Temperature measurement method and device based on infrared thermal imaging

Abstract

The embodiment of the invention discloses a temperature measurement method and device based on infrared thermal imaging, in the embodiment, a non-contact temperature measurement mode is adopted, the temperature measurement range is wider, the measurement speed is high, a plurality of temperature measurement groups are set according to actual requirements, each temperature measurement group comprises a plurality of temperature measurement elements, and the temperature measurement efficiency is improved. However, the types of the temperature measurement elements of each temperature measurement group must be the same, thereby guaranteeing the consistency of subsequent data analysis, and carrying out the real-time temperature data collection of each temperature measurement group through employing an infrared thermal imaging device. According to the method, the accuracy and the real-time performance of data acquisition are ensured, so that subsequent accurate data analysis is facilitated, a temperature measurement element of which the correlation with the temperature data is greater than a set threshold value is selected as a target factor, a temperature curve is generated according to the target factor and the temperature data, and a temperature measurement formula is established according to the temperature curve, so that the temperature measurement precision requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of infrared thermal imaging technology, and in particular to a temperature measurement method and device based on infrared thermal imaging. Background Art

[0002] At present, infrared thermal imaging equipment has shined in the fields of human body temperature measurement, industrial monitoring, etc., and is becoming more and more popular in military, civilian and industrial fields. However, the temperature measurement stability of infrared thermal imaging equipment is not high, and the temperature measurement effects of different equipment vary greatly.

[0003] Any object with a temperature above absolute zero (-273.15°C) will emit infrared energy (heat) based on its temperature; the infrared energy emitted by an object is called its thermal signature. Generally speaking, the hotter an object is, the more radiation it emits. A thermal imager is essentially a thermal sensor that can detect and capture tiny temperature differences. It collects infrared radiation from objects in the scene and creates pixels and images based on information about the temperature differences. Since objects rarely have exactly the same temperature as surrounding objects, thermal imagers can detect their differences and create a sharp contrast in the thermal image. This is the basic principle of infrared thermal imaging.

[0004] Temperature measurement methods are divided into contact and non-contact temperature measurement. The method described in this article is applicable to non-contact temperature measurement methods and equipment. Current non-contact temperature measurement equipment is affected by various practical factors, resulting in insufficient temperature measurement accuracy or a limited range of applicable environments. Typically, many temperature measurement equipment will have large deviations outdoors in winter, and some equipment needs to be warmed up for a long time before it can be put into normal use. Summary of the invention

[0005] In view of the above-mentioned defects, an embodiment of the present invention discloses a temperature measurement method and device based on infrared thermal imaging, which generates a more accurate temperature measurement method and realizes non-contact temperature measurement.

[0006] The first aspect of the embodiment of the present invention discloses a temperature measurement method based on infrared thermal imaging, comprising:

[0007] Set up several temperature measurement groups, each of which includes multiple temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category;

[0008] The temperature data of each temperature measurement group is collected in real time, and the real-time collected temperature data is analyzed to obtain the correlation between each group of temperature measurement elements and the temperature data;

[0009] A temperature measurement element whose correlation with the temperature data is greater than a set threshold is selected as a target factor, a temperature curve is generated according to the target factor and the temperature data, and a temperature measurement formula is established according to the temperature curve.

[0010] As an optional implementation, in the first aspect of the embodiment of the present invention, the temperature measurement factors include blackbody temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power, and power-on time.

[0011] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the real-time temperature data is collected for each temperature measurement group, and data analysis is performed on the real-time collected temperature data, including:

[0012] Three types of temperature measurement comparison data are set based on several groups of temperature measurement groups, the black body temperature in each type of temperature measurement comparison data is the same, and the black body temperature in different types of temperature measurement comparison data is different;

[0013] The temperature data of each temperature measurement group under each type of temperature measurement comparison data is collected in real time at preset intervals to obtain several groups of comparison temperature data; wherein, in each type of temperature measurement comparison data, among the multiple temperature measurement elements of any group of temperature measurement groups, except for the black body temperature, only one type of temperature measurement element has a different value from the temperature measurement elements of the same type in other groups;

[0014] Two groups of comparison temperature data are selected from the comparison temperature data for comparison each time.

