A thermal comfort detection device and system

Abstract

The application belongs to the field of thermal comfort evaluation, and particularly relates to a thermal comfort detection device and system, which comprises an internal data monitoring module, an external data monitoring module, a control module and a communication module, the internal data monitoring module is used for monitoring human physiological parameters, the external data monitoring module is used for monitoring environmental parameters, the control module is used for collecting data collected by the internal data monitoring module and the external data monitoring module, and sending the data to an upper computer through the communication module, and is also used for sending a control signal to an environmental device according to a comfort level evaluation signal received by the communication module. The application can obtain more objective comfort evaluation data, and can be widely applied to the field of comfort evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of thermal comfort evaluation, and in particular to a thermal comfort detection device and system. BACKGROUND

[0002] With the continuous improvement of people's living standards, the pursuit of life quality is constantly improving, and people's requirements for the environment they live in are also increasing. Through air conditioning, air purifiers, heating and other means, the living and working environment is constantly optimized and improved. Thermal comfort is the evaluation requirement system that arises therefrom. The most commonly used thermal comfort evaluation method is the PMV-PDD (Predicted Mean Vote-Percentage of Dissatisfied) model. This model considers factors such as human activity level, clothing thermal resistance (clothing condition), air temperature, average radiant temperature, air flow speed and air humidity, and determines the cold and warm feeling level of most people by subjective feeling test under the condition of satisfying the human heat balance equation. However, this system still has deficiencies in terms of rapidity and accuracy of measurement because it relies heavily on human subjective feelings. In addition, this system is an evaluation of people in a stable state, and cannot perform real-time detection and evaluation of thermal comfort for working groups, especially for special operation groups. For a certain number of people, the thermal comfort of some people cannot be met even if the thermal comfort of most people is met.

[0003] To achieve accurate detection of human physiological parameters, the commonly used method at present is to use a multi-parameter physiological monitoring system. By combining wearable clothes and various flexible sensors that detect different physiological parameters of the human body, a skin-friendly and portable device is designed to detect the human body at any time. For example, the invention with the patent number CN202011167352.8 emphasizes the integration of the sensing module. Unlike traditional wearable monitoring clothes that distribute multiple sensor nodes over most of the body area, the invention focuses on collecting and processing multiple physiological parameters such as body temperature, heart rate, heart rate variability, respiratory frequency and blood oxygen saturation on a single arm of the human body. The invention with the patent number CN202010834214.4 optimizes the start-up time of the system. According to the detection purpose, the time parameter is set to control the start and stop, and the endurance of the system is improved. However, the existing devices are medical devices, so the data detected by them are tests of complex physiological parameters of the human body, and the focus is on monitoring the physiological parameters of the human body. However, they do not involve the detection of external environment, and cannot be used for the evaluation of thermal comfort environment. SUMMARY

[0004] The present application overcomes the deficiencies of the prior art and solves the technical problem of providing a thermal comfort detection device and system that integrates objective environmental parameters and human physiological parameter monitoring to evaluate the thermal comfort of the environment and achieve optimal control of environmental equipment.

[0005] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows: a thermal comfort detection device, comprising: an internal data monitoring module, an external data monitoring module, a control module and a communication module,

[0006] The internal data monitoring module is used for monitoring human physiological parameters.

[0007] The external data monitoring module is used for monitoring environmental parameters.

[0008] The control module is used for collecting the data collected by the internal data monitoring module and the external data monitoring module, and sending the data to an upper computer through the communication module, and is also used for sending a control signal to an environmental device according to a comfort level rating signal received by the communication module.

[0009] The internal data monitoring module comprises at least three of a heart rate collection module, a blood pressure collection module, a sweat collection module and a body temperature detection module.

[0010] The thermal comfort detection device further comprises a first wearable fixing device and a first energy supply module, the first energy supply module is arranged on the first wearable fixing device, and the internal data monitoring module is arranged inside the first wearable fixing device and in contact with the skin.

[0011] The external data monitoring module is arranged on the first wearable fixing device.

[0012] The thermal comfort detection device further comprises a second wearable fixing device and a second energy supply module, and the second energy supply module and the external data monitoring module are arranged on the second wearable fixing device.

[0013] The external data monitoring module comprises an environmental illumination collection monitoring module, an environmental temperature collection monitoring module, an environmental humidity collection monitoring module and a wind speed collection monitoring module.

