Desulfurization system control method and device, electronic equipment and storage medium

Abstract

The invention discloses a control method and device of a desulfurization system, electronic equipment and a storage medium, and relates to the technical field of production control. Inputting the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model; inputting the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model; and controlling the desulfurization system according to the first control parameter. Compared with the prior art, the embodiment of the invention can effectively identify the disturbance information by acquiring the operation parameters of the power plant in real time and inputting the operation parameters into the preset data model, and realizes efficient and accurate control of the desulfurization system according to the obtained accurate control parameters for the desulfurization system, thereby improving the stability and reliability of the desulfurization system, optimizing the operation efficiency, and improving the reliability of the desulfurization system. The energy consumption and the maintenance cost are reduced, so that the overall environmental protection level and economic benefits of a power plant are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of production control technology, and in particular to a control method and device, electronic equipment and storage medium for a desulfurization system. Background Art

[0002] With the rapid development of my country's economy, energy demand continues to grow. As the main source of electricity in my country, thermal power generation is becoming increasingly important. However, pollutants such as sulfur dioxide generated during thermal power generation have a serious impact on the environment. In order to reduce pollutant emissions and protect the ecological environment, thermal power plants are required to install desulfurization systems to reduce sulfur dioxide emissions.

[0003] At present, due to the complexity of the desulfurization system, the desulfurization system equipment and operating parameter adjustments of thermal power plants mainly rely on the operator's experience and judgment to be manually performed, including: adjusting the amount of desulfurizer added, controlling the gas-liquid contact time in the desulfurization tower, optimizing the airflow distribution in the desulfurization tower, monitoring the operating status of the desulfurization equipment, etc. The operator needs to observe the changes in the conditions of the inlet and outlet in real time according to the actual working conditions to adjust the equipment, so the operator is required to have rich professional knowledge, keen observation and quick response ability. This adjustment method affects the desulfurization efficiency to a certain extent, and at the same time puts forward high requirements on the professional quality of the operator. Summary of the invention

[0004] The present disclosure provides a control method, device, electronic device and storage medium for a desulfurization system.

[0005] According to a first aspect of the present disclosure, a control method for a desulfurization system is provided, comprising:

[0006] Obtain power plant operating parameters;

[0007] Inputting the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model;

[0008] Inputting the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model;

[0009] Control of the desulfurization system is performed according to the first control parameter.

[0010] Optionally, inputting the disturbance information into the preset data model to obtain the first control parameter for the desulfurization system output by the preset data model further includes:

[0011] The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

[0012] Optionally, after executing control of the desulfurization system according to the first control parameter, the method further includes:

[0013] Determining whether there is fault information in the desulfurization system;

[0014] When fault information exists in the desulfurization system, the fault information and the first restriction condition are input into the preset data model to obtain second control parameters for modules in the desulfurization system that have not failed, output by the preset data model.

[0015] Optionally, before inputting the power plant operating parameters into a preset data model to obtain disturbance information output by the preset data model, the method further includes:

[0016] Acquiring historical power plant operating parameters and a second restriction condition; wherein the restriction condition is a restriction condition on the desulfurization system;

[0017] Constructing the preset data model based on a deep machine learning algorithm;

[0018] The preset data model is trained according to the historical power plant operating parameters and restriction conditions so that the preset data model can simulate the actual operation of the power plant.

[0019] Optionally, the desulfurization system includes at least two subsystems; after executing control of the desulfurization system according to the first control parameter, the method further includes:

[0020] Obtain the control objectives of the target subsystem;

[0021] Inputting the control target of the target subsystem and the power plant operation parameter into a preset data model to obtain a second control parameter for the target subsystem output by the preset data model;

[0022] Controlling the target subsystem is performed according to the second control parameter.

[0023] According to a second aspect of the present disclosure, a control device for a desulfurization system is provided, comprising:

[0024] A first acquisition unit, used to acquire power plant operation parameters;

[0025] A first prediction unit, configured to input the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model;

[0026] a second prediction unit, configured to input the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model;

[0027] A first control unit is used to control the desulfurization system according to the first control parameter.

[0028] Optionally, the second prediction unit is further used for:

[0029] The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

[0030] Optionally, the device further comprises:

[0031] a determination unit, configured to determine whether there is fault information in the desulfurization system after the first control unit performs control on the desulfurization system according to the first control parameter;

[0032] The third prediction unit is used to input the fault information and the first restriction condition into the preset data model when there is fault information in the desulfurization system, so as to obtain the second control parameter of the module in the desulfurization system where no fault occurs, which is output by the preset data model.

