Dynamic comprehensive quantitative evaluation method and system for human factor habitability in long-term isolation environment

Abstract

The invention discloses a dynamic comprehensive quantitative evaluation method and system for human factor habitability in a long-term isolation environment. The method comprises the following steps: acquiring long-term isolation environment parameters; based on the long-term isolation environment parameters in combination with a human factor habitability evaluation model, calculating an evaluation value of the influence of the influence factors on the human factor habitability evaluation model at the sample time; based on the evaluation value of the influence of the influence factor on the human factor habitability evaluation model at the current time, combining the weight coefficient to obtain a total evaluation value; and evaluating the habitability of the long-term isolation environment based on the total evaluation value. According to the long-term isolation environment human factor habitability dynamic comprehensive quantitative evaluation method and system, the design result can construct the habitability dynamic comprehensive quantitative evaluation method and system for human factor habitability multiple influence factors and evaluation indexes, and the method and system are used for human factor habitability evaluation in the long-term isolation environment. Through multi-type data acquisition, the human factor habitability of the environment is evaluated in the time dimension, so that a habitability evaluation report of the environment which dynamically changes along with time lapse is obtained.

Description

Technical Field

[0001] The present invention relates to the field of ergonomics, and in particular to a dynamic comprehensive quantitative evaluation method and system for human habitability in a long-term isolation environment. Background Art

[0002] Human habitability refers to the degree to which a certain environment, equipment, task arrangements, and other factors can ensure that people in the environment can live in good health and work efficiently for a long time. Continuously improving the human habitability of long-term isolation environments is the key to ensuring people's adaptation and long-term living and working in the human-machine-environment interactive system. It plays an important role in supporting individual life and work needs, improving the availability, livability, and flexibility of the environment, and maximizing the efficiency of humans and machines. The present invention proposes a dynamic, comprehensive, and multi-factor quantitative method for evaluating the habitability of long-term isolation environments, which effectively responds to the challenges of small sample data. It plays a strategic supporting role in major national needs such as space exploration and deep-sea operations, and at the same time provides new research tools and theoretical developments for related disciplines such as ergonomics and environmental psychology.

[0003] The assessment of human habitability requires comprehensive consideration of physical environmental factors, human physiological and psychological factors, and human-machine-environment interaction factors. Existing habitability assessment schemes have the following shortcomings:

[0004] The application number is 201911167355.9, and the name is a method for designing and evaluating the habitability of a manned spacecraft in a limited and confined space. It involves the field of manned spacecraft, and in particular, a method for designing and evaluating the habitability of a manned spacecraft in a limited and confined space. By analyzing the design factors of the habitability of the spacecraft, a design scheme for the habitability system of the spacecraft is constructed according to the design factors, and the scheme is evaluated to obtain the best scheme. It focuses on using the fuzzy-grey correlation hierarchical analysis method to evaluate the design scheme of the interior decoration design factors (environmental factors, spatial factors, and human-machine facility factors) of manned spacecraft (space stations, space laboratories) for medium and long term stay.

[0005] This scheme uses the fuzzy-grey correlation hierarchical analysis method to propose a method for designing and evaluating the habitability of manned spacecraft in a limited and confined space. However, this method requires experts or decision makers to provide preference matrices or weights and relies on expert experience. These subjective factors may lead to uncertainty and deviation in the results. In addition, due to the complexity of the method and the multiple factors involved, the results are not intuitive enough and the decision results are difficult to interpret.

[0006] The invention patent application with application number 202210454651.2 and titled "A method for designing thermal comfort of submarine ventilation and air-conditioning system" relates to the technical field of ship ventilation and air-conditioning system design, and in particular to a method for designing thermal comfort of submarine ventilation and air-conditioning system. A method for designing thermal comfort of ventilation and air-conditioning system suitable for enclosed cabin space is provided, which can improve the thermal comfort of personnel living environment and the habitability level of each cabin in the submarine on the basis of energy saving and improving energy utilization.