[0015] As an optional implementation, in the first aspect of the embodiment of the present invention, the blackbody temperatures corresponding to the three types of temperature measurement comparison data are 30° C., 60° C. and 90° C. respectively.

[0016] As an optional implementation, in the first aspect of the embodiment of the present invention, the selecting a temperature measuring element having a correlation with the temperature data greater than a set threshold as a target factor includes:

[0017] When the difference between one group of temperature data and another group of temperature data in each comparison exceeds the set threshold, the temperature measurement groups corresponding to the two groups of comparison temperature data in this comparison are obtained, and different types of temperature measurement elements in the two groups of temperature measurement groups are obtained as the primary selection factors;

[0018] Determine the calculation coefficient of each preliminary selection factor based on the multiple linear regression method, the calculation formula of the multiple linear regression method is: y=b0+b1*x1+…+bn*xn, wherein b0 is the preset deviation, bn is the calculation coefficient of the corresponding preliminary selection factor, xn is the preliminary selection factor, and y is the dependent variable;

[0019] The target factor is selected from the preliminary factors based on the calculated coefficients.

[0020] As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:

[0021] The verification data is collected to revise the temperature measurement formula.

[0022] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the collecting verification data to correct the temperature measurement formula includes:

[0023] Collect verification data, the verification data includes target factors and temperature data, input the target factors of the verification data into the temperature measurement formula to obtain the calculated temperature,

[0024] The difference between the calculated temperature and the temperature data is obtained, and the temperature measurement formula is corrected according to the difference.

[0025] A second aspect of an embodiment of the present invention discloses a temperature measurement device based on infrared thermal imaging, comprising:

[0026] Test grouping module: used to set several temperature measurement groups, each of which includes multiple temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category;

[0027] Data acquisition module: used to collect temperature data of each temperature measurement group in real time, and perform data analysis on the real-time collected temperature data to obtain the correlation between each group of temperature measurement elements and temperature data;

[0028] Factor determination module: used to select temperature measurement elements whose correlation with temperature data is greater than a set threshold as target factors, generate a temperature curve according to the target factors and temperature data, and establish a temperature measurement formula according to the temperature curve.

[0029] As an optional implementation, in the second aspect of the embodiment of the present invention, the temperature measurement factors include blackbody temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power, and power-on time.

[0030] As an optional implementation, in the second aspect of the embodiment of the present invention, collecting temperature data in real time for each temperature measurement group and performing data analysis on the real-time collected temperature data include:

[0031] Three types of temperature measurement comparison data are set based on several groups of temperature measurement groups, the black body temperature in each type of temperature measurement comparison data is the same, and the black body temperature in different types of temperature measurement comparison data is different;

[0032] The temperature data of each temperature measurement group under each type of temperature measurement comparison data is collected in real time at preset intervals to obtain several groups of comparison temperature data; wherein, in each type of temperature measurement comparison data, among the multiple temperature measurement elements of any group of temperature measurement groups, except for the black body temperature, only one type of temperature measurement element has a different value from the temperature measurement elements of the same type in other groups;

[0033] Two groups of comparison temperature data are selected from the comparison temperature data for comparison each time.

[0034] As an optional implementation, in the second aspect of the embodiment of the present invention, the blackbody temperatures corresponding to the three types of temperature measurement comparison data are 30° C., 60° C. and 90° C. respectively.

[0035] As an optional implementation, in the second aspect of the embodiment of the present invention, the selecting a temperature measuring element having a correlation with the temperature data greater than a set threshold as a target factor includes:

[0036] When the difference between one group of temperature data and another group of temperature data in each comparison exceeds the set threshold, the temperature measurement groups corresponding to the two groups of comparison temperature data in this comparison are obtained, and different types of temperature measurement elements in the two groups of temperature measurement groups are obtained as the primary selection factors;

[0037] Determine the calculation coefficient of each preliminary selection factor based on the multiple linear regression method, the calculation formula of the multiple linear regression method is: y=b0+b1*x1+…+bn*xn, wherein b0 is the preset deviation, bn is the calculation coefficient of the corresponding preliminary selection factor, xn is the preliminary selection factor, and y is the dependent variable;

[0038] The target factor is selected from the preliminary factors based on the calculated coefficients.

