Thermal power plant industrial system control method and device, computer readable storage medium

Abstract

The application provides a thermal power plant industrial system control method and device and a computer readable storage medium, and belongs to the field of thermal power plant industrial system control. The thermal power plant industrial system control method comprises the following steps: determining a plurality of regulated objects of a thermal power plant industrial system, a plurality of controlled objects corresponding to the regulated objects and a plurality of disturbance objects; establishing a transfer function model corresponding to each regulated object of each controlled object, obtaining an optimal control rate matrix through a plurality of transfer function models corresponding to the plurality of controlled objects; substituting a deviation amount of the regulated object and a deviation amount of the disturbance object collected in real time into the optimal control rate matrix to obtain a first control amount of each controlled object; calculating a second control amount of each controlled object according to a fuzzy calculation algorithm; and adjusting the controlled object according to the first control amount and the second control amount of the controlled object.

Description

Technical Field

[0001] This invention relates to the field of industrial system control in thermal power plants, and more specifically to a method for controlling an industrial system in a thermal power plant, a control device for an industrial system in a thermal power plant, a computer-readable storage medium, and an electronic device. Background Technology

[0002] In traditional analog control systems for thermal power plants, one control variable typically corresponds to one process variable, such as desuperheating water temperature control. It's also possible to control a process consisting of multiple independent single-loop systems, thereby controlling the entire process, such as the three-impulse control of feedwater in a boiler drum. However, power generation is inherently a multivariate system, and these variables influence each other. Therefore, when designing a control system, it's crucial to minimize mutual interference.

[0003] To avoid the deterioration of control performance due to the interaction of multiple variables, existing technologies sometimes choose not to control even variables that should be controlled. This inevitably leads to the so-called "out-of-control" phenomenon.

[0004] Existing technologies also employ multivariate generalized predictive control algorithms to control multiple variables. However, when generalized predictive control is applied to practical engineering, its algorithms are complex, require the adjustment of many parameters, and have a long online calculation time, which increases the complexity of practical engineering applications and makes it difficult to meet the control requirements of a fast and systematic system. Summary of the Invention

[0005] The purpose of this invention is to provide a control method and apparatus for an industrial system in a thermal power plant, as well as a computer-readable storage medium, to at least solve the above-mentioned problems.

[0006] To achieve the above objectives, a first aspect of the present invention provides a control method for an industrial system in a thermal power plant, the method comprising:

[0007] Identify multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system;

[0008] Establish a transfer function model for each controlled object corresponding to each controlled object, and obtain the optimal control law matrix through multiple transfer function models corresponding to multiple controlled objects;

[0009] Substitute the deviation of the controlled object and the deviation of the disturbed object collected in real time into the optimal control rate matrix to calculate the first control quantity for each controlled object.

[0010] The second control quantity for each controlled object is calculated using a fuzzy computing algorithm.

[0011] Adjust the controlled object according to the first control variable and the second control variable of the controlled object.

[0012] In this embodiment of the invention, establishing the transfer function model for each controlled object corresponding to each regulated object includes:

[0013] Acquire historical data for the controlled object, the adjusted object, and the disturbed object respectively;

[0014] The historical data of the controlled object, the regulated object, and the disturbed object are processed to obtain preprocessed data that meets the identification conditions;

[0015] The optimal data is obtained by performing optimization calculations on the preprocessed data using an optimization algorithm.

[0016] Establish a transfer function model for each controlled object and each controlled object based on the optimal data.

[0017] In this embodiment of the invention, the transfer function model is:

[0018]

[0019] Where K is the proportional coefficient, m is the order of the integrator, n is the order of the inertial element, T is the integration time constant, τ is the pure delay time, and α and β are the differential coefficients.

