Method and system for heating furnace coking prediction

Abstract

The application provides a heating furnace coking prediction method and system, and belongs to the technical field of heating furnaces.The method comprises the following steps: monitoring the tube temperature of a target heating furnace in real time based on the video monitoring information of the tube of the target heating furnace; determining the current tube hot zone and the temperature change range value in a preset time period according to the tube temperature; calculating the temperature change value delta T and the temperature change rate of the current tube hot zone in the preset time period according to the temperature change range value; and predicting the tube coking condition and the tube rupture condition according to the video monitoring information, the temperature change value delta T and the temperature change rate. Thus, the state of the current tube hot zone is obtained, and the tube coking condition and the tube rupture condition in the next second are predicted, so that the tube coking is predicted by means of the tube temperature, and the purpose of tube rupture early warning is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heating furnace, in particular to a heating furnace coking prediction method and system. BACKGROUND

[0002] The damage of the furnace tube of the heating furnace is generally caused by coking. Coking is a phenomenon that the oil temperature inside the furnace tube exceeds a certain limit and then thermal cracking occurs, and then free carbon is formed and accumulated on the upper part of the furnace tube. The damage process of the furnace tube is as follows: the coking in the furnace tube is started, the temperature of the tube wall is increased, and thus the oxidation of the surface of the tube wall and the corrosion of the furnace tube are aggravated. The thickness of the tube wall is thinned by the oxidation, and then the thinned part of the tube wall is first bulged under the double actions of the internal pressure and the heat. After the bulging of the furnace tube, the gap between the inner wall of the furnace tube and the coke layer is increased, and thus the temperature of the other part of the furnace tube is gradually increased, and thus the oxidation and thinning are further aggravated, and finally the furnace tube is broken. As can be seen, the increase of the temperature of the furnace tube leads to the increase of the oil temperature inside the furnace tube, and thus leads to the coking in the furnace tube, and the coking in the furnace tube further increases the temperature of the tube wall, aggravates the oxidation of the surface of the tube wall and the corrosion of the furnace tube, and then leads to the final rupture of the furnace tube. Therefore, the temperature of the furnace tube, the coking in the furnace tube and the rupture of the furnace tube have a certain correlation, among which only the temperature of the furnace tube can be monitored. How to establish the correlation among the temperature of the furnace tube, the coking in the furnace tube and the rupture of the furnace tube, predict the coking in the furnace tube by using the temperature of the furnace tube and realize the early warning of the rupture of the furnace tube is a problem to be solved at present. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a heating furnace coking prediction method and system to at least solve the problem that the coking in the furnace tube and the early warning of the rupture of the furnace tube cannot be accurately predicted by using the temperature of the furnace tube.

[0004] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a heating furnace coking prediction method, comprising:

[0005] monitoring the temperature of the furnace tube of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace;

[0006] determining the current hot zone of the furnace tube and the temperature change range value in the preset time period according to the temperature of the furnace tube;

[0007] calculating the temperature change value δT and the temperature change rate of the current hot zone of the furnace tube in the preset time period according to the temperature change range value;

[0008] predicting the coking condition and the rupture condition of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate.

[0009] Optionally, before the above-mentioned monitoring the temperature of the furnace tube of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace, the method further comprises:

[0010] According to the volume data of the furnace tube and the shooting angle of the preset camera, the number and the installation position of the preset camera are determined;

[0011] After the installation of the preset camera according to the determined installation position, the camera initialization correction is performed, and when the actual shooting angle of the camera matches the expected shooting angle, the configuration of the preset camera is completed.

[0012] Optionally, the coking condition of the furnace tube includes whether the furnace tube is continuously coking.

[0013] The determination rule of whether the furnace tube is continuously coking is that:

[0014] When δT* is greater than 1, it is determined that the furnace tube is continuously coking, otherwise, it is determined that the furnace tube will not continuously coke, wherein, δT* represents the temperature change rate.

[0015] Optionally, the coking condition of the furnace tube includes a coking risk level.

[0016] The prediction of the coking condition of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate includes:

[0017] The video monitoring information is analyzed and processed to obtain a furnace tube hot area image analysis result.

