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

Abstract

The present disclosure discloses a control method and device, an electronic device and a storage medium for a desulfurization system. The main technical solutions include: obtaining real-time parameters of boiler operation; determining whether there is a first disturbance parameter in the real-time parameters according to a preset parameter threshold; in the case of the presence of a disturbance parameter, inputting the first disturbance parameter into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model; and controlling the desulfurization system according to the first desulfurization system control parameter. The real-time parameters of boiler operation are obtained, and the presence of a first disturbance parameter is determined in combination with a preset parameter threshold. When a disturbance parameter is detected, it is input into a preset prediction model to obtain a control parameter for the desulfurization system. This automated control process greatly improves the response speed and accuracy of the desulfurization system, effectively reduces improper control caused by delays or errors in human judgment, and helps to optimize desulfurization efficiency, reduce pollutant emissions, and meet environmental protection requirements.

Description

Technical Field

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

[0002] With increasingly stringent environmental protection policies, the optimization and control of desulfurization systems in thermal power plants has become a key link in reducing pollutant emissions and improving environmental protection levels.

[0003] However, in actual operation, boilers are often affected by a variety of interference factors, such as fluctuations in fuel quality, unstable combustion, changes in external environmental conditions, etc., which cause the concentration of pollutants emitted by the boiler to exceed national or local environmental protection standards, thereby causing environmental pollution. Summary of the invention

[0004] The present disclosure provides a control method, device, electronic device and storage medium for a desulfurization system, the main purpose of which is to solve the problem that the concentration of pollutants discharged by boilers exceeds the national or local environmental protection standards, thereby causing environmental pollution.

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

[0006] Obtain real-time parameters of boiler operation;

[0007] Determining whether a first disturbance parameter exists in the real-time parameters according to a preset parameter threshold;

[0008] In the case where the disturbance parameter exists, the first disturbance parameter is input into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model;

[0009] The desulfurization system is controlled according to the first desulfurization system control parameter.

[0010] Optionally, after determining whether there is a disturbance parameter in the real-time parameter according to a preset parameter threshold, the method further includes:

[0011] In the absence of the first disturbance parameter, inputting the real-time parameter into the preset prediction model to obtain parameter change prediction data output by the preset prediction model;

[0012] Determining whether a second disturbance parameter exists in the parameter change prediction data according to the preset parameter threshold;

[0013] In the case where the second disturbance parameter exists, inputting the second disturbance parameter into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model;

[0014] The desulfurization system is controlled according to the second desulfurization system control parameter.

[0015] Optionally, after obtaining the real-time parameters of the boiler operation, the method further includes:

[0016] Obtaining a target real-time parameter of a target parameter item from the real-time parameters;

[0017] Analyze the target real-time parameters and target historical parameters of the target parameter item to obtain the parameter change trend of the target parameter item;

[0018] Inputting the parameter change trend into the preset prediction model to obtain the third desulfurization system control parameter output by the preset prediction model;

[0019] The desulfurization system is controlled according to the third desulfurization system control parameter.

[0020] Optionally, after executing control of the desulfurization system according to the first desulfurization system control parameter, the method further includes:

[0021] Generate a timestamp of the first desulfurization system control parameter, and store the first desulfurization system control parameter in a preset database according to the sequence of the timestamps.

[0022] Optionally, before inputting the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model, the method further includes:

[0023] Acquire historical boiler operating parameters; wherein the historical boiler operating parameters include historical disturbance parameters;

[0024] The preset data model is trained according to the historical boiler operating parameters so that the preset data model can simulate the actual operating conditions of the boiler.

[0025] According to a second aspect of the present disclosure, a control device for a desulfurization system is provided, comprising:

[0026] A first acquisition unit, used to acquire real-time parameters of boiler operation;

[0027] A first determining unit, configured to determine whether a first disturbance parameter exists in the real-time parameters according to a preset parameter threshold;

[0028] a first output unit, configured to input the first disturbance parameter into a preset prediction model in the presence of the disturbance parameter, and obtain a first desulfurization system control parameter output by the preset prediction model;

[0029] The first control unit is used to control the desulfurization system according to the first desulfurization system control parameter.

[0030] Optionally, the device further comprises:

[0031] A prediction unit, configured to, after the first determination unit determines whether there is a disturbance parameter in the real-time parameter according to a preset parameter threshold, input the real-time parameter into the preset prediction model in the absence of the first disturbance parameter, to obtain parameter change prediction data output by the preset prediction model;

[0032] A second determining unit, configured to determine whether a second disturbance parameter exists in the parameter change prediction data according to the preset parameter threshold;

[0033] a second output unit, configured to input the second disturbance parameter into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model when the second disturbance parameter exists;

[0034] The second control unit is used to control the desulfurization system according to the second desulfurization system control parameter.

