Oxide scale shedding prediction model generation and prediction method, device, equipment and medium

Abstract

The present application provides a scale shedding prediction model generation and prediction method, device, equipment and medium, which relates to the field of steam oxidation technology. The scale shedding prediction model generation method includes: constructing a training sample set based on multiple groups of experimental data groups with multi-parameter coordination generated by an oxide scale shedding experimental device; wherein the experimental data group includes the first experimental data at the beginning of the experiment and the second experimental data at the end of the experiment, and the first experimental data and the second experimental data both include equipment status data and operation data; the pre-constructed initial scale shedding prediction model is trained through the training sample set to obtain a target scale shedding prediction model. By inputting the data group to be predicted into the target scale shedding prediction model, its corresponding prediction result can be obtained. The present application can effectively solve the existing problem of being unable to predict scale shedding, increasing the risk of equipment failure and safety accidents, and reducing operating efficiency and operational benefits.

Description

Technical Field

[0001] The present application relates to the field of steam oxidation technology, and in particular to a method and device for generating an oxide scale shedding prediction model, an oxide scale shedding prediction method and device, an electronic device, and a machine-readable storage medium. Background Art

[0002] In high-temperature industrial applications, such as thermal power generation and petrochemicals, the inside of the equipment is often exposed to high temperatures for a long time, which often results in the formation of an oxide layer. The accumulation and shedding of this oxide layer not only affects the thermal efficiency of the equipment, but may also cause a series of safety hazards.

[0003] Taking the boiler system of a thermal power plant as an example, the pipelines of large-capacity units are usually made of high-quality austenitic stainless steel materials (such as TP304H, TP347H, etc.) to ensure strength and corrosion resistance in high-temperature environments. However, despite the good performance of these materials, it is still difficult to avoid oxidation reactions due to long-term exposure to high-temperature steam, resulting in the gradual formation of oxide scale on the inner wall of the pipeline.

[0004] Due to the large difference in expansion coefficient between the oxide scale and the base material, the oxide scale will peel off due to stress during the start-up and shutdown of the unit or load fluctuation. The oxide scale peeling may not only cause uneven inner surface of the pipeline, affecting the heat transfer efficiency, but also block the heating surface pipeline, and in severe cases, cause safety accidents such as overheating and pipe explosion, threatening the stability and operation safety of the unit.

[0005] The prediction of oxide scale shedding can not only improve the safety, thermal efficiency, production stability and economy of equipment, but also effectively reduce maintenance and operation risks, thereby achieving long-term stable operation of equipment and reducing failure rate. However, in the related technology, there is a lack of an effective method for predicting oxide scale shedding to improve equipment safety and operation efficiency. Summary of the invention

[0006] The purpose of the embodiments of the present application is to provide a method and device for generating an oxide scale shedding prediction model, an oxide scale shedding prediction method and device, an electronic device, and a machine-readable storage medium, so as to solve the problem that the prior art cannot predict the oxide scale shedding situation, increases the risk of equipment failure and safety accidents, and reduces operating efficiency and operational benefits.

[0007] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for generating an oxide scale shedding prediction model, the method comprising: A training sample set is constructed according to multiple groups of experimental data sets generated by the oxide scale shedding experimental device with multiple parameters coordinated; wherein the experimental data set includes first experimental data at the beginning of the experiment and second experimental data at the end of the experiment, and both the first experimental data and the second experimental data include equipment status data and operation data; The pre-built initial oxide scale shedding prediction model is trained by using the training sample set to obtain a target oxide scale shedding prediction model.

[0008] In an embodiment of the present application, the equipment status data includes weight data, the operation data includes vibration data, flow data, temperature data and pressure data, and the oxide scale shedding data includes at least one of the shedding amount, shedding rate and shedding speed.

