A method and device for estimating the life of bag filter bags of bag type dust collector

Abstract

The present application provides a method and device for estimating the life of filter bags of a bag dust collector, which relates to the technical field of bag dust collectors. First, the flow field data and temperature field data of the bag dust collector are obtained, and the filter bags are divided into zones according to the flow field data and temperature field data of the bag dust collector, and the zone level and the primary correction coefficient of the life of the filter bags of each zone are defined. According to the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector, the initial estimated life of the filter bags of each zone level is determined, and the cumulative proportion of the number of broken bags in each zone is obtained, and the secondary correction coefficient of the life of the filter bags of each zone is determined according to the cumulative proportion of the number of broken bags in each zone. According to the primary correction coefficient of the life of each zone, the initial estimated life of the filter bags of each zone level and the secondary correction coefficient of the life of the filter bags of each zone, the final estimated life of the filter bags of each zone level is determined. The life of the filter bags of the bag dust collector can be accurately estimated.

Description

Technical Field

[0001] The present application relates to the technical field of bag dust collectors, and in particular to a method and device for estimating the life of filter bags of bag dust collectors. Background Art

[0002] Bag filters offer high dust removal efficiency, large air volume processing capacity, and reliable operation, making them widely used in the steel, cement, aluminum, nonferrous metals, and waste incineration industries. The main causes of abnormal bag filter operation are filter bag damage and pulse valve failure. A damaged filter bag can lead to excessive flue gas dust emission concentrations, forcing the entire plant to shut down, resulting in significant economic losses. Machine vision bag breakage detection and positioning technology uses a camera installed at the top of the clean air chamber to capture real-time images of the filter bag opening within its field of view. An algorithm then detects dust leakage from the bag opening in the image to determine if the bag is broken and where it is broken. However, existing technologies do not yet offer a way to accurately estimate the lifespan of bag filter bags.

[0003] In summary, how to accurately estimate the life of bag filters is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present application provides a bag filter bag life estimation method and device, which aims to accurately estimate the life of the bag filter bag of the bag filter.

[0005] In a first aspect, the present application provides a method for estimating the life of a bag filter bag of a bag filter, comprising:

[0006] Obtain flow field data and temperature field data of bag filter;

[0007] According to the flow field data and temperature field data of the bag filter, the filter bags are divided into zones, and a zone grade and a life correction coefficient are defined for each zone of the filter bag;

[0008] Determine an initial estimated lifespan of the filter bags for each partition level based on the structural type data, dust type data, average operating temperature data, and average air volume data of the bag filter;

[0009] Get the cumulative percentage of broken bags in each area;

[0010] Determine the secondary correction coefficient of the filter bag life of each partition according to the cumulative proportion of the number of broken bags in each area;

[0011] The final estimated life of the filter bag at each partition level is determined according to the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

[0012] Optionally, obtaining the flow field data and temperature field data of the bag filter includes:

[0013] Obtaining operating parameters and structural parameters of the bag filter;

[0014] A simulation calculation is performed based on the operating parameters and structural parameters of the bag filter to determine the flow field data and temperature field data of the bag filter.

[0015] Optionally, the filter bags are divided into zones according to the flow field data and temperature field data of the bag filter, and a zone level and a life correction coefficient are defined for each zone of the filter bag, including:

[0016] According to the flow field data and temperature field data of the bag filter, the flow field data and temperature field data corresponding to the flower plate plane, the middle plane, and the bottom plane of the filter bag are determined respectively;

[0017] The maximum velocity and temperature of each filter bag in each plane are taken as the velocity and temperature of each filter bag partition;

[0018] The speed and temperature with the largest number of filter bags are taken as the benchmark speed and benchmark temperature;

[0019] The partition grade and life correction coefficient of the filter bag in each partition are determined according to the speed, temperature, the reference speed and the reference temperature of each filter bag partition.

