Intelligent soot blowing method, system and equipment for dry deslagging of tower type boiler and storage medium

Abstract

The embodiment of the invention provides an intelligent soot blowing method, system and equipment for dry deslagging of a tower type boiler and a storage medium. The method comprises the steps that the furnace bottom slag falling condition and the dry slag discharging transportation amount corresponding to a current soot blowing heating surface area are obtained in real time; according to the furnace bottom slag falling condition and the dry slag discharging transportation volume, the soot blowing effect of each heating surface area of the hearth is judged; controlling the soot blowing frequency of each heating surface area of the hearth according to the soot blowing effect of each heating surface area of the hearth and a pre-established soot blowing strategy; wherein the soot blowing strategy is obtained through fuzzy logic calculation according to a soot condition monitoring model, a soot blowing expert system and a soot blowing economic model. According to the embodiment of the invention, the boiler bottom slag falling condition and the slag discharging amount condition of the heating surface area are monitored in real time, so that fine control of boiler soot blowing is realized, and the problem that soot blowing of the heating surface area of a high-capacity and high-parameter combustion boiler is generally extensive is solved.

Description

Technical Field

[0001] The embodiments of the present disclosure belong to the technical field of dry slag discharge and soot blowing for tower boilers, and specifically relate to a tower boiler dry slag discharge and intelligent soot blowing method, system, equipment and storage medium. Background Art

[0002] Compared with the Π-type boiler, the tower boiler fundamentally solves the problem of superheater and reheater overheating caused by the imbalance of flue gas energy in the horizontal flue. At the same time, the tower boiler also has the advantages of small footprint, small wear of the heating surface, and convenient shutdown inspection and maintenance. In recent years, the air-cooled dry slag discharger system of coal-fired boilers in thermal power plants has been rapidly applied for its energy-saving and water-saving advantages, and the comprehensive utilization value of dry slag is high. The sales price as a by-product of power generation is 3 to 5 times that of wet slag. When the tower boiler is operating normally, the high-temperature slag falling from the cold ash hopper is transported through the bottom slag discharge device, and the cooling air enters the slag discharger through the small air ports on both sides of the dry slag discharger shell and the head air supply port. The hot slag is gradually cooled on the conveyor belt and reduced to about 150°C. The cooling air gradually heats up to 300°C to 400°C and enters the furnace, thereby recovering the physical sensible heat of the slag and the heat released by its reburning.

[0003] At present, large boilers mainly use soot blowers to remove ash and coke. Conventional soot blowing methods include steam soot blowing, sonic soot blowing and gas pulse soot blowing, among which steam soot blowing is the most widely used. There are two existing soot blowing strategies: (1) timed soot blowing, which starts soot blowing at a predetermined time regardless of whether the ash and coke are serious; (2) soot blowing mode based on the exhaust gas temperature, which simply starts soot blowing when the exhaust gas temperature is high. The common problems of the above two soot blowing methods are: regardless of whether the ash and coke are serious or not, and regardless of the location of the ash and coke, all heating surfaces of the boiler are simply soot blown, which not only wastes a lot of steam, but also causes serious damage to the heating tube surface where the ash and coke are not serious. Summary of the invention

[0004] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art, and provide a tower boiler dry slag intelligent soot blowing method, system, equipment and storage medium.

[0005] One aspect of the present disclosure provides a tower boiler dry slag discharge intelligent soot blowing method, the method comprising:

[0006] Obtain the furnace bottom slag falling situation and dry slag transportation volume corresponding to the current sootblowing heating surface area in real time;

[0007] According to the furnace bottom slag falling situation and the dry slag transportation volume, the soot blowing effect of each heating surface area of ​​the furnace is judged;

[0008] According to the sootblowing effect of each heating surface area of ​​the furnace and the pre-established sootblowing strategy, the sootblowing frequency of each heating surface area of ​​the furnace is controlled; wherein,

[0009] The soot blowing strategy is obtained by fuzzy logic calculation based on the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model.

