CN118328935A - Calculation method and device for thinning high-temperature corrosion wall pipe of boiler water wall - Google Patents

Abstract

The present application relates to the technical field of boiler testing, and in particular to a method and device for calculating thinning of high-temperature corrosion wall tubes of boiler water-cooled walls. The method comprises: setting at least one sensor installation point on the boiler water-cooled wall, and installing a data sensor on each sensor installation point; acquiring boiler water-cooled wall data collected by the data sensor for multiple times; calculating the corrosion rate of multiple groups of boiler water-cooled wall data based on a pre-trained neural network model, and determining the calculation results of thinning of high-temperature corrosion wall tubes of the boiler water-cooled wall. The present application can efficiently and accurately detect the tested tubes with thinned wall thickness, greatly improving the detection efficiency, so as to replace the tested tubes with thinned wall thickness as soon as possible, avoiding tube burst accidents caused by thinning of water-cooled wall tubes, and providing a strong safety guarantee for the safe operation of the unit.

Description

Calculation method and device for thinning of boiler water-cooled wall tubes due to high temperature corrosion

Technical Field

The present application relates to the technical field of boiler testing, and in particular to a method and device for calculating thinning of high-temperature corroded wall tubes of a boiler water-cooled wall.

Background technique

The heating surface of a power plant boiler includes water-cooled walls, superheaters, reheaters and economizers, which are located around and inside the furnace. They are the main heat exchange components of the boiler and are also subject to the harshest environment of the boiler. The lower water-cooled wall directly bears the high temperature generated by fuel combustion, and other heating surfaces are subjected to the erosion of high-temperature flue gas and high-speed flue gas. In addition, in order to prevent slag inclusion in the heating surface, the boiler is also equipped with a soot blower to blow soot on the heating surface regularly.

The erosion and thinning of the heating surface is a common hidden danger that cannot be cured. According to relevant standards, if the thinning of the heating surface exceeds 30%, it should be replaced. 30% is the value calculated based on the design conditions to ensure safe operation. However, with the adjustment of the national industry, most units are currently involved in peak load regulation, and some units are even involved in deep peak load regulation. In this way, when operating under variable load conditions, the original design of 30% cannot fully guarantee the safe operation of the boiler, and there is a risk of leakage.

Therefore, it is necessary to invent a calculation method and device for the thinning of the boiler water-cooled wall tubes due to high-temperature corrosion to determine the thinning amount of the boiler water-cooled wall tubes due to high-temperature corrosion.

Summary of the invention

Based on this, it is necessary to provide a calculation method and device for thinning of boiler water-cooled wall tubes due to high-temperature corrosion in response to technical issues.

In a first aspect, the present application provides a method for calculating the thinning of boiler water-cooled wall tubes due to high-temperature corrosion.

Methods include:

At least one sensor installation point is arranged on the boiler water-cooled wall, and a data sensor is installed on each sensor installation point;

Acquire boiler water wall data collected by data sensors multiple times;

The corrosion rate of multiple sets of boiler water wall data is calculated based on the pre-trained neural network model to determine the calculation results of the thinning of the boiler water wall tube due to high-temperature corrosion.

In one of the embodiments, the density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, the training process of the neural network model includes:

The neural network model is trained according to the preset size parameters of the boiler water-cooled wall, the preset neural network model and the sample water-cooled wall data.

In one embodiment, according to preset boiler water wall size parameters, a preset neural network model and sample water wall data, a neural network model is trained to obtain the neural network model, including:

Parameter configuration of a preset neural network model is performed according to preset size parameters of a boiler water-cooled wall to obtain a configured preset neural network model;

According to the sample water-cooled wall data, the configured preset neural network model is trained to obtain a trained neural network model.

In a second aspect, the present application also provides a device for calculating the thinning of boiler water-cooled wall tubes due to high-temperature corrosion. The device comprises:

An installation module is used to set at least one sensor installation point on the boiler water-cooled wall and install a data sensor on each sensor installation point;

An acquisition module, used for repeatedly acquiring boiler water wall data collected by a data sensor;

The calculation module is used to calculate the corrosion rate of multiple groups of boiler water-cooled wall data based on a pre-trained neural network model to determine the thinning results of the boiler water-cooled wall tubes due to high-temperature corrosion.

In one of the embodiments, the density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, the device further comprises:

The training module is used to train a neural network model according to preset boiler water-cooled wall size parameters, a preset neural network model, and sample water-cooled wall data.

