Infrared temperature measurement curve model construction method, medium and equipment

Abstract

The invention provides an infrared temperature measurement curve model construction method, a medium and equipment, and relates to the technical field of infrared temperature measurement, and the method comprises the steps: obtaining the focal plane temperature of an infrared thermal imaging equipment detector and AD values of a plurality of radiation calibration sources collected by the equipment; taking the focal plane temperature of the detector and the AD values of the radiation calibration sources at the lowest temperature point, the middle temperature point and the highest temperature point as input, taking the AD values of the radiation calibration sources at the other temperature points in the plurality of radiation calibration sources as output, and constructing a multiple linear regression model; acquiring the focal plane temperature of the infrared thermal imaging equipment detector to be constructed and the acquired AD values of the radiation calibration source at the lowest temperature point, the intermediate temperature point and the highest temperature point, inputting the multiple linear regression model to obtain the predicted AD values of the radiation calibration source at the other temperature points, and performing curve fitting based on the acquired AD values and the predicted AD values to obtain the radiation calibration source of the infrared thermal imaging equipment. And obtaining an infrared temperature measurement curve model. According to the method, the construction efficiency of the infrared temperature measurement curve model can be improved.

Description

Technical Field

[0001] This invention relates to the field of infrared temperature measurement technology, and in particular to a method, medium, and device for constructing an infrared temperature measurement curve model. Background Technology

[0002] Currently, infrared thermal imaging equipment is widely used in various fields such as medicine, construction, power, and fire protection. Due to its non-contact temperature measurement, visualization of heat distribution, and adaptability to complex environments, it plays an irreplaceable role in scenarios requiring rapid identification of temperature differences. However, the ability of infrared thermal imaging equipment to measure temperature is inseparable from the method of constructing the infrared temperature measurement model. Currently, most methods for constructing infrared temperature measurement models involve collecting AD values ​​from multiple different radiation calibration sources and performing curve fitting. To ensure the accuracy of temperature measurement results, this often requires a certain number of radiation calibration sources. Theoretically, the more radiation calibration sources, the more accurate the curve fitting, and the more accurate the infrared thermal imaging equipment will be in temperature measurement. Generally speaking, infrared thermal imaging equipment needs to collect AD values ​​from at least seven radiation calibration sources at different ambient temperatures for curve fitting, and the number of radiation calibration sources required varies for different temperature measurement levels. Furthermore, because there are certain differences between each device, each device needs to collect data individually, which greatly increases the production burden and reduces actual production efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide a method, medium, and device for constructing infrared thermometry curve models, aiming to achieve the construction of infrared thermometry curve models while reducing the number of radiation calibration sources, thereby reducing the amount of data to be collected from calibration sources during the construction process and improving the efficiency of infrared thermometry curve model construction. The specific technical solution is as follows:

[0004] A method for constructing an infrared temperature measurement curve model, the method comprising the following steps:

[0005] S100: Obtain the detector focal plane temperature of multiple infrared thermal imaging devices under multiple ambient temperatures, as well as the AD values ​​of multiple radiation calibration sources collected by the devices.

[0006] S200: Using the focal plane temperature of the infrared thermal imaging device detector and the AD values ​​of the lowest, middle and highest temperature points of the radiation calibration sources among multiple radiation calibration sources as inputs, and the AD values ​​of the remaining temperature points of the radiation calibration sources among multiple radiation calibration sources as outputs, a multiple linear regression model is constructed.

[0007] S300: Obtain the detector focal plane temperature of the infrared thermal imaging device to be constructed at an ambient temperature, as well as the AD values ​​of the radiation calibration sources at the lowest, intermediate, and highest temperature points. Input the AD values ​​into the multiple linear regression model to obtain the predicted AD values ​​of the radiation calibration sources at the remaining temperature points. Based on the collected AD values ​​and the predicted AD values, perform curve fitting to obtain the infrared temperature measurement curve model of the infrared thermal imaging device to be constructed.

[0008] Furthermore, in step S100, the number of infrared thermal imaging devices is no less than 10.

[0009] Furthermore, in step S100, the focal plane temperature of the infrared thermal imaging device detector and the AD values ​​of the multiple radiation calibration sources are obtained by placing multiple infrared thermal imaging devices under multiple ambient temperatures for a first time period, then turning on the devices and waiting for a second time period until the devices stabilize.