[0010] Furthermore, the sootblowing strategy is pre-established by the following steps:

[0011] Obtaining dust pollution characteristic parameters of the heating surface, and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters;

[0012] An expert experience database is established based on the experience of operators, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system;

[0013] Acquire sootblowing cost parameters and sootblowing benefit parameters, and establish a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters;

[0014] According to the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model, the soot blowing strategy is obtained by using fuzzy logic calculation.

[0015] Furthermore, the obtaining of dust pollution characteristic parameters of the heating surface and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters include:

[0016] Obtaining dust pollution characteristic parameters of the heating surface, and marking the dust pollution characteristic parameters to obtain a dust pollution characteristic parameter sample set;

[0017] A ash pollution condition monitoring model is established using a semi-supervised algorithm based on the ash pollution characteristic parameter sample set.

[0018] Furthermore, the real-time acquisition of the furnace bottom slag falling situation and dry slag transport volume corresponding to the current sootblowing heating surface area includes:

[0019] When blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling at the bottom of the furnace in real time;

[0020] When soot blowing in the current heating surface area, binocular vision technology is used to calculate the volume of slag transported by the dry slag discharger, and the dry slag transportation volume is obtained in combination with the slag density.

[0021] Another aspect of the present disclosure provides a tower boiler dry slag discharge intelligent soot blowing system, the system comprising:

[0022] The acquisition module is used to obtain the furnace bottom slag falling situation and dry slag transportation volume corresponding to the current soot blowing heating surface area in real time;

[0023] A judgment module, used to judge the soot blowing effect of each heating surface area of ​​the furnace according to the slag falling situation at the furnace bottom and the dry slag transportation volume;

[0024] A sootblowing module is used to control the sootblowing frequency of each heating surface area of ​​the furnace according to the sootblowing effect of each heating surface area of ​​the furnace and the pre-established sootblowing strategy; wherein,

[0025] The soot blowing strategy is obtained by fuzzy logic calculation based on a soot pollution status monitoring model, a soot blowing expert system and a soot blowing economic model.

[0026] Furthermore, the system further comprises a policy module; the policy module is used for:

[0027] Obtaining dust pollution characteristic parameters of the heating surface, and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters;

[0028] An expert experience database is established based on the experience of operators, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system;

[0029] Acquire sootblowing cost parameters and sootblowing benefit parameters, and establish a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters;

[0030] According to the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model, the soot blowing strategy is obtained by using fuzzy logic calculation.

[0031] Furthermore, the policy module is specifically used for:

[0032] Obtaining dust pollution characteristic parameters of the heating surface, and marking the dust pollution characteristic parameters to obtain a dust pollution characteristic parameter sample set;

[0033] A ash pollution condition monitoring model is established using a semi-supervised algorithm based on the ash pollution characteristic parameter sample set.

[0034] Furthermore, the acquisition module is specifically used for:

[0035] When blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling at the bottom of the furnace in real time;

[0036] When soot blowing in the current heating surface area, a line laser binocular stereo camera is used to calculate the volume of the transported slag on the conveyor belt of the dry slag discharger, and the dry slag transportation volume is obtained in combination with the slag density.

[0037] Another aspect of the present disclosure provides an electronic device, comprising:

[0038] at least one processor; and,

[0039] The memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the tower boiler dry slag discharge intelligent sootblowing method described above.

[0040] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned tower boiler dry slag discharge intelligent sootblowing method is implemented.

[0041] The disclosed embodiments provide a tower boiler dry slag intelligent soot blowing method, system, equipment and storage medium, which realizes refined control of boiler soot blowing by real-time monitoring of the furnace bottom slag falling and slag discharge amount in the heating surface area, and solves the problem of generally extensive soot blowing in the heating surface area of ​​large-capacity and high-parameter combustion boilers. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process of a tower boiler dry slag discharge intelligent soot blowing method according to an embodiment of the present disclosure;

[0043] Figure 2 This is a structural schematic diagram of a tower boiler dry slag discharge intelligent soot blowing system according to another embodiment of the present disclosure;

[0044] Figure 3 The figure is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0046] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0047] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0048] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present disclosure. As used in this disclosure, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0049] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the protection scope of the present disclosure.

