CN117346896A - Remote zoom infrared temperature measurement method - Google Patents
Remote zoom infrared temperature measurement method Download PDFInfo
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- CN117346896A CN117346896A CN202311374337.4A CN202311374337A CN117346896A CN 117346896 A CN117346896 A CN 117346896A CN 202311374337 A CN202311374337 A CN 202311374337A CN 117346896 A CN117346896 A CN 117346896A
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- 238000009529 body temperature measurement Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 12
- 230000005855 radiation Effects 0.000 claims abstract description 30
- 238000003062 neural network model Methods 0.000 claims abstract description 25
- 238000001514 detection method Methods 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims abstract description 5
- 230000007613 environmental effect Effects 0.000 claims description 9
- 238000004861 thermometry Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0096—Radiation pyrometry, e.g. infrared or optical thermometry for measuring wires, electrical contacts or electronic systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/52—Radiation pyrometry, e.g. infrared or optical thermometry using comparison with reference sources, e.g. disappearing-filament pyrometer
- G01J5/53—Reference sources, e.g. standard lamps; Black bodies
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Radiation Pyrometers (AREA)
Abstract
A remote zooming infrared temperature measurement method belongs to the field of temperature measurement. The infrared detection method aims at solving the problem that the receiving of the existing infrared radiation signal is interfered by various factors, so that the accuracy of infrared detection is poor. Setting a plurality of black bodies, wherein the actual temperature value of each black body is different; collecting infrared heat radiation values of the black body by adopting different focal lengths under various environments; taking the environment information in any sample and the infrared heat radiation value of the blackbody collected under any focal length as the input of the neural network model, taking the actual temperature value of the blackbody in the sample as the output of the neural network model, and training the neural network model to obtain a trained neural network model; and when the trained neural network model receives the current environment information and the infrared heat radiation value of the power equipment collected under the current focal length, predicting the current temperature value of the power equipment. The invention is used for measuring the temperature of the power equipment.
Description
Technical Field
The invention relates to infrared temperature measurement, and belongs to the field of temperature measurement.
Background
With the rapid development of unattended intelligent substations, power equipment is generally subjected to energy loss caused by work or faults so as to rise the temperature of the power equipment, and when the temperature is higher than a normal value, the service life of the power equipment is greatly reduced, so that potential safety hazards exist. Serious accidents can be directly caused. Affecting grid stability.
The existing infrared temperature measuring device is used for measuring the temperature of the power equipment, and the infrared temperature measuring device is divided into a contact type temperature measuring device and a non-contact type temperature measuring device; contact infrared temperature measurement system: the measuring range is simple, the measuring distance is small, the measuring range is small, and the measuring precision is difficult to meet the measuring requirement.
The non-contact temperature measurement infrared system has the following defects: 1. blackbody temperature measurement is carried out in a laboratory, and the influences of the real environment temperature and the humidity and wind speed are not considered; 2. only consider conventional factors such as the real ambient temperature, atmospheric humidity, etc., the temperature measurement precision is lower; 3. conventional factors such as real environment temperature, atmospheric humidity and the like are not considered; 4. the temperature measurement is carried out at the longest distance of 30 meters, and the precision error of the zoom factor is not considered to be larger.
Therefore, in the field practical application, the infrared diagnosis is extremely complex, because the receiving of the infrared radiation signal is interfered by various factors, so that the accuracy of the infrared detection is often not guaranteed.
Disclosure of Invention
The invention aims to solve the problem that the accuracy of infrared detection is poor due to the fact that the receiving of the existing infrared radiation signal is interfered by various factors, and provides a remote zooming infrared temperature measurement method.
The remote zoom infrared temperature measurement method comprises the following steps:
step 1, setting a plurality of black bodies to simulate power equipment with temperature, wherein the actual temperature value of each black body is different; placing each blackbody in different environments, collecting infrared heat radiation values of the blackbody by adopting different focal lengths in each environment, and forming a sample by environment information in any environment, the heat radiation values of the blackbody collected in any focal length in the environment and the actual temperature values of the blackbody in the environment;
step 2, taking the environment information in any sample and the infrared heat radiation value of the blackbody collected under any focal length as the input of a neural network model, taking the actual temperature value of the blackbody in the sample as the output of the neural network model, and training the neural network model to obtain a trained neural network model;
and 3, when the trained neural network model receives the current environment information and the infrared heat radiation value of the power equipment collected under the current focal length, predicting the current temperature value of the power equipment.
Preferably, the environmental information is collected using an environmental detection module.
Preferably, the environmental information includes temperature information and humidity information.
Preferably, infrared detectors are used to collect blackbody thermal infrared radiation values at different focal lengths.
Preferably, an infrared detector is used to collect the infrared thermal radiation value of the power equipment.
The beneficial effects of the invention are as follows:
the infrared detector is adopted to collect infrared radiation values of the black body under different focal lengths except for considering atmospheric temperature and atmospheric humidity, and because the focal lengths can influence the infrared radiation receiving rate and have influence on temperature measurement, the infrared detector overcomes the defect of low temperature measurement precision caused by considering only infrared thermal radiation values under a single focal length or a single distance.
