Computer system based on blast furnace-hot blast stove safe temperature control calculation model

Abstract

The invention discloses a computer system based on a blast furnace-hot blast stove safe temperature control calculation model, and the system comprises a feeding recognition module which is used for obtaining the smell, shape and color data of ores after the ores enter a blast furnace; the processing information database comprises two sub-databases; wherein information entries of the ore are pre-stored in the first sub-database and are used for realizing matching of the processing time and the vault temperature of the ore; the second sub-database pre-stores process entries of the ore and is used for realizing matching of initial processing parameters of the ore; the real-time monitoring module is used for collecting real-time data in the ore processing process; and the model prediction module is used for predicting and outputting prediction parameters of the blast furnace-hot blast stove at the next moment by utilizing the blast furnace-hot blast stove safe temperature control calculation model based on the ore processing information and the real-time data, and carrying out furnace-hot blast stove regulation and control according to the parameters so as to realize temperature control in the processing process.

Description

Technical Field

[0001] The invention relates to the field of computer systems based on model control, and in particular to a computer system based on a blast furnace-hot blast stove safety temperature control calculation model. Background Art

[0002] In the steel industry, temperature is an important environmental factor that affects the iron production rate and safety. Too high or too low temperature will have an adverse effect on the quality of molten iron and molten steel. However, in the steel industry, there are a large number of equipment and process links that require temperature control, such as blast furnaces, hot blast furnaces, electric arc furnaces, etc.

[0003] Traditional temperature control methods for ironmaking and steelmaking use valve control or manual adjustment, which have problems such as inaccurate temperature control, slow response, and high energy consumption. The more advanced control method is PID control, but it lacks the ability to monitor and dynamically adjust temperature distribution in real time, and these methods are difficult to adjust parameters, sensitive to interference, and have performance limitations. In addition, PID control systems may be sensitive to noise and external disturbances, causing the system output to become unstable when faced with high-frequency noise. Summary of the invention

[0004] The purpose of the present invention is to provide a computer system based on a blast furnace-hot blast stove safety temperature control calculation model while taking into account the safety and service life of the blast furnace and the hot blast stove, so as to solve the problems of inaccurate temperature control, slow response and high energy consumption in the processing of ironmaking and steelmaking.

[0005] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0006] The computer system based on the blast furnace-hot blast stove safety temperature control calculation model includes:

[0007] A feed identification module that captures the smell, shape and color data of the ore after it enters the blast furnace;

[0008] The processing information database includes two sub-databases; the first sub-database pre-stores information entries of the ore for matching the processing time and the dome temperature of the ore; the second sub-database pre-stores process entries of the ore for matching the initial processing parameters of the ore;

[0009] Real-time monitoring module, used to collect real-time data during ore processing;

[0010] The model prediction module, based on ore processing information and real-time data, uses the blast furnace-hot blast stove safety temperature control calculation model to predict and output the blast furnace-hot blast stove's cold air main flow, blast furnace gas branch flow, clean gas main flow, combustion air main flow, and steam injection volume at the next moment. The blast furnace-hot blast stove is regulated according to these parameters to achieve temperature control during the processing process.

[0011] Furthermore, the ore identification module includes an electronic nose for identifying the smell of ore and a high-definition camera for identifying the shape and color of ore; when the ore enters the blast furnace, the odor sampling operator and gas sensor array in the electronic nose identify the odor intensity and odor category of the ore, convert it into a digital signal of the odor, and transmit it to the processing information database together with the shape and color obtained and identified by the high-definition camera.

[0012] Furthermore, the first sub-database pre-stores information entries of ore, each of which includes: ore type, ore name, shape, color, smell, processing time and dome temperature, etc.; using the shape, smell and color collected by the ore recognition module as search conditions, the first sub-database matches the stored information entries, finds the ore type matching the search conditions, and obtains the processing time t and the dome temperature target value T from the corresponding entry information. g0 ;

[0013] The second sub-database pre-stores the process entries of the ore, each of which includes: the type of ore and the initial processing parameters matching the type of ore, including the initial value Q of the cold air main flow 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 The second sub-database selects the initial processing parameters in the corresponding process entry according to the ore type matched by the first sub-database, and sets the initial processing parameters and processing time t g , Dome temperature target value T g0 Transmitted to the real-time monitoring module as ore processing information.