[0014] The communication module is a Bluetooth module.

[0015] In addition, the present application also provides a thermal comfort detection system, comprising the thermal comfort detection device, and further comprising an upper computer, the upper computer is used for calculating comfort level data according to the data collected by the internal data monitoring module and the external data monitoring module, and sending the comfort level data to the control module.

[0016] The method for calculating the comfort level data by the upper computer is as follows:

[0017] S1, collecting and processing the monitoring data of the internal data monitoring module and the external data monitoring module in a sampling period, and taking the median of each monitoring data as the collection data in the sampling period;

[0018] S2, each item of data is listed together with each level of standard value data set as a decision matrix Am, the normalized matrix A' of which is calculated, and a weight vector B is constructed to weight the normalized vector A' to obtain a normalized weighted decision matrix A";

[0019] S3, determining the positive ideal solution A of the thermal comfort rating + and the negative ideal solution A - :

[0020] wherein

[0021] wherein,

[0022] wherein, m and n represent the number of rows and columns of the weighted decision matrix A", the rows correspond to different sampling times, and the columns correspond to different data types;

[0023] S4, the thermal comfort rating number is calculated, and the calculation formula is:

[0024]

[0025]

[0026]

[0027] wherein represents the relative gap between the normalized weighted decision matrix A" and the positive ideal solution, represents the relative gap between the normalized weighted decision matrix A" and the negative ideal solution, A" ij represents the matrix element of the i-th row and the j-th column in the normalized weighted decision matrix A" + j represents the j-th element in the positive ideal solution matrix of the thermal comfort rating, A - j represents the j-th element in the negative ideal solution matrix of the thermal comfort rating.

[0028] The method for the host computer to obtain the comfort data is:

[0029] S1, collecting monitoring data of internal data monitoring module and external data monitoring module in processing period; the processing period is 5-20 minutes;

[0030] S2, quantitizing the data in the sampling period to obtain a standard matrix AA; the rows of the standard matrix AA are the sampling times in the processing period, and the columns are n=s p wherein, p is equal to the number of sensor data types, and s is the number of quantized value types;

[0031] S3, obtaining the comfort rating in each sampling period by using the TOPSIS method, obtaining a comfort vector S according to the comfort rating in each sampling period, and obtaining an augmented matrix M by taking the comfort vector S as the last column of a standard matrix AA;

[0032] S4, calculating each synthetic variable alpha of the augmented matrix M i ;

[0033] S5, calculating a discriminant function J 2 , and obtaining the contribution of each discriminant factor combination by taking the partial derivative of the discriminant function to each discriminant factor;

[0034] S5, calculating the current comfort degree according to the contribution of each discriminant factor combination.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] The present application provides a thermal comfort detection device and system, which integrates objective environment and human physiological parameter monitoring, obtains the non-stable state of the relative objective local heat flow state of the human body by analyzing the comfort condition of the human body in the non-steady state environment, and then can improve the accuracy of data monitoring and analysis by analyzing the results for intelligent control of the environmental equipment.

[0037] In addition, the collected data can be transmitted to building equipment or environmental operation equipment, such as air conditioners, air purifiers, etc., to participate in the intelligent control of the building equipment. It is distinguished from the existing PMV-PPD steady-state model by analyzing the comfort condition of the human body in the non-steady state environment, and obtaining the non-stable state of the relative objective local heat flow state of the human body by remote data analysis. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A structural schematic diagram of a thermal comfort detection device provided for the first embodiment of the present application;

[0039] Figure 2 A circuit structure schematic diagram of a thermal comfort detection device provided for the first embodiment of the present application;

[0040] Figure 3 A structural schematic diagram of a thermal comfort detection device provided for the second embodiment of the present application;

[0041] Figure 4 Another structural schematic diagram of a thermal comfort detection device provided for the second embodiment of the present application;

[0042] In the diagram: 1 is the first wearable fixed device, 2 is the internal data monitoring module, 3 is the external data monitoring module, 4 is the heart rate acquisition module, 5 is the blood pressure acquisition module, 6 is the sweat acquisition module, 7 is the body temperature detection module, 8 is the second wearable fixed device, 9 is the ambient light acquisition and monitoring module, 10 is the ambient temperature acquisition and monitoring module, 11 is the ambient humidity acquisition and monitoring module, 12 is the wind speed acquisition and monitoring module, 13 is the power display module, and 14 is the communication module. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but 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.

