Infrared temperature measurement method and system with environment adaptive calibration function

Abstract

The invention discloses an infrared temperature measurement method and system with an environment adaptive calibration function, and the method comprises the following steps: S1, collecting an infrared radiation energy signal of a measured object through an infrared sensor, and synchronously collecting an environment radiation energy signal and a surface state characteristic signal of the measured object; s2, establishing an emissivity database according to the material and the surface state of the measured object; according to the invention, infrared radiation, environmental radiation and surface state characteristic signals of a measured object are synchronously collected, an emissivity database capable of covering different materials and surface states is constructed, initial emissivity parameters are matched, and the emissivity is dynamically calibrated through an environmental reflection radiation influence model and an adaptive algorithm, so that the emissivity is accurately measured. The emissivity can be adaptively adjusted according to the material, the surface state difference and the environmental reflection radiation, the temperature measurement deviation can be reduced compared with the direct infrared temperature measurement, and the temperature measurement accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of infrared temperature measurement technology, specifically to an infrared temperature measurement method and system with environmental adaptive calibration function. Background Technology

[0002] The principle of infrared thermometry is to detect the infrared radiation energy emitted by the object being measured and then deduce the object's temperature by combining the physical laws of infrared radiation. In practical applications, the accuracy of temperature measurement is greatly affected by the material and surface condition of the object being measured. This difference in emissivity caused by different materials and surface conditions will lead to a deviation between the radiation energy received by the infrared sensor and the actual temperature of the object. This is especially true for objects with low emissivity, which are greatly affected by the radiation energy of the environment, thus affecting the accuracy of temperature measurement.

[0003] To compensate for this bias, traditional methods include attaching high-emissivity auxiliary materials for short-term testing, adding metal light shields or radiation shielding devices, or replacing traditional lenses with reflectors to enhance target radiation collection. However, there is limited space for adding these to some of the tested objects. In addition, when switching between tested objects with different materials or surface conditions, it is necessary to manually calibrate the emissivity of the infrared thermometer to improve the accuracy of temperature testing. Before testing, it is necessary to consult the material emissivity table and input new reference values ​​according to the material and surface condition of the tested object. When testing different tested objects, it is necessary to repeatedly adjust the parameters, which is not quick enough. Summary of the Invention

[0004] The purpose of this invention is to provide an infrared temperature measurement method and system with environmental adaptive calibration function. By simultaneously collecting infrared radiation, environmental radiation and surface condition characteristic signals of the object under test, constructing an emissivity database covering different materials and surface conditions, establishing an environmental reflection radiation influence model, and realizing adaptive calibration of emissivity for objects under test with different materials and surface conditions, thereby improving the accuracy of temperature measurement.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an infrared temperature measurement method and system with environmental adaptive calibration function, comprising the following steps: S1. The infrared radiation energy signal of the object under test is collected by an infrared sensor, the ambient radiation energy signal is collected simultaneously, and the surface state characteristic signal of the object under test is collected. S2. Establish an emissivity database based on the material and surface condition of the object being tested; S3. Initially identify the material type and surface condition of the object being tested, and match the corresponding initial emissivity parameters; S4. Based on the collected environmental radiation energy signals, establish an environmental reflected radiation influence model; S5. Based on the initial emissivity parameters, the emissivity parameters are dynamically adjusted using an adaptive algorithm; S6. The actual temperature value is output after correction calculation.

[0006] Preferably, in step S1, the environmental radiation energy signal includes an environmental temperature signal and an environmental radiation intensity signal, and the surface state characteristic signal of the object being measured is set as the surface roughness parameter signal of the object being measured.

[0007] Preferably, in step S2, the material of the object being tested is divided into three categories: non-metallic, metallic, and composite materials, with metallic materials located in the low emissivity range, non-metallic materials located in the high emissivity range, and composite materials located in the medium emissivity range. The low emissivity range is set to ε≤0.3; The emissivity range is set to 0.3 < ε < 0.8; The high emissivity range is set to ε≥0.8.

[0008] Preferably, in step S2, the surface state of the object under test is divided into a natural state and a surface processed state. When the object under test is in a natural state, the emissivity is assumed to correspond to the low, medium and high emissivity ranges respectively.

