A flue gas emission secondary dust raising intelligent regulation method and system

Abstract

The application provides a flue gas emission secondary dust raising intelligent regulation method and system, relates to the technical field of intelligent dust removal, and comprises the following steps: collecting device signal data, clean flue gas concentration data and device parameter data of a dust removal electric field device in real time, preprocessing the clean flue gas concentration data and the device parameter data to obtain standardized working condition data; processing the standardized working condition data to obtain the concentration change slope degree of the dust removal electric field device in the current collection period; formulating a secondary dust raising algorithm, generating trace data of the corresponding dust removal electric field device according to the device signal data, the standardized working condition data and the concentration change slope degree of the current collection period; based on a deep learning algorithm, constructing an intelligent regulation model, and generating a secondary dust raising intelligent regulation scheme of the corresponding dust removal electric field device according to the trace data of the dust removal electric field device in the historical collection period, so that dust removal can be efficiently realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent dust removal technology, specifically to an intelligent control method and system for secondary dust generation from flue gas emissions. Background Technology

[0002] In the construction and operation of smart power plants, flue gas dust emission control is a core aspect of ensuring environmental compliance and efficient equipment operation. Among these, secondary dust pollution directly affects the stability of dust emission indicators, making it a key technical challenge that smart power plant systems need to address.

[0003] Therefore, a method and system for intelligent control of secondary dust generation from flue gas emissions are provided. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, the purpose of this invention is to provide a method and system for intelligent control of secondary dust generation in flue gas emissions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent control of secondary dust generation in flue gas emissions, the method comprising:

[0006] Real-time acquisition of equipment signal data, clean flue gas concentration data, and equipment parameter data of dust removal electric field equipment; preprocessing of the clean flue gas concentration data and equipment parameter data to obtain standardized operating condition data;

[0007] The standardized operating condition data is processed to obtain the slope of the concentration change of the dust removal electric field equipment in the current collection period;

[0008] A secondary dust emission algorithm is developed, which generates traceability data for the corresponding dust removal electric field equipment based on the equipment signal data of the current collection cycle, standardized operating condition data, and concentration change slope.

[0009] Based on deep learning algorithms, an intelligent control model is constructed, and based on the historical data of the dust removal electric field equipment, a secondary dust control scheme for the corresponding dust removal electric field equipment is generated.

[0010] Furthermore, the process of real-time acquisition of equipment signal data, clean flue gas concentration data, and equipment parameter data of the dust removal electric field equipment includes:

[0011] Each dust removal electric field device is assigned a unique signal group identifier, and the corresponding physical marker is associated with the dust removal electric field device corresponding to the unique signal group identifier; the device signal data of the corresponding dust removal electric field device is collected based on the unique signal group identifier and the physical marker.

[0012] The system's monitoring processor obtains the monitoring address of the dust removal electric field equipment; the monitoring processor collects the net flue gas concentration data of the corresponding dust removal electric field equipment according to the monitoring address and the preset collection period.

[0013] By mapping the correspondence between register addresses and entity markers, and according to a preset data reading method, the corresponding device parameter data is obtained. The device parameter data includes secondary current values ​​and conduction angles.

[0014] Furthermore, the process of preprocessing the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data includes:

[0015] Set the corresponding data format, convert the collected net flue gas concentration data and equipment parameter data according to the corresponding data format, and then obtain the corresponding standardized operating condition data. The standardized operating condition data includes standardized net flue gas concentration data and standardized equipment parameter data. The standardized equipment parameter data includes standardized secondary current value and standardized conduction angle.

[0016] If the standardized operating condition data is missing in a certain collection cycle, a fallback algorithm is called to obtain fallback data, and the missing standardized operating condition data is filled in based on the fallback data.

[0017] Furthermore, the process of obtaining fallback data by invoking the fallback algorithm includes:

[0018] A fallback data collection period is preset. If the standard operating condition data for the current collection period is missing, the timestamp of the current collection period will be obtained as the start timestamp. The start timestamp will be iterated backward according to the fallback data collection period to obtain the end timestamp. The historical standard operating condition data corresponding to the end timestamp will be used as fallback data to fill the gap.

