Electrostatic dust collection control method and device, medium, electronic equipment and program product

Abstract

The invention relates to an electrostatic dust collection control method and device, a medium, electronic equipment and a program product, and relates to the field of electrostatic dust collection, the method comprises the steps that real-time data in the electrostatic dust collection process are obtained, the real-time data comprise real-time environment information and operation parameters of an electrostatic dust collector, and the real-time environment information of the electrostatic dust collector is obtained based on the real-time data; operation of the electrostatic dust collector is controlled through various controllers, and the controllers comprise at least one of a fuzzy controller, a neural network controller and a robust controller. According to the technical scheme, based on the real-time data in the electrostatic dust collection process, the fuzzy controller is used for automatically controlling the operation of the electrostatic dust collector, the neural network is used for optimizing the control process, the robust controller is used for reducing the influence of external interference, the control precision of electrostatic dust collection can be improved, and therefore the dust collection efficiency and accuracy are improved; the labor cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of electrostatic dust removal, and in particular, to an electrostatic dust removal control method, device, medium, electronic equipment and program product. Background Art

[0002] Electrostatic precipitator is a highly efficient air purification equipment, widely used in industrial and commercial environments. Electrostatic precipitator is mainly composed of high-voltage power supply, discharge electrode and dust collecting electrode. Its working principle is to use the strong electric field generated by the high-voltage power supply to make the discharge electrode release negative charge, ionize the dust particles in the air and make them acquire negative charge. These charged particles are then attracted by the specially designed dust collecting electrode, thereby effectively removing dust from the air and improving air quality. Electrostatic precipitator can handle various types of particulate matter, including fine smoke and dust. In the prior art, the operation mode of electrostatic precipitator is mainly through manual operation of the crew, which depends on the experience and subjective judgment of the operator, and the dust removal efficiency is low. In addition, when the working conditions change, it is easy to cause waste of electricity and environmental pollution due to untimely manual processing. Summary of the invention

[0003] The purpose of the present disclosure is to provide an electrostatic dust removal control method, device, medium, electronic equipment and program product to solve the above-mentioned technical problems.

[0004] In order to achieve the above object, according to a first aspect of an embodiment of the present disclosure, there is provided an electrostatic dust removal control method, the method comprising: Acquiring real-time data during the electrostatic precipitator process; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator; Based on the real-time data, the operation of the electrostatic precipitator is controlled by a variety of controllers; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

[0005] Optionally, the obtaining of real-time data during the electrostatic precipitator process includes: Using an airflow sensor to measure the airflow velocity and direction of the electrostatic precipitator in real time; Use dust concentration sensors to detect smoke concentration in the dust removal environment; A voltage sensor is used to obtain the operating voltage of the discharge electrode of the electrostatic precipitator.

[0006] Optionally, the controller is a fuzzy controller, and the operation of the electrostatic precipitator is controlled by a plurality of controllers, including: The input variables of the fuzzy controller are obtained; the input variables include voltage deviation and voltage deviation change rate; wherein the voltage deviation is the difference between the output voltage of the electrostatic precipitator and the expected voltage; the voltage deviation change rate is the change rate of the voltage deviation over time; Based on the input variables, the pulse width and duty cycle of the pulse width modulation waveform are adjusted by a fuzzy control algorithm to determine the target output voltage of the electrostatic precipitator.

[0007] Optionally, the controller is a neural network controller, and the operation of the electrostatic precipitator is controlled by multiple controllers, including: A target model is established and trained through a feedforward neural network to determine the target supply voltage of the electrostatic precipitator; the target model is used to reflect the relationship between the outlet dust concentration of the electrostatic precipitator and the supply voltage.

[0008] Optionally, the feedforward neural network is a fully supervised radial basis function neural network, and the target model is established and trained by the feedforward neural network, including: Obtaining outlet dust concentration data of the electrostatic precipitator under different power supply voltages; Based on the outlet dust concentration data under the different power supply voltages, the target model is established and trained through the fully supervised radial basis function neural network to determine the weights of the hidden layer, the network center parameters and the output layer; The target supply voltage is determined by using the trained target model and the improved genetic algorithm.