[0020] In this embodiment of the invention, obtaining the optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects includes:

[0021] Multiple transfer function models are discretized using a multivariate generalized prediction algorithm to obtain the following multivariate generalized prediction formula:

[0022]

[0023]

[0024] The optimal control rate matrix is ​​obtained by calculating the multivariate generalized prediction formula as follows:

[0025]

[0026] Where Δ=1-z -1 For differential operators, u1(k-1), u2(k-1), v1(k-1), v2(k-1), and v3(k-1) are two control inputs and three external disturbance inputs, respectively. 1 (k), y 2 (k) represents the two controlled outputs, e(k) represents white noise with zero mean, and Q 1 G 1 Q 2 G 2 , All are matrix parameters obtained from the transfer function model by multivariate generalized prediction algorithms, Y 1 r (k+1) is the value of the setpoint 1 at time k+1, Y 1 (k) is the value of the controlled object 1 at time k, ΔU1(k-1) is the increment of the controlled object 1 at time k-1, ΔU2(k-1) is the increment of the controlled object 2 at time k-1, Δv1(k) is the increment of the disturbed object 1 at time k, Δv2(k) is the increment of the disturbed object 2 at time k, and Δv3(k) is the increment of the disturbed object 3 at time k.

[0027] In this embodiment of the invention, calculating the second control quantity for each controlled object according to the fuzzy calculation algorithm includes:

[0028] Based on the set value of the object being adjusted, the deviation of the object being adjusted is fuzzy segmented to obtain multiple fuzzy segments;

[0029] Obtain the membership degree of the control quantity for different fuzzy segments for different exact values ​​of the deviation of the controlled object;

[0030] Calculate the sum of the membership degrees of the control quantities of all fuzzy segments when the controlled object exhibits a precise deviation value;

[0031] Calculate the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the membership degrees of the control quantities of the multiple controlled objects.

[0032] In this embodiment of the invention, the calculation of the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the control quantity membership degrees of the multiple controlled objects specifically involves:

[0033] The sum of the membership degrees of the control variables of multiple controlled objects is superimposed to obtain the second control variable of the controlled object related to the multiple controlled objects.

[0034] In this embodiment of the invention, adjusting the controlled object according to the first control quantity and the second control quantity of the controlled object includes:

[0035] The optimal control quantity of the controlled object is obtained by superimposing the first control quantity and the second control quantity of the controlled object.

[0036] Adjust the controlled object according to the optimal control quantity.

[0037] A second aspect of the present invention provides a control device for an industrial system in a thermal power plant, comprising:

[0038] The control object determination unit is used to determine multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system.

[0039] The first control quantity acquisition unit is used to establish a transfer function model for each controlled object corresponding to each regulated object, and obtain the optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; the deviation of the regulated object and the deviation of the disturbance object collected in real time are substituted into the optimal control rate matrix to calculate the first control quantity of each controlled object.

[0040] The second control quantity acquisition unit is used to calculate the second control quantity of each controlled object according to the fuzzy calculation algorithm.

[0041] The adjustment unit is used to adjust the controlled object according to the first control quantity and the second control quantity of the controlled object.

[0042] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the control method for a thermal power plant industrial system as described above.

[0043] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the control method for a thermal power plant industrial system as described above.

[0044] This invention combines fuzzy computing algorithm with multivariate generalized predictive control algorithm. The multivariate generalized predictive control algorithm is used as the core and the fuzzy computing algorithm is used as the feedforward to control the industrial system of thermal power plant. While realizing multivariate control, it meets the system's requirements for rapid response and has the ability to suppress unknown interference.

[0045] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart of a control method for an industrial system in a thermal power plant provided by one embodiment of the present invention;

[0048] Figure 2 This is a control block diagram of a thermal power plant coordination system provided in one embodiment of the present invention;

[0049] Figure 3 This is a structural block diagram of a control device for an industrial system in a thermal power plant, provided by one embodiment of the present invention. Detailed Implementation

[0050] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0051] As described in the background section, existing technologies, in order to avoid the deterioration of control performance due to the mutual influence of multiple variables, sometimes choose not to control even variables that should be controlled. This inevitably leads to the so-called "out-of-control" phenomenon. Some existing technologies also use multivariate generalized predictive control algorithms to control multiple variables. However, when generalized predictive control is applied to practical engineering, its algorithms are relatively complex, require the adjustment of many parameters, and have a long online calculation time, increasing the complexity of practical engineering applications and making it difficult to quickly meet the control requirements of the system.