[0018] The furnace tube hot area image analysis result, the temperature change value δT and the temperature change rate are input into a preset database for matching to obtain a corresponding coking risk level, wherein, the preset database stores a corresponding relationship between the furnace tube hot area image analysis result, the temperature change value δT and the temperature change rate and the coking risk level.

[0019] Optionally, the furnace tube hot area image analysis result includes a furnace tube deformation parameter and a furnace tube wall thickness.

[0020] The analysis and processing of the video monitoring information to obtain the furnace tube hot area image analysis result includes:

[0021] The video monitoring information is decompressed into continuous image frames;

[0022] A current furnace tube hot area image in any image frame is compared with a factory initial image corresponding to the current furnace tube hot area to obtain a comparison result.

[0023] Based on the comparison result, a furnace tube deformation parameter is obtained.

[0024] Based on the furnace tube deformation parameter, a furnace tube wall thickness is measured.

[0025] Optionally, the furnace tube deformation parameter includes a tube wall deformation position and a tube wall deformation range.

[0026] The above measuring the thickness of the tube wall of the furnace tube based on the deformation parameter of the furnace tube comprises:

[0027] Measuring the thickness of the tube wall corresponding to the deformation position of the tube wall to obtain a first thickness value;

[0028] Measuring the thickness of the tube wall in the deformation range of the tube wall with the deformation position of the tube wall as the center to obtain at least one second thickness value;

[0029] Combining the first thickness value and the second thickness value to obtain the thickness of the tube wall of the furnace tube.

[0030] Optionally, the above furnace tube rupture condition comprises a furnace tube rupture risk level;

[0031] The above predicting the furnace tube rupture condition according to the temperature change value δT comprises:

[0032] When the following condition is met: The furnace tube rupture risk level is predicted to be the highest level, wherein N represents the length of time N seconds of collecting the temperature, i represents the i-th second in N seconds, represents the failure temperature of the furnace tube, represents the normal temperature of the outer wall of the furnace tube.

[0033] Optionally, the above heating furnace coking prediction method further comprises:

[0034] When the temperature of the furnace tube of the target heating furnace changes, updating the current furnace tube hot zone according to the changed furnace tube temperature;

[0035] Re-executing the coking prediction based on the updated furnace tube hot zone.

[0036] Optionally, the above heating furnace coking prediction method further comprises:

[0037] Inputting the service life and use environment information of the target heating furnace into the preset dangerous index model to obtain corresponding dangerous index data;

[0038] Obtaining historical prediction results of the coking condition and the rupture condition of the furnace tube of the target heating furnace;

[0039] Matching the dangerous index data and the historical prediction results to obtain a matching result;

[0040] According to the matching result, determining whether to stop using the target heating furnace, wherein if it is determined to stop using the target heating furnace, a stop using warning signal is sent.

[0041] The second aspect of the present application provides a heating furnace coking prediction system, comprising:

[0042] A real-time monitoring module for real-time monitoring the temperature of the furnace tube of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace;

[0043] a current furnace tube hot zone determination module configured to determine a current furnace tube hot zone and a temperature variation range value in a preset time period according to the furnace tube temperature;

[0044] a temperature variation calculation module configured to calculate a temperature variation value and a temperature variation rate of the current furnace tube hot zone in the preset time period according to the temperature variation range value;

[0045] a prediction module configured to predict a furnace tube coking condition and a furnace tube rupture condition according to the video monitoring information, the temperature variation value and the temperature variation rate.

[0046] In a third aspect of the present application, a machine readable storage medium is provided, which stores instructions configured to cause a processor to be configured to perform the above-mentioned heating furnace coking prediction method when the instructions are executed by the processor.

[0047] In a fourth aspect of the present application, an electronic device is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned heating furnace coking prediction method when executing the computer program.

