Infrared temperature detection device, air conditioner and control method of air conditioner

Abstract

The invention discloses an infrared temperature detection device, an air conditioner and a control method of the air conditioner, and the device comprises an infrared camera which is used for detecting an infrared image of a to-be-detected user; the first temperature sensor is used for detecting the current environment temperature; the second temperature sensor is used for detecting the equipment temperature of the infrared camera; the distance sensor is used for detecting the current distance of the to-be-detected user; the controller is configured to obtain the infrared image, the environment temperature, the equipment temperature and the current distance; inputting the infrared image into a preset filter for filtering; calculating lumen value data and a background lumen value of the to-be-detected user according to the filtered infrared image; and inputting the lumen value data, the background lumen value, the environment temperature, the equipment temperature and the current distance into a skin temperature detection model to obtain human body surface temperature data output. According to the scheme, the influence of the noise of the infrared image, the environment temperature and the change of the detection distance on the temperature detection precision can be reduced, and the detection precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature detection, and particularly to an infrared temperature detection device, an air conditioner, and a control method for an air conditioner. Background Art

[0002] Compared with traditional temperature measurement technologies, infrared temperature measurement technology has the advantages of high sensitivity, fast speed, wide temperature measurement range, etc., and is thus widely used in fields such as home appliances and medical treatment. An infrared camera can obtain an infrared image of a measured target in a non-contact and long-distance manner, and can also measure the surface temperature of the target according to the output image.

[0003] However, in the actual use process, in the existing infrared human body temperature measurement system, due to the noise of the infrared image, the environmental temperature, and the change of the detection distance, the accuracy of the temperature output by the infrared camera will be affected, resulting in a low temperature measurement accuracy. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an infrared temperature detection device, an air conditioner, and a control method for an air conditioner, which can reduce the influence of the noise of the infrared image, the environmental temperature, and the change of the detection distance on the temperature detection accuracy and improve the detection accuracy.

[0005] The embodiments of the present invention provide an infrared temperature detection device, including:

[0006] An infrared camera for detecting an infrared image of a user to be measured;

[0007] A first temperature sensor for detecting the current environmental temperature;

[0008] A second temperature sensor for detecting the device temperature of the infrared camera;

[0009] A distance sensor for detecting the current distance of the user to be measured;

[0010] A controller configured to:

[0011] Obtain the infrared image, the environmental temperature, the device temperature, and the current distance;

[0012] Input the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0013] Calculate the lumen value data of the user to be measured and the background lumen value according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured;

[0014] Input the luminescence value data, the background luminescence value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data.

[0015] Preferably, the controller is further configured to:

[0016] Perform a fast Fourier transform on the infrared image using a preset discrete Fourier transform formula, convert the infrared image from the spatial domain to the frequency domain space to obtain an image spectrum;

[0017] Input the image spectrum into a preset Butterworth band-pass filter for filtering to obtain a filtered frequency-domain image;

[0018] Use the inverse discrete Fourier transform formula to transfer the frequency-domain image from the frequency domain space to the spatial domain to obtain a filtered infrared image.

[0019] As a preferred solution, the discrete Fourier transform formula is

[0020] The transfer function of the Butterworth band-pass filter

[0021] The inverse discrete Fourier transform formula is:

[0022]

[0023] Among them, the frequency-domain image G(u,v) = F(u,v)H(u,v), F(u,v) is the image spectrum, f(x,y) is the luminescence value in the spatial domain of the infrared image, x and y are discrete real variables, respectively representing the number of rows and columns corresponding to the two-dimensional array of the infrared image f(x,y), x = 0, 1, 2,..., M - 1; y = 0, 1, 2,..., N - 1, u and v are discrete frequency variables, u = 0, 1, 2,..., M - 1; v = 0, 1, 2,..., N - 1, M and N are respectively the number of rows and columns corresponding to the two-dimensional array of the infrared image f(x,y); W is the passband bandwidth, D0 is the passband center radius, where n is the order of the filter; D(u,v) is the band-pass filter on the circular domain with D0 as the radius, D 2 (u,v) is the square of the filtering distance; g(x,y) is the filtered infrared image.

[0024] Preferably, the controller is further configured to:

[0025] Pre-acquire a training data set, and input the training data set into a pre-constructed fully connected neural network model for training;

[0026] Optimize the fully connected neural network model according to the pre-acquired test data set and the preset objective function, find the model parameters corresponding to the optimal objective function, and obtain the skin temperature detection model;

[0027] Among them, the training data set and the test data set include the lumen value data, background lumen value, ambient temperature, device temperature, and current distance as the model input data, and the human body surface temperature data as the model output data.

[0028] Further, the process of obtaining the training data set or the test data set includes:

[0029] In a preset experimental environment, obtain the current infrared image by changing the distance between the experimental personnel and the infrared camera and / or the current ambient temperature;

[0030] Input the current infrared image into a preset filter for filtering to obtain the filtered infrared image, determine the current model input data, and read the current model output data by setting temperature sensors at different parts of the experimental personnel;

[0031] Use the current model input data and the corresponding model output data as the training data set or the test data set.

[0032] Preferably, the background lumen value is specifically the average lumen value of several pixels at a preset pixel distance from the area where the user to be measured is located in the infrared image.

[0033] As a preferred solution, the fully connected neural network model includes an input layer, two hidden layers, and an output layer;

[0034] The output of the fully connected neural network model

[0035] The objective function is

[0036] Among them, A k (w, b, X) represents the calculation result of the k-th neuron in the hidden layer, p is the number of neurons in the hidden layer, σ(·) represents the Relu activation function, w is the weight, b is the bias, X is the input variable, x i represents the i-th input variable, w i represents the weight of the i-th input variable, q is the number of input variables; L is the mean absolute error, S is the number of skin surface temperature samples at each part in the test data set; z i is the predicted value calculated by the fully connected neural network model for the i-th model input data in the test data set, r iIt is the output data of the i-th model in the test dataset.