[0039] As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:

[0040] The verification data is collected to revise the temperature measurement formula.

[0041] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the collecting verification data to correct the temperature measurement formula includes:

[0042] Collect verification data, the verification data includes target factors and temperature data, input the target factors of the verification data into the temperature measurement formula to obtain the calculated temperature,

[0043] The difference between the calculated temperature and the temperature data is obtained, and the temperature measurement formula is corrected according to the difference.

[0044] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the temperature measurement method based on infrared thermal imaging disclosed in the first aspect of the embodiment of the present invention.

[0045] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the temperature measurement method based on infrared thermal imaging disclosed in the first aspect of an embodiment of the present invention.

[0046] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0047] In the embodiment of the present invention, a non-contact temperature measurement method is adopted, which has a wider temperature measurement range and a fast measurement speed. The embodiment sets several groups of temperature measurement groups according to actual needs. Each group of temperature measurement groups contains multiple temperature measurement elements, but the categories of temperature measurement elements of each group of temperature measurement groups must be the same, which ensures the consistency of subsequent data analysis, and uses infrared thermal imaging equipment to collect real-time temperature data for each group of temperature measurement groups. To ensure the accuracy and real-time nature of data collection, so as to conduct accurate data analysis later, select the temperature measurement elements whose correlation with temperature data is greater than the set threshold as the target factor, generate a temperature curve according to the target factor and temperature data, and establish a temperature measurement formula according to the temperature curve to meet the temperature measurement accuracy requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flow chart of a temperature measurement method based on infrared thermal imaging provided by an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a fitted temperature curve provided by an embodiment of the present invention;

[0051] Figure 3 is a structural schematic diagram of a temperature measuring device based on infrared thermal imaging provided by an embodiment of the present invention;

[0052] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] The embodiments of the present invention disclose a temperature measurement method, device, electronic device and storage medium based on infrared thermal imaging. The embodiments of the present invention adopt a non-contact temperature measurement method, which has a wider temperature measurement range and a fast measurement speed. The embodiments set several groups of temperature measurement groups according to actual needs. Each temperature measurement group contains multiple temperature measurement elements, but the categories of temperature measurement elements of each temperature measurement group must be the same, which ensures the consistency of subsequent data analysis. Infrared thermal imaging equipment is used to collect real-time temperature data for each temperature measurement group. To ensure the accuracy and real-time nature of data collection, so as to perform accurate data analysis later, select the temperature measurement elements whose correlation with the temperature data is greater than the set threshold as the target factor, generate a temperature curve based on the target factor and the temperature data, and establish a temperature measurement formula based on the temperature curve to meet the temperature measurement accuracy requirements.

[0056] Embodiment 1

[0057] See also Figure 1 , Figure 1 It is a flow chart of a temperature measurement method based on infrared thermal imaging disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the temperature measurement method based on infrared thermal imaging includes the following steps:

[0058] 101. Set a number of temperature measurement groups, each of which includes a variety of temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category.

[0059] Embodiment In order to obtain a more accurate temperature measurement calculation method, it is necessary to build a front-end infrared thermal imaging temperature measurement equipment test environment in advance, assemble infrared thermal imaging equipment including thermal imaging detectors, lenses and other components, and prepare multiple black bodies to prepare for subsequent real-time data collection. Among them, the black body is also a black body radiation source for releasing temperature. The infrared thermal imaging equipment must be fully cooled before it can be turned on to collect data. Because the internal components of the device will heat up when it is turned on for the first time after being stationary, the temperature in the device cavity will slowly rise. Taking into account the impact of the heat of the device itself on the temperature measurement will have a more ideal effect.

[0060] Correspondingly, the temperature measurement factors include blackbody temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power supply, and power-on time.

[0061] 102. Real-time temperature data is collected for each temperature measurement group, and data analysis is performed on the real-time collected temperature data to obtain the correlation between each group of temperature measurement elements and the temperature data.