[0009] In the embodiment of the present application, a training sample set is constructed based on multiple sets of experimental data groups generated by a multi-parameter coordinated oxide scale shedding experimental device, including: For each experimental data group, data analysis is performed based on the equipment status data in the first experimental data and the second experimental data to determine the oxide scale shedding data; and training samples corresponding to the experimental data group are generated based on the oxide scale shedding data, the operating data in the first experimental data and the operating data in the second experimental data; The training sample set includes training samples corresponding to each experimental data group.

[0010] In an embodiment of the present application, the oxide scale shedding experimental device includes a data acquisition system, a weighing device, a control device, and a steam generator, a high-temperature and high-pressure steam storage tank, a control valve, an experimental pipe and a condensation discharge device connected in sequence. The experimental pipe is provided with a temperature acquisition instrument, a pressure acquisition instrument, a flow acquisition instrument and a vibration generator. The temperature acquisition instrument, the pressure acquisition instrument and the flow acquisition instrument are all connected to the data acquisition system, and the vibration generator is connected to the control device.

[0011] In an embodiment of the present application, the oxide scale shedding experimental device also includes an endoscope and a display module connected to the endoscope, the endoscope is arranged on the inner wall of the experimental pipe, and the display module is used to display the image obtained by the endoscope.

[0012] A second aspect of the present application provides a method for predicting oxide scale shedding, the method comprising: Acquire a data group to be predicted; wherein the data group to be predicted includes initial data and target data, and both the initial data and the target data include operation data; The data group to be predicted is input into the target oxide scale shedding prediction model obtained by the oxide scale shedding prediction model generation method described in the first aspect above, to obtain the prediction result corresponding to the data group to be predicted.

[0013] The third aspect of the present application provides a device for generating an oxide scale shedding prediction model, the device comprising: A sample generation module is used to construct a training sample set according to multiple groups of experimental data groups with multi-parameter coordination generated by the oxide scale shedding experimental device; wherein the experimental data group includes first experimental data at the beginning of the experiment and second experimental data at the end of the experiment, and the first experimental data and the second experimental data both include equipment status data and operation data; The model training module is used to train the pre-built initial oxide scale shedding prediction model through the training sample set to obtain a target oxide scale shedding prediction model.

[0014] A fourth aspect of the present application provides an oxide scale shedding prediction device, the device comprising: A data acquisition module, used to acquire a data group to be predicted; wherein the data group to be predicted includes initial data and target data, and both the initial data and the target data include operation data; The prediction module is used to input the data group to be predicted into the target oxide scale shedding prediction model obtained by using the oxide scale shedding prediction model generation method described in the first aspect above to obtain the prediction result corresponding to the data group to be predicted.

[0015] The fifth aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for generating an oxide scale shedding prediction model as described in the first aspect when executing the computer program.

[0016] In a sixth aspect, the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the scale shedding prediction model generation method described in the first aspect above.

[0017] The oxide scale shedding prediction model generation and prediction method, device, equipment and medium provided in the present application generate a training sample set based on experimental data (i.e., an experimental data group) at two times before and after the experiment, and use the training sample set to train the pre-constructed initial oxide scale shedding prediction model, so that the target oxide scale shedding prediction model finally trained can predict the oxide scale shedding situation under different working conditions, so that relevant personnel can ensure the operating safety of the equipment and improve the operating efficiency and operational benefits based on the prediction results.

[0018] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 The schematic diagram of the process of the method for generating the oxide scale shedding prediction model in the embodiment of the present application is schematically shown; Figure 2 The schematic diagram of the structure of the oxide scale shedding experimental device of the embodiment of the present application is shown; Figure 3 The schematic diagram of the process of the oxide scale shedding prediction method of the embodiment of the present application is schematically shown; Figure 4 A structural block diagram of an oxide scale shedding prediction model generation device according to an embodiment of the present application is schematically shown; Figure 5 The structural block diagram of the oxide scale shedding prediction device of the embodiment of the present application is schematically shown; Figure 6 The internal structure diagram of the computer device according to the embodiment of the present application is schematically shown.

