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

Abstract

The invention 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 adjusted objects of a thermal power plant industrial system, and a plurality of controlled objects and a plurality of disturbance objects corresponding to the adjusted objects; establishing a transfer function model of each controlled object corresponding to each adjusted object, and obtaining an optimal control rate matrix through a plurality of transfer function models corresponding to a plurality of controlled objects; substituting the deviation value of the adjusted object and the deviation value of the disturbance object, which are acquired in real time, into the optimal control rate matrix, and calculating to obtain a first control quantity of each controlled object; calculating a second control quantity of each controlled object according to a fuzzy calculation algorithm; and adjusting the controlled object according to the first control quantity and the second control quantity of the controlled object.

Description

Technical Field

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

[0002] In traditional thermal power plant analog control, generally one control quantity corresponds to one process quantity, such as desuperheating water temperature control. It is also possible to control a process consisting of multiple independent single loops, thereby controlling the entire process, such as the three-impulse control of water supply to the drum boiler. However, the power production process is essentially a multivariable system, and the variables affect each other. Therefore, when designing a control system, it is necessary to avoid mutual influence as much as possible.

[0003] In order to avoid the mutual influence of multiple variables and thus deterioration of control performance, the prior art prefers to abandon some variables that need to be controlled even if they are originally controlled. This inevitably leads to the so-called "out-of-control" phenomenon.

[0004] The existing technology also uses a multivariable generalized prediction algorithm to control multiple variables, but when generalized predictive control is applied to actual engineering, its algorithm is relatively complex, requires more parameters to be adjusted, and takes a long time to calculate online, which increases the complexity of actual engineering applications and is not easy to meet the control requirements of a fast system. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a method and device for controlling an industrial system of a thermal power plant, and a computer-readable storage medium, so as to at least solve the above-mentioned problems.

[0006] In order to achieve the above-mentioned object, the first aspect of the present invention provides a method for controlling an industrial system of a thermal power plant, the method comprising:

[0007] Determine a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects;

[0008] Establishing a transfer function model for each controlled object corresponding to each regulated object, and obtaining an optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects;

[0009] Substituting the deviation of the controlled object and the deviation of the disturbing object collected in real time into the optimal control rate matrix, and calculating the first control amount of each controlled object;

[0010] Calculate the second control quantity of each controlled object according to the fuzzy calculation algorithm;

[0011] The controlled object is adjusted according to the first control amount and the second control amount of the controlled object.

[0012] In an embodiment of the present invention, the step of establishing a transfer function model of each controlled object corresponding to each regulated object includes:

[0013] Obtain historical data of the controlled object, the regulated object and the disturbed object respectively;

[0014] Processing the historical data of the controlled object, the regulated object and the disturbing object to obtain pre-processed data that meets the identification conditions;

[0015] Performing optimization calculation on the preprocessed data by using an optimization algorithm to obtain optimal data;

[0016] A transfer function model is established for each controlled object corresponding to each regulated object based on the optimal data.

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

[0018]

[0019] Among them, K is the proportional coefficient, m is the order of the integral link, n is the order of the inertia link, T is the integral time constant, τ is the pure delay time, and α and β are differential coefficients.

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

[0021] Multiple transfer function models are discretized using the multivariable generalized prediction algorithm to obtain the multivariable generalized prediction formula as follows:

[0022]

[0023]

[0024] The multivariable generalized prediction formula is calculated to obtain the optimal control rate matrix as follows:

[0025]

[0026] Where Δ = 1-z -1 is the difference operator, u 1 (k-1),u 2 (k-1), v 1 (k-1), v 2 (k-1), v 3 (k-1) is two control inputs and three external disturbance inputs, y 1 (k), y 2 (k) is the output of the two controlled quantities, e(k) is the white noise with zero mean, Q 1 , G1 , Q 2 , G 2 , are matrix parameters obtained by the multivariable generalized prediction algorithm based on the transfer function model, Y 1 r (k+1) is the value of setting 1 at time k+1, Y 1 (k) is the value of the controlled object 1 at time k, ΔU 1 (k-1) is the increment of the controlled object 1 at time k-1, ΔU 2 (k-1) is the increment of the controlled object 2 at time k-1, Δv 1 (k) is the increment of the disturbance object 1 at time k, Δv 2 (k) is the increment of the disturbance object 2 at time k, Δv 3 (k) is the increment of the disturbance object 3 at time k.

