Blast furnace top CO content soft measurement method based on multi-view data collaborative analysis

Abstract

The invention provides a blast furnace top CO content soft measurement method based on multi-view data collaborative analysis, and relates to the technical field of blast furnace ironmaking processes. The method comprises the following steps: firstly, collecting image data and related physical variables of a blast furnace tuyere and a furnace top, and carrying out gray processing and normalization processing to obtain image matrixes and physical variable matrixes of the tuyere and the furnace top; then, carrying out collaborative correlation analysis on the data to obtain a correlation information index matrix for prediction; further processing the information for calculating and removing the associated data through the index matrix; establishing a prediction model of the blast furnace top CO content by using the information without the associated data to obtain parameters for predicting the blast furnace top CO content; and finally, after the data obtained in real time are subjected to gray processing and information of associated data is removed, the blast furnace top CO content is predicted according to the parameters of the blast furnace top CO content prediction model. According to the invention, the accuracy of blast furnace top CO measurement can be improved, and the measurement difficulty and time lag are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of blast furnace ironmaking process, and in particular to a soft measurement method for CO content in a blast furnace top based on multi-view data collaborative analysis. Background Art

[0002] CO concentration is a key indicator of gas distribution and reduction reaction inside the blast furnace, and directly reflects the efficiency of chemical reactions in the blast furnace. High CO content generally indicates high reaction efficiency and optimal gas flow, while low CO content indicates poor reaction kinetics or uneven gas distribution and reduced output. Therefore, measuring CO content helps to actively adjust operating parameters, which is crucial to maintaining safe, continuous and stable operation of blast furnaces, energy conservation, emission reduction and quality improvement. It helps to promote high-quality development of the steel industry and accelerate the realization of the industry's "carbon peak". However, as the most energy-consuming process in modern processing industry and one of the largest and most complex production processes, the blast furnace ironmaking process is characterized by a continuous production environment of high temperature, high pressure, high dust, multi-phase and multi-field coupling, which leads to great difficulties in real-time measurement of CO content and severely increases the measurement time and cost requirements. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a soft measurement method for the CO content in the blast furnace top based on collaborative analysis of multi-perspective data in view of the shortcomings of the above-mentioned prior art, which can solve the problems of long measurement time, high cost and failure to fully utilize on-site production data and improve the accuracy of the measurement of the CO content in the furnace top.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A soft measurement method for CO content in a blast furnace top based on collaborative analysis of multi-view data includes the following steps:

[0006] Step 1: Collect images of the tuyere raceway, furnace top, and related physical variable data as well as CO content data of the furnace top during blast furnace production;

[0007] Step 1.1: Collect image data of the flame in the tuyere raceway of the blast furnace and its related physical variables;

[0008] Step 1.2: Collect image data of blast furnace top flame and its related physical variables;

[0009] Step 1.3: Collect blast furnace top CO content data;

[0010] Step 2: Perform collaborative analysis on the data obtained in step 1. The objective function model of data collaborative analysis is:

[0011]

[0012] Among them, X i , X j are images at adjacent moments under the same viewing angle, X a , X b is the image of the adjacent moment of another perspective, and the size of the image matrix is ​​m×n; is the projection matrix, which is used to analyze the correlation information of samples from multiple perspectives, l is the number of samples, and w ij 、w ab is the weight of the physical variable from different perspectives, β1 and β2 are coefficient parameters; y i is the kinetic energy of the wind, y a is the CO content at the furnace top;

[0013] w ij 、w ab The settings are as follows:

[0014]

[0015] Among them, r i 、r j is the relevant physical variable of the wind outlet raceway, r a 、r b are the relevant physical variables of the furnace top, σ is the core parameter;

[0016] Solving the data collaborative analysis objective function model to obtain U, V and a constant deviation b;

[0017] Step 3: According to the projection matrices U and V obtained in step 2, calculate the index Q of the correlation information under multiple perspectives;

[0018] First, we get P = UV T , P is an m×n matrix; convert the matrix P into a column vector, that is: Q = Vec(P),

[0019] Step 4: The correlation information index Q obtained in step 2 is applied to the image of the furnace top to obtain the correlation information and calculate the key information for prediction after removing the correlation data;

[0020] Step 5: Using the bilinear regression method, the prediction model of the CO content in the furnace top of each sample is established as follows:

[0021]

[0022] Among them, u pj 、v pj They are the left projection matrix u p , right projection matrix v p The j-th column vector of ; Set to u p=[u p1 ,u p2 ,...,u pj ,...,u pk ]、v p =[v p1 ,v p2 ,...,v pj ,...,v pk ];X s is the key information for CO content calculation, b p is a constant deviation, α is a system parameter, y s is the label vector corresponding to the CO content at the furnace top in the CO content prediction model, q = Vec(u p (v p ) T ).