[0009] Optionally, the controller is a robust controller, and the operation of the electrostatic precipitator is controlled by multiple controllers, including: Obtaining a changing relationship between at least one influencing factor and smoke concentration in a dust removal environment; the influencing factor includes one or more of coal quality change, load fluctuation, and air volume change; Based on the change relationship, a robust controller is designed to control the operation of the electrostatic precipitator; the control target of the robust controller is to control the error value within a set range; the error value is the difference between the actual measured smoke concentration value and the set value of the smoke concentration.

[0010] Optionally, the method further comprises: Based on the real-time data, determining the equipment operation status through an equipment operation status prediction model; When the operating state of the equipment is a risky state, a warning message is output.

[0011] According to a second aspect of an embodiment of the present disclosure, there is provided an electrostatic dust removal control device, the device comprising: An acquisition module is used to acquire real-time data during the electrostatic precipitator process; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator; A control module is used to control the operation of the electrostatic precipitator through multiple controllers based on the real-time data; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

[0012] According to a third aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods described in the first aspect of the embodiment of the present disclosure are implemented.

[0013] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of any one of the methods described in the first aspect of the embodiments of the present disclosure.

[0014] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the embodiment of the present disclosure.

[0015] In the above technical solution, real-time data in the electrostatic precipitator process is obtained, and the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator. Based on the real-time data, the operation of the electrostatic precipitator is controlled by multiple controllers, wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller. Through the above technical solution, based on the real-time data in the electrostatic precipitator process, the operation of the electrostatic precipitator is automatically controlled by a fuzzy controller, the control process is optimized by a neural network, and the influence of external interference is reduced by a robust controller, so that the control accuracy of the electrostatic precipitator can be improved, thereby improving the dust removal efficiency and accuracy and reducing labor costs.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment; Figure 2is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment; Figure 3 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment; Figure 4 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment; Figure 5 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment; Figure 6 is a block diagram of an electrostatic dust removal control device according to an exemplary embodiment; Figure 7 is a block diagram of an electronic device 700 according to an exemplary embodiment; Figure 8 is a block diagram of an electronic device 800 according to an exemplary embodiment. DETAILED DESCRIPTION

[0018] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0019] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0020] Figure 1 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0021] In step S11, real-time data in the electrostatic precipitator process is obtained; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator.

[0022] Among them, the environmental information may include the smoke concentration in the dust removal environment, and the operating parameters of the electrostatic precipitator may include air flow speed and direction, the operating voltage of the discharge electrode, etc.

[0023] In step S12, based on the real-time data, the operation of the electrostatic precipitator is controlled by a plurality of controllers; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

[0024] For example, fuzzy control algorithms and neural network algorithms can automatically optimize the control strategy of the electrostatic precipitator based on environmental information and operating data, and robust control algorithms can improve the electrostatic precipitator's resistance to external disturbances, for example, controlling the device to operate stably when smoke concentration changes sharply or airflow is unstable.

[0025] In the above technical solution, real-time data in the electrostatic precipitator process is obtained, and the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator. Based on the real-time data, the operation of the electrostatic precipitator is controlled by multiple controllers, wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller. Through the above technical solution, based on the real-time data in the electrostatic precipitator process, the operation of the electrostatic precipitator is automatically controlled by a fuzzy controller, the control process is optimized by a neural network, and the influence of external interference is reduced by a robust controller, so that the control accuracy of the electrostatic precipitator can be improved, thereby improving the dust removal efficiency and accuracy and reducing labor costs.

[0026] Figure 2 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment. Figure 2 As shown, the acquisition of real-time data in the electrostatic precipitator process described in the above step S11 may include the following steps: Step S111, using an airflow sensor to measure the airflow velocity and direction of the electrostatic precipitator in real time; Step S112, using a dust concentration sensor to detect the smoke concentration in the dust removal environment; Step S113, using a voltage sensor to obtain the operating voltage of the discharge electrode of the electrostatic precipitator.