[0020] Description of Reference Numerals 1-steam generator; 2-pressure regulating valve; 3-temperature sensor; 4-pressure sensor; 5-flange; 6-high-temperature and high-pressure steam storage tank; 7-flow sensor; 8-control valve; 9-experimental pipe; 10-vibration generator; 11-endoscope; 12-condensation discharge device; A01-processor; A02-network interface; A03-internal memory; A04-display screen; A05-input device; A06-non-volatile storage medium; B01-operating system; B02-computer program. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0023] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0024] At present, the monitoring and prevention of oxide scale shedding are mainly carried out through the following means: first, through boiler operation management, controlling metal temperature and strengthening operation adjustment to slow down the growth of oxide scale; second, using special detection instruments, such as oxide scale detectors and tube inner wall oxide scale thickness gauges, to quickly and quantitatively monitor the oxide scale thickness and peeling of the boiler high-temperature heating surface. However, these methods still have some problems in practical application, such as the inability to monitor in real time and low detection efficiency.

[0025] In view of the fact that the related art cannot predict the oxide scale shedding situation, which increases the risk of equipment failure and safety accidents, and reduces the operating efficiency and operational benefits, the present application provides an oxide scale shedding prediction model generation and prediction method, device, equipment and medium to better predict and prevent the shedding of oxide scale. The oxide scale shedding prediction model generation and prediction method, device, equipment and medium provided by the present application are described in detail below in conjunction with the accompanying drawings through specific embodiments and implementation methods.

[0026] Figure 1 The schematic diagram of the process of the method for generating the oxide scale shedding prediction model in the embodiment of the present application is shown schematically. Figure 1 As shown, in one embodiment of the present application, a method for generating an oxide scale shedding prediction model is provided, and the method may include the following steps.

[0027] Step A200, constructing a training sample set according to multiple groups of experimental data sets generated by the oxide scale shedding experimental device with multiple parameters coordinated.

[0028] The experimental data group includes the first experimental data at the beginning of the experiment and the second experimental data at the end of the experiment, and the first experimental data and the second experimental data both include equipment status data and operation data. It is worth mentioning that in the embodiment of the present application, multi-parameter synergy refers to the interaction of multiple factors (such as temperature and pressure) to synergistically affect the shedding behavior of the oxide scale.

[0029] In an embodiment of the present application, the equipment status data includes weight data, the operation data includes vibration data, flow data, temperature data and pressure data, and the oxide scale shedding data includes at least one of the shedding amount, shedding rate and shedding speed.

[0030] In the embodiment of the present application, the step A200 includes the following steps: Step A210, for each experimental data group, data analysis is performed based on the equipment status data in the first experimental data and the second experimental data to determine the oxide scale shedding data; based on the oxide scale shedding data, the operating data in the first experimental data and the operating data in the second experimental data, a training sample corresponding to the experimental data group is generated.

[0031] The training sample set includes training samples corresponding to each experimental data group, and each training sample in the training sample set includes oxide scale shedding data, operation data in the first experimental data, and operation data in the second experimental data.

[0032] In the embodiment of the present application, the structural schematic diagram of the oxide scale shedding experimental device is as follows: Figure 2 The oxide scale shedding experimental device comprises a data acquisition system, a weighing device, a control device, and a steam generator 1, a high-temperature and high-pressure steam storage tank 6, a control valve 8, an experimental pipe 9, and a condensation discharge device 12 connected in sequence. The experimental pipe 9 is provided with a temperature acquisition instrument, a pressure acquisition instrument, a flow acquisition instrument, and a vibration generator 10. The temperature acquisition instrument, the pressure acquisition instrument, and the flow acquisition instrument are all connected to the data acquisition system, and the vibration generator 10 is connected to the control device.