[0007] This scheme provides a thermal comfort design method for ventilation and air conditioning systems in enclosed cabin spaces, focusing only on the thermal comfort of physical environmental factors. Summary of the invention

[0008] In order to solve the problems that the existing technology lacks objective evaluation index data to quantify the habitability of the environment, only focuses on the physical environment, ignores the influence of time effect on people's perception of the habitability of the environment, and it is difficult to obtain the evaluation results using traditional statistical analysis methods, the present invention proposes a dynamic comprehensive quantitative evaluation method for the human habitability of a long-term isolation environment, including:

[0009] Obtain long-term isolation environment parameters;

[0010] Based on the long-term isolation environment parameters and a pre-built human habitability assessment model, calculating the assessment value of the impact of the influencing factors on the human habitability assessment model at the current sample time;

[0011] Obtaining a total evaluation value based on the evaluation value of the impact of the influencing factors on the human habitability evaluation model at the current time combined with a preset weight coefficient;

[0012] Assessing the human habitability of the long-term isolation environment based on the total assessment value;

[0013] The human habitability assessment model includes sub-models of various influencing factors affecting human habitability.

[0014] Furthermore, the long-term isolation environment parameters include: physical environment parameters, human psychological state and human physiological indicators.

[0015] Furthermore, the physical environment parameters include: space size, area layout, lighting, noise, ground support, dining facilities, adequacy of supplies, and error-proof design;

[0016] The psychological state of the person includes: stress level, team cohesion, social frequency, task clarity, resource adequacy, technical resources, technology acceptance, time management, task periodicity, and rest periodicity;

[0017] The physiological indicators of the person include: personal space, leisure activities, risk occurrence frequency, emergency response time, allocation efficiency, user interface usability, and interaction frequency.

[0018] Furthermore, the human habitability assessment model includes a physical environment sub-model, a psychosocial sub-model, a work support sub-model, a life support sub-model, a human safety sub-model, a material management sub-model, a human-technical system interaction sub-model, and a time perception sub-model.

[0019] Furthermore, the physical environment sub-model is shown in the following formula:

[0020] X1(t)=S(t)·L(t)·I(t)·(10-N(t))

[0021] Where X1(t) is the evaluation value of the impact of physical environmental factors on the human habitability assessment model at the current sample time t, S(t) is the space size, L(t) is the regional layout, I(t) is the lighting, and N(t) is the noise.

[0022] Furthermore, the psychosocial sub-model is shown as follows:

[0023]

[0024] Where X2(t) is the evaluation value of the impact of psychosocial factors on the human habitability assessment model at the current sample time t, P(t) is the stress level, C(t) is the team cohesion, F(t) is the social frequency, α is the weight of the stress level, β is the weight of the team cohesion, and γ is the weight of the social frequency.

[0025] Furthermore, the work support sub-model is shown in the following formula:

[0026]

[0027] Where X3(t) is the estimated value of the impact of work support factors on the human habitability assessment model at the current sample time t, and C task (t) is the task clarity, R resource (t) is the resource adequacy, R tech (t) is the technical resource, Gground(t) is the ground support, max(C task ,R resource ,R tech ,G ground ) is the maximum value of mission clarity, resource adequacy, technical resources, and ground support within the sample time.

[0028] Furthermore, the life support sub-model is shown as follows:

[0029]

[0030] Where X4(t) is the estimated value of the impact of life support factors on the human habitability assessment model at the current sample time t, D(t) is the dining facility, S space (t) is personal space, and A(t) is leisure activities.

[0031] Furthermore, the human safety sub-model is shown as follows:

[0032]

[0033] Where X5(t) is the estimated value of the impact of human safety factors on the human habitability assessment model at the current sample time t, R freq (t) is the frequency of risk occurrence, R time (t) is the emergency response time, E design (t) To prevent mistakes, R max is the maximum probability of risk occurrence, T max The maximum allowed time for emergency response.

[0034] Furthermore, the material management sub-model is shown as follows:

[0035] X6(t)=M supply (t)·M efficiency (t)

[0036] Where X6(t) is the estimated value of the impact of material management factors on the human habitability assessment model at the current sample time t, M supply (t) is the material sufficiency, M efficiency (t) is the allocation efficiency.

[0037] Furthermore, the human-technical system interaction sub-model is shown in the following formula:

[0038]

[0039] Where X7(t) is the estimated value of the impact of the interaction factors between people and technology systems on the human habitability assessment model at the current sample time t, U(t) is the usability of the user interface, and A tech (t) is the technology acceptance, F int (t) is the interaction frequency.