[0044] Example 1

[0045] like Figures 1-2 As shown, Embodiment 1 of the present invention provides a thermal comfort detection device, including an internal data monitoring module 2, an external data monitoring module 3, a control module, and a communication module. The internal data monitoring module is used to monitor human physiological parameters; the external data monitoring module is used to monitor environmental parameters; the control module is used to collect data collected by the internal and external data monitoring modules and send it to a host computer through the communication module, and is also used to send control signals to environmental devices based on the comfort rating signals received by the communication module.

[0046] Specifically, in this embodiment, the internal data monitoring module includes a heart rate acquisition module 4, a blood pressure acquisition module 5, a sweat acquisition module 6, and a body temperature detection module 7.

[0047] Specifically, the thermal comfort testing device of this embodiment further includes a first wearable fixing device 1 and a first power supply module. The first power supply module is disposed on the first wearable fixing device, and the internal data monitoring module is disposed inside the first wearable fixing device and in contact with the skin. The internal monitoring device, located on the skin-contacting side inside the wearable fixing device, is made of a flexible material with a large contact area to minimize human discomfort while ensuring accurate measurement data.

[0048] Specifically, in this embodiment, the external data monitoring module is installed on the first wearable fixed device. Furthermore, this embodiment also includes a power display module, which is located on the outside of the device and is used for power indication; it can be represented as a display screen showing the power level.

[0049] Specifically, in the embodiment, the external data monitoring module includes an ambient illumination acquisition monitoring module, an ambient temperature acquisition monitoring module, an ambient humidity acquisition monitoring module, and a wind speed acquisition monitoring module.

[0050] Specifically, in the embodiment, the communication module includes a Bluetooth module or other wireless communication module.

[0051] In the embodiment, the internal data monitoring module 2 and the external data monitoring module 3 are arranged in the shell, the shell can be fixed on the base of the wearable fixing device by screws, the external data monitoring module of the thermal comfort monitoring device is fixed on the wrist part by a magic tape, the internal monitoring module is fixed on the inner side of the thigh by a magic tape, the connection between the battery and each monitoring module is ensured to be normal, and the leg ring belt is fixed as much as possible at the root of the thigh.

[0052] For the external data monitoring module, the wind speed sensor is selected to be a thermal temperature difference type wind speed sensor based on CMOS integration. A constant power (CP) or constant temperature difference (CTD) control chip is used to measure the differential mode signals of two vertical heat stacks to obtain the wind speed and direction information. The sensor has the characteristics of high integration, low power consumption, simple process and low cost, and has a large measurement range.

[0053] The wind speed of the external monitoring device is measured in real time and sent to the flexible mainboard memory to wait for remote transmission to obtain the relative reliability of the data under the premise of ensuring sensor integration. The wind speed sensor adopts closed-loop control. When the body temperature is higher than the ambient temperature, the wind speed and direction information is obtained by measuring the vertical symmetric temperature difference ΔTx and ΔTy. The differential mode signal can be output in the vertical direction:

[0054]

[0055] In the formula, V 12 , V 34 is the differential mode signal output by the two groups of heat stacks; ΔT is the temperature value higher than the environment; α is the Seebeck coefficient, which can be 100 μV / K, and N is the number of thermocouple series of each heat stack; is the included angle between the wind direction and the reference axis X; V is the voltage related to the wind speed. Therefore, the measured wind speed and direction can be expressed as:

[0056]

[0057] According to King's theorem, the power dissipation generated by forced convection can be obtained from the sensor characteristics and material geometric properties to obtain the wind speed information:

[0058]

[0059] In the formula, P totalFor power, c1, c2 are constants related to the geometric and physical properties of the sensor, in the selection of the illuminance sensor, the price economical light sensor based on the photoelectric effect is selected instead of the illuminance sensor, the change of the electric signal is generated through the change of the optical signal, and then the electric signal is monitored and stored through the transistor amplification circuit. The light sensor is made of an external photoelectric effect device, which uses a highly integrated phototube to transfer the energy of photons to electrons in the cathode, and forms electron emission when the incident light frequency is greater than the limit frequency. The change of the current generated by the change of the light intensity of the photoelectron is monitored and transmitted.

[0060] For the internal data monitoring module 2, when wearing, the close contact with the measured part should also be ensured to ensure that the inside body temperature detection probe, the skin sweat detection probe and the skin of the thigh root are in good contact.