[0023] Solve the furnace top CO content prediction model to obtain u p 、v p and b p ;

[0024] Step 6: The furnace top camera is used to collect the furnace top image data in the blast furnace industrial production process online. According to the solutions of steps 2 to 5, the furnace top CO content is measured online in the ironmaking blast furnace production process. The furnace top CO content calculation model is:

[0025]

[0026] Furthermore, the specific steps of solving the objective function model in step 2 are as follows:

[0027] Step 2.1: Set the initial projection matrix V;

[0028] Step 2.2: Fix V and find U and b. The specific steps are as follows:

[0029] Step 2.2.1: Combine the multi-view image operations in equation (1) to obtain:

[0030]

[0031] in, u1, u2…u k is the column vector of U; E = [S, A] mk×2l ,A=[A1,A2,…,A a ,,A l ] mk×l , S=[s1,s2,…,s i ,,s l ] mk×l , Y is the label vector corresponding to the viewing angle,

[0032] Step 2.2.2: Calculate the operation from the perspective of physical variables and get:

[0033]

[0034] Where F = β1SL d S T +β2AL f A T , L d , L f are the Laplace matrices corresponding to the physical variables of the furnace top and the tuyere, L d , L f The calculation method is: L = DW, L represents L d or L f , D is a diagonal matrix, W is the corresponding adjacency matrix;

[0035] Step 2.2.3: Derivative equation (5) and set it to 0, and we get:

[0036]

[0037] Where G = I-1 / (2l)e T e, all elements in e are 1; I is the identity matrix;

[0038] Step 2.3: Fix U and find V and b. The specific steps are as follows:

[0039] Step 2.3.1: Convert equation (1) into:

[0040]

[0041] in, v1, v2…v k is the column vector of V; H = [J, K] nk×2l ,J=[j1,j2,…,j i ,…,j l ] nk×l , K=[k1,k2,…,k a ,…,k l ] nk×l ,

[0042] Step 2.3.2: Calculate the operation from the perspective of physical variables and get:

[0043]

[0044] Where, M = β1JL d J T +β2KL f K T ;

[0045] Step 2.3.3: Derivative (9) and set it to 0, we get:

[0046]

[0047] Step 2.4: Repeat steps 2.2 to 2.3 for c iterations, and finally obtain the projection vectors U, V and the constant deviation b.

[0048] Furthermore, in step 3, after obtaining Q, the elements of the vector Q are sorted from large to small, and their corresponding associations are arranged from strong to weak, so as to obtain the corresponding association rules.

[0049] Furthermore, the specific steps of solving the furnace top CO content prediction model in step 5 are as follows:

[0050] Step 5.1: Fix v p , find u p and b p , the specific steps are as follows:

[0051] Define as follows:

[0052] q′=Vec(v p (u p ) T ) T (15)

[0053] Step 5.1.1: Solve the prediction model of furnace top CO content and rewrite equation (14) as follows:

[0054]

[0055] Formula (16) is further transformed into:

[0056]

[0057] Among them, e p The elements of are all 1, I is the identity matrix; T = [t1, t2, …, t i ,…,t l ] mk×l ,

[0058] Step 5.1.2: Derivative (17) and set it to 0, we get:

[0059]

[0060] Step 5.2: Fix u p , find v p and b p , the specific steps are as follows:

[0061] Step 5.2.1: Rewrite equation (14) as:

[0062]

[0063] in, N=[n1,n2,…,n i ,…n l ] mk×l ,

[0064] Step 5.2.2: Derivative (20) and set it to 0, we get:

[0065]

[0066] Step 5.3: Repeat steps 5.1 to 5.2 for R iterations, and finally get the left and right projection vectors u p 、v p and constant deviation b p .