[0052] To address the aforementioned problems, this invention provides a control method for an industrial system in a thermal power plant, comprising: determining multiple controlled objects and corresponding controlled objects and disturbance objects within the industrial system; establishing a transfer function model for each controlled object corresponding to each controlled object, and obtaining an optimal control rate matrix through the multiple transfer function models corresponding to multiple controlled objects; substituting the real-time collected deviations of the controlled objects and the disturbance objects into the optimal control rate matrix to calculate a first control quantity for each controlled object; calculating a second control quantity for each controlled object using a fuzzy logic algorithm; and adjusting the controlled objects based on the first and second control quantities. This invention combines a fuzzy logic algorithm with a multivariate generalized predictive control algorithm, using the multivariate generalized predictive control algorithm as the core and the fuzzy logic algorithm as the feedforward to control the industrial system of a thermal power plant. This achieves multivariate control while meeting the system's rapid response requirements and possessing the ability to suppress unknown disturbances.

[0053] Figure 1 This is a flowchart of a control method for an industrial system in a thermal power plant according to one embodiment of the present invention. Figure 1 As shown, this invention provides a control method for an industrial system in a thermal power plant, the method comprising the following steps:

[0054] S1. Determine multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system.

[0055] In this embodiment, as Figure 2 As shown, Figure 2 This is a control block diagram of a thermal power plant coordination system, where the industrial system of the thermal power plant is the thermal power plant coordination control system.

[0056] The controlled objects are coal feed rate, water feed rate, and air feed rate;

[0057] The controlled objects are: the enthalpy value at the separator outlet and the main steam pressure;

[0058] The disturbance target is: the opening degree of the high-adjustment valve.

[0059] like Figure 2 As shown, by controlling the coal feed rate (i.e. Figure 2 The amount of wind and coal regulation), water supply (i.e. Figure 2 The feedwater regulation is used to adjust the enthalpy at the separator outlet and the main steam pressure.

[0060] exist Figure 2 In this context, the dynamic fuzzy feedforward BIR of coal and water supply based on the energy balance of unit capacity is the second control quantity obtained through fuzzy calculation algorithm.

[0061] exist Figure 2 In this context, f(x) is a piecewise linear function, and lag is an inertial element. By adding the piecewise linear function and the inertial element control, the water, coal, and air can be better matched, which is the current technology.

[0062] exist Figure 2 In this context, the static feedforward of the load baseline after unified conversion is a control variable specifically considered by the coordinated control system of thermal power plants, and it is also an existing technology.

[0063] In this embodiment, the first control quantity, the second control quantity, and the load baseline static feedforward empty control quantity are superimposed to obtain the final control quantity.

[0064] S2. Establish a transfer function model for each controlled object corresponding to each controlled object, and obtain the optimal control law matrix through multiple transfer function models corresponding to multiple controlled objects;

[0065] S3. Substitute the deviation of the controlled object and the deviation of the disturbed object collected in real time into the optimal control rate matrix to calculate the first control quantity of each controlled object.

[0066] In this embodiment, establishing the transfer function model for each controlled object corresponding to each called object includes:

[0067] Acquire historical data for the controlled object, the adjusted object, and the disturbed object respectively;

[0068] The historical data of the controlled object, the regulated object, and the disturbed object are processed to obtain preprocessed data that meets the identification conditions;

[0069] The optimal data is obtained by performing optimization calculations on the preprocessed data using an optimization algorithm.

[0070] Based on the optimal data, a transfer function model is established for each controlled object and corresponding to each controlled object, as follows:

[0071]

[0072] Where K is the proportional coefficient, m is the order of the integrator, n is the order of the inertial element, T is the integration time constant, τ is the pure delay time, and α and β are the differential coefficients.