[0010] Furthermore, multiple ambient temperatures are within the range of 5℃ to 35℃.

[0011] Furthermore, the first time period is greater than or equal to 1 hour.

[0012] Furthermore, the second time period is greater than or equal to 1.5 hours.

[0013] Furthermore, in step S200, the calculation formula for the multiple linear regression model is as follows:

[0014]

[0015] in, For the AD values ​​of the remaining temperature point radiation calibration sources among multiple radiation calibration sources, This refers to the focal plane temperature of the detector in an infrared thermal imaging device. The AD value is the lowest temperature point among multiple radiation calibration sources. AD value of the intermediate temperature point radiation calibration source. The AD value is the radiation calibration source at the highest temperature point. is a coefficient.

[0016] Furthermore, constructing a multiple linear regression model involves substituting the input and the output into the multiple linear regression model calculation formula to solve for the coefficients, thereby determining the multiple linear regression model expression between the input and the output.

[0017] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the infrared temperature measurement curve model construction method as described above.

[0018] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the infrared temperature measurement curve model construction method as described above.

[0019] The present invention provides a method, medium, and device for constructing an infrared temperature measurement curve model, which has the following beneficial effects:

[0020] This invention acquires the detector focal plane temperature of multiple infrared thermal imaging devices under various ambient temperatures, as well as the AD values ​​of multiple radiation calibration sources collected by the devices. Using the detector focal plane temperature of the infrared thermal imaging devices and the AD values ​​of the lowest, intermediate, and highest temperature points from the multiple radiation calibration sources as input, and the AD values ​​of the remaining temperature points from the multiple radiation calibration sources as output, a multiple linear regression model is constructed. The invention then acquires the detector focal plane temperature of the infrared thermal imaging device to be constructed under a certain ambient temperature, as well as the AD values ​​of the lowest, intermediate, and highest temperature points from the radiation calibration sources, and inputs these values ​​into the multiple linear regression model to obtain the predicted AD values ​​of the remaining temperature points from the radiation calibration sources. Based on the acquired AD values ​​and the predicted AD values, a curve fitting is performed to obtain the infrared temperature measurement curve model of the infrared thermal imaging device to be constructed. This allows for the construction of an infrared temperature measurement curve model while reducing the number of radiation calibration sources, thereby reducing the amount of data required to collect calibration sources during the construction process and improving the efficiency of infrared temperature measurement curve model construction. Attached Figure Description

[0021] Figure 1 A flowchart illustrating an infrared temperature measurement curve model construction method provided in an embodiment of the present invention;

[0022] Figure 2 A flowchart illustrating an infrared temperature measurement curve model construction method provided for verification embodiments of the present invention;

[0023] Figure 3 A comparison of the AD values ​​of the radiation calibration source at other temperature points predicted for the low temperature measurement range with the actual AD values ​​of the radiation calibration source at other temperature points.

[0024] Figure 4 A comparison chart of fitted curves for real data and fitted curves for predicted data in the low temperature measurement range;

[0025] Figure 5 A comparison of the AD values ​​of the radiation calibration source at other temperature points predicted for the high temperature measurement range with the actual AD values ​​of the radiation calibration source at other temperature points.

[0026] Figure 6 A comparison chart of fitted curves for real data and fitted curves for predicted data in the high temperature measurement range;

[0027] Figure 7 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0029] Example 1

[0030] This embodiment provides a method for constructing an infrared temperature measurement curve model. (See attached document.) Figure 1 As shown, the method includes the following steps:

[0031] S100: Obtain the focal plane temperature of the detectors of multiple infrared thermal imaging devices under multiple ambient temperatures, as well as the AD values ​​of multiple radiation calibration sources collected by the infrared thermal imaging devices.

[0032] In one embodiment, the number of infrared thermal imaging devices is no less than 10.

[0033] In one embodiment, the focal plane temperature of the infrared thermal imaging device detector and the AD values ​​of multiple radiation calibration sources collected by the infrared thermal imaging device are obtained by placing multiple infrared thermal imaging devices under multiple ambient temperatures for a first time period, and then turning on the device and waiting for a second time period. At this time, the internal temperature of the infrared thermal imaging device is basically stable.

[0034] In a preferred embodiment, multiple ambient temperatures are within the range of 5°C to 35°C.

[0035] In a preferred embodiment, the first time period is greater than or equal to 1 hour.