[0067] Furthermore, the specific steps of step 6 include:

[0068] Step 6.1: The furnace top image data in the blast furnace industrial production process is collected online through the furnace top camera, and the image data is grayed and normalized;

[0069] Step 6.2: Process the image in step 6.1 using the associated information index obtained in step 2 to obtain an image X with key information. s ;

[0070] Step 6.3: u obtained in step 5 p 、v p and b p As well as the system parameters α and the furnace top image X of key information s As the input of the CO content calculation model of the furnace top, the measurement and calculation of the CO content in the blast furnace top is realized.

[0071] The beneficial effect produced by adopting the above technical scheme is that: the soft measurement method of blast furnace top CO content based on multi-view data collaborative analysis provided by the present invention realizes the measurement and calculation of blast furnace top CO content using tuyere and furnace top images and physical variable data collected at the industrial site, replaces sensor measurement, solves the difficulties such as difficult sensor installation, high cost, poor application conditions and prone to failure; at the same time, compared with sampling measurement, the method of the present invention has low delay and fast response speed, which is more in line with the technical requirements of blast furnace ironmaking industry. Based on the principle of the process of blast furnace ironmaking process and the characteristics of strong coupling, the present invention uniformly grayscales the collected images, retains the light and dark features of the original image, introduces physical variables, and performs correlation and collaborative analysis on blast furnace production data from multiple perspectives, thereby extracting key information for measuring and calculating blast furnace CO content, and improving the accuracy of measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of a blast furnace ironmaking process provided by an embodiment of the present invention;

[0073] Figure 2 A flow chart of a soft-sensing method for CO content in a blast furnace top based on collaborative analysis of multi-view data provided by an embodiment of the present invention;

[0074] Figure 3 A comparison chart of the measured value and the true value provided by the embodiment of the present invention;

[0075] Figure 4 A relative error diagram between the measured value and the true value provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0077] like Figure 1 The figure shows a schematic diagram of the production process of blast furnace ironmaking. In the entire process, first, iron ore, coke and other furnace materials are loaded from the upper part of the blast furnace in a certain proportion. High-temperature hot air, coal powder and the like are blown into the bottom of the blast furnace, where oxygen and coke react to produce carbon monoxide gas. Subsequently, carbon monoxide gradually flows upward in the blast furnace and undergoes a reduction reaction with the iron ore on the upper layer, reducing the iron oxide to iron. At the same time, impurities in the iron ore react with limestone to form slag, which falls to the bottom of the blast furnace together with molten iron and residual coke. When the temperature at the bottom of the blast furnace reaches above 1500°C, the molten iron and slag are separated, the molten iron is discharged through the iron outlet, and the slag is discharged through the slag outlet. Blast furnace ironmaking not only produces the main product of pig iron, but also produces by-products such as blast furnace gas (CO is the main combustible component) and slag. The blast furnace gas is recycled through the treatment system. As Figure 2 As shown, the method of this embodiment is described as follows.

[0078] Step 1: Collect images of the tuyere raceway, furnace top, and related physical variable data as well as CO content data of the furnace top during blast furnace production;

[0079] Step 1.1: Collect image data of the flame in the tuyere raceway of the blast furnace and its related physical variables;

[0080] Step 1.2: Collect image data of blast furnace top flame and its related physical variables;

[0081] Step 1.3: Collect blast furnace top CO content data;

[0082] In this embodiment, the number of various samples collected under the stable operation condition of the blast furnace is 1500.

[0083] Step 2: Perform collaborative analysis on the data obtained in step 1, and the objective function model is:

[0084]

[0085] Among them, X i , X j are images at adjacent moments under the same viewing angle, X a , X b is the image of the adjacent moment of another perspective, and the size of the image matrix is ​​m×n; is the projection vector, which is used to analyze the correlation information of samples from multiple perspectives, l is the number of samples, y i is the kinetic energy of the wind, y a is the CO content in the furnace top; w ij 、w ab is the weight of the physical variable from different perspectives, β1 and β2 are coefficient parameters.

[0086] Solve the objective function model to obtain U, V and a constant deviation b.

[0087] In this embodiment, m, n and k are 258, 255 and 15 respectively, the number of samples l=1500, U and V are matrices of 258×15 and 255×15 respectively, β1=1000, β2=1, and b=-0.0243.