[0037] Preferably, the lumen value data includes the lumen values of the forehead, cheeks, and back of the hand of the user to be measured.

[0038] An embodiment of the present invention also provides an air conditioner, which includes:

[0039] An air conditioner body;

[0040] An infrared camera for detecting an infrared image of the user to be measured;

[0041] A first temperature sensor for detecting the current ambient temperature;

[0042] A second temperature sensor for detecting the device temperature of the infrared camera;

[0043] A distance sensor for detecting the current distance of the user to be measured;

[0044] A controller configured to:

[0045] Obtain the infrared image, the ambient temperature, the device temperature, and the current distance;

[0046] Input the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0047] Calculate the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, and the lumen value data includes the lumen values of different parts of the user to be measured;

[0048] Input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data;

[0049] Match a corresponding control instruction in a preset control instruction matching library according to the human body surface temperature data, and control the operation of the air conditioner body according to the control instruction.

[0050] An embodiment of the present invention also provides a control method for an air conditioner, which includes:

[0051] An air conditioner body;

[0052] An infrared camera for detecting an infrared image of the user to be measured;

[0053] A first temperature sensor for detecting the current ambient temperature;

[0054] A second temperature sensor for detecting the device temperature of the infrared camera;

[0055] A distance sensor for detecting the current distance of the user to be measured;

[0056] A controller;

[0057] The method includes:

[0058] Obtaining the infrared image, the ambient temperature, the device temperature, and the current distance;

[0059] Inputting the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0060] Calculating the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured;

[0061] Inputting the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data;

[0062] Matching a corresponding control instruction in a preset control instruction matching library according to the human body surface temperature data, and controlling the operation of the air conditioner body according to the control instruction.

[0063] Compared with the prior art, the infrared temperature detection device, the air conditioner, and the control method of the air conditioner disclosed in the present invention include an infrared camera for detecting the infrared image of the user to be measured; a first temperature sensor for detecting the current ambient temperature; a second temperature sensor for detecting the device temperature of the infrared camera; a distance sensor for detecting the current distance of the user to be measured; a controller configured to: obtain the infrared image, the ambient temperature, the device temperature, and the current distance; input the infrared image into a preset filter for filtering to obtain a filtered infrared image; calculate the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured; input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data. It can reduce the influence of the noise of the infrared image, the ambient temperature, and the change of the detection distance on the temperature detection accuracy and improve the detection accuracy. Description of the Drawings

[0064] Figure 1 is a schematic structural diagram of the infrared temperature detection device provided by an embodiment of the present invention;

[0065] Figure 2It is a schematic flowchart of the work performed by the controller provided in an embodiment of the present invention;

[0066] Figure 3 It is another schematic flowchart of the work performed by the controller provided in an embodiment of the present invention;

[0067] Figure 4 It is a schematic flowchart of the filtering process provided in an embodiment of the present invention;

[0068] Figure 5 It is yet another schematic flowchart of the work performed by the controller provided in an embodiment of the present invention;

[0069] Figure 6 It is a schematic diagram of an infrared image provided in an embodiment of the present invention;

[0070] Figure 7 It is a schematic diagram of the structure of the fully connected neural network model provided in an embodiment of the present invention;

[0071] Figure 8 It is a schematic diagram of the structure of an air conditioner body in an embodiment provided in an embodiment of the present invention;

[0072] Figure 9 It is a partial schematic diagram of the refrigerant circuit of the air conditioner body in an embodiment of the present invention;

[0073] Figure 10 It is yet another schematic flowchart of the work performed by the controller provided in an embodiment of the present invention. Detailed implementation manners

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0075] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0076] The terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0077] In the description of this application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0078] See Figure 1 , which is a schematic structural diagram of an infrared temperature detection device provided by an embodiment of the present invention. The infrared detection device includes:

[0079] An infrared camera for detecting an infrared image of a user to be measured;

[0080] A first temperature sensor for detecting the current ambient temperature;

[0081] A second temperature sensor for detecting the device temperature of the infrared camera;

[0082] A distance sensor for detecting the current distance of the user to be measured;

[0083] A controller.

[0084] Specifically, the controller 140 is respectively connected to the infrared camera 150, the first temperature sensor 160, the second temperature sensor 170, and the distance sensor 180 to obtain data and perform corresponding control processes.

[0085] Among them, an infrared camera can obtain an infrared image of a target to be measured under non-contact and long-distance conditions, and can also measure the surface temperature of the target according to the output lumen value. Although infrared temperature measurement technology has many advantages, there are many factors that affect the accuracy of the lumen value output by the infrared camera. The main factors include stripe noise, environmental temperature, distance, etc. Stripe noise is formed when different readout circuits are used for sensors in different columns on the infrared focal plane array, and the difference in the bias voltage of the readout circuit generates light and dark on the infrared image. This strip noise usually appears as vertical stripes, and the stripes are relatively narrow, which will affect the maximum lumen value of the human body part in the infrared image, thereby affecting the temperature measurement accuracy. The environmental temperature will affect the internal environmental temperature of the system where the drive circuit board is located, and has a non-uniform effect on the output lumen value. The distance will directly affect the size of the target to be measured, make the generated image blurred, and also have a non-linear effect on the output lumen value, affecting the temperature measurement accuracy.

[0086] To avoid these technical problems in the infrared temperature measurement device, the solution of this application uses a first temperature sensor, a second temperature sensor, and a distance sensor for the user to detect the influence of the environmental temperature, device temperature, and current distance on infrared temperature measurement.