[0019] Further, the process of processing the standardized operating condition data to obtain the slope of the concentration change of the dust removal electric field equipment in the current acquisition period includes:

[0020] Two data collection points are preset, and the time difference between the two data collection points is recorded as the data collection interval;

[0021] The slope of concentration change is calculated based on the coordinates of the data collection points corresponding to the current collection period and the coordinates of the data collection points corresponding to the historical collection periods.

[0022] Furthermore, the process of developing a secondary dust emission algorithm includes:

[0023] Set judgment conditions and preset the highest threshold for clean flue gas concentration;

[0024] Set an execution cycle, and obtain the corresponding output current and primary clean flue gas concentration data according to the execution cycle; obtain the corresponding conduction angle difference and secondary clean flue gas concentration data according to the primary clean flue gas concentration data and the judgment relationship of the highest threshold of clean flue gas concentration; perform corresponding operations on the output current and conduction angle difference according to the secondary clean flue gas concentration data and the judgment relationship of the highest threshold of clean flue gas concentration.

[0025] The minimum threshold of net flue gas concentration is preset. Based on the judgment relationship between the real-time collected net flue gas concentration data, the minimum threshold of net flue gas concentration, and the maximum threshold of net flue gas concentration, the corresponding output current, conduction angle difference, and number of cycles are obtained.

[0026] Based on the aforementioned judgment conditions, execution cycle, output current, and conduction angle difference, a secondary dust control algorithm is constructed.

[0027] Furthermore, the process of generating traceability data for the corresponding dust removal electric field equipment based on the secondary dust emission algorithm and according to the equipment signal data, standardized operating condition data, and concentration change slope in the current acquisition period includes:

[0028] Based on the equipment signal data of the current acquisition cycle, obtain the standard operating condition data of the corresponding dust removal electric field equipment; based on the standardized operating condition data, obtain the concentration change slope degree of the corresponding acquisition cycle;

[0029] When the slope of the concentration change and the standardized operating condition data both meet the judgment conditions in the secondary dust emission algorithm, the number of cycles of the secondary dust emission algorithm executed in the corresponding collection cycle, the secondary current value obtained in each cycle, and the conduction angle are obtained.

[0030] The corresponding dust removal electric field equipment's traceability data is generated by taking the concentration change slope degree of the corresponding collection period, the standardized operating condition data, the number of cycles of the secondary dust emission algorithm, the secondary current value obtained in each cycle, and the conduction angle.

[0031] Furthermore, the process of constructing an intelligent regulation model based on deep learning algorithms includes:

[0032] Acquire traceability data of several sets of dust removal electric field equipment with different historical acquisition periods; and obtain the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution period, the output current and the conduction angle difference of the dust removal electric field equipment corresponding to the historical acquisition period.

[0033] A training sample set is formed based on the traceability data of several sets of dust removal electric field equipment with different historical collection periods, the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution period, the output current, and the conduction angle difference.

[0034] A standard model is constructed based on deep learning algorithms; the training sample set is then input into the standard model to train it, and the trained standard model is denoted as the intelligent control model.

[0035] Furthermore, the process of generating a corresponding intelligent control scheme for secondary dust control of the dust removal electric field equipment based on historical data collection cycles includes:

[0036] The historical data collected from the corresponding dust removal electric field equipment is input into the intelligent control model. The intelligent control model outputs the number of cycles of the electric field of the corresponding dust removal electric field equipment, as well as the adjustment amount of the secondary current value and the conduction angle. Based on the number of cycles, the adjustment amount of the secondary current value and the conduction angle, a secondary dust control scheme for the corresponding dust removal electric field equipment is generated. Relevant technical personnel can adjust the dust removal process in real time according to the secondary dust control scheme to achieve efficient dust removal.

[0037] A second aspect of the present invention also provides an intelligent control system for secondary dust emission from flue gas, the system comprising: a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, an intelligent control module, and a data storage module;

[0038] The data acquisition module is used to collect real-time equipment signal data, clean flue gas concentration data, and equipment parameter data of the dust removal electric field equipment.

[0039] The data preprocessing module is used to preprocess the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data.

[0040] The data processing module is used to process the standardized operating condition data to obtain the slope degree of the concentration change of the dust removal electric field equipment in the current collection period.

