Signal processing method based on proportional-integral control

Abstract

The invention provides a signal processing method based on proportional-integral control. The signal processing method comprises the steps that a to-be-processed signal of a thermal power generating unit denitration control system at the current moment and a feedback signal of the thermal power generating unit denitration control system at the last moment are obtained; obtaining a deviation value of the two; processing the deviation value through 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 an advanced observation operation processing result of the target output signal at the previous moment. Proportional-integral control and advanced observation operation processing are separated, the phenomenon of output jump is remarkably restrained, and the influence of external disturbance or given input change can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of industrial process control, and in particular 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) method is widely used because its parameter setting is relatively simple. However, as the power system's requirements for thermal power unit control performance increase, the traditional proportional-integral control gradually reveals its limitations in feedback control performance.

[0003] At present, in order to overcome the limitations of traditional proportional-integral control, Acceleration Engineering Fastest Proportional-Integral Control (AEFPI control) is one of the solutions. This control method uses an acceleration strategy to improve the response speed of the denitration control system of thermal power units. However, the pure lag of the controlled process of many thermal power unit denitration control systems is relatively high, 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 accelerated engineering fastest proportional-integral control, an improved proportional-integral control method is urgently needed to further optimize the target output signal quality of this type of control method. Summary of the invention

[0004] The present invention 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 a thermal power unit is not smooth enough and improve the quality of the output signal.

[0005] In order to solve the above technical problems, the first aspect of the present invention application provides a signal processing method based on proportional-integral control, comprising: Obtaining a signal to be processed at a current moment and a feedback signal at a previous moment of a denitration control system of a thermal power unit; and obtaining a deviation value between the signal to be processed at a current moment and the feedback signal at a previous moment; The deviation value is processed by a preset proportional control gain to obtain a proportional processing result; the sum of the integral signals of the proportional processing result is obtained by a preset integrator; and the target output signal at the current moment is obtained based on the proportional processing result and the integral signal; 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.

[0006] In the implementation of the present invention, the feedback signal at the previous moment is obtained through the result of the advance observation operation processing 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 target output signal at the current moment is obtained by proportional-integral control of the deviation value. In this way, compared with the processing method of the prior art that places both the proportional-integral control and the advance observation operation processing on the inner side of the loop of the feedback control system, the present application places the proportional-integral control part on the inner side of the loop and the advance observation operation processing on the outer side of the loop during the processing of the signal to be processed of the denitration control system of the thermal power unit, that is, the proportional-integral control and the advance observation operation processing are separated in a feedback control system, which significantly suppresses the phenomenon of target output signal jumps. When facing complex working conditions such as rapidly changing loads and coal quality changes of the thermal power unit, the influence of external disturbances or changes in given inputs can be reduced, and the smoothness and quality of the target output signal of the denitration control system of the thermal power unit at the current moment are effectively improved.

[0007] 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; wherein, The acquisition module is used to acquire the to-be-processed signal of the thermal power unit denitration control system at the current moment and the feedback signal at the previous moment; and to acquire the deviation value between the to-be-processed signal at the current moment and the feedback signal at the previous moment; 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 a target output signal at the current moment based on the sum of the proportional processing result and the integral signal; 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.

[0008] The third aspect of the present application provides a proportional-integral control device, including a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the signal processing method based on proportional-integral control.

[0009] The fourth aspect of the present application provides a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction is executed on a signal processing device / apparatus based on proportional-integral control, the signal processing device / apparatus based on proportional-integral control performs the operation of the signal processing method based on proportional-integral control. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 : A flow chart of the first embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0011] Figure 2 : A flow chart of a second embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0012] Figure 3 : A schematic diagram of the principle of a second embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0013] Figure 4 : A flow chart of the third embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0014] Figure 5 : A schematic diagram of the principle of the third embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0015] Figure 6 : A schematic diagram of the principles of the fourth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0016] Figure 7 : A schematic diagram of the principles of the fifth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0017] Figure 8 : A flow chart of the sixth embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0018] Fig. 9 : A flow chart of the seventh embodiment of the signal processing method based on proportional-integral control provided by the present invention.

[0019] Fig.10 : A schematic diagram of the principles of an application example of the signal processing method based on proportional-integral control provided by the present invention.

[0020] Fig.11 : It is a principle schematic diagram of an application example of the accelerated engineering fastest proportional-integral control method of the prior art.