[0061] Embodiment two

[0062] The embodiment two of the present application provides a thermal comfort detection device, which is the same as the embodiment one, comprising an internal data monitoring module 2, an external data monitoring module 3, a control module and a communication module, the internal data monitoring module is used for monitoring the physiological parameters of the human body; the external data monitoring module is used for monitoring the environmental parameters; the control module is used for collecting the data collected by the internal data monitoring module and the external data monitoring module, and sending the data to the upper computer through the communication module, and is also used for sending a control signal to an environmental device according to a comfort level rating signal received by the communication module.

[0063] As shown in Figures 3-4 Unlike the integrated design of the embodiment one, in the embodiment, a split design is adopted, comprising a first wearable fixing device and a second wearable fixing device, which are used for setting the internal data monitoring module 2 and the external data monitoring module 3 respectively, in addition, the control module and the communication module respectively comprise two control units and two communication units, and the first wearable fixing device and the second wearable fixing device are respectively provided with one control unit and one communication unit.

[0064] Specifically, in the embodiment, the internal data monitoring module comprises at least three of a heart rate acquisition module, a blood pressure acquisition module, a sweat acquisition module and a body temperature detection module.

[0065] Specifically, the thermal comfort detection device of the embodiment comprises two energy supply modules, a first energy supply module is arranged on the first wearable fixing device, and a second energy supply module is arranged on the second wearable fixing device.

[0066] Specifically, in the embodiment, the external data monitoring module is arranged on the first wearable fixing device.

[0067] Specifically, in the embodiment, the external data monitoring module comprises an ambient illuminance acquisition monitoring module, an ambient temperature acquisition monitoring module, an ambient humidity acquisition monitoring module and a wind speed acquisition monitoring module.

[0068] Specifically, in the embodiment, the communication module comprises a Bluetooth module or other wireless communication module.

[0069] Embodiment three

[0070] The embodiment three of the present application provides a thermal comfort detection system, comprising the thermal comfort detection device of the embodiment one or two, and further comprising a host computer.

[0071] Specifically, in the embodiment, the data processing and analysis is mainly realized based on a TOPSIS comprehensive evaluation algorithm.

[0072] S1, monitoring data of the internal data monitoring module and the external data monitoring module in a first processing period is collected, and the median of each monitoring data is taken as the collected data in the sampling period; in the embodiment, the data collected by each monitoring module is collected at an interval of 10s as a processing period.

[0073] S2, each item of data and each level of standard value data group are listed as a decision matrix A m , a normalized matrix A' is calculated, a weight vector B is constructed to perform weighted processing on the normalized vector A', and a normalized weighted decision matrix A'' is obtained; wherein the number of rows of the weighted decision matrix A'' is n, each row corresponds to different sampling times, the number of columns is m, and each column represents different data types, for example, the first column represents wind speed data, the second column represents illuminance data, the third column represents temperature data, and the fourth column represents humidity data.

[0074] S3, a positive ideal solution A + and a negative ideal solution A - of the thermal comfort rating are determined.

[0075] wherein

[0076] wherein,

[0077] S4, calculate the thermal comfort rating number, the calculation formula is:

[0078]

[0079]

[0080]

[0081] Wherein The relative gap between the normalized weighted decision matrix A" and the positive ideal solution is represented, The relative gap between the normalized weighted decision matrix A" and the negative ideal solution is represented, A" ij The matrix element of the normalized weighted decision matrix A" in the ith row and the jth column is represented, A + j The jth element in the positive ideal solution matrix of the thermal comfort rating is represented, A - j The jth element in the negative ideal solution matrix of the thermal comfort rating is represented.

[0082] Embodiment four

[0083] Embodiment four of the present application provides a thermal comfort detection system, which is the same as embodiment three, comprising a thermal comfort detection device according to embodiment one or two, further comprising a host computer, wherein the host computer is used to calculate the comfort data according to the data collected by the internal data monitoring module and the external data monitoring module, and send the comfort data to the control module.