[0048] According to the above technical solution, the heating furnace coking prediction method and system can obtain the video monitoring information of the furnace tube of the target heating furnace in real time, thereby monitoring the furnace tube temperature of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace. According to the furnace tube temperature, all segments with a temperature higher than a preset temperature are comprehensively considered to obtain the current furnace tube hot zone and the temperature variation range value in a preset time period. Then, according to the temperature variation range value, the temperature variation value and the temperature variation rate of the current furnace tube hot zone in the preset time period are calculated. In combination with the video monitoring information, the temperature variation value and the temperature variation rate, the state of the current furnace tube hot zone can be obtained, and the furnace tube coking condition and the furnace tube rupture condition in the next second can be predicted, so as to realize the prediction of the furnace tube coking and the early warning of the furnace tube rupture by means of the furnace tube temperature.

[0049] Other features and advantages of the present application will be described in detail in the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0050] The accompanying drawings are included to provide a further understanding of the present application and constitute a part of the specification, which together with the detailed description, serve to explain the present application. In the drawings:

[0051] Figure 1 is a flowchart of a heating furnace coking prediction method provided by an embodiment of the present application;

[0052] Figure 2is a block diagram of a heating furnace coking prediction system provided by an embodiment of the present application;

[0053] Figure 3 is a schematic diagram of an electronic device structure provided by a preferred embodiment of the present application.

[0054] Explanation of reference signs

[0055] 10 - electronic device, 100 - processor, 101 - memory, 102 - computer program. DETAILED DESCRIPTION

[0056] The specific embodiments of the present application are described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present application, and are not intended to limit the present application.

[0057] Example 1

[0058] Figure 1 is a flowchart of a heating furnace coking prediction method provided by an embodiment of the present application. As shown in the figure, Figure 1 the embodiment of the present application provides a heating furnace coking prediction method, which comprises:

[0059] S110: Real-time monitoring of the tube temperature of the target heating furnace based on the video monitoring information of the tube of the target heating furnace;

[0060] Specifically, the tube of the target heating furnace can be monitored in real time by a preset camera to obtain the video monitoring information of the tube of the target heating furnace.

[0061] The preset camera can be a camera coupled with an infrared camera and a thermometer.

[0062] Specifically, in the process of real-time monitoring of the target heating furnace by the preset camera, the tube of the target heating furnace is photographed and recorded by the infrared camera, and the tube temperature of the target heating furnace is obtained by the infrared camera and the thermometer at the same time, so as to ensure the accuracy of the obtained tube temperature.

[0063] In the above implementation process, if the tube temperatures obtained by the infrared camera and the thermometer are inconsistent and the difference between the tube temperatures obtained by the two is within a preset temperature error range, the average of the temperatures measured by the two is taken as the obtained tube temperature. If the tube temperatures obtained by the infrared camera and the thermometer are inconsistent and the difference between the tube temperatures obtained by the two is not within the preset temperature error range, it indicates that the infrared camera or the thermometer has failed, then a test object with a temperature (the temperature of the test object can be adjusted) is used to test the infrared camera and the thermometer respectively, so as to determine and replace the failed infrared camera or thermometer, and further avoid affecting the accuracy of the obtained tube temperature.

[0064] S120: determining a current hot zone of the furnace tube and a temperature variation range value within a preset time period according to the temperature of the furnace tube;

[0065] Specifically, if the temperature of any section of the furnace tube is higher than a preset temperature (for example, 400℃), it indicates that the section belongs to the current hot zone of the furnace tube. Thus, the current hot zone of the furnace tube is obtained by comprehensively considering all sections whose temperature is higher than the preset temperature according to the temperature of the furnace tube.

[0066] The preset time period can be 2 seconds.

[0067] Specifically, after the preset camera monitors the current hot zone of the furnace tube, the current hot zone of the furnace tube is monitored frame by frame, and video monitoring information and a temperature variation range value of the current hot zone of the furnace tube within 2 seconds are obtained.

[0068] S130: calculating a temperature variation value δT and a temperature variation rate of the current hot zone of the furnace tube within a preset time period according to the temperature variation range value.

[0069] Specifically, the temperature variation value δT and the temperature variation rate of the current hot zone of the furnace tube within 2 seconds are calculated according to the obtained temperature variation range value of the current hot zone of the furnace tube within 2 seconds.