[0027] For example, during the electrostatic dust removal process, the airflow sensor is used to measure the airflow speed and direction in real time to ensure that the equipment operates under target conditions, improve the dust removal efficiency, and can issue an alarm message when the airflow is too low; the dust concentration sensor is used to detect the dust concentration in the air and provide timely feedback on the current dust removal effect; the voltage sensor is used to monitor the working voltage of the discharge electrode to ensure operation within the safe range of the electrostatic precipitator.

[0028] It is understandable that after acquiring the real-time data, the data needs to be cleaned and preprocessed to improve the availability of the data and ensure the accuracy of subsequent analysis and modeling. Among them, data cleaning can include steps such as missing value processing, duplicate data removal, outlier detection and data consistency check. For example, the mean filling method is used to process missing values; duplicate data is removed; outliers are identified by statistical methods and visualization tools; and data consistency is checked by identifying the consistency between different sources and fields. Data preprocessing includes converting the cleaned data into a format suitable for analysis; using Min-Max normalization for data standardization, correlation analysis and principal component analysis for feature selection and extraction, unique hot encoding and time series decomposition methods for data conversion and data segmentation. After cleaning and preprocessing, the real-time data can be transmitted to the controller in a standardized format through serial communication, CAN (Controller Area Network) bus or Ethernet for subsequent data analysis.

[0029] In a possible implementation, the controller in step S12 is a fuzzy controller. Figure 3 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment. Figure 3 As shown, the control of the operation of the electrostatic precipitator by multiple controllers described in step S12 may include the following steps: Step S121, obtaining input variables of the fuzzy controller; the input variables include voltage deviation and voltage deviation change rate.

[0030] The voltage deviation is the difference between the output voltage of the electrostatic precipitator and the expected voltage; the voltage deviation change rate is the change rate of the voltage deviation over time; Step S122, based on the input variable, the pulse width and duty cycle of the pulse width modulation waveform are adjusted by a fuzzy control algorithm to determine the target output voltage of the electrostatic precipitator.

[0031] It is understandable that the control method of high-voltage electrostatic precipitator equipment is mainly based on voltage regulation of PID (Proportional-Integral-Derivative) control, but in actual application, due to the hysteresis characteristics of the dust collector control system and the complex working environment, the system is affected by nonlinear factors, and it is difficult to establish an accurate mathematical model, so it is impossible to track and adjust in real time. In order to solve this problem, the present disclosure proposes an electrostatic precipitator control method based on a fuzzy control algorithm, which converts expert experience into fuzzy control rules, and adjusts the output voltage of the electrostatic precipitator by adjusting the pulse width and duty cycle of the pulse width modulation (PWM) waveform to cope with the nonlinear characteristics of the system and model uncertainty.

[0032] For example, a two-bit fuzzy controller is used, and the input variables are voltage deviation e and voltage deviation rate ee. According to the experimental data of the actual electric field, the final output voltage physical range of the dust removal power supply is U=[0,7*10 5 ], the voltage change rate is [-250, 250]. Based on this, the fuzzy range of the input variable voltage deviation e of the fuzzy controller is set to [-1, 1], and its quantization factor is ke=2.86*10 -5 The fuzzy range of the input variable voltage deviation change rate ee is [-0.1, 0.1], and the quantization factor is kec = 0.0004. The physical range of the output variable u is [-0.5, 0.5], the fuzzy range is also [-1, 1], and the proportional factor is set to ku = 0.7.

[0033] Among them, the fuzzy subsets covering variables in the fuzzy domain of input variable e include: negative (high); zero (reasonable); positive (low); the fuzzy subsets covering variables in the fuzzy domain of input variable ee include: negative (high); zero (reasonable); positive (low); the fuzzy subsets covering variables in the fuzzy domain of output variable u include: close-fast (fast drop); close-slow (slow drop); no-change (unchanged); open-slow (slow rise); open-fast (fast rise). The fuzzy subsets of fuzzy controller are detailed in Table 1.

