Method and system for evaluating attention level of human body in high-temperature environment

Abstract

The invention belongs to the technical field of human body attention evaluation in a high-temperature environment, and provides a human body attention level evaluation method and system in a high-temperature environment, and the method comprises the steps: dividing an environment temperature range into a plurality of temperature ranges according to a value sequence of temperatures from low to high; performing feature extraction on the near-infrared fNIRS data in the plurality of temperature ranges to obtain feature data in each temperature range; according to the method, range division is carried out on the environment temperature, feature extraction and screening are carried out on the corresponding near-infrared fNIRS data in different environment temperature ranges on the basis, the expression of the near-infrared fNIRS data features in different high-temperature ranges can be reflected, and the near-infrared fNIRS data features in different temperature sections in the high-temperature environment can be reflected. According to the method, the attention performance value is calculated through different attention performance value calculation models, so that the influence caused by obvious change of human brain metabolic rate difference in a high-temperature range is avoided, the coupling relationship between the blood oxygen signal of the fNIRS and the neural activity can be better expressed, and the evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of human attention assessment in high-temperature environments, and in particular relates to a method and system for evaluating human attention levels in high-temperature environments. Background Art

[0002] With global warming and frequent heat waves, the impact of sustained high temperatures on human health and work efficiency has become increasingly prominent. High temperatures not only affect the body's physical and mental health, but also cause cognitive decline, especially attention, which can lead to work errors and even accidents. Therefore, it is of great significance to evaluate the performance of human attention during high temperature exposure. Functional Near-Infrared Spectroscopy (fNIRS), a non-invasive brain imaging technique, can reflect neural activity by measuring changes in hemoglobin concentration in the cerebral cortex, making it suitable for assessing attention in dynamic environments. However, current fNIRS cognitive assessments are mostly focused on normal temperature environments. However, the human brain's metabolic rate varies significantly under high-temperature conditions, and as temperatures increase, these differences in brain metabolic rate become more pronounced within different temperature ranges. This causes the coupling relationship between the fNIRS blood oxygenation signal (HbO / HbR) and neural activity to deviate from the normal temperature model, leading to inaccurate assessments. Summary of the Invention

[0003] To address the above-mentioned issues, the present invention proposes a method and system for evaluating human attention levels in high-temperature environments. The present invention divides the ambient temperature into ranges and, on this basis, extracts and filters features from the near-infrared fNIRS data corresponding to different ambient temperature ranges. This facilitates reflecting the performance of near-infrared fNIRS data features within different high-temperature ranges. Furthermore, in a high-temperature environment, different attention performance value calculation models are used to calculate attention performance values ​​for different temperature segments. This avoids the impact of significant differences in the human brain's metabolic rate within the high-temperature range, better expresses the coupling relationship between fNIRS blood oxygenation signals and neural activity, and improves assessment accuracy.

[0004] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for evaluating a person's attention level in a high temperature environment, comprising: Acquire ambient temperature data and near-infrared fNIRS data of the left frontal lobe of the human brain; The ambient temperature range is divided into multiple temperature ranges according to the order of temperature values ​​from low to high; feature extraction is performed on the near-infrared fNIRS data in multiple temperature ranges to obtain feature data in each temperature range; Determining the temperature range to which the ambient temperature data belongs, and obtaining an attention performance value based on the characteristic data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; wherein the attention performance value calculation model is a polynomial including the characteristic data, exposure time, and corresponding coefficients; Evaluation is performed based on attention performance values.

[0005] Furthermore, the ambient temperature range is divided into multiple temperature ranges including: when the temperature is greater than or equal to the first preset temperature value and less than the second preset temperature value, it is near high temperature; when the temperature is greater than or equal to the second preset temperature value and less than the third preset temperature value, it is high temperature; when the temperature is greater than or equal to the third preset temperature value and less than the fourth preset temperature value, it is extremely high temperature; the first preset temperature value is less than the second preset temperature value, the second preset temperature value is less than the third preset temperature value, and the third preset temperature value is less than the fourth preset temperature value.

[0006] Furthermore, when performing feature extraction, the extracted features include time domain indicators and frequency domain indicators.