[0084] In this embodiment, the method for the host computer to calculate the comfort data is:

[0085] S1, collect the monitoring data of the internal data monitoring module and the external data monitoring module in the second processing period; the second processing period can be 5-20 minutes;

[0086] S2, quantitatively process the data in the sampling period to obtain a standard matrix AA; the number of rows of the standard matrix AA is the sampling times in the processing period, and the number of columns is n=S1*S2*... P Wherein, p is equal to the number of sensor data types, which is 4 in this embodiment, corresponding to wind speed, illumination, temperature and humidity respectively, Sp is the quantitative assignment type number of the pth sensor, in this embodiment, S1=S2=...=S P =2, that is, n=S P =2 P That is, the data quantization processing of each sensor is two, corresponding to 0 and 1 respectively.

[0087] In this embodiment, the thermal comfort rating calculated by the host computer is received through the communication module, and the rating is output to the air conditioner, air purifier and other related devices or relevant data monitoring center. After outputting the above rating, the data output every ten seconds is stored in the host computer using the quantification II method, and the comfort data under a certain time scale is stored. Since the data collected by the TOPSIS method has a short step, frequent output signal changes are easy to occur, which leads to difficult control. Therefore, the collected data is read in 10 minutes, the standard matrix A measured is quantitatively processed, the test data of the single index within the recommended index range is determined as comfortable, and the rest of the range is determined as uncomfortable, and the value is 0, and the matrix AA is obtained. Taking the four types of external data collected by the sensor of the device as an example: A represents wind speed, B represents illumination, C represents temperature, and D represents humidity;

[0088] Each of them has a recommended value as follows:

[0089]

[0090] In this embodiment, all data types that need to be evaluated can obtain a code, and each element x (A，B，C，D) in the synthetic variable is assigned a value of 1 or 0:

[0091] x (A，B，C，D) = x (0，0，0，0) ... x (1，1，1，1) = 1 or 0; (10)

[0092] The value of the data that needs to be evaluated is written as a row vector, and the data obtained at different times is written as a column vector, and the matrix AA is obtained.

[0093] Through the above assignment, the data of each sensor collected can be converted. Assuming that in the first sampling data, A=1, B=0, C=0, and D=1, the first row element of the standard matrix AA is [1000, 0000, 0000, 0001], and there are 16 column data in total.

[0094] S3, using the TOPSIS method to obtain the comfort rating in each sampling period, obtaining the comfort vector S according to the comfort rating in each sampling period, and taking the comfort vector S as the last column of the standard matrix AA to obtain the augmented matrix M. Assuming:

[0095]

[0096] Then:

[0097]

[0098] S4, calculate each synthetic variable αi ;

[0099] At this point, the augmented matrix M is actually about x. (A，B，C，D) The system of linear equations can also be expressed as follows, where α1 is called the composite variable. In the general equation (14), to ensure the universality of the calculation of the composite variable, x is used. jk In this example, x represents (A，B，C，D) :

[0100] α1=x (0，0，0，0) +x (0，0，0，1) +...+x (1，1，1，1) (13)

[0101]

[0102] in:

[0103] Where: x jk This represents the element in row j and column k of matrix AA;

[0104] Q represents the number of labels. In this example, it is A, B, C, and D, so Q = 4.

[0105] Jj represents the Jjth category of the jth label, which in this example is either the 0th or 1st category corresponding to the A label;

[0106] δ(j, k) j The explanation of ) = 0 or 1: For the j-th label, the value of the k-th data in the j-th category is either "not within the recommended range" or "is within the recommended range".

[0107] Extending this to multiple evaluation variables, we have the following calculation formula: where the inter-group variance is S. B The within-group variance is S W Population variance S A For J 2 For element x (A，...Z) Take the partial derivatives separately, then let Let the discriminant function J 2 The formula for obtaining the maximum value is as follows:

[0108]

[0109]

[0110]

[0111]

[0112] Where g is the number of judgment benchmark groups, corresponding to comfort / discomfort in this embodiment, i.e. g = 2 (i.e., the two groups where the last column element in the augmented matrix M is 1 or 0), α li It is the i-th composite variable within the l-th judgment benchmark group; It is the average value of the composite variables within the l-th judgment benchmark group; It is the average of all composite variables; n l It determines the number of composite variables within benchmark group 1.

[0113] By examining each term x separately (A，...Z) Find the partial derivative, fate It can form a concept about x (A，...Z) The system of equations thus yields each x (A，...Z) That is, the degree of dependence of each combination of discriminant factors is used as the basis for the final judgment. Here, we still take the four variables A, B, C, and D in this example as an example. The degree of dependence of the example is shown in the table below:

[0114] Table 1. Assignment Value Table

[0115]

[0116] Generally, when the degree of attachment is greater than 0, the degree of tendency toward that direction is greater, and vice versa. In this example, the greater the degree of attachment, the greater the certainty of the judgment of comfort.