[0036] In a preferred embodiment, the second time period is greater than or equal to 1.5 hours.

[0037] S200 uses the focal plane temperature of the infrared thermal imaging device detector, as well as the AD values ​​of the lowest, middle, and highest temperature radiation calibration sources among multiple radiation calibration sources, as inputs, and the AD values ​​of the remaining temperature radiation calibration sources among multiple radiation calibration sources as outputs to construct a multiple linear regression model.

[0038] In one embodiment, the calculation formula for the multiple linear regression model is as follows:

[0039]

[0040] in, For the AD values ​​of the remaining temperature point radiation calibration sources among multiple radiation calibration sources, This refers to the focal plane temperature of the detector in an infrared thermal imaging device. The AD value is the lowest temperature point among multiple radiation calibration sources. AD value of the intermediate temperature point radiation calibration source. The AD value is the radiation calibration source at the highest temperature point. is a coefficient.

[0041] In one embodiment, constructing a multiple linear regression model includes substituting the input and the output into the multiple linear regression model calculation formula to solve for the coefficients, thereby determining the multiple linear regression model expression between the input and the output.

[0042] It should be understood that when constructing a multiple linear regression model, the number of radiation calibration sources is more than three, and each radiation calibration source has a different temperature point. This allows us to determine the AD values ​​for the radiation calibration sources at the lowest, intermediate, and highest temperatures, as well as the AD values ​​for the remaining temperature points. For example, in constructing a multiple linear regression model, the number of radiation calibration sources N is seven, with temperatures ranging from -10℃, 10℃, 30℃, 50℃, 70℃, 90℃, and 100℃. This represents the AD value collected by the -10℃ radiation calibration source equipment. This represents the AD value collected by the 10℃ radiation calibration source device. This represents the AD value collected by the 30℃ radiation calibration source device. This represents the AD value collected by the 50℃ radiation calibration source device. This represents the AD value collected by the 70℃ radiation calibration source device. This represents the AD value collected by the 90℃ radiation calibration source device. This represents the AD value collected by the 100℃ radiation calibration source device.

[0043] The constructed multiple linear regression model can reveal the relationship between the AD values ​​of the radiation calibration sources at other temperature points and the AD values ​​of the lowest, intermediate, and highest temperature radiation calibration sources. For any infrared thermal imaging device, knowing the AD values ​​of its lowest, intermediate, and highest temperature radiation calibration sources allows for the prediction of the AD values ​​of the other four temperature point radiation calibration sources. Therefore, when constructing an infrared temperature measurement curve model for the infrared thermal imaging device, only three radiation calibration sources are needed to collect the AD values ​​of the lowest, intermediate, and highest temperature radiation calibration sources to obtain the AD values ​​of the seven temperature point radiation calibration sources. This eliminates the need to collect the AD values ​​of all seven temperature point radiation calibration sources as required by existing technologies, reducing the amount of data required for calibration source collection during the construction process and improving the efficiency of infrared temperature measurement curve model construction.

[0044] S300: Obtain the focal plane temperature of the detector of the infrared thermal imaging device to be constructed at an ambient temperature, the AD values ​​of the lowest temperature point radiation calibration source, the intermediate temperature point radiation calibration source, and the highest temperature point radiation calibration source collected by the infrared thermal imaging device to be constructed, input them into the multiple linear regression model to obtain the predicted AD values ​​of the radiation calibration sources at the remaining temperature points, and perform curve fitting using the AD values ​​of the lowest temperature point radiation calibration source, the intermediate temperature point radiation calibration source, the highest temperature point radiation calibration source collected by the infrared thermal imaging device to be constructed, and the predicted AD values ​​of the radiation calibration sources at the remaining temperature points to obtain the infrared temperature measurement curve model of the infrared thermal imaging device to be constructed.

[0045] The infrared thermal imaging device to be constructed refers to the object for which the constructed multiple linear regression model is used to construct the infrared temperature measurement curve model in the future, i.e., the infrared thermal imaging device for which the infrared temperature measurement curve model is to be constructed.

[0046] When constructing an infrared temperature measurement curve model for an infrared thermal imaging device, only three radiation calibration sources are needed to collect the AD values ​​of the lowest temperature point, the middle temperature point, and the highest temperature point. This yields AD values ​​for seven temperature points. The temperature measurement curve can then be fitted using these seven AD values, unlike existing technologies that require collecting AD values ​​from seven different temperature points. This reduces the amount of data collected from calibration sources during the construction process and improves the efficiency of infrared temperature measurement curve model construction.

