CN105371957A - Transformer station equipment infrared temperature registration positioning and method - Google Patents
Transformer station equipment infrared temperature registration positioning and method Download PDFInfo
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Abstract
The invention discloses a transformer station equipment infrared temperature registration positioning system comprising an infrared camera, a visible light camera, a video server, and a data processing analytical unit. The data processing analytical unit receives an infrared thermal image, captured by the infrared camera, of transformer station equipment, and a visible image, captured by the visible light camera, of the transformer station equipment, and registers the infrared thermal image and the visible image of the same target scene in order to match the measuring points of the infrared thermal image and the visible image of the same target scene. The data processing analytical unit establishes a radial basis function neural network, predicts the predicted temperature values of the measuring points on the acquired the infrared thermal image by means of the radial basis function neural network, and enables the predicted temperature values to correspond to various measuring points, matching the measuring points on the infrared thermal image, of the visible image. The invention also discloses a transformer station equipment infrared temperature registration positioning method.
Description
Technical field
The present invention relates to a kind of system and method substation equipment being carried out to temperature prediction, particularly relate to a kind of by carrying out the system and method for registered placement realization to the temperature prediction of substation equipment to Infrared Thermogram and visible images.
Background technology
Many equipment in power industry all run under high voltage, current state, have extremely close contacting with temperature.In numerous power outage, the interruption maintenance caused because of equipment local overheating happens occasionally.Therefore, Timeliness coverage equipment heating defect, eliminates heating defect in original state, is the key ensureing the generation of equipment safety operation, minimizing accident, avoid being forced to power failure.The ultimate principle of infrared detection is exactly the infrared radiation signal by detecting object, obtains the Warm status feature of object, and judges the state of object according to this Warm status feature and corresponding basis for estimation.Contact at a distance, not because infrared detection technology has, in real time, the feature such as quick, thus the on-line monitoring and fault diagonosing realizing power equipment is had great importance.Converting station electric power equipment mainly adopts infrared thermal imaging and infrared point temperature to measure two kinds of infrared detection technologies, thus realizes the runnability of full transformer station main electric power equipment and the comprehensive monitoring of heat condition.
Although infrared thermal imaging technique has above-mentioned plurality of advantages, but compared with normal image, Infrared Thermogram is subject to the impact of the factors such as principle of work, external environment and self device, visual effect is clear not, target device and background contrasts poor, inconvenience is caused to follow-up fault analysis and handling.In addition, infrared monitoring equipment Market is almost occupied by external big companies, due to the reason such as trade monopoly, blockade on new techniques, Utilities Electric Co. can only be passive the analysis software that carries of purchase, use equipment, cannot personalized requirements preferably, seriously constrain the raising of Fault Diagnosis for Electrical Equipment level in transformer station, be unfavorable for the safe and stable operation of intelligent grid.
Summary of the invention
The object of the present invention is to provide a kind of substation equipment infrared temperature registered placement system, its can predict obtain substation equipment Infrared Thermogram on the temperature prediction value of each measuring point, realize coupling and the information sharing of each measuring point on the Infrared Thermogram of substation equipment and visible images, this information comprises the temperature prediction value to described each measuring point.
Another object of the present invention is to provide a kind of substation equipment infrared temperature registered placement method, the method has above-mentioned functions equally.
To achieve these goals, the present invention proposes a kind of substation equipment infrared temperature registered placement system, it comprises:
Thermal camera, it gathers the Infrared Thermogram of target scene;
Visible light camera, it gathers the visible images of target scene;
Video server, it is connected by video line respectively with thermal camera and visible light camera;
Data Management Analysis unit, this Data Management Analysis unit is generally computing machine, it is connected with described video server, it receives Infrared Thermogram and the visible images of the substation equipment of thermal camera and visible light camera collection, and registration is carried out, to mate the Infrared Thermogram of same target scene and each measuring point of visible images to the Infrared Thermogram of same target scene and visible images; Described Data Management Analysis unit sets up radial base neural net, and obtained the temperature prediction value of each measuring point on Infrared Thermogram by radial base neural net prediction, and this temperature prediction value is corresponded on each measuring point of the visible images mated with each measuring point on Infrared Thermogram.
