CN112488191B - Metal corrosion distribution map drawing method based on KNN intelligent algorithm - Google Patents
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- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 4
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 4
- 239000003595 mist Substances 0.000 claims description 4
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Abstract
The invention provides a metal corrosion distribution map drawing method based on a KNN intelligent algorithm, which comprises the following steps: acquiring metal corrosion rate data, and establishing a metal corrosion model by adopting a KNN intelligent algorithm; evaluating the prediction effect of the metal corrosion model based on the goodness of fit, optimizing and adjusting model parameters, and drawing a metal corrosion prediction distribution map; drawing a corrosion medium distribution diagram according to the corrosion medium data; the metal corrosion prediction distribution map and the corrosion medium distribution map are superposed and synthesized to obtain a metal corrosion distribution map, so that the corrosion distribution maps of various metals can be drawn finely, the actual corrosion conditions of the metals can be well fitted, and effective guidance is provided for metal corrosion prevention in different regions.
Description
Technical Field
The invention relates to the technical field of map drawing, in particular to a metal corrosion distribution map drawing method based on a KNN intelligent algorithm.
Background
According to statistics, about 1 hundred million tons of metal scrapped due to corrosion worldwide each year accounts for more than 10% of the annual metal yield. With the continuous advance of industrialization, sulfide, nitrogen oxide and other industrial gases are discharged into the atmospheric environment, so that the corrosion rate of metals is further increased. In coastal areas in south China, high temperature and high radiation all the year round, large precipitation, humid air and high content of erosive sea salt particles in the air are caused, and various metal equipment is easy to be seriously corroded and lose efficacy to cause very serious loss when being used in the environment for a long time. Therefore, it is necessary to research a metal corrosion distributed drawing method, and it is possible to adopt metals and protection measures with different corrosion protection technical requirements for areas with different corrosion grades, so as to improve the corrosion resistance of metal equipment and reduce the loss caused by metal corrosion.
The mechanism of metal corrosion is complex, and the influence factors are many. Most of metal corrosion belongs to micro electrochemical corrosion, and a dissolving solution and a conductive medium are needed; acid rain exists in part of areas, and the corrosion speed is increased if acidic substances exist in the corrosion solution; atmospheric contaminants dissolve in solution with the metal surface and act as electrolytes, also accelerating metal corrosion. Meanwhile, the corrosion rate data of the metal is difficult to obtain, the acquisition time span is long, and the model overfitting is easily caused in the drawing process of the corrosion distribution diagram, so that the accuracy of the calculation effect is low. Therefore, the invention adopts an artificial intelligence algorithm to establish a metal corrosion model.
The invention provides a metal corrosion distribution map drawing method based on a KNN intelligent algorithm, which can be used for finely drawing corrosion distribution maps of various typical metals, better fitting the actual corrosion condition of the metals and providing effective guidance for metal corrosion prevention.
Disclosure of Invention
The invention aims to provide a metal corrosion distribution map drawing method based on a KNN intelligent algorithm, which can adopt metal and protection measures with different corrosion protection technical requirements aiming at areas with different corrosion grades, thereby improving the corrosion resistance of metal equipment and reducing the loss caused by metal corrosion.
The invention provides a metal corrosion distribution map drawing method based on a KNN intelligent algorithm, which comprises the following steps:
step S1: obtaining metal corrosion rate data of a plurality of places, and dividing the metal corrosion rate data of the plurality of places into a training sample set and a testing sample set;
step S2: processing the training sample set and the test sample based on a KNN algorithm, and establishing a metal corrosion model;
and step S3: based on goodness-of-fit testing and estimation of the prediction effect of the metal corrosion model, carrying out optimization adjustment on model parameters according to the prediction effect until the goodness-of-fit value is greater than 0.8, calculating a metal corrosion prediction result, and drawing a metal corrosion prediction distribution map;
and step S4: extracting corrosion medium data of a plurality of places, and drawing a corrosion medium distribution diagram according to the corrosion medium data;
step S5: and superposing the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram to obtain a metal corrosion distribution diagram.
Preferably, in step S2, the training sample set and the test sample are processed based on a KNN algorithm, and a metal corrosion model is established, including:
step S2.1: calculating the distance between each test sample in the test sample set and all training samples in the training sample set, wherein the calculation formula of the distance is as follows:
wherein,
for the (i) th test sample,
for the ith metal corrosion rate data for the ith test sample,
for the (j) th training sample,
the ith metal corrosion rate data of the jth training sample is obtained, and L is the sample number of the training sample set;
step S2.2: selecting the first K training samples from the training sample set according to the priority that the distance between each test sample and all the training samples in the training sample set is from small to large;
step S2.3: judging the metal corrosion grades of the K training samples, selecting the metal corrosion grade category with the largest quantity in the K training samples as C max And the metal corrosion prediction result is used as the metal corrosion prediction result of the ith test sample.
Preferably, the goodness-of-fit calculation formula is as follows:
wherein z is the true value of the metal corrosion data,
as a result of the prediction of the metal corrosion relationship model,
the value is the average value of the truth, and N is the predicted result number of the metal corrosion model.