[0050] like Figure 1 As shown, an embodiment of the present disclosure provides a tower boiler dry slag discharge intelligent soot blowing method, the method comprising:

[0051] Step S1, real-time acquisition of the furnace bottom slag falling situation and dry slag transport volume corresponding to the current sootblowing heating surface area.

[0052] Specifically, according to the actual situation of the dry slag discharger, when blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling below the furnace bottom slag well in real time, and dynamically grasp the furnace slag falling situation. In some embodiments, through system development, it is also possible to realize intelligent recognition of large slag and issue large slag signal warnings for large slag falling from the boiler. At the same time, binocular vision technology is used to calculate the volume of the stacked continuous transport slag on the transmission steel belt of the dynamic dry slag discharger through a line laser binocular stereo camera, and the real-time statistics of the dry slag transportation volume (quality, flow) are combined with the stacked slag density provided by the power plant, and finally the real-time and accurate monitoring of the furnace bottom slag volume is realized.

[0053] Step S2: judging the soot blowing effect of each heating surface area of ​​the furnace according to the furnace bottom slag falling situation and the dry slag transportation volume.

[0054] Specifically, when slag falling frequently occurs at the furnace bottom during sootblowing in the heating surface area, it indicates that the sootblowing effect in this area is better; conversely, when there is no obvious slag falling at the furnace bottom during sootblowing in the heating surface area, it indicates that the sootblowing effect in this area is poor, thereby realizing the identification of the sootblowing effect in the entire boiler furnace.

[0055] Step S3, controlling the sootblowing frequency of each heating surface area of ​​the furnace according to the sootblowing effect of each heating surface area of ​​the furnace and a pre-established sootblowing strategy.

[0056] Specifically, the sootblowing strategy is pre-established through the following steps:

[0057] (1) Obtain dust pollution characteristic parameters of the heating surface, and establish a dust pollution status monitoring model based on the dust pollution characteristic parameters.

[0058] By fully exploring the high-order mapping relationships contained in the historical operating data, the relationship between the dust pollution status of the heating surface and the operating parameters is established, and the dust pollution status monitoring model is established based on machine learning. First, the operating parameters related to the dust pollution characteristics of the heating surface are screened out, and this embodiment defines them as the dust pollution characteristic parameters of the heating surface. Data cleaning can be used to remove noise data and invalid data to improve data quality. Subsequently, through field tests and expert analysis, the data is labeled "clean" and "polluted" to obtain a sample set of dust pollution characteristic parameters. Finally, a dust pollution status monitoring model is established based on the dust pollution characteristic parameter sample set using a semi-supervised machine learning algorithm.

[0059] (2) An expert experience database is established based on the experience of the operating personnel, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system.

[0060] The sootblowing strategy needs to focus on the safety of unit operation. First, the experience of operators is collected and digitized to form an operation expert experience database; then, the relationship contained in the data is mined through feature engineering and data association analysis, and the expert experience database is supplemented, and finally a sootblowing expert system is formed to ensure the safety of sootblowing.

[0061] (3) Obtaining sootblowing cost parameters and sootblowing benefit parameters, and establishing a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters.

[0062] Another key point to consider in sootblowing strategy is the economic efficiency of unit operation. First, the economic efficiency of sootblowing is analyzed in detail. The cost of sootblowing is mainly composed of sootblowing medium consumption, drive motor power consumption, sootblower depreciation and maintenance, etc., while the main benefits of sootblowing include improved boiler efficiency, improved steam temperature and steam temperature deviation, and reduced operation and maintenance work. The costs and benefits of sootblowing are quantified, and a sootblowing economic model is established.

[0063] (4) Based on the soot pollution monitoring model, the sootblowing expert system and the sootblowing economic model obtained above, a sootblowing strategy is obtained by using fuzzy logic calculation.

[0064] Thereby, the establishment of the sootblowing strategy in this embodiment is completed.

[0065] Finally, the whole furnace soot blowing effect is characterized based on the continuous online and accurate measurement of the bottom slag falling and the dry slag transportation volume. Combined with the establishment of the above mathematical models, the soot blowing signal used to control the soot blowing is linked with the slag discharge volume and the large slag signal, etc., to increase the soot blowing frequency in the area with better soot blowing effect, and appropriately reduce the soot blowing frequency in the area with weaker soot blowing effect, so as to realize the intelligent soot blowing of the boiler furnace and greatly improve the soot blowing efficiency. Practice has shown that the tower boiler dry slag intelligent soot blowing method of this embodiment can save 30% of the steam consumption of soot blowing.