[0087] An infrared image of the user to be measured is obtained through the infrared camera, and the detected infrared image is output to the controller.

[0088] The first temperature sensor is deployed outside the housing of the infrared temperature detection device and is used to detect the environmental temperature of the current environment and output the detected environmental temperature to the controller.

[0089] The second temperature sensor is deployed inside the infrared camera for the user to detect the device temperature inside the infrared camera and output the detected device temperature to the controller.

[0090] The distance sensor is close to the infrared camera device and is used to detect the current distance between the user to be measured and the infrared camera and output the detected current distance to the controller.

[0091] The infrared temperature detection process is executed by the controller. Specifically, during the detection, refer to Figure 2 , which is a schematic flow chart of the work executed by the controller provided in the embodiment of the present invention. The controller executes the following steps:

[0092] Step S1, obtain the infrared image, the environmental temperature, the device temperature, and the current distance;

[0093] Step S2, input the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0094] Step S3: Calculate the lumen value data of the user to be measured and the background lumen value based on the filtered infrared image. The lumen value data includes the lumen values of different parts of the user to be measured.

[0095] Step S4: Input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human surface temperature data.

[0096] Specifically, refer to Figure 3 , which is another schematic flowchart of the work performed by the controller provided in the embodiment of the present invention. When the controller specifically performs temperature detection, the following steps are specifically executed;

[0097] Step S301: Obtain an infrared image. That is, obtain an infrared image of the user to be measured through an infrared camera.

[0098] Step S302: Filter the infrared image using a Butterworth band-pass filter. That is, input the infrared image into the Butterworth band-pass filter for filtering to obtain a filtered infrared image. Using the Butterworth band-pass filter to filter the stripe noise of the infrared image, removing the abnormal phenomenon of lumen value caused by noise, and improving the temperature measurement accuracy.

[0099] Step S303: Calculate the lumen value data and the background lumen value in the filtered infrared image. Among them, the lumen value data includes the lumen values of different parts of the user to be measured.

[0100] Specifically when obtaining, according to the existing infrared image temperature measurement and recognition method, identify the human body in the infrared image, mark different parts of the human body, and obtain the lumen values of different parts as the lumen value data. After identifying the human body in the infrared image, the background lumen value can identify the pixels outside the human body area as the background area, and determine the lumen value of the background area. Among them, by identifying the part interval of the user to be measured, that is, identifying a certain area as a certain part, taking the maximum lumen value in the area as the lumen value of the part, or taking the average value of the lumen values of all pixels in the area as the lumen value of the part, determine the lumen values of different parts of the user to be measured to obtain the lumen value data.

[0101] Among them, the background lumen value can also be based on identifying the background area outside the human body, and taking the average value of the lumen values of the background area as the background lumen value.

[0102] Step S304: Obtain the ambient temperature, the device temperature, and the current distance.

[0103] It should be noted that in this embodiment, the device temperature can specifically be the substrate temperature detected and obtained by a second temperature sensor configured on the circuit board of the infrared camera, and / or the shutter temperature detected and obtained by a second temperature sensor configured on the shutter of the infrared camera. By using the substrate temperature and / or the shutter temperature as the device temperature and considering the influence of the infrared camera device temperature on the infrared image, the accuracy of temperature detection is improved.

[0104] Step 305: Calculate using the skin temperature detection model to obtain the output of the human body surface temperature data. That is, input the lumen value data, background lumen value, ambient temperature, device temperature, and the current distance into the pre-trained skin temperature detection model to obtain the output of the human body surface temperature data.

[0105] When training the skin temperature detection model, a neural network model is designed to fit the skin surface temperature of the human body part. The lumen value, distance, ambient temperature, background lumen value, and device temperature data of the human body part in the infrared image are used as the input training data of the neural network, and the skin surface temperature data of each part of the human body collected by the corresponding thermocouple are used as the output training data of the neural network for fitting training to obtain the fitted skin surface temperature detection model.

[0106] Use the powerful non-linear learning ability of the pre-trained skin temperature detection model to fit the non-uniformity law of the influence of ambient temperature, device temperature, and distance on the lumen value, so as to obtain the correct temperature of the human body part.

[0107] This application uses a denoising method to eliminate stripe noise and solve the abnormal lumen value phenomenon caused by stripe noise. Then, use the powerful non-linear learning ability of the neural network to fit the non-uniformity law of the influence of ambient temperature, device temperature, and distance on the lumen value, improve the temperature measurement accuracy, and thus obtain the correct temperature of the human body part.

[0108] In another embodiment provided by the present invention, the controller is further configured to:

[0109] Perform a fast Fourier transform on the infrared image using a preset discrete Fourier transform formula to transform the infrared image from the spatial domain to the frequency domain space to obtain an image spectrum;

[0110] Input the image spectrum into a preset Butterworth band-pass filter for filtering to obtain a filtered frequency-domain image;

[0111] Use the inverse discrete Fourier transform formula to transform the frequency-domain image from the frequency domain space to the spatial domain to obtain a filtered infrared image.

[0112] When specifically implementing this embodiment, refer to Figure 4, which is a schematic flowchart of the filtering process provided by an embodiment of the present invention. When the controller filters the infrared image using a Butterworth bandpass filter, the following specific operations are performed:

[0113] Frequency domain transformation: That is, perform a fast Fourier transform on the infrared image using a preset discrete Fourier transform formula, convert the infrared image from the spatial domain to the frequency domain space, and obtain the image spectrum. The principle of the Butterworth bandpass filter is based on the frequency domain characteristics of the signal. The signal can be represented as a spectrum distribution, where components of different frequencies exhibit different amplitudes. The design goal of the Butterworth bandpass filter is to select the signal components within a specific frequency range and filter out the signal components of other frequencies. Therefore, it is necessary to first transform the infrared image in the spatial domain into the frequency domain.