[0041] The data analysis module is used to develop secondary dust control algorithms. Based on the equipment signal data, standardized operating condition data, and concentration change slope of the current collection period, it generates traceability data for the corresponding dust removal electric field equipment.

[0042] The intelligent control module, based on deep learning algorithms, constructs an intelligent control model and generates a corresponding intelligent control scheme for secondary dust emission of the dust removal electric field equipment based on the historical data of the dust removal electric field equipment.

[0043] The data storage module is used to store equipment signal data, clean flue gas concentration data, equipment parameter data, traceability data, and secondary dust control process optimization solutions during the control process.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] 1. By associating unified signal group identifiers with monitoring addresses, accurate acquisition and standardized processing of equipment signal data, clean flue gas concentration data, and equipment parameter data are achieved. A fallback mechanism to fill data gaps ensures the continuity of operating data and provides a reliable foundation for subsequent analysis.

[0046] 2. Incorporate the slope of concentration change into the control logic, and combine it with the multi-condition judgment and cyclic execution rules of the secondary dust emission algorithm to improve the timeliness of secondary dust emission control;

[0047] 3. The intelligent control model built based on deep learning algorithms optimizes parameter adjustment schemes using historical traceability data, reduces manual intervention, improves the accuracy and adaptability of control, and avoids blind parameter adjustment; through system division of labor and collaboration and full-process data storage, the traceability and optimization of secondary dust control are realized, and the overall stability of electrostatic precipitator operation and dust emission control effect are improved, meeting the dual needs of the industrial field for environmental protection and production efficiency. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0049] Figure 1 This is a schematic diagram illustrating the steps of an intelligent control method for secondary dust generation in flue gas emissions.

[0050] Figure 2 This is a schematic diagram of a module for an intelligent control system for secondary dust generation in flue gas emissions. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] like Figure 1 As shown, a method for intelligent control of secondary dust emission from flue gas is disclosed, the method comprising the following steps:

[0054] Step S1: Collect equipment signal data, clean flue gas concentration data and equipment parameter data of the dust removal electric field equipment in real time, and preprocess the clean flue gas concentration data and equipment parameter data to obtain standardized operating condition data.

[0055] Step S2: Process the standardized operating condition data to obtain the slope of the concentration change of the dust removal electric field equipment in the current acquisition period;

[0056] Step S3: Develop a secondary dust control algorithm. Based on the equipment signal data, standardized operating condition data, and concentration change slope of the current collection period, generate traceability data for the corresponding dust removal electric field equipment.

[0057] Step S4: Based on deep learning algorithms, construct an intelligent control model and generate a corresponding intelligent control scheme for secondary dust control of the dust removal electric field equipment based on the historical data of the dust removal electric field equipment.

[0058] It should be further explained that, in the specific implementation process, the real-time acquisition of equipment signal data, clean flue gas concentration data, and equipment parameter data of the dust removal electric field equipment includes:

[0059] Optionally, in this embodiment of the application, a unique signal group identifier is assigned to each dust removal electric field device, and a corresponding physical marker is associated with the dust removal electric field device corresponding to the unique signal group identifier. The physical marker includes, but is not limited to, a secondary current marker and a conduction angle marker. Based on the unique signal group identifier and the physical marker, the device signal data of the corresponding dust removal electric field device is collected and stored.

[0060] In an exemplary embodiment, the unique signal group identifier for a five-field dust removal electric field device is "5DC-2CDL", and the unique signal group identifier for a four-field dust removal electric field device is "4DC-PULSE"; the secondary current is marked as "CURRENT", and the conduction angle is marked as "CONDUCTION_ANGLE". Before performing parameter adjustment, the system calls a method in the system to obtain the device signal data of the corresponding dust removal electric field device by reading key-value pairs in the format power:signal:{unique signal group identifier}:{entity identifier} from the database; if there is no data in the Redis database (e.g., the device has just started), the system calls a method to query the most recently stored device signal data from the database.

[0061] Optionally, in this embodiment of the application, the monitoring address of the dust removal electric field device is obtained through the system's monitoring processor; the monitoring processor collects and stores the net flue gas concentration data of the corresponding dust removal electric field device according to the monitoring address and a preset collection period;

[0062] In an exemplary embodiment, the corresponding listening address, such as 3646, is obtained through a listening processor. The listening processor has a built-in data receiving thread that receives the net flue gas concentration data sent by the dust removal electric field device once per second, such as 4.2 or 3.8, and temporarily stores it in a memory buffer.