[0014] Furthermore, the monitoring module comprises two sub-monitoring modules, the first sub-monitoring module is used to detect the dome temperature T in the blast furnace. g , exhaust gas temperature T f, optimal air-fuel ratio A / F, supply air humidity RH; the second sub-monitoring module is used to detect the cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh; cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, steam injection amount Q5, and transmit these real-time data and ore processing information to the model prediction module.

[0015] Furthermore, the construction process of the blast furnace-hot blast stove safety temperature control calculation model is as follows:

[0016] Based on the ore processing information and real-time data in the ore processing process, a blast furnace-hot blast stove safety temperature control calculation model is constructed; the calculation model is a nonlinear model, and the nonlinear model is converted to construct a state space equation;

[0017] For the established state space equation, the Taylor series expansion method is applied to determine the coefficient matrix of the state space equation, thereby linearizing the state space equation;

[0018] The state space equation obtained by linearization is discretized using a zero-order holder to construct a discretized system.

[0019] For the discretized system, a rolling optimization method is used to predict the system state for a period of time in the future;

[0020] A quadratic control law is designed, and the entropy weight method is used to obtain the weight coefficients of the system state and the weight coefficients of the input variables in the quadratic control law; the predicted system state for a period of time in the future is brought in, and constraints are set, the system input variable sequence is solved, and the first input variable in the solved sequence is extracted. The computer system uses the parameters in the input variable to regulate the blast furnace-hot blast stove processing process.

[0021] Furthermore, the ore processing information and real-time data in the ore processing process are used to construct a safe temperature control calculation model for a blast furnace and a hot blast stove; the calculation model is a nonlinear model, and the nonlinear model is converted to construct a state space equation, including:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] Wherein, t is the time parameter, P1 is the cold air main pressure, T1 is the cold air main temperature, P2 is the blast furnace gas branch pressure, T2 is the blast furnace gas branch temperature, P3 is the clean gas main pressure, T3 is the clean gas main temperature, P4 is the combustion air main pressure, T4 is the combustion air main temperature, rh is the atmospheric humidity; Q1 is the cold air main flow, Q2 is the blast furnace gas branch flow, Q3 is the clean gas main flow, Q4 is the combustion air main flow, Q5 is the amount of steam injected; T g is the dome temperature, T f is the exhaust gas temperature, A / F is the optimal air-fuel ratio, RH is the air humidity, and the parameters are marked represents its first-order derivative;

[0031] Set the state variable ,in ; Input variables ,in ; Output variables ,in , the superscript T indicates transposition;

[0032] The state space equation constructed by the nonlinear model is as follows:

[0033] ;

[0034] ;

[0035] in, is the first-order derivative of the state variable, A is the system matrix, B is the control matrix, C is the output matrix or observation matrix, and D is the direct transfer matrix.

[0036] Furthermore, the method of applying Taylor series expansion is used to determine the coefficient matrix of the state space equation, which is specifically expressed as:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] in, is the partial derivative, It means taking the coefficient of the corresponding element after integrating the derivative, and t is the time parameter.

[0042] Furthermore, the state space equation obtained by linearization processing is discretized by using the zero-order holder to construct a discretized system, which is expressed as:

[0043] ;

[0044] Among them, k is the current time, is the value of the state variable x at time k, Enter the value of variable u at time k, represents the system state at time k+1 predicted at time k, , is the discrete system coefficient matrix, we have:

[0045] ;

[0046] ;

[0047] Among them, I is the identity matrix, A is the system matrix, B is the control matrix, and T is the sampling period.

[0048] Furthermore, for the discretized system, a rolling optimization method is used to predict the system state for a period of time in the future, including:

[0049] The system state at a certain time in the future to The expression is as follows:

[0050] ; ; ;…… ;

[0051] in, is the initial system state at time k, and its value is the state variable collected at time k ; The system state at time k+N predicted at time k; is the input variable at time k ; is the input variable at time k+N-1 predicted at time k, and N is the length of the prediction interval.

