk levels, different types of health hazards (such as radiation sickness, respiratory diseases, etc.) and their probability and severity of occurrence.

[0042] In this embodiment, by analyzing the correlation between the fluctuation characteristic data and the gas characteristic data, multi-source hazard correlation information can be obtained, which provides a comprehensive hazard background for further analysis. According to this correlation information, the parameters of the initial multi-source data analysis algorithm are set so that the multi-source data analysis algorithm of the target barracks can accurately adapt to the specific hazard environment. After the fluctuation and gas characteristic data are input into the algorithm, the accurate calculation and prediction of the health hazard data can be achieved, thereby comprehensively evaluating the combined hazards of the fluctuation source and gas in the target barracks. Ultimately, it can provide a basis for formulating more scientific and effective health risk management measures, ensuring the pertinence and effectiveness of hazard protection measures.

[0043] In an exemplary embodiment, Figure 4 As shown, when the multi-source hazard association information is less than the preset hazard association threshold, the parameters of the initial multi-source data analysis algorithm of the target barracks are set according to the multi-source hazard association information to obtain the target multi-source data analysis algorithm, including steps 402 to 408. Among them: Step 402, constructing an initial fluctuation hazard analysis item and an initial gas hazard analysis item of an initial multi-source data analysis algorithm according to the multi-source hazard association information.

[0044] Among them, the initial fluctuation hazard analysis item can be the initial radiation hazard assessment module built based on fluctuation characteristic data (such as radiation type, intensity, time distribution, etc.) when building a multi-source data analysis algorithm. It mainly focuses on the potential impact of radiation sources on human health and analyzes key factors such as radiation exposure dose, dose rate, and exposure time.

[0045] Among them, the initial gas hazard analysis item can be the initial gas hazard assessment module built based on gas characteristic data (such as gas type, concentration, exposure duration, etc.) when building a multi-source data analysis algorithm. It mainly analyzes the potential threat to health of different gases at specific concentrations, especially the impact on the respiratory system and other physiological functions.

[0046] Specifically, when the multi-source hazard association information is less than the preset hazard association threshold, based on the multi-source hazard association information between the hazards of the fluctuation characteristic data and the gas characteristic data, for example, there may be a certain interaction between the radiation intensity and the gas concentration, or in some cases, the concentration change of a certain gas will aggravate the radiation hazard, the initial analysis items are constructed for the radiation hazard and the gas hazard respectively, and the initial fluctuation hazard analysis items and the initial gas hazard analysis items of the initial multi-source data analysis algorithm are obtained. The fluctuation hazard analysis item will focus on factors such as the type, intensity, and exposure time of radiation, and consider its potential hazards to human health; while the gas hazard analysis item focuses on the type, concentration, and exposure duration of the gas, and analyzes its impact on the respiratory system or other physiological systems.

[0047] Step 404, setting the parameters of the initial fluctuation hazard analysis item according to the fluctuation characteristic data to obtain the target fluctuation hazard analysis item.

[0048] Among them, the target fluctuation hazard analysis item can be an analysis module that is optimized and adjusted based on the initial fluctuation hazard analysis item in combination with the specific target barracks environment and fluctuation characteristic data (such as radiation intensity, exposure duration, etc.).

[0049] Specifically, analyze the fluctuation characteristic data, including radiation intensity, radiation type (such as α, β, γ rays), radiation source distribution, and the time and space characteristics of radiation exposure. According to the above analysis data, set the threshold and weighting coefficient of radiation exposure in the initial fluctuation hazard analysis item to ensure that the initial fluctuation hazard analysis item can reasonably reflect the impact of different radiation sources and different exposure conditions on health. For example, for high-intensity radiation sources, it may be necessary to set a higher hazard weight; and for long-term low-dose radiation, it may be necessary to calculate the potential impact of cumulative doses on health. In addition, it is necessary to adjust the parameters in the initial fluctuation hazard analysis item in combination with radiation protection standards and human physiological response models to ensure that the health risks under different radiation conditions can be accurately assessed, and finally construct the target fluctuation hazard analysis item.

[0050] Step 406, setting parameters of initial gas hazard analysis items according to the gas characteristic data to obtain target gas hazard analysis items.

[0051] Among them, the target gas hazard analysis item can be an analysis module obtained by optimizing and adjusting the gas characteristic data (such as gas concentration, type, time and space changes, etc.) on the basis of the initial gas hazard analysis item.

[0052] Specifically, the gas characteristic data is analyzed in detail, including factors such as the type of gas (such as carbon monoxide, ammonia, methane, etc.), concentration level, exposure time, fluctuation trend, etc. Based on the data analyzed above, the hazard weighting coefficient of each gas is determined, and the weight of its impact on human health at different concentrations is set. For example, for high concentrations of carbon monoxide, a higher hazard weight may need to be set because it is more harmful to the respiratory and nervous systems; while for lower concentrations of gas, adjustments may need to be made based on exposure time and dose effects. At the same time, the combined effects of gas and radiation need to be considered, especially when the concentrations of certain gases reach a certain threshold, they may increase the potential threat of radiation to health, ensuring that the target gas hazard analysis items can accurately reflect the health risks under different gas exposure conditions in the target barracks.