[0034] Table 1 Fuzzy subsets of fuzzy controller

[0035] According to the field experimental data and manual operation experience, the rules for adjusting the output voltage are summarized as follows: 1) If the voltage is low, quickly increase the voltage; 2) If the voltage is reasonable, the voltage regulation is zero; 3) If the voltage is too high, quickly reduce the voltage; 4) If the voltage is reasonable and the voltage deviation is small, increase the voltage slowly; 5) If the voltage is reasonable but the voltage deviation is large, slowly reduce the voltage.

[0036] In another possible implementation, the controller in step S12 is a neural network controller, and the control of the operation of the electrostatic precipitator by multiple controllers in step S12 may also include: A target model is established and trained through a feedforward neural network to determine the target supply voltage of the electrostatic precipitator; the target model is used to reflect the relationship between the outlet dust concentration of the electrostatic precipitator and the supply voltage.

[0037] Optionally, the feedforward neural network may be a fully supervised radial basis function (RBF) neural network, and establishing the target model and performing training through the feedforward neural network may include: (1) Obtaining the outlet dust concentration data of the electrostatic precipitator under different power supply voltages; (2) Based on the outlet dust concentration data under different power supply voltages, the target model is established and trained through the fully supervised radial basis function neural network to determine the weights of the hidden layer, the network center parameters and the output layer; (3) Determine the target supply voltage using the trained target model and the improved genetic algorithm.

[0038] It is understandable that the RBF neural network is a feedforward neural network with superior performance, a fast learning rate, and no local minimum problem. Based on the RBF neural network, the relationship between the outlet dust concentration and the power supply voltage of the electrostatic precipitator is modeled and applied to m-unit one-room n-field electrostatic precipitator equipment. The secondary voltage of n electric fields is used as the input variable X of the RBF neural network, and the outlet dust concentration of the electrostatic precipitator is used as the output of the RBF neural network. The least squares method is used to identify the electric power model, determine the relationship between dust removal efficiency and power consumption, and use the improved genetic algorithm (IGA) to optimize the parameters to determine the target power supply voltage.

[0039] In another possible implementation, the controller in step S12 is a robust controller. Figure 4 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment. Figure 4 As shown, the control of the operation of the electrostatic precipitator by multiple controllers described in step S12 may include the following steps: Step S123, obtaining a changing relationship between at least one influencing factor and the smoke concentration in the dust removal environment; the influencing factor includes one or more of coal quality change, load fluctuation and air volume change.

[0040] Step S124: Based on the change relationship, a robust controller is designed to control the operation of the electrostatic precipitator.

[0041] The control target of the robust controller is to control the error value within a set range; the error value is the difference between the actual measured smoke concentration value and the set value of the smoke concentration.

[0042] It can be understood that the H∞ control method (H infinite control method) as a robust control method based on frequency domain analysis can ensure the stability of the system and make the system more robust to uncertainty and interference. In the electrostatic dust removal control method provided in the present disclosure, a robust controller is designed by the H∞ control method, a suitable weighting function is selected, and the minimization of the smoke concentration control error is set as the control target.

[0043] For example, suppose the smoke concentration system The state equation is: ; in, is the system state vector, including smoke concentration and related dynamic variables; It is the control input, such as the manipulated variable such as the air volume; is external interference, such as uncertain factors in the production process; A, B, D are the corresponding system matrices.

[0044] The output equation is: ; in, is the measurement output, i.e. the actual measured smoke concentration value; C and E are the output matrices.

[0045] Control error for: ;in It is the set value of smoke concentration.

[0046] Defining generalized output for:

[0047] in, is the transfer function of the controller.

[0048] In the control process, the appropriate weighting function can be determined , To determine the generalized output:

[0049] The goal of the robust control method is to determine an appropriate controller transfer function , so that the interference To generalized output The H∞ norm of the transfer function is less than a given positive number ,Right now For example, the controller parameters can be solved by using methods such as Linear Matrix Inequality (LMI), thereby converting the H∞ control problem into a set of linear matrix inequality solutions to obtain the optimal parameters of the robust controller. Applying the robust control algorithm to hardware devices such as Programmable Logic Controller (PLC) can control the dust removal system to operate stably in an interference environment.