[0007] Furthermore, the time domain indicators include mean, standard deviation, skewness, kurtosis, peak-to-peak value, root mean square value, entropy and area under the curve; the frequency domain indicators include spectral centroid, spectral standard deviation, spectral skewness and spectral kurtosis.

[0008] Furthermore, the attention performance value calculation model for the near-high temperature section, the attention performance value calculation model for the high temperature section, and the attention performance value calculation model for the extremely high temperature section are respectively: ; ; ; in, a 1 、b 1 、c 1 、d 1. e 1. a 2 、b 2 、c 2. d 2. 、 a 3 、b 3 、c 3. d 3 and is the coefficient; X is the characteristic data; t is the exposure time; The preset unit time.

[0009] Furthermore, when the attention performance value is greater than or equal to the first attention performance value and less than the second attention performance value, it is a low attention level; when the attention performance value is greater than or equal to the second attention performance value and less than the third attention performance value, it is a medium attention level; when the attention performance value is greater than or equal to the third attention performance value and less than the fourth attention performance value, it is a high attention level; wherein, the first attention performance value is less than or equal to the second attention performance value, the second attention performance value is less than or equal to the third attention performance value, and the third attention performance value is less than or equal to the fourth attention performance value.

[0010] In a second aspect, the present invention further provides a system for evaluating human attention level in a high temperature environment, comprising: The data acquisition module is configured to: acquire ambient temperature data and near-infrared fNIRS data of the left frontal lobe of the human brain; The feature extraction module is configured to: divide the ambient temperature range into multiple temperature ranges according to the order of temperature values ​​from low to high; extract features from the near-infrared fNIRS data within the multiple temperature ranges to obtain feature data within each temperature range; a calculation module configured to: determine a temperature range to which the ambient temperature data belongs, and obtain an attention performance value based on characteristic data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; wherein the attention performance value calculation model is a polynomial including the characteristic data, exposure time, and corresponding coefficients; The evaluation module is configured to: perform evaluation based on the attention performance value.

[0011] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for evaluating the human attention level in a high temperature environment described in the first aspect.

[0012] In a fourth aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the method for evaluating the human attention level in a high temperature environment described in the first aspect are implemented.

[0013] In a fifth aspect, the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the method for evaluating the human attention level in a high temperature environment described in the first aspect.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, first, the ambient temperature range is divided into multiple temperature ranges according to the order of temperature values ​​from low to high; feature extraction is performed on the near-infrared fNIRS data within the multiple temperature ranges to obtain feature data in each temperature range; then, the temperature range to which the ambient temperature data belongs is determined, and the attention performance value is obtained based on the feature data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; by dividing the ambient temperature into ranges, feature extraction and screening are performed on the corresponding near-infrared fNIRS data within different ambient temperature ranges on this basis, which is conducive to reflecting the performance of near-infrared fNIRS data features in different high temperature ranges, and calculating the attention performance value by different attention performance value calculation models in different temperature segments in a high temperature environment, avoiding the influence of significant changes in the metabolic rate of the human brain within the high temperature range, and can better express the coupling relationship between the fNIRS blood oxygen signal and neural activity, thereby improving the evaluation accuracy.

[0015] 2. This invention only collects fNIRS data from the left frontal lobe. This acquisition method is simple, portable, and highly stable, minimally affected by the user's activity status. It also has a wide range of applications, enabling rapid deployment and flexible testing. It adjusts in real time to changes in the high-temperature environment, flexibly assessing human attention levels in high temperatures with high accuracy, meeting real-world work needs. It provides objective, data-driven, real-time assessments of attention, avoiding biases inherent in subjective evaluations and significantly improving assessment accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0017] Figure 1 Schematic diagram of the method flow of Example 1 of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0020] With global warming and the increasing frequency of heat waves, high temperature records are constantly being broken, and heat waves are gradually showing new characteristics: long duration, large coverage, high intensity, and high frequency. The impact of the persistent high temperatures associated with heat waves on human health and work efficiency is becoming increasingly prominent. High temperatures not only affect the body's physical and mental health but also cause cognitive decline, especially attention deficit, which can lead to work errors and even accidents. Therefore, maintaining a high level of attention is particularly important for human safety during heat exposure, and achieving objective and real-time evaluation of human attention during heat exposure is of great significance.