Preferably, step S4: and extracting corrosion medium data of a plurality of places, wherein the corrosion medium data comprise salt mist concentration, hydrogen sulfide concentration, sulfur dioxide concentration and nitrogen dioxide concentration in the atmosphere.
Preferably, the drawing of the corrosion medium distribution map according to the corrosion medium data comprises:
step S5.1: synthesizing the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram into a matrix, and then superposing;
step S5.2: drawing according to the superposed gray value;
step S5.3: and performing color matching according to the brightness value in the gray value to further form a metal corrosion distribution diagram.
Compared with the prior art, the invention has the beneficial effects that:
(1) A metal corrosion model based on a KNN intelligent algorithm is established, and based on the existing metal corrosion data, the metal corrosion model is established by adopting an artificial intelligence algorithm KNN algorithm, so that the corrosion data of the unmonitored area can be predicted finely;
(2) The metal corrosion distribution diagram drawing method based on the KNN intelligent algorithm can be used for finely drawing the corrosion distribution diagrams of various typical metals, better fitting the actual corrosion condition of the metals, providing effective guidance for metal corrosion prevention and further reducing the loss caused by metal corrosion.
Drawings
FIG. 1 is a schematic flow chart of a metal corrosion distribution diagram drawing method based on a KNN intelligent algorithm.
FIG. 2 is a graph of metal corrosion rate versus sulfur dioxide concentration.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
Fig. 1 reflects a specific flow of a KNN intelligent algorithm-based metal corrosion distribution map drawing method, including the following steps:
step S1: obtaining metal corrosion rate data of a plurality of places, and dividing the metal corrosion rate data of the plurality of places into a training sample set and a testing sample set;
step S2: processing the training sample set and the test sample based on a KNN algorithm, and establishing a metal corrosion model;
step S2.1: calculating the distance between each test sample in the test sample set and all training samples in the training sample set, wherein the calculation formula of the distance is as follows:
wherein,
for the (i) -th test sample,
for the ith metal corrosion rate data for the ith test sample,
for the jth training sample, the number of training samples,
the ith metal corrosion rate data of the jth training sample is obtained, and L is the sample number of the training sample set;
step S2.2: selecting the first K training samples from the training sample set according to the priority that the distance between each test sample and all the training samples in the training sample set is from small to large;
step S2.3: judging the metal corrosion grades of the K training samples, selecting the metal corrosion grade category with the largest quantity in the K training samples as C max And as a prediction of metal corrosion for the ith test specimen, i.e.
C(i)＝C max
Wherein, C (i) is a prediction result of the metal corrosion grade of the ith test sample, and the metal corrosion grade comprises six types: c1, C2, C3, C4, C5-1, C5-M, respectively, for very low, medium, high, very high (industrial environment), very high (marine environment)
And step S3: based on goodness-of-fit testing and estimation of the prediction effect of the metal corrosion model, carrying out optimization adjustment on model parameters according to the prediction effect until the goodness-of-fit value is greater than 0.8, calculating a metal corrosion prediction result, and drawing a metal corrosion prediction distribution map;
the goodness-of-fit calculation formula is as follows:
wherein z is the true value of the metal corrosion data,
as a result of the prediction of the metal corrosion relationship model,
the value is the average value of the truth, and N is the predicted result number of the metal corrosion model.
The goodness of fit value is closer to 1, which means that the prediction effect is better, and the effect is worse as the goodness of fit value is closer to 0.
And drawing a metal corrosion prediction distribution map according to the plurality of metal corrosion prediction results.
And step S4: extracting corrosion medium data of a plurality of places, wherein the corrosion medium data comprise salt mist concentration, hydrogen sulfide concentration, sulfur dioxide concentration and nitrogen dioxide concentration in the atmosphere, and drawing a corrosion medium distribution diagram according to the corrosion medium data;
specifically, the drawing method includes: the drawing mode comprises the following steps: obtaining corrosion rate equivalent points, drawing an isoline, smoothing the isoline, filling the isoline and marking the isoline.
Step S5: and superposing the metal corrosion prediction distribution map and the corrosion medium distribution map to obtain a metal corrosion distribution map, wherein the specific steps comprise:
step S5.1: superposing the gray values of the same geographical position points of the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram;
step S5.2: drawing according to the superposed gray value;
step S5.3: and performing color matching according to the brightness value in the gray value to further form a metal corrosion distribution diagram.
The following is a practical example of the method of the present invention, and the concrete implementation of the metal corrosion distribution diagram drawing method based on the KNN intelligent algorithm is explained by combining the example.
Fig. 1 reflects a specific flow of a KNN intelligent algorithm-based metal corrosion distribution diagram drawing method, which includes the following steps:
step S1: acquiring metal corrosion rate data of a plurality of places, preprocessing the metal corrosion rate data of the plurality of places, and dividing the data into a training sample set and a test sample set; partial metal corrosion rate data for some regions are shown in table 1, which uses Q235 steel, pure zinc and pure copper as examples of partial metals.