Substation equipment infrared temperature registered placement system of the present invention, based on to the same Infrared Thermogram of target scene and the registration of visible images, realize the coupling of each measuring point on the Infrared Thermogram of substation equipment and visible images, by radial base neural net, the pixel of the measuring point of described Infrared Thermogram and temperature are carried out the temperature prediction value that matching obtains each measuring point on Infrared Thermogram simultaneously, and this temperature prediction value is corresponded on each measuring point of the visible images mated with each measuring point on Infrared Thermogram.Two width obtained under different time, different sensors (imaging device) or different condition or multiple image are carried out the process of mating, superposing by described registration exactly; Conventional method has based on half-tone information method, transpositions domain and feature based method etc., and its technology is comparatively ripe, is to well known to a person skilled in the art technology, and the present invention is no longer described in detail.The method of described matching is, first temperature prediction training is carried out to described radial base neural net, then with the radial base neural net of training through temperature prediction as model of fit, realize matching using the pixel of the measuring point of Infrared Thermogram and temperature as the input and output of described model of fit.The method of described temperature prediction training is, with some pixel values for input amendment, export accordingly as described input amendment using the temperature strip temperature value that described some pixel values are corresponding, temperature prediction training is carried out to described radial base neural net, solves the parameter of radial base neural net.
On embody rule, substation equipment infrared temperature registered placement system of the present invention can by arranging human-computer interaction interface, realize Infrared Thermogram and the monitoring of visible images binary channels: by clicking the arbitrary measuring point on Infrared Thermogram or visible images, obtain the information such as temperature prediction value, coordinate of this measuring point.
Substation equipment infrared temperature registered placement system of the present invention can carry out omnibearing infrared temperature monitoring and registered placement to substation equipment, by directly consulting visible images, obtain the information such as temperature prediction value, coordinate of each measuring point, therefore the thermal anomaly position positioning difficulty caused because Infrared Thermogram is fuzzy can be reduced, reduce positional accuracy error, simplify the consequent malfunction analyzing and processing difficulty in transformer station's infrared temperature monitoring, thus improve the intelligent level of transformer station.
Further, in substation equipment infrared temperature registered placement system of the present invention, described thermal camera is also directly connected by netting twine with Data Management Analysis unit, to make Data Management Analysis unit to thermal camera transmission control parameters, thus control the operations such as thermal camera carries out focusing on, aperture electric discharge, setting area.
Further, rotating The Cloud Terrace is also comprised in of the present invention or above-mentioned substation equipment infrared temperature registered placement system, described thermal camera and visible light camera are arranged on described The Cloud Terrace, described The Cloud Terrace is connected with video server, with the control signal of receiver, video server, thus make it possible to the rotation being controlled The Cloud Terrace by video server.
Further, at the inverter that of the present invention or above-mentioned substation equipment infrared temperature registered placement system also comprises electric battery and is connected with electric battery, described inverter is connected respectively with thermal camera, visible light camera, video server and Data Management Analysis unit, changes alternating current into be supplied to thermal camera, visible light camera, video server and Data Management Analysis unit with direct current electric battery provided.
Correspondingly, present invention also offers a kind of substation equipment infrared temperature registered placement method, it comprises step:
(1) Infrared Thermogram and the visible images of the same target scene of substation equipment is obtained respectively;
(2) visible images is mated with the picture element matrix of Infrared Thermogram, to make each measuring point on each measuring point complete Corresponding matching Infrared Thermogram on visible images;
(3) build radial base neural net and temperature prediction training is carried out to it;
(4) using pixel value corresponding to the measuring point on Infrared Thermogram as the input of the radial base neural net of training through temperature prediction, solve the output of the radial base neural net through temperature prediction training, this output is the temperature prediction value of the measuring point on Infrared Thermogram, is also the temperature prediction value of the measuring point on visible images.