[0088] w ij 、w ab The settings are as follows:

[0089]

[0090] In this embodiment, σ=0.5.

[0091] The specific steps to solve the objective function model are as follows:

[0092] Step 2.1: Set the initial projection matrix V.

[0093] Step 2.2: Fix V and find U and b. The specific steps are as follows:

[0094] Step 2.2.1: Combine the multi-view image operations in equation (1) to obtain:

[0095]

[0096] in, u1, u2…u k is the column vector of U; E = [S, A] 3870×3000 ,A=[A1,A2,…,A a ,…,A l ] 3870×1500 , S=[s1,s2,…,s i ,…,s l ] 3870×1500 , Y is the label vector corresponding to the viewing angle,

[0097] Step 2.2.2: Calculate the operation from the perspective of physical variables and get:

[0098]

[0099] Where F = β1SL d S T +β2AL f A T , L d , L f are the Laplace matrices corresponding to the physical variables of the furnace top and the tuyere, L d , L f The calculation method is: L = DW, L represents L d or L f , D is a diagonal matrix, W is the corresponding adjacency matrix.

[0100] Step 2.2.3: Derivative equation (5) and set it to 0, and we get:

[0101]

[0102] Where G = I-1 / (2l)e T e, all elements in e are 1, and I is the identity matrix.

[0103] Step 2.3: Fix U and find V and b. The specific steps are as follows:

[0104] Step 2.3.1: Convert equation (1) into:

[0105]

[0106] in, v1, v2…v k is the column vector of V; H = [J, K] 3825×1500 ,J=[j1,j2,…,j i ,…,j l ] 3825×1500 , K=[k1,k2,…,k a ,…,k l ] 3825×1500

[0107] Step 2.3.2: Calculate the operation from the perspective of physical variables and get:

[0108]

[0109] Where, M = β1JL d J T +β2KL f K T .

[0110] Step 2.3.3: Derivative (9) and set it to 0, we get:

[0111]

[0112]

[0113] Step 2.4: Repeat steps 2.2 to 2.3 above for c iterations, and finally obtain the projection vectors U, V and the constant deviation b.

[0114] Step 3: Calculate the index of the associated information under multiple perspectives. The specific steps are as follows:

[0115] Step 3.1: Based on the projection vectors U and V obtained in step 2, we get:

[0116] P=UV T (12)

[0117] Step 3.2: The m×n matrix P obtained by formula (12) is converted into a column vector, that is:

[0118] Q=Vec(P) (13)

[0119] in,

[0120] Step 3.3: Sort the elements of vector Q from large to small, and arrange the corresponding association rules from strong to weak, so as to obtain the index of the corresponding association analysis.

[0121] Step 4: The correlation information index Q obtained in step 2 is applied to the image of the furnace top to obtain the correlation information and the key information for prediction after calculating the removal of the correlation data.

[0122] Step 5: Using the bilinear regression method, the prediction model of the CO content in the furnace top of each sample is established as follows:

[0123]

[0124] Among them, u pj 、v pj They are the left projection matrix u p , right projection matrix v p The j-th column vector of ; Set to u p =[u p1 ,u p2 ,...,u pj ,...,u pk ]、v p =[v p1 ,v p2 ,...,v pj ,...,v pk ];X s is the key information for calculating CO content, α is a system parameter, and in this embodiment, α=10000. s is the label vector corresponding to the CO content at the furnace top in the CO content prediction model, q = Vec(u p (v p ) T ).

[0125] Solve the furnace top CO content prediction model to obtain u p 、v p and b p .

[0126] The specific steps to solve the prediction model of furnace top CO content are as follows:

[0127] Step 5.1: Fix v p , find u p and b p , the specific steps are as follows:

[0128] Define as follows:

[0129] q′=Vec(v p (u p ) T ) T (15)

[0130] Step 5.1.1: Solve the prediction model of furnace top CO content and rewrite equation (14) as follows:

[0131]

[0132] Formula (16) is further transformed into:

[0133]

[0134] Among them, e p The elements of are all 1, I is the identity matrix; T = [t1, t2, …, t i ,…,t l ] mk×l ,

[0135] Step 5.1.2: Derivative (17) and set it to 0, we can get:

[0136]

[0137] Step 5.2: Fix u p , find v p and b p , the specific steps are as follows:

[0138] Step 5.2.1: Rewrite equation (14) as:

[0139]

[0140] in, N=[n1,n2,…,n i ,n l ] mk×l ,

[0141] Step 5.2.2: Take the derivative of equation (20) and set it to 0, and we get:

[0142]

[0143] Step 5.3: Repeat steps 5.1 to 5.2 for 100 iterations, and finally get the left and right projection vectors u p 、v p and constant deviation b p .