[0033] Optionally, the device further comprises:

[0034] A second acquisition unit is used to acquire historical power plant operation parameters and a second restriction condition before the first prediction unit inputs the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model; wherein the restriction condition is a restriction condition on the desulfurization system;

[0035] A construction unit, used to construct the preset data model based on a deep machine learning algorithm;

[0036] The training unit is used to train the preset data model according to the historical power plant operating parameters and restriction conditions so that the preset data model can simulate the actual operation of the power plant.

[0037] Optionally, the desulfurization system comprises at least two subsystems; the device further comprises:

[0038] a third acquisition unit, configured to acquire a control target of a target subsystem after the first control unit performs control on the desulfurization system according to the first control parameter;

[0039] An input unit, used to input the control target of the target subsystem and the power plant operation parameter into a preset data model to obtain a second control parameter for the target subsystem output by the preset data model;

[0040] A second control unit is used to control the target subsystem according to the second control parameter.

[0041] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0042] at least one processor; and

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0045] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0046] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0047] The control method, device, electronic device and storage medium of the desulfurization system provided by the present disclosure mainly include the following technical solutions: obtaining the power plant operating parameters; inputting the power plant operating parameters into a preset data model to obtain the disturbance information output by the preset data model; inputting the disturbance information into the preset data model to obtain the first control parameter for the desulfurization system output by the preset data model; and executing the control of the desulfurization system according to the first control parameter. Compared with the related art, the embodiment of the present application can effectively identify the disturbance information and obtain the precise control parameters for the desulfurization system according to the real-time acquisition of the power plant operating parameters and the input of the preset data model, thereby achieving efficient and accurate control of the desulfurization system, improving the stability and reliability of the desulfurization system, optimizing the operating efficiency, reducing energy consumption and maintenance costs, and thus improving the overall environmental protection level and economic benefits of the power plant.

[0048] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0050] Figure 1 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0051] Figure 2A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0052] Figure 3 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0053] Figure 4 A schematic diagram of the structure of a control device for a desulfurization system provided in an embodiment of the present disclosure;

[0054] Figure 5 A schematic diagram of the structure of a control device for a desulfurization system provided in an embodiment of the present disclosure;

[0055] Figure 6 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0057] The control method, device, electronic device and storage medium of the desulfurization system according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0058] Figure 1 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure.

[0059] like Figure 1 As shown, the method comprises the following steps:

[0060] Step 101, obtaining power plant operating parameters.

[0061] In some embodiments, data is collected in real time from various key equipment and systems of the power plant, including but not limited to the combustion efficiency of the boiler, flue gas composition, inlet and outlet concentrations of the desulfurization tower, pH value of the limestone slurry, flow rate and pressure difference of the desulfurization tower, degree of coal combustion, etc.; these parameters are the basis for evaluating the operating status of the power plant and the performance of the desulfurization system.

[0062] In some embodiments, the power plant operating parameters can be obtained by installing sensors and monitoring systems, which can ensure the accuracy and timeliness of the data and provide a reliable basis for subsequent data analysis and model calculations.

[0063] Step 102: input the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model.

[0064] The acquired power plant operating parameters are input into a pre-established data model. This model can be built based on a machine learning algorithm or a physical model and trained based on the historical operating parameters of the power plant. It can identify and quantify the disturbance factors that affect the performance of the desulfurization system, such as changes in fuel composition, fluctuations in flue gas temperature, changes in environmental conditions, etc. Through model analysis, a series of disturbance information can be obtained.

[0065] Step 103: input the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

[0066] After obtaining the disturbance information, the disturbance information is sent back to the data model to calculate the optimal control parameters for the desulfurization system. These control parameters may include the limestone slurry replenishment rate, flue gas flow regulation, water spraying volume of the spray system, etc. The data model will adjust these parameters according to the nature and degree of the disturbance to achieve the best desulfurization effect. This step is the key to achieving intelligent control and adaptive regulation.

[0067] Step 104: Control the desulfurization system according to the first control parameter.