[0009] Preferably, the surface processing state includes polishing, roughening, oxidation, and coating. When the surface roughness Ra of the polishing process is ≤0.1μm, it is considered highly polished, and the emissivity range is set to 0.02-0.08. When the surface roughness of the polished surface is 0.1μm < Ra ≤ 1μm, it is considered medium polishing, and the emissivity range is set to 0.08-0.15. When the surface roughness of the polished surface is 1μm < Ra ≤ 5μm, it is considered light polishing, and the emissivity range is set to 0.15-0.3. Roughening processes include sandblasting and wire drawing. The emissivity range for sandblasting is set to 0.4-0.7, and the emissivity range for wire drawing is set to 0.35-0.6.

[0010] Preferably, when the oxide layer thickness of the oxidation process is <1μm, it is considered light oxidation, and the emissivity range is set to 0.3-0.5; When the oxide layer thickness is 1-10μm, it is considered medium oxidation, and the emissivity range is set to 0.5-0.8. When the oxide layer thickness of the oxidation process is ≥10μm, it is considered heavy oxidation, and the emissivity range is set to 0.8-0.97. When the coating is made of metal, the emissivity range is set to 0.05-0.3; when the coating is made of non-metal, the emissivity range is set to 0.8-0.95; and when the coating is made of composite material, the emissivity range is set to 0.1-0.5.

[0011] Preferably, in step S4, when establishing the environmental reflection and radiation influence model, it is necessary to calculate the object's reflectivity ρ and geometric influence factor f. geo and the surrounding directional heat source radiation correction factor f dir Finally, the ambient radiation energy E reflected by the measured object is calculated. ref =E env *ρ*f geo *f dir And convert the reflected radiation energy into the corresponding equivalent reflection temperature T. ref The conversion formula is E ref =(C1 / λ 5 )*{1 / [e C2 / (λ*Tref) -1]}.

[0012] Preferably, in step S5, an emissivity-environmental interference correlation model is established by combining Kirchhoff's laws with the proportion of environmental reflected radiation r, and the formula is ε. cal1 =ε init ×[1+k1×(r−r0)+k2×(Ra−Ra0)]; The total radiation E received by the sensor total It is the object's own radiation E self With reflected environmental radiation E ref The superposition of these factors requires first removing interference, stripping away reflected interference, and restoring the object's own radiation. The formula is E. self =E total -E ref Then, the temperature is deduced by applying the physical laws of infrared radiation to simplify Planck's law for the commonly used 8-14μm wavelength band. The formula is: E self =ε cal1 ×σ×T 4 true T 4 true For the actual temperature, σ = 5.670374419 × 10 −8 W / (m 2 *K 4 ).

[0013] Preferably, it includes a signal acquisition module to acquire raw data of the object under test; The database module is used to store emissivity data corresponding to different materials and surface conditions; The algorithm processing module includes an environmental reflection radiation modeling module, an emissivity calibration algorithm module, and a temperature correction calculation module. The environmental reflection radiation modeling module establishes an influence model based on the signal from the environmental radiation acquisition unit. The emissivity calibration algorithm module achieves emissivity matching and adaptive calibration through machine learning algorithms. The temperature correction calculation module distinguishes between the object's own radiation and reflected radiation, corrects the radiation energy signal, and calculates the true temperature. The temperature output module outputs the actual temperature value calculated by the algorithm processing module in the form of a digital display or signal transmission. The signal acquisition module, database module, algorithm processing module and temperature output module are all electrically connected to the main control chip.

[0014] Preferably, the signal acquisition module includes a thermopile infrared sensor for acquiring infrared radiation energy signals of the object under test, a digital temperature sensor for acquiring ambient temperature signals, an infrared radiation sensor for acquiring ambient radiation intensity signals, and an optical roughness sensor for acquiring surface roughness signals of the object under test. The thermopile infrared sensor, the digital temperature sensor, and the infrared radiation sensor are all electrically connected to the main control chip.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs an emissivity database that covers different materials and surface conditions by simultaneously collecting infrared radiation, environmental radiation, and surface condition characteristic signals of the object under test. It matches the initial emissivity parameters and then dynamically calibrates the emissivity through an environmental reflection radiation influence model and an adaptive algorithm. It can adaptively adjust the emissivity according to the differences in material, surface condition, and environmental reflection radiation. Compared with direct infrared thermometry, it can reduce the temperature measurement deviation and improve the temperature measurement accuracy.