[0053] Step 408, integrating the target fluctuation hazard analysis item and the target gas hazard analysis item to obtain a target multi-source data analysis algorithm.

[0054] Specifically, the parameters in the target fluctuation hazard analysis item and the target gas hazard analysis item are weighted and integrated to ensure that each hazard factor (such as radiation intensity, gas concentration, etc.) is appropriately allocated according to its actual risk level and interaction. During the fusion process, techniques such as multivariate regression, weighted average, or machine learning models may be used to combine the influencing factors of the two to reflect the combined effects of radiation and gas hazards in the same environment. Secondly, during the fusion process, the potential synergistic effects between radiation and gas must also be considered. For example, the concentration of certain gases may increase health risks in a high radiation environment. This interactive effect needs to be quantified through a model to obtain a target multi-source data analysis algorithm.

[0055] In this embodiment, by constructing the fluctuation hazard analysis item and the gas hazard analysis item of the initial multi-source data analysis algorithm according to the multi-source hazard association information, and setting the respective parameters according to the fluctuation characteristic data and the gas characteristic data, the hazards of the fluctuation source and the gas to the target barracks can be accurately identified and quantified. By integrating these two target hazard analysis items to form a comprehensive multi-source data analysis algorithm, it is possible not only to comprehensively evaluate the independent hazards of the fluctuation source and the gas, but also to consider the interaction effect between the two, thereby improving the prediction accuracy of the comprehensive hazard and the effectiveness of the response strategy. Ultimately, it can provide a more accurate and comprehensive health risk assessment for the target barracks, and provide a reliable basis for formulating effective protective measures and emergency response plans.

[0056] In an exemplary embodiment, Figure 5 As shown, when the multi-source hazard association information is greater than the preset hazard association threshold, the parameters of the initial multi-source data analysis algorithm of the target barracks are set according to the multi-source hazard association information to obtain the target multi-source data analysis algorithm, including steps 502 to 510. Among them: Step 502: constructing initial hazard analysis weighted items and initial hazard analysis association items of an initial multi-source data analysis algorithm based on the multi-source hazard association information.

[0057] Among them, the initial hazard analysis weighted items can be the relative impact weights of various hazard sources on health risks set according to the characteristics of hazard sources such as radiation and gas when constructing a multi-source data analysis algorithm.

[0058] The initial hazard analysis association items may be association items constructed based on the relationship between different hazard sources such as radiation and gas data in multi-source data analysis.

[0059] Specifically, when the multi-source hazard association information is greater than the preset hazard association threshold, the multi-source hazard association information obtained by the association analysis of the fluctuation characteristic data and the gas characteristic data, such as the radiation intensity and gas concentration may have a certain interaction, or in some cases, the concentration change of a certain gas will aggravate the radiation hazard, construct the analysis items of the initial multi-source data analysis algorithm for radiation hazard and gas hazard respectively. The fluctuation hazard analysis item will focus on factors such as the type, intensity, and exposure time of radiation, and consider its potential hazards to human health; the gas hazard analysis item will focus on the type, concentration, exposure duration, etc. of the gas, and analyze its impact on the respiratory system or other physiological systems.

[0060] Step 504, performing multi-dimensional nonlinear transformation on the wave characteristic data and the gas characteristic data respectively to obtain a wave nonlinear matrix and a gas nonlinear matrix.

[0061] Among them, multidimensional nonlinear transformation can be to convert characteristic data such as radiation and gas into a higher dimensional representation through nonlinear mapping methods, thereby revealing the potential nonlinear relationship in the data.

[0062] The fluctuation nonlinear matrix can be a fluctuation characteristic data matrix after multi-dimensional nonlinear transformation, which reflects the potential nonlinear relationship and complex patterns in the radiation data.

[0063] The gas nonlinear matrix may be a gas characteristic data matrix after multi-dimensional nonlinear transformation, which reflects the nonlinear pattern of gas concentration, type and its impact on health.

[0064] Specifically, since the fluctuation characteristic data and gas characteristic data themselves usually have complex nonlinear relationships, simple linear analysis methods may not be able to fully reveal the underlying patterns and interactions. Therefore, multidimensional nonlinear transformation techniques, such as principal component analysis (PCA), support vector machine (SVM) or neural network, are used to map the original fluctuation characteristic data and gas characteristic data to a high-dimensional space, respectively, to extract the nonlinear features in the data and eliminate the complex dependencies that may exist between variables, so that the data can better reflect the implicit interaction effects between different hazard factors, and obtain the fluctuation nonlinear matrix and the gas nonlinear matrix to capture the deep-level features in the data that are not recognized by the linear model.

[0065] Step 506, setting the initial hazard analysis weighted items according to the fluctuation nonlinear matrix and the gas nonlinear matrix to obtain the target hazard analysis weighted items.

[0066] The target hazard analysis weighted item may be a weighted item obtained by optimizing and adjusting the initial hazard analysis weighted item in combination with the fluctuation nonlinear matrix and the gas nonlinear matrix.