[0114] Butterworth bandpass filter filtering: Use the Butterworth bandpass filter to filter the spectrum of the image, multiply the image spectrum in the frequency domain by the corresponding pixels of the Butterworth bandpass filter, and the filtered image in the frequency domain can be obtained to get the filtered frequency domain image.

[0115] The performance of stripe noise in the spatial domain is relatively complex and it is not easy to find the pattern. However, in the frequency domain, this noise is a signal with a specific frequency. Therefore, by converting the image to the frequency domain and using the Butterworth bandpass filter to filter out this signal with a specific frequency, the purpose of denoising can be achieved.

[0116] Inverse transformation: After filtering the image spectrum in the frequency domain, it is also necessary to transform the image in the frequency domain back to the spatial domain in order to read the corresponding lumen data, that is, use the inverse discrete Fourier transform formula to transfer the frequency domain image from the frequency domain space to the spatial domain, and obtain the filtered infrared image.

[0117] Filter the infrared image in the frequency domain through the Butterworth bandpass filter to eliminate the stripe noise caused by the bias voltage difference of the sensor array reading circuit of the infrared camera, and improve the accuracy of the infrared image.

[0118] In another embodiment provided by the present invention, the discrete Fourier transform formula is

[0119] The transfer function of the Butterworth bandpass filter

[0120] The inverse discrete Fourier transform formula is:

[0121]

[0122] Among them, the frequency-domain image \(G(u, v)=F(u, v)H(u, v)\), where \(F(u, v)\) is the image spectrum, \(f(x, y)\) is the luminescence value in the spatial domain of the infrared image, \(x\) and \(y\) are discrete real variables, respectively representing the number of rows and columns corresponding to the two-dimensional array of the infrared image \(f(x, y)\), \(x = 0, 1, 2, \cdots, M - 1\); \(y = 0, 1, 2, \cdots, N - 1\), \(u\) and \(v\) are discrete frequency variables, \(u = 0, 1, 2, \cdots, M - 1\); \(v = 0, 1, 2, \cdots, N - 1\), \(M\) and \(N\) are respectively the number of rows and columns corresponding to the two-dimensional array of the infrared image \(f(x, y)\); \(W\) is the passband bandwidth, \(D_0\) is the passband center radius, where \(n\) is the order of the filter; \(D(u, v)\) is a band-pass filter on a circular domain with radius \(D_0\), and \(D\) 2 (u, v) is the square of the filtering distance; \(g(x, y)\) is the filtered infrared image.

[0123] In the specific implementation of this embodiment, taking an infrared image as an example, first perform a fast Fourier transform on the image, and use the preset discrete Fourier transform formula to convert the image from the spatial domain to the frequency-domain space;

[0124] The discrete Fourier transform formula is:

[0125]

[0126] Among them, \(F(u, v)\) is the image spectrum, \(u\) and \(v\) are discrete frequency variables, \(u = 0, 1, 2, \cdots, M - 1\); \(v = 0, 1, 2, \cdots, N - 1\), \(f(x, y)\) is the luminescence value in the spatial domain of the infrared image, which is a two-dimensional array, \(x\) and \(y\) are discrete real variables, respectively representing the number of rows and columns corresponding to the two-dimensional array of the infrared image \(f(x, y)\), \(x = 0, 1, 2, \cdots, M - 1\); \(y = 0, 1, 2, \cdots, N - 1\), \(M\) and \(N\) are respectively the number of rows and columns corresponding to the two-dimensional array of the infrared image \(f(x, y)\).

[0127] Then, use a Butterworth band-pass filter to filter the spectrum of the image. The transfer function \(H(u, v)\) of an \(n\)-th order radially symmetric Butterworth band-pass filter is defined as:

[0128]

[0129] Among them, \(W\) is the passband bandwidth, \(D_0\) is the passband center radius, where \(n\) is the order of the filter; \(D(u, v)\) is a band-pass filter on a circular domain with radius \(D_0\), and \(D\) 2 (u, v) is the square of the filtering distance; let the center point of this band-pass filter be the point \((u_0, v_0)\), D 2 (u, v)=(u - u_0) 2 +(v - v_0) 2。

[0130] Multiply the data F(u, v) in the frequency domain with the corresponding pixels of the Butterworth band-pass filter H(u, v), and the filtered frequency-domain image G(u, v) in the frequency domain can be obtained as G(u, v) = F(u, v)H(u, v).

[0131] Use the inverse discrete Fourier transform formula to transfer the frequency-domain image from the frequency domain space to the spatial domain, obtaining the filtered infrared image.

[0132] The inverse discrete Fourier transform formula is as follows:

[0133]

[0134] where g(x, y) is the filtered infrared image.

[0135] Complete the frequency-domain transformation, filtering, and spatial-domain transformation processes in sequence through the discrete Fourier transform formula, transfer function, and inverse discrete Fourier transform formula.

[0136] In another embodiment provided by the present invention, the controller is further configured to:

[0137] Pre-acquire a training data set, and input the training data set into a pre-constructed fully-connected neural network model for training;

[0138] Optimize the fully-connected neural network model according to a pre-acquired test data set and a preset objective function, find the model parameters corresponding to the optimal objective function, and obtain the skin temperature detection model;

[0139] Among them, the training data set and the test data set include the lumen value data, background lumen value, ambient temperature, device temperature, and current distance as the model input data, and the human body surface temperature data as the model output data.

[0140] In the specific implementation of this embodiment, refer to Figure 5 , which is another schematic flow diagram of the work performed by the controller provided by the embodiment of the present invention. When the controller performs model training, the following steps are specifically executed:

[0141] Step S501, acquire the training data set and the test data set;

[0142] It should be noted that the test data set and the training data set are specifically obtained by dividing the same acquired data set into different proportions to obtain the test data set and the training data set.