[0016] 2. The temperature measuring device of the present invention does not rely on any additional devices added to the outside of the object being measured. It does not require manual querying of the emissivity table and repeated adjustment of device parameters when switching between different objects being measured. The temperature measuring operation process can be simplified through automated signal acquisition, emissivity matching and adaptive calibration. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a technical solution: an infrared temperature measurement method with environmental adaptive calibration function, comprising the following steps: S1. The infrared radiation energy signal of the object under test is collected by an infrared sensor as the original effective signal source for temperature measurement. At the same time, the ambient radiation energy signal and the surface state characteristic signal of the object under test are collected. Objects with low emissivity emit weak radiation but strong reflection, reflecting radiation from the surrounding environment to the sensor. This causes the sensor to mistakenly include the reflected energy in the object's own radiation. By collecting environmental radiation energy signals, the proportion of reflected radiation and equivalent temperature can be calculated using a model, and the degree of interference can be determined. Objects with different materials and surface conditions have different degrees of matching between radiation energy and actual temperature. After collecting this signal, it can be combined with an emissivity database and environmental data to determine the deviation between the signal and the theoretical radiation energy, and then the emissivity can be corrected using algorithms.

[0020] S2. Establish an emissivity database based on the material and surface condition of the object being tested; S3. Initially identify the material type and surface condition of the object being tested, and match the corresponding initial emissivity parameters; S4. Based on the collected environmental radiation energy signal, establish an environmental reflection radiation influence model, calculate the proportion of environmental radiation energy reflected by the object being measured, and obtain the degree of interference of environmental reflection radiation on temperature measurement. S5. Based on the initial emissivity parameter, the emissivity parameter is dynamically adjusted through an adaptive algorithm; at the same time, the radiation energy of the object being measured is distinguished from the reflected environmental radiation energy, the radiation energy signal received by the infrared sensor is corrected, and the error caused by the difference in emissivity and the superposition of reflected environmental radiation is eliminated. S6. The actual temperature value is output after correction calculation.

[0021] In step S1, the environmental radiation energy signal includes the environmental temperature signal and the environmental radiation intensity signal, and the surface state characteristic signal of the object under test is set as the surface roughness parameter signal of the object under test.

[0022] In step S2, the material of the object being tested is divided into three categories: non-metallic, metallic, and composite. The metallic material is located in the low emissivity range, the non-metallic material is located in the high emissivity range, and the composite material is located in the medium emissivity range. The low emissivity range is set to ε≤0.3; The emissivity range is set to 0.3 < ε < 0.8; The high emissivity range is set to ε≥0.8.

[0023] In step S2, the surface state of the object under test is divided into natural state and surface processed state. When the object under test is in natural state, the emissivity is respectively in the low, medium and high emissivity ranges.

[0024] The surface processing states include polishing, roughening, oxidation, and coating. When the surface roughness Ra of the polishing process is ≤0.1μm, it is considered highly polished, and the emissivity range is set to 0.02-0.08. Among them, the emissivity of commonly used highly polished aluminum is ε=0.09 at 100℃ and ε=0.04 at 440℃, highly polished brass is ε=0.03 at 247℃, polished unoxidized steel is ε=0.05-0.10, and mirror-grade polished silver can have a lower limit of ε down to 0.02, all of which conform to this range.

[0025] When the surface roughness of polishing is 0.1μm < Ra ≤ 1μm, it is considered medium polishing, and the emissivity range is set to 0.08-0.15. Among them, the emissivity of commonly used metal rough polished aluminum at 100℃ is ε=0.18, and the emissivity of polished slightly oxidized copper is ε=0.10-0.15. The medium polished surface is slightly rough, and the emissivity is higher than that of highly polished surfaces, which falls within this range.