[0035] Optionally, the device further comprises:

[0036] A second acquisition unit is used to acquire a target real-time parameter of a target parameter item from the real-time parameters after the first acquisition unit acquires the real-time parameters of the boiler operation;

[0037] An analysis unit, configured to analyze the target real-time parameters and the target historical parameters of the target parameter item to obtain a parameter change trend of the target parameter item;

[0038] A third output unit, used to input the parameter change trend into the preset prediction model to obtain a third desulfurization system control parameter output by the preset prediction model;

[0039] The third control unit is used to control the desulfurization system according to the third desulfurization system control parameter.

[0040] Optionally, the device further comprises:

[0041] The storage unit is used to generate a timestamp of the first desulfurization system control parameter after the first control unit controls the desulfurization system according to the first desulfurization system control parameter, and store the first desulfurization system control parameter in a preset database according to the sequence of the timestamps.

[0042] Optionally, the device further comprises:

[0043] A third acquisition unit is used to acquire historical boiler operation parameters before the first output unit inputs the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model; wherein the historical boiler operation parameters include historical disturbance parameters;

[0044] A training unit is used to train the preset data model according to the historical boiler operating parameters so that the preset data model can simulate the actual operating conditions of the boiler.

[0045] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0046] at least one processor; and

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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 real-time parameters of the boiler operation; determining whether there is a first disturbance parameter in the real-time parameters according to a preset parameter threshold; in the case of the presence of the disturbance parameter, inputting the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model; and executing control of the desulfurization system according to the first desulfurization system control parameter. Compared with the related art, the embodiment of the present application obtains the real-time parameters of the boiler operation and determines whether there is a first disturbance parameter in combination with a preset parameter threshold. When the disturbance parameter is detected, it is input into a preset prediction model to obtain the control parameter for the desulfurization system. This automated control process greatly improves the response speed and accuracy of the desulfurization system, effectively reduces improper control caused by delays or errors in human judgment, helps to optimize the desulfurization efficiency, reduce pollutant emissions, and meet environmental protection requirements.

[0052] 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

[0053] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0054] Figure 1 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0055] Figure 2 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0056] Figure 3 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure;

[0057] Figure 4 A schematic diagram of the structure of a control device for a desulfurization system provided in an embodiment of the present disclosure;

[0058] Figure 5 A schematic diagram of the structure of a control device for a desulfurization system provided in an embodiment of the present disclosure;

[0059] Figure 6 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] 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.

[0061] 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.

[0062] Figure 1 A schematic flow chart of a control method for a desulfurization system provided in an embodiment of the present disclosure.

[0063] like Figure 1 As shown, the method comprises the following steps:

[0064] Step 101, obtaining real-time parameters of boiler operation.

[0065] The real-time parameters of the boiler include but are not limited to the inlet and outlet flue gas temperature, pressure, flow, oxygen content, fuel consumption, and furnace negative pressure. By installing high-precision sensors and data acquisition systems, the real-time and accuracy of these parameters can be ensured. These data are not only the basis for evaluating the current operating status of the boiler, but also the key to subsequent analysis and control strategy formulation. The acquisition of real-time parameters helps operators to promptly discover minor changes in boiler performance, prevent potential safety risks, and provide a basis for optimizing the combustion process.

[0066] Step 102: Determine whether there is a first disturbance parameter in the real-time parameters according to a preset parameter threshold.

[0067] After obtaining the real-time parameters, they need to be compared with the preset parameter thresholds. These thresholds are set based on the boiler's design parameters, historical operating data, and safety standards. If any real-time parameter exceeds these thresholds, it indicates that the boiler operation may have been disturbed. The disturbance parameter will disturb the operating state of the boiler, causing its operating power to fail to meet the standard or the exhaust gas content to exceed the standard, etc.

[0068] Step 103: In the case where the disturbance parameter exists, the first disturbance parameter is input into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model.

[0069] After the disturbance parameters are detected, they are input into a preset prediction model, which is built on a machine learning algorithm and historical data analysis to predict the impact of the disturbance on the desulfurization system. The model will output a series of control parameters designed to adjust the operation of the desulfurization system to compensate for the impact of the disturbance.