According to the invention, the environment detection module is used for detecting the atmospheric temperature and the atmospheric humidity, the focal length is changed to perform ultra-long distance infrared data calibration, and finally, the neural network model nonlinear fitting is adopted to perform long distance infrared temperature measurement, and experiments show that more heat radiation can be absorbed by adding the varifocal factor under the same distance, so that the noise influence is reduced, and the temperature measurement model is more accurate.
Drawings
FIG. 1 is a schematic diagram of a remote zoom infrared temperature measurement calibration device;
FIG. 2 is a schematic diagram of a neural network model;
FIG. 3 is a graph comparing accuracy of predictions of a neural network model with and without zoom consideration.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
Example 1:
a remote zoom infrared temperature measurement method according to the present embodiment is described with reference to fig. 1 to 3, and includes the following steps:
step 1, setting a plurality of black bodies to simulate power equipment with temperature, wherein the actual temperature value of each black body is different; placing each blackbody in different environments, collecting infrared heat radiation values of the blackbody by adopting different focal lengths in each environment, and forming a sample by environment information in any environment, the heat radiation values of the blackbody collected in any focal length in the environment and the actual temperature values of the blackbody in the environment;
step 2, taking the environment information in any sample and the infrared heat radiation value of the blackbody collected under any focal length as the input of a neural network model, taking the actual temperature value of the blackbody in the sample as the output of the neural network model, and training the neural network model to obtain a trained neural network model;
and 3, when the trained neural network model receives the current environment information and the infrared heat radiation value of the power equipment collected under the current focal length, predicting the current temperature value of the power equipment.
The environment detection module is used for collecting factors such as atmospheric temperature, atmospheric humidity and the like, so that inaccurate infrared temperature measurement can be avoided, and an infrared detector is used for ultra-long-distance infrared temperature measurement; the atmospheric temperature, atmospheric humidity and variable magnification factors all belong to nonlinear relations with the infrared temperature measurement results. And finally, carrying out nonlinear fitting on the neural network model to obtain an infrared temperature measurement result.
In the temperature measurement process, if the test distance is slightly changed, the farther the distance is, the lower the detection temperature is and the influence of the absorption and scattering capability of the propagation medium can be caused under the condition that other conditions are unchanged. The variable-magnification (zoom) temperature measurement can make up for the blurring of images caused by the change of the distance between the equipment and the device, and the high-temperature part can be accurately positioned through a temperature measurement model.
In this embodiment, an environment detection module is used to collect environmental information.
In the present embodiment, the environmental information includes temperature information and humidity information.
In this embodiment, infrared detectors are used to collect blackbody thermal infrared radiation values at different focal lengths.
In this embodiment, an infrared detector is used to collect the infrared thermal radiation value of the electrical device.
In the step 1, environmental information can be recorded every 30 minutes; in the step 2, camera focal plane values are acquired and recorded every 10 meters along the center direction of the infrared detector, the black body occupies 2/3 of a thermal imaging picture, and variable multiple (zoom) is recorded; as shown in fig. 2, the atmospheric temperature, atmospheric humidity, infrared detector focal plane value, blackbody infrared thermal radiation value and variable power are input into a neural network model for training.
Experimental data are as follows:
FIG. 3 is a graph showing the influence of the variable factor on the temperature measurement precision, and it can be seen that other factors remain unchanged, and adding the variable factor at the same distance can absorb more heat radiation, reduce the influence of noise, and make the temperature measurement model more accurate.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways other than as described in the original claims. It is also to be understood that features described in connection with separate embodiments may be used in other described embodiments.
Claims (5)
1. The remote zoom infrared temperature measurement method is characterized by comprising the following steps of:
step 1, setting a plurality of black bodies to simulate power equipment with temperature, wherein the actual temperature value of each black body is different; placing each blackbody in different environments, collecting infrared heat radiation values of the blackbody by adopting different focal lengths in each environment, forming a sample by environment information in any environment, the heat radiation values of the blackbody collected in any focal length in the environment and actual temperature values of the blackbody in the environment,
step 2, taking the environment information in any sample and the infrared heat radiation value of the blackbody collected under any focal length as the input of a neural network model, taking the actual temperature value of the blackbody in the sample as the output of the neural network model, and training the neural network model to obtain a trained neural network model;
and 3, when the trained neural network model receives the current environment information and the infrared heat radiation value of the power equipment collected under the current focal length, predicting the current temperature value of the power equipment.
2. The method of claim 1, wherein the environmental information is collected by an environmental detection module.
3. The remote zoom infrared thermometry method of claim 1, wherein the environmental information comprises temperature information and humidity information.
4. The method for remote zoom infrared temperature measurement according to claim 1, wherein infrared detectors are used to collect the blackbody thermal infrared radiation values at different focal lengths.
5. The method for remote zoom infrared temperature measurement according to claim 1, wherein an infrared detector is used to collect the infrared heat radiation value of the power equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311374337.4A CN117346896A (en) | 2023-10-23 | 2023-10-23 | Remote zoom infrared temperature measurement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311374337.4A CN117346896A (en) | 2023-10-23 | 2023-10-23 | Remote zoom infrared temperature measurement method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117346896A true CN117346896A (en) | 2024-01-05 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311374337.4A Pending CN117346896A (en) | 2023-10-23 | 2023-10-23 | Remote zoom infrared temperature measurement method |
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| CN (1) | CN117346896A (en) |
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2023
- 2023-10-23 CN CN202311374337.4A patent/CN117346896A/en active Pending
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