[0063] Optionally, in this embodiment of the application, the corresponding device parameter data is obtained and stored by mapping the correspondence between the register address and the entity marker and according to the preset data reading method.

[0064] It should be noted that the device parameter data includes, but is not limited to, secondary current values ​​and conduction angle.

[0065] In an exemplary embodiment, if the original value in the register address 20550 of the five-field dust removal electric field device is read as 0258 (hexadecimal), then 0258 is converted to decimal 600 according to the data reading method, and the database is updated. That is, the value of power:signal:5DC-2CDL:CURRENT of the five-field dust removal electric field device is 600, which is then available for other components to call.

[0066] It should be further explained that, in the specific implementation process, the preprocessing of the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data includes:

[0067] Optionally, in this embodiment of the application, a corresponding data format is set, and the collected net flue gas concentration data and equipment parameter data are converted according to the corresponding data format to obtain the corresponding standardized operating condition data. The standardized operating condition data includes, but is not limited to, standardized net flue gas concentration data and standardized equipment parameter data. The standardized equipment parameter data includes, but is not limited to, standardized secondary current value and standardized conduction angle.

[0068] Optionally, in this embodiment of the application, if the standardized operating condition data is missing in a certain collection cycle, a fallback algorithm is called to obtain fallback data, and the missing standardized operating condition data is filled in based on the fallback data, thereby achieving data integrity.

[0069] It should be noted that the fallback data includes, but is not limited to, fallback data on net flue gas concentration and fallback data on equipment parameters; the fallback data on equipment parameters includes, but is not limited to, fallback data on secondary current and fallback data on conduction angle.

[0070] It should be further explained that, in the specific implementation process, the process of obtaining the fallback data by calling the fallback algorithm includes:

[0071] Optionally, in this embodiment of the application, a fallback collection period is preset. If the standard operating condition data of the current collection period is missing, the timestamp of the current collection period is obtained as the start timestamp. The start timestamp is traversed backward according to the fallback collection period to obtain the end timestamp. The historical standard operating condition data corresponding to the end timestamp is then used as fallback data to fill the gap.

[0072] It should be noted that if the fallback collection period does not meet the corresponding collection period length, the fallback collection period will be divided into two parts according to the binary search algorithm to meet the requirements.

[0073] In an exemplary embodiment, when calculating the net flue gas concentration slope using net flue gas concentration data collected over a period of time, if the collected net flue gas concentration data returns a service timeout or an empty result, a method in the system is invoked, passing in the start timestamp. If the fallback collection period is 5 minutes, then based on the start timestamp and the fallback collection period, historical net flue gas concentration data (such as "4.0") corresponding to the end timestamp is queried from the corresponding association table as the fallback data for net flue gas concentration.

[0074] It should be further explained that, in the specific implementation process, the process of processing the standardized operating condition data to obtain the slope of the concentration change of the dust removal electric field equipment in the current collection period includes:

[0075] Optionally, in this embodiment of the application, two data acquisition points are preset, and the time difference between the two data acquisition points is recorded as the data acquisition interval; the data acquisition interval can be set according to specific circumstances.

[0076] It should be noted that there is a time sequence between the two data collection points. One data point represents the data collection point of the current collection period, while the other data collection point represents the data collection point of a historical collection period.

[0077] The x-coordinate of the data acquisition point corresponding to the current acquisition period is recorded as x2, and the y-coordinate of the data acquisition point corresponding to the current acquisition period is recorded as y2; the x-coordinate of the data acquisition point corresponding to the historical acquisition period is recorded as x1, and the y-coordinate of the data acquisition point corresponding to the historical acquisition period is recorded as y1.

[0078] Based on the coordinates (x2, y2) of the data acquisition point corresponding to the current acquisition period and the coordinates (x1, y1) of the data acquisition point corresponding to the historical acquisition period, the slope degree of the concentration change is calculated. ;

[0079] It should be noted that x2 and x1 are used to represent the collection order, and x2 is generally set to 2 and x1 to 1 to prevent the denominator from being 0; y2 and y1 represent the net flue gas concentration data collected at the corresponding data collection points; the slope of the concentration change is... The calculation process does not involve the influence of physical dimensions; it only takes values ​​for calculation.

