Signal Processing Method Based on Proportional-Integral Control

Abstract

The present invention application provides a signal processing method based on proportional-integral control, including: obtaining a signal to be processed at the current moment and a feedback signal at the previous moment of the denitration control system of a thermal power unit; and obtaining the deviation value between the two; processing the deviation value through a proportional control gain to obtain a proportional processing result; obtaining an integral signal of the proportional processing result through a preset integrator; obtaining a target output signal at the current moment based on the sum of the proportional processing result and the integral signal; wherein the feedback signal at the previous moment is obtained according to the lead observation operation processing result of the target output signal at the previous moment. This application separates proportional-integral control and lead observation operation processing, significantly suppressing the phenomenon of output jump and reducing the influence of external disturbances or changes in the given input.

Description

Technical Field

[0001] This invention application relates to the field of industrial process control, and particularly to a signal processing method based on proportional-integral control. Background Art

[0002] In some control loops of thermal power units, the traditional proportional-integral control (PI control for short) method is widely used because its parameter tuning is relatively simple. However, with the improvement of the control performance requirements of the power system for thermal power units, the limitations of the traditional proportional-integral control gradually emerge in the feedback control performance.

[0003] Currently, to overcome the limitations of the traditional proportional-integral control, the Acceleration Engineering Fastest Proportional-Integral Control (AEFPI control for short) is one of the solutions. This control method improves the response speed of the denitration control system of thermal power units through an acceleration strategy. However, the pure time delay of the controlled process in many denitration control systems of thermal power units accounts for a relatively high proportion, such as 90%, resulting in the final target output signal not being smooth enough. Therefore, for the denitration control system of thermal power units, based on the existing Acceleration Engineering Fastest Proportional-Integral Control, an improved proportional-integral control method is urgently needed to further optimize the quality of the target output signal of this type of control method. Summary of the Invention

[0004] This invention application provides a signal processing method based on proportional-integral control, which can solve the technical problem that the output signal of the denitration control system of thermal power units is not smooth enough and improve the quality of the output signal.

[0005] To solve the above technical problem, the first aspect of this invention application provides a signal processing method based on proportional-integral control, including:

[0006] Obtain the signal to be processed at the current moment and the feedback signal at the previous moment of the denitration control system of the thermal power unit; and obtain the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment;

[0007] Process the deviation value through a preset proportional control gain to obtain a proportional processing result; obtain the sum of the integral signals of the proportional processing result through a preset integrator; based on the proportional processing result and the integral signal, obtain the target output signal at the current moment;

[0008] Among them, the feedback signal at the previous moment is obtained according to the result of the lead observation operation and processing of the target output signal at the previous moment.

[0009] Implementing the present invention application, the feedback signal at the previous moment is obtained through the result of the lead observation operation and processing of the target output signal at the previous moment. Based on the signal to be processed at the current moment and the feedback signal at the previous moment, a deviation value is obtained, and proportional-integral control is performed on the deviation value to obtain the target output signal at the current moment. In this way, compared with the prior art where both proportional-integral control and lead observation operation and processing are placed inside the loop of the feedback control system, in this application, the proportional-integral control part is placed inside the loop and the lead observation operation and processing is placed outside the loop when processing the signal to be processed in the denitration control system of a thermal power unit. That is, the proportional-integral control and the lead observation operation and processing are separated in a feedback control system, significantly suppressing the phenomenon of the target output signal jumping. When facing complex working conditions such as rapid changes in the load and coal quality of the thermal power unit, it can reduce the influence of external disturbances or changes in the given input, and effectively improve the smoothness and quality of the target output signal at the current moment of the denitration control system of the thermal power unit.

[0010] The second aspect of the present invention application provides a signal processing device based on proportional-integral control, including an acquisition module and a proportional-integral control module; among them,

[0011] The acquisition module is used to acquire the signal to be processed at the current moment and the feedback signal at the previous moment of the denitration control system of the thermal power unit; and acquire the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment;

[0012] The proportional-integral control module is used to process the deviation value through a preset proportional control gain to obtain a proportional processing result; obtain an integral signal of the proportional processing result through a preset integrator; and obtain the target output signal at the current moment based on the sum of the proportional processing result and the integral signal;

[0013] Among them, the feedback signal at the previous moment is obtained according to the result of the lead observation operation and processing of the target output signal at the previous moment.

[0014] The third aspect of the present invention application provides a proportional-integral control device, including a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations of the signal processing method based on proportional-integral control.

[0015] The fourth aspect of the present invention application provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction runs on a signal processing device / apparatus based on proportional-integral control, it enables the signal processing device / apparatus based on proportional-integral control to execute the operations of the signal processing method based on proportional-integral control described above. Description of the Drawings

[0016] Figure 1 : Schematic flowchart of the first embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0017] Figure 2 : Schematic flowchart of the second embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0018] Figure 3 : Schematic diagram of the principle of the second embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0019] Figure 4 : Schematic flowchart of the third embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0020] Figure 5 : Schematic diagram of the principle of the third embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0021] Figure 6 : Schematic diagram of the principle of the fourth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0022] Figure 7 : Schematic diagram of the principle of the fifth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0023] Figure 8 : Schematic flowchart of the sixth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0024] Figure 9 : Schematic flowchart of the seventh embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0025] Figure 10 : Schematic diagram of the principle of an application example of the signal processing method based on proportional-integral control provided by the present invention.

[0026] Figure 11 : Schematic diagram of the principle of an application example of the accelerated engineering fastest proportional-integral control method of the prior art.

[0027] Figure 12 : Schematic diagram of the simulation results of an application example of the signal processing method based on proportional-integral control provided by the present invention.

[0028] Figure 13 : Schematic diagram of the simulation results of an application example of the signal processing method based on proportional-integral control in the prior art.

[0029] Figure 14 : Schematic structural diagram of the first embodiment of the signal processing device based on proportional-integral control provided by the present invention.

[0030] Figure 15 : Schematic structural diagram of the first embodiment of the signal processing equipment based on proportional-integral control provided by the present invention. Detailed implementation manners

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Embodiment 1

[0033] Please refer to Figure 1 , Figure 1 , which is a schematic flowchart of the first embodiment of the signal processing method based on proportional-integral control provided by the present invention application. The first embodiment includes steps S101 to S102; wherein,

[0034] Step S101, obtaining the signal to be processed at the current moment and the feedback signal at the previous moment of the denitration control system of the thermal power unit; and obtaining the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment.