[0052] Furthermore, the design of the quadratic control law includes:

[0053] The general form of designing the quadratic control law J is:

[0054] ;

[0055] Among them, Q is The weight coefficient of the system state at the moment, R is the weight coefficient of the input variable, and F is The weight coefficient of the system state at the moment; is the system state at time k+i predicted at time k, is the input variable at the k+ith moment predicted at the kth moment, i=1,2,…,N-1;

[0056] The constraints of the quadratic control law J are:

[0057] ;

[0058] .

[0059] Furthermore, the entropy weight method is used to obtain the weight coefficients of the system state and the input variables in the quadratic control law, including:

[0060] Obtain the historical data of the currently processed ore, extract the following fourteen parameters: cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh, cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, and steam injection amount Q5, at the same prediction interval length from time k+1 to k+N, and take each data value of each parameter as a sample;

[0061] Establish positive indicators for each sample :

[0062] ;

[0063] in, For sample a ij Positive indicator, min and max are for minimum and maximum value operations, a ij Represents the data value of the j-th historical data at the k+i-th moment;

[0064] Then, the positive index of each sample is calculated in turn The proportion of positive indicators at all times , the entropy value of the positive indicator corresponding to each type of historical data :

[0065] ;

[0066] ;

[0067] in, ;

[0068] Finally, we get the weight of each positive indicator of historical data. :

[0069] ;

[0070] in, ; The weight coefficient Q of the system state at a certain moment is a positive definite matrix, and each element on its diagonal is given by The weight coefficient R is a unit matrix, and its diagonal elements are ;

[0071] but The weight coefficient F of the system state at a moment is:

[0072] ;

[0073] in, is the discrete system coefficient matrix, ;

[0074] In the above formula, I is the unit matrix, A is the system matrix, and T is the sampling period.

[0075] Furthermore, at the initial time k, the computer system controls the calcining machine to first use the initial processing parameter cold air main pipe flow initial value Q in the ore processing information 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 Start working and set the processing time t g , Dome temperature target value T g0 , start the first cycle, the process of each cycle is:

[0076] The real-time monitoring module obtains the state variables at time k , the state variable x is used as the initial state of the system at time k in the rolling optimization method , predict and obtain the system status at future times to , then to As Substitute it into the quadratic control law J, and obtain the input variable sequence by taking the derivative of J and setting the derivative equal to zero , take its first value Control the blast furnace-hot blast stove at the next time k+1;

[0077] At the next time k+1, the second cycle starts; and the above cycle process is repeated until the ore processing is completed or the processing time t is reached. g and the target value of the vault temperature T g0 .

[0078] Compared with the prior art, the present invention has the following technical features:

[0079] 1. The present invention integrates modules such as feed identification, real-time monitoring, and model prediction, maps historical data of flow control and temperature control systems with real-time data, and realizes coupled control of flow and temperature by the computer system by constructing a nonlinear computer model; the model predictive control method is adopted to improve the robustness and stability of the system and realize dynamic and precise control of temperature.

[0080] 2. The present invention uses large model technology to compare the collected iron ore property data with the historical data in the database, and uses electronic noses and high-definition cameras to replace manual identification and selection, thereby reducing labor costs, reducing working hours, and realizing the automation of iron ore and its ingredient identification work.

[0081] 3. The present invention dynamically updates the weight coefficient of the linear quadratic control law, and applies entropy weight to obtain the weight coefficient of the system state and the system input; the real-time data in the database is processed in the same way, and the weight coefficient is continuously updated to make the prediction result closer to the expected trajectory, thereby achieving more accurate and rapid temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a structural diagram of the system of the present invention;

[0083] Figure 2 A schematic diagram of a model building and data analysis process in one embodiment of the method of the present invention;

[0084] Figure 3 The figure is a schematic diagram of the data processing flow in one embodiment of the method of the present invention. DETAILED DESCRIPTION

[0085] See attached Figure 1 The present invention provides a computer system based on a blast furnace-hot blast stove safety temperature control calculation model, comprising:

[0086] The feed identification module includes an electronic nose for identifying the smell of ore and a high-definition camera for identifying the shape and color of ore. When the ore enters the blast furnace, the odor sampling operator and gas sensor array in the electronic nose identify the odor intensity and odor category of the ore, convert it into a digital signal of the odor, and transmit it to the processing information database together with the shape and color obtained and identified by the high-definition camera.