[0020] Optionally, determining the estimated initial life of the filter bags of each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter includes:

[0021] The structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector are input into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag for each partition level.

[0022] Optionally, before inputting the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter into a life estimation deep learning algorithm to obtain the initial estimated life of the filter bag at each partition level, the method further includes:

[0023] The flow field data and temperature field data of the bag dust collector, the partition level of the filter bags in each partition, the structural type data of the bag dust collector, the dust type data, the average operating temperature data and the average air volume data are used as input parameter samples, and the actual life of the filter bags at each partition level is used as the output parameter sample for training to obtain the life prediction deep learning algorithm.

[0024] In a second aspect, the present application provides a bag filter bag life estimation device for a bag filter, comprising:

[0025] The first acquisition module is used to obtain the flow field data and temperature field data of the bag filter;

[0026] A partitioning module is used to partition the filter bags according to the flow field data and temperature field data of the bag filter, and define a partition level and a life correction coefficient for the filter bags in each partition;

[0027] A first determination module is configured to determine an initial estimated lifespan of a filter bag at each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter;

[0028] The second acquisition module is used to obtain the cumulative percentage of broken bags in each area;

[0029] The second determination module is used to determine the secondary correction coefficient of the filter bag life of each partition according to the cumulative proportion of the number of broken bags in each area;

[0030] The third determination module is used to determine the final estimated life of the filter bag at each partition level according to the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

[0031] Optionally, the first acquisition module includes:

[0032] An acquisition unit, configured to acquire operating parameters and structural parameters of the bag filter;

[0033] The first determining unit is used to perform simulation calculations based on the operating parameters and structural parameters of the bag filter to determine the flow field data and temperature field data of the bag filter.

[0034] Optionally, the partitioning module includes:

[0035] The second determining unit is used to determine the flow field data and temperature field data corresponding to the flower plate plane, the middle plane, and the bottom plane of the filter bag respectively according to the flow field data and the temperature field data of the bag filter;

[0036] The third determining unit is used to use the maximum velocity and the maximum temperature of each filter bag in each plane as the velocity and the temperature of each filter bag partition;

[0037] a fourth determining unit, configured to use the speed and temperature at which the number of filter bags is the largest as a reference speed and a reference temperature;

[0038] The fifth determining unit is used to determine the partition level and life correction coefficient of the filter bag in each partition according to the speed, temperature, the reference speed and the reference temperature of each filter bag partition.

[0039] Optionally, the third determining module includes:

[0040] The sixth determination unit is used to input the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag for each partition level.

[0041] Optionally, the device further includes:

[0042] A training unit is used to use the flow field data and temperature field data of the bag dust collector, the partition level of the filter bag in each partition, the structural type data of the bag dust collector, the dust type data, the average operating temperature data and the average air volume data as input parameter samples, and use the actual life of the filter bags of each partition level as output parameter samples for training to obtain the life prediction deep learning algorithm.

[0043] The present application provides a method for estimating the life of filter bags of a bag dust collector. When executing the method, the flow field data and temperature field data of the bag dust collector are first obtained, and then the filter bags are partitioned according to the flow field data and temperature field data of the bag dust collector, and the partition level and the life correction coefficient are defined for the filter bags of each partition. Then, according to the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector, the initial estimated life of the filter bags of each partition level is determined. Then, the cumulative proportion of the number of broken bags in each area is obtained, and according to the cumulative proportion of the number of broken bags in each area, the life correction coefficient of the filter bags of each partition is determined. Finally, according to the life correction coefficient of each partition, the initial estimated life of the filter bags of each partition level and the life correction coefficient of the filter bags of each partition level, the final estimated life of the filter bags of each partition level is determined. In this way, by taking the flow field data and temperature field data of the bag dust collector, the life of the filter bags of the bag dust collector can be estimated. In this way, the life of the filter bags of the bag dust collector can be accurately estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following briefly introduces the drawings required for use in the embodiment or the prior art description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1A flow chart of a method for estimating the life of a bag filter bag of a bag filter provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of a bag filter life estimation device for a bag filter provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following, in conjunction with the accompanying drawings, provides a clear and complete description of the technical solutions in the embodiments of this application. This application provides a method and device for estimating the life of bag filters for use in the field of bag filter technology. The foregoing is merely illustrative and does not limit the application areas of the methods and devices provided herein.

