A method and system for extracting indicators for building a thermal comfort model

Abstract

The present application belongs to the technical field of thermal comfort, and particularly relates to an index extraction method for constructing a thermal comfort model, comprising the following steps: S1, selecting a test site, and cleaning up articles and sundries that are not needed in the site; S2, setting up an infrared thermal image temperature measurement sensor, an air temperature and humidity sensor, an air speed sensor, a visible light sensor, and a resistance type dew point hygrometer in the site. The present application proposes that two groups of different test data are respectively collected in the process of data collection, and the two groups of data are respectively screened, and finally in the process of data preprocessing, the site data containing the subjects in the two groups of data are compared with the site data not containing the subjects in the other group, whether the same items in the two groups of data exist abnormities is judged, if accurate, the process continues, if the data is abnormal, data collection is re-performed, so that the index data accuracy for constructing the thermal comfort model is improved.

Description

Technical Field

[0001] This invention belongs to the field of thermal comfort technology, specifically relating to a method and system for extracting indicators for constructing a thermal comfort model. Background Technology

[0002] Thermal comfort is defined as a person's subjective evaluation of satisfaction with the surrounding thermal environment. In the ASHRAE 55 standard, human thermal comfort is explained as a state of consciousness expressing satisfaction with the thermal environment. From these definitions, it can be seen that both environmental thermal comfort and human thermal comfort are determined by human thermal sensation. Humans are the subject of thermal comfort sensation; without the subject of humans, the quality of the environment is meaningless. Therefore, thermal comfort is a sensation, a subjective response of humans to the coupling effect of environmental factors and their own factors. When constructing a thermal comfort model, it is necessary to extract various data indicators to facilitate the construction of the thermal comfort model.

[0003] Currently, in the process of extracting various data indicators required for thermal comfort models, data are usually collected from subjects and environmental and human variables in the test site through various monitoring devices. However, when subjects enter the test site, it may affect the operation of the site data monitoring equipment, resulting in a large deviation between the site data with and without subjects, which leads to inaccurate indicator data used to construct thermal comfort models. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for extracting indicators for constructing a thermal comfort model. This addresses the problem in the existing technology where, in the process of extracting various data indicators required for thermal comfort models, data is typically collected from subjects and environmental and human variables in the testing area using various monitoring devices. However, when subjects enter the testing area, it may affect the operation of the site data monitoring equipment, resulting in a significant discrepancy between site data with and without subjects, leading to inaccurate indicator data used to construct the thermal comfort model.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for extracting indicators for constructing a thermal comfort model, comprising the following steps: S1. Select a test site and clean up any unnecessary items and debris in the site; S2. An infrared thermal imaging temperature sensor, an air temperature and humidity sensor, an air velocity sensor, a visible light sensor, and a resistive dew point hygrometer are set up in the site. Then, the environmental variables and human variables in the site are monitored through various monitoring devices. The visible light sensor is a CMOS sensor. S3. Collect various data according to the monitoring equipment set up in S2, and collect two sets of data respectively: site data including subjects and site data excluding subjects. S4. Filter the data collected in S3, retain the data related to thermal comfort, and filter and save the two sets of data collected in S3 separately. S5. Compare the site data containing subjects with the site data without subjects, and determine whether there are any anomalies in the same items in the two sets of data. If accurate, continue; if the data is abnormal, return to S3 to collect data again. S6. Construct a thermal comfort model based on the preprocessed thermal comfort-related data, and then conduct subsequent evaluation and optimization.

[0006] As a preferred method for extracting indicators to construct a thermal comfort model according to the present invention, the environmental variables in S2 include: air temperature, mean radiant temperature, relative humidity, and airflow velocity.

[0007] As a preferred method for extracting indicators to construct a thermal comfort model according to the present invention, the human body variables in S2 include: human body temperature and human sweating rate.