[0117] It should be noted that in this embodiment, the degree of entitlement can also be calculated using the eigenvalues ​​of the matrix. The method of using matrix transformations to obtain the eigenvector corresponding to the largest eigenvalue as the degree of entitlement also belongs to a type of quantitative method II. (η = J, x represents the variable to be evaluated, x' represents the transpose of x)

[0118] At x′S T Under the condition x = 1, let (S B -η 2 S T Given x = 0, we need to find the determinant S. B -η 2 S T |=0. The intrinsic value η can be solved. 2 And the intrinsic vector x, that is, the degree of association of each vector to be evaluated.

[0119] By using each combination of discriminant factors x that has been obtained (A，...Z) The degree of dependence can be directly calculated and determined by the value of the composite variable. The degree of dependence is used to judge comfort. In this embodiment, examples of comfort output are shown in the table below.

[0120] Table 2 Comfort Output

[0121]

[0122] By calculating the degree of receptivity and using its positive or negative value to issue control signals, valves in air conditioning equipment can be controlled to rotate in both directions, and the flow rate can be increased or decreased to achieve the control purpose.

[0123] The wearable fixing device described in this invention includes, but is not limited to, leg rings, sleeves, headbands, etc. Therefore, similar devices not limited to the above-mentioned implementations fall within the scope of protection of this invention.

[0124] Those skilled in the art should recognize that the sensor selection scheme and sensor arrangement scheme described in the specific embodiments of the present invention are convenient measures to facilitate the understanding of the working method and specific implementation of the present invention by relevant thermal personnel. Therefore, all improved schemes based on the above-mentioned "external monitoring module" and "internal monitoring module", as well as the equipment and technology based on the present invention that utilize external parameters and physiological parameters to monitor, transmit, and analyze data, and provide data support and formulate operating strategies for related equipment, are all within the scope of patent protection.

[0125] The power supply systems for the aforementioned external and internal monitoring modules can be replaced in various ways, such as battery power, rechargeable lithium battery power, and solar power.

[0126] The aforementioned data transmission analysis forms and contents that differ from PMV and are combined with human body parameters are not limited to the cross-analysis mentioned above. They can be discriminant analysis, quantitative analysis, and other analysis methods based on statistical principal component analysis, deep learning, and other computer technology-based analysis methods. The spirit of using PMV as a reference and combining it with the objective physiological parameters of the human body obtained from monitoring is also within the scope of patent protection.

[0127] The comfort detection device of the present invention includes, but is not limited to, the separate design and integrated design of the above-mentioned "external monitoring module" and "internal monitoring module" (see attached figures). It should be noted that the scope of protection of the present invention is the form that simultaneously measures external environmental and human body parameters and improves based on PMV, and is not limited to the actual product design. The form in the attached figures of this patent is a more recommended form derived from the experimental research of the applicant's team.

[0128] The present invention provides various forms of battery level indication, including but not limited to displays, vibration devices, buzzers, and other methods that attract the user's attention.

[0129] The present application comprises the technical process of calculating the current thermal comfort of human body by analyzing the physiological data obtained by remote transmission on the host computer. The data analysis method described in the present application is based on the PMV equation and the human body heat balance equation. The dynamic parameters of the human body and the environment can be obtained in real time by each monitoring module, and transmitted to the host computer by the communication module. The host computer analyzes and calculates the local heat flow change rule of the human body, thereby analyzing and evaluating the thermal comfort of the environment where the human body is currently located. This can make up for the limitations of the steady-state thermal balance comfort discrimination method of PMV.

[0130] Meanwhile, the present application can take advantage of wearable devices. By changing the wearing position, the physiological data of different parts of the human body can be monitored, and multiple sets of relevant external parameters affecting the PMV index and physiological parameters of the human body can be obtained and cross-compared. The overall thermal discomfort brought by multiple sets of non-steady-state local discomfort of different parts, such as wind feeling, local heat exposure, and cold radiation, cannot be obtained by the steady-state model. The data are analyzed and the thermal comfort analysis is completed through the technical process.