[0070] S140: predicting a coking condition and a rupture condition of the furnace tube according to the video monitoring information, the temperature variation value δT and the temperature variation rate.

[0071] The coking condition of the furnace tube includes whether the furnace tube is continuously coking. The determination rule of whether the furnace tube is continuously coking is: when δT*>1, it is determined that the furnace tube is continuously coking, otherwise, it is determined that the furnace tube will not continuously coking, wherein δT* represents the temperature variation rate. Thus, whether the furnace tube is continuously coking in the next second is determined by the temperature variation rate, and the purpose of predicting the coking condition of the furnace tube in the next second is achieved.

[0072] In addition, the coking condition of the furnace tube includes a coking risk degree level. The prediction of the coking condition of the furnace tube according to the video monitoring information, the temperature variation value δT and the temperature variation rate includes:

[0073] The video monitoring information is analyzed and processed to obtain a furnace tube hot zone image analysis result;

[0074] The furnace tube hot zone image analysis result, the temperature variation value δT and the temperature variation rate are input into a preset database for matching to obtain a corresponding coking risk degree level, wherein the preset database stores a corresponding relationship between the furnace tube hot zone image analysis result, the temperature variation value δT and the temperature variation rate and the coking risk degree level.

[0075] Specifically, according to the coking risk degree level of the current target heating furnace, the purpose of predicting the coking risk degree is achieved.

[0076] Exemplarily, in the preset database, when the temperature change rate reaches 1 and / or the temperature change value δT reaches 2 and / or the obvious pipe wall bulge appears in the image analysis result of the hot zone of the furnace tube and the volume of the bulge meets the preset volume size, the highest coking risk degree level is taken as an example.

[0077] In addition, the above-mentioned furnace tube rupture condition includes a furnace tube rupture risk level. The above-mentioned prediction of the furnace tube rupture condition according to the temperature change value δT includes: When the condition of the temperature change value δT is met, the furnace tube rupture risk level is predicted to be the highest level, wherein N represents the time length N seconds of collecting the temperature, i represents the i-th second in N seconds, represents the failure temperature of the furnace tube, represents the normal temperature of the outer wall of the furnace tube. Specifically, when the condition of the temperature change value δT is met, the furnace tube rupture risk is predicted to be extremely high, thereby achieving the purpose of predicting the furnace tube rupture risk by using the temperature change value of the current hot zone of the furnace tube.

[0078] In the above-mentioned implementation process, the method uses a preset camera to obtain the furnace tube temperature and the furnace tube monitoring video of the target heating furnace in real time. According to the furnace tube temperature, all sections with a temperature higher than a preset temperature are comprehensively obtained to obtain the current hot zone of the furnace tube. When the preset camera monitors the current hot zone of the furnace tube, the current hot zone of the furnace tube is monitored frame by frame, and the video monitoring information and the temperature change range value of the current hot zone of the furnace tube in a preset time period are obtained. According to the temperature change range value, the temperature change value δT and the temperature change rate of the current hot zone of the furnace tube in the preset time period are calculated. In combination with the video monitoring information, the temperature change value δT and the temperature change rate, the state of the current hot zone of the furnace tube is obtained, and then the coking condition and the furnace tube rupture condition of the next second are predicted, thereby achieving the purpose of predicting the coking of the furnace tube and realizing the early warning of the furnace tube rupture by means of the furnace tube temperature.

[0079] Embodiment 2

[0080] The embodiment provides a heating furnace coking prediction method, which is basically the same as the heating furnace coking prediction method provided in embodiment 1, and the main difference is that the above-mentioned image analysis result of the hot zone of the furnace tube includes a furnace tube deformation parameter and a furnace tube wall thickness. The above-mentioned video monitoring information is analyzed and processed to obtain the image analysis result of the hot zone of the furnace tube, which includes:

[0081] The video monitoring information is decompressed into continuous image frames;

[0082] comparing the current furnace tube hot zone image in any image frame with the factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result;

[0083] based on the comparison result, obtaining the furnace tube deformation parameter;

[0084] based on the furnace tube deformation parameter, measuring the furnace tube wall thickness.