[0021] Methods for assessing attention performance are mainly divided into two categories: subjective and objective. Subjective methods rely on self-reports from users, are easily affected by subjective factors, and have low reliability. Objective methods include behavioral measurements and neurophysiological measurements. Although behavioral measurements can reflect attention performance, they cannot provide real-time information on brain activity. Neurophysiological measurements, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), can directly reflect brain neural activity and provide new ideas for assessing attention performance. fNIRS (functional Near-Infrared Spectroscopy), as a non-invasive brain imaging technology, can reflect neural activity by measuring changes in hemoglobin concentration in the cerebral cortex. It has the advantages of high spatial resolution, strong resistance to motion interference, and easy portability, making it more suitable for attention assessment in dynamic environments.

[0022] As described in the background technology, currently, fNIRS cognitive assessments are mostly focused on normal temperature environments. However, in high temperature environments, the human brain's metabolic rate varies significantly, causing the coupling relationship between the fNIRS blood oxygen signal (HbO / HbR) and neural activity to deviate from the model at normal temperature, resulting in inaccurate assessments.

[0023] In order to solve at least one of the above problems, the present invention provides a method for evaluating the human attention level in a high temperature environment, which is of great significance for the accurate evaluation of human attention in a high temperature environment and the health and safety protection of outdoor people.

[0024] The present invention provides a method for evaluating the level of human attention in a high-temperature environment, which is used to solve the technical problems in the prior art caused by insufficient data application, complex integration algorithms, and poor real-time performance, resulting in inaccurate evaluation results and insufficient coverage. At the same time, it fills the gap in the current evaluation of human attention in high-temperature environments. By accurately identifying the ambient temperature and integrating fNIRS data, the real-time performance and coverage of the evaluation are enhanced, thereby improving the accuracy and reliability of the evaluation results. Figure 1 As shown, the method includes: S1. Parameter collection: Optionally, ambient temperature data can be acquired using a temperature sensor attached to the portable device, while simultaneously collecting near-infrared fNIRS data from the left frontal lobe of the brain in a high-temperature environment. In this embodiment, high temperature can refer to a temperature above a preset value, such as above 30°C. Near-infrared fNIRS data from the left frontal lobe can be detected using a near-infrared brain imaging system or other equipment.

[0025] S2. Feature parameter screening: S2.1. Based on the ambient temperature data, determine the ambient temperature range of the user and categorize the ambient temperature range into three ranges: near high temperature, high temperature, and extreme high temperature, in descending order of temperature values. For example, if the temperature is greater than or equal to the first preset temperature value T1 and less than the second preset temperature value T2, it is near high temperature and is recorded as near high temperature: [T1, T2]; if the temperature is greater than or equal to the second preset temperature value T2 and less than the third preset temperature value T3, it is high temperature and is recorded as high temperature: [T2, T3]; if the temperature is greater than or equal to the third preset temperature value T3 and less than the fourth preset temperature value T4, it is extremely high temperature and is recorded as near high temperature: [T3, T4]; if the first preset temperature value T1 is less than the second preset temperature value T2, the second preset temperature value T2 is less than the third preset temperature value T3, and the third preset temperature value T3 is less than the fourth preset temperature value T4.

[0026] The ambient temperature is divided into ranges, and on this basis, feature extraction and screening of the near-infrared fNIRS data corresponding to different ambient temperature ranges are performed. This is conducive to reflecting the performance of near-infrared fNIRS data characteristics in different high temperature ranges and solving the evaluation impact problem caused by large differences in human brain metabolic rate in the high temperature range.

[0027] S2.2. The raw near-infrared fNIRS data in the above different temperature ranges are preprocessed by the built-in data processor, and the feature extraction method is used to extract the feature X of the near-infrared fNIRS data, and the sensitive feature X is screened out. n .