[0035] The signal processing method based on proportional-integral control in this embodiment can be applied to the denitration control system of thermal power units. To solve the problem of new energy consumption, the peak shaving function of thermal power units provides a favorable guarantee for the power grid to consume new energy. However, the safe and stable operation of thermal power units is affected by factors such as combustion stability and the adaptability of the denitration system. Therefore, in order to ensure the stable operation of thermal power units and the denitration (control) system, it is necessary to perform certain processing on the signals involved to face complex operating environments and working conditions.

[0036] The signal to be processed at the current moment obtained in step S101, and this signal to be processed can be nitrogen oxides NO xGiven value, the feedback signal at the previous moment can be nitrogen oxides NO x Output value at the previous moment to achieve negative feedback control of the denitration control system for thermal power units. Among them, nitrogen oxides NO x The output value of includes the process value of nitrogen oxides NO x And external disturbances.

[0037] Generally, the above external disturbances are caused by the instability of thermal power units or denitration (control) systems. Thermal power units often face complex operating conditions such as rapidly changing loads and coal quality changes. Therefore, the denitration control system will be subject to certain disturbances, which in turn affect the stable operation of thermal power units or denitration control systems.

[0038] Exemplarily, the feedback signal at the previous moment in this step can be obtained based on the result of the lead observation operation and processing of the target output signal at the previous moment.

[0039] Step S101 lays the foundation for the proportional-integral control processing in the subsequent step S102 by obtaining the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment.

[0040] Step S102 processes the deviation value through a preset proportional control gain to obtain a proportional processing result; obtains the integral signal of the proportional processing result through a preset integrator; and obtains the target output signal at the current moment based on the sum of the proportional processing result and the integral signal.

[0041] In this step, the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment can be processed through a proportional control gain. For example, the proportional control gain can be:

[0042] ;

[0043] Among them, K INT Is the proportional control gain, with no unit, and K AEFPI Is the proportional gain of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control, with no unit.

[0044] It can be understood that the target output signal at the current moment can be processed through lead observation operation to obtain the feedback signal at the current moment, which is used for processing the signal to be processed at the next moment, and then obtaining the target output signal at the next moment.

[0045] As described above, by implementing the embodiments of the present application, the feedback signal at the previous moment is obtained through the lead observation operation processing result of the target output signal at the previous moment, the deviation value is obtained based on the signal to be processed at the current moment and the feedback signal at the previous moment, and the proportional-integral control is performed on the deviation value to obtain the target output signal at the current moment. In this way, compared with the prior art in which both the proportional-integral control and the lead observation operation processing are placed inside the loop of the feedback control system, in this embodiment, part of the proportional-integral control is placed inside the loop and the lead observation operation processing is placed outside the loop during the process of processing the signal to be processed in the denitration control system of the thermal power unit. That is, the proportional-integral control and the lead observation operation processing are separated in a feedback control system, significantly suppressing the phenomenon of signal jump of the target output signal. When facing complex working conditions such as rapid load change and coal quality change of the thermal power unit, the influence of external disturbance or given input change can be reduced, effectively improving the smoothness and quality of the target output signal at the current moment of the denitration control system of the thermal power unit.

[0046] Based on the Figure 1 embodiment shown, Figure 2 a schematic flowchart of the second embodiment of the signal processing method based on proportional-integral control according to the present invention application is provided, Figure 3 and a schematic principle diagram of the second embodiment of the signal processing method based on proportional-integral control according to the present invention application is provided. It should be noted that the same steps as those in Figure 1 are not described herein again.

[0047] As Figure 2 shown, in some embodiments of the second embodiment, after step S102, steps S201 to S202 are included, which are described in detail as follows:

[0048] Step S201: Filter the target output signal at the current moment to obtain a filtered signal (at the current moment).

[0049] Step S202: Subtract the filtered signal (at the current moment) from the target output signal at the current moment, and perform a lead observation ratio operation on the obtained difference to obtain the feedback signal at the current moment.

[0050] Based on the Figure 1 embodiment shown, Figure 4 a schematic flowchart of the third embodiment of the signal processing method based on proportional-integral control according to the present invention application is provided, Figure 5 and a schematic principle diagram of the third embodiment of the signal processing method based on proportional-integral control according to the present invention application is provided. It should be noted that the same steps as those in Figure 1 are not described herein again.

[0051] As Figure 4 shown, before step S101, the third embodiment includes steps S401 to S402, which are described in detail as follows:

[0052] Step S401: By filtering the target output signal at the previous moment, a filtered signal (at the previous moment) is obtained.

[0053] Step S402: Subtract the filtered signal (at the previous moment) from the target output signal at the previous moment, and perform a lead observation proportional operation on the obtained difference to obtain a feedback signal at the previous moment.

[0054] It can be understood that the lead observation operation methods adopted in the second embodiment and the third embodiment are basically the same. The difference between the two is that the second embodiment processes the target output signal at the current moment, while the third embodiment processes the target output signal at the previous moment.

[0055] As Figure 3 and Figure 5 shown, the subtraction process in step S202 and / or step S302 can be implemented by the subtractor shown in the figure (the symbol shown in the figure is Σ, the input terminal with the symbol “+” is the minuend terminal, and the input terminal with the symbol “-” is the subtrahend terminal).

[0056] In addition, the lead observation proportional operation described in step S202 and / or step S402 can be implemented by a lead observation proportional operator. The gain K LO of this lead observation proportional operator can take a value of 11, and the unit is dimensionless.

[0057] Based on the second embodiment and / or the third embodiment, Figure 6 this is a schematic diagram of the principle of the fourth embodiment of the signal processing method based on proportional-integral control of the present invention application. The same steps as in the first embodiment, the second embodiment, and the third embodiment will not be described again here.

[0058] The filtering process in step S201 and / or step S301 can be implemented by a combined filter. The combined filter includes a first-order inertial filter, a second-order inertial filter, a third-order inertial filter, a fourth-order inertial filter, a fifth-order inertial filter, a sixth-order inertial filter, a seventh-order inertial filter, an eighth-order inertial filter, a ninth-order inertial filter, a tenth-order inertial filter, an eleventh-order inertial filter, a twelfth-order inertial filter, a thirteenth-order inertial filter, a fourteenth-order inertial filter, and a fifteenth-order inertial filter.