[0143] Step S502: Input the training data set into the fully connected neural network model for training. That is, input the lumen value data, background lumen value, ambient temperature, device temperature, and current distance in the training data set, which are used as the model input data, and the human body surface temperature data, which is used as the model output data, into the fully connected neural network model for training respectively, to fit the relationship between the lumen value data, background lumen value, ambient temperature, device temperature, current distance and the human body surface temperature data, where the human body surface temperature data includes the temperature values of different parts of the human body.

[0144] Step S503: Verify the current model using the prediction data set and a preset objective function.

[0145] Step S504: Determine whether the verified objective function is optimal.

[0146] Verify the maximum error when the model fits the skin surface temperature of each part of the human body by predicting the test data, determine whether the calculated error reaches the preset range, or whether the continuously calculated errors meet the preset optimal conditions, so as to judge whether it reaches the optimal.

[0147] If not, return to Step S501. During training, when the model does not reach the optimal, continue to obtain training data for training and optimize the model parameters until the model reaches the optimal.

[0148] If so, execute Step S505;

[0149] Step S505: Obtain the skin temperature detection model according to the model parameters of the current model.

[0150] Optimize the model through the objective function, output the skin temperature detection model that meets the preset accuracy requirements, and use the output skin temperature detection model for temperature detection, which can ensure the accuracy of temperature detection.

[0151] In another embodiment provided by the present invention, the process of obtaining the training data set or the test data set includes:

[0152] In a preset experimental environment, by changing the distance between the experimental personnel and the infrared camera and / or the current ambient temperature, obtain the current infrared image;

[0153] Input the current infrared image into a preset filter for filtering to obtain the filtered infrared image, determine the current model input data, and read the current model output data by setting temperature sensors at different parts of the experimental personnel;

[0154] Use the current model input data and the corresponding model output data as the training data set or the test data set.

[0155] Specifically, when obtaining the training dataset or test dataset for model training, a sealed space of about 10 - 15 square meters is built, and a wall-mounted air conditioner is installed to create the ambient temperature changes in the bedroom throughout the four seasons as the experimental environment.

[0156] Two experimenters sit on stools, and the distance between the stools and the infrared camera is between 100 cm and 400 cm. The experimental environment simulates the heating and cooling processes when people use air conditioners indoors throughout the year. During the experiment, the indoor ambient temperature is heated and cooled at a constant speed. The ambient temperature at this time is read through the first temperature sensor, the current distance between the stool and the infrared camera is read through the distance sensor, the device temperature of the infrared camera is detected through the second temperature sensor, and the infrared image at this time is read through the infrared camera.

[0157] Thermocouples for collecting the surface temperature of the human body are attached to three parts of the experimenters' foreheads, cheeks, and the backs of their hands. The thermocouples record the temperatures of the three parts of the human body every second as the output human surface temperature data.

[0158] The current infrared image is input into a preset filter for filtering to obtain the filtered infrared image. The lumen values corresponding to the forehead, cheek, and back of the hand parts of the human body in the filtered infrared image are obtained as the lumen value data.

[0159] Through a preset number of experiments, in each experiment, one of the background lumen value, ambient temperature, device temperature, and current distance is changed, and the temperatures of different parts are recorded as the output human surface temperature data to obtain the training output set.

[0160] In another embodiment provided by the present invention, the background lumen value is specifically the average lumen value of several pixels at a preset pixel distance from the area where the user to be measured is located in the infrared image.

[0161] When specifically implementing this embodiment, refer to Figure 6 , which is a schematic diagram of an infrared image provided by an embodiment of the present invention.

[0162] The background lumen value is calculated from the average values of 3 pixels each located above, below, to the left, and to the right outside the area where the identified user to be measured is located in the infrared image, that is Figure 6Among them, outside the area where the user A to be measured is located, three pixel points a1, a2, and a3 are randomly selected from all the pixel points above the user to be measured and 3 pixel points away from the edge of the area where the user to be measured is located; three pixel points a4, a5, and a6 are randomly selected from all the pixel points below the user to be measured and 3 pixel points away from the edge of the area where the user to be measured is located; three pixel points a7, a8, and a9 are randomly selected from all the pixel points to the left of the user to be measured and 3 pixel points away from the edge of the area where the user to be measured is located; three pixel points a10, a11, and a12 are randomly selected from all the pixel points to the right of the user to be measured and 3 pixel points away from the edge of the area where the user to be measured is located. Calculate the mean value of the lumen values of the pixel points a1 to a12 as the background lumen value.

[0163] It should be noted that in this embodiment, 3 pixels at a distance of 3 pixels are respectively taken in the four directions of up, down, left, and right outside the area where the user to be measured is located. In other embodiments, M pixels can be randomly selected from all the pixels at a distance of N pixels outside the area where the user to be measured is located, and the mean value is calculated without distinguishing the direction. The pixel distance and the number of pixel points can also be adjusted according to the actual situation.

[0164] It is also possible to determine how many pixels to take according to the distance. For example, select a point in the background of each part of the human body, and take infrared images of this point and the human body at different distances. On the infrared image, the distance between this point and each part of the human body changes. The farther the distance, the smaller the distance between this point and each part of the human body, and the background lumen value can be taken as the mean value of two pixels around each part of the human body; the closer the distance, the larger the distance between this point and each part of the human body, and the background lumen value can be taken as the mean value of six pixels around each part of the human body.

[0165] By taking the mean value of the background pixels around the human body, the error of the background lumen value can be reduced.