In one embodiment, the training module includes:

A configuration unit, used to configure parameters of a preset neural network model according to preset size parameters of a boiler water wall, to obtain a configured preset neural network model;

The training unit is used to perform model training on the configured preset neural network model according to the sample water-cooled wall data to obtain a trained neural network model.

In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

At least one sensor installation point is arranged on the boiler water-cooled wall, and a data sensor is installed on each sensor installation point;

Acquire boiler water wall data collected by data sensors multiple times;

The corrosion rate of multiple sets of boiler water wall data is calculated based on the pre-trained neural network model to determine the calculation results of the thinning of the boiler water wall tube due to high-temperature corrosion.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

At least one sensor installation point is arranged on the boiler water-cooled wall, and a data sensor is installed on each sensor installation point;

Acquire boiler water wall data collected by data sensors multiple times;

The corrosion rate of multiple sets of boiler water wall data is calculated based on the pre-trained neural network model to determine the calculation results of the thinning of the boiler water wall tube due to high-temperature corrosion.

The present invention provides a method and device for calculating thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion. The present invention provides at least one sensor installation point on the boiler water-cooled wall and installs a data sensor on each sensor installation point. Then, the boiler water-cooled wall data collected by the data sensor is obtained multiple times. The corrosion rate of multiple groups of boiler water-cooled wall data is calculated based on a pre-trained neural network model to determine the calculation result of thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion. The method can efficiently and accurately detect the thinned wall tubes under test, greatly improving the detection efficiency, so that the thinned wall tubes under test can be replaced as soon as possible, avoiding tube explosion accidents caused by thinned water-cooled wall tubes, and providing a strong safety guarantee for the safe operation of the unit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a method for calculating thinning of boiler water-cooled wall tubes subjected to high-temperature corrosion in one embodiment;

FIG2 is a flow chart of a training process of a neural network model in one embodiment;

FIG3 is a structural block diagram of a first computing device for thinning of boiler water-cooled wall tubes due to high-temperature corrosion in one embodiment;

FIG4 is a structural block diagram of a second device for calculating thinning of boiler water-cooled wall tubes due to high-temperature corrosion in another embodiment;

FIG5 is a structural block diagram of a third computing device for thinning of boiler water-cooled wall tubes due to high-temperature corrosion in another embodiment;

FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

In the description of the embodiments of the present invention, it should be noted that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the invention product is usually placed when in use, or the orientation or positional relationship commonly understood by those skilled in the art, or the orientation or positional relationship in which the invention product is usually placed when in use, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first" and "second" are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the terms in the present invention can be understood according to specific circumstances; the drawings in the embodiments are used to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations.

It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. In the description of the present application, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the terms does not necessarily have to be directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradicting each other.

In one embodiment, as shown in FIG1 , a method for calculating thinning of boiler water-cooled wall tubes due to high-temperature corrosion is provided, the method comprising the following steps:

Step 101: set at least one sensor installation point on the boiler water-cooled wall, and install a data sensor on each sensor installation point.

It should be noted that when it is necessary to set a sensor installation point, the following may be specifically included: determining transverse lines and longitudinal lines on the back-fire side of the boiler water-cooled wall, determining the number of transverse lines according to the number of burner layers, distributing multiple longitudinal lines equidistantly on the transverse lines, and vertically intersecting the multiple transverse lines and the multiple longitudinal lines to obtain multiple intersection points, and the intersection points are sensor installation points.

It is further explained that the setting of the sensor installation point can also be determined based on the historical experience of the staff, and the location of the sensor installation point is not limited here.

In one embodiment of the present application, the density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

Step 102, repeatedly acquiring boiler water wall data collected by a data sensor.

It should be noted that the data sensor collects the data of the boiler water-cooled wall in real time, and the computer equipment that executes the calculation method for thinning the wall tubes of the boiler water-cooled wall due to high-temperature corrosion performs matrix grouping on the data sensors to obtain multiple matrix group data sensors, and then obtains multiple real-time matrix groups of boiler water-cooled wall data. The microprocessor converts the boiler water-cooled wall data of the multiple real-time matrix groups into digital signals through data processing and signal conversion, and then transmits them to the computer equipment that executes the calculation method for thinning the wall tubes of the boiler water-cooled wall due to high-temperature corrosion through optical cables. The computer equipment that executes the calculation method for thinning the wall tubes of the boiler water-cooled wall due to high-temperature corrosion processes the boiler water-cooled wall data of the multiple real-time matrix groups. When the data of any real-time matrix group is less than the threshold value, the central control room sends an alarm signal, indicating that the water-cooled wall tubes in the matrix need to be inspected and replaced, and the replaced new tubes are processed according to the steps of setting at least one sensor installation point on the boiler water-cooled wall and installing data sensors on each sensor installation point.