[0059] The process of filtering the target output signal at the current moment in step S201 and / or step S301 to obtain a filtered signal is specifically:

[0060] Input the target output signal at the current moment multiplied by 135 into a first-order inertial filter; input the target output signal at the current moment multiplied by 133 into a second-order inertial filter; input the target output signal at the current moment multiplied by 130 into a third-order inertial filter; input the target output signal at the current moment multiplied by 126 into a fourth-order inertial filter; input the target output signal at the current moment multiplied by 121 into a fifth-order inertial filter; input the target output signal at the current moment multiplied by 115 into a sixth-order inertial filter; input the target output signal at the current moment multiplied by 108 into a seventh-order inertial filter; input the target output signal at the current moment multiplied by 100 into an eighth-order inertial filter; input the target output signal at the current moment multiplied by 91 into a ninth-order inertial filter; input the target output signal at the current moment multiplied by 81 into a tenth-order inertial filter; input the target output signal at the current moment multiplied by 70 into an eleventh-order inertial filter; input the target output signal at the current moment multiplied by 58 into a twelfth-order inertial filter; input the target output signal at the current moment multiplied by 45 into a thirteenth-order inertial filter; input the target output signal at the current moment multiplied by 31 into a fourteenth-order inertial filter; input the target output signal at the current moment multiplied by 16 into a fifteenth-order inertial filter.

[0061] Sum up the outputs of the first-order inertial filter, second-order inertial filter, third-order inertial filter, fourth-order inertial filter, fifth-order inertial filter, sixth-order inertial filter, seventh-order inertial filter, eighth-order inertial filter, ninth-order inertial filter, tenth-order inertial filter, eleventh-order inertial filter, twelfth-order inertial filter, thirteenth-order inertial filter, fourteenth-order inertial filter and fifteenth-order inertial filter through the Laplace transfer function of the combined filter, and process the obtained sum through a preset proportional operation gain to obtain the filtered signal.

[0062] Implementing this embodiment, the combined filter is a filter designed after separating the proportional-integral control part and the lead observation processing part. The design of each-order inertial filter and the design of the input to each-order inertial filter enable the combined filter to have the ability to quickly track the input signal (so that the output signal can accelerate to track the input signal), and its output signal can reach a stable state in a short time, thus realizing the fast control of the system. At the same time, the design of this combined filter can accurately track the target signal, improve the control efficiency of the denitration control system of thermal power units. As the output signal tracks the input signal, the denitration control system of thermal power units can quickly respond when facing complex and changeable working conditions, and maintain stable tracking performance, effectively coping with various interferences and uncertainties, ensuring the stable operation of the system. It is not only applicable to simple tracking problems, but also can maintain excellent tracking performance in complex engineering environments, showing strong adaptability.

[0063] Exemplarily, the Laplace transfer function of a first-order inertial filter is:

[0064] ;

[0065] where f O1IF (s) is the Laplace transfer function of the first-order inertial filter, and the numerator represents multiplying by 135; T AEFPI is the equivalent acceleration type engineering fastest proportional-integral controller time constant of the signal processing method based on proportional-integral control described in this application, with the unit of seconds, and s is the Laplace operator.

[0066] Similarly, the Laplace transfer function of a second-order inertial filter is:

[0067] ;

[0068] In the formula, f O2IF (s) is the Laplace transfer function of the second-order inertial filter, and the numerator represents multiplying by 133.

[0069] The Laplace transfer function of a third-order inertial filter is:

[0070] ;

[0071] In the formula, f O3IF (s) is the Laplace transfer function of the third-order inertial filter, and the numerator represents multiplying by 130.

[0072] The Laplace transfer function of a fourth-order inertial filter is:

[0073] ;

[0074] In the formula, f O4IF (s) is the Laplace transfer function of the fourth-order inertial filter, and the numerator represents multiplying by 126.

[0075] The Laplace transfer function of a fifth-order inertial filter is:

[0076] ;

[0077] In the formula, f O5IF (s) is the Laplace transfer function of the fifth-order inertial filter, and the numerator represents multiplying by 121.

[0078] The Laplace transfer function of a sixth-order inertial filter is:

[0079] ;

[0080] In the formula, f O6IF(s) is the Laplace transfer function of a sixth-order inertial filter, and the numerator represents multiplying by 115.

[0081] The Laplace transfer function of a seventh-order inertial filter is:

[0082] ;

[0083] where f O7IF (s) is the Laplace transfer function of a seventh-order inertial filter, and the numerator represents multiplying by 108.

[0084] The Laplace transfer function of an eighth-order inertial filter is:

[0085] ;

[0086] where f O8IF (s) is the Laplace transfer function of an eighth-order inertial filter, and the numerator represents multiplying by 100.

[0087] The Laplace transfer function of a ninth-order inertial filter is:

[0088] ;

[0089] where f O9IF (s) is the Laplace transfer function of a ninth-order inertial filter, and the numerator represents multiplying by 91.

[0090] The Laplace transfer function of a tenth-order inertial filter is:

[0091] ;

[0092] where f O10IF (s) is the Laplace transfer function of a tenth-order inertial filter, and the numerator represents multiplying by 81.

[0093] The Laplace transfer function of an eleventh-order inertial filter is:

[0094] ;

[0095] where f O11IF (s) is the Laplace transfer function of an eleventh-order inertial filter, and the numerator represents multiplying by 70.

[0096] The Laplace transfer function of a twelfth-order inertial filter is:

[0097] ;

[0098] where f O12IF (s) is the Laplace transfer function of a twelfth-order inertial filter, and the numerator represents multiplying by 58.

[0099] The Laplace transfer function of the thirteen - order inertia filter is:

[0100] ;

[0101] where f O13IF (s) is the Laplace transfer function of the thirteen - order inertia filter, and the numerator represents multiplying by 45.

[0102] The Laplace transfer function of the fourteen - order inertia filter is:

[0103] ;

[0104] where f O14IF (s) is the Laplace transfer function of the fourteen - order inertia filter, and the numerator represents multiplying by 31.

[0105] The Laplace transfer function of the fifteen - order inertia filter is:

[0106] ;

[0107] where f O15IF (s) is the Laplace transfer function of the fifteen - order inertia filter, and the numerator represents multiplying by 16.

[0108] The Laplace transfer function of the combined filter can be expressed by the following formula:

[0109] ;

[0110] where f CF (s) is the Laplace transfer function of the said combined filter, l is a dynamic variable used to calculate the gain of each order filter in the combined filter, which is a dimensionless positive integer, i is a dynamic variable used to calculate the order of each order filter in the combined filter, which is a dimensionless positive integer, K OUT is the said proportional operation gain, and the unit is dimensionless. In some embodiments, this proportional operation gain can be K OUT = 1 / 136.