[0073] In this embodiment, obtaining the optimal control law matrix through multiple transfer function models corresponding to multiple controlled objects includes:

[0074] Multiple transfer function models are discretized using a multivariate generalized prediction algorithm to obtain the following multivariate generalized prediction formula:

[0075]

[0076]

[0077] The optimal control rate matrix is ​​obtained by calculating the multivariate generalized prediction formula as follows:

[0078]

[0079] Where Δ=1-z -1 For differential operators, u1(k-1), u2(k-1), v1(k-1), v2(k-1), and v3(k-1) are two control inputs and three external disturbance inputs, respectively. 1 (k), y 2 (k) represents the two controlled outputs, e(k) represents white noise with zero mean, and Q 1 G 1 Q 2 G 2 , All are matrix parameters obtained from the transfer function model by multivariate generalized prediction algorithms, Y 1 r (k+1) is the value of the setpoint 1 at time k+1, Y 1 (k) is the value of the controlled object 1 at time k, ΔU1(k-1) is the increment of the controlled object 1 at time k-1, ΔU2(k-1) is the increment of the controlled object 2 at time k-1, Δv1(k) is the increment of the disturbed object 1 at time k, Δv2(k) is the increment of the disturbed object 2 at time k, and Δv3(k) is the increment of the disturbed object 3 at time k.

[0080] The transformation of the multivariate generalized prediction algorithm to obtain the optimal control rate matrix is ​​an existing technology and will not be elaborated here.

[0081] S4. Calculate the second control quantity for each controlled object according to the fuzzy calculation algorithm.

[0082] In this embodiment, calculating the second control quantity for each controlled object using the fuzzy computing algorithm includes:

[0083] Based on the set value of the object being adjusted, the deviation of the object being adjusted is fuzzy segmented to obtain multiple fuzzy segments;

[0084] Obtain the membership degree of the control quantity for different fuzzy segments for different exact values ​​of the deviation of the controlled object;

[0085] Calculate the sum of the membership degrees of the control quantities of all fuzzy segments when the controlled object exhibits a precise deviation value;

[0086] Calculate the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the membership degrees of the control quantities of the multiple controlled objects.

[0087] In this embodiment, the calculation of the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the control quantity membership degrees of the multiple controlled objects specifically involves:

[0088] The sum of the membership degrees of the control variables of multiple controlled objects is superimposed to obtain the second control variable of the controlled object related to the multiple controlled objects.

[0089] Specifically, taking the main steam pressure of the controlled object as an example:

[0090] The deviation between the main steam pressure and the main steam pressure setpoint is fuzzy segmented into Liu Ge fuzzy segments, namely [NB, NM, NS, PS, PM, PB].

[0091] NB indicates a large negative deviation, NM indicates a medium negative deviation, and NS indicates a small negative deviation.

[0092] PB indicates a large positive deviation, PM indicates a medium positive deviation, and PS indicates a small positive deviation. The specific deviation values ​​and their corresponding membership degrees of the fuzzy segment control variables are shown in Table 1 below.

[0093] X represents the exact deviation between the main steam pressure and the set value of the main steam pressure.

[0094] X Fuzzy segmentation representing the deviation between the main steam pressure and the main steam pressure setpoint.

[0095] Table 1: Membership of Control Quantities for Different Fuzzy Segments Based on the Exact Values ​​of Different Deviations of the Controlled Object

[0096]

[0097] The corresponding values ​​in the table represent the membership degree (the relationship between the deviation value and the fuzzy segment). For example, if the deviation between the main steam pressure and the main steam pressure setpoint is 1.5, then this value belongs to PB (1), PM (0.5), and PS (0.3).

[0098] The rate of change of the main steam pressure is fuzzily divided into three segments [NB, ZO, PB].

[0099] NB indicates a large negative rate of change.

[0100] ZO indicates a rate of change of 0.

[0101] PB indicates a large positive rate of change.

[0102] The membership degrees of the fuzzy segment control quantity corresponding to the exact value of the specific rate are shown in Table 2 below.

[0103] Table 2: Membership degree of control quantities for different fuzzy segments of the exact values ​​of different deviations of the controlled object

[0104]

[0105] The fuzzy segments corresponding to the main steam pressure deviation and the main steam pressure change rate are then mapped to the control variables of the controlled object (taking coal feed rate as an example).

[0106] Main steam pressure deviation:

[0107]

[0108]

[0109] The table above shows the control actions for the controlled object when the positive deviation between the main steam pressure and the main steam pressure setpoint is large (PB): -3 (reducing 3 tons of coal); -2 when the positive deviation is moderate (PM): -1; and -1 when the positive deviation is small (PS): -3 (adding 3 tons of coal); when the negative deviation is large (NB): 2; when the negative deviation is moderate (NM): 2; and 1 when the negative deviation is small (NS): 1.