[0062] Real-time temperature measurement data collection of infrared thermal imaging equipment. Set different black body temperatures, turn on the infrared thermal imaging equipment for the first time after it has cooled down sufficiently, start the data collection program, collect real-time data, and prepare for data analysis.

[0063] In the embodiment, a plurality of different blackbody temperatures are set, and the blackbody reading is waited for to stabilize. Since the temperature in the device cavity will slowly rise during the temperature measurement process, the infrared thermal imaging device must be fully cooled before starting to collect data. The temperature collection function has been implemented on the device. Turn on the infrared thermal imaging device and start the data collection program to collect real-time data in preparation for data analysis. The collected data includes blackbody temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, distance, ambient temperature, power, power-on time, etc. The collection time can be set to twice a minute and classified according to distance and lens size, and written to files separately.

[0064] Specifically, temperature data is collected in real time for each temperature measurement group, and data analysis is performed on the real-time collected temperature data, including: three types of temperature measurement comparison data are set based on several groups of temperature measurement groups, the black body temperature in each type of temperature measurement comparison data is the same, and the black body temperature in different types of temperature measurement comparison data is different; the temperature data of each temperature measurement group under each type of temperature measurement comparison data is collected in real time every preset time, and several groups of comparison temperature data are obtained; wherein, in each type of temperature measurement comparison data, among the multiple temperature measurement elements of any group of temperature measurement groups, except for the black body temperature, only one type of temperature measurement element has a different value from the temperature measurement elements of the same type in other groups; two groups of comparison temperature data are selected from the comparison temperature data for comparison each time. The black body temperatures corresponding to the three types of temperature measurement comparison data are 30°C, 60°C and 90°C respectively.

[0065] Assume that there is a temperature measuring device, and the device is facing a 30℃ black body for temperature measurement, and a temperature measurement data is recorded every minute from power on to power off. The data includes black body temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, distance, ambient temperature, power, power on time, etc. Repeat the above steps to collect data for 60℃ and 100℃ black bodies. Perform a pairwise correlation analysis on all the collected data, mainly looking at the impact of changes in other factors on the module temperature. Thus, the biggest influencing factors are determined to be movement temperature, shutter temperature, distance, ambient temperature, etc. For example, nine groups of temperature measurement groups are set, each of which contains the corresponding values ​​of black body temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power, and power on time. Each group of temperature measurement groups forms a temperature measurement environment. During the test, there are three black body temperatures, namely 30℃, 60℃ and 90℃ mentioned above. Corresponding to the blackbody temperature of 30°C, the above nine temperature measurement groups all set the blackbody temperature to 30°C, and then each group chooses to change the value of any one of the other temperature measurement elements. The same is true for the blackbody temperatures of 60°C and 90°C. Therefore, a total of 3x 9=27 groups of temperature data can be measured.

[0066] 103. Select a temperature measurement element whose correlation with the temperature data is greater than a set threshold as a target factor, generate a temperature curve according to the target factor and the temperature data, and establish a temperature measurement formula according to the temperature curve.

[0067] This step is to analyze and fit the temperature curve based on the collected data. The collected data is summarized and analyzed, the correlation is determined, and the key factors are selected to determine the coefficients to fit the temperature curve.

[0068] In this step, the temperature measurement elements whose correlation with the temperature data is greater than the set threshold are selected as the target factors, including: when the difference between the temperature data of one group and the temperature data of another group in each comparison exceeds the set threshold, the temperature measurement groups corresponding to the two groups of comparison temperature data in this comparison are obtained, and the temperature measurement elements of different types in the two groups of temperature measurement groups are obtained as the preliminary factors; the calculation coefficient of each preliminary factor is determined based on the multiple linear regression method, and the calculation formula of the multiple linear regression method is: y=b0+b1*x1+…+bn*xn, wherein b0 is the preset deviation, bn is the calculation coefficient of the corresponding preliminary factor, xn is the preliminary factor, and y is the dependent variable; the target factor is selected from the preliminary factors based on the calculation coefficient. The embodiment uses a multiple linear regression method to assign calculation coefficients corresponding to different preliminary factors. It can be considered that x1, x2...xn represent specific values ​​of preliminary factors such as movement temperature, shutter temperature, distance, and ambient temperature. For this formula, the final fitting result y is the set of data closest to the actual temperature, which is the ideal calculation coefficient. At this time, the criticality of different preliminary factors is determined according to the numerical value of the calculation coefficient. In the calculation formula, y is not a fixed value. When different calculation coefficients are set, the result of y is affected. The embodiment needs to evaluate the difference between y and the actual temperature, and finally select a set of calculation coefficients with the smallest difference between y and the actual temperature.