[0087] The processing information database includes two sub-databases; the first sub-database pre-stores ore information entries, each of which includes: ore type, ore name, shape, color, smell, processing time and vault temperature, etc.; using the shape, smell and color collected by the ore recognition module as search conditions, the first sub-database matches the stored information entries, finds the ore type that matches the search conditions, and obtains the processing time t from the corresponding entry information. g And the target value of the vault temperature T g0 .

[0088] The second sub-database pre-stores the process entries of the ore, each of which includes: the type of ore and the initial processing parameters matching the type of ore, including the initial value Q of the cold air main flow 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 The second sub-database selects the initial processing parameters in the corresponding process entry according to the ore type matched by the first sub-database, and sets the initial processing parameters and processing time t g , Dome temperature target value T g0 Transmitted to the real-time monitoring module as ore processing information.

[0089] The real-time monitoring module includes two sub-monitoring modules for collecting real-time data during ore processing; the first sub-monitoring module is used to detect the dome temperature T in the blast furnace. g , exhaust gas temperature T f , optimal air-fuel ratio A / F, supply air humidity RH; the second sub-monitoring module is used to detect the cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh; cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, steam injection amount Q5, and transmit these real-time data and ore processing information to the model prediction module.

[0090] The model prediction module, based on ore processing information and real-time data, uses the blast furnace-hot blast stove safety temperature control calculation model to predict and output the blast furnace-hot blast stove's cold air main flow, blast furnace gas branch flow, clean gas main flow, combustion air main flow, and steam injection volume at the next moment. The blast furnace-hot blast stove is regulated according to these parameters to achieve temperature control during the processing process.

[0091] The model prediction module in this scheme is based on the blast furnace-hot blast stove safety temperature control calculation model. The specific construction process is as follows:

[0092] Step 1, based on the ore processing information and real-time data in the ore processing process, a blast furnace-hot blast stove safety temperature control calculation model is constructed; the calculation model is a nonlinear model, the nonlinear model is converted and processed to construct a state space equation.

[0093] This step uses the heating and heat conduction mechanism to identify the system based on the relationship between the dome temperature and the processing time, builds a nonlinear calculation model, and converts the calculation model to obtain the state space equation. First, the system needs to be identified; the nonlinear model of blast furnace-hot blast stove temperature control is established as follows:

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] Wherein, t is the time parameter, P1 is the cold air main pressure, T1 is the cold air main temperature, P2 is the blast furnace gas branch pressure, T2 is the blast furnace gas branch temperature, P3 is the clean gas main pressure, T3 is the clean gas main temperature, P4 is the combustion air main pressure, T4 is the combustion air main temperature, rh is the atmospheric humidity; Q1 is the cold air main flow, Q2 is the blast furnace gas branch flow, Q3 is the clean gas main flow, Q4 is the combustion air main flow, Q5 is the amount of steam injected; T g is the dome temperature, T f is the exhaust gas temperature, A / F is the optimal air-fuel ratio, and RH is the supply air humidity.

[0103] The parameters in this scheme are superscripted represents its first-order derivative, such as is the first derivative of the cold air main pressure; , , , , is a composite function.

[0104] Set the state variable ,in ; Input variables ,in ; Output variables ,in , the superscript T indicates transpose.

[0105] The state space equation constructed by the nonlinear model is as follows:

[0106] ;

[0107] ;

[0108] in, is the first-order derivative of the state variable, A is the system matrix, B is the control matrix, C is the output matrix or observation matrix, and D is the direct transfer matrix.

[0109] Step 2: For the established state-space equation, apply the Taylor series expansion method to determine the coefficient matrix of the state-space equation, thereby linearizing the state-space equation.

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] in, is the partial derivative, It means taking the coefficient of the corresponding element after integrating the derivative, and t is the time parameter.

[0115] Step 3: Use a zero-order holder to discretize the state space equation obtained by linearization and construct a discretized system.

[0116] Since the state space equation is continuous after the coefficient matrix is ​​determined in step 2, in order to simplify the model, improve model stability, reduce the risk of overfitting and improve computational efficiency, the linearized state space equation is discretized using a zero-order holder to obtain a discretized system:

[0117] ;

[0118] Among them, k is the current time, is the value of the state variable x at time k, Enter the value of variable u at time k, represents the system state at time k+1 predicted at time k, , is the discrete system coefficient matrix, we have:

[0119] ;

[0120] ;

[0121] Where I is the identity matrix, is the time constant, is a natural constant, A is the system matrix, B is the control matrix, and T is the sampling period.