[0026] When the surface roughness of polished metal is 1μm<Ra≤5μm, it is considered light polishing, and the emissivity range is set to 0.15-0.3. For brushed metal, ε=0.35-0.6, but when there is no obvious oxidation after light polishing, ε=0.15-0.25, which connects with the upper limit of low emissivity of 0.3.

[0027] Roughening processes include sandblasting and wire drawing. The emissivity range for sandblasting is set at 0.4-0.7, and for wire drawing at 0.35-0.6. Among these, commonly used metals for sandblasting are stainless steel (ε=0.4-0.7) and aluminum alloys (ε=0.5-0.7). The larger the abrasive grain size, the rougher the surface, and the closer the emissivity is to 0.7, which falls within the medium emissivity range. The texture depth of the wire-drawn metal surface is greater than that of light polishing, with an emissivity of 0.35-0.6, slightly lower than that of sandblasting, which falls within the lower limit of the medium emissivity range.

[0028] When the oxide layer thickness of the oxidation process is <1μm, it is considered light oxidation, and the emissivity range is set to 0.3-0.5. After oxidation treatment of the commonly used alloy A3003, ε=0.40, the thickness of the artificial light oxide layer is uniform, and the emissivity is stable at 0.3-0.5, which is in line with the medium emissivity range.

[0029] When the oxide layer thickness is 1-10μm, it is considered medium oxidation, and the emissivity range is set to 0.5-0.8. For anodized aluminum alloys, ε=0.5-0.7. The increase in oxide layer thickness leads to an increase in emissivity, which is in the high-mid range of medium emissivity.

[0030] When the oxide layer thickness is ≥10μm, it is considered heavily oxidized, and the emissivity range is set to 0.8-0.97. Commonly used alloys such as 20-Ni-24-Cr-55-Fe have an emissivity (ε) of 0.97 at 500℃ and 60-Ni-12-Cr-28-Fe have an emissivity (ε) of 0.82 at 560℃. Heavily oxidized layers approach the characteristics of non-metallic oxides, and their emissivity enters the high emissivity range. When the coating is made of metal, the emissivity range is set to 0.05-0.3; when the coating is made of non-metal, the emissivity range is set to 0.8-0.95; when the coating is made of composite components, the emissivity range is set to 0.1-0.5. Among these, commonly used coatings, such as black matte paint coatings, have an emissivity range of 0.9-0.98, ceramic coatings have an emissivity range of 0.85-0.95, and non-metallic coatings have radiation characteristics close to those of high-emissivity non-metals, which falls within this range. For architectural low-emissivity glass coatings, the emissivity range is 0.05-0.2, and for industrial low-emissivity metal coatings, the emissivity range is 0.3-0.5. Functional coatings can be adjusted as needed to cover low and medium emissivity ranges.

[0031] In step S4, when establishing the environmental reflection radiation influence model, it is necessary to calculate the object's reflectivity ρ and geometric influence factor f. geo and the surrounding directional heat source radiation correction factor f dir Finally, the ambient radiation energy E reflected by the measured object is calculated. ref =E env *ρ*f geo *f dir And convert the reflected radiation energy into the corresponding equivalent reflection temperature T. ref The conversion formula is E ref =(C1 / λ 5 )*{1 / [e C2 / (λ*Tref) -1]}.

[0032] The first radiation constant C1 = 3.7418 × 10 8 W*μm 4 / m 2 The second radiation constant C2 = 1.4388 × 10 4 μmK, λ=10μm (center wavelength of the 8-14μm band). According to Kirchhoff's laws of infrared radiation, the emissivity ε, reflectivity ρ, and transmittance τ of an object in thermal equilibrium satisfy ε + ρ + τ = 1. For a solid object being measured, the transmittance τ ≈ 0, and the reflectivity can be simplified to ρ = 1 − ε. init , where ε init For example, if the initial emissivity parameter is ε=0.1 for polished stainless steel, then the reflectivity ρ=0.9.