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

[0028] Fuzzy segmenting the deviation of the regulated object based on the set value of the regulated object to obtain a plurality of fuzzy segments;

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

[0030] Calculate the sum of the control quantity membership of all fuzzy segments when the controlled object has the exact value of deviation;

[0031] The second control quantity of the controlled object related to the multiple regulated objects is calculated according to the sum of the control quantity memberships of the multiple regulated objects.

[0032] In the embodiment of the present invention, the second control quantity of the controlled object related to the multiple regulated objects is calculated according to the sum of the control quantity memberships of the multiple regulated objects as follows:

[0033] The control quantity membership degrees of the multiple regulated objects are summed and added to obtain the second control quantity of the controlled object related to the multiple regulated objects.

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

[0035] The first control amount and the second control amount of the controlled object are superimposed to obtain the optimal control amount of the controlled object;

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

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

[0038] A control object determination unit, used for determining a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects;

[0039] A first control quantity acquisition unit is used to establish a transfer function model of each controlled object corresponding to each regulated object, and obtain an 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, and calculate and obtain the first control quantity of each controlled object;

[0040] A second control quantity acquisition unit, used for calculating the second control quantity of each controlled object according to a fuzzy calculation algorithm;

[0041] The regulating unit is used to regulate the controlled object according to the first control amount and the second control amount of the controlled object.

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

[0043] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned thermal power plant industrial system control method when executing the computer program.

[0044] The present invention combines a fuzzy computing algorithm with a multivariable generalized predictive control algorithm, takes the multivariable generalized predictive control algorithm as the core and the fuzzy computing algorithm as the feedforward to control the industrial system of a thermal power plant, and satisfies the system's rapid response requirements and has the ability to suppress unknown interference while achieving multivariable control.

[0045] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0047] Figure 1 It is a flow chart of a method for controlling an industrial system of a thermal power plant provided by an embodiment of the present invention;

[0048] Figure 2 It is a control block diagram of a thermal power plant coordination system provided by an embodiment of the present invention;

[0049] Figure 3 It is a structural block diagram of an industrial system control device for a thermal power plant provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0050] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0051] As introduced in the background technology, in order to avoid the mutual influence of multiple variables and the deterioration of control performance, the existing technology would rather give up some variables that need to be controlled. This will inevitably lead to the so-called "out of control" phenomenon. Some existing technologies also use multivariable generalized prediction algorithms to control multiple variables, but when generalized predictive control is applied to actual engineering, its algorithm is more complicated, more parameters need to be adjusted, and the online calculation time is longer, which increases the complexity of actual engineering applications and is not easy to meet the control requirements of fast systems.

[0052] In order to solve the above problems, the embodiment of the present invention provides a control method for an industrial system of a thermal power plant, including: determining multiple regulated objects of the industrial system of the thermal power plant and multiple controlled objects and multiple disturbance objects corresponding to the regulated objects; establishing a transfer function model corresponding to each controlled object, and obtaining an optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; substituting the deviation of the regulated object and the deviation of the disturbance object collected in real time into the optimal control rate matrix, and calculating the first control quantity of each controlled object; calculating the second control quantity of each controlled object according to the fuzzy calculation algorithm; adjusting the controlled object according to the first control quantity and the second control quantity of the controlled object. The present invention combines the fuzzy calculation algorithm with the multivariable generalized predictive control algorithm, takes the multivariable generalized predictive control algorithm as the core, and uses the fuzzy calculation algorithm as the feedforward to control the industrial system of the thermal power plant, and while realizing multivariable control, it meets the system's rapid response requirements and has the ability to suppress unknown interference.

[0053] Figure 1 FIG. 1 is a flow chart of a method for controlling an industrial system of a thermal power plant provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for controlling an industrial system of a thermal power plant, the method comprising the following steps:

[0054] S1. Determine a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects.