[0131] The present application can detect the physiological parameters of the human microenvironment and the external environment parameters in real time and evaluate and reflect the objective monitoring data of various labor groups and even special working groups. The data are transmitted and analyzed by the remote transmission module including but not limited to Bluetooth, and the final output is more accurate thermal comfort data compared with personal sensory data. On this basis, the technical equipment related to human comfort is provided with parameters in time, and participates in the control strategy of building equipment such as air conditioning unit. The output results can be used for optimizing and improving the working environment or for analyzing and controlling air purifiers, air conditioners, fans and other equipment to create a more comfortable working environment.

[0132] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A thermal comfort detecting device, characterized by, The application relates to a thermal comfort detection device, which comprises an internal data monitoring module, an external data monitoring module, a control module and a communication module. The internal data monitoring module is used for monitoring human physiological parameters. The external data monitoring module is used for monitoring environmental parameters. The control module is used for collecting data collected by the internal data monitoring module and the external data monitoring module, and sending the data to an upper computer through the communication module to calculate comfort degree, and is also used for sending a control signal to an environmental device according to the comfort degree received by the communication module. The application further comprises a first wearable fixing device and a first energy supply module, the first energy supply module is arranged on the first wearable fixing device, the internal data monitoring module is arranged on the inner side of the first wearable fixing device and is in contact with the skin. The application further comprises a second wearable fixing device and a second energy supply module, the second energy supply module and the external data monitoring module are arranged on the second wearable fixing device. The upper computer is used for calculating comfort degree data according to the data collected by the internal data monitoring module and the external data monitoring module. S1, monitoring data of the internal data monitoring module and the external data monitoring module in a processing period is collected; the processing period is 5-20 minutes; S2, quantizing the data in the sampling period to obtain a standard matrix AA; the number of rows of the standard matrix AA is the sampling number in the processing period, and the number of columns is s p wherein p is equal to the quantity of the sensing data categories, and s is the quantity of the quantization assignment categories. S3, a thermal comfort degree rating in each sampling period is obtained by using a TOPSIS method, a comfort degree vector S is obtained according to the thermal comfort degree rating in each sampling period, and the comfort degree vector S is taken as the last column of a standard matrix AA to obtain an augmented matrix M; S4. Calculate each synthetic variable of the augmented matrix M ; S5, calculating the discriminant function J 2 And the discriminant function to each discriminant factor, get each discriminant factor combination of the contribution degree; S6, the current comfort degree is calculated according to the contribution of each discriminant factor combination.

2. The thermal comfort detection device according to claim 1, wherein The internal data monitoring module comprises at least three of a heart rate collecting module, a blood pressure collecting module, a sweat collecting module and a body temperature detecting module.

3. The thermal comfort detection device of claim 1, wherein, The external data monitoring module comprises an environmental illumination collecting monitoring module, an environmental temperature collecting monitoring module, an environmental humidity collecting monitoring module and a wind speed collecting monitoring module.

4. The thermal comfort detection device of claim 1, wherein, The communication module is a Bluetooth module.

5. A thermal comfort detection system characterized by, The application further comprises an upper computer, the upper computer is used for calculating comfort degree data according to the data collected by the internal data monitoring module and the external data monitoring module, and sending the data to the control module.

6. A thermal comfort detection system according to claim 5, wherein, The method for calculating the thermal comfort degree rating data by the upper computer is as follows: S1, monitoring data of the internal data monitoring module and the external data monitoring module in a processing period is collected; the processing period is 5-20 minutes; S2, each data and each level of standard value data group are jointly listed as a decision matrix Am, a normalized matrix A' is calculated, a weight vector B is constructed to perform weighted processing on the normalized matrix A', and a normalized weighted decision matrix A'' is obtained; S3, positive ideal solution for determining thermal comfort rating and negative ideal solution : Wherein, m and n represent the number of columns and rows of the normalized weighted decision matrix A'', the rows correspond to different sampling times, and the columns correspond to different data types. S4, the thermal comfort degree rating data is calculated, and the calculation formula is as follows: wherein represents the relative gap between the normalized weighted decision matrix A" and the positive ideal solution, represents the relative gap between the normalized weighted decision matrix A" and the negative ideal solution, represents the matrix element of the i-th row and j-th column of the normalized weighted decision matrix A", represents the j-th element of the positive ideal solution matrix of thermal comfort rating, represents the j-th element of the negative ideal solution matrix of thermal comfort rating.

Images