The design of substation equipment infrared temperature registered placement method of the present invention is consistent with the design of substation equipment infrared temperature registered placement system of the present invention, does not repeat them here.
Substation equipment infrared temperature registered placement method of the present invention can predict obtain substation equipment Infrared Thermogram on the temperature prediction value of each measuring point, realize coupling and the information sharing of each measuring point on the Infrared Thermogram of substation equipment and visible images, this information comprises the temperature prediction value to described each measuring point.
Further, in substation equipment infrared temperature registered placement method of the present invention, Image semantic classification step is also comprised between described step (1) and (2), described Image semantic classification step at least comprises carries out image equalization process and filtering process to Infrared Thermogram and visible images, this processing procedure belongs to the basic content in Digital Image Processing, be well known to those skilled in the art, therefore the present invention is no longer explained in detail explanation.
Further, in substation equipment infrared temperature registered placement method of the present invention, described step (3) comprises the steps:
(3a) hidden layer basis function is built;
(3b) radial base neural net is built based on described hidden layer basis function;
(3c) with some pixel values for input amendment, export accordingly as described input amendment using the temperature strip temperature value that described some pixel values are corresponding, temperature prediction training carried out to described radial base neural net, solves the parameter of radial base neural net.
In such scheme, the hidden layer basis function of radial base neural net has various ways, and the most frequently used is gaussian kernel function.
Further, in above-mentioned substation equipment infrared temperature registered placement method,
The hidden layer basis function built in step (3a) is gaussian kernel function, and its expression formula is:
Wherein, X is that n ties up input vector, X=[x
1, x
2..., x
n], n is the number of input layer; c
jfor the center of a jth hidden layer basis function, it is the vector with X with same dimension; R
j(X-c
j) be the output valve of a jth hidden layer neuron, p is the number of hidden layer neuron; σ
jfor generalized constant, i.e. the variance of gaussian kernel function;
The expression formula of the radial base neural net built in step (3b) is:
Wherein, y
kfor the neuronic output valve of a kth output layer, m is the neuronic number of output layer; w
j,ifor the connection weights between a jth hidden layer neuron and i-th input layer;
Described in step (3c), parameter comprises the data center c of hidden layer basis function
j, generalized constant σ
jand connection weight w
j,i, by least square method, it is solved.
In such scheme, described parameter, more than equation number, is solving of an overdetermined equation, needs to use numerical solution to solve, and the present invention adopts least square method.Using least square method to solve over-determined systems is the method that those skilled in the art all know, and no longer does detailed introduction at this.
Further, in above-mentioned substation equipment infrared temperature registered placement method, σ
jspan be [0.01,0.05].
Substation equipment infrared temperature registered placement system of the present invention compared with prior art, has following beneficial effect:
1) the temperature prediction value of each measuring point on the Infrared Thermogram obtaining substation equipment can be predicted, realize coupling and the information sharing of each measuring point on the Infrared Thermogram of substation equipment and visible images;
2) omnibearing infrared temperature monitoring and registered placement can be carried out to substation equipment, by directly consulting visible images, obtain the information such as temperature prediction value, coordinate of each measuring point, therefore the thermal anomaly position positioning difficulty caused because Infrared Thermogram is fuzzy can be reduced, reduce positional accuracy error, simplify the consequent malfunction analyzing and processing difficulty in transformer station's infrared temperature monitoring, thus improve the intelligent level of transformer station.
Substation equipment infrared temperature registered placement method of the present invention has above-mentioned effect equally.
Accompanying drawing explanation
Fig. 1 is the general frame schematic diagram of substation equipment infrared temperature registered placement system of the present invention under a kind of embodiment.
Fig. 2 is the workflow diagram of substation equipment infrared temperature registered placement system of the present invention under a kind of embodiment.