[0068] According to the first control parameter output by the data model, the desulfurization system is controlled in real time. In some embodiments, the first control parameter is an actual data value, and the calculated parameter value is converted into an actual mechanical action or process adjustment, such as automatically adjusting the pumping speed of the limestone slurry, changing the opening of the flue damper, adjusting the nozzle state of the spray layer, etc. Through this precise control, it can be ensured that the desulfurization system can still maintain efficient and stable operation in the face of various disturbances, thereby meeting environmental emission standards and reducing operating costs.

[0069] The control method of the desulfurization system provided in the present disclosure mainly includes the following technical solutions: obtaining the power plant operating parameters; inputting the power plant operating parameters into a preset data model to obtain the disturbance information output by the preset data model; inputting the disturbance information into the preset data model to obtain the first control parameter for the desulfurization system output by the preset data model; and executing the control of the desulfurization system according to the first control parameter. Compared with the related art, the embodiment of the present application can effectively identify the disturbance information and obtain the precise control parameters for the desulfurization system by obtaining the power plant operating parameters in real time and inputting the preset data model, thereby achieving efficient and accurate control of the desulfurization system, improving the stability and reliability of the desulfurization system, optimizing the operating efficiency, reducing energy consumption and maintenance costs, and thus improving the overall environmental protection level and economic benefits of the power plant.

[0070] In some embodiments, after executing step 301 to input the disturbance information into the preset data model and obtain the first control parameter of the desulfurization system output by the preset data model, a first restriction condition is further considered, such as:

[0071] The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

[0072] When inputting the disturbance information into the preset data model, the first constraints need to be considered, which may include but are not limited to the operating range of the desulfurization system, the physical limits of the equipment, environmental emission standards, economic requirements, etc. The disturbance information is input into the preset data model together with these first constraints.

[0073] After receiving this information, the preset data model will perform calculations and logical judgments to determine how to adjust the operating parameters of the desulfurization system while meeting all constraints. The model will comprehensively consider the real-time nature of the disturbance information and the strictness of the constraints, and output a series of first control parameters for the desulfurization system through algorithm optimization.

[0074] These first control parameters not only reflect the adjustment required by the desulfurization system to cope with the current disturbance, but also ensure that the adjusted operation does not exceed any preset limit range. For example, if the disturbance information indicates a sudden increase in the sulfur content in the flue gas, the model will calculate the need to increase the supply of limestone slurry, but at the same time take into account the maximum working capacity of the pump, the capacity limit of the slurry tank and the upper limit of the emission standard, thus deriving a control parameter that can cope with the increase in sulfur content while meeting all constraints.

[0075] In this way, the preset data model not only improves the adaptability of the desulfurization system, but also ensures the safety and economy of the entire power plant operation, enabling the desulfurization system to maintain the optimal operating state in a complex and changeable operating environment.

[0076] Figure 2 A flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure, after controlling the desulfurization system according to the first control parameter, the method further includes:

[0077] Step 201, determining whether there is fault information in the desulfurization system.

[0078] In some embodiments, various components and processes of the desulfurization system are monitored in real time to detect any abnormal conditions or signs of failure, including continuous analysis of performance data of key equipment such as pumps, fans, spray systems, absorption towers, etc., and monitoring of control system and sensor signals. Fault detection may involve comparing real-time data with historical data, equipment performance standards or preset thresholds. If any parameter is detected to be out of the normal range, the system will record the fault information, including the type of fault, time of occurrence, scope of impact and possible root cause.

[0079] Step 202, when there is fault information in the desulfurization system, the fault information and the first restriction condition are input into the preset data model to obtain the second control parameter of the module in the desulfurization system where no fault occurs, which is output by the preset data model.

[0080] When the system detects a fault in the desulfurization system, it will immediately take action to minimize the impact of the fault. In this step, the fault information will be input into the preset data model together with the first constraint mentioned above to recalculate and optimize the control parameters while taking into account the fault situation to ensure that the modules in the desulfurization system that are not affected by the fault can continue to operate normally, and may even need to take on additional processing tasks to compensate for the absence of the faulty module. The output second control parameters will guide these modules to adjust their operations, such as changing flow, pressure or chemical reaction conditions, to maintain overall desulfurization efficiency.

[0081] In some embodiments, before inputting the power plant operating parameters into a preset data model to obtain disturbance information output by the preset data model, the method further includes:

[0082] Acquiring historical power plant operating parameters and a second restriction condition; wherein the restriction condition is a restriction condition on the desulfurization system;

[0083] Constructing the preset data model based on a deep machine learning algorithm;

[0084] The preset data model is trained according to the historical power plant operating parameters and restriction conditions so that the preset data model can simulate the actual operation of the power plant.