[0047] The infrared thermography curve model construction method provided by this invention obtains the detector focal plane temperature of multiple infrared thermal imaging devices under multiple ambient temperatures, as well as the AD values ​​of multiple radiation calibration sources collected by the devices. Using the detector focal plane temperature of the infrared thermal imaging device and the AD values ​​of the lowest, intermediate, and highest temperature points of the multiple radiation calibration sources as input, and the AD values ​​of the remaining temperature points of the multiple radiation calibration sources as output, a multiple linear regression model is constructed. The method then obtains the detector focal plane temperature of the infrared thermal imaging device to be constructed under one ambient temperature, as well as the AD values ​​of the lowest, intermediate, and highest temperature points of the collected radiation calibration sources, and inputs these values ​​into the multiple linear regression model to obtain the predicted AD values ​​of the remaining temperature points of the radiation calibration sources. Based on the collected AD values ​​and the predicted AD values, a curve fitting is performed to obtain the infrared thermography curve model of the infrared thermal imaging device to be constructed. This method can achieve the construction of an infrared thermography curve model while reducing the number of radiation calibration sources, thereby reducing the amount of data to be collected from calibration sources during the construction process and improving the efficiency of infrared thermography curve model construction.

[0048] Verification Example:

[0049] See Figure 2 As shown, this invention proposes a method for constructing an infrared temperature measurement curve model, the specific implementation of which is as follows:

[0050] 1. Place the infrared thermal imaging equipment in any ambient temperature. Place for a certain period of time This ensures the equipment reaches thermal equilibrium. (Ambient temperature) The value ranges from 5℃ to 35℃, and the placement time... It should be greater than or equal to 1 hour;

[0051] 2. Power on the infrared thermal imaging equipment and wait for the power-on time. The equipment is stable and has a short startup time. The time should be greater than or equal to 1.5 hours. This step ensures that the internal electronic components of the equipment reach a stable operating state, reducing temperature measurement errors caused by insufficient power-on time;

[0052] 3. Record the focal plane temperature of the infrared thermal imaging device detector at this time. And the AD values ​​of the equipment at different radiation calibration sources;

[0053] 4. Repeatedly collect data from multiple infrared thermal imaging devices (at least 10 devices) at various ambient temperatures. The data below is used to construct a dataset and divide it into a training set and a test set according to the proportion of data samples (80% of the data is used for model training and the remaining 20% ​​of the data is used for testing).

[0054] 5. Confirm the input and output. The input data is the focal plane temperature of the infrared thermal imaging device's detector. The AD value of the radiation calibration source at the lowest temperature point collected by the equipment. AD value of the radiation calibration source at the highest temperature point collected by the equipment. AD values ​​at intermediate temperature points collected by the equipment The output is the AD value of the remaining temperature points collected by the device. ;

[0055] 6. Construction of the multiple linear regression model. The calculation formula for the multiple linear regression model is as follows:

[0056]

[0057] The coefficients are obtained by substituting the input and output of the training set into the formula of the multiple linear regression model. ;

[0058] 7. Substitute the input of the test set into the multiple linear regression model to calculate the predicted AD values ​​of the radiation calibration sources at other temperature points. The output of the test set is the AD value of the actual radiation calibration source at the remaining temperature points. AD values ​​of the radiation calibration source at the other predicted temperature points Analysis and comparison:

[0059] Figure 3 , Figure 5 The graph reflects the relationship between the AD values ​​predicted by the model and the AD values ​​acquired by the infrared thermal imaging device in the low and high temperature measurement ranges. The slope of the dashed line in the figure is 1. It can be seen that the coordinate points formed by the model's predicted AD values ​​and the AD values ​​acquired by the infrared thermal imaging device basically fall on the dashed line with a slope of 1, indicating that the model's predicted AD values ​​are close to the actual AD values ​​acquired by the infrared thermal imaging device.

[0060] Figure 4 , Figure 6 The temperature measurement curves fitted by the model's predicted AD values ​​for both low and high temperature measurement ranges are compared with the temperature measurement curves fitted by the actual AD values ​​acquired by the infrared thermal imaging equipment. It can be seen that the temperature measurement curves fitted by the model's predicted AD values ​​and the temperature measurement curves fitted by the actual AD values ​​acquired by the infrared thermal imaging equipment maintain a high degree of overlap, indicating a good fitting effect.