[0040] Furthermore, the time perception sub-model is shown in the following formula:

[0041]

[0042] Where X8(t) is the evaluation value of the impact of time perception factors on the human habitability assessment model at the current sample time t, T is the period, and C task(t) is the task periodicity, C rest (t) is the rest periodicity, M time (t) is time management.

[0043] Furthermore, the total evaluation value is calculated as follows:

[0044]

[0045] In the formula, X i (t) is X i The estimated value of the factor’s impact on the human habitability assessment model at the current sample time t, α i is factor X i The weight coefficient, β ij Factor X i and X j The interaction weight coefficient, f ij is a function that describes the factor X i and factor X j Interaction affects the total evaluation value H(t), X j (t) is X j The independent impact assessment value of the factor on the human habitability assessment model at the current sample time t.

[0046] Furthermore, the human habitability of the isolation environment is evaluated based on the total evaluation value, including judging whether the total evaluation value of the current time node is greater than the total evaluation value of the previous time node based on the total evaluation value corresponding to each time node set within a preset sample time. If so, it is determined that the human habitability of the long-term isolation environment at the current time node is strong; otherwise, it is determined that the human habitability of the isolation environment at the current time node is weak.

[0047] Furthermore, after evaluating the habitability of the isolation environment based on the total evaluation value, the method further includes:

[0048] Based on the assessment results, recommendations are made on the design of habitability for isolated environments.

[0049] The present invention provides a dynamic comprehensive quantitative evaluation system for human habitability in a long-term isolation environment, the system comprising:

[0050] Data layer: used to collect and monitor long-term isolation environment parameters.

[0051] Model layer: used to calculate the evaluation values ​​of various influencing factors of human habitability based on long-term isolation environment parameters combined with a pre-built human habitability assessment model, and combine the pre-set weight coefficients to obtain the total evaluation value.

[0052] Application layer: used to evaluate the habitability of the isolation environment based on the total evaluation value;

[0053] The human habitability assessment model includes sub-models of various influencing factors affecting human habitability.

[0054] Furthermore, the data layer includes:

[0055] Environmental monitoring module: collects physical environment data, including space size, area layout, lighting, noise, ground support, dining facilities, material adequacy, and error-proofing design.

[0056] Psychological assessment module: Assess the psychological state of people in the environment, including stress level, team cohesion, social frequency, task clarity, resource adequacy, technical resources, technology acceptance, time management, task periodicity, and rest periodicity.

[0057] Physiological data monitoring: monitors physiological indicators of people in the environment, including personal space, leisure activities, frequency of risk occurrence, emergency response time, allocation efficiency, user interface usability, and interaction frequency.

[0058] Furthermore, the human habitability assessment model in the model layer includes: physical environment sub-model, psychosocial sub-model, work support sub-model, life support sub-model, human safety sub-model, material management sub-model, human and technical system interaction sub-model, and time perception sub-model.

[0059] Furthermore, the psychosocial sub-model is shown as follows:

[0060]

[0061] Where X2(t) is the evaluation value of the impact of psychosocial factors on the human habitability assessment model at the current sample time t, P(t) is the stress level, C(t) is the team cohesion, F(t) is the social frequency, α is the weight of the stress level, β is the weight of the team cohesion, and γ is the weight of the social frequency.

[0062] Furthermore, the work support sub-model is shown in the following formula:

[0063]

[0064] Where X3(t) is the estimated value of the impact of work support factors on the human habitability assessment model at the current sample time t, and C task (t) is the task clarity, R resource (t) is the resource adequacy, R tech (t) is technical resources, G ground (t) is the ground support, max(C task ,R resource ,R tech ,Gground ) is the maximum value of mission clarity, resource adequacy, technical resources, and ground support within the sample time.

[0065] Furthermore, the human safety sub-model is shown as follows:

[0066]

[0067] Where X5(t) is the estimated value of the impact of human safety factors on the human habitability assessment model at the current sample time t, R freq (t) is the frequency of risk occurrence, R time (t) is the emergency response time, E design (t) To prevent mistakes, R max is the maximum probability of risk occurrence, T max The maximum allowed time for emergency response.

[0068] Furthermore, the human-technical system interaction sub-model is shown in the following formula:

[0069]

[0070] Where X7(t) is the estimated value of the impact of the interaction factors between people and technology systems on the human habitability assessment model at the current sample time t, U(t) is the usability of the user interface, and A tech (t) is the technology acceptance, F int (t) is the interaction frequency.