Embodiment
Below in conjunction with Figure of description and specific embodiment, further explanation and explanation are made to substation equipment infrared temperature registered placement system and method for the present invention.
Fig. 1 illustrates the general frame of substation equipment infrared temperature registered placement system of the present invention under a kind of embodiment.
As shown in Figure 1, the present embodiment comprises: thermal camera, and it gathers the Infrared Thermogram of target scene; Visible light camera, it gathers the visible images of target scene; Video server, it is connected by video line respectively with thermal camera and visible light camera; As the computing machine of Data Management Analysis unit, it is connected with video server, it receives Infrared Thermogram and the visible images of the substation equipment of thermal camera and visible light camera collection, and registration is carried out, to mate the Infrared Thermogram of same target scene and each measuring point of visible images to the Infrared Thermogram of same target scene and visible images; Radial base neural net set up by computing machine, and obtained the temperature prediction value of each measuring point on Infrared Thermogram by radial base neural net prediction, and this temperature prediction value is corresponded on each measuring point of the visible images mated with each measuring point on Infrared Thermogram.In the present embodiment, thermal camera is also directly connected by netting twine with computing machine, to make computing machine to thermal camera transmission control parameters, thus controls the operations such as thermal camera carries out focusing on, aperture electric discharge, setting area.The present embodiment also comprises rotating The Cloud Terrace, and thermal camera and visible light camera are arranged on The Cloud Terrace, and The Cloud Terrace is connected with video server, with the control signal of receiver, video server, thus makes it possible to the rotation being controlled The Cloud Terrace by video server.The inverter that the present embodiment also comprises lithium battery group and is connected with lithium battery group, this inverter is connected respectively with thermal camera, visible light camera, rotating The Cloud Terrace, video server and computing machine, changes alternating current into be supplied to thermal camera, visible light camera, rotating The Cloud Terrace, video server and computing machine with direct current electric battery provided.
Fig. 2 illustrates the workflow of substation equipment infrared temperature registered placement system of the present invention under a kind of embodiment.As shown in Figure 2, the workflow of the present embodiment is:
Computing machine receives Infrared Thermogram and the visible images of the same target scene of the substation equipment of thermal camera and visible light camera collection, and image equalization, the pre-service of filtering and registration are carried out to this Infrared Thermogram and visible images merge, to mate each measuring point of this Infrared Thermogram and visible images; Computing machine exports human-computer interaction interface by display, and this human-computer interaction interface comprises two can carry out clicking the form choosing measuring point operation, the form display visible images on the left side, the form display Infrared Thermogram on the right; The radial base neural net being used for temperature prediction set up by computing machine, comprises step:
Build hidden layer basis function based on gaussian kernel function, its expression formula is:
Wherein, X is that n ties up input vector, X=[x
1, x
2..., x