[0048] Bag filters offer high dust removal efficiency, large air volume processing capacity, and reliable operation, making them widely used in the steel, cement, aluminum, nonferrous metals, and waste incineration industries. The main causes of abnormal bag filter operation are filter bag damage and pulse valve failure. A damaged filter bag can lead to excessive flue gas dust emission concentrations, forcing the entire plant to shut down, resulting in significant economic losses. Machine vision bag breakage detection and positioning technology uses a camera installed at the top of the clean air chamber to capture real-time images of the filter bag opening within its field of view. An algorithm then detects dust leakage from the bag opening in the image to determine if the bag is broken and where it is broken. However, existing technologies do not yet offer a way to accurately estimate the lifespan of bag filter bags.

[0049] After research, the inventor proposed the technical solution of this application. When executing the method, the flow field data and temperature field data of the bag dust collector are first obtained. Then, based on the flow field data and temperature field data of the bag dust collector, the filter bags are divided into zones, and the zone level and the primary correction coefficient of the life of the filter bags in each zone are defined. Then, based on the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector, the initial estimated life of the filter bags at each zone level is determined. Then, the cumulative proportion of broken bags in each zone is obtained, and based on the cumulative proportion of broken bags in each zone, the secondary correction coefficient of the life of the filter bags in each zone is determined. Finally, based on the primary correction coefficient of the life of each zone, the initial estimated life of the filter bags at each zone level and the secondary correction coefficient of the life of the filter bags at each zone level, the final estimated life of the filter bags at each zone level is determined. In this way, by obtaining the flow field data and temperature field data of the bag dust collector, the life of the filter bags of the bag dust collector can be estimated. In this way, the life of the filter bags of the bag dust collector can be accurately estimated.

[0050] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0051] See also Figure 1 , Figure 1 A flow chart of a method for estimating the life of a bag filter bag of a bag filter provided in an embodiment of the present application includes:

[0052] S101: Acquire flow field data and temperature field data of a bag filter.

[0053] First, the operating parameters and structural parameters of the bag dust collector are obtained, and simulation calculations are performed based on the operating parameters and structural parameters of the bag dust collector to obtain the flow field data and temperature field data of the bag dust collector.

[0054] S102: The filter bags are divided into zones according to the flow field data and temperature field data of the bag filter, and a zone level and a life correction coefficient are defined for the filter bags in each zone.

[0055] According to the flow field data and temperature field data of the bag dust collector, the filter bags are divided into zones, and the zone level En and the life correction coefficient kn are defined for each zone of the filter bags. Specifically, the flow field data and temperature field data need to include at least the flow field data and temperature field data of three planes: the flower plate plane, the middle plane, and the filter bag bottom plane. Among them, multiple middle planes can be added. The maximum velocity and temperature of each filter bag in each plane are used as the velocity and temperature of each filter bag zone, and the maximum velocity and temperature of each filter bag in each plane are rounded up or down. The number of filter bags at each speed and temperature is counted, and the speed and temperature with the largest number of filter bags are used as the benchmark speed and temperature. According to the speed, temperature, benchmark speed and benchmark temperature of each filter bag zone, the zone level and life correction coefficient of the filter bag in each zone are determined. The relationship between the zone level En, the filter bag life correction coefficient kn, and the speed and temperature of each filter bag is shown in the following table:

[0056] Table 1 Relationship between partition level En, filter bag life correction coefficient kn and filter bag speed and temperature

[0057]

[0058]

[0059] If the temperature of each filter bag exceeds the reference temperature x (100% ± 15%), or the speed of each filter bag exceeds the reference speed x (100% ± 25%), it is considered an abnormal operating condition and an alarm is issued. The temperature and speed threshold ranges are determined by the actual filter bag tolerance temperature and the required filter bag outlet air velocity.