TABLE 1 partial area partial Metal Corrosion Rate data
Step S2.1: calculating the distance between each test sample in the test sample set and all training samples in the training sample set, and respectively selecting K nearest training samples with the minimum distance from each test sample, wherein the calculation formula of the distance is as follows:
wherein,
for the (i) th test sample,
for the ith metal corrosion rate data for the ith test sample,
for the jth training sample, the number of training samples,
the data is the ith metal corrosion rate data of the jth training sample, and L is the sample number of the training sample set;
step S2.2: calculating the most number of classes in the K nearest neighbor training samples of each test sample, and taking the classes as the classes of the test samples;
and step S3: based on goodness-of-fit testing and estimation of the prediction effect of the metal corrosion model, optimizing and adjusting model parameters according to the prediction effect until the goodness-of-fit value is greater than 0.8, wherein the closer the goodness-of-fit value is to 1, the better the prediction effect is, and the closer the goodness-of-fit value is to 0, the worse the effect is; and calculating a metal corrosion prediction result, and drawing a metal corrosion prediction distribution map.
The goodness-of-fit calculation formula is as follows:
wherein z is the true value of the metal corrosion data,
as a result of the prediction of the metal corrosion relationship model,
is true valueAnd the average value is N, and the number of the predicted results of the metal corrosion model is N.
And step S4: extracting corrosion medium data of a plurality of places, wherein the corrosion medium data comprises salt mist concentration, hydrogen sulfide concentration, sulfur dioxide concentration and nitrogen dioxide concentration in the atmosphere, drawing a corrosion medium distribution diagram according to the corrosion medium data, taking a sulfur dioxide corrosion medium as an example, a change curve of metal corrosion rate under different sulfur dioxide concentrations is shown in figure 2, and TOW in the diagram represents the wetting time of a metal surface.
Step S5.1: synthesizing the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram into a matrix, and then superposing;
step S5.2: drawing according to the superposed gray value;
step S5.3: and performing color matching according to the brightness value in the gray value to further form a metal corrosion distribution diagram.
The present invention is not limited to the above embodiments, and various other equivalent modifications, substitutions or alterations can be made on the basis of the above description and the common general technical knowledge and conventional means in the field without departing from the basic technical idea of the invention.
Claims (5)
1. A metal corrosion distribution map drawing method based on a KNN intelligent algorithm is characterized by comprising the following steps:
step S1: obtaining metal corrosion rate data of a plurality of places, and dividing the metal corrosion rate data of the plurality of places into a training sample set and a testing sample set;
step S2: processing the training sample set and the test sample based on a KNN algorithm, and establishing a metal corrosion model;
and step S3: based on goodness-of-fit testing and estimation of the prediction effect of the metal corrosion model, carrying out optimization adjustment on model parameters according to the prediction effect until the goodness-of-fit value is greater than 0.8, calculating a metal corrosion prediction result, and drawing a metal corrosion prediction distribution map;
and step S4: extracting corrosion medium data of a plurality of places, and drawing a corrosion medium distribution map according to the corrosion medium data;
step S5: and superposing the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram to obtain a metal corrosion distribution diagram.
2. The KNN intelligent algorithm-based metal corrosion distribution map drawing method as claimed in claim 1, wherein: in step S2, the training sample set and the test sample are processed based on the KNN algorithm, and a metal corrosion model is established, wherein the metal corrosion model comprises the following steps:
step S2.1: calculating the distance between each test sample in the test sample set and all training samples in the training sample set, wherein the calculation formula of the distance is as follows:
wherein,
for the (i) -th test sample,
for the ith metal corrosion rate data for the ith test sample,
for the jth training sample, the number of training samples,
the ith metal corrosion rate data of the jth training sample is obtained, and L is the sample number of the training sample set;
step S2.2: selecting the first K training samples from the training sample set according to the priority that the distance between each test sample and all the training samples in the training sample set is from small to large;
step S2.3: judging the metal corrosion grade of K training samples, and selecting the K training samples with the highest quantityHigh metal corrosion class C max And taking the predicted result as the metal corrosion prediction result of the ith test sample.
3. The KNN intelligent algorithm-based metal corrosion distribution map drawing method according to claim 1, characterized in that: the goodness of fit calculation formula in step S3 is:
wherein z is the true value of the metal corrosion data,
as a result of the prediction of the metal corrosion model,
the value is the average value of the truth, and N is the predicted result number of the metal corrosion model.
4. The KNN intelligent algorithm-based metal corrosion distribution map drawing method as claimed in claim 1, wherein: and S4, the data of the corrosion medium comprise the concentration of salt mist, the concentration of hydrogen sulfide, the concentration of sulfur dioxide and the concentration of nitrogen dioxide in the atmosphere.
5. The KNN intelligent algorithm-based metal corrosion distribution map drawing method as claimed in claim 1, wherein: the metal corrosion distribution map superposition synthesis method in the step S5 comprises the following steps:
step S5.1: synthesizing the metal corrosion prediction distribution diagram and the corrosion medium distribution diagram into a matrix, and then superposing;
step S5.2: drawing according to the superposed gray value;
step S5.3: and performing color matching according to the brightness value in the gray value to form a metal corrosion distribution diagram.
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