[0008] As a preferred method for extracting indicators to construct a thermal comfort model according to the present invention, the infrared thermal imaging temperature sensor in S2 is a FLIR Lepton 3.5 infrared thermal imaging temperature sensor.

[0009] As a preferred method for extracting indicators to construct a thermal comfort model according to the present invention, the air temperature and humidity sensor in S2 is an SHT31 air temperature and humidity sensor.

[0010] As a preferred method for extracting indicators to construct a thermal comfort model according to the present invention, the air velocity sensor in S2 is a SIEMENS air velocity sensor.

[0011] In a preferred embodiment of the index extraction method for constructing a thermal comfort model according to the present invention, in step S5, if the error of the same item in the two sets of data is less than ±5%, it is determined to be accurate; if the error of the same item in the two sets of data is greater than ±5%, it is determined to be abnormal.

[0012] An index extraction system for constructing a thermal comfort model includes a monitoring module, an acquisition module, a data filtering module, a data processing module, and a model component module. The monitoring module is associated with the acquisition module, the acquisition module is associated with the data filtering module, the data filtering module is associated with the data processing module, and the data processing module is associated with the model component module.

[0013] In a preferred embodiment of the index extraction system for constructing a thermal comfort model according to the present invention, the data acquisition module, data filtering module, and data processing module are performed using a computer for data processing.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By collecting two different sets of experimental data during the data acquisition process and filtering the data in each set, and then comparing the site data containing subjects with the site data without subjects in the two sets of data during the data preprocessing process, it was determined whether there were any anomalies in the same items of the two sets of data. If the data were accurate, the process continued; if the data was abnormal, the data acquisition was repeated. This improved the accuracy of the index data used to construct the thermal comfort model. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the logic of the present invention; Figure 2 This is a schematic diagram of the system modules of the present invention.

[0016] In the diagram: 1. Monitoring module; 2. Data acquisition module; 3. Data filtering module; 4. Data processing module; 5. Model component module. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figures 1-2 The present invention provides the following technical solution: a method for extracting indicators for constructing a thermal comfort model, comprising the following steps: S1. Select a test site and clean up any unnecessary items and debris in the site; S2. An infrared thermal imaging temperature sensor, an air temperature and humidity sensor, an air velocity sensor, a visible light sensor, and a resistive dew point hygrometer are set up in the site. Then, the environmental variables and human variables in the site are monitored through various monitoring devices. The visible light sensor is a CMOS sensor. S3. Collect various data according to the monitoring equipment set up in S2, and collect two sets of data respectively: site data including subjects and site data excluding subjects. S4. Filter the data collected in S3, retain the data related to thermal comfort, and filter and save the two sets of data collected in S3 separately. S5. Compare the site data containing subjects with the site data without subjects, and determine whether there are any anomalies in the same items in the two sets of data. If accurate, continue; if the data is abnormal, return to S3 to collect data again. S6. Construct a thermal comfort model based on the preprocessed thermal comfort-related data, and then conduct subsequent evaluation and optimization; There are many methods for constructing thermal comfort models, including the PMV-PPD index, effective temperature ET, new effective temperature ET*, standard effective temperature SET*, thermal stress index HIS, and wet black sphere temperature WBGT. However, under steady-state conditions, the PMV-PPD index is currently the most widely used thermal comfort evaluation and prediction index both domestically and internationally. The PMV model represents human thermal sensation as a function of six objective physical parameters, including two human parameters (activity level and clothing thermal resistance) and four environmental parameters (dry-bulb air temperature, mean radiant temperature, wind speed, and air humidity), covering all major factors affecting human thermal sensation. The PMV model quantifies the impact of these six objective parameters on human thermal sensation and can accurately predict the thermal sensation of indoor occupants under different activity levels, clothing conditions, and air conditioning operating conditions. The mathematical expression of the PMV model is as follows:

[0019]

[0020]

[0021]

[0022] In the formula: M is the metabolic rate, 1 met = 58.15 W / m², W / m²; W is the mechanical work done by the human body, W / m². For the thermal resistance of clothing, 1 clo = 0.155 m²•℃ / W, m²•℃ / W; This refers to the surface area coefficient of clothing. Air temperature, °C; (-) represents the mean radiant temperature, in °C; The wind speed is relative, in m / s; The partial pressure of water vapor is Pa; tcl is the convective heat transfer coefficient, W / (㎡•K); tcl is the surface temperature of the garment, °C.