[0028] As shown in Table 1, feature X includes but is not limited to eight time-domain metrics (Mean, Std, Skewness, Kurtosis, Peak2Peak, RMS, AUC, and Entropy) and four frequency-domain metrics (Spectral Centroid, Spectral Std, Spectral Skewness, and Spectral Kurtosis). Because different brain activation levels at different temperatures result in different fNIRS signal changes, different high-temperature environments have different effects on different features. Based on the extracted signal features, sensitive features Xn, such as the mean and / or standard deviation, are further screened.

[0029] Table 1 Characteristic indicators

[0030] S3. Setting the attention evaluation criteria: Optionally, different attention intervals are set to distinguish different levels of attention. The interval [Y', Y'') greater than or equal to the first attention performance value Y' and less than the second attention performance value Y'' is defined as a low attention level; the interval [Y'', Y''') greater than or equal to the second attention performance value Y'' and less than the third attention performance value Y''' is defined as a medium attention level; the interval [Y''', Y'''') greater than or equal to the third attention performance value Y''' and less than the fourth attention performance value Y'''' is defined as a high attention level. Among them, the first attention performance value Y' is less than or equal to the second attention performance value Y'', the second attention performance value Y'' is less than or equal to the third attention performance value Y''', and the third attention performance value Y''' is less than or equal to the fourth attention performance value Y''''.

[0031] S4. Attention evaluation model and evaluation: Construct evaluation models under different high temperature environment ranges. According to different temperature ranges, different attention evaluation models are corresponding to different temperature ranges to calculate the real-time attention performance value Y. Specifically: The attention performance value calculation model Y1 for the near-high temperature segment [T1, T2), the attention performance value calculation model Y2 for the high temperature segment [T2, T3), and the attention performance value calculation model Y3 for the extremely high temperature segment [T3, T4) are: ; ; ; in, a 1 、b 1 、c 1 、d 1. e 1. a 2 、b 2 、c 2. d 2. 、 a 3 、b 3 、c 3. d 3 and is the coefficient, which can be obtained from the experiment; X is the characteristic data; t is the exposure time, which can refer to the duration of human body in high temperature environment; The preset unit time is used to eliminate the dimension of the corresponding item in the model. The ambient temperature range zone to which the user belongs is determined based on the ambient temperature tested in step S1. Calculations are performed using the corresponding calculation model, combined with the evaluation criteria in step S3, to output a real-time attention evaluation result.

[0032] In high-temperature environments, different attention performance calculation models are used to calculate attention performance values ​​for different temperature ranges. This avoids the impact of significant differences in the human brain's metabolic rate within high-temperature ranges, better expresses the coupling relationship between fNIRS blood oxygenation signals and neural activity, and improves assessment accuracy. Specifically, compared to near-high-temperature ranges, high-temperature ranges have a greater time effect (the corresponding index for time is 2.2). In extremely high-temperature ranges, the brain is more strongly affected by the environment, and feature changes have a greater impact on attention performance (the corresponding index for features is 2.4). By changing the exponential of the model corresponding to different temperatures, local features of the data can be more flexibly captured, improving the accuracy of the model.

[0033] S5. User display terminal displays in real time: Optionally, the display terminal can be combined with the user terminal to output the attention performance to the display terminal, such as a mobile phone or tablet computer, in real time via Bluetooth, and use different data levels and colors to display the user's real-time attention level.

[0034] Based on fNIRS technology, the present invention also provides a system for evaluating human attention level in a high-temperature environment, including a data acquisition and preprocessing module, an attention performance calculation module, a logic judgment module, and a user display module; The data acquisition and preprocessing module performs the steps in step S1 of the method. Optionally, the ambient temperature is collected and the temperature range is confirmed using a temperature sensor. Simultaneously, the portable module can collect and preprocess raw fNIRS data from the left frontal lobe in real time. Time and frequency domain features of the fNIRS data signal are then extracted, and sensitive feature indicators are selected based on these features to match the attention calculation model at the corresponding temperature.

[0035] The attention performance calculation module executes the content in step S2 of the method. Optionally, the user's real-time attention performance is calculated through a built-in attention calculation model.

[0036] The logic judgment module executes the content in step S3 of the method. Optionally, the real-time attention level is obtained for judgment.