[0069] The specific temperature measurement formula varies according to different lenses. For example, for a 7mm lens, the temperature measurement formula for its low temperature section (-50℃ to 120℃) is: Y=0.993*x1+0.805*x2-0.714*x3-0.126*x4+0.093*x5+0.3; the temperature measurement formula for its high temperature section (120℃ to 600℃) is: Y=0.975*x1-4.65*x2+5.29*x3-0.113*x4+0.072*x5+12, where x1 represents the module output temperature, x2 represents the movement temperature, x3 represents the shutter temperature, x4 represents the ambient temperature, and x5 represents the distance.

[0070] The temperature curve fitted in the embodiment is as follows Figure 2As shown in the figure, for the data of various factors collected, first analyze their correlation with the blackbody temperature to determine the approximate influencing factors of the influencing factors. Screen out irrelevant data, and try to use big data analysis to program and determine the influencing factors of important data. Because the data collected each time is single, it is necessary to summarize the data for multiple times, so that a discrete data image will be formed. The influencing factors and the corresponding influencing factors will be found based on image comparison. Design formula, the objective function is the function of each influencing factor. If the temperature measurement result is ideal, it is required that the difference between the value of the objective function and the actual blackbody value in various cases must be within the accuracy range. Excellent temperature measurement algorithms are based on sufficient testing. Because the temperature output by the temperature detector itself will be affected by factors such as lens, distance, ambient temperature, emissivity, and temperature in the equipment cavity, try to use the control variable method to collect experimental data and conduct comprehensive analysis.

[0071] Furthermore, the embodiment also includes collecting verification data to correct the temperature measurement formula. Specifically, verification data is collected, and the verification data includes target factors and temperature data. The target factors of the verification data are input into the temperature measurement formula to obtain the calculated temperature, the difference between the calculated temperature and the temperature data is obtained, and the temperature measurement formula is corrected according to the difference. By correcting the temperature measurement formula, errors caused by factors such as the measurement environment and equipment performance can be eliminated or reduced, thereby improving the accuracy and reliability of the measurement. Different temperature measurement scenarios and applications may have different requirements for temperature measurement accuracy. The corrected temperature measurement formula can be adjusted according to specific needs to meet the measurement requirements in specific scenarios. Temperature measurement is affected by a variety of environmental factors, such as ambient temperature, humidity, wind speed, etc. The corrected temperature measurement formula can take into account the influence of these environmental factors and improve the stability and accuracy of the measurement.

[0072] The embodiment designs a comprehensive temperature measurement data processing mechanism, which forms a closed-loop system from real-time data collection to data analysis and comparison, and then to the selection of target factors and the correction of temperature measurement formula. The system can continuously monitor and adjust the accuracy of temperature measurement data, providing reliable data support for subsequent temperature measurement applications. At the same time, the accuracy and adaptability of the temperature measurement formula are ensured through the multiple linear regression method and the correction of verification data.

[0073] Infrared thermal imaging systems are divided into refrigeration type and non-refrigeration type. The embodiment is a non-refrigeration type, which has the advantages of low price, small size, light weight, low power consumption, long life, and high reliability compared to the refrigeration type system. The temperature measurement method can be divided into two categories: contact type and non-contact type. The embodiment is a non-contact type, which has the advantages of wide measurement temperature range, wide measurement object range, and fast measurement speed compared to the contact type. Since the infrared temperature measurement equipment contains many components, the infrared detector is just one of them. The isolated test of a single infrared detector can meet the accuracy requirements, but when it is installed in the equipment, it will be affected by other heating components as the equipment is turned on, thereby affecting its accuracy. At this time, the infrared detector data must be corrected and compensated according to the specific situation of the equipment to make the temperature measurement result meet the accuracy requirements. At present, most traditional temperature measurement algorithms only consider a single factor, lack of systematic analysis of influencing factors, resulting in insufficient temperature measurement stability. The algorithm and equipment obtained in the embodiment comprehensively consider multiple factors, and the temperature measurement algorithm is more rigorous and has wider adaptability. Moreover, most of the current infrared thermal imaging temperature measurement devices need to be warmed up for a period of time after being turned on before they can work normally. The temperature measurement accuracy cannot be guaranteed before the device reaches a steady state within a period of time after being turned on. However, the present embodiment can meet the accuracy requirements from the first time the device is turned on, and the accuracy requirements can be met without the need for "warming up".