[0085] In some embodiments, a large amount of operating parameter data is first extracted from the historical database of the power plant. These data cover the performance of the desulfurization system under different operating conditions, including but not limited to flue gas flow, sulfur oxide concentration, desulfurization efficiency, energy consumption, limestone consumption, etc. At the same time, the second restriction conditions related to the desulfurization system are collected. These conditions may include environmental emission standards, equipment operating limits, material supply conditions, energy consumption limits, etc. The acquisition of these historical data and restriction conditions is the basis for building and training data models. The two will help the model better understand the behavior and limitations of the desulfurization system under different operating conditions.

[0086] In some embodiments, an advanced deep machine learning algorithm, such as a neural network, a convolutional neural network, or a deep belief network, is used to construct a preset data model. This model is designed to capture the complex relationship between power plant operating parameters and desulfurization system performance. After the model is built, the model is trained using the acquired historical power plant operating parameters and a second constraint. The training process includes adjusting the parameters within the model to minimize the difference between the model prediction and the actual historical data, and learning the complex patterns of the data through multiple iterations so that the trained model should be able to accurately simulate the actual operation of the power plant, including the response of the desulfurization system under different operating conditions, and its adaptability when faced with various constraints. In this way, the model can provide reliable decision support for the optimal control and fault prediction of the desulfurization system. "

[0087] Figure 3 A flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure, wherein the desulfurization system comprises at least two subsystems; after controlling the desulfurization system according to the first control parameter, the method further comprises:

[0088] Step 301, obtaining the control target of the target subsystem.

[0089] Clarify the specific control objectives of the target subsystem. The control objectives are set according to the overall operation strategy of the power plant, environmental protection requirements, energy consumption indicators or system optimization objectives. For example, if the target subsystem is the spray tower of the desulfurization system, the control objective may be to maintain a specific desulfurization efficiency, reduce limestone consumption or optimize energy consumption. In some embodiments, the determination of the control objectives also needs to consider the current status of the subsystem, historical performance data and possible future change trends to ensure that the control objectives are practical.

[0090] Step 302: Input the control target of the target subsystem and the power plant operation parameter into a preset data model to obtain a second control parameter for the target subsystem output by the preset data model.

[0091] After the control objectives are determined, these objectives are then combined with the real-time power plant operating parameters and input into the preset data model that has been built and trained. The preset data model will use its learned knowledge to analyze the relationship between the current operating parameters and the control objectives and calculate a series of optimized secondary control parameters. These parameters may include but are not limited to the amount of water sprayed from the spray tower, the concentration of the limestone slurry, the distribution of flue gas flow, etc. The secondary control parameters output by the model are intended to guide the target subsystem to achieve the established control objectives in the most efficient way, while taking into account the overall operating conditions and external constraints of the power plant.

[0092] Step 303: Control the target subsystem according to the second control parameter.

[0093] According to the second control parameter output by the preset data model, the target subsystem is controlled in real time. In some embodiments, precise control of the target subsystem is achieved through an automated control system such as a DCS (Distributed Control System) or a PLC (Programmable Logic Controller). For example, if the second control parameter indicates that the water spray volume of the spray tower needs to be increased, the control system will adjust the speed of the water pump or open additional nozzles accordingly. Through this closed-loop control, it can be ensured that the target subsystem operates according to the preset control target, thereby improving the operating efficiency and environmental protection performance of the entire power plant. At the same time, this automated control method can also reduce errors in human operation and improve the reliability and stability of the system.

[0094] Corresponding to the above-mentioned desulfurization system control method, the present invention also provides a desulfurization system control device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0095] Figure 4 A schematic diagram of the structure of a control device for a desulfurization system provided in an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, including:

[0096] A first acquisition unit 41 is used to acquire power plant operation parameters;

[0097] A first prediction unit 42, configured to input the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model;

[0098] A second prediction unit 43 is used to input the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model;

[0099] The first control unit 44 is used to control the desulfurization system according to the first control parameter.