[0037] The user display module executes the steps in step S4 of the method. Optionally, the real-time attention level data is displayed in real time via the user display module. The display module can be integrated with a user terminal, such as a mobile phone or tablet computer, to display the user's real-time attention level using different data levels and colors.

[0038] The method and system of the present invention can monitor and provide feedback on the user's attention level in real time. Once a decrease in attention is detected, measures can be taken immediately, such as adjusting the difficulty of the task or providing other physical stimulation (such as cooling) to prevent the rapid decline of attention.

[0039] Example 1: This embodiment provides a method for evaluating the human attention level in a high temperature environment, which evaluates and provides feedback on the attention of a user exposed to high temperature for 30 minutes at 33°C in real time.

[0040] S1. Synchronously collect ambient temperature T a The user's fNIRS data is used to determine the temperature range (near high temperature: [30°C, 35°C), high temperature: [35°C, 40°C), and extreme high temperature: [40°C, 50°C) based on the collected parameters. In this case, the ambient temperature T is monitored in real time. a The built-in data processing module pre-processes the user's fNIRS data and then extracts the fNIRS data features X1, X2, X3, X4, X5...X n , feature X is the mean, std, etc., and n is the number of extracted features. Multiple extracted features are selected through the built-in machine learning model to obtain the characteristic index Xn that is most sensitive to changes under high temperature.

[0041] S2. Set attention evaluation criteria. This embodiment sets different attention intervals to distinguish different levels of attention: low attention level [0, 0.6], medium attention level [0.6, 1], and high attention level [1, 1.5]. Subsequently, the real-time acquired feature data X or sensitive feature Xn is input into the built-in near-high temperature environment attention performance calculation model Y1, while taking into account the time variable t. According to the near-high temperature binary quadratic attention evaluation model: ( a 1 ，b 1 ，c 1 ，d 1 and e 1 are obtained from experiments, such as a 1=1.6, b 1=-0.00011, c 1=0.21, d 1=-0.002, e 1=0.5, ). In this case, Y1=1.6X 2 -0.00011 2 +0.21X-0.002X +0.5, calculate the user's real-time attention performance Y; in this case, the real-time feature X1 obtained at the 30th minute is 0.5, and the corresponding attention performance Y calculated by the model is 0.876.

[0042] S3. The real-time attention level is determined and categorized into three levels: low, medium, and high. Y > Y''' represents high attention, indicating that the user is focused, alert, and alert; Y''' > Y > Y'' represents medium attention, indicating that the user's attention is being depleted and showing signs of fading; and Y < Y'' represents low attention, indicating that the user's attention is poor in high temperatures and requires timely intervention to prevent accidents. In this example, the attention level is 0.876. The judgment logic indicates that 1 > 0.876 > 0.6, which is in the medium attention level range. The result is then transmitted to the user.

[0043] S4. The user's attention level is fed back in real time by the user's terminal, transmitted via Bluetooth to a display screen, including but not limited to mobile phones, computers, and watches. The attention level in this example is 0.876, which is in the medium attention level range. Observation or light intervention is required to ensure user safety.

[0044] This embodiment can monitor the attention level of an individual in real time under high temperature conditions to ensure the work efficiency and safety of the individual in a high temperature environment.

[0045] Example 2: This embodiment provides a system for evaluating human attention level in a high temperature environment, including: The data acquisition module is configured to: acquire ambient temperature data and near-infrared fNIRS data of the left frontal lobe of the human brain; The feature extraction module is configured to: divide the ambient temperature range into multiple temperature ranges according to the order of temperature values ​​from low to high; extract features from the near-infrared fNIRS data within the multiple temperature ranges to obtain feature data within each temperature range; a calculation module configured to: determine a temperature range to which the ambient temperature data belongs, and obtain an attention performance value based on characteristic data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; wherein the attention performance value calculation model is a polynomial including the characteristic data, exposure time, and corresponding coefficients; The evaluation module is configured to: perform evaluation based on the attention performance value.

[0046] The working method of the system is the same as the method for evaluating the human attention level in a high temperature environment of the present invention and Example 1, and will not be repeated here.