[0021] Fig.12 : A schematic diagram of simulation results of an application example of the signal processing method based on proportional-integral control provided by the present invention.

[0022] Fig.13 : It is a schematic diagram of simulation results of an application example of the signal processing method based on proportional-integral control in the prior art.

[0023] Fig.14 : A schematic structural diagram of the first embodiment of the signal processing device based on proportional-integral control provided by the present invention.

[0024] Fig.15 : A schematic structural diagram of the first embodiment of the signal processing device based on proportional-integral control provided by the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1 Please refer to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the signal processing method based on proportional-integral control provided by the present invention. The first embodiment includes steps S101 to S102; wherein: Step S101, obtaining a signal to be processed at a current moment and a feedback signal at a previous moment of a denitration control system of a thermal power unit; and obtaining a deviation value between the signal to be processed at a current moment and the feedback signal at a previous moment.

[0027] The signal processing method based on proportional-integral control described in this embodiment can be applied to the denitration control system of thermal power units. In order to solve the problem of absorbing new energy, the peak-shaving effect of thermal power units provides a favorable guarantee for the power grid to absorb 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 denitration (control) systems, it is necessary to process the signals involved in them to face complex operating environments and working conditions.

[0028] Step S101 obtains the signal to be processed of the denitration control system of the thermal power unit at the current moment, and the signal to be processed may be nitrogen oxides NO x The feedback signal at the previous moment may be a nitrogen oxide NO x The output value of the previous moment is used to realize the negative feedback control of the denitrification control system of the thermal power unit. x The output value includes nitrogen oxides NO x process value and external disturbances.

[0029] Generally, the above external disturbances are caused by the instability of thermal power units or denitrification (control) systems. Thermal power units often face complex working conditions such as rapidly changing loads and changes in coal quality. Therefore, the denitrification control system will be subject to certain interference, which in turn affects the stable operation of the thermal power units or denitrification control systems.

[0030] Exemplarily, the feedback signal at the previous moment in this step can be obtained according to the advance observation operation processing result of the target output signal at the previous moment.

[0031] Step S101 obtains the deviation value between the signal to be processed at the current moment and the feedback signal at the previous moment, thereby laying a foundation for the proportional-integral control processing of the subsequent step S102.

[0032] Step S102, processing the deviation value through a preset proportional control gain to obtain a proportional processing result; obtaining an integral signal of the proportional processing result through a preset integrator; and obtaining a target output signal at the current moment based on the sum of the proportional processing result and the integral signal.

[0033] 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 by a proportional control gain. For example, the proportional control gain can be: ; Among them, K INT is the proportional control gain, dimensionless, 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, and the unit is dimensionless.

[0034] It can be understood that the target output signal at the current moment can be processed through the advance observation operation to obtain the feedback signal at the current moment, which is used to process the signal to be processed at the next moment, and then obtain the target output signal at the next moment.

[0035] From the above, the embodiment of the present application is implemented, the feedback signal of the previous moment is obtained through the advance observation operation processing result of the target output signal of the previous moment, the deviation value is obtained based on the signal to be processed at the current moment and the feedback signal of the previous moment, and the deviation value is subjected to proportional-integral control to obtain the target output signal of the current moment. In this way, compared with the processing method of the prior art that places both the proportional-integral control and the advance observation operation processing on the inner side of the loop of the feedback control system, this embodiment places the proportional-integral control part on the inner side of the loop and the advance observation operation processing on the outer side of the loop during the processing of the signal to be processed of the denitration control system of the thermal power unit, that is, the proportional-integral control and the advance observation operation processing are separated in a feedback control system, which significantly suppresses the phenomenon of target output signal jump. When facing complex working conditions such as rapidly changing load and coal quality changes of the thermal power unit, the influence of external disturbances or given input changes can be reduced, and the smoothness and quality of the target output signal of the denitration control system of the thermal power unit at the current moment of the denitration control system of the thermal power unit are effectively improved.

[0036] exist Figure 1 Based on the embodiment shown, Figure 2 A flow chart of a second embodiment of a signal processing method based on proportional-integral control of the present invention is provided. Figure 3 The schematic diagram of the principle of the second embodiment of the signal processing method based on proportional-integral control of the present invention is provided. It should be noted that the second embodiment is similar to Figure 1 The same steps will not be repeated here.