[0033] Among them, the steam generator 1 is used to generate high-temperature and high-pressure steam to simulate the heat source of the high-temperature environment; the high-temperature and high-pressure steam storage tank 6 is used to store the high-temperature and high-pressure steam generated by the steam generator 1 to ensure a stable supply of steam; the control valve 8 is used to adjust the flow and pressure of the steam, and the steam output from the output end of the control valve 8 enters the inside of the experimental pipe 9 to simulate different working conditions; the experimental pipe 9 is supported by the weighing device; the temperature acquisition instrument, the pressure acquisition instrument, and the flow acquisition instrument are respectively used to monitor the temperature, pressure, and flow in the experimental pipe 9; the vibration generator 10 is externally placed on the experimental pipe 9, and the vibration generator 10 is connected to the power supply and the control device through wires, and is used to apply controllable vibration conditions to simulate the vibration environment during operation; the output end of the condensation discharge device 12 is connected to the outside through a discharge pipe, and the condensation discharge device 12 is used to condense the steam generated after the experiment and safely discharge it to the outside to ensure the closure and safety of the experimental environment.

[0034] In a specific example, the steam generator 1, the high-temperature and high-pressure steam storage tank 6, the control valve 8, the experimental pipe 9 and the condensation discharge device 12 are detachably connected in sequence through a flange 5 to ensure stable transmission of steam under high temperature and high pressure conditions; a pressure regulating valve 2, a temperature sensor 3 and a pressure sensor 4 are arranged between the steam generator 1 and the high-temperature and high-pressure steam storage tank 6 to monitor and control the steam entering the high-temperature and high-pressure steam storage tank 6; a temperature sensor 3, a pressure sensor 4 and a flow sensor 7 are arranged between the high-temperature and high-pressure steam storage tank 6 and the experimental pipe 9 to monitor and control the steam entering the experimental pipe 9; both ends of the experimental pipe 9 are connected by a high-temperature resistant hose for easy installation and disassembly; the experimental pipe 9 is connected to the vibration generator 10 by an electric wire; the weighing device adopts a precision electronic scale; the temperature acquisition instrument, the pressure acquisition instrument and the flow acquisition instrument respectively adopt thermocouples, pressure gauges and flow meters, and are connected to the data acquisition system through wires or data cables.

[0035] In this specific example, the number of the temperature acquisition instrument, the pressure acquisition instrument, and the flow acquisition instrument are all two, and the same type of acquisition instruments are respectively arranged at the upper and lower ends of the inner wall of the experimental pipe 9 to monitor the temperature, pressure and flow changes in the experimental pipe 9 in real time. It should be noted that the operating data in the experimental data group are all obtained by the acquisition instrument arranged at the lower end (i.e., the output end) of the inner wall of the experimental pipe 9.

[0036] The oxide scale shedding experimental device provided in the embodiment of the present application can monitor in real time the shedding of oxide scales of different materials and thicknesses under different working conditions (temperature, pressure, flow rate, vibration, etc.), and then generate a large number of training samples to ensure the prediction accuracy of the model.

[0037] Based on the above example, the oxide scale removal experimental device also includes an endoscope 11 and a display module connected to the endoscope 11 . The endoscope 11 is arranged on the inner wall of the experimental pipe 9 . The display module is used to display the image obtained by the endoscope 11 .

[0038] The lens portion of the endoscope 11 extends into the interior of the experimental tube 9 and is connected to the display module (such as a display) via a video cable or a data cable, so that relevant personnel can observe the generation and shedding of oxide scale and provide an intuitive visual monitoring method.

[0039] The working principle of the oxide scale shedding experimental device is as follows: Setting working conditions: According to the experimental requirements, the flow rate and pressure of steam are adjusted by the control valve 8, and the initial conditions are monitored and recorded by using thermocouples, pressure gauges and flow meters.

[0040] Heating and vibration: Start the steam generator 1 , pass high-temperature and high-pressure steam into the experimental pipe 9 to a preset temperature and pressure, and apply vibration of a specific frequency and amplitude through the vibration generator 10 .