[0166] In another embodiment provided by the present invention, the fully connected neural network model includes an input layer, two hidden layers, and an output layer;

[0167] The output of the fully connected neural network model

[0168] The objective function is

[0169] Among them, A k (w, b, X) represents the calculation result of the kth neuron in the hidden layer, p is the number of neurons in the hidden layer, σ(·) represents the Relu activation function, w is the weight, b is the bias, X is the input variable, and x i represents the ith input variable, w iRepresents the weight of the i-th input variable, q is the number of input variables; L is the mean absolute error, and S is the number of skin surface temperature samples at each part in the test dataset; z i Is the predicted value calculated by the fully connected neural network model for the i-th model input data in the test dataset, r i Is the i-th model output data in the test dataset.

[0170] During the specific implementation of this embodiment, refer to Figure 7 , which is the structural schematic diagram of the fully connected neural network model provided by the embodiment of the present invention; the fully connected neural network model consists of an input layer, two hidden layers, and an output layer.

[0171] This network has a total of 5 input variables, namely the lumen value data x1 of the human body part, the environmental temperature x2, the device temperature x3, the current distance x4, and the background lumen value x5. The five input variables are input into the neurons of the input layer, and after passing through two hidden layers and an output layer, the output result z is obtained.

[0172] Each hidden layer is composed of multiple neurons. The first hidden layer includes p neurons A 1 1 to A 1 p The second hidden layer includes p neurons A 2 1 to A 2 p .

[0173] The output of the fully connected neural network model

[0174] Among them, A k (w, b, X) represents the calculation result of the k-th neuron in the hidden layer, p is the number of neurons in the hidden layer, σ(·) represents the Relu activation function, w is the weight, b is the bias, X is the input variable, and x i Represents the i-th input variable, and w i Represents the weight of the i-th input variable, q is the number of input variables, and in this application, it is 5.

[0175] The objective function of the mean absolute error used to optimize the model is

[0176] Among them, L is the mean absolute error, S is the number of skin surface temperature samples at each part in the test dataset; z i Is the predicted value calculated by the fully connected neural network model for the i-th model input data in the test dataset, r i Is the i-th model output data in the test dataset.

[0177] In another embodiment provided by the present invention, the lumen value data includes the lumen values of the forehead, cheeks, and back of the hand of the user to be measured.

[0178] When specifically performing temperature detection, by detecting the lumen values of the forehead, cheeks, and back of the hand of the user exposed, the temperature of different parts of the user is determined, so as to realize the monitoring of the user's body temperature, facilitate the monitoring of the user's current state, and facilitate the application in the fields of household appliances, medical treatment, etc.

[0179] Another embodiment of the present invention provides an air conditioner, which includes:

[0180] An air conditioner body;

[0181] An infrared camera for detecting an infrared image of the user to be measured;

[0182] A first temperature sensor for detecting the current ambient temperature;

[0183] A second temperature sensor for detecting the device temperature of the infrared camera;

[0184] A distance sensor for detecting the current distance of the user to be measured;

[0185] A controller.

[0186] In the specific implementation of this embodiment, the air conditioner includes an air conditioner body, an infrared camera, a first temperature sensor, a second temperature sensor, a distance sensor, and a controller.

[0187] See Figure 8 , which is a schematic structural diagram of an air conditioner body provided by an embodiment of the present invention in an implementation manner. An embodiment of the present invention provides an air conditioner body 100, which includes an indoor unit 110 and an outdoor unit 120. The indoor unit 110 is usually installed indoors and can be in the form of an indoor wall-mounted unit, an indoor cabinet unit, etc. The outdoor unit 120 is usually installed outdoors and is used for heat exchange of the indoor environment. The air conditioner 100 has a refrigerant circuit 130. By circulating the refrigerant in the refrigerant circuit 130, a vapor compression refrigeration cycle can be performed, and a connecting pipe is used to connect the indoor unit 110 and the outdoor unit 120 to form a refrigerant circuit for refrigerant circulation.

[0188] See Figure 9, which is a partial structural schematic diagram of the refrigerant circuit of the air conditioner body in the embodiment of the present invention. In this application, the air conditioner performs a refrigeration cycle by using a compressor 131, an indoor heat exchanger 132, a throttle valve 133, and an outdoor heat exchanger 134. The refrigeration cycle includes a series of processes, involving compression, condensation, expansion, and evaporation, and supplies refrigerant to the air that has been conditioned and heat-exchanged. Among them, the indoor heat exchanger 132 is usually arranged in the indoor unit 110, the compressor 131 and the outdoor heat exchanger 134 are usually arranged in the outdoor unit 120, the throttle valve 133 can be arranged in the indoor unit 110 or the outdoor unit 120, and the indoor heat exchanger 132 and the outdoor heat exchanger 134 are used as condensers or evaporators. When the indoor heat exchanger 132 is used as a condenser, the air conditioner serves as a heater in the heating mode, and when the indoor heat exchanger 132 is used as an evaporator, the air conditioner serves as a cooler in the cooling mode.

[0189] The compressor 131 compresses the refrigerant gas in a high-temperature and high-pressure state and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into the condenser. The condenser condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through the condensation process. The throttle valve 133 expands the liquid-phase refrigerant in a high-temperature and high-pressure state condensed in the condenser into a low-pressure liquid-phase refrigerant. The evaporator evaporates the refrigerant expanded in the throttle valve 133 and returns the refrigerant gas in a low-temperature and low-pressure state to the compressor 131. The evaporator can achieve a refrigeration effect by using the latent heat of evaporation of the refrigerant to perform heat exchange with the material to be cooled. Throughout the cycle, the air conditioner can adjust the temperature of the indoor space.

[0190] The air conditioner provided in the embodiment of the present invention further includes: an infrared camera 150, a first temperature sensor 160, a second temperature sensor 170, and a distance sensor 180 connected to the controller.