[0100] The control device of the desulfurization system provided by the present disclosure has the following main technical solutions: obtaining the power plant operating parameters; inputting the power plant operating parameters into a preset data model to obtain the disturbance information output by the preset data model; inputting the disturbance information into the preset data model to obtain the first control parameter for the desulfurization system output by the preset data model; and executing the control of the desulfurization system according to the first control parameter. Compared with the related art, the embodiment of the present application can effectively identify the disturbance information and obtain the precise control parameters for the desulfurization system by obtaining the power plant operating parameters in real time and inputting the preset data model, thereby achieving efficient and accurate control of the desulfurization system, improving the stability and reliability of the desulfurization system, optimizing the operating efficiency, reducing energy consumption and maintenance costs, and thus improving the overall environmental protection level and economic benefits of the power plant.

[0101] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the second prediction unit 43 is further used for:

[0102] The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

[0103] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0104] A determination unit 45, configured to determine whether there is fault information in the desulfurization system after the first control unit 44 performs control on the desulfurization system according to the first control parameter;

[0105] The third prediction unit 46 is used to input the fault information and the first restriction condition into the preset data model when there is fault information in the desulfurization system, so as to obtain the second control parameter output by the preset data model for the module in the desulfurization system where no fault occurs.

[0106] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0107] The second acquisition unit 47 is used to acquire the historical power plant operation parameters and the second restriction condition before the first prediction unit 42 inputs the power plant operation parameters into the preset data model to obtain the disturbance information output by the preset data model; wherein the restriction condition is a restriction condition for the desulfurization system;

[0108] A construction unit 48, configured to construct the preset data model based on a deep machine learning algorithm;

[0109] The training unit 49 is used to train the preset data model according to the historical power plant operating parameters and restriction conditions, so that the preset data model can simulate the actual operation of the power plant.

[0110] In a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the desulfurization system comprises at least two subsystems; the device also includes:

[0111] A third acquisition unit 410 is used to acquire a control target of a target subsystem after the first control unit 41 performs control on the desulfurization system according to the first control parameter;

[0112] An input unit 411 is used to input the control target of the target subsystem and the power plant operation parameter into a preset data model to obtain a second control parameter for the target subsystem output by the preset data model;

[0113] The second control unit 412 is used to control the target subsystem according to the second control parameter.

[0114] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principle is the same, which is no longer limited in the embodiment of the present disclosure.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0116] Figure 6 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0117] like Figure 6 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0118] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a control method for a desulfurization system. For example, in some embodiments, the control method for a desulfurization system may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the aforementioned control method of the desulfurization system in any other appropriate manner (for example, by means of firmware).

[0120] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0122] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0125] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0126] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0127] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0128] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A control method for a desulfurization system, characterized in that: include: Obtain power plant operating parameters; Inputting the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model; Inputting the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model; Control of the desulfurization system is performed according to the first control parameter.

2. The method according to claim 1, characterized in that: The step of inputting the disturbance information into the preset data model to obtain the first control parameter for the desulfurization system output by the preset data model further includes: The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

3. The method according to claim 2, characterized in that After executing control of the desulfurization system according to the first control parameter, the method further includes: Determining whether there is fault information in the desulfurization system; When fault information exists in the desulfurization system, the fault information and the first restriction condition are input into the preset data model to obtain second control parameters for modules in the desulfurization system that have not failed, output by the preset data model.

4. The method according to claim 1, characterized in that Before inputting the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model, the method further includes: Acquiring historical power plant operating parameters and a second restriction condition; wherein the restriction condition is a restriction condition on the desulfurization system; Constructing the preset data model based on a deep machine learning algorithm; The preset data model is trained according to the historical power plant operating parameters and restriction conditions so that the preset data model can simulate the actual operation of the power plant.

5. The method according to claim 1, characterized in that The desulfurization system comprises at least two subsystems; after executing control of the desulfurization system according to the first control parameter, the method further comprises: Obtain the control objectives of the target subsystem; Inputting the control target of the target subsystem and the power plant operation parameter into a preset data model to obtain a second control parameter for the target subsystem output by the preset data model; Controlling the target subsystem is performed according to the second control parameter.

6. A control device for a desulfurization system, characterized in that: include: A first acquisition unit, used to acquire power plant operation parameters; A first prediction unit, configured to input the power plant operation parameters into a preset data model to obtain disturbance information output by the preset data model; a second prediction unit, configured to input the disturbance information into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model; A first control unit is used to control the desulfurization system according to the first control parameter.

7. The device according to claim 6, characterized in that The second prediction unit is further configured to: The disturbance information and the first restriction condition are input into the preset data model to obtain a first control parameter for the desulfurization system output by the preset data model.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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