[0037] like Figure 2 As shown, in some implementations of the second embodiment, after step S102, steps S201 to S202 are included, which are described in detail as follows: Step S201 , filtering the target output signal at the current moment to obtain a filtered signal (at the current moment).

[0038] Step S202, subtracting the filtered signal (at the current moment) from the target output signal at the current moment, performing an advance observation ratio operation on the obtained difference, and obtaining a feedback signal at the current moment.

[0039] exist Figure 1 Based on the embodiment shown, Figure 4 A flow chart of a third embodiment of a signal processing method based on proportional-integral control of the present invention is provided. Figure 5 The schematic diagram of the principle of the third embodiment of the signal processing method based on proportional-integral control of the present invention is provided. Figure 1 The same steps will not be repeated here.

[0040] like Figure 4 As shown, before step S101, the third embodiment includes steps S401 to S402, which are described in detail as follows: Step S401 , filtering the target output signal at the previous moment to obtain a filtered signal (at the previous moment).

[0041] Step S402, subtract the filtered signal (at the previous moment) from the target output signal at the previous moment, perform an advance observation ratio operation on the obtained difference, and obtain the feedback signal at the previous moment.

[0042] It can be understood that the advance observation calculation method adopted by the second embodiment and the third embodiment is basically the same. The difference between the two is that the second embodiment processes the target output signal at the current moment, and the third embodiment processes the target output signal at the previous moment.

[0043] like Figure 3 and Figure 5 As shown, the subtraction processing of 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 end with the symbol "+" is the minuend end, and the input end with the symbol "-" is the subtrahend end).

[0044] In addition, the advance observation ratio operation in step S202 and / or step S402 can be implemented by an advance observation ratio operator. The gain K of the advance observation ratio operator LO The value can be 11 and the unit is dimensionless.

[0045] On the basis of 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. The same steps as those of the first embodiment, the second embodiment and the third embodiment are not repeated here.

[0046] The filtering process of step S201 and / or step S301 can be implemented by a combination filter. 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.

[0047] Step S201 and / or step S301 filter the target output signal at the current moment 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.

[0048] 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.

[0049] In the implementation of this embodiment, the combined filter is a filter designed after separating the proportional-integral control part and the advance observation processing part. The design of each order inertial filter and the design of each order inertial filter input can enable the combined filter to have the ability to quickly track the input signal (so that the output signal can accelerate the tracking of the input signal), and its output signal can reach a stable state in a short time, thereby realizing rapid control of the system. At the same time, the combined filter design can accurately track the target signal and improve the control efficiency of the denitration control system of the thermal power unit. With the tracking of the input signal by the output signal, the denitration control system of the thermal power unit can respond quickly when facing complex and changeable working conditions, and maintain stable tracking performance, effectively deal with various interferences and uncertainties, and ensure the stable operation of the system. It is not only suitable for simple tracking problems, but also can maintain excellent tracking performance in complex engineering environments, showing strong adaptability.

[0050] Exemplarily, the Laplace transfer function of a first-order inertial filter is: ; Among them, f O1IF (s) is the Laplace transfer function of the first-order inertial filter, and the numerator represents multiplication by 135; 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 described in this application, in seconds, and s is the Laplace operator.

[0051] Similarly, the Laplace transfer function of the second-order inertial filter is: ; In the formula, f O2IF (s) is the Laplace transfer function of the second-order inertial filter, and the numerator represents multiplication by 133.

[0052] The Laplace transfer function of the third-order inertial filter is: ; In the formula, f O3IF (s) is the Laplace transfer function of the third-order inertial filter, and the numerator represents multiplication by 130.

[0053] The Laplace transfer function of the fourth-order inertial filter is: ; In the formula, f O4IF (s) is the Laplace transfer function of the fourth-order inertial filter, and the numerator represents multiplication by 126.

[0054] The Laplace transfer function of the fifth-order inertial filter is: ; In the formula, f O5IF (s) is the Laplace transfer function of the fifth-order inertial filter, and the numerator represents multiplication by 121.

[0055] The Laplace transfer function of the sixth-order inertial filter is: ; In the formula, f O6IF (s) is the Laplace transfer function of the sixth-order inertial filter, and the numerator represents multiplication by 115.

[0056] The Laplace transfer function of the seventh-order inertial filter is: ; In the formula, f O7IF (s) is the Laplace transfer function of the seventh-order inertial filter, and the numerator represents multiplication by 108.