[0067] Specifically, since the fluctuation nonlinear matrix and the gas nonlinear matrix contain complex features extracted by nonlinear transformation, they reflect the deep information of nonlinear relationships in radiation and gas hazard data. Therefore, the analysis data obtained by statistical analysis, correlation analysis, etc. of the ongoing data of the fluctuation nonlinear matrix and the gas nonlinear matrix, combined with the multi-source hazard association information, can accurately adjust the initial hazard analysis weighting items to make them more in line with the actual risks. For example, some nonlinear relationships may indicate that the combined effects of radiation and gas may produce more significant health hazards at specific concentrations or doses, so it is necessary to assign higher weights to such high-risk combinations. In addition, the information in the nonlinear matrix can help reveal the complex interaction effects that may exist between radiation and gas data, thereby better guiding the weighting setting. The target hazard analysis weighting items obtained after weighting can more accurately reflect the relative contribution of different radiation and gas characteristics to health risks.

[0068] Step 508, according to the multi-source hazard association information, the multi-source data association matrix of the initial hazard analysis association items is set to obtain the target hazard analysis association items.

[0069] Among them, the multivariate data correlation matrix is ​​a mathematical tool that describes the relationship between multiple variables. It calculates the correlation between multiple data sources (such as radiation, gas and other hazard data) to establish a matrix representation. Each element of the matrix represents the correlation coefficient between two variables, which is usually used to measure the strength and direction of the linear relationship between variables.

[0070] Among them, the target hazard analysis association item can be a data association matrix optimized based on the initial hazard analysis association item combined with the multi-source hazard association matrix. It represents the interaction between different hazard sources (such as radiation and gas) and can more accurately reflect the relationship between these data sources after optimization.

[0071] Specifically, since the role of the initial hazard analysis association items is to represent the interrelationships and interaction effects between different hazard sources (such as radiation and gas), the construction of these association items relies on multi-source hazard association information, which reveals the potential correlation between different data sources such as radiation and gas. Therefore, correlation analysis, synergistic effect analysis or interaction modeling is performed on the multi-source hazard association information, and a multi-source data association matrix is ​​set up to accurately quantify and represent the interaction relationship between these data sources. For example, multi-source hazard association information characterizes that certain gases may aggravate the hazards of radiation at high concentrations, or that different types of radiation respond differently to gas exposure. These factors need to be captured and quantified through an association matrix. By adjusting and optimizing the association matrix, the target hazard analysis association items obtained can better handle the complex interactions of multi-source hazard data.

[0072] Step 510, construct a nonlinear fusion of target hazard analysis weighted items and target hazard analysis associated items to obtain a target multi-source data analysis algorithm.

[0073] Among them, nonlinear fusion can be to combine multiple hazard analysis weighted items and related items through nonlinear methods (such as neural networks, deep learning, etc.) to form a comprehensive multi-source data analysis algorithm.

[0074] Specifically, through nonlinear fusion technology (such as neural network, nonlinear regression, etc.), the complex interactive relationship between weighted terms and related terms is integrated, so that the model can simultaneously handle the nonlinear characteristics of radiation and gas data and the interaction between them. The final target multi-source data analysis algorithm is obtained.

[0075] Among them, the expression of the target multi-source data analysis algorithm is: in, H for health hazard data; is a fusion function used to map the feature data of each part to the health hazard assessment space corresponding to the health hazard data; is the time-varying weighting coefficient of the fluctuation characteristic data; is the time-varying weighting coefficient of gas characteristic data; is the fluctuation nonlinear matrix; is the gas nonlinear matrix; is the fluctuation characteristic data; is the gas characteristic data; time-varying association weight coefficient; is the multi-source data association matrix; It is nonlinear fusion; T is the number of feature dimensions; M and N are the number of rows and columns of the multi-source data association matrix, respectively; S For barracks structure data; Mapping functions for structured data; Influence weights for barrack structures; It is the index variable of the nonlinear transformation channel. It is the associated item for initial hazard analysis; Add weighting items to the initial hazard analysis.

[0076] In this embodiment, by constructing initial hazard analysis weighted items and hazard analysis associated items based on multi-source hazard association information, and performing multidimensional nonlinear transformation on fluctuation characteristic data and gas characteristic data, the nonlinear relationship between complex environmental hazards and biological responses can be effectively captured. The generation and application of these nonlinear matrices make the hazard analysis of the target barracks more accurate, and can carefully consider the complex interactions between fluctuation data and gas. By adjusting the initial weighted items and associated items, the target hazard analysis weighted items and associated items are obtained, thereby providing high-quality data support for subsequent nonlinear fusion. The target multi-source data analysis algorithm finally constructed not only improves the analysis capability of the combined hazards of fluctuations and gases, but also enhances the accuracy of its predictions and protective measures, which helps to formulate more effective health protection strategies and emergency response plans.

[0077] In an exemplary embodiment, Figure 6 As shown, a hazard protection simulation is performed based on the barracks structure data and the health hazard data to obtain the hazard protection data of the target barracks, including steps 602 to 608. Among them: Step 602, performing hazard treatment simulation based on the barracks structure data and the health hazard data to obtain hazard treatment simulation data.

[0078] Among them, hazard treatment simulation can be a process of simulating the impact of hazard sources (such as radiation, gas leakage, etc.) on personnel or organisms in the barracks under different environmental conditions by comprehensively considering the structural data and health hazard data of the barracks.