n], in the present invention, input refers to the pixel value (R, G, B) of each point in image; N is the number of input layer, because the pixel value of each point only has 3 in image, and therefore n=3 in the present invention; c
jfor the center of a jth hidden layer basis function, be the vector with X with same dimension, c
jcomponent span identical with the pixel value components of input picture, i.e. 0≤c
j≤ 255; R
j(X-c
j) be the output valve of a jth hidden layer neuron, p is the number of hidden layer neuron; σ
jfor generalized constant, i.e. the variance of gaussian kernel function;
Build radial base neural net based on above-mentioned hidden layer basis function, expression formula is:
Wherein, y
kfor the neuronic output valve of a kth output layer, m is the neuronic number of output layer, output layer neuron representation temperature predicted value in the present invention, therefore m=1; w
j,ifor the connection weights between a jth hidden layer neuron and i-th input layer;
With some pixel values for input amendment, export accordingly using the temperature strip temperature value that this some pixel value is corresponding as input amendment, carry out temperature prediction training to radial base neural net, solve the parameter of radial base neural net, this parameter comprises the data center c of hidden layer basis function
j, generalized constant σ
jand connection weight w
j,i, by least square method, it is solved:
Accuracy is taken into account and computation complexity gets 45 to choosing of the number p of hidden layer neuron; Difficulty is solved, assuming that the σ in hidden layer basis function for simplifying
jequal, σ
jspan between 0.01 ~ 0.05, the present embodiment gets 0.028; In the iterative process of temperature prediction training, get any integer value between 0 to 255 at random, finally solve the c obtained
jfor:
| R | G | B | |
| c 1 | 98 | 124 | 16 |
| c 2 | 211 | 45 | 78 |
| c 3 | 33 | 121 | 92 |
| c 4 | 204 | 133 | 145 |
| c 5 | 59 | 23 | 245 |
| c 6 | 238 | 231 | 190 |
| c 7 | 195 | 226 | 169 |
| c 8 | 211 | 112 | 133 |
| c 9 | 146 | 199 | 66 |
| c 10 | 202 | 38 | 245 |
| c 11 | 84 | 158 | 138 |
| c 12 | 57 | 66 | 8 |
| c 13 | 80 | 114 | 178 |
| c 14 | 149 | 215 | 133 |
| c 15 | 212 | 50 | 15 |
| c 16 | 74 | 77 | 227 |
| c 17 | 103 | 123 | 84 |
| c 18 | 220 | 86 | 59 |
| c 19 | 157 | 204 | 29 |
| c 20 | 253 | 252 | 79 |
| c 21 | 52 | 41 | 58 |
| c 22 | 211 | 60 | 166 |
| c 23 | 172 | 179 | 17 |
| c 24 | 63 | 96 | 70 |
| c 25 | 121 | 248 | 72 |
| c 26 | 102 | 248 | 224 |
| c 27 | 153 | 164 | 113 |
| c 28 | 204 | 219 | 193 |
| c 29 | 27 | 102 | 154 |
| c 30 | 209 | 161 | 200 |
| c 31 | 214 | 251 | 29 |
| c 32 | 90 | 143 | 250 |
| c 33 | 110 | 238 | 216 |
| c 34 | 146 | 184 | 13 |
| c 35 | 179 | 123 | 119 |
| c 36 | 189 | 163 | 83 |
| c 37 | 193 | 226 | 161 |
| c 38 | 99 | 51 | 59 |
| c 39 | 109 | 101 | 148 |
| c 40 | 244 | 253 | 154 |
| c 41 | 146 | 103 | 153 |
| c 42 | 217 | 168 | 114 |
| c 43 | 70 | 230 | 9 |
| c 44 | 159 | 254 | 131 |
| c 45 | 150 | 167 | 104 |
Solve the connection weight w obtained
j,itable is (going in j correspondence table, row in i correspondence table):
| 221.5928652 | 179.6582716 | 61.6122449 |
| 233.9444619 | 230.3126588 | 183.8514565 |
| 202.7950514 | 104.755969 | 41.71688667 |
| 226.9271137 | 215.6851312 | 108.7026239 |
| 174.1148756 | 46.45351002 | 94.09030251 |
| 255 | 217 | 235 |
| 127.0308205 | 19.00102848 | 127.9914406 |
| 213.0395243 | 156.088203 | 49.01078632 |
| 233.9841563 | 227.0374844 | 147.3673979 |
| 181.7492711 | 67.59766764 | 67.22157434 |
| 219.7627689 | 183.7478517 | 63.73127693 |
| 62.78984947 | 10.91224745 | 108.8839429 |
| 241.6939286 | 234.3474573 | 211.9652781 |
| 164.911797 | 37.93877551 | 115.0612245 |
| 100.426183 | 17.32960756 | 121.4033098 |
| 223.0571531 | 198.1744171 | 71.52620932 |
| 200.9854227 | 118.1428571 | 37.98542274 |