[0066] An intelligent sootblowing method for dry slag discharge of a tower boiler in an embodiment of the present disclosure realizes refined control of boiler sootblowing by real-time monitoring of the slag falling and slag discharge amount at the furnace bottom in the heating surface area, thereby solving the problem of generally extensive sootblowing in the heating surface area of ​​large-capacity and high-parameter combustion boilers.

[0067] like Figure 2 As shown, another embodiment of the present disclosure provides a tower boiler dry slag discharge intelligent soot blowing system, the system comprising:

[0068] The acquisition module 210 is used to obtain the furnace bottom slag falling situation and dry slag transportation volume corresponding to the current sootblowing heating surface area in real time;

[0069] A judgment module 220, for judging the soot blowing effect of each heating surface area of ​​the furnace according to the furnace bottom slag falling situation and the dry slag transportation volume;

[0070] The sootblowing module 230 is used to control the sootblowing frequency of each heating surface area of ​​the furnace according to the sootblowing effect of each heating surface area of ​​the furnace and a pre-established sootblowing strategy; wherein the sootblowing strategy is obtained by fuzzy logic calculation based on the soot pollution monitoring model, the sootblowing expert system and the sootblowing economic model.

[0071] For example, Figure 2 As shown, the system further includes a policy module 240; the policy module 240 is used to:

[0072] Obtaining dust pollution characteristic parameters of the heating surface, and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters;

[0073] An expert experience database is established based on the experience of operators, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system;

[0074] Acquire sootblowing cost parameters and sootblowing benefit parameters, and establish a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters;

[0075] According to the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model, the soot blowing strategy is obtained by using fuzzy logic calculation.

[0076] Exemplarily, the policy module is specifically used for:

[0077] Obtaining dust pollution characteristic parameters of the heating surface, and marking the dust pollution characteristic parameters to obtain a dust pollution characteristic parameter sample set;

[0078] A ash pollution condition monitoring model is established using a semi-supervised algorithm based on the ash pollution characteristic parameter sample set.

[0079] Exemplarily, the acquisition module is specifically used for:

[0080] When blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling at the bottom of the furnace in real time;

[0081] When soot blowing in the current heating surface area, a line laser binocular stereo camera is used to calculate the volume of the transported slag on the conveyor belt of the dry slag discharger, and the dry slag transportation volume is obtained in combination with the slag density.

[0082] Specifically, a tower boiler dry slag discharge intelligent soot blowing system in an embodiment of the present disclosure is used to implement the tower boiler dry slag discharge intelligent soot blowing method described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0083] An intelligent sootblowing system for dry slag discharge of a tower boiler in an embodiment of the present disclosure realizes refined control of boiler sootblowing by real-time monitoring of the slag falling and slag discharge amount at the furnace bottom in the heating surface area, thereby solving the problem of generally extensive sootblowing in the heating surface area of ​​large-capacity and high-parameter combustion boilers.

[0084] like Figure 3 As shown, another embodiment of the present disclosure provides an electronic device, including:

[0085] At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301, for storing one or more programs, which, when executed by the at least one processor 301, enable the at least one processor 301 to implement the above-mentioned tower boiler dry slag discharge intelligent sootblowing method.

[0086] The memory 302 and the processor 301 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 301 and the memory 302 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 301 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 301.

[0087] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 302 can be used to store data used by the processor 301 when performing operations.

[0088] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the above-mentioned tower boiler dry slag discharge intelligent sootblowing method is implemented.

[0089] The computer-readable storage medium may be included in the system or electronic device of the present disclosure, or may exist independently.