[0071] Furthermore, the time perception sub-model is shown in the following formula:

[0072]

[0073] Where X8(t) is the evaluation value of the impact of time perception factors on the human habitability assessment model at the current sample time t, T is the period, and C task (t) is the task periodicity, C rest (t) is the rest periodicity, M time (t) is time management.

[0074] Furthermore, the total evaluation value is calculated as follows:

[0075]

[0076] In the formula, X i (t) is X i The estimated value of the factor’s impact on the human habitability assessment model at the current sample time t, α i is factor X i The weight coefficient, β ij Factor Xi and X j The interaction weight coefficient, f ij is a function that describes the factor X i and factor X j Interaction affects the total evaluation value H(t), X j (t) is X j The independent impact assessment value of the factor on the human habitability assessment model at the current sample time t.

[0077] Comprehensive assessment engine: integrates all data and calculates habitability scores;

[0078] Interaction Assessment Model: Evaluate the impact of interactions between different factors on the habitability of people in isolated environments;

[0079] The model layer also includes: Time perception model: simulates people's perception of the passage of time in an isolated environment and evaluates its impact on work and rest cycles.

[0080] The application layer includes:

[0081] Recommendation generation module: based on the assessment results, proposes recommendations to improve habitability;

[0082] User Interface: Provides an intuitive interface for viewing assessment results and recommendations.

[0083] Report Generator: Generates detailed assessment reports for further analysis and documentation.

[0084] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0085] The memory is used to store one or more programs;

[0086] When the one or more programs are executed by the at least one processor, a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment as described above is implemented.

[0087] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, it implements a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment as described above.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] The present invention discloses a dynamic comprehensive quantitative evaluation method and system for human habitability of a long-term isolation environment, comprising: obtaining long-term isolation environment parameters; calculating the evaluation value of the influence of influencing factors on the human habitability evaluation model at the current sample time based on the long-term isolation environment parameters combined with a pre-constructed human habitability evaluation model; obtaining a total evaluation value based on the evaluation value of the influence of the influencing factors on the human habitability evaluation model at the current time combined with a pre-set weight coefficient; evaluating the habitability of the long-term isolation environment based on the total evaluation value; wherein the human habitability evaluation model includes sub-models of various influencing factors that affect human habitability. According to the dynamic comprehensive quantitative evaluation method and system for human habitability of a long-term isolation environment of the present invention, the present invention cites long-term isolation parameters for evaluation, which solves the problem that the prior art lacks objective evaluation index data to quantify environmental habitability and only focuses on the impact of the physical environment on human habitability; the present invention also designs a human habitability evaluation model, which combines long-term isolation parameters to calculate the evaluation value of the impact of influencing factors on the human habitability evaluation model at the current sample time, which solves the problem that the prior art ignores the impact of time effects on people's perception of environmental habitability, and it is difficult to obtain data using traditional statistical analysis methods to derive evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 This is a flow chart of the dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment of the present invention;

[0091] Figure 2 A diagram of the steps for evaluating human habitability of the present invention;

[0092] Figure 3 This is a diagram of the architecture of the human habitability assessment system in a long-term isolation environment of the present invention;

[0093] Figure 4 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0094] The present invention aims to propose a quantitative evaluation method for human habitability in long-term isolation environments such as manned spacecraft, deep-sea research platforms, polar research stations, isolation experimental facilities, and long-term medical isolation, so as to get rid of the dependence of environmental habitability evaluation on qualitative methods. The determination of this dynamic comprehensive quantitative evaluation method and system for human habitability is to break the limitations of subjective evaluation of environmental habitability, make up for the lack of attention to the dynamic changes of environmental habitability over time and the impact of the physiological and psychological state of people in the interactive system, and realize the dynamic changes, quantitative evaluation, and application of design suggestions for the optimization process of environmental human habitability.

[0095] The present invention aims at multiple factors affecting human habitability and evaluation indicators, constructs a dynamic comprehensive quantitative evaluation method and system for habitability, which is used for human habitability evaluation in long-term isolation environment. Through multi-type data collection, the human habitability of the environment is evaluated in the time dimension, so as to obtain a habitability evaluation report of the environment that changes dynamically over time, and can make human habitability optimization suggestions for the human-machine-environment system.