[0080] It should be further explained that, in the specific implementation process, the process of developing the secondary dust control algorithm includes:

[0081] Optionally, in this embodiment of the application, a judgment condition is set and a maximum threshold for net flue gas concentration is preset. The judgment condition includes, but is not limited to, a concentration judgment condition and a concentration slope judgment condition. The concentration judgment condition is that the net flue gas concentration data is greater than or equal to the maximum threshold for net flue gas concentration. The concentration slope judgment condition is that the degree of concentration change slope is greater than or equal to the degree of concentration change slope threshold.

[0082] In an exemplary embodiment, the maximum threshold for net flue gas concentration is set to 4. If the real-time collected net flue gas concentration data is greater than or equal to the maximum threshold of net flue gas concentration 4, the concentration judgment condition is met.

[0083] The threshold for the slope of concentration change is set to 20°. If the slope of concentration change calculated in real time is greater than or equal to the net flue gas concentration threshold of 4, then the concentration slope judgment condition is met.

[0084] Optionally, in this embodiment of the application, an execution cycle is set. During the execution cycle, the standard secondary current value of the high-frequency power supply of the corresponding electric field of the dust removal electric field equipment is first increased, and the increased secondary current value is recorded as the output current. The corresponding clean flue gas concentration data is collected in real time and recorded as the first-level clean flue gas concentration data.

[0085] If the primary clean flue gas concentration data is greater than or equal to the highest threshold of clean flue gas concentration, the conduction angle of the corresponding electric field pulse power supply of the dust removal electric field equipment is increased and recorded as the conduction angle difference. The corresponding clean flue gas concentration data is collected in real time and recorded as the secondary clean flue gas concentration data. If the secondary clean flue gas concentration data is greater than or equal to the highest threshold of clean flue gas concentration, the process is repeated according to the output current and the conduction angle difference until the final clean flue gas concentration data reaches the standard.

[0086] In one exemplary embodiment, the execution cycle is set to 40 minutes, with the secondary current of the high-frequency power supply corresponding to the dust removal electric field device increased for the first 20 minutes and the conduction angle of the pulse power supply corresponding to the dust removal electric field device increased for the next 20 minutes.

[0087] It should be noted that the above-mentioned cyclic execution process will stop after a maximum of 3 consecutive cycles (i.e., a maximum of 3 increases), regardless of the result, to prevent over-adjustment.

[0088] Optionally, in this embodiment of the application, a minimum threshold for net flue gas concentration is preset. When the real-time collected net flue gas concentration data is greater than or equal to the minimum threshold for net flue gas concentration and less than the maximum threshold for net flue gas concentration, the loop execution stops, and the output current and conduction angle difference of the current loop execution are maintained until the final net flue gas concentration data reaches the standard, and the number of loops is recorded and stored.

[0089] When the real-time collected net flue gas concentration data is less than the minimum threshold of net flue gas concentration, the loop execution stops, and the original secondary current and conduction angle are restored. The number of loops is recorded and stored.

[0090] In an exemplary embodiment, the minimum threshold for net flue gas concentration is 3, the maximum threshold for net flue gas concentration is 4, and the real-time collected net flue gas concentration data is within the range [3, 4). The current increased output current and conduction angle difference are maintained. When the real-time collected net flue gas concentration data is less than 3, the loop execution stops, and the current secondary current value and conduction angle are restored to the original secondary current value and conduction angle, and the loop count is recorded.

[0091] Optionally, in this embodiment of the application, a secondary dust control algorithm is formed based on the judgment conditions, execution cycle, output current, and conduction angle difference.

[0092] It should be noted that the execution cycle refers to the time required to execute one secondary dust suppression algorithm. The output current and the conduction angle difference can be set according to actual conditions.

[0093] It should be further explained that, in the specific implementation process, the process of generating traceability data for the corresponding dust removal electric field equipment based on the secondary dust emission algorithm and according to the equipment signal data of the current collection period, standardized operating condition data, and concentration change slope, includes:

[0094] Optionally, in this embodiment of the application, standard operating condition data of the corresponding dust removal electric field equipment is obtained based on the equipment signal data of the current acquisition cycle; and the concentration change slope degree of the corresponding acquisition cycle is obtained based on the standardized operating condition data.