[0041] Monitoring and recording: By adjusting the steam generator 1 and the control valve 8, the temperature, pressure, flow rate and vibration change curves are continuously monitored and obtained, and the inner wall high temperature endoscope 11 is used to observe and record the oxide scale shedding process.

[0042] Weighing: Before and after the experiment, the weight change of the experimental pipe 9 is weighed using a precision electronic scale to calculate the amount, rate and speed of oxide scale shedding.

[0043] When describing the relationship between the temperature, pressure, flow rate, vibration changes and oxide scale shedding during the operation of the experimental pipe 9, the influence of each variable on oxide scale shedding can be analyzed from a qualitative perspective. The following is a specific example of oxide scale shedding when a single variable is analyzed based on the above oxide scale shedding experimental device and other conditions remain unchanged.

[0044] Take the values ​​of various operating parameters under multiple groups of load increase and decrease states (that is, the internal operating pressure of the experimental pipe 9, the fluid flow rate in the experimental pipe 9, the vibration intensity of the experimental pipe 9, and the internal temperature of the experimental pipe 9 (hereinafter referred to as temperature) as single variables, and the others are constants).

[0045] Take temperature as the variable, and the internal operating pressure, fluid flow rate, and vibration intensity of the experimental pipe 9 as constants for example: The initial weight of the experimental pipe 9 before the experiment started was , the initial tube pressure is , the flow rate is The vibration intensity is After the heating curve is applied, the weight of the experimental pipe 9 is , it can be seen that the weight change of the experimental tube 9 in the temperature dynamic change range is , the time variation is , oxide scale shedding amount , oxide scale shedding rate , oxide scale shedding rate , that is, determine the oxide scale shedding amount, oxide scale shedding rate and oxide scale shedding rate under the heating curve. Similarly, under the heating curve, take any other N (N is a preset constant, which can be selected as 10) groups of experimental data for experiments, and take different constants for the pressure, fluid flow rate and vibration intensity in each experimental data (that is, the first experimental data). According to the experimental results, the oxide scale shedding situation is determined, that is, the oxide scale shedding amount, oxide scale shedding rate and oxide scale shedding rate are calculated, so that the corresponding change relationship between different operating parameters and oxide scale shedding parameters (that is, shedding amount, shedding rate and shedding rate) under the heating curve can be summarized.

[0046] Take another new experimental pipe 9 for experiment. The initial weight of the experimental pipe 9 before the experiment is , the initial tube pressure is , the flow rate is The vibration intensity is After the cooling curve, the weight of the experimental pipe 9 is , it can be seen that the weight change of the experimental tube 9 in the temperature dynamic change range is , the time variation is , oxide scale shedding amount , oxide scale shedding rate , oxide scale shedding rate , that is, determine the oxide scale shedding amount, oxide scale shedding rate and oxide scale shedding speed under the cooling curve. Similarly, under the cooling curve, take any other N groups of experimental data for experiments, take different constants for the pressure, fluid flow rate and vibration intensity in each experimental data, and determine the oxide scale shedding situation according to the experimental results, that is, calculate the oxide scale shedding amount, oxide scale shedding rate and oxide scale shedding speed, so as to summarize the corresponding change relationship between different operating parameters and oxide scale shedding parameters under the cooling curve.

[0047] By analogy, the internal operating pressure of the experimental pipe 9, the fluid flow rate, and the vibration intensity are used as single variables (when the experimental conditions change, a new experimental pipe 9 is replaced for the experiment), and the others are constants for the experiment, so as to obtain multiple sets of experimental data sets with multi-parameter coordination. By calculating the oxide scale shedding amount, shedding rate, and shedding speed under load increase and load decrease, multiple training samples are obtained. Of course, it is understandable that multiple operating parameters can also be used as variables or each operating parameter can be used as a constant to generate an experimental data set.