[0070] Step 104: Control the desulfurization system according to the first desulfurization system control parameter.

[0071] In some embodiments, the control parameters may include adjusting the injection rate of the desulfurizer, changing the flue gas flow distribution, optimizing the operating conditions of the spray tower, etc. These parameters are converted into specific operating instructions to achieve real-time adjustment of the desulfurization system.

[0072] The control method of the desulfurization system provided by the present disclosure mainly includes the following technical solutions: obtaining the real-time parameters of the boiler operation; determining whether there is a first disturbance parameter in the real-time parameters according to a preset parameter threshold; in the case where the disturbance parameter exists, inputting the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model; and controlling the desulfurization system according to the first desulfurization system control parameter. Compared with the related art, the embodiment of the present application obtains the real-time parameters of the boiler operation and determines whether there is a first disturbance parameter in combination with a preset parameter threshold. When the disturbance parameter is detected, it is input into a preset prediction model to obtain the control parameter for the desulfurization system. This automated control process greatly improves the response speed and accuracy of the desulfurization system, effectively reduces improper control caused by delays or errors in human judgment, and helps to optimize the desulfurization efficiency, reduce pollutant emissions, and meet environmental protection requirements.

[0073] See also Figure 2 , Figure 2 A flow chart of a control method for a desulfurization system provided by an embodiment of the present disclosure, after determining whether there is a disturbance parameter in the real-time parameter according to a preset parameter threshold, further includes:

[0074] Step 201: In the absence of the first disturbance parameter, the real-time parameter is input into the preset prediction model to obtain parameter change prediction data output by the preset prediction model.

[0075] Based on the preset prediction model, the operation status of the boiler in the future is predicted to generate parameter change prediction data. These data include but are not limited to the change trends of key indicators such as boiler efficiency, energy consumption, and emission concentration, providing forward-looking information for operators.

[0076] Step 202: Determine whether there is a second disturbance parameter in the parameter change prediction data according to the preset parameter threshold.

[0077] According to the preset parameter threshold, the parameter change prediction data output by the prediction model is deeply analyzed to determine whether there is a second disturbance parameter. Potential abnormal signs are found from the parameter change prediction data. If the prediction data is beyond the normal range, it indicates that there may be a second disturbance parameter.

[0078] Step 203: In the case where the second disturbance parameter exists, the second disturbance parameter is input into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model.

[0079] After the second disturbance parameter is detected, it is input into the preset prediction model to obtain the second set of control parameters for the desulfurization system. These parameters are designed for the possible future operating conditions of the boiler and are intended to optimize the desulfurization system in advance.

[0080] Step 204: Control the desulfurization system according to the second desulfurization system control parameter.

[0081] According to the control parameters of the second desulfurization system output by the prediction model, the system performs control operations on the desulfurization system. This may include adjusting the injection rate of the desulfurizer, changing the flue gas flow distribution, optimizing the operating conditions of the absorption tower, etc. Through this forward-looking control strategy, the system can take action before the disturbance actually occurs, effectively prevent potential problems, ensure the efficiency and stability of the desulfurization system operation, and reduce the impact on the environment.

[0082] See also Figure 3 , Figure 3 A flow chart of a control method for a desulfurization system provided by an embodiment of the present disclosure is provided. After obtaining real-time parameters of boiler operation, the method further includes:

[0083] Step 301, obtaining a target real-time parameter of a target parameter item in the real-time parameters.

[0084] Among the real-time parameters of boiler operation, the target parameter items are usually key factors affecting the performance of the desulfurization system, such as sulfur oxide concentration, temperature, humidity, etc. in the flue gas. Before executing this step, the target parameter items can be set according to actual needs. Specifically, this embodiment of the application does not limit this.

[0085] Step 302: Analyze the target real-time parameters and target historical parameters of the target parameter item to obtain the parameter change trend of the target parameter item.

[0086] Compare and analyze the target real-time parameters of the target parameter items with the historical parameters to reveal the changing trend of the parameters. This analysis process may include time series analysis, statistical pattern recognition and trend prediction techniques. Through these methods, the periodic changes, abnormal fluctuations and long-term trends of the parameters are identified, thus providing data support for the operation of the desulfurization system.

[0087] Step 303: input the parameter variation trend into the preset prediction model to obtain the third desulfurization system control parameter output by the preset prediction model.