[0095] When the slope of the concentration change and the standardized operating condition data both meet the judgment conditions in the secondary dust emission algorithm, the number of cycles of the secondary dust emission algorithm executed in the corresponding collection cycle, the secondary current value obtained in each cycle, and the conduction angle are obtained.

[0096] The corresponding dust removal electric field equipment's traceability data is generated by taking the concentration change slope degree of the corresponding collection period, the standardized operating condition data, the number of cycles of the secondary dust emission algorithm, the secondary current value obtained in each cycle, and the conduction angle.

[0097] It should be further explained that, in the specific implementation process, the process of constructing an intelligent control model based on deep learning algorithms includes:

[0098] Optionally, in this embodiment of the application, several sets of traceability data of dust removal electric field equipment with different historical collection periods are obtained; and the lowest threshold value of net flue gas concentration, the highest threshold value of net flue gas concentration, the execution period, the output current and the conduction angle difference value of the dust removal electric field equipment corresponding to the historical collection period are obtained.

[0099] Based on several sets of historical data collection periods for different dust removal electric field equipment, including the lowest threshold of clean flue gas concentration, the highest threshold of clean flue gas concentration, the execution cycle, the output current, and the conduction angle difference;

[0100] The specific process of constructing the training sample set includes:

[0101] The traceability data of several sets of dust removal electric field equipment with different historical collection periods, the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution cycle, the output current, and the conduction angle difference are grouped and labeled as f=1, 2, 3, ..., p; p is a natural number.

[0102] The traceability data of several groups of dust removal electric field equipment with different historical collection periods in the ph group, the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution cycle, the output current, and the conduction angle difference are used as sample data, and h is a natural number less than p. The mean of the sample data is obtained using the sample data and is denoted as the sample set.

[0103] The remaining historical data collection periods of different dust removal electric field equipment, the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution cycle, the output current, and the conduction angle difference are used as the test set; a training sample set is formed based on the sample set and the test set.

[0104] A standard model is constructed based on deep learning algorithms; the training sample set is then input into the standard model to train it, and the trained standard model is denoted as the intelligent control model.

[0105] It should be noted that the deep learning algorithm can employ a multi-layer neural network algorithm.

[0106] It should be further explained that, in the specific implementation process, the process of generating a corresponding intelligent control scheme for secondary dust control of the dust removal electric field equipment based on the historical data collection period includes:

[0107] Optionally, in this embodiment of the application, the traceability data of the historical collection cycle of the corresponding dust removal electric field equipment is input into the intelligent control model. The intelligent control model outputs the number of cycles of the electric field of the corresponding dust removal electric field equipment, as well as the adjustment amount of the secondary current value and the conduction angle. Based on the number of cycles, the adjustment amount of the secondary current value and the conduction angle, a secondary dust control scheme for the corresponding dust removal electric field equipment is generated. Relevant technicians can adjust the dust removal process in real time according to the secondary dust control scheme to achieve efficient dust removal.

[0108] like Figure 2 As shown, a smart control system for secondary dust emission from flue gas includes: a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, a smart control module, and a data storage module.

[0109] The data acquisition module is used to collect real-time equipment signal data, clean flue gas concentration data, and equipment parameter data of the dust removal electric field equipment.

[0110] The data preprocessing module is used to preprocess the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data.

[0111] The data processing module is used to process the standardized operating condition data to obtain the slope degree of the concentration change of the dust removal electric field equipment in the current collection period.

[0112] The data analysis module is used to develop secondary dust control algorithms. Based on the equipment signal data, standardized operating condition data, and concentration change slope of the current collection period, it generates traceability data for the corresponding dust removal electric field equipment.

[0113] The intelligent control module, based on deep learning algorithms, constructs an intelligent control model and generates a corresponding intelligent control scheme for secondary dust emission from the dust removal electric field equipment based on the historical data collected from the equipment.

[0114] The data storage module is used to store equipment signal data, clean flue gas concentration data, equipment parameter data, traceability data, and secondary dust control process optimization solutions during the control process.