[0060] S103: Determine the initial estimated life of the filter bags of each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag dust collector.

[0061] Based on data such as the bag filter structure type, dust type, average operating temperature, and average air volume, a lifespan prediction deep learning algorithm is used to calculate the initial estimated lifespan An of the filter bags at each partition level. Prior to this, a lifespan prediction deep learning algorithm must be established. The steps for establishing this algorithm are as follows:

[0062] 1. Perform simulation calculations based on the operating parameters and structural parameters of the bag filter to obtain flow field data and temperature field data. Then, divide the filter bags into zones and number them according to the flow field data and temperature field data, and define the zone level En for the filter bags in each zone.

[0063] 2. Obtain data such as bag filter structure type, dust type, average operating temperature, average air volume, etc.

[0064] 3. Obtain the actual life span Cn of filter bags of each partition level detected by the bag filter broken bag detection and positioning system;

[0065] 4. Establish a bag filter life database based on the data obtained in steps 1 to 3;

[0066] 5. Use the data from steps 1 to 2 as input parameters and the data from step 3 as output parameters, and use the deep learning algorithm to train to obtain a life prediction deep learning algorithm.

[0067] S104: Obtain the cumulative percentage of broken bags in each area.

[0068] Obtain the cumulative percentage of broken bags in each area, Mn. The current cumulative percentage of broken bags in each area, Mn, is the percentage of the cumulative number of broken bags in each area calculated after the new filter bag is replaced, as a percentage of the total number of filter bags in each area. After the new filter bag is replaced, Mn is reset and recalculated, and updated in real time.

[0069] S105: Determine the secondary correction coefficient of the filter bag life of each partition based on the cumulative proportion of the number of broken bags in each area.

[0070] According to the cumulative proportion of broken bags in each area Mn, the secondary correction coefficient gn of the filter bag life of each partition is determined. The relationship between the current cumulative proportion of broken bags in each area Mn and the secondary correction coefficient gn of the filter bag life is shown in the following table:

[0071] Table 2 Relationship between the cumulative proportion of broken bags in each area Mn and the secondary correction coefficient gn of filter bag life

[0072] The current cumulative proportion of broken bags in each region Mn Second correction factor gn for filter bag life Mn<2% 0 2%≤Mn<5% 5％ 5%≤Mn<8% 10％ 8%≤Mn<11% 15％

[0073] If the cumulative percentage of broken bags in each area, Mn, is ≥ 11%, an abnormal operating condition is detected and an alarm is issued, indicating a risk of exceeding the standard for flue gas outlet concentration. The alarm index value of 11% is determined by the cumulative percentage of broken bags in each area, Mn, when the actual outlet flue gas concentration exceeds the standard.

[0074] S106: Determine the final estimated life of the filter bag at each partition level according to the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

[0075] The calculation formula for the filter bag life Bn of each partition is:

[0076] Bn=An×(1-kn-gn).

[0077] In an embodiment of the present application, when executing the method, the flow field data and temperature field data of the bag dust collector are first obtained, and then the filter bags are partitioned according to the flow field data and temperature field data of the bag dust collector, and the partition level and the life correction coefficient are defined for the filter bags of each partition. Then, according to the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector, the initial estimated life of the filter bags of each partition level is determined. Then, the cumulative proportion of the number of broken bags in each area is obtained, and according to the cumulative proportion of the number of broken bags in each area, the life correction coefficient of the filter bags of each partition is determined. Finally, according to the life correction coefficient of each partition, the initial estimated life of the filter bags of each partition level and the life correction coefficient of the filter bags of each partition level, the final estimated life of the filter bags of each partition level is determined. In this way, by taking the flow field data and temperature field data of the bag dust collector, the life of the filter bags of the bag dust collector can be estimated. In this way, the life of the filter bags of the bag dust collector can be accurately estimated.