[0074] Embodiment 2

[0075] See also Figure 3 , Figure 3 Schematic diagram of a temperature measuring device based on infrared thermal imaging disclosed in an embodiment of the present invention. Figure 3 As shown, the temperature measuring device based on infrared thermal imaging may include: a test grouping module 301, a data acquisition module 302 and a factor determination module 303, wherein the test grouping module 301 is used to set a plurality of temperature measurement groups, each temperature measurement group includes a plurality of temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category; the data acquisition module 302 is used to respectively collect temperature data of each temperature measurement group in real time, and perform data analysis on the real-time collected temperature data to obtain the correlation between each group of temperature measurement elements and the temperature data; the factor determination module 303 is used to select the temperature measurement elements whose correlation with the temperature data is greater than a set threshold as target factors, generate a temperature curve according to the target factors and the temperature data, and establish a temperature measurement formula according to the temperature curve.

[0076] The temperature measurement factors of the embodiment include black body temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power, and boot time.

[0077] The data acquisition module 302 specifically sets three types of temperature measurement comparison data based on several groups of temperature measurement groups, wherein the black body temperature in each type of temperature measurement comparison data is the same, and the black body temperature in different types of temperature measurement comparison data is different; the temperature data of each group of temperature measurement groups under each type of temperature measurement comparison data is collected in real time at preset time intervals to obtain several groups of comparison temperature data; wherein, in each type of temperature measurement comparison data, among the multiple temperature measurement elements of any group of temperature measurement groups, except for the black body temperature, only one type of temperature measurement element has a different value from the temperature measurement elements of the same type in other groups; and two groups of comparison temperature data are selected from the comparison temperature data for comparison each time. Among them, the black body temperatures corresponding to the three types of temperature measurement comparison data are 30°C, 60°C and 90°C respectively.

[0078] In the factor determination module 303, the temperature measurement elements whose correlation with the temperature data is greater than the set threshold are selected as the target factors, including: when the difference between the temperature data of one group and the temperature data of another group in each comparison exceeds the set threshold, the temperature measurement groups corresponding to the two groups of comparison temperature data in this comparison are obtained, and the temperature measurement elements of different types in the two groups of temperature measurement groups are obtained as the preliminary factors; the calculation coefficient of each preliminary factor is determined based on the multiple linear regression method, and the calculation formula of the multiple linear regression method is: y=b0+b1*x1+…+bn*xn, wherein b0 is the preset deviation, bn is the calculation coefficient of the corresponding preliminary factor, xn is the preliminary factor, and y is the dependent variable; the target factor is selected from the preliminary factors based on the calculation coefficient.

[0079] The embodiment may further include a formula correction module for collecting verification data to correct the temperature measurement formula. Specifically, the verification data is collected, the verification data includes a target factor and temperature data, the target factor of the verification data is input into the temperature measurement formula to obtain the calculated temperature, the difference between the calculated temperature and the temperature data is obtained, and the temperature measurement formula is corrected according to the difference.

[0080] Embodiment 3

[0081] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 4 As shown, the electronic device may include:

[0082] A memory 401 storing executable program codes;

[0083] a processor 402 coupled to the memory 401;

[0084] The processor 402 calls the executable program code stored in the memory 401 to execute part or all of the steps in the temperature measurement method based on infrared thermal imaging in the first embodiment.

[0085] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the temperature measurement method based on infrared thermal imaging in the first embodiment.