[0048] In the embodiment of the present application, data analysis is performed based on the equipment status data in the first experimental data and the second experimental data, which means that before and after the end of the experiment, the weight change of the experimental pipe 9 is weighed by the weighing device to calculate the amount of oxide scale shedding, the shedding rate and the shedding speed.

[0049] In a specific example, in addition to the scale shedding data, the operating data in the first experimental data, and the operating data in the second experimental data, the training samples also include the difference between the values ​​of each operating parameter at the end of the experiment and the values ​​at the beginning of the experiment, so as to help the model better understand the dynamic process and changing trend of the operating parameters in the experiment, thereby improving the prediction accuracy and explanatory ability of the model.

[0050] It should be noted that in the embodiments of the present application, the operating parameters specifically refer to parameters related to operating data such as temperature, pressure, flow rate, vibration amplitude, vibration frequency, etc.

[0051] Step A400: training the pre-built initial oxide scale shedding prediction model using the training sample set to obtain a target oxide scale shedding prediction model.

[0052] In the embodiments of the present application, the initial oxide scale shedding prediction model may adopt an existing structure, such as a simple neural network model. The present application does not specifically limit the structure of the initial oxide scale shedding prediction model.

[0053] Specifically, when the model is trained, the operating data in the first experimental data and the operating data in the second experimental data are inputs of the initial oxide scale shedding prediction model, and the oxide scale shedding data are outputs of the initial oxide scale shedding prediction model.

[0054] It can be seen that the scale shedding prediction model generation method provided in the embodiment of the present application generates a training sample set based on the experimental data at two times before and after the experiment (i.e., the experimental data group), and uses the training sample set to perform model training on the pre-constructed initial scale shedding prediction model, so that the target scale shedding prediction model finally generated by the training can predict the scale shedding situation under different working conditions, so that relevant personnel can ensure the operating safety of the equipment and improve the operating efficiency and operational benefits based on the prediction results.

[0055] Figure 1 FIG. 1 is a flow chart of a method for generating a scale shedding prediction model in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0056] Figure 3 The schematic diagram of the process flow of the oxide scale shedding prediction method of the embodiment of the present application is shown schematically. Figure 3 As shown, in one embodiment of the present application, a method for predicting oxide scale shedding is provided, and the method may include the following steps.

[0057] Step B200, obtaining a data group to be predicted.

[0058] The data group to be predicted includes initial data and target data, and both the initial data and the target data include operating data.

[0059] Step B400: input the data group to be predicted into the target oxide scale shedding prediction model obtained by the oxide scale shedding prediction model generation method described in the above embodiment to obtain the prediction result corresponding to the data group to be predicted.

[0060] The prediction result includes at least one of the oxide scale shedding amount, the oxide scale shedding rate and the oxide scale shedding speed.

[0061] It can be seen that the oxide scale shedding prediction method provided in the embodiment of the present application can analyze the influence of factors such as temperature, pressure, flow, vibration, etc. in the data group to be predicted on the generation and shedding of oxide scale by inputting the data group to be predicted into the target oxide scale shedding prediction model, thereby assisting relevant personnel to improve equipment safety and operating efficiency.

[0062] Figure 4 The structural block diagram of the oxide scale shedding prediction model generation device of the embodiment of the present application is schematically shown. Figure 4 As shown, in one embodiment of the present application, a device for generating an oxide scale shedding prediction model is provided, and the device for generating an oxide scale shedding prediction model may include the following functional modules.

[0063] The sample generation module is used to construct a training sample set according to multiple sets of experimental data groups generated by the oxide scale shedding experimental device with multiple parameters. The experimental data group includes the first experimental data at the beginning of the experiment and the second experimental data at the end of the experiment, and the first experimental data and the second experimental data both include equipment status data and operation data.

[0064] The model training module is used to train the pre-built initial oxide scale shedding prediction model through the training sample set to obtain a target oxide scale shedding prediction model.