[0191] An infrared image of the user to be measured is obtained through the infrared camera, and the detected infrared image is output to the controller.

[0192] The first temperature sensor is deployed outside the housing of the infrared temperature detection device and is used to detect the ambient temperature of the current environment and output the detected ambient temperature to the controller.

[0193] The second temperature sensor is deployed inside the infrared camera to detect the device temperature inside the infrared camera and output the detected device temperature to the controller.

[0194] The distance sensor, close to the infrared camera device, is used to detect the current distance between the user to be measured and the infrared camera and output the detected current distance to the controller.

[0195] The controller performs temperature monitoring and controls the operation of the air conditioner body according to the monitoring results;

[0196] See Figure 10 , which is another schematic flowchart of the work performed by the controller provided in the embodiment of the present invention. The controller performs the following steps:

[0197] Step S1001, obtain the infrared image, the ambient temperature, the device temperature, and the current distance;

[0198] Step S1002, input the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0199] Step S1003, calculate the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured;

[0200] Step S1004, input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data;

[0201] Step S1005, match a corresponding control instruction in a preset control instruction matching library according to the human body surface temperature data, and control the operation of the air conditioner body according to the control instruction.

[0202] In specific implementation, obtain an infrared image. That is, obtain the infrared image of the user to be measured through an infrared camera.

[0203] Filter the infrared image using a Butterworth band-pass filter. That is, input the infrared image into the Butterworth band-pass filter for filtering to obtain a filtered infrared image. Use the Butterworth band-pass filter to filter the stripe noise of the infrared image, remove the abnormal phenomenon of the lumen value caused by the noise, and improve the temperature measurement accuracy.

[0204] Calculate the lumen value data and the background lumen value in the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured.

[0205] When specifically obtaining, according to the existing infrared image temperature measurement and recognition method, identify the human body in the infrared image, mark different parts of the human body, and obtain the lumen values of different parts as the lumen value data. After identifying the human body in the infrared image, the background lumen value can be obtained by identifying the pixels outside the area where the human body is located as the background area and determining the lumen value of the background area. By identifying the part interval of the user to be measured, that is, identifying a certain area as a certain part, taking the maximum lumen value in the area as the lumen value of the part, or taking the average value of the lumen values of all pixels in the area as the lumen value of the part, determine the lumen values of different parts of the user to be measured to obtain the lumen value data.

[0206] Among them, the background lumen value can also be obtained by identifying the background area outside the human body and taking the average lumen value of the background area as the background lumen value.

[0207] Obtain the ambient temperature, device temperature, and current distance.

[0208] It should be noted that in this embodiment, the device temperature can specifically be the substrate temperature detected by the second temperature sensor configured on the circuit board of the infrared camera and / or the shutter temperature detected by the second temperature sensor configured on the shutter of the infrared camera. By using the substrate temperature and / or the shutter temperature as the device temperature, the influence of the infrared camera device temperature on the infrared image is considered, and the accuracy of temperature detection is improved.

[0209] Use the skin temperature detection model for calculation to obtain the output of the human body surface temperature data. That is, input the lumen value data, background lumen value, ambient temperature, device temperature, and current distance into the pre-trained skin temperature detection model to obtain the output of the human body surface temperature data.

[0210] When training the skin temperature detection model, design a neural network model to fit the skin surface temperature of the human body part. Use the lumen value, distance, ambient temperature, background lumen value, and device temperature data of the human body part in the infrared image as the input training data of the neural network, and use the skin surface temperature data of each part of the human body collected by the corresponding thermocouple as the output training data of the neural network for fitting training to obtain the fitted skin surface temperature detection model.

[0211] The temperature monitoring process of steps S1001 to S1004 of the solution of this application is the same as the specific implementation process in the embodiment of the above infrared temperature measurement device, and will not be elaborated here.

[0212] After obtaining the human body surface temperature data, that is, obtaining the temperatures of different parts of the human body, according to the temperatures of different parts of the human body, match the corresponding control instructions in the preset control instruction matching library, and the control instruction matching library includes the corresponding relationship between different control instructions and the human body surface temperature data.

[0213] By monitoring the skin surface temperatures of different parts of the user using the air conditioner, judge the current state of the user. It is also possible to judge whether the skin surface temperature of the human body rises or falls according to the difference between the skin surface temperature at the end point and the starting skin surface temperature of the set time period, judge the current state of the user, and then perform intelligent air conditioner control through the control logic to avoid the user's body temperature being too high or too low, thereby improving the user experience.

[0214] The embodiment of the present invention also provides a control method for an air conditioner, which is applied to an air conditioner, and the air conditioner includes:

[0215] Air conditioner body;

[0216] An infrared camera for detecting an infrared image of a user to be measured;

[0217] A first temperature sensor for detecting the current ambient temperature;

[0218] A second temperature sensor for detecting the device temperature of the infrared camera;

[0219] A distance sensor for detecting the current distance of the user to be measured;

[0220] A controller;

[0221] The method includes:

[0222] Obtaining the infrared image, the ambient temperature, the device temperature, and the current distance;

[0223] Inputting the infrared image into a preset filter for filtering to obtain a filtered infrared image;

[0224] Calculating the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured;

[0225] Inputting the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human body surface temperature data;

[0226] Matching a corresponding control instruction in a preset control instruction matching library according to the human body surface temperature data, and controlling the operation of the air conditioner body according to the control instruction.

[0227] It should be noted that a control method of an air conditioner provided in an embodiment of the present invention is the same as all the process steps executed by a controller of an air conditioner in the above embodiment, and their working principles and beneficial effects correspond one by one, so details are not described herein again.

[0228] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc.