[0115] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0116] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of this application.

[0118] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed application can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for intelligent control of secondary dust generation from flue gas emissions, characterized in that, The method includes: Real-time acquisition of equipment signal data, clean flue gas concentration data, and equipment parameter data of dust removal electric field equipment; preprocessing of the clean flue gas concentration data and equipment parameter data to obtain standardized operating condition data; The standardized operating condition data is processed to obtain the slope of the concentration change of the dust removal electric field equipment in the current collection period; Develop a secondary dust control algorithm, including: The judgment conditions are set and the maximum threshold of net flue gas concentration is preset. The judgment conditions include concentration judgment conditions and concentration slope judgment conditions. The concentration judgment condition is that the net flue gas concentration data is greater than or equal to the maximum threshold of net flue gas concentration. The concentration slope judgment condition is that the degree of concentration change slope is greater than or equal to the threshold of concentration change slope. Set an execution cycle. Within the execution cycle, first increase the standard secondary current value of the high-frequency power supply of the corresponding electric field of the dust removal electric field equipment, and record the increased secondary current value as the output current and collect the corresponding clean flue gas concentration data in real time as the first-level clean flue gas concentration data. If the primary clean flue gas concentration data is greater than or equal to the highest threshold of clean flue gas concentration, the conduction angle of the corresponding electric field pulse power supply of the dust removal electric field equipment is increased and recorded as the conduction angle difference. The corresponding clean flue gas concentration data is collected in real time and recorded as the secondary clean flue gas concentration data. If the secondary clean flue gas concentration data is greater than or equal to the highest threshold of clean flue gas concentration, the process is repeated cyclically according to the output current and the conduction angle difference. A minimum threshold for clean flue gas concentration is preset. When the real-time collected clean flue gas concentration data is greater than or equal to the minimum threshold and less than the maximum threshold, the loop execution stops, and the output current and conduction angle difference of the current loop execution are maintained until the final clean flue gas concentration data reaches the standard. The number of loops is recorded and stored. When the real-time collected net flue gas concentration data is less than the minimum threshold of net flue gas concentration, the loop execution stops, and the original secondary current and conduction angle are restored. The number of loops is recorded and stored. Based on the judgment conditions, execution cycle, output current, and conduction angle difference, a secondary dust control algorithm is formed. The process of generating traceability data for the corresponding dust removal electric field equipment based on the secondary dust emission algorithm, and according to the equipment signal data of the current collection period, standardized operating condition data, and concentration change slope, includes: Based on the equipment signal data of the current acquisition cycle, obtain the standard operating condition data of the corresponding dust removal electric field equipment; based on the standardized operating condition data, obtain the concentration change slope degree of the corresponding acquisition cycle; When the slope of the concentration change and the standardized operating condition data both meet the judgment conditions in the secondary dust emission algorithm, the number of cycles of the secondary dust emission algorithm executed in the corresponding collection cycle, the secondary current value obtained in each cycle, and the conduction angle are obtained. The concentration change slope degree of the corresponding collection period, standardized operating condition data, number of cycles of the secondary dust emission algorithm, secondary current value obtained in each cycle, and conduction angle are used to generate traceability data for the corresponding dust removal electric field equipment. Based on deep learning algorithms, an intelligent control model is constructed, and based on the historical data of the dust removal electric field equipment, a secondary dust control scheme for the corresponding dust removal electric field equipment is generated.

2. The intelligent control method for secondary dust emission from flue gas according to claim 1, characterized in that, The process of real-time acquisition of equipment signal data, clean flue gas concentration data, and equipment parameter data of dust removal electric field equipment includes: Each dust removal electric field device is assigned a unique signal group identifier, and the corresponding physical marker is associated with the dust removal electric field device corresponding to the unique signal group identifier; the device signal data of the corresponding dust removal electric field device is collected based on the unique signal group identifier and the physical marker. The system's monitoring processor obtains the monitoring address of the dust removal electric field equipment; the monitoring processor collects the net flue gas concentration data of the corresponding dust removal electric field equipment according to the monitoring address and the preset collection period. By mapping the correspondence between register addresses and entity markers, and according to a preset data reading method, the corresponding device parameter data is obtained. The device parameter data includes secondary current values ​​and conduction angles.