[0057] The Laplace transfer function of the eighth-order inertial filter is: ; In the formula, f O8IF (s) is the Laplace transfer function of the eighth-order inertial filter, and the numerator represents multiplication by 100.

[0058] The Laplace transfer function of the ninth-order inertial filter is: ; In the formula, f O9IF (s) is the Laplace transfer function of the ninth-order inertial filter, and the numerator represents multiplication by 91.

[0059] The Laplace transfer function of the tenth-order inertial filter is: ; In the formula, f O10IF (s) is the Laplace transfer function of the tenth-order inertial filter, and the numerator represents multiplication by 81.

[0060] The Laplace transfer function of the eleventh-order inertial filter is: ; In the formula, f O11IF (s) is the Laplace transfer function of the eleventh-order inertial filter, and the numerator represents multiplication by 70.

[0061] The Laplace transfer function of the twelfth-order inertial filter is: ; In the formula, f O12IF (s) is the Laplace transfer function of the twelfth-order inertial filter, and the numerator represents multiplication by 58.

[0062] The Laplace transfer function of the thirteenth-order inertial filter is: ; In the formula, f O13IF (s) is the Laplace transfer function of the thirteenth-order inertial filter, and the numerator represents multiplication by 45.

[0063] The Laplace transfer function of the fourteenth-order inertial filter is: ; In the formula, f O14IF (s) is the Laplace transfer function of the fourteenth-order inertial filter, and the numerator represents multiplication by 31.

[0064] The Laplace transfer function of the fifteenth-order inertial filter is: ; In the formula, fO15IF (s) is the Laplace transfer function of the fifteenth-order inertial filter, and the numerator represents multiplication by 16.

[0065] The Laplace transfer function of the combined filter can be expressed as follows: ; 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, 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 proportional operation gain, in dimensionless units. In some embodiments, the proportional operation gain may be K OUT =1 / 136.

[0066] In addition, in some preferred implementations, the step S202 and / or the step S402 performs an advanced observation ratio operation on the obtained difference, 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, in dimensionless units. In some embodiments, K LO The value can be 11, f CF (s) is the Laplace transfer function of the combined filter.

[0067] exist Figure 1 Based on the embodiment shown, Figure 7 FIG. 5 is a schematic diagram showing the principle of step S102 of the fifth embodiment of the signal processing method based on proportional-integral control of the present invention. Figure 1 The same steps as those in the illustrated embodiment will not be repeated in this embodiment.

[0068] The step S102 of obtaining the integral signal of the proportional processing result by a preset integrator is specifically: The Laplace transfer function of the preset integrator is: ; Among them, f I (s) is the Laplace transfer function of the preset integrator, TAEFPI is the time constant of the equivalent acceleration type engineering fastest proportional-integral controller of the signal processing method based on proportional-integral control, in seconds, and s is the Laplace operator.

[0069] exist Figure 2 Based on the embodiment shown, Figure 8 A flow chart of step S201 of a sixth embodiment of a signal processing method based on proportional-integral control is provided. Figure 2 The same steps will not be repeated here. Figure 8 As shown, step S201 includes steps S801 to S804, which are described in detail as follows: Step S801, obtaining a target output signal in a preset time interval and a maximum value of the target output signal in the preset time interval; and calculating the sum of the target output signals in the preset time interval.

[0070] Step S802: 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 value according to the target output statistical value.

[0071] Step S803, 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 using the target output statistical value to obtain the filtered signal.

[0072] Step S804: 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 by using a smoothing filter constructed by the target output statistical value to obtain the filtered signal.

[0073] In this embodiment, in view of the problem that the target output signal may be too large or too small in some cases, it is impossible to obtain a statistical quantity with reference value at a larger or smaller location. In order to overcome this problem, this embodiment obtains the target output signal of a preset time interval and the maximum value of the target output signal of the preset time interval; divides the sum of the target output signals of 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, the target output signal at the current moment is reduced to the first upper limit threshold, and the smoothing filter constructed by the target output statistical value is used to smooth and filter the reduced target output signal to obtain the filtered signal; when the target output signal at the current moment is less than the first upper limit threshold, the smoothing filter constructed by the target output statistical value is used to smooth and filter the reduced target output signal to obtain the filtered signal. In this way, the target output signal can be smoothed and filtered by a smoothing filter with non-fixed parameters (target output statistical value) to obtain an effective and high-quality filtered signal, so as to facilitate the processing of subsequent steps.