[0111] In addition, in some preferred embodiments, the step of performing the lead - observation proportional operation on the obtained difference in step S202 and / or step S402 is specifically:

[0112] Process the obtained difference through a preset lead - observation proportional operator;

[0113] The Laplace transfer function of the equivalent controller of the step of processing the obtained difference through the preset lead - observation proportional operator is expressed by the following formula:

[0114] ;

[0115] where f AEFPI:LO (s) is the Laplace transfer function of the equivalent controller, and K LO is the gain of the lead observer proportional operator, with the unit being dimensionless. In some embodiments, K LO can take the value of 11, and f CF (s) is the Laplace transfer function of the combined filter.

[0116] Based on Figure 1 the embodiment shown, Figure 7 a schematic diagram of the principle of the fifth embodiment of step S102 of the signal processing method based on proportional-integral control according to the present invention application is shown. It should be noted that the steps same as those in Figure 1 the embodiment shown are not described herein again.

[0117] The step of obtaining the integral signal of the proportional processing result through the preset integrator in step S102 is specifically:

[0118] The Laplace transfer function of the preset integrator is:

[0119] ;

[0120] where f I (s) is the Laplace transfer function of the preset integrator, T AEFPI is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control, with the unit being seconds, and s is the Laplace operator.

[0121] Based on Figure 2 the embodiment shown, Figure 8 a flowchart of step S201 of the sixth embodiment of the signal processing method based on proportional-integral control is provided. The steps same as those in Figure 2 are not described herein again. As Figure 8 shown, step S201 includes steps S801 to S804, which are described in detail as follows:

[0122] Step S801, obtain the target output signal in the preset time interval, and the maximum value of the target output signal in the preset time interval; and calculate the sum of the target output signals in the preset time interval.

[0123] Step S802, divide the sum of the target output signals in the preset time interval by the maximum value to obtain the target output statistical value; and calculate the first upper limit threshold according to the target output statistical value.

[0124] Step S803: When the target output signal at the current moment is greater than or equal to the first upper limit threshold, reduce the target output signal at the current moment to the first upper limit threshold, and perform smoothing filtering on the reduced target output signal by using a smoothing filter constructed with the target output statistical value to obtain the filtered signal.

[0125] Step S804: When the target output signal at the current moment is less than the first upper limit threshold, perform smoothing filtering on the target output signal at the current moment by using a smoothing filter constructed with the target output statistical value to obtain the filtered signal.

[0126] In this embodiment, in view of the problem that the target output signal may be too large or too small in some cases, and valuable statistics cannot be obtained at the larger or smaller values. To overcome this problem, this embodiment obtains the target output signal in a preset time interval and the maximum value of the target output signal in the preset time interval; divides the sum of the target output signals in the preset time interval by the maximum value to obtain the target output statistical value, and then calculates the first upper limit threshold; when the target output signal at the current moment is greater than or equal to the first upper limit threshold, reduce the target output signal at the current moment to the first upper limit threshold, and perform smoothing filtering on the reduced target output signal by using a smoothing filter constructed with the target output statistical value to obtain the filtered signal; when the target output signal at the current moment is less than the first upper limit threshold, perform smoothing filtering on the reduced target output signal by using a smoothing filter constructed with the target output statistical value to obtain the filtered signal. In this way, a smoothing filter with non-fixed parameters (target output statistical value) can be used to perform smoothing filtering on the target output signal to obtain an effective and high-quality filtered signal, which is convenient for subsequent step processing.

[0127] In Figure 1 On the basis of the Figure 9 illustrated embodiment, Figure 1 a schematic flowchart of step S101 of the seventh embodiment of the signal processing method based on proportional-integral control is provided. The Figure 9 same steps are not elaborated here. As

[0128] shown, step S101 includes steps S901 to S905, which are described in detail as follows:

[0129] Step S901: Construct a multi-period system model of the denitration control system of the thermal power unit; the multi-period system model includes a first sub-model in units of days, a second sub-model in units of weeks, and a third sub-model in units of months, and the first sub-model, the second sub-model, and the third sub-model are connected through a data interaction interface.Step S902: Obtain the target operation data of the denitration control system of the thermal power unit. The target operation data includes the inlet nitrogen oxide concentration, ammonia injection amount, inlet temperature, and inlet flow rate of the denitration control system.

[0130] Step S903: Divide the target operation data according to the daily cycle, weekly cycle, and monthly cycle to obtain a daily cycle dataset, a weekly cycle dataset, and a monthly cycle dataset.

[0131] Step S904: Use the daily cycle dataset to train the first sub-model, use the weekly cycle dataset to train the second sub-model, and use the monthly cycle dataset to train the third sub-model. When the first sub-model, the second sub-model, and the third sub-model all converge, obtain the trained multi-cycle system model, and determine the trained multi-cycle system model as the optimized system model.

[0132] Step S905: Use the particle swarm optimization algorithm to determine the activation energy parameter and reaction rate parameter of the denitration reaction of the denitration control system of the thermal power unit; based on the activation energy parameter of the denitration reaction, the reaction rate parameter, and the target operation data, solve the optimized system model, obtain the solution result according to the output of the optimized system model, and further obtain the signal to be processed.

[0133] In this embodiment, the multi-cycle system model can be constructed by Aspen Plus software, and the particle swarm optimization algorithm (model) can be constructed by MATLAB software. The particle swarm optimization algorithm (model) and the optimized system model can be connected through a data interface and perform data interaction. The multi-cycle system model includes a first sub-model in units of days, a second sub-model in units of weeks, and a third sub-model in units of months. The first sub-model, the second sub-model, and the third sub-model are connected through a data interaction interface. In this embodiment, the target operation data is divided according to the daily cycle, weekly cycle, and monthly cycle to obtain a daily cycle dataset, a weekly cycle dataset, and a monthly cycle dataset; each dataset is respectively used for the training of different sub-models to obtain the trained multi-cycle system model, and the trained multi-cycle system model is determined as the optimized system model; and the solution is carried out under the conditions of the activation energy parameter and reaction rate parameter of the denitration reaction of the denitration control system of the thermal power unit given by the particle swarm optimization algorithm. Compared with the prior art of directly constructing a single system model, the solution result is more in line with the actual denitration reaction environment of the thermal power system. The optimized system model constructed in this embodiment also has better performance than the existing single system model.