[0065] In an embodiment of the present application, the equipment status data includes weight data, the operation data includes vibration data, flow data, temperature data and pressure data, and the oxide scale shedding data includes at least one of the shedding amount, shedding rate and shedding speed.

[0066] In the embodiment of the present application, the sample generation module is specifically used for: For each experimental data group, data analysis is performed based on the equipment status data in the first experimental data and the second experimental data to determine the oxide scale shedding data; based on the oxide scale shedding data, the operating data in the first experimental data and the operating data in the second experimental data, a training sample corresponding to the experimental data group is generated.

[0067] The training sample set includes training samples corresponding to each experimental data group.

[0068] Since the oxide scale shedding prediction model generation device provided in the embodiment of the present application is a virtual device corresponding to the oxide scale shedding prediction model generation method of the above embodiment, it can also solve the problem that the prior art cannot predict oxide scale shedding, increases the risk of equipment failure and safety accidents, and reduces operating efficiency and operational benefits.

[0069] Figure 5 The structural block diagram of the oxide scale shedding prediction device of the embodiment of the present application is schematically shown. Figure 5 As shown, in one embodiment of the present application, an oxide scale shedding prediction device is provided, and the oxide scale shedding prediction device may include the following functional modules.

[0070] The data acquisition module is used to acquire a data group to be predicted, wherein the data group to be predicted includes initial data and target data, and both the initial data and the target data include operation data.

[0071] The prediction module is used to input the data group to be predicted into the target oxide scale shedding prediction model obtained by using the oxide scale shedding prediction model generation method described in the above embodiment to obtain the prediction result corresponding to the data group to be predicted.

[0072] Since the oxide scale shedding prediction device provided in the embodiment of the present application is a virtual device corresponding to the oxide scale shedding prediction method of the above embodiment, it can also solve the problem that the prior art cannot predict oxide scale shedding, which increases the risk of equipment failure and safety accidents, while reducing operating efficiency and operational benefits.

[0073] An embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the scale shedding prediction model generation method or scale shedding prediction method described in the above embodiment is implemented.

[0074] The electronic device provided in the embodiment of the present application includes a processor capable of running the oxide scale shedding prediction model generation method of the aforementioned embodiment, and can therefore also solve the problem that the prior art cannot predict oxide scale shedding, which increases the risk of equipment failure and safety accidents, while reducing operating efficiency and operational benefits.

[0075] An embodiment of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the oxide scale shedding prediction model generation method or oxide scale shedding prediction method described in the above embodiment.

[0076] The machine-readable storage medium provided in the embodiment of the present application stores instructions for enabling the machine to execute the oxide scale shedding prediction model generation method of the above-mentioned embodiment. Therefore, it can also solve the problem that the prior art cannot predict oxide scale shedding, increases the risk of equipment failure and safety accidents, and reduces operating efficiency and operational benefits.

[0077] Figure 6 The internal structure diagram of the computer device of the embodiment of the present application is schematically shown. Figure 6 As shown, in one embodiment of the present application, a computer device is provided, which may be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for generating an oxide scale shedding prediction model or an oxide scale shedding prediction method is implemented. The display screen A04 of the computer device can be a liquid crystal display screen A04 or an electronic ink display screen A04, and the input device A05 of the computer device can be a touch layer covering the display screen A04, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0078] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0079] In one embodiment, the oxide scale shedding prediction model generation device and the oxide scale shedding prediction device provided in the present application can each be implemented in the form of a computer program B02, and the computer program B02 can be used in the following manner: Figure 6 The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the oxide scale shedding prediction model generation device / oxide scale shedding prediction device, and the computer program B02 composed of various program modules enables the processor A01 to execute the steps of the oxide scale shedding prediction model generation method / oxide scale shedding prediction method of various embodiments of the present application described in this specification.