[0110] Main steam change rate:

[0111]

[0112] The table above shows that when the rate of change of the main steam pressure is large in the positive direction (PB), the control quantity is -10 (reducing 10 tons of coal); when the rate is constant (PM), the control quantity is 0; when the rate of change is large in the negative direction (NB), the control quantity is 10 (adding 10 tons of coal).

[0113] Actual coal feed output = ∑ membership degree * pressure deviation coal feed action + ∑ membership degree * pressure change coal feed action

[0114] For example, if the pressure deviation is 1 and the pressure change rate is 2, then according to the above fuzzy rule, the actual coal feed rate is...

[0115] Coal feed rate action = 0.5*-3 + 1*-2 + 0.5*-1 + 0.3*1 + 0*2 + 0*3 +

[0116] 0.5 * -10 + 0 * 0 + 0 * 10 = -8.7

[0117] For example, when the main steam pressure deviation is 1 and the main steam pressure change rate is 2, the coal feed rate should be adjusted by -8.7.

[0118] In this embodiment, the membership degree of the control quantity for different fuzzy segments is obtained by selecting a membership function (normal function, trigonometric function or trapezoidal function) and then taking the cut set.

[0119] In other embodiments of the present invention, the membership degree of the control quantity for different fuzzy segments is obtained by manually setting the exact values ​​of different deviations of the controlled object.

[0120] S5. Adjust the controlled object according to the first control variable and the second control variable of the controlled object.

[0121] In this embodiment, adjusting the controlled object according to the first control quantity and the second control quantity includes:

[0122] The optimal control quantity of the controlled object is obtained by superimposing the first control quantity and the second control quantity of the controlled object.

[0123] Adjust the controlled object according to the optimal control quantity.

[0124] Figure 3 This is a structural block diagram of a control device for an industrial system in a thermal power plant, provided by one embodiment of the present invention. Figure 3 As shown, this invention provides a control device for a thermal power plant industrial system. The device includes: a control object determination unit, used to determine multiple controlled objects and multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system; a first control quantity acquisition unit, used to establish a transfer function model for each controlled object corresponding to each controlled object, and obtain an optimal control rate matrix through the multiple transfer function models corresponding to multiple controlled objects; substituting the deviations of the controlled objects and the disturbance objects collected in real time into the optimal control rate matrix to calculate a first control quantity for each controlled object; a second control quantity acquisition unit, used to calculate a second control quantity for each controlled object according to a fuzzy calculation algorithm; and an adjustment unit, used to adjust the controlled objects according to the first and second control quantities. The thermal power plant industrial system device is used to implement a control method for a thermal power plant industrial system, the method including:

[0125] Identify multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system;

[0126] Establish a transfer function model for each controlled object corresponding to each regulated object, and obtain the optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; substitute the deviation of the regulated object and the deviation of the disturbance object collected in real time into the optimal control rate matrix to calculate the first control quantity of each controlled object.

[0127] The second control quantity for each controlled object is calculated using a fuzzy computing algorithm.

[0128] Adjust the controlled object according to the first control variable and the second control variable of the controlled object.

[0129] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the control method for a thermal power plant industrial system as described above.

[0130] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the control method for a thermal power plant industrial system as described above.

[0131] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0132] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0133] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A control method for an industrial system in a thermal power plant, characterized in that, The method includes: Identify multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the industrial system of the thermal power plant; wherein, the controlled objects are coal feed rate, water feed rate and air feed rate, the controlled objects are: separator outlet enthalpy value and main steam pressure, and the disturbance objects are: high-pressure valve opening degree; Establish a transfer function model for each controlled object corresponding to each controlled object, and obtain the optimal control law matrix through multiple transfer function models corresponding to multiple controlled objects; Substitute the deviation of the controlled object and the deviation of the disturbed object collected in real time into the optimal control rate matrix to calculate the first control quantity for each controlled object. The second control quantity for each controlled object is calculated using a fuzzy computing algorithm, including: Based on the set value of the object being adjusted, the deviation of the object being adjusted is fuzzy segmented to obtain multiple fuzzy segments; Obtain the membership degree of the control quantity for different fuzzy segments for different exact values ​​of the deviation of the controlled object; Calculate the sum of the membership degrees of the control quantities of all fuzzy segments when the controlled object exhibits a precise deviation value; Calculate the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the membership degrees of the control quantities of the multiple controlled objects; Adjust the controlled object according to the first control variable and the second control variable of the controlled object.