| 189.9642921 | 99.11218965 | 42.83484773 |
| 233.5897373 | 233.969171 | 198.0237146 |
| 212.9773564 | 163.7101633 | 48.302986 |
| 31.23217367 | 7.147030574 | 87.30131153 |
| 149.0072674 | 22.04602674 | 127.1647273 |
| 201.9852867 | 110.9412065 | 39.98448776 |
| 233.0763542 | 227.9741434 | 162.8921623 |
| 138.607485 | 20.98342527 | 125.8541424 |
| 118.8619963 | 16.88569389 | 126.9539988 |
| 229.029112 | 221.8932843 | 130.6620881 |
| 209.8709721 | 147.8709721 | 46.85781435 |
| 50 | 0 | 36 |
| 190.8705556 | 90.56609916 | 48.575959 |
| 170.0405698 | 39.03399094 | 104.8717116 |
| 218.5510204 | 171.4460641 | 55.63848397 |
| 209.9913301 | 145.4614489 | 47.99133014 |
| 190.2831133 | 82.42055606 | 52.82347491 |
| 226.2851023 | 211.2114 | 93.2726245 |
| 206.637702 | 135.6938011 | 45.70145093 |
| 177.7475202 | 60.67346939 | 77.4364848 |
| 110.9545597 | 16 | 123.6137579 |
| 221.9953761 | 202.3076354 | 81.41629763 |
| 222.9987675 | 193.9473349 | 68.00855936 |
| 153.9825073 | 24.03498542 | 124.2099125 |
| 158.7777457 | 30.58002193 | 116.5233534 |
| 202.453561 | 125.2524798 | 42.60166257 |
| 39.07679623 | 8.863679249 | 101.6191808 |
| 56.73435388 | 13.65833964 | 107.2892672 |
After the radial base neural net being used for temperature prediction set up by computing machine, computing machine just can pixel value corresponding to measuring point on Infrared Thermogram as the input of the radial base neural net of training through temperature prediction, solve the output of the radial base neural net through temperature prediction training, this output is the temperature prediction value of the measuring point on Infrared Thermogram, is also the temperature prediction value of the measuring point on visible images.Operating personnel click " selection visible images " or " selection thermal-induced imagery " button by human-computer interaction interface and select visible images or Infrared Thermogram:
The present embodiment first selects visible images, click a measuring point, computing machine is marked red circle 1, computing machine judges whether this measuring point exceeds the measuring point scope of the Infrared Thermogram mated with it, if exceeded, computing machine requirement is clicked again, if do not exceeded, computing machine obtains the Infrared Thermogram measuring point mated with it by the matching relationship between measuring point, and the temperature prediction value of this Infrared Thermogram measuring point is obtained by radial base neural net prediction, and this temperature prediction value is carried out output display as the temperature prediction value of red circle 1 place visible images measuring point, showing the present embodiment output valve in figure is 21.6311,
The present embodiment selects Infrared Thermogram again, and click a measuring point, computing machine is marked green circle 2, and the visible images measuring point with green circle 2 Corresponding matching is labeled as Huang and encloses 3 by computing machine, and exports its coordinate X-axis 123, Y-axis 100; Predicted the temperature prediction value output display that obtain green circle 2 place Infrared Thermogram measuring point by radial base neural net, showing the present embodiment output valve in figure is 8.293.
Substation equipment infrared temperature registered placement method of the present invention can the workflow of said system as a kind of embodiment, do not repeat them here.
That enumerates it should be noted that above is only specific embodiments of the invention, obviously the invention is not restricted to above embodiment, has many similar changes thereupon.If all distortion that those skilled in the art directly derives from content disclosed by the invention or associates, protection scope of the present invention all should be belonged to.