[0078] The above are some specific implementations of the bag filter life estimation method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiment of the present application will be introduced from the perspective of functional modularization.

[0079] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a bag filter bag life prediction device provided in an embodiment of the present application. The bag filter bag life prediction device 200 includes:

[0080] The first acquisition module 210 is used to acquire the flow field data and temperature field data of the bag filter;

[0081] A partitioning module 220 is used to partition the filter bags according to the flow field data and temperature field data of the bag filter, and define a partition level and a life correction coefficient for each partition of the filter bag;

[0082] A first determination module 230 is configured to determine an initial estimated lifespan of filter bags at each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter;

[0083] The second acquisition module 240 is used to obtain the cumulative percentage of broken bags in each area;

[0084] The second determination module 250 is configured to determine the secondary correction coefficient of the filter bag life of each partition according to the cumulative proportion of the number of broken bags in each area;

[0085] The third determination module 260 is used to determine the final estimated life of the filter bag at each partition level based on the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

[0086] Optionally, the first acquisition module 210 includes:

[0087] An acquisition unit, configured to acquire operating parameters and structural parameters of the bag filter;

[0088] The first determining unit is used to perform simulation calculations based on the operating parameters and structural parameters of the bag filter to determine the flow field data and temperature field data of the bag filter.

[0089] Optionally, the partitioning module 220 includes:

[0090] The second determining unit is used to determine the flow field data and temperature field data corresponding to the flower plate plane, the middle plane, and the bottom plane of the filter bag respectively according to the flow field data and the temperature field data of the bag filter;

[0091] The third determining unit is used to use the maximum velocity and the maximum temperature of each filter bag in each plane as the velocity and the temperature of each filter bag partition;

[0092] a fourth determining unit, configured to use the speed and temperature at which the number of filter bags is the largest as a reference speed and a reference temperature;

[0093] The fifth determining unit is used to determine the partition level and life correction coefficient of the filter bag in each partition according to the speed, temperature, the reference speed and the reference temperature of each filter bag partition.

[0094] Optionally, the third determining module 260 includes:

[0095] The sixth determination unit is used to input the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag for each partition level.

[0096] Optionally, the apparatus 200 further includes:

[0097] A training unit is used to use the flow field data and temperature field data of the bag dust collector, the partition level of the filter bag in each partition, the structural type data of the bag dust collector, the dust type data, the average operating temperature data and the average air volume data as input parameter samples, and use the actual life of the filter bags of each partition level as output parameter samples for training to obtain the life prediction deep learning algorithm.

[0098] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0099] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method described in any embodiment of the present application.

[0100] The computer storage medium stores code, and when the code is executed, the device executing the code implements the method described in any embodiment of the present application.

[0101] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above embodiment methods can be implemented by means of software plus a general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment or certain parts of the embodiments of the present application.

[0102] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0103] It should also be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the equipment and device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without expending creative work.

[0104] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for estimating the life of a bag filter bag of a bag filter, characterized in that: include: Obtain flow field data and temperature field data of bag filter; According to the flow field data and temperature field data of the bag filter, the filter bags are divided into zones, and a zone grade and a life correction coefficient are defined for each zone of the filter bag; Determine an initial estimated lifespan of the filter bags for each partition level based on the structural type data, dust type data, average operating temperature data, and average air volume data of the bag filter; Get the cumulative percentage of broken bags in each area; Determine the secondary correction coefficient of the filter bag life of each partition according to the cumulative proportion of the number of broken bags in each area; The final estimated life of the filter bag at each partition level is determined according to the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

2. The method according to claim 1, characterized in that The obtaining of flow field data and temperature field data of the bag filter includes: Obtaining operating parameters and structural parameters of the bag filter; A simulation calculation is performed based on the operating parameters and structural parameters of the bag filter to determine the flow field data and temperature field data of the bag filter.