[0079] Among them, the hazard treatment simulation data can be the output result obtained through the hazard treatment simulation process, which specifically reflects the potential health impact of the hazard source on the personnel in the barracks under different structures and protective measures. These data include information such as the intensity of the hazard source, the range of transmission, and the protective effect.

[0080] Specifically, the barracks' structural data provides the physical characteristics of the building, such as wall materials, door and window designs, etc. These factors will affect the transmission path and intensity of hazards such as radiation and gas leakage. At the same time, the health hazard data reflects the potential impact of different hazard sources (such as radiation, gas, etc.) on human health. By inputting this information into the simulation system, the spread of hazards under different structural conditions and the degree of impact on organisms are simulated, and hazard treatment simulation data is generated to provide a preliminary evaluation of the hazard control effect under different environments.

[0081] Step 604, based on the hazard treatment simulation data, biofeedback monitoring is performed on each simulated organism in the target barracks to obtain biofeedback monitoring simulation data.

[0082] Among them, biofeedback monitoring can be to monitor the physiological responses of simulated organisms (such as simulated people, experimental animals, etc.) in the barracks to evaluate their health status when facing specific hazards. This monitoring usually involves real-time detection of physiological indicators of simulated organisms (such as heart rate, body temperature, blood pressure, respiratory rate, etc.) to help evaluate the specific impact of hazards (such as radiation or toxic gases) on the health of organisms.

[0083] Among them, biofeedback monitoring simulation data can be output data obtained by monitoring the health response of simulated organisms in a hazard treatment simulation environment. These data include physiological changes and health indicators of simulated organisms (such as abnormal reactions, health damage or fluctuations in physiological indicators of organisms).

[0084] Specifically, based on the hazard treatment simulation data, the biological responses of different organisms in the barracks (such as staff, residents, animals, plants, etc.) in the environment are simulated to monitor their physical conditions under different hazard conditions. For example, by simulating the health status of organisms (such as the physiological effects of radiation exposure on organisms) and monitoring the biological health indicators (such as heart rate, respiratory rate, etc.) based on the simulation data, biofeedback monitoring simulation data can be generated to evaluate the effectiveness of current hazard protection measures.

[0085] Step 606, when the biofeedback monitoring simulation data indicates that any simulated organism has physical abnormalities, the simulation parameters of the hazard handling simulation are adjusted.

[0086] The simulation parameters may be parameters used to adjust and set various protection variables in the simulation environment during the hazard handling simulation process.

[0087] Specifically, if the biofeedback monitoring simulation data indicates that the simulated organism has physical abnormalities (such as excessive radiation dose or excessive gas concentration causing abnormal physical indicators of the organism), the hazard handling simulation needs to be adjusted. Specifically, the hazard protection parameters in the simulation can be adjusted based on the health response of the biofeedback monitoring simulation data (such as the occurrence of radiation poisoning, breathing difficulties and other symptoms). These parameters may include increasing the strength of the protective barrier, adjusting the airflow channel, optimizing the operation of the air purification equipment, etc., to reduce the impact of the hazard on the organism. The adjusted simulation parameters will be used in the next round of hazard handling simulation to optimize the protection measures.

[0088] Step 608, using the new simulation parameters, returns to the step of performing hazard handling simulation based on the barracks structure data and the health hazard data to obtain hazard handling simulation data, until all simulated organisms have no physical abnormalities or the simulation parameters trigger the hazard handling constraint rules, and obtain hazard protection data.

[0089] Among them, hazard treatment constraint rules can be set during the hazard treatment simulation process to ensure that the simulation results meet safety standards and actual needs. For example, it may be set that the radiation dose cannot exceed a certain safety threshold, the gas concentration cannot exceed the standard, or the cost and feasibility of the protective measures cannot exceed a certain range.

[0090] Specifically, after adjusting the simulation parameters, the hazard treatment simulation is performed again using the new simulation parameters to evaluate whether the adjusted protective measures effectively reduce the impact of the hazard on the organism. If the health status of the simulated organism returns to normal after adjustment, or reaches the set protection standard (such as radiation dose, gas concentration reaches a safe level), the final hazard protection data is generated. If there are still abnormal reactions, continue to adjust the simulation parameters and simulate until the health status of all simulated organisms meets the requirements, or reaches the hazard treatment constraint rules (such as the maximum allowable range of protective measures), and finally obtain the hazard protection data.

[0091] In this embodiment, by performing hazard treatment simulation based on barracks structure data and health hazard data, and performing biofeedback monitoring on simulated organisms, the effectiveness of hazard treatment measures can be dynamically evaluated and health abnormalities of organisms can be identified in a timely manner. When physical abnormalities are detected in simulated organisms, iterative optimization is performed by adjusting simulation parameters to ensure that the hazard treatment plan can respond to various health threats more accurately. Repeated adjustments are made until the health status of the simulated organism returns to normal or the constraint rules are triggered, and accurate hazard protection data is finally obtained, which can realize real-time optimization of hazard protection measures, ensure the effectiveness of the protection plan in the target barracks under different hazard conditions, and provide decision makers with a more scientific basis, thereby effectively reducing health risks and improving protection effects.

[0092] In an exemplary embodiment, Figure 7 As shown, a hazard treatment simulation is performed according to the barracks structure data and the health hazard data to obtain hazard treatment simulation data, including steps 702 to 710. Among them: Step 702, simulating the hazard treatment air duct of the target barracks according to the barracks structure data and the health hazard data to obtain hazard treatment ventilation data.