[0074] exist Figure 1 Based on the embodiment shown, Fig. 9 A flow chart of step S101 of a seventh embodiment of a signal processing method based on proportional-integral control is provided. Figure 1 The same steps will not be repeated here. Fig. 9 As shown, step S101 includes steps S901 to S905, which are described in detail as follows: Step S901, constructing a multi-period system model of the denitrification 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, and the first sub-model, the second sub-model and the third sub-model are connected through a data interaction interface.

[0075] Step S902, obtaining target operation data of the target load of the denitration control system of the thermal power unit, wherein the target operation data includes the inlet nitrogen oxide concentration, ammonia injection amount, inlet temperature and inlet flow rate of the denitration control system.

[0076] Step S903: Divide the target operation data into daily cycles, weekly cycles and monthly cycles to obtain daily cycle data sets, weekly cycle data sets and monthly cycle data sets.

[0077] Step S904, using the daily cycle data set to train the first sub-model, using the weekly cycle data set to train the second sub-model, using the monthly cycle data set to train the third sub-model, 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.

[0078] Step S905, using a particle swarm optimization algorithm 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.

[0079] In this embodiment, the multi-period system model can be constructed by Aspen Plus software, the particle swarm optimization algorithm (model) can be constructed by MATLAB software, and the particle swarm optimization algorithm (model) and the optimization system model can be connected through a data interface and interact with data. 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, and 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 data set, a weekly cycle data set, and a monthly cycle data set; each data set is used for training different sub-models to obtain a trained multi-period system model, and the trained multi-period system model is determined as an optimized system model; and the denitration reaction activation energy parameter and the denitration reaction rate parameter of the denitration control system of the thermal power unit given by the particle swarm optimization algorithm are solved. Compared with the prior art directly constructing a single system model, the solution result is more in line with the actual denitration reaction environment of the thermal power system, and the optimized system model constructed in this embodiment also has better performance than the existing single system model.

[0080] In some preferred embodiments, the target operating data of the target load of the denitration control system of the thermal power unit is obtained by: obtaining the historical operating data and historical load data of the denitration control system of the thermal power unit; according to the historical load data, eliminating the historical operating data with load outside the preset interval to obtain the first operating data; based on a preset fitting formula, fitting the historical load data and the first operating data to obtain the functional relationship between the historical load data and the first operating data; according to the preset inlet temperature constraint, determining the corresponding unit constraint load by using the functional relationship; dynamically calculating the target load by using the preset operating load calculation model and the unit constraint load, and then obtaining the target operating data of the target load of the denitration control system of the thermal power unit.

[0081] In this preferred embodiment, after eliminating historical operating data with loads outside a preset range and obtaining first operating data, the first operating data and the historical load data are fit-processed to obtain a functional relationship between the historical load data and the first operating data, thereby simulating the relationship between the operating parameters and the load of the thermal power unit denitrification control system. Furthermore, under the preset inlet temperature constraint condition, the corresponding unit constraint load is determined using the functional relationship, and the target load is dynamically calculated using a preset operating load calculation model and the unit constraint load, thereby obtaining target operating data of the target load of the thermal power unit denitrification control system. The target operating data obtained in this way is closer to the actual operating conditions and actual load of the thermal power unit denitrification control system, thereby improving the accuracy of the obtained target operating data.

[0082] Exemplarily, the target load is dynamically calculated by using a preset operating load calculation model and the unit constraint load, specifically: the target load is calculated according to the following formula: L m =L d +L p -f(T); Among them, 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.

[0083] In some preferred embodiments, the particle swarm optimization algorithm is used to determine the denitration reaction activation energy parameters and denitration reaction rate parameters of the denitration control system of the thermal power unit, specifically: setting the denitration reaction parameters of the denitration control system of the thermal power unit, the denitration reaction parameters including reaction type parameters, component parameters and physical property parameters; according to the denitration reaction parameters, setting the type, dimension and pressure of the reaction equipment to obtain the first configuration parameters; according to the denitration reaction parameters, setting the corresponding simulation reaction process, catalyst parameters, bed void ratio and stoichiometry to obtain the second configuration parameters; according to the first configuration parameters and the second configuration parameters, constructing a particle swarm model of the denitration control system of the thermal power unit, and under the preset denitration reaction activation energy constraints, denitration reaction rate constraints and target constraints, calculating the denitration reaction activation energy parameters and denitration reaction rate parameters; wherein 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.