[0088] The obtained parameter change trend is input into the preset prediction model, which will predict the future operation of the desulfurization system based on these trend data and output the third set of desulfurization system control parameters. Through the comprehensive analysis of historical data and real-time trends, the prediction model can provide the best control strategy for specific operating conditions. These control parameters may include the injection rate of the desulfurizer, the adjustment of the operating point of the flue gas treatment system, etc., aiming to optimize the desulfurization effect and reduce operating costs.

[0089] Step 304: Control the desulfurization system according to the third desulfurization system control parameter.

[0090] The system controls the desulfurization system according to the third desulfurization system control parameter output by the prediction model.

[0091] In some embodiments, after controlling the desulfurization system according to the first desulfurization system control parameter, the method further includes:

[0092] Generate a timestamp of the first desulfurization system control parameter, and store the first desulfurization system control parameter in a preset database according to the sequence of the timestamps.

[0093] After generating the first desulfurization system control parameters, the system will attach an accurate timestamp to them to ensure the traceability of the control parameter history and time series analysis.

[0094] According to the sequence of timestamps, these first desulfurization system control parameters with time stamps are stored in a preset database in an orderly manner to store a large amount of data and support fast retrieval and analysis.

[0095] By analyzing the stored historical data of control parameters, operators and technicians can identify long-term trends and periodic changes in the performance of the desulfurization system, thus providing a basis for system maintenance and optimization. When a system failure occurs, historical control parameters can be reviewed to help diagnose the cause of the failure and analyze whether it is caused by operating errors, equipment failures or external factors.

[0096] In some embodiments, before inputting the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model, the method further includes:

[0097] Acquire historical boiler operating parameters; wherein the historical boiler operating parameters include historical disturbance parameters;

[0098] The preset data model is trained according to the historical boiler operating parameters so that the preset data model can simulate the actual operating conditions of the boiler.

[0099] Historical data includes the operation records of the boiler in the past period of time, including but not limited to key indicators such as temperature, pressure, flow, combustion efficiency, and various historical disturbance parameters that appear in these records. These disturbance parameters may be caused by changes in fuel quality, equipment wear, operating errors or external environmental factors, which have an impact on the stable operation of the boiler.

[0100] Training the preset data model based on historical boiler operating parameters is a key step in model optimization and accurate simulation of the actual operation of the boiler. For the training process, please refer to any implementation method in the prior art, and the embodiments of this application will not be described one by one here. Through training, the preset data model can more accurately simulate the actual operation of the boiler, more accurately predict the behavior of the boiler under different working conditions, and formulate more effective boiler operation strategies to cope with various disturbances and maintain efficient and stable operation of the boiler.

[0101] 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.

[0102] Figure 4 A schematic diagram 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:

[0103] A first acquisition unit 41 is used to acquire real-time parameters of boiler operation;

[0104] A first determining unit 42, configured to determine whether a first disturbance parameter exists in the real-time parameters according to a preset parameter threshold;

[0105] A first output unit 43 is used to input the first disturbance parameter into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model when the disturbance parameter exists;

[0106] The first control unit 44 is used to control the desulfurization system according to the first desulfurization system control parameter.

[0107] The control device of the desulfurization system provided by the present disclosure has the following main technical solutions: obtaining the real-time parameters of the boiler operation; determining whether there is a first disturbance parameter in the real-time parameters according to a preset parameter threshold; in the case where the disturbance parameter exists, inputting the first disturbance parameter into a preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model; and executing control of the desulfurization system according to the first desulfurization system control parameter. Compared with the related art, the embodiment of the present application obtains the real-time parameters of the boiler operation and determines whether there is a first disturbance parameter in combination with a preset parameter threshold. When the disturbance parameter is detected, it is input into the preset prediction model to obtain the control parameter for the desulfurization system. This automated control process greatly improves the response speed and accuracy of the desulfurization system, effectively reduces improper control caused by delays or errors in human judgment, and helps to optimize the desulfurization efficiency, reduce pollutant emissions, and meet environmental protection requirements.

[0108] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0109] The prediction unit 45 is used for, after the first determination unit 42 determines whether there is a disturbance parameter in the real-time parameter according to a preset parameter threshold, inputting the real-time parameter into the preset prediction model in the absence of the first disturbance parameter, to obtain parameter change prediction data output by the preset prediction model;

[0110] A second determination unit 46, configured to determine whether a second disturbance parameter exists in the parameter change prediction data according to the preset parameter threshold;

[0111] A second output unit 47 is used to input the second disturbance parameter into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model when the second disturbance parameter exists;

[0112] The second control unit 48 is used to control the desulfurization system according to the second desulfurization system control parameter.