[0023] Preferably, the environmental variables in S2 include: air temperature, mean radiant temperature, relative humidity, and airflow velocity; It is worth noting that ambient temperature is one of the most important factors affecting thermal comfort. Both excessively high and low temperatures can cause discomfort. Generally, the human body feels most comfortable with indoor temperatures between 20°C and 26°C. Humidity refers to the moisture content in the air. High humidity makes it difficult for the body to sweat, leading to a stuffy feeling; low humidity causes dry skin and discomfort. Indoor humidity is generally controlled between 40% and 60%. Wind speed also has a significant impact on thermal comfort. Moderate wind speed can accelerate heat dissipation and improve comfort. However, excessive wind speed can cause the body to lose heat too quickly, leading to a feeling of cold. Radiation refers to the radiated heat from a heat source. For example, sunlight radiation makes the body feel hot. The impact of radiation on thermal comfort varies depending on the environment and activity.

[0024] Preferred: Human variables in S2 include: human body temperature and human sweating rate.

[0025] Preferred model: The infrared thermal imaging temperature sensor in S2 is the FLIR Lepton 3.5 infrared thermal imaging temperature sensor, which is used to monitor and record the subject's perceived temperature data; It is important to note that the infrared thermal imaging temperature sensor has a sensitivity of 40mK, a resolution of 160*120, and a maximum frame rate of 9Hz. When combined with a corresponding development board (which can be used to read images, set camera parameters, etc.), the infrared thermal imaging temperature sensor can be connected to the processor via a Serial Peripheral Interface (SPI) or a USB interface. The infrared thermal imaging temperature sensor is used to acquire infrared images (e.g., color infrared images or grayscale infrared images) of the person being measured in real time and transmits the infrared images to the processor via the Serial Peripheral Interface (SPI) or USB interface.

[0026] Preferred model: The air temperature and humidity sensor in S2 is an SHT31 air temperature and humidity sensor, which is used to monitor and record the air humidity in the site; It is worth noting that the air temperature and humidity sensor has a temperature accuracy of 0.3℃, a relative humidity accuracy of 2%, and a maximum acquisition frequency of 30Hz. The temperature and humidity sensor and the air velocity sensor are connected to the processor via an I2C interface.

[0027] Preferably, the air velocity sensor in S2 is a SIEMENS air velocity sensor, which is used to monitor and record the air velocity in the field.

[0028] Preferred method: In S5, if the error of the same item in the two sets of data is less than ±5%, it is judged as accurate; if the error of the same item in the two sets of data is greater than ±5%, it is judged as abnormal. It is worth noting that by collecting two different sets of experimental data during the data acquisition process and filtering these two sets of data separately, and then comparing the site data containing subjects with the site data without subjects in the two sets of data during the data preprocessing process, it was determined whether there were any anomalies in the identical items in the two sets of data. If the error of the identical items in the two sets of data was less than ±5%, it was judged as accurate; if the error of the identical items in the two sets of data was greater than ±5%, it was judged as abnormal. When the data was abnormal, the data was re-collected, thereby improving the accuracy of the index data used to construct the thermal comfort model.

[0029] A system for extracting indicators for constructing a thermal comfort model includes a monitoring module 1, a data acquisition module 2, a data filtering module 3, a data processing module 4, and a model component module 5. The monitoring module 1 is associated with the data acquisition module 2, the data acquisition module 2 is associated with the data filtering module 3, the data filtering module 3 is associated with the data processing module 4, and the data processing module 4 is associated with the model component module 5. Preferably, the data acquisition module 2, data filtering module 3, and data processing module 4 use computers for data processing.