[0096] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0097] Embodiment 1:

[0098] The present invention proposes a dynamic comprehensive quantitative evaluation method for the human habitability of a long-term isolation environment. Figure 1 As shown, the specific implementation method includes:

[0099] S1 obtains all indicator data involved in the evaluation;

[0100] S2: based on the long-term isolation environment parameters and the pre-built human habitability assessment model, calculating the assessment value of the impact of the influencing factors on the human habitability assessment model at the current sample time;

[0101] S3 obtains a total evaluation value based on the evaluation value of the impact of the influencing factors on the human habitability evaluation model at the current time combined with a preset weight coefficient;

[0102] S4 assessing the habitability of the long-term isolation environment based on the total assessment value;

[0103] S5 evaluates the habitability of the isolation environment based on the total evaluation value and proposes suggestions for the design of the habitability of the isolation environment.

[0104] The human habitability assessment model includes sub-models of various influencing factors affecting human habitability.

[0105] In order to better describe the present invention, the following specific embodiments are combined with Figure 2 The present invention is further introduced.

[0106] Evaluation of a long-term isolation environment H(t) at t=(1,30,60,90,120,160):

[0107] S1 obtains all indicator data involved in the evaluation, including:

[0108] Obtain physical environment data: including space size, area layout, lighting, noise, ground support, dining facilities, material adequacy, and error-proofing design.

[0109] Obtain people's psychological state: including stress level, team cohesion, social frequency, task clarity, resource adequacy, technical resources, technology acceptance, time management, task periodicity, and rest periodicity.

[0110] Obtain human physiological indicators: including personal space, leisure activities, risk frequency, emergency response time, allocation efficiency, user interface usability, and interaction frequency.

[0111] Before step S2, the human habitability assessment model involved in the present invention is introduced:

[0112] The pre-built human habitability assessment model includes 8 sub-models: physical environment, psychosocial, work support, life support, human safety, material management, human-technical system interaction, and time perception.

[0113] 1. The calculation formula of the physical environment sub-model is as follows:

[0114] X1(t)=S(t)·L(t)·I(t)·(10-N(t))

[0115] Where X1(t) is the evaluation value of the impact of physical environmental factors on the human habitability assessment model at the current sample time t, S(t) is the space size, L(t) is the regional layout, I(t) is the lighting, and N(t) is the noise.

[0116] 2. The calculation formula of the psychosocial sub-model is as follows:

[0117]

[0118] Where X2(t) is the evaluation value of the impact of psychosocial factors on the human habitability assessment model at the current sample time t, P(t) is the stress level, C(t) is the team cohesion, and F(t) is the social frequency.

[0119] 3. The calculation formula of the work support sub-model is as follows:

[0120]

[0121] Where X3(t) is the estimated value of the impact of work support factors on the human habitability assessment model at the current sample time t, and C task (t) is the task clarity, R resource (t) is the resource adequacy, R tech (t) is the technical resource, Gground(t) is the ground support, max(C task ,R resource ,R tech ,G ground) is the maximum value of mission clarity, resource adequacy, technical resources, and ground support within the sample time.

[0122] 4. The calculation formula of the life support sub-model is as follows:

[0123]

[0124] Where X4(t) is the estimated value of the impact of life support factors on the human habitability assessment model at the current sample time t, D(t) is the dining facility, S space (t) is personal space, and A(t) is leisure activities.

[0125] 5. The calculation formula of the human safety sub-model is as follows:

[0126]

[0127] Where X5(t) is the estimated value of the impact of human safety factors on the human habitability assessment model at the current sample time t, R freq (t) is the frequency of risk occurrence, R time (t) is the emergency response time, E design (t) To prevent mistakes, R max is the maximum probability of risk occurrence, T max The maximum allowed time for emergency response.

[0128] 6. The calculation formula of the material management sub-model is as follows:

[0129] X6(t)=M supply (t)·M efficiency (t)

[0130] Where X6(t) is the estimated value of the impact of material management factors on the human habitability assessment model at the current sample time t, M supply (t) is the material sufficiency, M efficiency (t) is the allocation efficiency.