[0113] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0114] A second acquisition unit 49 is used to acquire a target real-time parameter of a target parameter item from the real-time parameters after the first acquisition unit 41 acquires the real-time parameters of the boiler operation;

[0115] An analysis unit 410 is used to analyze the target real-time parameters and the target historical parameters of the target parameter item to obtain a parameter change trend of the target parameter item;

[0116] A third output unit 411 is used to input the parameter change trend into the preset prediction model to obtain a third desulfurization system control parameter output by the preset prediction model;

[0117] The third control unit 412 is used to control the desulfurization system according to the second desulfurization system control parameter.

[0118] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0119] The storage unit 413 is used to generate a timestamp of the first desulfurization system control parameter after the first control unit 44 controls the desulfurization system according to the first desulfurization system control parameter, and store the first desulfurization system control parameter in a preset database according to the sequence of the timestamps.

[0120] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0121] The third acquisition unit 414 is used to acquire historical boiler operation parameters before the first output unit 43 inputs the first disturbance parameter into the preset prediction model to obtain the first desulfurization system control parameter output by the preset prediction model; wherein the historical boiler operation parameters include historical disturbance parameters;

[0122] The training unit 415 is used to train the preset data model according to the historical boiler operating parameters so that the preset data model can simulate the actual operating conditions of the boiler.

[0123] 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.

[0124] 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.

[0125] Figure 6A 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 real-time parameters of boiler operation; Determining whether a first disturbance parameter exists in the real-time parameters according to a preset parameter threshold; In the case where the disturbance parameter exists, the first disturbance parameter is input into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model; The desulfurization system is controlled according to the first desulfurization system control parameter.

2. The method according to claim 1, characterized in that After determining whether there is a disturbance parameter in the real-time parameter according to the preset parameter threshold, the method further includes: In the absence of the first disturbance parameter, inputting the real-time parameter into the preset prediction model to obtain parameter change prediction data output by the preset prediction model; Determining whether a second disturbance parameter exists in the parameter change prediction data according to the preset parameter threshold; In the case where the second disturbance parameter exists, inputting the second disturbance parameter into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model; The desulfurization system is controlled according to the second desulfurization system control parameter.

3. The method according to claim 1, characterized in that After obtaining the real-time parameters of the boiler operation, the method further includes: Obtaining a target real-time parameter of a target parameter item from the real-time parameters; Analyze the target real-time parameters and target historical parameters of the target parameter item to obtain the parameter change trend of the target parameter item; The parameter variation trend is input into the preset prediction model to obtain the third desulfurization system control parameter output by the preset prediction model.

4. The method according to claim 1, characterized in that: After executing control of the desulfurization system according to the first desulfurization system control parameter, the method further includes: Generate a timestamp of the first desulfurization system control parameter, and store the first desulfurization system control parameter in a preset database according to the sequence of the timestamps.

5. The method according to any one of claims 1 to 3, characterized in that: Before inputting the first disturbance parameter into a preset prediction model to obtain a first desulfurization system control parameter output by the preset prediction model, the method further includes: Acquire historical boiler operating parameters; wherein the historical boiler operating parameters include historical disturbance parameters; The preset data model is trained according to the historical boiler operating parameters so that the preset data model can simulate the actual operating conditions of the boiler.

6. A control device for a desulfurization system, characterized in that: include: A first acquisition unit, used to acquire real-time parameters of boiler operation; A first determining unit, configured to determine whether a first disturbance parameter exists in the real-time parameters according to a preset parameter threshold; a first output unit, configured to input the first disturbance parameter into a preset prediction model in the presence of the disturbance parameter, and obtain a first desulfurization system control parameter output by the preset prediction model; The first control unit is used to control the desulfurization system according to the first desulfurization system control parameter.

7. The device according to claim 6, characterized in that The device also includes: A prediction unit, configured to, after the first determination unit determines whether there is a disturbance parameter in the real-time parameter according to a preset parameter threshold, input the real-time parameter into the preset prediction model in the absence of the first disturbance parameter, to obtain parameter change prediction data output by the preset prediction model; A second determining unit, configured to determine whether a second disturbance parameter exists in the parameter change prediction data according to the preset parameter threshold; a second output unit, configured to input the second disturbance parameter into a preset prediction model to obtain a second desulfurization system control parameter output by the preset prediction model when the second disturbance parameter exists; The second control unit is used to control the desulfurization system according to the second desulfurization system control parameter.

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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