[0033] The calculation basis for the geometric impact factor is as follows: 1) Sensor field of view θ: The smaller the field of view, the more concentrated the ambient radiation reflected by the measured object. θ ≤ reference field of view θ0, where the reference field of view θ0 = 2°, f geo The closer to 1.0, the range of values ​​is 0.8-1.0; the larger the field of view, the greater θ > θ0, resulting in more dispersed reflected radiation, f geo Reduced to 0.3-0.5; 2) Surface roughness Ra of the object being measured: The smoother the surface, the closer the reflection is to specular reflection, f geo Take a value of 0.8-1.0; the rougher the surface, such as sandblasted metal, Ra≥5μm, the diffuse reflection occurs. geo Taking values ​​of 0.1-0.3, the uniformity of environmental radiation: In a closed environment, environmental radiation is uniform, f... geo Take 0.7-1.0; open environment, environmental radiation is dispersed, f geo Taking a value between 0.3 and 0.7, the final formula for calculating the geometric impact factor is f. geo =0.2×θ0 / θ+0.5×(1-Ra / Ra max )+0.3×f env-uni Where θ0 = 2°, Ra max =10μm, f env-uni The coefficient represents the environmental uniformity, with 1.0 for a closed environment and 0.5 for an open environment.

[0034] In step S5, combining Kirchhoff's laws and the proportion of environmental reflected radiation r, an emissivity-environmental interference correlation model is established, with the formula ε. cal1 =ε init ×[1+k1×(r−r0)+k2×(Ra−Ra0)].

[0035] ε cal1 The calibrated emissivity is given by k1, which is the environmental interference correction coefficient. For low emissivity objects, k1 = 0.8; for medium emissivity, k1 = 0.3; and for high emissivity, k1 = 0.1. R0 is the reference reflected radiation ratio. For a closed environment, r0 = 20; and for an open environment, r0 = 30. K2 is the surface roughness correction coefficient, which is fixed at 0.02. For every deviation of the roughness from the reference value Ra0 = 1 μm, the emissivity is corrected accordingly. Ra is the surface roughness of the object being measured, collected in step S1.

[0036] The total radiation E received by the sensor total It is the object's own radiation E self With reflected environmental radiation E ref The superposition of these factors requires first removing interference, stripping away reflected interference, and restoring the object's own radiation. The formula is E. self =E total -E ref Then, the temperature is deduced by applying the physical laws of infrared radiation to simplify Planck's law for the commonly used 8-14μm wavelength band. The formula is: E self =ε cal1 ×σ×T 4 true T 4 true For the actual temperature, σ = 5.670374419 × 10 −8 W / (m 2 *K 4 By dynamically adjusting the emissivity parameter, the corrected emissivity ε is achieved. cal1 Match the true radiation characteristics of the object under test, and then use T... 4 true=E self / (ε cal1 The true temperature value is obtained by calculating ×σ, which reduces the temperature measurement deviation caused by differences in emissivity and environmental interference.

[0037] An infrared temperature measurement system with environmental adaptive calibration function is applied to the aforementioned infrared temperature measurement method with environmental adaptive calibration function, including a signal acquisition module for acquiring raw data of the object being measured; The signal acquisition module includes a thermopile infrared sensor for acquiring infrared radiation energy signals of the object under test, a digital temperature sensor for acquiring ambient temperature signals, an infrared radiation sensor for acquiring ambient radiation intensity signals, and an optical roughness sensor for acquiring surface roughness signals of the object under test. The optical roughness sensor uses a Proscan2000 with an S3 / 011 probe. The thermopile infrared sensor, digital temperature sensor, and infrared radiation sensor are all electrically connected to the main control chip.

[0038] Among them, the thermopile infrared sensor adopts the GY-906 based on the MLX90614 chip, which outputs the initial ambient temperature signal. If the deviation exceeds ±0.5℃, a calibration command is triggered.

[0039] The digital temperature sensor is model SD5820A, covering a range of -10 to +85℃. The infrared radiation sensor is model PMA2144, which detects the ambient radiation baseline and records the initial value. The main control chip is STM32F103RCT6, which receives signals from the four sensors.

[0040] The database module is used to store emissivity data corresponding to different materials and surface conditions; it has a built-in FLASH storage and automatically loads preset datasets, including more than 30 types of materials such as aluminum, steel, copper, PP, PE, and ceramics, as well as emissivity reference data corresponding to different roughnesses from Ra0.025μm to Ra10μm, with an accuracy of ±0.01.