[0131] 7. The calculation formula of the human-technical system interaction sub-model is as follows:

[0132]

[0133] Where X7(t) is the estimated value of the impact of the interaction factors between people and technology systems on the human habitability assessment model at the current sample time t, U(t) is the usability of the user interface, and A tech (t) is the technology acceptance, F int (t) is the interaction frequency.

[0134] 8. The calculation formula of the time perception sub-model is as follows:

[0135]

[0136] Where X8(t) is the evaluation value of the impact of time perception factors on the human habitability assessment model at the current sample time t, T is the period, and C task (t) is the task periodicity, C rest (t) is the rest periodicity, M time (t) is time management.

[0137] S2, based on the long-term isolation environment parameters and the pre-built human habitability assessment model, calculate the assessment value of the impact of the influencing factors on the human habitability assessment model at the current sample time, specifically including:

[0138] The calculation results are shown in Table 1.

[0139] Table 1 Evaluation values ​​of influencing factors on the human habitability assessment model

[0140] Time t <![CDATA[X1(t)]]> <![CDATA[X2(t)]]> <![CDATA[X3(t)]]> <![CDATA[X4(t)]]> <![CDATA[X5(t)]]> <![CDATA[X6(t)]]> <![CDATA[X7(t)]]> <![CDATA[X8(t)]]> 1 90 85 80 75 95 70 85 60 30 85 80 75 70 90 65 80 55 60 70 65 70 65 85 60 75 50 90 65 60 65 60 80 55 70 45 120 70 65 70 65 85 60 75 50 160 80 70 75 70 90 65 80 60

[0141] From the above data, we can see that:

[0142] - The physical environment (X1(t)) and psychosocial (X2(t)) are good in the early stage, then decrease, indicating maladaptation, and then increase again, indicating readaptation.

[0143] -Work support (X3(t)) and life support (X4(t)) are established in the early stage, may decline in the middle stage due to resource issues, and then increase again through improvements in the later stage.

[0144] -Time perception (X8(t)) may reach its lowest point in the mid-term, indicating the least accurate perception of time, and then gradually improve.

[0145] S3 obtains a total evaluation value based on the evaluation value of the impact of the influencing factors on the human habitability evaluation model at the current time combined with a preset weight coefficient, specifically including:

[0146] 1. In this example, the preset weight coefficient is:

[0147] α1=0.1, α2=0.2,…, α3=0.1

[0148] β 13 =0.05, β 28 =0.05, β 24 =0.05

[0149] where α i is the weight coefficient of the independent influencing factor, β ij is the weight coefficient of the interaction between the two factors.

[0150] 2. The total evaluation value is calculated as follows:

[0151]

[0152] In the formula, X i (t) is X i The estimated value of the factor’s impact on the human habitability assessment model at the current sample time t, α i is factor X i The weight coefficient, β ij Factor X i and X j The interaction weight coefficient, f ij is a function that describes the factor X i and factor X j Interaction affects the total evaluation value H(t), X j (t) is X j The independent impact assessment value of the factor on the human habitability assessment model at the current sample time t.

[0153] 3. The calculation results are shown in Table 2.

[0154] Table 2 Total evaluation value

[0155] Time t <![CDATA[H ind (t)]]> <![CDATA[H int (t)]]> H(t) 1 8.65 3.675 12.325 30 7.55 2.7375 10.2875 60 6.45 1.8225 8.2725 90 5.825 1.35 7.175 120 6.45 1.8225 8.2725 160 7.55 3.675 11.225

[0156] Among them, H ind (t) is the weighted sum of independent influencing factors, H int (t) is the weighted sum of the pairwise interaction terms, and H(t) is the total evaluation value.

[0157] S4 evaluates the habitability of the long-term isolation environment based on the total evaluation value, specifically including:

[0158] It can be seen from Table 2 that:

[0159] Time t = 0: In the initial stage, people just enter the environment and may feel unfamiliar with it. The psychosocial factors X2(t) and time perception X8(t) may be low, resulting in a low initial value of H(t).

[0160] Time t=30: People begin to adapt to the environment, the physical environment X1(t) and work support X3(t) improve, and H(t) begins to rise.

[0161] Time t=60: An “inadaptability period” may occur. Due to long-term isolation and monotonous life, the psychosocial factor X2(t) decreases and H(t) reaches a low point.