[0122] Step 4: For the discretized system, use the rolling optimization method to predict the system state for a period of time in the future.

[0123] The system state at a certain time in the future to The expression is as follows:

[0124] ; ; ;…… ;

[0125] in, is the initial system state at time k, and its value is the state variable collected at time k ; The system state at time k+N predicted at time k; is the input variable at time k ; is the input variable at time k+N-1 predicted at time k, and N is the length of the prediction interval.

[0126] Step 5, design a quadratic control law, and use the entropy weight method to obtain the weight coefficient of the system state and the weight coefficient of the input variable in the quadratic control law; bring in the predicted system state for a period of time in the future, set constraints, solve the system input variable sequence, extract the first input variable in the solved sequence, and the computer system uses the parameters in the input variable to regulate the blast furnace-hot blast stove processing process.

[0127] First, the general form of the quadratic control law J is designed:

[0128] ;

[0129] Among them, Q is The weight coefficient of the system state at the moment, R is the weight coefficient of the input variable, and F is The weight coefficient of the system state at the moment; is the system state at time k+i predicted by time k obtained in step 4, is the input variable at time k+i predicted at time k, i=1,2,…,N-1, and the superscript T indicates transposition.

[0130] The temperature fluctuation range of the hot blast stove is usually designed to be within ±10℃ to ensure the stability and accuracy of the ironmaking and steelmaking processes; for the above quadratic control law J, considering the safety and service life of the hot blast stove and blast furnace, its constraints are:

[0131] ;

[0132] ;

[0133] For the above quadratic control law J, The weight coefficient Q of the system state at the moment, The weight coefficient F of the system state at the moment and the weight coefficient R of the input variable are obtained by the entropy weight method, as follows:

[0134] Obtain the historical data of the currently processed ore, and extract the fourteen parameters of cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh, cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, and steam injection amount Q5 at the same prediction interval length from k+1 to k+N. Take each data value of each parameter as a sample; express it in a tabular form with N rows and 14 columns:

[0135]

[0136] The sample a in the i-th (i=1,2,…,N) row and j-th (j=1,2,…14) column ij is the data value of the jth parameter historical data at the k+ith moment; a positive indicator is established for each sample :

[0137] ;

[0138] in, For sample a ij Positive indicator, min and max are operations for finding minimum and maximum values.

[0139] Then, the positive index of each sample is calculated in turn The proportion of positive indicators at all times , the entropy value of the positive indicator corresponding to each type of historical data :

[0140] ;

[0141] ;

[0142] in, .

[0143] Finally, we get the weight of each positive indicator of historical data. :

[0144] ;

[0145] in, The weight coefficient Q of the system state at a certain moment is a positive definite matrix, and each element on its diagonal is given by The weight coefficient R is a unit matrix, and its diagonal elements are .

[0146] but The weight coefficient F of the system state at a moment is:

[0147] ;

[0148] The first N-1 values ​​of the system state in the future period obtained in step 4, that is, to As , the Nth value As Substitute it into the quadratic control law J, take the derivative of the quadratic control law J, and set the derivative to 0, and the input variable sequence of the system can be obtained: , i=1…N-1.

[0149] The obtained system input variables are the combination matrix of the system input variables u from the kth moment to the k+N-1th moment, and the first input variable is taken The predicted cold air main flow, blast furnace gas branch flow, clean gas main flow, combustion air main flow and injected steam volume are used to control the cold air main, blast furnace gas branch, clean gas main, combustion air main and injected steam engine respectively to adjust the ore processing process.