3. The intelligent control method for secondary dust emission from flue gas according to claim 2, characterized in that, The process of preprocessing the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data includes: Set the corresponding data format, convert the collected net flue gas concentration data and equipment parameter data according to the corresponding data format, and then obtain the corresponding standardized operating condition data. The standardized operating condition data includes standardized net flue gas concentration data and standardized equipment parameter data. The standardized equipment parameter data includes standardized secondary current value and standardized conduction angle. If the standardized operating condition data is missing in a certain collection cycle, a fallback algorithm is called to obtain fallback data, and the missing standardized operating condition data is filled in based on the fallback data.

4. The intelligent control method for secondary dust emission from flue gas according to claim 3, characterized in that, The process of obtaining fallback data by calling the fallback algorithm includes: A fallback data collection period is preset. If the standard operating condition data for the current collection period is missing, the timestamp of the current collection period will be obtained as the start timestamp. The start timestamp will be iterated backward according to the fallback data collection period to obtain the end timestamp. The historical standard operating condition data corresponding to the end timestamp will be used as fallback data to fill the gap.

5. The intelligent control method for secondary dust emission from flue gas according to claim 4, characterized in that, The process of processing the standardized operating condition data to obtain the slope of the concentration change of the dust removal electric field equipment in the current collection period includes: Two data collection points are preset, and the time difference between the two data collection points is recorded as the data collection interval; The slope of concentration change is calculated based on the coordinates of the data collection points corresponding to the current collection period and the coordinates of the data collection points corresponding to the historical collection periods.

6. The intelligent control method for secondary dust emission from flue gas according to claim 5, characterized in that, The process of building an intelligent regulation model based on deep learning algorithms includes: Acquire traceability data of several sets of dust removal electric field equipment with different historical acquisition periods; and obtain the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution period, the output current and the conduction angle difference of the dust removal electric field equipment corresponding to the historical acquisition period. A training sample set is formed based on the traceability data of several sets of dust removal electric field equipment with different historical collection periods, the minimum threshold of net flue gas concentration, the maximum threshold of net flue gas concentration, the execution period, the output current, and the conduction angle difference. A standard model is constructed based on deep learning algorithms; the training sample set is then input into the standard model to train it, and the trained standard model is denoted as the intelligent control model.

7. The intelligent control method for secondary dust emission from flue gas according to claim 6, characterized in that, The process of generating a corresponding intelligent control scheme for secondary dust control of dust removal electric field equipment based on historical data collection periods includes: The historical data collected from the corresponding dust removal electric field equipment is input into the intelligent control model. The intelligent control model outputs the number of cycles of the electric field of the corresponding dust removal electric field equipment, as well as the adjustment amount of the secondary current value and the conduction angle. Based on the number of cycles, the adjustment amount of the secondary current value and the conduction angle, a secondary dust control scheme for the corresponding dust removal electric field equipment is generated. Relevant technical personnel can adjust the dust removal process in real time according to the secondary dust control scheme to achieve efficient dust removal.

8. A smart control system for secondary dust emission from flue gas, implementing the smart control method for secondary dust emission from flue gas as described in any one of claims 1 to 7, characterized in that, The system includes: a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, an intelligent control module, and a data storage module; The data acquisition module is used to collect real-time equipment signal data, clean flue gas concentration data, and equipment parameter data of the dust removal electric field equipment. The data preprocessing module is used to preprocess the net flue gas concentration data and equipment parameter data to obtain standardized operating condition data. The data processing module is used to process the standardized operating condition data to obtain the slope degree of the concentration change of the dust removal electric field equipment in the current collection period. The data analysis module is used to develop secondary dust control algorithms. Based on the equipment signal data, standardized operating condition data, and concentration change slope of the current collection period, it generates traceability data for the corresponding dust removal electric field equipment. The intelligent control module, based on deep learning algorithms, constructs an intelligent control model and generates a corresponding intelligent control scheme for secondary dust emission of the dust removal electric field equipment based on the historical data of the dust removal electric field equipment. The data storage module is used to store equipment signal data, clean flue gas concentration data, equipment parameter data, traceability data, and secondary dust control process optimization solutions during the control process.

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