[0134] In some preferred embodiments, the obtaining of the target operation data of the denitration control system of the thermal power unit specifically includes: obtaining the historical operation data and historical load data of the denitration control system of the thermal power unit; according to the historical load data, eliminating the historical operation data with the load outside the preset interval to obtain the first operation data; based on a preset fitting formula, performing fitting processing on the historical load data and the first operation data to obtain the functional relationship between the historical load data and the first operation data; according to the preset inlet temperature constraint, using the functional relationship to determine the corresponding unit constraint load; through a preset operation load calculation model and the unit constraint load, dynamically calculating the target load, and further obtaining the target operation data of the target load of the denitration control system of the thermal power unit.

[0135] In this preferred embodiment, after eliminating the historical operation data with the load outside the preset interval to obtain the first operation data, fitting processing is performed on the first operation data and the historical load data to obtain the functional relationship between the historical load data and the first operation data, simulating the relationship between the operation parameters and the load of the denitration control system of the thermal power unit. Furthermore, under the preset inlet temperature constraint condition, using this functional relationship to determine the corresponding unit constraint load, through a preset operation load calculation model and the unit constraint load, dynamically calculating the target load, and further obtaining the target operation data of the target load of the denitration control system of the thermal power unit. The target operation data obtained in this way is closer to the actual operation condition and actual load of the denitration control system of the thermal power unit, improving the accuracy of the obtained target operation data.

[0136] Exemplarily, the dynamically calculating the target load through a preset operation load calculation model and the unit constraint load specifically includes: calculating the target load according to the following formula:

[0137] L m =L d +L p -f(T);

[0138] Wherein, L m is the target load, T is the current inlet temperature of the denitration control system of the thermal power unit, L d is the unit constraint load, L p is the current load of the denitration control system of the thermal power unit, and f(T) is the result of preprocessing the current inlet temperature.

[0139] In some preferred embodiments, the use of the particle swarm optimization algorithm to determine the denitration reaction activation energy parameter and the denitration reaction rate parameter of the denitration control system of the thermal power unit is specifically as follows: Set the denitration reaction parameters of the denitration control system of the thermal power unit, where the denitration reaction parameters include reaction type parameters, component parameters, and physical property parameters; According to the denitration reaction parameters, set the type, dimension, and pressure of the reaction equipment to obtain the first configuration parameter; According to the denitration reaction parameters, set the corresponding simulated reaction process, catalyst parameters, bed void fraction, and stoichiometry to obtain the second configuration parameter; According to the first configuration parameter and the second configuration parameter, construct the particle swarm model of the denitration control system of the thermal power unit, and calculate the denitration reaction activation energy parameter and the denitration reaction rate parameter under the preset denitration reaction activation energy constraint, denitration reaction rate constraint, and target constraint; where the target constraint is set according to the nitrogen oxide concentration output by the optimization system model and the actual outlet nitrogen oxide concentration of the denitration control system of the thermal power unit.

[0140] Implementing this preferred embodiment, by setting the type, dimension, and pressure of the reaction equipment, the first configuration parameter is obtained; by setting the corresponding simulated reaction process, catalyst parameters, bed void fraction, and stoichiometry, the second configuration parameter is obtained; and then, using the first configuration parameter and the second configuration parameter, a particle swarm model is constructed through MATLAB software, and under the preset denitration reaction activation energy constraint, denitration reaction rate constraint, and target constraint, the denitration reaction activation energy parameter and the denitration reaction rate parameter are calculated. Compared with the prior art, it can achieve further refinement of the denitration reaction environment, accurately construct the particle swarm model, and accurately obtain the denitration reaction activation energy parameter and the denitration reaction rate parameter.

[0141] In addition, the present invention application also provides an application example of a signal processing method based on proportional-integral control, such as Figure 10 and Figure 11 shown. Figure 10 The figure shows the schematic diagram of the principle of the signal processing method based on proportional-integral control adopted in the first embodiment or other embodiments of this application. And Figure 11 is the schematic diagram of the principle of the accelerated engineering fastest proportional-integral control method of the prior art.

[0142] For comparison, Figure 11 the Laplace function of the accelerated engineering fastest proportional-integral controller in

[0143] ;

[0144] In the formula, f AEFPI (s) is the Laplace transfer function of the accelerated engineering fastest proportional-integral controller, s is the Laplace operator; K AEFPIis the proportional gain of the accelerated engineering fastest proportional-integral controller, with the unit being dimensionless; T AEFPI is the time constant of the accelerated engineering fastest proportional-integral controller, with the unit being seconds; I is the order of each filter, and the range is a dimensionless positive integer from 1 to 16.

[0145] It can be seen that, compared with the prior art, in the process of processing the signal to be processed in the denitration control system of the thermal power unit in the embodiment of the present application, the proportional-integral control part is placed inside the loop, and the lead observation operation processing is placed outside the loop, separating the proportional-integral control and the lead observation operation processing in a feedback control system.

[0146] While in Figure 10 and Figure 11 , a 100% pure time delay process treatment and a disturbance model (Disturbance model, abbreviated as DM) are adopted, as follows:

[0147] ;

[0148] ;

[0149] where s is the Laplace operator, and f P (s) is the transfer function representing 100% pure time delay; f DM (s) is the transfer function of the disturbance model.

[0150] The disturbance model of the application example is to simulate the complex working conditions and variable loads actually faced by the denitration control system of the thermal power unit, so as to reflect the difference between the signal processing method based on proportional-integral control in the present application and the existing signal processing method based on proportional-integral control.

[0151] The relevant parameters of the signal processing method based on proportional-integral control in the present application obtained by the mathematical optimization method include: K AEFPI = 0.6379, T AEFPI = 137.5 seconds.

[0152] The external disturbance (disturbance model) can adopt a ramp function, the process given is a unit step, the length of the ramp function is 1000 seconds, and the rate of the ramp function is 500 -1 / second, and the obtained simulation results are as shown in Figure 12 . Figure 12 The ordinate PV AEFPI (t) is the target output signal of the signal processing method based on proportional-integral control in the present application, and its process overshoot is 0. The abscissa t is time (unit: second), Figure 12The adjustment time of the shown application example is 388.5 seconds, and the adjustment time refers to the time when the target output signal enters a value greater than 0.95; the maximum deviation of the target output signal relative to the signal to be processed during the ramp function is 0.2955.