[0084] In implementing this preferred embodiment, the first configuration parameter is obtained by setting the type, dimension and pressure of the reaction equipment; the second configuration parameter is obtained by setting the corresponding simulated reaction process, catalyst parameters, bed porosity and stoichiometry; and the first configuration parameter and the second configuration parameter are used to construct a particle swarm model through MATLAB software, and the denitrification reaction activation energy parameter and the denitrification reaction rate parameter are calculated under the preset denitrification reaction activation energy constraint, denitrification reaction rate constraint and target constraint. Compared with the prior art, the denitrification reaction environment can be further refined, and the denitrification reaction activation energy parameter and the denitrification reaction rate parameter can be accurately obtained while accurately constructing the particle swarm model.

[0085] In addition, the present invention also provides an application example of a signal processing method based on proportional-integral control, such as Fig.10 and Fig.11 shown. Fig.10 FIG. 1 is a schematic diagram showing the principle of a signal processing method based on proportional-integral control adopted in the first embodiment or other embodiments of the present application. Fig.11 It is a schematic diagram of the principle of the accelerated engineering fastest proportional-integral control method of the prior art.

[0086] For comparison, Fig.11 The Laplace function of the medium acceleration engineering maximum proportional-integral controller is: ; 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 AEFPI is the proportional gain of the accelerating engineering fastest proportional-integral controller, unit is dimensionless; TAEFPI is the time constant of the fastest proportional-integral controller of the acceleration engineering, in seconds; I is the order of each order filter, ranging from 1 to 16 dimensionless positive integers.

[0087] It can be seen that compared with the prior art, the embodiment of the present application places the proportional-integral control part on the inside of the loop and the advance observation operation processing on the outside of the loop in the process of processing the pending signal of the denitrification control system of the thermal power unit, and separates the proportional-integral control and the advance observation operation processing in a feedback control system.

[0088] And in Fig.10 and Fig.11 In the paper, 100% pure lag process processing and disturbance model (Disturbancemodel, abbreviated as DM) are adopted, as follows: ; ; Among them, s is the Laplace operator, f P (s) represents the transfer function of 100% pure hysteresis; f DM (s) is the transfer function of the disturbance model.

[0089] The disturbance model of the application example is intended to simulate the complex operating conditions and variable loads actually faced by the denitrification control system of a thermal power unit, so as to reflect the difference between the signal processing method based on proportional-integral control of the present application and the existing signal processing method based on proportional-integral control.

[0090] The relevant parameters of the signal processing method based on proportional-integral control of the present invention obtained by the mathematical optimization method include: K AEFPI =0.6379, T AEFPI =137.5 seconds.

[0091] The external disturbance (disturbance model) can be a ramp function, the process is given as a unit step, the ramp function length is 1000 seconds, and the ramp function rate is 500 -1 / second, the simulation results are as follows Fig.12 shown. Fig.12 The vertical coordinate PV AEFPI (t) is the target output signal of the signal processing method based on proportional-integral control of the present application, and its process overshoot is 0. The horizontal axis t is time (in seconds). Fig.12 The adjustment time of the application example shown is 388.5 seconds, and the adjustment time refers to the time for the target output signal to enter 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.

[0092] For comparison purposes, Fig.13 The simulation results of the prior art are given. Under the same given parameters, the signal processing method based on proportional-integral control of the accelerated engineering speed of the prior art obviously has obvious jump problems. However, the present application separates the proportional-integral control part and the advance observation operation processing part, making the target output signal smoother.

[0093] Correspondingly, such as Fig.14 As shown, Fig.14 A schematic diagram of the structure of a first embodiment of a signal processing device 1400 based on proportional-integral control according to the present invention is provided. The signal processing device 1400 based on proportional-integral control provided by the present invention comprises an acquisition module 1401 and a proportional-integral control module 1402; wherein: The acquisition module 1401 is used to acquire the to-be-processed signal of the thermal power unit denitration control system at the current moment and the feedback signal at the previous moment; and to acquire the deviation value between the to-be-processed signal at the current moment and the feedback signal at the previous moment; 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 a target output signal at the current moment based on the sum of the proportional processing result and the integral signal; 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.