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

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

[0057] The objects to be adjusted are: separator outlet enthalpy and main steam pressure;

[0058] The disturbance object is: high-adjustment valve opening.

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

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

[0061] exist Figure 2 In the figure, f(x) is a broken line function, lag is an inertia link, and by adding a broken line function and inertia link control, water, coal and wind can be more matched, which is an existing technology.

[0062] exist Figure 2 In the present invention, the load baseline static feedforward after uniform conversion is the control quantity specifically considered in the coordinated control system of thermal power plants, and is also the existing technology.

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

[0064] S2. Establish a transfer function model for each controlled object corresponding to each regulated object, and obtain an optimal control rate 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 disturbing object collected in real time into the optimal control rate matrix, and calculate the first control amount of each controlled object.

[0066] In this embodiment, the step of establishing a transfer function model of each controlled object corresponding to each regulated object includes:

[0067] Obtain historical data of the controlled object, the regulated object and the disturbed object respectively;

[0068] Processing the historical data of the controlled object, the regulated object and the disturbed object to obtain pre-processed data that meets the identification conditions;

[0069] Performing optimization calculation on the preprocessed data by using an optimization algorithm to obtain optimal data;

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

[0071]

[0072] Among them, K is the proportional coefficient, m is the order of the integral link, n is the order of the inertia link, T is the integral time constant, τ is the pure delay time, and α and β are differential coefficients.

[0073] In this embodiment, the optimal control rate matrix is ​​obtained by using multiple transfer function models corresponding to multiple controlled objects, including:

[0074] Multiple transfer function models are discretized using the multivariable generalized prediction algorithm to obtain the multivariable generalized prediction formula as follows:

[0075]

[0076]

[0077] The multivariable generalized prediction formula is calculated to obtain the optimal control rate matrix as follows:

[0078]

[0079] Where Δ = 1-z -1 is the difference operator, u 1 (k-1),u 2 (k-1), v 1 (k-1), v 2 (k-1), v 3 (k-1) is two control inputs and three external disturbance inputs, y 1 (k), y 2 (k) is the output of the two controlled quantities, e(k) is the white noise with zero mean, Q 1 , G 1 , Q 2 , G 2 , are matrix parameters obtained by the multivariable generalized prediction algorithm based on the transfer function model, Y 1 r (k+1) is the value of setting 1 at time k+1, Y 1 (k) is the value of the controlled object 1 at time k, ΔU 1 (k-1) is the increment of the controlled object 1 at time k-1, ΔU 2 (k-1) is the increment of the controlled object 2 at time k-1, Δv 1(k) is the increment of the disturbance object 1 at time k, Δv 2 (k) is the increment of the disturbance object 2 at time k, Δv 3 (k) is the increment of the disturbance object 3 at time k.

[0080] The multivariable generalized prediction algorithm transformation to obtain the optimal control rate matrix is ​​a prior art and need not be elaborated here.

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

[0082] In this embodiment, the step of calculating the second control variable of each controlled object according to the fuzzy calculation algorithm includes:

[0083] Fuzzy segmenting the deviation of the regulated object based on the set value of the regulated object to obtain a plurality of fuzzy segments;

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

[0085] Calculate the sum of the control quantity membership of all fuzzy segments when the controlled object has the exact value of deviation;

[0086] The second control quantity of the controlled object related to the multiple regulated objects is calculated according to the sum of the control quantity memberships of the multiple regulated objects.

[0087] In this embodiment, the second control quantity of the controlled object related to the multiple regulated objects is calculated according to the sum of the control quantity memberships of the multiple regulated objects as follows:

[0088] The control quantity membership degrees of the multiple regulated objects are summed and added to obtain the second control quantity of the controlled object related to the multiple regulated objects.

[0089] Specifically, take the main steam pressure as an example:

[0090] The deviation between the main steam pressure and the main steam pressure setting value is fuzzy segmented into Liuge fuzzy segments, which are [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 ​​correspond to the fuzzy segment control quantity membership as shown in Table 1.

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

[0094] XIndicates the deviation between the main steam pressure and the main steam pressure setting value.