[0153] For the purpose of comparison, Figure 13 the simulation results of the prior art are given. When the given parameters are the same, there are obvious jump problems in the accelerated engineering fastest signal processing method based on proportional-integral control using the prior art. In this application, by separating the proportional-integral control part and the lead observation operation processing part, the target output signal becomes smoother.

[0154] Correspondingly, as Figure 14 shown, Figure 14 a structural schematic diagram of the first embodiment of the signal processing device 1400 based on proportional-integral control according to the present invention application is provided. The signal processing device 1400 based on proportional-integral control provided by the present invention application includes an acquisition module 1401 and a proportional-integral control module 1402; wherein,

[0155] the acquisition module 1401 is used to acquire the signal to be processed at the current moment and the feedback signal at the previous moment of the denitration control system of the thermal power unit; and acquire the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment;

[0156] the proportional-integral control module 1402 is used to process the deviation value through a preset proportional control gain to obtain a proportional processing result; obtain an integral signal of the proportional processing result through a preset integrator; and obtain the target output signal at the current moment based on the sum of the proportional processing result and the integral signal;

[0157] wherein, the feedback signal at the previous moment is obtained according to the lead observation operation processing result of the target output signal at the previous moment.

[0158] In some preferred embodiments, the signal processing device 1400 based on proportional-integral control further includes a lead observation operation processing module, and the lead observation operation processing module is used to filter the target output signal at the current moment to obtain a filtered signal; subtract the filtered signal from the target output signal at the current moment, and perform a lead observation proportional operation on the obtained difference to obtain the feedback signal at the current moment.

[0159] In some preferred embodiments, the filtering process is implemented by a combined filter, and the combined filter includes a first-order inertial filter, a second-order inertial filter, a third-order inertial filter, a fourth-order inertial filter, a fifth-order inertial filter, a sixth-order inertial filter, a seventh-order inertial filter, an eighth-order inertial filter, a ninth-order inertial filter, a tenth-order inertial filter, an eleventh-order inertial filter, a twelfth-order inertial filter, a thirteenth-order inertial filter, a fourteenth-order inertial filter, and a fifteenth-order inertial filter;

[0160] In some preferred embodiments, the lead observation operation processing module includes a first filtering unit, and the first filtering unit is configured to input 135 times the target output signal at the current moment into a first-order inertial filter; input 133 times the target output signal at the current moment into a second-order inertial filter; input 130 times the target output signal at the current moment into a third-order inertial filter; input 126 times the target output signal at the current moment into a fourth-order inertial filter; input 121 times the target output signal at the current moment into a fifth-order inertial filter; input 115 times the target output signal at the current moment into a sixth-order inertial filter; input 108 times the target output signal at the current moment into a seventh-order inertial filter; input 100 times the target output signal at the current moment into an eighth-order inertial filter; input 91 times the target output signal at the current moment into a ninth-order inertial filter; input 81 times the target output signal at the current moment into a tenth-order inertial filter; input 70 times the target output signal at the current moment into an eleventh-order inertial filter; input 58 times the target output signal at the current moment into a twelfth-order inertial filter; input 45 times the target output signal at the current moment into a thirteenth-order inertial filter; input 31 times the target output signal at the current moment into a fourteenth-order inertial filter; input 16 times the target output signal at the current moment into a fifteenth-order inertial filter; sum up the outputs of the first-order inertial filter, the second-order inertial filter, the third-order inertial filter, the fourth-order inertial filter, the fifth-order inertial filter, the sixth-order inertial filter, the seventh-order inertial filter, the eighth-order inertial filter, the ninth-order inertial filter, the tenth-order inertial filter, the eleventh-order inertial filter, the twelfth-order inertial filter, the thirteenth-order inertial filter, the fourteenth-order inertial filter, and the fifteenth-order inertial filter through the Laplace transfer function of the combined filter, and process the obtained sum through a preset proportional operation gain to obtain the filtered signal.

[0161] In some preferred embodiments, the Laplace transfer function of the combined filter is represented by the following formula:

[0162] ;

[0163] where f CF(s) is the Laplace transfer function of the combined filter, l is a dynamic variable used to calculate the gains of each order filter in the combined filter, i is a dynamic variable used to calculate the orders of each order filter in the combined filter, T AEFPI is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control, s is the Laplace operator, K OUT is the proportional operation gain.

[0164] In some preferred embodiments, the lead observation operation processing module includes a lead observation operation processing unit, and the lead observation operation processing unit is used to process the obtained difference through a preset lead observation proportional operator; the Laplace transfer function of the equivalent controller of the step of processing the obtained difference through the preset lead observation proportional operator is represented by the following formula:

[0165] ;

[0166] wherein, f AEFPI:LO (s) is the Laplace transfer function of the equivalent controller, K LO is the gain of the lead observation proportional operator, f CF (s) is the Laplace transfer function of the combined filter, s is the Laplace operator.

[0167] In some preferred embodiments, the lead observation operation processing module includes a second filtering unit, and the second filtering unit is used to obtain the target output signal in a preset time interval and the maximum value of the target output signal in the preset time interval; and calculate the sum of the target output signals in the preset time interval;

[0168] divide the sum of the target output signals in the preset time interval by the maximum value to obtain a target output statistical value; and calculate a first upper limit threshold according to the target output statistical value;

[0169] When the target output signal at the current moment is greater than or equal to the first upper limit threshold, reduce the target output signal at the current moment to the first upper limit threshold, and perform smoothing filtering on the reduced target output signal by using a smoothing filter constructed by the target output statistical value to obtain the filtered signal;

[0170] When the target output signal at the current moment is less than the first upper limit threshold, perform smoothing filtering on the target output signal at the current moment by using a smoothing filter constructed by the target output statistical value to obtain the filtered signal.

[0171] In some preferred embodiments, the proportional control gain is:

[0172] ;

[0173] wherein, K INT is the proportional control gain, and K AEFPI is the proportional gain of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control.

[0174] In some preferred embodiments, the Laplace transfer function of the preset integrator is:

[0175] ;

[0176] wherein, f I (s) is the Laplace transfer function of the preset integrator, s is the Laplace operator, and T AEFPI is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control.