Step 103, based on the pre-trained neural network model, corrosion rate calculation is performed on multiple groups of boiler water-cooled wall data to determine the calculation result of thinning of the boiler water-cooled wall tube due to high-temperature corrosion.

It should be noted that the neural network model can be trained based on preset size parameters of the boiler water-cooled wall, a preset neural network model, and sample water-cooled wall data.

The sample water-cooled wall data can be divided into three parts: training set, optimization set and test set. Considering that the data required for the training set needs to have strong reference, half of all preset training samples can be selected as the training set, the remaining half as the optimization set, and some samples in the preset training samples as the test set.

The calculation method of the thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion is provided in this application by setting at least one sensor installation point on the boiler water-cooled wall and installing a data sensor on each sensor installation point; then, the boiler water-cooled wall data collected by the data sensor is obtained multiple times; the corrosion rate calculation of multiple groups of boiler water-cooled wall data is realized based on a pre-trained neural network model, and the calculation result of the thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion is determined. In this way, the measured tube with thinned wall thickness can be detected efficiently and accurately, which greatly improves the detection efficiency, so that the measured tube with thinned wall thickness can be replaced as soon as possible, and the explosion accident caused by thinning of water-cooled wall tube can be avoided, which provides a strong safety guarantee for the safe operation of the unit.

In one embodiment, as shown in FIG2 , a method for calculating thinning of high-temperature corrosion wall tubes of a boiler water-cooled wall is provided. Specifically, when it is necessary to train a neural network model based on preset boiler water-cooled wall size parameters, a preset neural network model and sample water-cooled wall data, the method includes the following steps:

Step 201, configuring parameters of a preset neural network model according to preset size parameters of a boiler water-cooled wall to obtain a configured preset neural network model.

Step 202: Perform model training on the configured preset neural network model according to the sample water-cooled wall data to obtain a trained neural network model.

It should be noted that the sample water-cooled wall data can be divided into three parts: training set, optimization set and test set. Considering that the data required for the training set needs to have a strong reference, half of all the preset training samples can be selected as the training set, the remaining half as the optimization set, and some samples in the preset training samples as the test set.

The calculation method of the thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion is provided in this application by setting at least one sensor installation point on the boiler water-cooled wall and installing a data sensor on each sensor installation point; then, the boiler water-cooled wall data collected by the data sensor is obtained multiple times; the corrosion rate calculation of multiple groups of boiler water-cooled wall data is realized based on a pre-trained neural network model, and the calculation result of the thinning of the wall tube of the boiler water-cooled wall due to high-temperature corrosion is determined. In this way, the measured tube with thinned wall thickness can be detected efficiently and accurately, which greatly improves the detection efficiency, so that the measured tube with thinned wall thickness can be replaced as soon as possible, and the explosion accident caused by thinning of water-cooled wall tube can be avoided, which provides a strong safety guarantee for the safe operation of the unit.

It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the present application also provides a cold wall high temperature corrosion wall tube thinning calculation device for implementing the cold wall high temperature corrosion wall tube thinning calculation method involved. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitations in one or more cold wall high temperature corrosion wall tube thinning calculation device embodiments provided below can refer to the limitations of the cold wall high temperature corrosion wall tube thinning calculation method above, and will not be repeated here.

In one embodiment, as shown in FIG3 , a calculation device for cold wall high temperature corrosion wall tube thinning is provided, comprising: an installation module 10, an acquisition module 20 and a calculation module 30, wherein:

The installation module 10 is used to set at least one sensor installation point on the boiler water-cooled wall and install a data sensor on each sensor installation point.

Among them, the density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

The acquisition module 20 is used to acquire the boiler water wall data collected by the data sensor multiple times.

The calculation module 30 is used to calculate the corrosion rate of multiple groups of boiler water-cooled wall data based on a pre-trained neural network model to determine the thinning results of the boiler water-cooled wall tubes due to high-temperature corrosion.

In one embodiment, as shown in FIG4 , a computing device for cold wall high temperature corrosion wall tube thinning is provided, further comprising: a training module 40, wherein:

The training module 40 is used to train a neural network model according to preset boiler water wall size parameters, a preset neural network model, and sample water wall data.

In one embodiment, as shown in FIG5 , a computing device for cold wall high temperature corrosion wall tube thinning is provided, wherein a training module 40 in the device comprises: a configuration unit 41 and a training unit 42, wherein:

The configuration unit 41 is used to configure the parameters of the preset neural network model according to the size parameters of the preset boiler water-cooled wall to obtain the configured preset neural network model.