[0093] Among them, the hazard handling air duct can be a temporary ventilation path designed or simulated inside the barracks for handling hazards. The path is specifically used to deal with specific hazard sources, such as toxic gas leakage, fire smoke or radiation diffusion.

[0094] Among them, the hazard treatment ventilation data can be the output data after simulating the hazard treatment air duct, reflecting the concentration distribution, flow path and ventilation effect of harmful substances (such as gas, smoke, etc.) in the barracks under the above-mentioned air duct, which also includes the current air duct information.

[0095] Specifically, since the barracks' structural data provides basic information about the ventilation system, such as the vents, duct layout, and wall permeability in the room, and the health hazard data reflects the potential threat to the human body from different hazard sources (such as radiation, gas concentration, etc.), this information can be used to simulate the hazard treatment ducts in the target barracks that currently treat hazardous substances through air discharge, and simulate how different duct combinations affect air flow and changes in the concentration of hazardous substances, so as to evaluate the optimal duct combination of the ventilation system and ultimately obtain hazard treatment ventilation data.

[0096] Step 704, adjusting the health hazard data according to the hazard processing ventilation data to obtain ventilation adjusted hazard data.

[0097] The ventilation adjustment hazard data can be the result of adjustments based on the hazard treatment ventilation data. These adjustments reflect how the ventilation system affects the health hazard data in actual operation, especially the changes in factors such as gas concentration and radiation intensity.

[0098] Specifically, based on the hazard treatment ventilation data, the health hazard data is further adjusted. For example, the hazard treatment ventilation data characterizes that the ventilation effect may lead to a decrease in gas concentration or a decrease in radiation intensity, so the original health hazard data is adjusted based on these data to ensure that the actual effect of the ventilation measures is accurately reflected and the ventilation adjustment hazard data is obtained.

[0099] Step 706, when the ventilation adjustment hazard data is not a null value, the spray data of the target barracks is simulated according to the barracks structure data and the ventilation adjustment hazard data to obtain the hazard treatment spray data.

[0100] The sprinkler data can be the data obtained by simulating the effect of the sprinkler system on the treatment of hazardous sources (such as fire or toxic gas leakage). The sprinkler system releases water mist or liquid to reduce the concentration or temperature of harmful substances in the air, thereby reducing the threat of harmful sources to personnel. Sprinkler data usually includes information such as spraying range, spraying volume, and spraying pressure, which is used to evaluate the sprinkler system's ability to control harmful substances under specific circumstances.

[0101] Specifically, when the ventilation adjustment hazard data is not null, it indicates that the target barracks still have dangerous factors to organisms. Since the concentration of harmful substances is reduced or the overheated area is cooled by releasing water mist or liquid, the simulation is performed based on the ventilation adjustment hazard data and the structural data of the barracks (such as the location of the sprinkler head, the spraying range, the spraying pressure, etc.). The goal is to evaluate the protective effect of the sprinkler system under specific conditions and determine whether it can effectively reduce the intensity of the hazard source or reduce the spread of harmful substances. For example, the sprinkler system may further reduce the health risks to personnel by reducing the concentration of harmful gases, inhibiting the spread of fire, or absorbing toxic substances. Finally, the simulation obtains the hazard treatment sprinkler data.

[0102] Step 708, adjusting the ventilation adjustment hazard data according to the hazard processing spray data to obtain the spray adjustment hazard data.

[0103] Among them, the sprinkler adjustment hazard data can be the result of adjustment based on the sprinkler data, reflecting the actual impact of the sprinkler system on the health hazard data when dealing with the hazard source. Through the action of the sprinkler system, the gas concentration, radiation intensity, etc. may be further reduced. The adjusted hazard data demonstrates the effectiveness of the sprinkler system in slowing down the spread of harmful substances and reducing health hazards.

[0104] Specifically, based on the hazard treatment spray data, the ventilation adjustment hazard data is adjusted again. For example, the introduction of the spray system may further reduce the concentration or impact of certain hazard sources, thereby further improving the ventilation adjustment hazard data. Therefore, the impact of the hazard source is re-evaluated in combination with the spray data to obtain updated hazard data, namely the spray adjustment hazard data, to ensure that the effect of the spray measures is accurately reflected in the adjusted health hazard data and optimize the comprehensive protection effect.

[0105] Step 710, when the sprinkler adjustment hazard data is not a null value, construct the evacuation channel information of each simulated creature as hazard processing simulation data according to the barracks structure data and the distribution of the sprinkler adjustment hazard data.

[0106] The evacuation channel information can simulate how organisms (such as people) can safely evacuate through the escape path inside the barracks when a hazard occurs. This information is constructed based on the structural data of the barracks (such as the location of doors and windows, corridor layout, number of exits, etc.) and the distribution of hazard sources (such as the distribution of toxic gases or fire hot spots).