[0095] Table 1: The degree of control quantity membership for different fuzzy segments for different exact deviation values ​​of the controlled object

[0096]

[0097] The corresponding values ​​in the table represent the degree of membership (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 setting value is 1.5, then this value belongs to PB as 1, to PM as 0.5, to PS as 0.3,

[0098] The main steam pressure change rate is fuzzy divided into three segments [NB, ZO, PB]

[0099] NB indicates a large negative rate of change

[0100] ZO means the rate of change is 0

[0101] PB means the positive rate of change is large

[0102] The specific rate values ​​corresponding to the fuzzy segment control quantity membership are shown in Table 2.

[0103] Table 2: The degree of control quantity membership for different fuzzy segments for different exact deviation values ​​of the controlled object

[0104]

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

[0106] Main steam pressure deviation:

[0107]

[0108]

[0109] The above table shows that when the positive deviation between the main steam pressure and the main steam pressure setting value is large (PB), the control object control amount action is -3 (subtract 3 tons of coal); when the positive deviation is medium (PM), the control object control amount action is -2; when the positive deviation is small (PS), the control object control amount action is -1; when the negative deviation is large (NB), the control object control amount action is 3 (add 3 tons of coal); when the negative deviation is medium (NM), the control object control amount action is 2; when the negative deviation is small (NS), the control object control amount action is 1.

[0110] Main steam change rate:

[0111]

[0112] The above table shows that when the main steam pressure change rate changes greatly in the positive direction (PB), the control amount acts as -10 (minus 10 tons of coal); when the rate remains unchanged (PM), the control amount acts as 0; when the main steam pressure change rate changes greatly in the negative direction (NB), the control amount acts as 10 (adding 10 tons of coal).

[0113] Output of actual coal feed rate = ∑ membership degree * pressure deviation coal feed rate action + ∑ membership degree * pressure change coal feed rate action

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

[0115] Coal feed amount = 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] That is to say, 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 -8.7.

[0118] In this embodiment, the membership of the control amount for different fuzzy segments of the exact values ​​of the deviations of the controlled object is obtained by selecting a membership function (normal function, trigonometric function or trapezoidal function) and taking a cutoff set after calculation.

[0119] In other embodiments of the present invention, the control amount membership of different fuzzy segments for different exact deviation values ​​of the regulated object is obtained through artificial setting.

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

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

[0122] The first control amount and the second control amount of the controlled object are superimposed to obtain the optimal control amount 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 thermal power plant industrial system control device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides a thermal power plant industrial system control device, the device comprising: a control object determination unit, used to determine multiple regulated objects of the thermal power plant industrial system and multiple controlled objects and multiple disturbance objects corresponding to the regulated objects; a first control quantity acquisition unit, used to establish a transfer function model of each controlled object corresponding to each regulated object, and obtain an 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, and calculate the first control quantity of each controlled object; a second control quantity acquisition unit, used to calculate the second control quantity of each controlled object according to a fuzzy calculation algorithm; an adjustment unit, used to adjust the controlled object according to the first control quantity and the second control quantity of the controlled object. The thermal power plant industrial system device is used to implement a thermal power plant industrial system control method, the method comprising:

[0125] Determine a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects;

[0126] Establishing a transfer function model for each controlled object corresponding to each regulated object, and obtaining an optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; substituting the deviation of the regulated object and the deviation of the disturbance object collected in real time into the optimal control rate matrix, and calculating and obtaining a first control amount of each controlled object;

[0127] Calculate the second control quantity of each controlled object according to the fuzzy calculation algorithm;

[0128] The controlled object is adjusted according to the first control amount and the second control amount of the controlled object.

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

[0130] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned thermal power plant industrial system control method when executing the computer program.

[0131] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0132] The optional embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, the technical scheme of the embodiments of the present invention can be subjected to a variety of simple modifications, and these simple modifications all belong to 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. In order to avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0133] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A method for controlling an industrial system of a thermal power plant, characterized in that: The method comprises: Determine a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects; Establishing a transfer function model for each controlled object corresponding to each regulated object, and obtaining an optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; Substituting the deviation of the controlled object and the deviation of the disturbing object collected in real time into the optimal control rate matrix, and calculating the first control amount of each controlled object; Calculate the second control quantity of each controlled object according to the fuzzy calculation algorithm; The controlled object is adjusted according to the first control amount and the second control amount of the controlled object.