[0090] Computer-readable storage media may be any tangible media that contains or stores a program, which may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, optical fiber, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0091] The computer-readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, in which the computer-readable program code is carried. Specific examples include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0092] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A tower boiler dry slag intelligent soot blowing method, characterized in that: The method comprises: Obtain the furnace bottom slag falling situation and dry slag transportation volume corresponding to the current sootblowing heating surface area in real time; According to the furnace bottom slag falling situation and the dry slag transportation volume, the soot blowing effect of each heating surface area of ​​the furnace is judged; According to the sootblowing effect of each heating surface area of ​​the furnace and the pre-established sootblowing strategy, the sootblowing frequency of each heating surface area of ​​the furnace is controlled; wherein, The soot blowing strategy is obtained by fuzzy logic calculation based on the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model.

2. The method according to claim 1, characterized in that The sootblowing strategy is pre-established by the following steps: Obtaining dust pollution characteristic parameters of the heating surface, and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters; An expert experience database is established based on the experience of operators, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system; Acquire sootblowing cost parameters and sootblowing benefit parameters, and establish a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters; According to the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model, the soot blowing strategy is obtained by using fuzzy logic calculation.

3. The method according to claim 2, characterized in that The step of obtaining dust pollution characteristic parameters of the heating surface and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters includes: Obtaining dust pollution characteristic parameters of the heating surface, and marking the dust pollution characteristic parameters to obtain a dust pollution characteristic parameter sample set; A ash pollution condition monitoring model is established using a semi-supervised algorithm based on the ash pollution characteristic parameter sample set.

4. The method according to any one of claims 1 to 3, characterized in that: The real-time acquisition of the furnace bottom slag falling situation and dry slag discharge transportation volume corresponding to the current sootblowing heating surface area includes: When blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling at the bottom of the furnace in real time; When soot blowing in the current heating surface area, binocular vision technology is used to calculate the volume of slag transported by the dry slag discharger, and the dry slag transportation volume is obtained in combination with the slag density.

5. A tower boiler dry slag discharge intelligent soot blowing system, characterized in that: The system comprises: The acquisition module is used to obtain the furnace bottom slag falling situation and dry slag transportation volume corresponding to the current soot blowing heating surface area in real time; A judgment module, used to judge the soot blowing effect of each heating surface area of ​​the furnace according to the slag falling situation at the furnace bottom and the dry slag transportation volume; A sootblowing module is used to control the sootblowing frequency of each heating surface area of ​​the furnace according to the sootblowing effect of each heating surface area of ​​the furnace and the pre-established sootblowing strategy; wherein, The soot blowing strategy is obtained by fuzzy logic calculation based on the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model.

6. The system according to claim 5, characterized in that The system further comprises a policy module; the policy module is used for: Obtaining dust pollution characteristic parameters of the heating surface, and establishing a dust pollution status monitoring model according to the dust pollution characteristic parameters; An expert experience database is established based on the experience of operators, and the expert experience database is supplemented through feature engineering and data association to obtain a sootblowing expert system; Acquire sootblowing cost parameters and sootblowing benefit parameters, and establish a sootblowing economic model according to the sootblowing cost parameters and the sootblowing benefit parameters; According to the soot pollution status monitoring model, the soot blowing expert system and the soot blowing economic model, the soot blowing strategy is obtained by using fuzzy logic calculation.

7. The system according to claim 6, characterized in that The strategy module is specifically used for: Obtaining dust pollution characteristic parameters of the heating surface, and marking the dust pollution characteristic parameters to obtain a dust pollution characteristic parameter sample set; A ash pollution condition monitoring model is established using a semi-supervised algorithm based on the ash pollution characteristic parameter sample set.

8. The system according to any one of claims 5 to 7, characterized in that: The acquisition module is specifically used for: When blowing soot in the current heating surface area, image recognition technology is used to monitor the slag falling at the bottom of the furnace in real time; When soot blowing in the current heating surface area, a line laser binocular stereo camera is used to calculate the volume of the transported slag on the conveyor belt of the dry slag discharger, and the dry slag transportation volume is obtained in combination with the slag density.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs, and when the one or more programs are executed by the at least one processor, the at least one processor can implement the tower boiler dry slag intelligent sootblowing method according to any one of claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the tower boiler dry slag discharge intelligent soot blowing method according to any one of claims 1 to 4 is implemented.

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