[0162] Time t = 90: Personnel and the task force take measures to improve the situation, such as enhancing team cohesion and improving life support X4(t), and H(t) begins to rise.

[0163] Time t = 120 and later: As personnel adapt to the environment and become familiar with the task, all factors tend to stabilize and H(t) reaches a high and stable state.

[0164] The present invention discloses a dynamic comprehensive quantitative evaluation method for human habitability of a long-term isolation environment, which further includes:

[0165] Considering the time effect, a diachronic assessment report and suggestions are put forward for the habitability design of the isolation environment, including:

[0166] Environmental design: Ensure that the physical environment is comfortable, including appropriate lighting, temperature, and spatial layout, to promote the physiological health and work efficiency of personnel.

[0167] Psychological support: Provide psychosocial support X2(t), such as mental health monitoring, team building activities, and psychological counseling to enhance teamwork and social frequency.

[0168] Work-life balance: Design effective work supports X3(t) and life supports X4(t) to ensure task clarity, resource adequacy, and diversity of leisure activities.

[0169] Safety and Health: Strengthen human safety X5(t) measures, including error-proofing design, risk assessment, and emergency response training to reduce the risk of operating errors and accidents.

[0170] Material and information management: Optimize material management X6(t) and information interaction X7(t) to ensure material adequacy and distribution efficiency, while improving user interface usability and technology acceptance.

[0171] Time management: Through effective time-aware X8(t) design, such as reasonable arrangement of tasks and rest cycles, it helps adjust the biological clock and improve work efficiency.

[0172] Dynamic monitoring and adjustment: H(t) is continuously monitored and dynamically adjusted based on personnel feedback to address adaptability issues that may arise in long-term isolation environments.

[0173] These measures can improve the habitability of the space and ensure the health, safety and efficiency of people in long-term isolation environments.

[0174] Embodiment 2:

[0175] The present invention based on the same inventive concept also provides a dynamic comprehensive quantitative evaluation system for human habitability in a long-term isolation environment, such as Figure 3 As shown, including:

[0176] Data layer: used to collect and monitor long-term isolation environment parameters.

[0177] Model layer: used to calculate the evaluation values ​​of various influencing factors of human habitability based on long-term isolation environment parameters combined with a pre-built human habitability assessment model, and combine the pre-set weight coefficients to obtain the total evaluation value.

[0178] Application layer: used to evaluate the habitability of the isolation environment based on the total evaluation value.

[0179] The data layer includes:

[0180] Environmental monitoring module: collects physical environment data, including space size, area layout, lighting, noise, ground support, dining facilities, material sufficiency, and error-proof design.

[0181] Psychological assessment module: assesses the psychological state of people in the environment, including stress level, team cohesion, social frequency, task clarity, resource adequacy, technical resources, technology acceptance, time management, task periodicity, and rest periodicity.

[0182] Physiological data monitoring: monitoring physiological indicators of people in the environment, including personal space, leisure activities, frequency of risk occurrence, emergency response time, allocation efficiency, user interface usability, and interaction frequency.

[0183] The model layer includes:

[0184] Comprehensive Assessment Engine: Integrates all data to calculate habitability scores.

[0185] Interaction Assessment Model: Evaluate the impact of interactions between different factors on human habitability in isolated environments.

[0186] Time perception model: simulates people's perception of the passage of time in an isolated environment and evaluates its impact on work and rest cycles.

[0187] The application layer includes:

[0188] Recommendation generation module: Based on the assessment results, recommendations are made to improve habitability.

[0189] User Interface: Provides an intuitive interface for viewing assessment results and recommendations.

[0190] Report Generator: Generates detailed assessment reports for further analysis and documentation.

[0191] Example 3

[0192] like Figure 4As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0193] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment in the above-mentioned embodiment.

[0194] Example 4

[0195] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment in the above embodiment.

[0196] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0197] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0198] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0199] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0200] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A dynamic comprehensive quantitative evaluation method for human habitability of a long-term isolation environment, characterized in that: The method comprises: Obtain long-term isolation environment parameters; Based on the long-term isolation environment parameters and a pre-built human habitability assessment model, calculating the assessment value of the impact of the influencing factors on the human habitability assessment model at the current sample time; Obtaining a total evaluation value based on the evaluation value of the impact of the influencing factors on the human habitability evaluation model at the current time combined with a preset weight coefficient; Assessing the human habitability of the long-term isolation environment based on the total assessment value; The human habitability assessment model includes sub-models of various influencing factors affecting human habitability.