[0177] In some preferred embodiments, the obtaining module 1401 includes a solving unit, and the solving unit is configured to:

[0178] Construct a multi-period system model of the denitration control system of the thermal power unit; the multi-period system model includes a first sub-model in units of days, a second sub-model in units of weeks, and a third sub-model in units of months, and the first sub-model, the second sub-model, and the third sub-model are connected through a data interaction interface;

[0179] Obtain the target operation data of the target load of the denitration control system of the thermal power unit, where the target operation data includes the inlet nitrogen oxide concentration, ammonia injection amount, inlet temperature, and inlet flow rate of the denitration control system;

[0180] Divide the target operation data according to the daily cycle, weekly cycle, and monthly cycle to obtain a daily cycle data set, a weekly cycle data set, and a monthly cycle data set;

[0181] Use the daily cycle data set to train the first sub-model, use the weekly cycle data set to train the second sub-model, use the monthly cycle data set to train the third sub-model, and when the first sub-model, the second sub-model, and the third sub-model all converge, obtain the trained multi-period system model, and determine the trained multi-period system model as the optimized system model;

[0182] Use the particle swarm optimization algorithm to determine the activation energy parameter and reaction rate parameter of the denitration reaction of the denitration control system of the thermal power unit; based on the activation energy parameter of the denitration reaction, the reaction rate parameter, and the target operation data, solve the optimized system model, and obtain the solution result according to the output of the optimized system model, and then obtain the signal to be processed.

[0183] In some preferred embodiments, the integration unit includes an operation data acquisition subunit, and the operation data acquisition subunit is used to acquire the historical operation data and historical load data of the denitration control system of the thermal power unit;

[0184] According to the historical load data, eliminate the historical operation data with the load outside the preset interval to obtain the first operation data;

[0185] Based on a preset fitting formula, perform fitting processing on the historical load data and the first operation data to obtain the functional relationship between the historical load data and the first operation data;

[0186] According to the preset inlet temperature constraint, use the functional relationship to determine the corresponding unit constraint load;

[0187] Through a preset operation load calculation model and the unit constraint load, dynamically calculate the target load, and then obtain the target operation data of the target load of the denitration control system of the thermal power unit.

[0188] In some preferred embodiments, the operation data acquisition subunit includes a dynamic calculation unit module, and the dynamic calculation unit module is used for: calculating the target load according to the following formula:

[0189] L m =L d +L p -f(T);

[0190] Wherein, L m is the target load, T is the current inlet temperature of the denitration control system of the thermal power unit, L d is the unit constraint load, L p is the current load of the denitration control system of the thermal power unit, and f(T) is the result of preprocessing the current inlet temperature.

[0191] In some preferred embodiments, the solving unit includes a solving subunit, and the solving subunit is configured to: set the denitration reaction parameters of the denitration control system of the thermal power unit, where the denitration reaction parameters include reaction type parameters, component parameters, and physical property parameters; set the type, dimension, and pressure of the reaction equipment according to the denitration reaction parameters to obtain first configuration parameters; set the corresponding simulated reaction process, catalyst parameters, bed void fraction, and stoichiometry according to the denitration reaction parameters to obtain second configuration parameters; construct a particle swarm model of the denitration control system of the thermal power unit according to the first configuration parameters and the second configuration parameters, and calculate the denitration reaction activation energy parameter and the denitration reaction rate parameter under the preset denitration reaction activation energy constraint, denitration reaction rate constraint, and target constraint; where the target constraint is set according to the nitrogen oxide concentration output by the optimization system model and the actual outlet nitrogen oxide concentration of the denitration control system of the thermal power unit.

[0192] For the device embodiment, since it is basically similar to the method embodiment, the relevant description can refer to the partial description of the method embodiment.

[0193] The present invention application also provides a signal processing device based on proportional-integral control, including a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations of the signal processing method based on proportional-integral control.

[0194] Figure 15 The structural schematic diagram of the embodiment of the signal processing device based on proportional-integral control of the present invention is shown. The specific implementation of the signal processing device based on proportional-integral control is not limited in the specific embodiments of the present invention.

[0195] As Figure 15 shown, the signal processing device based on proportional-integral control may include: a processor 1502, a communications interface 1504, a memory 1506, and a communication bus 1508.

[0196] Wherein: the processor 1502, the communication interface 1504, and the memory 1506 complete mutual communication through the communication bus 1508. The communication interface 1504 is used to communicate with network elements of other devices such as clients or other servers. The processor 1502 is used to execute the program 1510, and specifically can execute the relevant steps in the embodiment of the signal processing method based on proportional-integral control described above.

[0197] Specifically, the program 1510 may include program code that includes computer-executable instructions.

[0198] The processor 1502 may be a central processing unit (CPU), or a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the signal processing device based on proportional-integral control may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0199] The memory 1506 is used to store the program 1510. The memory 1506 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0200] In addition, another embodiment of the present invention application further provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction runs on a signal processing device / apparatus based on proportional-integral control, the signal processing device / apparatus based on proportional-integral control is caused to execute the operations of the signal processing method based on proportional-integral control.

[0201] Wherein, if the modules integrated in the signal processing device / apparatus based on proportional-integral control are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0202] The specific embodiments described above further elaborate on the objective, technical solution and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not intended to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A signal processing method based on proportional-integral control, characterized in that: include: Obtain the current signal to be processed and the previous feedback signal of the denitration control system of the thermal power unit; and obtaining a deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment; The deviation value is processed by a preset proportional control gain to obtain a proportional processing result; Obtaining an integral signal of the proportional processing result through a preset integrator; Obtaining a target output signal at a current moment based on the sum of the proportional processing result and the integral signal; Wherein, the feedback signal at the previous moment is obtained according to the advance observation operation processing result of the target output signal at the previous moment; The proportional control gain is: ； Among them, K INT is the proportional control gain, K AEFPI It is the proportional gain of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control.

2. A signal processing method based on proportional-integral control as claimed in claim 1, characterized in that: The signal processing method based on proportional-integral control also includes: Filtering the target output signal at the current moment to obtain a filtered signal; The target output signal at the current moment is subtracted from the filtered signal, and the obtained difference is subjected to an advance observation ratio operation to obtain a feedback signal at the current moment.