[0107] Specifically, when the sprinkler adjustment hazard data is not null, it indicates that the target barracks still have dangerous factors to organisms. Analysis of barracks structural data, such as the location of doors and windows, the layout of corridors, the number and location of exits, and the distribution of sprinkler adjustment hazard data will lead to a decrease in gas concentration or heat in some areas, and chemical reactions in some areas will lead to unpredictable situations. Therefore, it is necessary to consider the impact of these changes on the biological evacuation route. For example, the effect of the sprinkler system may make some exit areas safer, while other areas may become dangerous due to slippery or high water mist concentration. Based on these safe and unsafe factors, the system simulates how organisms choose paths to avoid hazard sources according to current environmental conditions. For example, if a certain exit is unsafe due to high gas concentration, the simulated organism will choose other safer channels. By considering different biological reactions, risk avoidance decisions and environmental factors, the optimal evacuation route for each simulated organism is comprehensively constructed as the evacuation channel information to ensure that it can avoid hazards in the shortest time and evacuate safely to a safe area. This process is achieved through simulation algorithms to accurately evaluate the evacuation efficiency and safety when hazards occur.

[0108] In this embodiment, by simulating the hazard treatment air duct according to the barracks structure data and health hazard data, the ventilation system can be effectively optimized, the accumulation of harmful substances can be reduced, and health hazards can be reduced. After adjusting the ventilation system, the spray simulation is performed according to the updated hazard data to further enhance the hazard treatment effect and reduce the impact of the hazard source. Each adjusted data can be optimized without being null, so as to accurately adjust the hazard treatment plan and ensure the effectiveness of protective measures. In addition, by analyzing the hazard data after the spray adjustment and constructing the evacuation channel information, a more reasonable risk avoidance path can be provided for the simulated organisms, ensuring timely evacuation in an emergency and minimizing the harm to personnel. Ultimately, it can provide comprehensive and effective hazard protection data, providing a scientific basis for actual emergency response and health risk management.

[0109] In an exemplary embodiment, Figure 8 As shown, according to the hazard treatment simulation data, biofeedback monitoring is performed on each simulated organism in the target barracks to obtain biofeedback monitoring simulation data, including steps 802 to 810. Among them: Step 802, based on the hazard treatment simulation data, monitor the biofeedback data of each simulated organism in the target barracks to obtain the biomonitoring characteristic data of each simulated organism.

[0110] Among them, biofeedback data can be data obtained by simulating and monitoring the physiological response of organisms in specific environments or conditions. It usually includes indicators such as heart rate, body temperature, blood oxygen level, respiratory rate, etc., reflecting the immediate physiological response of organisms to external hazards or environmental changes.

[0111] The biological monitoring characteristic data may be physiological characteristic data of an organism within a certain period of time obtained by extracting characteristics from biological feedback data.

[0112] Specifically, based on the hazard treatment simulation data of the target barracks, simulated biofeedback monitoring is performed on each simulated organism (such as personnel or other organisms) in the target barracks, and simulated biofeedback data such as heart rate, body temperature, respiratory rate, blood oxygen saturation, etc. are collected in real time to reflect their physiological responses in the current hazard environment. The biofeedback data is further feature extracted to obtain biological monitoring feature data for each simulated organism.

[0113] Step 804, for any simulated organism, a physiological state development model of the simulated organism is constructed based on the biological monitoring characteristic data and the hazard treatment simulation data.

[0114] Among them, the physiological state development model can be a model used to describe the changes in the physiological state of an organism under a specific hazardous environment. The model is based on biological principles, combines biological monitoring data and environmental hazard data, and uses mathematical equations and algorithms to predict the evolution of the physiological state of an organism over time when exposed to different hazard sources (such as toxic gases, radiation, etc.).

[0115] Specifically, for any simulated organism, the collected biological monitoring data (such as heart rate, body temperature, respiratory rate, etc.) and environmental hazard data (such as gas concentration, temperature, etc.) are used as input variables to construct a multidimensional data set. Then, regression analysis methods (such as linear regression, nonlinear regression, etc.) are used to model the relationship between the physiological response of the organism and the environmental hazard, and mathematical equations are established to describe the response pattern of the organism under different hazard conditions. Alternatively, machine learning methods (such as random forests, support vector machines, neural networks, etc.) are used to train the model in combination with a large amount of historical data so that it can self-learn the physiological changes of organisms under different environments. For more complex physiological changes, physiological models based on biological principles (such as dynamic models based on physiological systems) can be used to model the intrinsic physiological processes of the organism and simulate the adaptation and response of the organism under different hazard conditions. Any of the above methods can combine biological monitoring data and hazard treatment simulation data to form a physiological state development model that can predict changes in the physiological state of the organism.

[0116] Step 806, evaluating the current physical condition of the simulated organism according to the physiological state development model to obtain the current physical state data of the organism.

[0117] The current physical state data of the organism can be the health data of the organism at a specific moment obtained through real-time simulation monitoring. These data include physiological parameters such as heart rate, body temperature, blood oxygen level, respiratory rate, etc., which reflect the immediate physiological response of the organism in the current hazardous environment.

[0118] Specifically, the physiological state development model is used to combine the current biological monitoring feature data with the hazard treatment simulation data to calculate the current health status of the simulated organism, such as whether there are physiological abnormalities such as excessive fatigue, toxic poisoning, and heat stress. The model will quantify the physiological responses caused by different hazard sources and give evaluation results, such as whether the organism is within the normal health range, whether emergency measures need to be taken or the protection strategy needs to be adjusted, and obtain the current physical state data of the organism.