[0085] Specifically, the video monitoring information is decomposed into a plurality of continuous image frames, the current furnace tube hot zone image is located in any image frame according to the position of the current furnace tube hot zone on the furnace tube, the comparison between the current furnace tube hot zone image in any image frame and the factory initial image corresponding to the current furnace tube hot zone is performed to determine the furnace tube deformation parameter, and the furnace tube wall thickness is measured based on the furnace tube deformation parameter. Thus, the furnace tube hot zone image analysis result is obtained by combining the furnace tube deformation parameter and the furnace tube wall thickness.

[0086] Embodiment 3

[0087] The embodiment provides a heating furnace coking prediction method, which is basically the same as the heating furnace coking prediction method provided in embodiment 2, and the main difference lies in that the above-mentioned furnace tube deformation parameter includes a tube wall deformation position and a tube wall deformation range.

[0088] For example, when the furnace tube bulges due to the double effects of internal pressure and heat caused by oxidation thinning, the center position of the bulge is taken as the tube wall deformation position, and the range of the deformed tube wall is taken as the tube wall deformation range.

[0089] The above-mentioned measurement of the furnace tube wall thickness based on the furnace tube deformation parameter includes:

[0090] measuring the tube wall thickness corresponding to the tube wall deformation position to obtain a first thickness value;

[0091] centering on the tube wall deformation position, the tube wall thickness in the tube wall deformation range is measured in turn to obtain at least one second thickness value;

[0092] combining the first thickness value and the second thickness value to obtain the furnace tube wall thickness.

[0093] For example, when the furnace tube deforms to bulge, the center position of the bulge is first measured to obtain a first thickness value, and then the tube wall thickness in the tube wall deformation range is measured along the center position of the bulge in turn, and when the measured thickness value is inconsistent with the first thickness value, it is recorded as a second thickness value. Further, the first thickness value and all the second thickness values are combined to obtain the furnace tube wall thickness. The first thickness value and all the second thickness values can reflect the thickness change of the furnace tube wall, thereby reflecting the deformation of the furnace tube.

[0094] Embodiment 4

[0095] The embodiment provides a heating furnace coking prediction method which is basically the same as the heating furnace coking prediction method provided in embodiment 1, and the main difference lies in that before the above-mentioned real-time monitoring of the tube temperature of the target heating furnace based on the video monitoring information of the tube of the target heating furnace, the method further comprises the following steps of:

[0096] According to the volume data of the tube and the shooting angle of the preset camera, the number and installation position of the preset camera are determined;

[0097] After the installation of each preset camera based on the determined installation position is completed, camera initialization correction is performed, and when the actual shooting angle matches the expected shooting angle, the preset camera configuration is completed.

[0098] Specifically, according to the volume data of the tube and the shooting angle of the preset camera, the field of view for shooting the tube is divided into multiple monitoring areas for separate monitoring, and one preset camera is configured for each monitoring area, so that for each monitoring area, the corresponding camera is used for targeted monitoring, which not only ensures comprehensive monitoring of the tube, but also improves the image recognition speed and the safety of the tube use.

[0099] Exemplarily, the number of the preset cameras can be four.

[0100] Embodiment 5

[0101] The embodiment provides a heating furnace coking prediction method which is basically the same as the heating furnace coking prediction method provided in embodiment 1, and the main difference lies in that the above-mentioned heating furnace coking prediction method further comprises the following steps of:

[0102] When the tube temperature of the target heating furnace changes, the current tube hot area is updated according to the changed tube temperature;

[0103] The coking prediction is re-executed based on the updated tube hot area.

[0104] Specifically, as the target heating furnace is used, the tube temperature of the target heating furnace will continuously increase, and the current tube hot area is updated in real time according to the changed tube temperature, so as to ensure the real-time performance of the current tube hot area. Meanwhile, the preset camera is used to monitor the current tube hot area frame by frame, so as to ensure that the video monitoring information of the current tube hot area and the temperature change range value in the preset time period are obtained, thereby facilitating the judgment of the temperature change influence of the current tube hot area.