[0229] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. An infrared temperature detection device, characterized in that, Including: An infrared camera for detecting an infrared image of a user to be measured; A first temperature sensor for detecting the current ambient temperature; A second temperature sensor for detecting the device temperature of the infrared camera; A distance sensor for detecting the current distance of the user to be measured; A controller configured to: Obtain the infrared image, the ambient temperature, the device temperature, and the current distance; Input the infrared image into a preset filter for filtering to obtain a filtered infrared image; Calculate the lumen value data and the background lumen value of the user to be measured according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured; Input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human surface temperature data.

2. The infrared temperature detection device according to claim 1, wherein The controller is further configured to: Perform a fast Fourier transform on the infrared image using a preset discrete Fourier transform formula, convert the infrared image from the spatial domain to the frequency domain space to obtain an image spectrum; Input the image spectrum into a preset Butterworth band-pass filter for filtering to obtain a filtered frequency-domain image; Use the inverse discrete Fourier transform formula to transfer the frequency-domain image from the frequency domain space to the spatial domain to obtain a filtered infrared image.

3. The infrared temperature detection device according to claim 2, characterized in that, The discrete Fourier transform formula is Transfer function of the Butterworth band-pass filter The inverse discrete Fourier transform formula is: Among them, the frequency-domain image G(u, v) = F(u, v)H(u, v), where F(u, v) is the image spectrum, f(x, y) is the luminance value in the spatial domain of the infrared image, x and y are discrete real variables, representing the number of rows and columns corresponding to the two-dimensional array of the infrared image f(x, y) respectively, x = 0, 1, 2, …, M - 1; y = 0, 1, 2, …, N - 1, u and v are discrete frequency variables, u = 0, 1, 2, …, M - 1; v = 0, 1, 2, …, N - 1, M and N are the number of rows and columns corresponding to the two-dimensional array of the infrared image f(x, y) respectively; W is the passband bandwidth, D0 is the passband center radius, where n is the order of the filter; D(u, v) is a band-pass filter on a circular domain with radius D0, and D 2 (u, v) is the square of the filtering distance; g(x, y) is the filtered infrared image.

4. The infrared temperature detection device according to claim 1, characterized in that, The controller is further configured to: Pre-obtain a training data set, and input the training data set into a pre-constructed fully connected neural network model for training; Optimize the fully connected neural network model according to a pre-obtained test data set and a preset objective function, search for model parameters corresponding to the optimal objective function to obtain the skin temperature detection model; Wherein, the training data set and the test data set include lumen value data, background lumen value, ambient temperature, device temperature, and current distance as model input data, and human surface temperature data as model output data.

5. The infrared temperature detection device according to claim 4, characterized in that The process of obtaining the training data set or the test data set includes: In a preset experimental environment, by changing the distance between the experimental personnel and the infrared camera and / or the current ambient temperature, obtain the current infrared image; Input the current infrared image into a preset filter for filtering to obtain a filtered infrared image, determine the current model input data, and read the current model output data by setting temperature sensors at different parts of the experimental personnel; Use the current model input data and the corresponding model output data as the training data set or the test data set.

6. The infrared temperature detection device according to claim 1, characterized in that, The background lumen value is specifically the average lumen value of several pixels at a preset pixel distance from the area where the user to be measured is located in the infrared image.

7. The infrared temperature detection device according to claim 4, characterized in that, The fully connected neural network model includes an input layer, two hidden layers, and an output layer; Output of the fully connected neural network model The objective function is Among them, A k (w, b, X) represents the calculation result of the k-th neuron in the hidden layer, p is the number of neurons in the hidden layer, σ(·) represents the Relu activation function, w is the weight, b is the bias, X is the input variable, and x i represents the i-th input variable, and w i represents the weight of the i-th input variable, and q is the number of input variables; L is the mean absolute error, and S is the number of skin surface temperature samples at each part in the test dataset; z i is the predicted value calculated by the fully connected neural network model for the i-th model input data in the test dataset, and r i is the i-th model output data in the test dataset.

8. The infrared temperature detection device according to claim 1, characterized in that, The lumen value data includes the lumen values of the forehead, cheeks, and back of the hand of the user to be measured.

9. An air conditioner, characterized in that, The air conditioner includes: An air conditioner body; An infrared camera for detecting an infrared image of a user to be measured; A first temperature sensor for detecting the current ambient temperature; A second temperature sensor for detecting the device temperature of the infrared camera; A distance sensor for detecting the current distance of the user to be measured; A controller configured to: Obtain the infrared image, the ambient temperature, the device temperature, and the current distance; Input the infrared image into a preset filter for filtering to obtain a filtered infrared image; Calculate the lumen value data of the user to be measured and the background lumen value according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured; Input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human surface temperature data; Match a corresponding control instruction in a preset control instruction matching library according to the human surface temperature data, and control the operation of the air conditioner body according to the control instruction.

10. A control method for an air conditioner, characterized in that, The air conditioner includes: An air conditioner body; An infrared camera for detecting an infrared image of a user to be measured; A first temperature sensor for detecting the current ambient temperature; A second temperature sensor for detecting the device temperature of the infrared camera; A distance sensor for detecting the current distance of the user to be measured; A controller; The method includes: Obtain the infrared image, the ambient temperature, the device temperature, and the current distance; Input the infrared image into a preset filter for filtering to obtain a filtered infrared image; Calculate the lumen value data of the user to be measured and the background lumen value according to the filtered infrared image, where the lumen value data includes the lumen values of different parts of the user to be measured; Input the lumen value data, the background lumen value, the ambient temperature, the device temperature, and the current distance into a pre-trained skin temperature detection model to obtain an output of human surface temperature data; Match a corresponding control instruction in a preset control instruction matching library according to the human surface temperature data, and control the operation of the air conditioner body according to the control instruction.

Images