[0047] Example 3: This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for evaluating the human attention level in a high temperature environment described in Example 1 are implemented.

[0048] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the method for evaluating the human attention level in a high temperature environment described in Example 1 are implemented.

[0049] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the method for evaluating the human attention level in a high temperature environment described in Example 1 are implemented.

[0050] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A method for evaluating human attention level in a high temperature environment, characterized in that: include: Acquire ambient temperature data and near-infrared fNIRS data of the left frontal lobe of the human brain; The ambient temperature range is divided into multiple temperature ranges according to the order of temperature values ​​from low to high; feature extraction is performed on the near-infrared fNIRS data in multiple temperature ranges to obtain feature data in each temperature range; Determining the temperature range to which the ambient temperature data belongs, and obtaining an attention performance value based on the characteristic data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; wherein the attention performance value calculation model is a polynomial including the characteristic data, exposure time, and corresponding coefficients; Evaluation is performed based on attention performance values.

2. The method for evaluating human attention level in a high temperature environment according to claim 1, wherein: Dividing the ambient temperature range into multiple temperature ranges includes: when the temperature is greater than or equal to the first preset temperature value and less than the second preset temperature value, it is near high temperature; when the temperature is greater than or equal to the second preset temperature value and less than the third preset temperature value, it is high temperature; when the temperature is greater than or equal to the third preset temperature value and less than the fourth preset temperature value, it is extremely high temperature; the first preset temperature value is less than the second preset temperature value, the second preset temperature value is less than the third preset temperature value, and the third preset temperature value is less than the fourth preset temperature value.

3. The method for evaluating human attention level in a high temperature environment according to claim 2, wherein: When performing feature extraction, the extracted features include time domain indicators and frequency domain indicators.

4. The method for evaluating human attention level in a high temperature environment according to claim 3, wherein: The time domain indicators include mean, standard deviation, skewness, kurtosis, peak-to-peak value, root mean square value, entropy and area under the curve; the frequency domain indicators include spectral centroid, spectral standard deviation, spectral skewness and spectral kurtosis.

5. The method for evaluating human attention level in a high temperature environment according to claim 4, wherein: The calculation models for the attention performance value in the near-high temperature section, the high temperature section, and the extreme high temperature section are: ； ； ； in, a 1 、b 1 、c 1 、d 1. e 1. a 2 、b 2 、c 2. d 2. 、 a 3 、b 3 、c 3. d 3 and is the coefficient; X is the characteristic data; t is the exposure time; The preset unit time.

6. The method for evaluating human attention level in a high temperature environment according to claim 1, wherein: When the attention performance value is greater than or equal to the first attention performance value and less than the second attention performance value, it is a low attention level; when the attention performance value is greater than or equal to the second attention performance value and less than the third attention performance value, it is a medium attention level; when the attention performance value is greater than or equal to the third attention performance value and less than the fourth attention performance value, it is a high attention level; wherein, the first attention performance value is less than or equal to the second attention performance value, the second attention performance value is less than or equal to the third attention performance value, and the third attention performance value is less than or equal to the fourth attention performance value.

7. A system for evaluating human attention level in a high temperature environment, characterized in that: include: The data acquisition module is configured to: acquire ambient temperature data and near-infrared fNIRS data of the left frontal lobe of the human brain; The feature extraction module is configured to: divide the ambient temperature range into multiple temperature ranges according to the order of temperature values ​​from low to high; extract features from the near-infrared fNIRS data within the multiple temperature ranges to obtain feature data within each temperature range; a calculation module configured to: determine a temperature range to which the ambient temperature data belongs, and obtain an attention performance value based on characteristic data within the corresponding temperature range and a preset attention performance value calculation model within the corresponding temperature range; wherein the attention performance value calculation model is a polynomial including the characteristic data, exposure time, and corresponding coefficients; The evaluation module is configured to: perform evaluation based on the attention performance value.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for evaluating the level of human attention in a high temperature environment as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the method for evaluating the human attention level in a high temperature environment as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method for evaluating the human attention level in a high temperature environment according to any one of claims 1 to 6 are implemented.

Images