[0119] Step 808, evaluating the future physical condition of the simulated organism according to the physiological state development model to obtain the organism's future physical change data.

[0120] Among them, the data of future physical changes of organisms can be predicted by the physiological state development model, describing the health change trend of organisms in the future. These data are calculated based on the current physiological condition, environmental hazards and physiological adaptability, and predict the possible physiological changes of organisms under continuous exposure to specific hazardous environments, such as health deterioration, recovery or adaptation.

[0121] Specifically, the physiological state development model will combine the current biological monitoring characteristic data with the hazard treatment simulation data to analyze and predict the possible explicit physical changes in the future. For example, the physiological state development model will consider the cumulative effects of continuous exposure to harmful gases or radiation, or the stress response of the organism to long-term exposure to high temperature, high humidity and other environmental conditions. Then, the newly obtained explicit physical changes (such as changes in gas concentration or temperature fluctuations in the next few hours) are input, and the physiological state development model will simulate the evolution of the physiological state of the simulated organism, assess its possible health risks in the future, such as toxic accumulation, physical exhaustion, and impaired immune system, and obtain the biological future physical change data.

[0122] Step 810, integrating the current body state data of the organism and the future body change data of the organism to obtain biofeedback monitoring simulation data.

[0123] Specifically, the current body state data reflects the immediate health status of the organism, while the future body change data of the organism provides an early warning of future health risks, by combining these two parts of data according to certain weights. In practice, the current body state data is usually considered to have a higher weight because it directly reflects the immediate health status of the organism in a hazardous environment and can provide the most timely and accurate risk information. The future body change data of the organism considers the long-term impact. Although it is important, it usually has a certain degree of uncertainty due to its model-based prediction, so the weight is relatively low. The specific value of the weight can be used to evaluate the correlation between the current body state data and the future body change data of the organism and the health outcomes through regression analysis, so as to assign a suitable weight to each type of data. Finally, the overall health risk of the organism under the current environmental hazard is calculated, and a complete evaluation result is generated to obtain the biofeedback monitoring simulation data.

[0124] In this embodiment, by monitoring the biofeedback data of the simulated organisms and combining them with the hazard treatment simulation data, the biological monitoring characteristic data of each simulated organism can be accurately obtained, thereby providing a scientific basis for its physiological state. A physiological state development model is constructed based on these data, which can evaluate the current physical condition of the simulated organism in real time and predict possible physiological changes in the future. By integrating the current physical state and future physical change data, the health status of the organism in a specific hazard environment can be fully reflected, providing a more accurate reference for health risk assessment. Ultimately, it provides a practical basis for formulating effective protective measures and emergency response strategies, helps to improve the health protection of simulated organisms in the target barracks, and optimizes hazard response strategies.

[0125] Based on the same inventive concept, the embodiment of the present application also provides a barracks health monitoring system based on multi-parameter sensing for implementing the above-mentioned barracks health monitoring method based on multi-parameter sensing, the system including a computer device and a data monitoring terminal; Fig. 9 As shown, the computer device includes: a barracks data acquisition module 902, a hazard data extraction module 904, a hazard data analysis module 906, a barracks protection simulation module 908 and a barracks health analysis module 910. Further, the computer device can be a server, and its internal structure diagram can be as follows: Fig.10 The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface; the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A barracks health monitoring method based on multi-parameter sensing, characterized in that: The method comprises: Obtain the target barracks’ fluctuation monitoring data, gas monitoring data, and barracks structure data; Performing feature extraction on the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data; Performing object hazard analysis according to the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks; Performing a hazard protection simulation according to the barracks structure data and the health hazard data to obtain hazard protection data of the target barracks; The health hazard data is adjusted according to the hazard protection data to obtain actual health data of the target barracks.

2. The method according to claim 1, characterized in that The object hazard analysis is performed according to the fluctuation characteristic data and the gas characteristic data to obtain the health hazard data of the target barracks, including: Analyzing the correlation between the fluctuation characteristic data and the gas characteristic data to obtain multi-source hazard correlation information; According to the multi-source hazard association information, parameters of the initial multi-source data analysis algorithm of the target barracks are set to obtain a target multi-source data analysis algorithm; The fluctuation characteristic data and the gas characteristic data are input into the target multi-source data analysis algorithm to obtain the health hazard data.

3. The method according to claim 2, characterized in that When the multi-source hazard association information is less than a preset hazard association threshold, setting parameters of the initial multi-source data analysis algorithm of the target barracks according to the multi-source hazard association information to obtain a target multi-source data analysis algorithm includes: Constructing an initial fluctuation hazard analysis item and an initial gas hazard analysis item of the initial multi-source data analysis algorithm according to the multi-source hazard association information; According to the fluctuation characteristic data, setting the parameters of the initial fluctuation hazard analysis item to obtain the target fluctuation hazard analysis item; According to the gas characteristic data, setting the parameters of the initial gas hazard analysis item to obtain the target gas hazard analysis item; The target fluctuation hazard analysis item and the target gas hazard analysis item are integrated to obtain the target multi-source data analysis algorithm.