2. The thermal power plant industrial system control method according to claim 1, characterized in that: The step of establishing a transfer function model of each controlled object corresponding to each regulated object includes: Obtain historical data of the controlled object, the regulated object and the disturbed object respectively; Processing the historical data of the controlled object, the regulated object and the disturbing object to obtain pre-processed data that meets the identification conditions; Performing optimization calculation on the preprocessed data by using an optimization algorithm to obtain optimal data; A transfer function model is established for each controlled object corresponding to each regulated object based on the optimal data.

3. The thermal power plant industrial system control method according to claim 2, characterized in that: The transfer function model is: Among them, K is the proportional coefficient, m is the order of the integral link, n is the order of the inertia link, T is the integral time constant, τ is the pure delay time, and α and β are differential coefficients.

4. The thermal power plant industrial system control method according to claim 2, characterized in that: The optimal control rate matrix is ​​obtained by using multiple transfer function models corresponding to multiple controlled objects, including: Multiple transfer function models are discretized using the multivariable generalized prediction algorithm to obtain the multivariable generalized prediction formula as follows: The multivariable generalized prediction formula is calculated to obtain the optimal control rate matrix as follows: Where Δ = 1-z -1 is a differential operator, u1(k-1), u2(k-1), v1(k-1), v2(k-1), v3(k-1) are two control inputs and three external disturbance inputs, y 1 (k), y 2 (k) is the output of the two controlled quantities, e(k) is the white noise with zero mean, Q 1 , G 1 , Q 2 , G 2 , are matrix parameters obtained by the multivariable generalized prediction algorithm based on the transfer function model, Y 1 r (k+1) is the value of setting 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.

5. The thermal power plant industrial system control method according to claim 1, characterized in that: The step of calculating the second control quantity of each controlled object according to the fuzzy calculation algorithm includes: Fuzzy segmenting the deviation of the regulated object based on the set value of the regulated object to obtain a plurality of fuzzy segments; Obtain the control quantity membership of different fuzzy segments for different exact deviation values ​​of the controlled object; Calculate the sum of the control quantity membership of all fuzzy segments when the controlled object has the exact value of deviation; The second control quantity of the controlled object related to the multiple regulated objects is calculated according to the sum of the control quantity memberships of the multiple regulated objects.

6. The thermal power plant industrial system control method according to claim 5, characterized in that: The method of calculating the second control quantity of the controlled object related to the multiple regulated objects according to the sum of the control quantity membership of the multiple regulated objects is specifically: The control quantity membership degrees of the multiple regulated objects are summed and added to obtain the second control quantity of the controlled object related to the multiple regulated objects.

7. The thermal power plant industrial system control method according to claim 1, characterized in that: The step of adjusting the controlled object according to the first control amount and the second control amount of the controlled object comprises: The first control amount and the second control amount of the controlled object are superimposed to obtain the optimal control amount of the controlled object; Adjust the controlled object according to the optimal control quantity.

8. A thermal power plant industrial system control device, characterized in that: include: A control object determination unit, used for determining a plurality of regulated objects of the industrial system of the thermal power plant and a plurality of controlled objects and a plurality of disturbance objects corresponding to the regulated objects; A first control quantity acquisition unit is used to establish a transfer function model for each controlled object corresponding to each regulated object, and obtain an optimal control rate matrix through multiple transfer function models corresponding to multiple controlled objects; Substituting the deviation of the controlled object and the deviation of the disturbing object collected in real time into the optimal control rate matrix, and calculating the first control amount of each controlled object; A second control quantity acquisition unit, used for calculating the second control quantity of each controlled object according to a fuzzy calculation algorithm; The regulating unit is used to regulate the controlled object according to the first control amount and the second control amount of the controlled object.

9. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on a computer, the computer is enabled to execute the thermal power plant industrial system control method according to any one of claims 1 to 7.

10. 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, the thermal power plant industrial system control method described in any one of claims 1 to 7 is implemented.

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