[0061] Based on the above comparison results, this method can obtain the temperature measurement curve model faster and the obtained temperature measurement curve model has better performance. It is beneficial to reduce the amount of data to be collected from the calibration source during the construction process and improve the efficiency of infrared temperature measurement curve model construction.

[0062] Example 2

[0063] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the infrared temperature measurement curve model construction method described above.

[0064] The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0065] Example 3

[0066] This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the infrared temperature measurement curve model construction method described above.

[0067] like Figure 7 As shown, the computer device 70 may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to enable communication between these components. The communication interface 73 may include a display screen and a keyboard; optionally, the communication interface 73 may also include a standard wired interface or a wireless interface. The memory 74 may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 74 may also be at least one storage device located remotely from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above-described method steps.

[0068] The communication bus 72 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 72 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0069] The memory 74 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 74 may also include a combination of the above types of memory.

[0070] The processor 71 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0071] The processor 71 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0072] Optionally, the memory 74 is also used to store program instructions. The processor 71 can call the program instructions to implement the infrared temperature measurement curve model construction method of the present invention.

[0073] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the invention. Any changes or modifications made by those skilled in the art based on the embodiments of the present invention and the above disclosure shall fall within the protection scope of the claims.

Claims

1. A method for constructing an infrared temperature measurement curve model, characterized in that, The method includes the following steps: S100: Obtain the detector focal plane temperature of multiple infrared thermal imaging devices under multiple ambient temperatures, as well as the AD values ​​of multiple radiation calibration sources collected by the devices. S200: Using the focal plane temperature of the infrared thermal imaging device detector and the AD values ​​of the lowest, middle and highest temperature points of the radiation calibration sources among multiple radiation calibration sources as inputs, and the AD values ​​of the remaining temperature points of the radiation calibration sources among multiple radiation calibration sources as outputs, a multiple linear regression model is constructed. S300: Obtain the detector focal plane temperature of the infrared thermal imaging device to be constructed at an ambient temperature, as well as the AD values ​​of the radiation calibration sources at the lowest, intermediate, and highest temperature points. Input the AD values ​​into the multiple linear regression model to obtain the predicted AD values ​​of the radiation calibration sources at the remaining temperature points. Based on the collected AD values ​​and the predicted AD values, perform curve fitting to obtain the infrared temperature measurement curve model of the infrared thermal imaging device to be constructed.

2. The method for constructing an infrared temperature measurement curve model according to claim 1, characterized in that, In step S100, the number of infrared thermal imaging devices shall not be less than 10.

3. The method for constructing an infrared temperature measurement curve model according to claim 1, characterized in that, In step S100, the focal plane temperature of the infrared thermal imaging device detector and the AD values ​​of multiple radiation calibration sources are obtained by placing multiple infrared thermal imaging devices under multiple ambient temperatures for a first time period, then turning on the devices and waiting for a second time period until the devices stabilize.

4. The method for constructing an infrared temperature measurement curve model according to claim 3, characterized in that, Multiple ambient temperatures range from 5℃ to 35℃.

5. The method for constructing an infrared temperature measurement curve model according to claim 3, characterized in that, The first time period is greater than or equal to 1 hour.

6. The method for constructing an infrared temperature measurement curve model according to claim 3, characterized in that, The second time period is greater than or equal to 1.5 hours.

7. The method for constructing an infrared temperature measurement curve model according to claim 1, characterized in that, In step S200, the calculation formula for the multiple linear regression model is as follows: in, For the AD values ​​of the remaining temperature point radiation calibration sources among multiple radiation calibration sources, This refers to the focal plane temperature of the detector in an infrared thermal imaging device. The AD value is the lowest temperature point among multiple radiation calibration sources. AD value of the intermediate temperature point radiation calibration source. The AD value is the radiation calibration source at the highest temperature point. is a coefficient.

8. The method for constructing an infrared temperature measurement curve model according to claim 7, characterized in that, Constructing a multiple linear regression model involves substituting the input and the output into the multiple linear regression model calculation formula to solve for the coefficients, thereby determining the multiple linear regression model expression between the input and the output.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the infrared temperature measurement curve model construction method as described in any one of claims 1-8.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the infrared temperature measurement curve model construction method as described in any one of claims 1-8.

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