[0041] The algorithm processing module includes an environmental reflection radiation modeling module, an emissivity calibration algorithm module, and a temperature correction calculation module. The environmental reflection radiation modeling module establishes an influence model based on the signal from the environmental radiation acquisition unit. The emissivity calibration algorithm module achieves emissivity matching and adaptive calibration through machine learning algorithms. The temperature correction calculation module distinguishes between the object's own radiation and reflected radiation, corrects the radiation energy signal, and calculates the true temperature. The environmental reflected radiation modeling unit calls upon the radiation intensity data collected by the infrared radiation sensor and the ambient temperature data from the digital temperature sensor to establish a reflected radiation model.

[0042] The emissivity calibration algorithm module extracts the surface roughness Ra value and material characteristics, matches the initial emissivity in the database, and uses the improved SA-BFGS machine learning algorithm, combined with the radiation signal of GY-906 and the output of the environmental model, to iteratively correct the initial emissivity. The number of iterations is ≤20, and the final output is the calibrated emissivity ε. cal1 .

[0043] The improved SA-BFGS algorithm, also known as the IMP-SA-BFGS algorithm, is an algorithm that combines machine learning and numerical optimization, optimized from the SA-BFGS hybrid algorithm. BFGS, a classic quasi-Newton method, constructs an approximate Hessian matrix iteratively, quickly converging to a local optimum, but is prone to getting trapped in local optima. The SA algorithm simulates the physical annealing process, accepting poor solutions with a certain probability, possessing global optimization capabilities, but with a slow convergence speed. Combining the two, the SA algorithm first globally explores the approximate region of the optimum, and then the BFGS algorithm quickly converges to the exact optimum within that region. This is currently the most widely used algorithm, capable of rapidly adjusting the emissivity ε. cal1 Calibrate to within ±0.02 of the actual value.

[0044] The temperature output module outputs the actual temperature value calculated by the algorithm processing module via digital display or signal transmission. The signal acquisition module, database module, algorithm processing module, and temperature output module are all electrically connected to the main control chip. The main control chip receives the actual temperature value output by the algorithm processing module and presents or outputs it through local display or remote transmission.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An infrared temperature measurement method with environmental adaptive calibration function, characterized in that, Includes the following steps: S1. The infrared radiation energy signal of the object under test is collected by an infrared sensor, the ambient radiation energy signal is collected simultaneously, and the surface state characteristic signal of the object under test is collected. S2. Establish an emissivity database based on the material and surface condition of the object being tested; S3. Initially identify the material type and surface condition of the object being tested, and match the corresponding initial emissivity parameters; S4. Based on the collected environmental radiation energy signals, establish an environmental reflected radiation influence model; S5. Based on the initial emissivity parameters, the emissivity parameters are dynamically adjusted using an adaptive algorithm; S6. The actual temperature value is output after correction calculation.

2. The infrared temperature measurement method with environmental adaptive calibration function according to claim 1, characterized in that: In step S1, the environmental radiation energy signal includes the environmental temperature signal and the environmental radiation intensity signal, and the surface state characteristic signal of the object under test is set as the surface roughness parameter signal of the object under test.

3. The infrared temperature measurement method with environmental adaptive calibration function according to claim 2, characterized in that: In step S2, the material of the object being tested is divided into three categories: non-metallic, metallic, and composite. The metallic material is located in the low emissivity range, the non-metallic material is located in the high emissivity range, and the composite material is located in the medium emissivity range. The low emissivity range is set to ε≤0.3; The emissivity range is set to 0.3 < ε < 0.8; The high emissivity range is set to ε≥0.

8.

4. The infrared temperature measurement method with environmental adaptive calibration function according to claim 3, characterized in that: In step S2, the surface state of the object under test is divided into natural state and surface processed state. When the object under test is in natural state, the emissivity is respectively in the low, medium and high emissivity ranges.

5. The infrared temperature measurement method with environmental adaptive calibration function according to claim 4, characterized in that: The surface processing states include polishing, roughening, oxidation, and coating. When the surface roughness Ra of the polishing process is ≤0.1μm, it is considered highly polished, and the emissivity range is set to 0.02-0.