[0150] When the present invention is applied in a specific way, the working process is as follows:

[0151] At the initial time k, the computer system controls the calcining machine to first use the initial processing parameter cold air main pipe flow initial value Q in the ore processing information. 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 Start working and set the processing time t g , Dome temperature target value T g0 , start the first cycle, the process of each cycle is:

[0152] The real-time monitoring module obtains the state variables at time k , the state variable x is used as the initial state of the system at time k in the rolling optimization method in step 4 , predict and obtain the system status at future times to , then to As Substitute it into the quadratic control law J in step 5, and obtain the input variable sequence by taking the derivative of J and setting the derivative equal to zero , take its first value Control the blast furnace-hot blast stove at the next time k+1;

[0153] At the next time k+1, the second cycle starts; and the above cycle process is repeated until the ore processing is completed or the processing time t is reached. g and the target value of the vault temperature T g0 .

[0154] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A computer system based on a blast furnace-hot blast stove safety temperature control calculation model, characterized in that: include: A feed identification module to obtain smell, shape and color data of the ore after it enters the blast furnace; The processing information database includes two sub-databases; the first sub-database pre-stores information entries of the ore for matching the processing time and the dome temperature of the ore; the second sub-database pre-stores process entries of the ore for matching the initial processing parameters of the ore; Real-time monitoring module, used to collect real-time data during ore processing; The model prediction module, based on ore processing information and real-time data, uses the blast furnace-hot blast stove safety temperature control calculation model to predict and output the blast furnace-hot blast stove's cold air main flow, blast furnace gas branch flow, clean gas main flow, combustion air main flow, and steam injection volume at the next moment. The blast furnace-hot blast stove is regulated according to these parameters to achieve temperature control during the processing process.

2. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 1 is characterized in that: The ore identification module includes an electronic nose for identifying the smell of ore and a high-definition camera for identifying the shape and color of ore; when the ore enters the blast furnace, the odor sampling operator and gas sensor array in the electronic nose identify the odor intensity and odor category of the ore, convert it into a digital signal of the odor, and transmit it to the processing information database together with the shape and color obtained and identified by the high-definition camera.

3. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 2 is characterized in that: The first sub-database pre-stores ore information entries, each of which includes: ore type, ore name, shape, color, smell, processing time and dome temperature, etc.; using the shape, smell and color collected by the ore recognition module as search conditions, the first sub-database matches the stored information entries, finds the ore type that matches the search conditions, and obtains the processing time t and the dome temperature target value T from the corresponding entry information. g0 ; The second sub-database pre-stores the process entries of the ore, each of which includes: the type of ore and the initial processing parameters matching the type of ore, including the initial value Q of the cold air main flow 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 The second sub-database selects the initial processing parameters in the corresponding process entry according to the ore type matched by the first sub-database, and sets the initial processing parameters and processing time t g , Dome temperature target value T g0 Transmitted to the real-time monitoring module as ore processing information.

4. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 3 is characterized in that: The monitoring module includes two sub-monitoring modules. The first sub-monitoring module is used to detect the dome temperature T in the blast furnace. g , exhaust gas temperature T f , optimal air-fuel ratio A / F, supply air humidity RH; the second sub-monitoring module is used to detect the cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh; cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, steam injection amount Q5, and transmit these real-time data and ore processing information to the model prediction module.

5. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 1, characterized in that: The construction process of the blast furnace-hot blast stove safety temperature control calculation model is as follows: Based on the ore processing information and real-time data in the ore processing process, a blast furnace-hot blast stove safety temperature control calculation model is constructed; the calculation model is a nonlinear model, and the nonlinear model is converted to construct a state space equation; For the established state space equation, the Taylor series expansion method is applied to determine the coefficient matrix of the state space equation, thereby linearizing the state space equation; The state space equation obtained by linearization is discretized using a zero-order holder to construct a discretized system. For the discretized system, a rolling optimization method is used to predict the system state for a period of time in the future; A quadratic control law is designed, and the entropy weight method is used to obtain the weight coefficients of the system state and the weight coefficients of the input variables in the quadratic control law; the predicted system state for a period of time in the future is brought in, and constraints are set, the system input variable sequence is solved, and the first input variable in the solved sequence is extracted. The computer system uses the parameters in the input variable to regulate the processing process of the blast furnace-hot blast stove.

6. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 5, characterized in that: The ore processing information and real-time data in the ore processing process are used to construct a safe temperature control calculation model based on a blast furnace and a hot blast stove; the calculation model is a nonlinear model, and the nonlinear model is converted to construct a state space equation, including: ； ； ； ； ； ； ； ； Wherein, t is the time parameter, P1 is the cold air main pressure, T1 is the cold air main temperature, P2 is the blast furnace gas branch pressure, T2 is the blast furnace gas branch temperature, P3 is the clean gas main pressure, T3 is the clean gas main temperature, P4 is the combustion air main pressure, T4 is the combustion air main temperature, rh is the atmospheric humidity; Q1 is the cold air main flow, Q2 is the blast furnace gas branch flow, Q3 is the clean gas main flow, Q4 is the combustion air main flow, Q5 is the amount of steam injected; T g is the dome temperature, T f is the exhaust gas temperature, A / F is the optimal air-fuel ratio, RH is the air humidity, and the parameters are marked represents its first-order derivative; Set the state variable ,in ; Input variables ,in ; Output variables ,in , the superscript T indicates transposition; The state space equation constructed by the nonlinear model is as follows: ； ； in, is the first-order derivative of the state variable, A is the system matrix, B is the control matrix, C is the output matrix or observation matrix, and D is the direct transfer matrix.

7. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 6, characterized in that: The method of applying Taylor series expansion is used to determine the coefficient matrix of the state space equation, which is specifically expressed as: ； ； ； ； in, is the partial derivative, It means taking the coefficient of the corresponding element after integrating the derivative, and t is the time parameter.

8. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 6, characterized in that: The design of the quadratic control law comprises: The general form of designing the quadratic control law J is: ； Among them, Q is The weight coefficient of the system state at the moment, R is the weight coefficient of the input variable, and F is The weight coefficient of the system state at the moment; is the system state at time k+i predicted at time k, is the input variable at the k+ith moment predicted at the kth moment, i=1,2,…,N-1; The constraints of the quadratic control law J are: ； 。 9. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 8, characterized in that: The entropy weight method is used to obtain the weight coefficients of the system state and the input variables in the quadratic control law, including: Obtain the historical data of the currently processed ore, extract the following fourteen parameters: cold air main pressure P1, cold air main temperature T1, blast furnace gas branch pressure P2, blast furnace gas branch temperature T2, clean gas main pressure P3, clean gas main temperature T3, combustion air main pressure P4, combustion air main temperature T4, atmospheric humidity rh, cold air main flow Q1, blast furnace gas branch flow Q2, clean gas main flow Q3, combustion air main flow Q4, and steam injection amount Q5, at the same prediction interval length from time k+1 to k+N, and take each data value of each parameter as a sample; Establish positive indicators for each sample : ； in, For sample a ij Positive indicator, min and max are for minimum and maximum value operations, a ij Represents the data value of the j-th historical data at the k+i-th moment; Then, the positive index of each sample is calculated in turn The proportion of positive indicators at all times , the entropy value of the positive indicator corresponding to each type of historical data : ； ； in, ; Finally, we get the weight of each positive indicator of historical data. : ； in, ; The weight coefficient Q of the system state at a certain moment is a positive definite matrix, and each element on its diagonal is given by The weight coefficient R is a unit matrix, and its diagonal elements are ; but The weight coefficient F of the system state at a moment is: ； in, is the discrete system coefficient matrix, ; In the above formula, I is the unit matrix, A is the system matrix, and T is the sampling period.

10. The computer system based on the blast furnace-hot blast stove safety temperature control calculation model according to claim 9, characterized in that: At the initial time k, the computer system controls the calcining machine to first use the initial processing parameter cold air main pipe flow initial value Q in the ore processing information. 01 , Initial value of blast furnace gas branch flow Q 02 , Initial value of net gas main flow Q 03 , initial value of combustion air main flow Q 04 , initial value of steam injection amount Q 05 Start working and set the processing time t g , Dome temperature target value T g0 , start the first cycle, the process of each cycle is: The real-time monitoring module obtains the state variables at time k , the state variable x is used as the initial state of the system at time k in the rolling optimization method , predict and obtain the system status at future times to , then to As Substitute it into the quadratic control law J, and obtain the input variable sequence by taking the derivative of J and setting the derivative equal to zero , take its first value Control the blast furnace-hot blast stove at the next time k+1; At the next time k+1, the second cycle starts; and the above cycle process is repeated until the ore processing is completed or the processing time t is reached. g and the target value of the vault temperature T g0 .

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