[0050] Figure 5 is a flow chart of an electrostatic dust removal control method according to an exemplary embodiment. Figure 5 As shown, the method may also include the following steps: Step S13, based on the real-time data, determining the equipment operation status through an equipment operation status prediction model; Step S14: outputting warning information when the operating state of the equipment is a risky state.

[0051] For example, determining the device operating status in step S13 may include the following steps: (1) Establish a prediction model for equipment operation status; The operating data of the electrostatic precipitator is non-dimensionalized and normalized, and the historical value of the characteristic information of the equipment is transformed to the interval [0,1]. The normalization formula is:

[0052] Where: is the normalized data; is the original data, and are the maximum and minimum values ​​of the data respectively.

[0053] (2) Neural network model design; According to multiple characteristic information of the equipment, the historical operation data of the electrostatic precipitator is obtained, and the BP (Backpropagation) neural network is used to establish the operation status prediction model of the characteristic information of the equipment. It can be understood that the complete neural network includes structures such as input layer, hidden layer, output layer and activation function. Among them, the input layer is the operation data including multiple characteristic information; the output layer generates the prediction results of the model, which can correspond to multiple output dimensions; in order to reduce the complexity of model training and ensure the prediction accuracy of the model, the hidden layer can choose a single hidden layer structure, and the number of neurons in the hidden layer can be customized according to the actual working conditions using empirical formulas to avoid the problem of excessive calculation and overfitting caused by too many neurons, or the problem of insufficient number of neurons affecting the prediction accuracy. The activation function can include linear function, ramp function, threshold function and tansig function (hyperbolic tangent function), etc., which can be selected according to the actual situation, and the present disclosure does not limit this.

[0054] It is understandable that when the equipment operation state is determined to be a risk state by the equipment operation state prediction model, warning information can be output. The warning process may include the following steps: (1) Threshold determination and threshold classification management; Based on the design specifications, operating experience and safety standards of the electrostatic precipitator, the normal range and alarm threshold of each operating parameter are determined, and the alarm threshold is divided into different levels. For example: level 1, level 2 and level 3, the severity of the alarm increases in sequence, level 1 is triggered when the parameter is close to the abnormal range, reminding relevant personnel to pay attention; level 3 is triggered when the parameter is seriously beyond the normal range, indicating that the equipment or system may have a serious failure and immediate measures need to be taken.

[0055] (2) Trend analysis and forecasting; By analyzing the trend of historical data and real-time data, the change trend of parameters can be predicted. If the change trend of parameters is found to be abnormal, such as continuous increase or decrease, even if the current parameter value is still within the normal range, it may indicate that the equipment or system is about to have problems, thus issuing an early warning.

[0056] (3) Comparison and judgment; Comprehensively analyze and judge multiple parameters. For example, there is a correlation between certain parameters. When one parameter is abnormal, it may cause other related parameters to change. By analyzing this correlation, the operating status of the equipment or system can be judged more accurately.

[0057] (4) Customized warnings; The dust removal site can customize early warning conditions according to different scenarios and needs to promptly detect abnormal conditions of the equipment and respond quickly.

[0058] (5) Generation and storage of alarm records; When it is detected that the operating parameters exceed the alarm threshold, the alarm system generates corresponding alarm information and displays the alarm information on the display screen of the monitoring system to notify relevant personnel. The alarm system can record and store the alarm information of each trigger, including the alarm time, alarm type, alarm parameters, alarm level, etc. These records can be used for subsequent data analysis, fault diagnosis and accident investigation, which helps to evaluate and improve the operating status of equipment or systems.

[0059] In the above technical solution, real-time data in the electrostatic precipitator process is obtained, and the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator. Based on the real-time data, the operation of the electrostatic precipitator is controlled by multiple controllers, wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller. Through the above technical solution, based on the real-time data in the electrostatic precipitator process, the operation of the electrostatic precipitator is automatically controlled by a fuzzy controller, the control process is optimized by a neural network, and the influence of external interference is reduced by a robust controller, so that the control accuracy of the electrostatic precipitator can be improved, thereby improving the dust removal efficiency and accuracy and reducing labor costs.