2. The method according to claim 1, characterized in that The long-term isolation environment parameters include: physical environment parameters, human psychological state and human physiological indicators.

3. The method according to claim 1, characterized in that The human habitability assessment model includes: a physical environment sub-model, a psychosocial sub-model, a work support sub-model, a life support sub-model, a human safety sub-model, a material management sub-model, a human-technical system interaction sub-model, and a time perception sub-model.

4. The method according to claim 3, characterized in that The psychosocial sub-model is shown below: Where X2(t) is the evaluation value of the impact of psychosocial factors on the human habitability assessment model at the current sample time t, P(t) is the stress level, C(t) is the team cohesion, F(t) is the social frequency, α is the weight of the stress level, β is the weight of the team cohesion, and γ is the weight of the social frequency.

5. The method according to claim 3, characterized in that The work support sub-model is shown as follows: Where X3(t) is the estimated value of the impact of work support factors on the human habitability assessment model at the current sample time t, and C task (t) is the task clarity, R resource (t) is the resource adequacy, R tech (t) is the technical resource, Gground(t) is the ground support, max(C task ,R resource ,R tech ,G ground ) is the maximum value of mission clarity, resource adequacy, technical resources, and ground support within the sample time.

6. The method according to claim 3, characterized in that The human safety sub-model is shown as follows: Where X5(t) is the estimated value of the impact of human safety factors on the human habitability assessment model at the current sample time t, R freq (t) is the frequency of risk occurrence, R time (t) is the emergency response time, E design (t) To prevent mistakes, R max is the maximum probability of risk occurrence, T max The maximum allowed time for emergency response.

7. The method according to claim 3, characterized in that The human-technical system interaction sub-model is shown as follows: Where X7(t) is the estimated value of the impact of the interaction factors between people and technology systems on the human habitability assessment model at the current sample time t, U(t) is the usability of the user interface, and A tech (t) is the technology acceptance, F int (t) is the interaction frequency.

8. The method according to claim 3, characterized in that The time perception sub-model is shown in the following formula: Where X8(t) is the evaluation value of the impact of time perception factors on the human habitability assessment model at the current sample time t, T is the period, and C task (t) is the task periodicity, C rest (t) is the rest periodicity, M time (t) is time management.

9. The method according to claim 1, characterized in that The total evaluation value is calculated as follows: Where Xi(t) is X i The estimated value of the factor’s impact on the human habitability assessment model at the current sample time t, α i is factor X i The weight coefficient, β ij Factor X i and X j The interaction weight coefficient, f ij is a function that describes the factor X i It interacts with factor Xj to affect the total evaluation value H(t), and Xj(t) is the independent impact evaluation value of factor Xj on the human habitability assessment model at the current sample time t.

10. The method according to claim 1, characterized in that The assessment of the human habitability of the long-term isolation environment based on the total assessment value includes: Based on the total evaluation values ​​corresponding to each time node set within the preset sample time, determine whether the total evaluation value of the current time node is greater than the total evaluation value of the previous time node. If so, it is determined that the long-term isolation environment at the current time node has strong human habitability; otherwise, it is determined that the isolation environment at the current time node has weak human habitability.

11. A long-term isolation environment human habitability assessment system, characterized in that: The system comprises: A data layer to collect and monitor long-term isolation environment parameters; The model layer is used to calculate the evaluation values ​​of various factors affecting human habitability based on the long-term isolation environment parameters combined with the pre-built human habitability evaluation model and obtain the total evaluation value in combination with the pre-set weight coefficients; An application layer, for evaluating the habitability of the isolation environment based on the total evaluation value; The human habitability assessment model includes sub-models of various influencing factors affecting human habitability.

12. The system according to claim 11, characterized in that The data layer includes: Environmental monitoring module: collects physical environment data; Psychological assessment module: assess the psychological state of people in the environment; Physiological data monitoring: monitoring physiological indicators of people in the environment.

13. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment as described in any one of claims 1 to 10 is implemented.

14. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a dynamic comprehensive quantitative evaluation method for human habitability in a long-term isolation environment as described in any one of claims 1 to 10 is implemented.

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