[0086] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the temperature measurement method based on infrared thermal imaging in the first embodiment.

[0087] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the temperature measurement method based on infrared thermal imaging in embodiment one.

[0088] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0092] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0093] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0094] The above is a detailed introduction to the temperature measurement method, device, electronic device and storage medium based on infrared thermal imaging disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A temperature measurement method based on infrared thermal imaging, characterized in that: include: Set up several temperature measurement groups, each of which includes multiple temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category; The temperature data of each temperature measurement group is collected in real time, and the real-time collected temperature data is analyzed to obtain the correlation between each group of temperature measurement elements and the temperature data; A temperature measurement element whose correlation with the temperature data is greater than a set threshold is selected as a target factor, a temperature curve is generated according to the target factor and the temperature data, and a temperature measurement formula is established according to the temperature curve.

2. The temperature measurement method according to claim 1, characterized in that: The temperature measurement factors include black body temperature, module temperature, shutter temperature, movement temperature, CPU temperature, lens size, lens position, ambient temperature, power, and power-on time.

3. The temperature measurement method according to claim 2, characterized in that: The real-time temperature data collection of each temperature measurement group is respectively carried out, and the real-time collected temperature data is analyzed, including: Three types of temperature measurement comparison data are set based on several groups of temperature measurement groups, the black body temperature in each type of temperature measurement comparison data is the same, and the black body temperature in different types of temperature measurement comparison data is different; The temperature data of each temperature measurement group under each type of temperature measurement comparison data is collected in real time at preset intervals to obtain several groups of comparison temperature data; wherein, in each type of temperature measurement comparison data, among the multiple temperature measurement elements of any group of temperature measurement groups, except for the black body temperature, only one type of temperature measurement element has a different value from the temperature measurement elements of the same type in other groups; Two groups of comparison temperature data are selected from the comparison temperature data for comparison each time.

4. The temperature measurement method according to claim 3, characterized in that: The blackbody temperatures corresponding to the three types of temperature measurement comparison data are 30°C, 60°C and 90°C respectively.

5. The temperature measurement method according to claim 3, characterized in that: The step of selecting a temperature measuring element whose correlation with the temperature data is greater than a set threshold as a target factor includes: When the difference between one group of temperature data and another group of temperature data in each comparison exceeds the set threshold, the temperature measurement groups corresponding to the two groups of comparison temperature data in this comparison are obtained, and different types of temperature measurement elements in the two groups of temperature measurement groups are obtained as the primary selection factors; Determine the calculation coefficient of each preliminary selection factor based on the multiple linear regression method, the calculation formula of the multiple linear regression method is: y=b0+b1*x1+…+bn*xn, wherein b0 is the preset deviation, bn is the calculation coefficient of the corresponding preliminary selection factor, xn is the preliminary selection factor, and y is the dependent variable; The target factor is selected from the preliminary factors based on the calculated coefficients.

6. The temperature measurement method according to claim 1, characterized in that: Also includes: The verification data is collected to revise the temperature measurement formula.

7. The temperature measurement method according to claim 6, characterized in that: The collecting and verifying data to correct the temperature measurement formula includes: Collect verification data, the verification data includes target factors and temperature data, input the target factors of the verification data into the temperature measurement formula to obtain the calculated temperature, The difference between the calculated temperature and the temperature data is obtained, and the temperature measurement formula is corrected according to the difference.

8. A temperature measuring device based on infrared thermal imaging, characterized in that: include: Test grouping module: used to set several temperature measurement groups, each of which includes multiple temperature measurement elements, and the temperature measurement elements of each temperature measurement group are of the same category; Data acquisition module: used to collect temperature data of each temperature measurement group in real time, and perform data analysis on the real-time collected temperature data to obtain the correlation between each group of temperature measurement elements and temperature data; Factor determination module: used to select temperature measurement elements whose correlation with temperature data is greater than a set threshold as target factors, generate a temperature curve according to the target factors and temperature data, and establish a temperature measurement formula according to the temperature curve.

9. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the temperature measurement method based on infrared thermal imaging as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the temperature measurement method based on infrared thermal imaging according to any one of claims 1 to 7.

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