[0060] Figure 6 is a block diagram of an electrostatic dust removal control device according to an exemplary embodiment. Figure 6 As shown, the device 600 includes an acquisition module 610 and a control module 620 .

[0061] The acquisition module 610 is used to acquire real-time data during the electrostatic precipitator process; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator; The control module 620 is used to control the operation of the electrostatic precipitator through multiple controllers based on the real-time data; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

[0062] Optionally, the acquisition module 610 includes a measurement submodule, a detection submodule and a voltage acquisition submodule; The measurement submodule is used to measure the airflow velocity and direction of the electrostatic precipitator in real time using an airflow sensor; The detection submodule is used to detect the smoke concentration in the dust removal environment using a dust concentration sensor; The voltage acquisition submodule is used to acquire the operating voltage of the discharge electrode of the electrostatic precipitator using a voltage sensor.

[0063] Optionally, the controller is a fuzzy controller, and the control module 620 includes a first acquisition submodule and a first control submodule; The first acquisition submodule is used to acquire the input variables of the fuzzy controller; the input variables include voltage deviation and voltage deviation change rate; wherein the voltage deviation is the difference between the output voltage of the electrostatic precipitator and the expected voltage; the voltage deviation change rate is the change rate of the voltage deviation over time; The first control submodule is used to adjust the pulse width and duty cycle of the pulse width modulation waveform through a fuzzy control algorithm based on the input variable to determine the target output voltage of the electrostatic precipitator.

[0064] Optionally, the controller is a neural network controller, and the control module 620 further includes a determination submodule; The determination submodule is used to establish a target model and perform training through a feedforward neural network to determine the target supply voltage of the electrostatic precipitator; the target model is used to reflect the relationship between the outlet dust concentration of the electrostatic precipitator and the supply voltage.

[0065] Optionally, the feedforward neural network is a fully supervised radial basis function neural network, and the determination submodule is further used for: Obtaining the outlet dust concentration data of the electrostatic precipitator under different power supply voltages; Based on the outlet dust concentration data under different power supply voltages, the target model is established and trained through the fully supervised radial basis function neural network to determine the weights of the hidden layer, the network center parameters and the output layer; The target supply voltage is determined using the trained target model and an improved genetic algorithm.

[0066] Optionally, the controller is a robust controller, and the control module 620 further includes a second acquisition submodule and a second control submodule; The second acquisition submodule is used to obtain the change relationship between at least one influencing factor and the smoke concentration in the dust removal environment; the influencing factor includes one or more of coal quality change, load fluctuation and air volume change; The second control submodule is used to design a robust controller to control the operation of the electrostatic precipitator based on the change relationship; the control target of the robust controller is to control the error value within a set range; the error value is the difference between the actual measured smoke concentration value and the set value of the smoke concentration.

[0067] Optionally, the device 600 further includes a determination module and an output module; The determination module is used to determine the equipment operation status through the equipment operation status prediction model based on the real-time data; The output module is used to output warning information when the operating state of the equipment is a risky state.

[0068] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0069] Figure 7 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 may include: a processor 701 , a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0070] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned electrostatic dust removal control method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. These data may include instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0071] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned electrostatic dust removal control method.

[0072] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the electrostatic dust removal control method described above are implemented. For example, the computer-readable storage medium may be the memory 702 including the program instructions described above, and the program instructions may be executed by the processor 701 of the electronic device 700 to complete the electrostatic dust removal control method described above.

[0073] Figure 8 8 is a block diagram of an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be provided as a server. Figure 8 The electronic device 800 includes a processor 822, which may be one or more, and a memory 832 for storing a computer program executable by the processor 822. The computer program stored in the memory 832 may include one or more modules each corresponding to a set of instructions. In addition, the processor 822 may be configured to execute the computer program to perform the above-mentioned electrostatic dust removal control method.