[0080] For example, Figure 6 The computer device shown can be Figure 4 The sample generation module in the oxide scale shedding prediction model generation device shown executes the step A200 and the model training module executes the step A400.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program B02 instructions. These computer program B02 instructions can be provided to a processor A01 of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor A01 of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program B02 instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program B02 instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] In a typical configuration, a computing device includes one or more processors A01 (CPU), input / output interfaces, a network interface A02, and memory.

[0086] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0087] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0088] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0089] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for generating an oxide scale shedding prediction model, characterized in that: The method comprises: A training sample set is constructed according to multiple groups of experimental data sets generated by the oxide scale shedding experimental device with multiple parameters coordinated; wherein the experimental data set includes first experimental data at the beginning of the experiment and second experimental data at the end of the experiment, and both the first experimental data and the second experimental data include equipment status data and operation data; The pre-built initial oxide scale shedding prediction model is trained by using the training sample set to obtain a target oxide scale shedding prediction model.

2. The method according to claim 1, characterized in that The equipment status data includes weight data, the operation data includes vibration data, flow data, temperature data and pressure data, and the oxide scale shedding data includes at least one of shedding amount, shedding rate and shedding speed.

3. The method according to claim 1, characterized in that According to the multi-parameter collaborative experimental data sets generated by the oxide scale shedding experimental device, a training sample set is constructed, including: For each experimental data group, data analysis is performed based on the equipment status data in the first experimental data and the second experimental data to determine the oxide scale shedding data; and training samples corresponding to the experimental data group are generated based on the oxide scale shedding data, the operating data in the first experimental data and the operating data in the second experimental data; The training sample set includes training samples corresponding to each experimental data group.

4. The method according to any one of claims 1 to 3, characterized in that The oxide scale shedding experimental device includes a data acquisition system, a weighing device, a control device, and a steam generator, a high-temperature and high-pressure steam storage tank, a control valve, an experimental pipe and a condensation discharge device connected in sequence. The experimental pipe is provided with a temperature acquisition instrument, a pressure acquisition instrument, a flow acquisition instrument and a vibration generator. The temperature acquisition instrument, the pressure acquisition instrument and the flow acquisition instrument are all connected to the data acquisition system, and the vibration generator is connected to the control device.

5. The method according to claim 4, characterized in that The oxide scale shedding experimental device also includes an endoscope and a display module connected to the endoscope. The endoscope is arranged on the inner wall of the experimental pipe, and the display module is used to display the image acquired by the endoscope.

6. A method for predicting oxide scale shedding, characterized in that: The method comprises: Acquire a data group to be predicted; wherein the data group to be predicted includes initial data and target data, and both the initial data and the target data include operation data; The data group to be predicted is input into a target oxide scale shedding prediction model obtained by using the oxide scale shedding prediction model generation method according to any one of claims 1 to 5 to obtain a prediction result corresponding to the data group to be predicted.

7. A device for generating a scale shedding prediction model, characterized in that: The device comprises: A sample generation module is used to construct a training sample set according to multiple groups of experimental data groups with multi-parameter coordination generated by the oxide scale shedding experimental device; wherein the experimental data group includes first experimental data at the beginning of the experiment and second experimental data at the end of the experiment, and the first experimental data and the second experimental data both include equipment status data and operation data; The model training module is used to train the pre-built initial oxide scale shedding prediction model through the training sample set to obtain a target oxide scale shedding prediction model.

8. An oxide scale shedding prediction device, characterized in that: The device comprises: A data acquisition module, used to acquire a data group to be predicted; wherein the data group to be predicted includes initial data and target data, and both the initial data and the target data include operation data; A prediction module is used to input the data group to be predicted into a target oxide scale shedding prediction model obtained by using the oxide scale shedding prediction model generation method according to any one of claims 1 to 5 to obtain a prediction result corresponding to the data group to be predicted.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the scale shedding prediction model generation method according to any one of claims 1 to 5 or the scale shedding prediction method according to claim 6 is implemented.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the scale shedding prediction model generation method according to any one of claims 1 to 5 or the scale shedding prediction method according to claim 6.

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