Claims (9)
1. a substation equipment infrared temperature registered placement system, is characterized in that, comprising:
Thermal camera, it gathers the Infrared Thermogram of target scene;
Visible light camera, it gathers the visible images of target scene;
Video server, it is connected by video line respectively with thermal camera and visible light camera;
Data Management Analysis unit, it is connected with described video server, it receives Infrared Thermogram and the visible images of the substation equipment of thermal camera and visible light camera collection, and registration is carried out, to mate the Infrared Thermogram of same target scene and each measuring point of visible images to the Infrared Thermogram of same target scene and visible images; Described Data Management Analysis unit sets up radial base neural net, and obtained the temperature prediction value of each measuring point on Infrared Thermogram by radial base neural net prediction, and this temperature prediction value is corresponded on each measuring point of the visible images mated with each measuring point on Infrared Thermogram.
2. substation equipment infrared temperature registered placement system as claimed in claim 1, it is characterized in that, described thermal camera is also directly connected by netting twine with Data Management Analysis unit, to make Data Management Analysis unit to thermal camera transmission control parameters.
3. substation equipment infrared temperature registered placement system as claimed in claim 1 or 2, it is characterized in that, also comprise rotating The Cloud Terrace, described thermal camera and visible light camera are arranged on described The Cloud Terrace, described The Cloud Terrace is connected with video server, with the control signal of receiver, video server.
4. substation equipment infrared temperature registered placement system as claimed in claim 1, it is characterized in that, the inverter also comprising electric battery and be connected with electric battery, described inverter is connected respectively with thermal camera, visible light camera, video server and Data Management Analysis unit, changes alternating current into be supplied to thermal camera, visible light camera, video server and Data Management Analysis unit with direct current electric battery provided.
5. a substation equipment infrared temperature registered placement method, is characterized in that, comprise step:
(1) Infrared Thermogram and the visible images of the same target scene of substation equipment is obtained respectively;
(2) visible images is mated with the picture element matrix of Infrared Thermogram, to make each measuring point on each measuring point complete Corresponding matching Infrared Thermogram on visible images;
(3) build radial base neural net and temperature prediction training is carried out to it;
(4) using pixel value corresponding to the measuring point on Infrared Thermogram as the input of the radial base neural net of training through temperature prediction, solve the output of the radial base neural net through temperature prediction training, this output is the temperature prediction value of the measuring point on Infrared Thermogram, is also the temperature prediction value of the measuring point on visible images.
6. substation equipment infrared temperature registered placement method as claimed in claim 5, it is characterized in that, between described step (1) and (2), also comprise Image semantic classification step, described Image semantic classification step at least comprises carries out image equalization process and filtering process to Infrared Thermogram and visible images.
7. the substation equipment infrared temperature registered placement method as described in claim 5 or 6, it is characterized in that, described step (3) comprises the steps:
(3a) hidden layer basis function is built;
(3b) radial base neural net is built based on described hidden layer basis function;
(3c) with some pixel values for input amendment, export accordingly as described input amendment using the temperature strip temperature value that described some pixel values are corresponding, temperature prediction training carried out to described radial base neural net, solves the parameter of radial base neural net.
8. substation equipment infrared temperature registered placement method as claimed in claim 7, is characterized in that:
The hidden layer basis function built in step (3a) is gaussian kernel function, and its expression formula is:
Wherein, X is that n ties up input vector, X=[x
1, x
2..., x
n], n is the number of input layer; c
jfor the center of a jth hidden layer basis function, it is the vector with X with same dimension; R
j(X-c
j) be the output valve of a jth hidden layer neuron, p is the number of hidden layer neuron; σ
jfor generalized constant, i.e. the variance of gaussian kernel function;
The expression formula of the radial base neural net built in step (3b) is:
Wherein, y
kfor the neuronic output valve of a kth output layer, m is the neuronic number of output layer; w
j,ifor the connection weights between a jth hidden layer neuron and i-th input layer;
Described in step (3c), parameter comprises the data center c of hidden layer basis function
j, generalized constant σ
jand connection weight w
j,i, by least square method, it is solved.
9. substation equipment infrared temperature registered placement method as claimed in claim 8, is characterized in that: σ
jspan be [0.01,0.05].
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