2. The industrial system control method for thermal power plants according to claim 1, characterized in that, The establishment of the transfer function model for each controlled object corresponding to each called object includes: Acquire historical data for the controlled object, the adjusted object, and the disturbed object respectively; The historical data of the controlled object, the regulated object, and the disturbed object are processed to obtain preprocessed data that meets the identification conditions; The optimal data is obtained by performing optimization calculations on the preprocessed data using an optimization algorithm. Based on the optimal data, establish a transfer function model for each controlled object corresponding to each controlled object.

3. The industrial system control method for thermal power plants according to claim 2, characterized in that, The transfer function model is as follows: ； in, This is the proportionality coefficient. For the order of the integration stage, For the order of inertial components, The integral time constant is... For pure delay time, is the differential coefficient.

4. The industrial system control method for thermal power plants according to claim 2, characterized in that, The process of obtaining the optimal control law matrix through multiple transfer function models corresponding to multiple controlled objects includes: Multiple transfer function models are discretized using a multivariate generalized prediction algorithm to obtain the following multivariate generalized prediction formula: ； ； The optimal control rate matrix is ​​obtained by calculating the multivariate generalized prediction formula as follows: ； in, For difference operators, It has two control inputs and three external disturbance inputs. For two controlled outputs, It is white noise with zero mean. , , , , , All of these are matrix parameters obtained from the transfer function model by a multivariate generalized prediction algorithm. for The value of the time setting value 1, for The value of object 1 is being called at all times. for The increment of controlled object 1 at time 1. for The increment of controlled object 2 at time step, for The increment of perturbation object 1 at time 1. for The increment of perturbation object 2 at time 2. for The increment of perturbation object 3 at time t.

5. The industrial system control method for thermal power plants according to claim 1, characterized in that, The calculation of the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the control quantity membership degrees of the multiple controlled objects specifically involves: The sum of the membership degrees of the control variables of multiple controlled objects is superimposed to obtain the second control variable of the controlled object related to the multiple controlled objects.

6. The industrial system control method for thermal power plants according to claim 1, characterized in that, The adjustment of the controlled object based on the first control variable and the second control variable includes: The optimal control quantity of the controlled object is obtained by superimposing the first control quantity and the second control quantity of the controlled object. Adjust the controlled object according to the optimal control quantity.

7. A control device for an industrial system in a thermal power plant, characterized in that, include: The control object determination unit is used to determine multiple controlled objects and multiple disturbance objects corresponding to the controlled objects in the thermal power plant industrial system; wherein, the controlled objects are coal feed rate, water feed rate and air feed rate, the controlled objects are: separator outlet enthalpy value and main steam pressure, and the disturbance objects are: high-pressure valve opening degree; The first control quantity acquisition unit is used to establish a transfer function model for each controlled object corresponding to each regulated object, and obtain the optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; the deviation of the regulated object and the deviation of the disturbance object collected in real time are substituted into the optimal control rate matrix to calculate the first control quantity of each controlled object. The second control quantity acquisition unit is used to calculate the second control quantity for each controlled object according to the fuzzy calculation algorithm, including: Based on the set value of the object being adjusted, the deviation of the object being adjusted is fuzzy segmented to obtain multiple fuzzy segments; Obtain the membership degree of the control quantity for different fuzzy segments for different exact values ​​of the deviation of the controlled object; Calculate the sum of the membership degrees of the control quantities of all fuzzy segments when the controlled object exhibits a precise deviation value; Calculate the second control quantity of the controlled object related to the multiple controlled objects based on the sum of the membership degrees of the control quantities of the multiple controlled objects; The adjustment unit is used to adjust the controlled object according to the first control quantity and the second control quantity of the controlled object.

8. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed on the computer, the computer causes the computer to perform the industrial system control method for thermal power plants as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the industrial system control method for thermal power plants as described in any one of claims 1-6.

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