[0030] Working principle: When using the index extraction method and system for constructing a thermal comfort model, firstly, a test site is selected, and unnecessary items and debris are removed. Infrared thermal imaging temperature sensors, air temperature and humidity sensors, air velocity sensors, visible light sensors, CMOS sensors, and resistive dew point hygrometers are installed within the site. Then, environmental and human variables within the site are monitored using these devices. Data is collected based on the established monitoring equipment, and two sets of data are collected separately: site data including subjects and site data excluding subjects. The collected data is then filtered, retaining only the data related to thermal comfort. Furthermore, the two sets of data collected in S3 need to be filtered and retained separately. The data collection process involves: 1) Comparing site data containing subjects with site data excluding subjects to determine if any identical items exist. If accurate, the process continues; otherwise, data collection is repeated. 2) Constructing a thermal comfort model based on the preprocessed thermal comfort-related data, followed by evaluation and optimization. This is achieved by collecting two different sets of experimental data during the data acquisition process and filtering each set. During data preprocessing, the site data containing subjects is compared with the site data excluding subjects to determine if any identical items exist. If accurate, the process continues; otherwise, data collection is repeated. This improves the accuracy of the indicator data used to construct the thermal comfort model.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for extracting indicators to construct a thermal comfort model, characterized in that, Includes the following steps: S1. Select a test site and clean up any unnecessary items and debris in the site; S2. An infrared thermal imaging temperature sensor, an air temperature and humidity sensor, an air velocity sensor, a visible light sensor, and a resistive dew point hygrometer are set up in the site. Then, the environmental variables and human variables in the site are monitored through various monitoring devices. The visible light sensor is a CMOS sensor. S3. Collect various data according to the monitoring equipment set up in S2, and collect two sets of data respectively: site data including subjects and site data excluding subjects. S4. Filter the data collected in S3, retain the data related to thermal comfort, and filter and save the two sets of data collected in S3 separately. S5. Compare the site data containing subjects with the site data without subjects, and determine whether there are any anomalies in the same items in the two sets of data. If accurate, continue; if the data is abnormal, return to S3 to collect data again. S6. Construct a thermal comfort model based on the preprocessed thermal comfort-related data, and then conduct subsequent evaluation and optimization.

2. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: The environmental variables in S2 include: air temperature, mean radiant temperature, relative humidity, and airflow speed.

3. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: The human body variables in S2 include: human body temperature and human sweating rate.

4. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: The infrared thermal imaging temperature sensor in S2 is a FLIR Lepton 3.5 infrared thermal imaging temperature sensor.

5. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: The air temperature and humidity sensor in S2 is a SHT31 air temperature and humidity sensor.

6. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: The air velocity sensor in S2 is a SIEMENS air velocity sensor.

7. The method for extracting indicators for constructing a thermal comfort model according to claim 1, characterized in that: In step S5, if the error of the same item in the two sets of data is less than ±5%, it is determined to be accurate; if the error of the same item in the two sets of data is greater than ±5%, it is determined to be abnormal.

8. An index extraction system for constructing a thermal comfort model, used to implement the index extraction method for constructing a thermal comfort model as described in any one of claims 1-7, characterized in that, It includes a monitoring module (1), an acquisition module (2), a data filtering module (3), a data processing module (4), and a model component module (5). The monitoring module (1) is associated with the acquisition module (2), the acquisition module (2) is associated with the data filtering module (3), the data filtering module (3) is associated with the data processing module (4), and the data processing module (4) is associated with the model component module (5).

9. The index extraction system for constructing a thermal comfort model according to claim 8, characterized in that: The data acquisition module (2), data filtering module (3), and data processing module (4) use computers for data processing.

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