The training unit 42 is used to perform model training on the configured preset neural network model according to the sample water-cooled wall data to obtain a trained neural network model.

Each module in the calculation device for thinning the wall tube of the water-cooled wall of the furnace due to high temperature corrosion can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input/output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input/output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input/output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a calculation method for thinning the wall tube of a furnace water-cooled wall due to high-temperature corrosion is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

At least one sensor installation point is arranged on the boiler water-cooled wall, and a data sensor is installed on each sensor installation point;

Acquire boiler water wall data collected by data sensors multiple times;

The corrosion rate of multiple sets of boiler water wall data is calculated based on the pre-trained neural network model to determine the calculation results of the thinning of the boiler water wall tube due to high-temperature corrosion.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

Parameter configuration of a preset neural network model is performed according to preset size parameters of a boiler water-cooled wall to obtain a configured preset neural network model;

According to the sample water-cooled wall data, the configured preset neural network model is trained to obtain a trained neural network model.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

…

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Parameter configuration of a preset neural network model is performed according to preset size parameters of a boiler water-cooled wall to obtain a configured preset neural network model;

According to the sample water-cooled wall data, the configured preset neural network model is trained to obtain a trained neural network model.

In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

At least one sensor installation point is arranged on the boiler water-cooled wall, and a data sensor is installed on each sensor installation point;

Acquire boiler water wall data collected by data sensors multiple times;

The corrosion rate of multiple sets of boiler water wall data is calculated based on the pre-trained neural network model to determine the calculation results of the thinning of the boiler water wall tube due to high-temperature corrosion.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

The density of data sensors installed in the sensor installation points on the boiler water-cooled wall is 4-70/㎡.

In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

Parameter configuration of a preset neural network model is performed according to preset size parameters of a boiler water-cooled wall to obtain a configured preset neural network model;

According to the sample water-cooled wall data, the configured preset neural network model is trained to obtain a trained neural network model.

A person of ordinary skill in the art can understand that all or part of the processes in the implementation method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of each method. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims (10)

1. The calculation method for thinning the high-temperature corrosion wall pipe of the water-cooled wall of the boiler is characterized by comprising the following steps of:

At least one sensor mounting point is arranged on the water-cooled wall of the boiler, and a data sensor is arranged on each sensor mounting point;

acquiring boiler water wall data acquired by a data sensor for a plurality of times;

And (3) performing corrosion rate calculation on a plurality of groups of boiler water wall data based on a pre-trained neural network model, and determining a calculation result of thinning the high-temperature corrosion wall pipe of the boiler water wall.

2. The method of claim 1, wherein the density of data sensors installed in the sensor mounting points on the water wall of the boiler is 4-70 per square meter.

3. The method of claim 1, wherein the training process of the neural network model comprises:

The density of the data sensors installed in the sensor installation points on the water-cooled wall of the boiler is 4-70 per square meter.

4. The method of claim 3, wherein training the neural network model based on the predetermined boiler water wall dimensional parameters, the predetermined neural network model, and the sample water wall data comprises:

performing parameter configuration on the preset neural network model according to the size parameters of the preset boiler water wall to obtain a configured preset neural network model;

and carrying out model training on the configured preset neural network model according to the sample water-cooled wall data to obtain a trained neural network model.

5. A computing device for thinning a high-temperature corrosion wall pipe of a water-cooled wall of a boiler, the device comprising:

The installation module is used for setting at least one sensor installation point on the boiler water wall and installing a data sensor on each sensor installation point;

the acquisition module is used for acquiring the boiler water wall data acquired by the data sensor for a plurality of times;

The calculation module is used for calculating the corrosion rate of the plurality of groups of boiler water wall data based on a pre-trained neural network model and determining the thinning result of the high-temperature corrosion wall pipe of the boiler water wall.

6. The apparatus of claim 5, wherein the density of data sensors mounted in the sensor mounting points on the water wall of the boiler is 4-70 per square meter.

7. The apparatus of claim 5, wherein the apparatus further comprises:

the training module is used for training to obtain the neural network model according to the dimension parameters of the preset boiler water wall, the preset neural network model and the sample water wall data.

8. The apparatus of claim 7, wherein the training module comprises:

The configuration unit is used for carrying out parameter configuration on the preset neural network model according to the size parameters of the preset boiler water wall to obtain the configured preset neural network model;

the training unit is used for carrying out model training on the configured preset neural network model according to the sample water-cooled wall data to obtain a trained neural network model.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.