[0094] In some preferred embodiments, the signal processing device 1400 based on proportional-integral control also includes an advance observation operation processing module, which 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 an advance observation proportional operation on the obtained difference to obtain a feedback signal at the current moment.

[0095] In some preferred embodiments, 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; In some preferred embodiments, the advanced observation operation processing module includes a first filtering unit, which is used 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; and input 81 times the target output signal at the current moment into a tenth-order inertial filter; Inputting 70 times the target output signal at the current moment into an eleventh-order inertial filter; inputting 58 times the target output signal at the current moment into a twelfth-order inertial filter; inputting 45 times the target output signal at the current moment into a thirteenth-order inertial filter; inputting 31 times the target output signal at the current moment into a fourteenth-order inertial filter; inputting 16 times the target output signal at the current moment into a fifteenth-order inertial filter; summing 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 processing the obtained sum through a preset proportional operation gain to obtain the filtered signal.

[0096] In some preferred embodiments, the Laplace transfer function of the combined filter is expressed by the following formula: ; 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.

[0097] In some preferred embodiments, the advance observation operation processing module includes an advance observation operation processing unit, and the advance observation operation processing unit is used to process the obtained difference through a preset advance observation ratio operator; the Laplace transfer function of the equivalent controller of the step of processing the obtained difference through a 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.

[0098] In some preferred embodiments, the advance observation operation processing module includes a second filtering unit, which is used to obtain the target output signal of a preset time interval and the maximum value of the target output signal of the preset time interval; and calculate 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 using 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.

[0099] In some preferred embodiments, 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.

[0100] In some preferred implementations, 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, TAEFPI 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.

[0101] In some preferred implementations, the acquisition module 1401 includes a solution unit, and the solution unit is used to: 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.

[0102] In some preferred embodiments, the integration unit includes an operation data acquisition subunit, and the operation data acquisition subunit is used to acquire 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.

[0103] In some preferred embodiments, the operation data acquisition subunit includes a dynamic calculation unit module, and the dynamic calculation unit module is used to calculate the target load according to the following formula: L m =L d +L p -f(T); Among them, 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.

[0104] In some preferred embodiments, the solution unit includes a solution subunit, which is used to: set the denitration reaction parameters of the denitration control system of the thermal power unit, and 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 parameters; according to the denitration reaction parameters, set the corresponding simulation reaction process, catalyst parameters, bed void ratio and stoichiometry to obtain the second configuration parameters; according to the first configuration parameters and the second configuration parameters, construct a particle swarm model of the denitration control system of the thermal power unit, and under the preset denitration reaction activation energy constraint, denitration reaction rate constraint and target constraint, calculate the denitration reaction activation energy parameter and the denitration reaction rate parameter; wherein 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.

[0105] As for the device embodiment, since it is basically similar to the method embodiment, the relevant description may refer to the partial description of the method embodiment.

[0106] 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, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operation of the signal processing method based on proportional-integral control.

[0107] Fig.15 A schematic structural diagram of an embodiment of a signal processing device based on proportional-integral control of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the signal processing device based on proportional-integral control.

[0108] like Fig.15 As shown, the signal processing device based on proportional-integral control may include: a processor (processor) 1502 , a communication interface (Communications Interface) 1504 , a memory (memory) 1506 , and a communication bus 1508 .

[0109] The processor 1502, the communication interface 1504, and the memory 1506 communicate with each other via a communication bus 1508. The communication interface 1504 is used to communicate with other devices such as a client or other server network elements. The processor 1502 is used to execute a program 1510, which can specifically execute the relevant steps in the above-mentioned signal processing method embodiment based on proportional-integral control.

[0110] Specifically, program 1510 may include program code including computer executable instructions.

[0111] The processor 1502 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. The one or more processors included in the signal processing device based on proportional-integral control may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0112] The memory 1506 is used to store the program 1510. The memory 1506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0113] In addition, another embodiment of the present application also provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction is executed on a signal processing device / apparatus based on proportional-integral control, the signal processing device / apparatus based on proportional-integral control performs the operation of the signal processing method based on proportional-integral control.

[0114] Wherein, if the module integrated by the signal processing device / equipment based on proportional-integral control is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, 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, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0115] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection 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; 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.

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 using 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 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.

8. The signal processing method based on proportional-integral control according to 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.

9. 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.

10. A signal processing method based on proportional-integral control as claimed in claim 9, 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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