4. The method according to claim 2, characterized in that: When the multi-source hazard association information is greater than a preset hazard association threshold, setting parameters of the initial multi-source data analysis algorithm of the target barracks according to the multi-source hazard association information to obtain a target multi-source data analysis algorithm includes: Constructing initial hazard analysis weighted items and initial hazard analysis association items of the initial multi-source data analysis algorithm according to the multi-source hazard association information; Performing multi-dimensional nonlinear transformation on the fluctuation characteristic data and the gas characteristic data respectively to obtain a fluctuation nonlinear matrix and a gas nonlinear matrix; The initial hazard analysis weighted items are set according to the fluctuation nonlinear matrix and the gas nonlinear matrix to obtain target hazard analysis weighted items; According to the multi-source hazard association information, a multi-source data association matrix of the initial hazard analysis association items is set to obtain target hazard analysis association items; A nonlinear fusion of the target hazard analysis weighted items and the target hazard analysis associated items is constructed to obtain the target multi-source data analysis algorithm.

5. The method according to claim 4, characterized in that The expression of the target multi-source data analysis algorithm is: in, H for health hazard data; is the fusion function; is the time-varying weighting coefficient of the fluctuation characteristic data; is the time-varying weighting coefficient of gas characteristic data; is the fluctuation nonlinear matrix; is the gas nonlinear matrix; is the fluctuation characteristic data; is the gas characteristic data; time-varying association weight coefficient; is the multi-source data association matrix; It is nonlinear fusion; T is the number of feature dimensions; M and N are the number of rows and columns of the multi-source data association matrix, respectively; S For barracks structure data; Mapping functions for structured data; Influence weights for barrack structures; It is the index variable of the nonlinear transformation channel.

6. The method according to claim 1, characterized in that The performing of hazard protection simulation according to the barracks structure data and the health hazard data to obtain the hazard protection data of the target barracks includes: Perform hazard treatment simulation according to the barracks structure data and the health hazard data to obtain hazard treatment simulation data; According to the hazard treatment simulation data, biofeedback monitoring is performed on each simulated organism in the target barracks to obtain biofeedback monitoring simulation data; When the biofeedback monitoring simulation data indicates that any of the simulated organisms has physical abnormalities, adjusting simulation parameters of the hazard treatment simulation; Using the new simulation parameters, return to the step of performing the hazard handling simulation based on the barracks structure data and the health hazard data to obtain the hazard handling simulation data, until each of the simulated organisms has no physical abnormalities or the simulation parameters trigger the hazard handling constraint rules, and obtain the hazard protection data.

7. The method according to claim 6, characterized in that The performing of hazard treatment simulation according to the barracks structure data and the health hazard data to obtain hazard treatment simulation data includes: Simulating the hazard treatment air duct of the target barracks according to the barracks structure data and the health hazard data to obtain hazard treatment ventilation data; adjusting the health hazard data according to the hazard processing ventilation data to obtain ventilation adjusted hazard data; When the ventilation adjustment hazard data is not a null value, the spray data of the target barracks is simulated according to the barracks structure data and the ventilation adjustment hazard data to obtain hazard treatment spray data; Adjust the ventilation adjustment hazard data according to the hazard treatment spray data to obtain spray adjustment hazard data; When the sprinkler adjustment hazard data is not a null value, the evacuation channel information of each simulated creature is constructed as the hazard handling simulation data according to the barracks structure data and the distribution of the sprinkler adjustment hazard data.

8. The method according to claim 6, characterized in that The biofeedback monitoring is performed on each simulated organism in the target barracks according to the hazard treatment simulation data to obtain the biofeedback monitoring simulation data, including: According to the hazard treatment simulation data, the biofeedback data of each simulated organism in the target barracks is monitored to obtain the biomonitoring characteristic data of each simulated organism; For any of the simulated organisms, constructing a physiological state development model of the simulated organism based on the biological monitoring characteristic data and the hazard treatment simulation data; Evaluate the current physical condition of the simulated organism according to the physiological state development model to obtain current physical state data of the organism; and, evaluating the future physical condition of the simulated organism according to the physiological state development model to obtain future physical change data of the organism; The biological feedback monitoring simulation data is obtained by integrating the current body state data of the biological and the future body change data of the biological.

9. A barracks health monitoring system based on multi-parameter sensing, characterized in that: The system comprises: a computer device and a data monitoring terminal; the computer device comprises a barracks data acquisition module, a hazard data extraction module, a hazard data analysis module, a barracks protection simulation module and a barracks health analysis module; The barracks data acquisition module is used to acquire the fluctuation monitoring data, gas monitoring data and barracks structure data of the target barracks; The hazard data extraction module is used to extract features from the fluctuation monitoring data and the gas monitoring data to obtain fluctuation feature data and gas feature data; The hazard data analysis module is used to perform object hazard analysis based on the fluctuation characteristic data and the gas characteristic data to obtain health hazard data of the target barracks; The barracks protection simulation module is used to perform hazard protection simulation according to the barracks structure data and the health hazard data to obtain the hazard protection data of the target barracks; The barracks health analysis module is used to adjust the health hazard data according to the hazard protection data to obtain actual health data of the target barracks.

10. The system according to claim 9, characterized in that The computer device further comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods of claims 1 to 8 when executing the computer program.

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