[0144] Step 6: Online calculation of the CO content at the top of the blast furnace during the ironmaking process. The specific steps are as follows:

[0145] Step 6.1: The furnace top image data in the blast furnace industrial production process is collected online through the furnace top camera, and the image data is grayed and normalized;

[0146] Step 6.2: Process the image in step 6.1 using the associated information index obtained in step 2 to obtain an image X with key information. s ;

[0147] Step 6.3: u obtained in step 5 p 、v p and b p As well as the system parameters α and the furnace top image X of key information s As the input of the calculation model, the prediction calculation of the CO content in the blast furnace top is realized. The calculation formula is:

[0148]

[0149] Figure 3 , Figure 4 They are the comparison diagram between the measured value and the true value and the relative error diagram of the measured value. Figure 3 , Figure 4 It can be seen that the present invention has achieved good results in measuring the CO content in the blast furnace top.

[0150] In the overall measurement process of this embodiment, the RMSE between the sample measurement value and the true value is 0.181%. It can be seen that the present invention can accurately calculate and measure the CO content of the furnace top in the blast furnace ironmaking production process, thereby assisting the operator to understand the operating status of the blast furnace, and providing a guarantee for the safe and stable operation of the blast furnace.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A soft measurement method for CO content in a blast furnace top based on collaborative analysis of multi-view data, characterized in that: The steps include: Step 1: Collect images of the tuyere raceway, furnace top, and related physical variable data as well as CO content data of the furnace top during blast furnace production; Step 1.1: Collect image data of the flame in the tuyere raceway of the blast furnace and its related physical variables; Step 1.2: Collect image data of blast furnace top flame and its related physical variables; Step 1.3: Collect blast furnace top CO content data; Step 2: Perform collaborative analysis on the data obtained in step 1. The objective function model of data collaborative analysis is: Among them, X i , X j are images at adjacent moments under the same viewing angle, X a , X b is the image of the adjacent moment of another perspective, and the size of the image matrix is ​​m×n; is the projection matrix, which is used to analyze the correlation information of samples from multiple perspectives, l is the number of samples, and w ij 、w ab is the weight of the physical variable from different perspectives, β1 and β2 are coefficient parameters; y i is the kinetic energy of the wind, y a is the CO content at the furnace top; w ij 、w ab The settings are as follows: Among them, r i 、r j is the relevant physical variable of the tuyere raceway, r a 、r b are the relevant physical variables of the furnace top, σ is the core parameter; Solving the data collaborative analysis objective function model to obtain U, V and a constant deviation b; Step 3: According to the projection matrices U and V obtained in step 2, calculate the index Q of the correlation information under multiple perspectives; First, we get P = UV T , P is an m×n matrix; convert the matrix P into a column vector, that is: Q = Vec(P), Step 4: The correlation information index Q obtained in step 2 is applied to the image of the furnace top to obtain the correlation information and calculate the key information for prediction after removing the correlation data; Step 5: Using the bilinear regression method, the prediction model of the CO content in the furnace top of each sample is established as follows: Among them, u pj 、v pj They are the left projection matrix u p , right projection matrix v p The j-th column vector of ; Set to u p =[u p1 ,u p2 ,...,u pj ,...,u pk ]、v p =[v p1 ,v p2 ,...,v pj ,...,v pk ];X s is the key information for CO content calculation, b p is a constant deviation, α is a system parameter, y s is the label vector corresponding to the CO content at the furnace top in the CO content prediction model, q = Vec(u p (v p ) T ); Solve the furnace top CO content prediction model to obtain u p 、v p and b p ; Step 6: The furnace top camera is used to collect the furnace top image data in the blast furnace industrial production process online. According to the solutions of steps 2 to 5, the furnace top CO content is measured online in the ironmaking blast furnace production process. The furnace top CO content calculation model is:

2. The soft sensing method for CO content in a blast furnace top based on multi-view data collaborative analysis according to claim 1 is characterized in that: The specific steps of solving the objective function model in step 2 are as follows: Step 2.1: Set the initial projection matrix V; Step 2.2: Fix V, find U and b, and we get: in, u1, u2…u k is the column vector of U; E = [S, A] mk×2l ,A=[A1,A2,…,A a ,…,A l ] mk×l , S=[s1,s2,…,s i ,…,s l ] mk×l , F=β1SL d S T +β2AL f A T , L d , L f are the Laplace matrices corresponding to the physical variables of the furnace top and the tuyere, L d , L f The calculation method is: L = DW, L represents L d or L f , D is a diagonal matrix, W is the corresponding adjacency matrix; Y is the label vector of the corresponding view. G=I-1 / (2l)e T e, all elements in e are 1; I is the identity matrix; Step 2.3: Fix U, find V and b, and we get: in, v1, v2…v k is the column vector of V; H = [J, K] nk×2l ,J=[j1,j2,…,j i ,…,j l ] nk×l , K=[k1,k2,…,k a ,…,k l ] nk×l , M=β1JL d J T +β2KL f K T ; Step 2.4: Repeat steps 2.2 to 2.3 for c iterations, and finally obtain the projection vectors U, V and the constant deviation b.

3. The soft sensing method for CO content in a blast furnace top based on multi-view data collaborative analysis according to claim 2 is characterized in that: The specific steps of step 2.2 are as follows: Step 2.2.1: Combine the multi-view image operations in equation (1) to obtain: Step 2.2.2: Calculate the operation from the perspective of physical variables and get: Step 2.2.3: Derivative (5) and set it to 0 to obtain (6) and (7).

4. The soft sensing method for CO content in a blast furnace top based on multi-view data collaborative analysis according to claim 2 is characterized in that: The specific steps of step 2.3 are as follows: Step 2.3.1: Convert equation (1) into: Step 2.3.2: Calculate the operation from the perspective of physical variables and get: Step 2.3.3: Derivative (9) and set it to 0 to obtain (10) and (11).

5. The soft sensing method for CO content in a blast furnace top based on multi-view data collaborative analysis according to claim 2 is characterized in that: In step 3, after obtaining Q, the elements of vector Q are sorted from large to small, and their corresponding associations are arranged from strong to weak, so as to obtain the corresponding association rules.

6. The method for soft sensing of CO content in a blast furnace top based on collaborative analysis of multi-view data according to claim 5, characterized in that: The specific steps of solving the furnace top CO content prediction model in step 5 are as follows: Step 5.1: Fix v p , find u p and b p , the specific steps are as follows: Define as follows: q′=Thing(in p (at p ) T ) T (15) Step 5.1.1: Solve the prediction model of furnace top CO content and rewrite equation (14) as follows: Formula (16) is further transformed into: Among them, e p The elements of are all 1, I is the identity matrix; T = [t1, t2, …, t i ,…,t l ] mk×l , Step 5.1.2: Derivative (17) and set it to 0, we get: Step 5.2: Fix u p , find v p and b p , the specific steps are as follows: Step 5.2.1: Rewrite equation (14) as: in, N=[n1,n2,…,n i ,…n l ] mk×l , Step 5.2.2: Derivative (20) and set it to 0, we get: Step 5.3: Repeat steps 5.1 to 5.2 for R iterations, and finally get the left and right projection vectors u p 、v p and constant deviation b p .

7. The soft sensing method for CO content in a blast furnace top based on multi-view data collaborative analysis according to claim 6 is characterized in that: The specific steps of step 6 include: Step 6.1: The furnace top image data in the blast furnace industrial production process is collected online through the furnace top camera, and the image data is grayed and normalized; Step 6.2: Process the image in step 6.1 using the associated information index obtained in step 2 to obtain an image X with key information. s ; Step 6.3: u obtained in step 5 p 、v p and b p As well as the system parameters α and the furnace top image X of key information s As the input of the CO content calculation model of the furnace top, the measurement and calculation of the CO content in the blast furnace top is realized.

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