08. When the surface roughness of the polished surface is 0.1μm < Ra ≤ 1μm, it is considered medium polishing, and the emissivity range is set to 0.08-0.

15. When the surface roughness of the polished surface is 1μm < Ra ≤ 5μm, it is considered light polishing, and the emissivity range is set to 0.15-0.

3. Roughening processes include sandblasting and wire drawing. The emissivity range for sandblasting is set to 0.4-0.7, and the emissivity range for wire drawing is set to 0.35-0.

6.

6. The infrared temperature measurement method with environmental adaptive calibration function according to claim 5, characterized in that: When the oxide layer thickness of the oxidation process is <1μm, it is considered light oxidation, and the emissivity range is set to 0.3-0.

5. When the oxide layer thickness is 1-10μm, it is considered medium oxidation, and the emissivity range is set to 0.5-0.

8. When the oxide layer thickness of the oxidation process is ≥10μm, it is considered heavy oxidation, and the emissivity range is set to 0.8-0.

97. When the coating is made of metal, the emissivity range is set to 0.05-0.3; when the coating is made of non-metal, the emissivity range is set to 0.8-0.95; and when the coating is made of composite material, the emissivity range is set to 0.1-0.

5.

7. The infrared temperature measurement method with environmental adaptive calibration function according to claim 6, characterized in that: In step S4, when establishing the environmental reflection radiation influence model, it is necessary to calculate the object's reflectivity ρ and geometric influence factor f. geo and the surrounding directional heat source radiation correction factor f dir Finally, the ambient radiation energy E reflected by the measured object is calculated. ref =E env *ρ*f geo *f dir And convert the reflected radiation energy into the corresponding equivalent reflection temperature T. ref The conversion formula is E ref =(C1 / λ 5 )*{1 / [e C2 / (λ*Tref) -1]}.

8. The infrared temperature measurement method with environmental adaptive calibration function according to claim 7, characterized in that: In step S5, combining Kirchhoff's laws and the proportion of environmental reflected radiation r, an emissivity-environmental interference correlation model is established, with the formula ε. cal1 =ε init ×[1+k1×(r−r0)+k2×(Ra−Ra0)]; Total radiation E received by the sensor total It is the object's own radiation E self With reflected environmental radiation E ref The superposition of these factors requires first removing interference, stripping away reflected interference, and restoring the object's own radiation. The formula is E. self =E total -E ref Then, the temperature is deduced by applying the physical laws of infrared radiation to simplify Planck's law for the commonly used 8-14μm wavelength band. The formula is: E self =ε cal1 ×σ×T 4 true T 4 true For the actual temperature, σ = 5.670374419 × 10 −8 W / (m 2 *K 4 ).

9. An infrared temperature measurement system with environmental adaptive calibration function, applied to the infrared temperature measurement method with environmental adaptive calibration function as described in any one of claims 1-8, characterized in that: It includes a signal acquisition module to collect raw data of the object being measured; The database module is used to store emissivity data corresponding to different materials and surface conditions; The algorithm processing module includes an environmental reflection radiation modeling module, an emissivity calibration algorithm module, and a temperature correction calculation module. The environmental reflection radiation modeling module establishes an influence model based on the signal from the environmental radiation acquisition unit. The emissivity calibration algorithm module achieves emissivity matching and adaptive calibration through machine learning algorithms. The temperature correction calculation module distinguishes between the object's own radiation and reflected radiation, corrects the radiation energy signal, and calculates the true temperature. The temperature output module outputs the actual temperature value calculated by the algorithm processing module in the form of a digital display or signal transmission. The signal acquisition module, database module, algorithm processing module and temperature output module are all electrically connected to the main control chip.

10. An infrared temperature measurement system with environmental adaptive calibration function according to claim 9, characterized in that: The signal acquisition module includes a thermopile infrared sensor for acquiring infrared radiation energy signals of the object under test, a digital temperature sensor for acquiring ambient temperature signals, an infrared radiation sensor for acquiring ambient radiation intensity signals, and an optical roughness sensor for acquiring surface roughness signals of the object under test. The thermopile infrared sensor, the digital temperature sensor, and the infrared radiation sensor are all electrically connected to the main control chip.

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