3. A signal processing method based on proportional-integral control as claimed in claim 2, characterized in that: The filtering process is implemented by a combination filter, and the combination filter includes a first-order inertial filter, a second-order inertial filter, a third-order inertial filter, a fourth-order inertial filter, a fifth-order inertial filter, a sixth-order inertial filter, a seventh-order inertial filter, an eighth-order inertial filter, a ninth-order inertial filter, a tenth-order inertial filter, an eleventh-order inertial filter, a twelfth-order inertial filter, a thirteenth-order inertial filter, a fourteenth-order inertial filter, and a fifteenth-order inertial filter; The target output signal at the current moment is filtered to obtain a filtered signal, specifically: Input the target output signal of the current moment at 135 times into the first order inertial filter; input the target output signal of the current moment at 133 times into the second order inertial filter; input the target output signal of the current moment at 130 times into the third order inertial filter; input the target output signal of the current moment at 126 times into the fourth order inertial filter; input the target output signal of the current moment at 121 times into the fifth order inertial filter; input the target output signal of the current moment at 115 times into the sixth order inertial filter; input the target output signal of the current moment at 108 times into the seventh order inertial filter; input the target output signal of the current moment at 100 times into the seventh order inertial filter; input the target output signal of the current moment at 100 times into the sixth order inertial filter; input the target output signal of the current moment at 108 times into the seventh order inertial filter; input the target output signal of the current moment at 100 times into the The target output signal is input into an eighth-order inertial filter; 91 times the target output signal at the current moment is input into a ninth-order inertial filter; 81 times the target output signal at the current moment is input into a tenth-order inertial filter; 70 times the target output signal at the current moment is input into an eleventh-order inertial filter; 58 times the target output signal at the current moment is input into a twelfth-order inertial filter; 45 times the target output signal at the current moment is input into a thirteenth-order inertial filter; 31 times the target output signal at the current moment is input into a fourteenth-order inertial filter; 16 times the target output signal at the current moment is input into a fifteenth-order inertial filter; The outputs of the first-order inertial filter, the second-order inertial filter, the third-order inertial filter, the fourth-order inertial filter, the fifth-order inertial filter, the sixth-order inertial filter, the seventh-order inertial filter, the eighth-order inertial filter, the ninth-order inertial filter, the tenth-order inertial filter, the eleventh-order inertial filter, the twelfth-order inertial filter, the thirteenth-order inertial filter, the fourteenth-order inertial filter and the fifteenth-order inertial filter are summed up through the Laplace transfer function of the combined filter, and the obtained sum is processed through a preset proportional operation gain to obtain the filtered signal.

4. A signal processing method based on proportional-integral control as claimed in claim 3, characterized in that: The Laplace transfer function of the combined filter is expressed by the following equation: ； Among them, f CF (s) is the Laplace transfer function of the combined filter, l is a dynamic variable used to calculate the gain of each order filter in the combined filter, i is a dynamic variable used to calculate the order of each order filter in the combined filter, T AEFPI is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control, s is the Laplace operator, K OUT is the proportional operation gain.

5. A signal processing method based on proportional-integral control as claimed in claim 3, characterized in that: The obtained difference is subjected to an advance observation ratio operation, specifically: Processing the obtained difference by a preset advance observation ratio operator; The Laplace transfer function of the equivalent controller of the step of processing the obtained difference by the preset advance observation ratio operator is expressed by the following formula: ； Among them, f AEFPI:LO (s) is the Laplace transfer function of the equivalent controller, K LO is the gain of the lead observation ratio operator, f CF (s) is the Laplace transfer function of the combined filter, and s is the Laplace operator.

6. A signal processing method based on proportional-integral control as claimed in claim 2, characterized in that: The target output signal at the current moment is filtered to obtain a filtered signal, specifically: Obtaining a target output signal of a preset time interval and a maximum value of the target output signal of the preset time interval; and calculating the sum of the target output signals of the preset time interval; Dividing the sum of the target output signals in the preset time interval by the maximum value to obtain a target output statistical value; and calculating a first upper threshold value according to the target output statistical value; When the target output signal at the current moment is greater than or equal to the first upper limit threshold, the target output signal at the current moment is reduced to the first upper limit threshold, and the reduced target output signal is smoothed and filtered using a smoothing filter constructed by the target output statistical value to obtain the filtered signal; When the target output signal at the current moment is less than the first upper limit threshold, the target output signal at the current moment is smoothed and filtered by a smoothing filter constructed by using the target output statistical value to obtain the filtered signal.

7. A signal processing method based on proportional-integral control as claimed in claim 1, characterized in that: The Laplace transfer function of the preset integrator is: ； Among them, f I (s) is the Laplace transfer function of the preset integrator, s is the Laplace operator, T AEFPI It is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control.

8. The signal processing method based on proportional-integral control according to claim 1, characterized in that: The method of obtaining the signal to be processed of the denitration control system of the thermal power unit at the current moment is specifically as follows: Constructing a multi-period system model of the denitration control system of the thermal power unit; the multi-period system model includes a first sub-model based on days, a second sub-model based on weeks, and a third sub-model based on months, wherein the first sub-model, the second sub-model, and the third sub-model are connected via a data interaction interface; Acquiring target operation data of the target load of the denitration control system of the thermal power unit, wherein the target operation data includes an inlet nitrogen oxide concentration, an injection amount of ammonia, an inlet temperature and an inlet flow rate of the denitration control system; Dividing the target operation data into daily cycles, weekly cycles and monthly cycles to obtain a daily cycle data set, a weekly cycle data set and a monthly cycle data set; The first sub-model is trained using the daily cycle data set, the second sub-model is trained using the weekly cycle data set, and the third sub-model is trained using the monthly cycle data set, and when the first sub-model, the second sub-model and the third sub-model are all converged, a trained multi-cycle system model is obtained, and the trained multi-cycle system model is determined as the optimized system model; The particle swarm optimization algorithm is used to determine the denitration reaction activation energy parameters and the denitration reaction rate parameters of the denitration control system of the thermal power unit; based on the denitration reaction activation energy parameters, the denitration reaction rate parameters and the target operation data, the optimization system model is solved, and a solution result is obtained according to the output of the optimization system model, thereby obtaining the signal to be processed.

9. A signal processing method based on proportional-integral control as claimed in claim 8, characterized in that: The step of obtaining the target operation data of the target load of the denitration control system of the thermal power unit is specifically: Acquiring historical operation data and historical load data of the denitration control system of the thermal power unit; According to the historical load data, historical operation data with load outside a preset range is eliminated to obtain first operation data; Based on a preset fitting formula, fitting processing is performed on the historical load data and the first operating data to obtain a functional relationship between the historical load data and the first operating data; According to the preset inlet temperature constraint, the corresponding unit constraint load is determined using the functional relationship; The target load is dynamically calculated through a preset operation load calculation model and the unit constraint load, thereby obtaining target operation data of the target load of the denitration control system of the thermal power unit.

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