[0074] In addition, the electronic device 800 may further include a power supply component 826 and a communication component 850, wherein the power supply component 826 may be configured to perform power management of the electronic device 800, and the communication component 850 may be configured to implement communication of the electronic device 800, for example, wired or wireless communication. In addition, the electronic device 800 may further include an input / output (I / O) interface 858. The electronic device 800 may operate based on an operating system stored in the memory 832.

[0075] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by the processor, the steps of the electrostatic dust removal control method described above are implemented. For example, the non-transitory computer-readable storage medium may be the memory 832 including the program instructions described above, and the program instructions may be executed by the processor 822 of the electronic device 800 to complete the electrostatic dust removal control method described above.

[0076] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned electrostatic precipitator control method when executed by the programmable device.

[0077] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0078] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0079] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. An electrostatic dust removal control method, characterized in that: The method comprises: Acquiring real-time data during the electrostatic precipitator process; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator; Based on the real-time data, the operation of the electrostatic precipitator is controlled by a variety of controllers; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

2. The electrostatic dust removal control method according to claim 1, characterized in that: The real-time data obtained during the electrostatic dust removal process includes: Using an airflow sensor to measure the airflow velocity and direction of the electrostatic precipitator in real time; Use dust concentration sensors to detect smoke concentration in the dust removal environment; A voltage sensor is used to obtain the operating voltage of the discharge electrode of the electrostatic precipitator.

3. The electrostatic dust removal control method according to claim 1, characterized in that: The controller is a fuzzy controller, and the operation of the electrostatic precipitator is controlled by multiple controllers, including: The input variables of the fuzzy controller are obtained; the input variables include voltage deviation and voltage deviation change rate; wherein the voltage deviation is the difference between the output voltage of the electrostatic precipitator and the expected voltage; the voltage deviation change rate is the change rate of the voltage deviation over time; Based on the input variables, the pulse width and duty cycle of the pulse width modulation waveform are adjusted by a fuzzy control algorithm to determine the target output voltage of the electrostatic precipitator.

4. The electrostatic dust removal control method according to claim 1, characterized in that: The controller is a neural network controller, and the operation of the electrostatic precipitator is controlled by multiple controllers, including: A target model is established and trained through a feedforward neural network to determine the target supply voltage of the electrostatic precipitator; the target model is used to reflect the relationship between the outlet dust concentration of the electrostatic precipitator and the supply voltage.

5. The electrostatic dust removal control method according to claim 4, characterized in that: The feedforward neural network is a fully supervised radial basis function neural network. The target model is established and trained by the feedforward neural network, including: Obtaining outlet dust concentration data of the electrostatic precipitator under different power supply voltages; Based on the outlet dust concentration data under the different power supply voltages, the target model is established and trained through the fully supervised radial basis function neural network to determine the weights of the hidden layer, the network center parameters and the output layer; The target supply voltage is determined by using the trained target model and the improved genetic algorithm.

6. The electrostatic dust removal control method according to claim 1, characterized in that: The controller is a robust controller, and the operation of the electrostatic precipitator is controlled by multiple controllers, including: Obtaining a changing relationship between at least one influencing factor and smoke concentration in a dust removal environment; the influencing factor includes one or more of coal quality change, load fluctuation, and air volume change; Based on the change relationship, a robust controller is designed to control the operation of the electrostatic precipitator; the control target of the robust controller is to control the error value within a set range; the error value is the difference between the actual measured smoke concentration value and the set value of the smoke concentration.

7. The electrostatic dust removal control method according to claim 1, characterized in that: The method further comprises: Based on the real-time data, determining the equipment operation status through an equipment operation status prediction model; When the operating state of the equipment is a risky state, a warning message is output.

8. An electrostatic dust removal control device, characterized in that: The device comprises: An acquisition module is used to acquire real-time data during the electrostatic precipitator process; the real-time data includes real-time environmental information and operating parameters of the electrostatic precipitator; A control module is used to control the operation of the electrostatic precipitator through multiple controllers based on the real-time data; wherein the controller includes at least one of a fuzzy controller, a neural network controller and a robust controller.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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