3. The method according to claim 1, characterized in that The filter bags are divided into zones according to the flow field data and temperature field data of the bag filter, and a zone level and a life correction coefficient are defined for each zone of the filter bag, including: According to the flow field data and temperature field data of the bag filter, the flow field data and temperature field data corresponding to the flower plate plane, the middle plane, and the bottom plane of the filter bag are determined respectively; The maximum velocity and temperature of each filter bag in each plane are taken as the velocity and temperature of each filter bag partition; The speed and temperature with the largest number of filter bags are taken as the benchmark speed and benchmark temperature; The partition grade and life correction coefficient of the filter bag in each partition are determined according to the speed, temperature, the reference speed and the reference temperature of each filter bag partition.

4. The method according to claim 1, wherein Determining the estimated initial life of the filter bags of each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter includes: The structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector are input into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag for each partition level.

5. The method according to claim 4, characterized in that Before inputting the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag at each partition level, the method further includes: The flow field data and temperature field data of the bag dust collector, the partition level of the filter bags in each partition, the structural type data of the bag dust collector, the dust type data, the average operating temperature data and the average air volume data are used as input parameter samples, and the actual life of the filter bags at each partition level is used as the output parameter sample for training to obtain the life prediction deep learning algorithm.

6. A bag filter bag life prediction device, characterized in that: include: The first acquisition module is used to obtain the flow field data and temperature field data of the bag filter; A partitioning module is used to partition the filter bags according to the flow field data and temperature field data of the bag filter, and define a partition level and a life correction coefficient for the filter bags in each partition; A first determination module is configured to determine an initial estimated lifespan of a filter bag at each partition level based on the structure type data, dust type data, average operating temperature data, and average air volume data of the bag filter; The second acquisition module is used to obtain the cumulative percentage of broken bags in each area; The second determination module is used to determine the secondary correction coefficient of the filter bag life of each partition according to the cumulative proportion of the number of broken bags in each area; The third determination module is used to determine the final estimated life of the filter bag at each partition level according to the primary life correction coefficient of each partition, the initial estimated life of the filter bag at each partition level, and the secondary life correction coefficient of the filter bag at each partition.

7. The device according to claim 6, characterized in that The first acquisition module includes: An acquisition unit, configured to acquire operating parameters and structural parameters of the bag filter; The first determining unit is configured to perform simulation calculations based on the operating parameters and structural parameters of the bag filter to determine the flow field data and temperature field data of the bag filter.

8. The device according to claim 6, characterized in that The partition module includes: The second determining unit is used to determine the flow field data and temperature field data corresponding to the flower plate plane, the middle plane, and the bottom plane of the filter bag respectively according to the flow field data and the temperature field data of the bag filter; The third determining unit is used to use the maximum velocity and the maximum temperature of each filter bag in each plane as the velocity and the temperature of each filter bag partition; a fourth determining unit, configured to use the speed and temperature at which the number of filter bags is the largest as a reference speed and a reference temperature; The fifth determining unit is used to determine the partition level and life correction coefficient of the filter bag in each partition according to the speed, temperature, the reference speed and the reference temperature of each filter bag partition.

9. The device according to claim 6, characterized in that The third determining module includes: The sixth determination unit is used to input the structural type data, dust type data, average operating temperature data and average air volume data of the bag dust collector into the life estimation deep learning algorithm to obtain the initial estimated life of the filter bag for each partition level.

10. The device according to claim 9, characterized in that Also includes: A training unit is used to use the flow field data and temperature field data of the bag dust collector, the partition level of the filter bag in each partition, the structural type data of the bag dust collector, the dust type data, the average operating temperature data and the average air volume data as input parameter samples, and use the actual life of the filter bags of each partition level as output parameter samples for training to obtain the life prediction deep learning algorithm.

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