[0105] Embodiment 6

[0106] The embodiment provides a heating furnace coking prediction method which is basically the same as the heating furnace coking prediction method provided in embodiment 1, and the main difference lies in that the above-mentioned heating furnace coking prediction method further comprises the following steps of:

[0107] input the service life and the use environment information of the target heating furnace into the preset danger index model to obtain corresponding danger index data;

[0108] obtain historical prediction results of the coking condition and the tube rupture condition of the target heating furnace;

[0109] match the danger index data and the historical prediction results to obtain a matching result;

[0110] determine whether to stop using the target heating furnace according to the matching result, wherein if it is determined to stop using the target heating furnace, a stop-using warning signal is sent.

[0111] Specifically, as the service life of the target heating furnace increases, the coking speed caused by the service temperature of the tube of the target heating furnace of different ages and the tube rupture condition caused by the coking are different. The method analyzes the service life and the use environment information of the target heating furnace by the preset danger index model to obtain the danger index data corresponding to the current use stage of the target heating furnace. At the same time, the prediction results of the coking condition and the tube rupture condition of the target heating furnace are obtained, and the prediction results are taken as the historical prediction results to match the danger index data. According to the matching degree of the two, it is determined whether to stop using the target heating furnace, and at the same time, when it is determined to stop using the target heating furnace, a stop-using warning signal is sent to remind the user to close the target heating furnace. Thus, the tube rupture condition of the target heating furnace is further judged in combination with the actual use scene and the service life of the target heating furnace.

[0112] In some embodiments of the present embodiment, the above-mentioned heating furnace coking prediction method further comprises:

[0113] obtain a plurality of samples, wherein the plurality of samples include the service life and the tube rupture condition of the heating furnace under various use scenes;

[0114] establish a danger index initial model;

[0115] train the danger index initial model by using the plurality of samples to obtain the preset danger index model.

[0116] Embodiment 7

[0117] Figure 2 is a block diagram of a heating furnace coking prediction system provided by an embodiment of the present application. As shown in Figure 2 the present embodiment provides a heating furnace coking prediction system, which comprises:

[0118] a real-time monitoring module, configured to monitor the tube temperature of the target heating furnace in real time based on the video monitoring information of the tube of the target heating furnace;

[0119] Specifically, the preset camera coupled by the infrared camera and the thermometer can be used to monitor the furnace tube of the target heating furnace in real time to obtain the video monitoring information of the furnace tube of the target heating furnace.

[0120] A current furnace tube hot zone determination module is configured to determine a current furnace tube hot zone and a temperature variation range value in a preset time period according to the furnace tube temperature.

[0121] A temperature variation calculation module is configured to calculate a temperature variation value δT and a temperature variation rate of the current furnace tube hot zone in the preset time period according to the temperature variation range value.

[0122] A prediction module is configured to predict a furnace tube coking condition and a furnace tube rupture condition according to the video monitoring information, the temperature variation value δT and the temperature variation rate.

[0123] The furnace tube coking condition includes whether the furnace tube is continuously coking and a coking risk level. The furnace tube rupture condition includes a furnace tube rupture risk level.

[0124] In the implementation process, the system uses the preset camera to obtain the furnace tube temperature and the furnace tube monitoring video of the target heating furnace in real time. According to the furnace tube temperature, all sections with a temperature higher than a preset temperature are comprehensively determined to obtain a current furnace tube hot zone. When the preset camera monitors the current furnace tube hot zone, the current furnace tube hot zone is monitored frame by frame to obtain the video monitoring information and the temperature variation range value of the current furnace tube hot zone in a preset time period. Then, the temperature variation value δT and the temperature variation rate of the current furnace tube hot zone in the preset time period are calculated according to the temperature variation range value. The state of the current furnace tube hot zone can be obtained by combining the video monitoring information, the temperature variation value δT and the temperature variation rate, and the furnace tube coking condition and the furnace tube rupture condition in the next second can be predicted, thereby achieving the purpose of predicting the furnace tube coking and the furnace tube rupture warning by using the furnace tube temperature.

[0125] Embodiment 8

[0126] The embodiment of the present application provides a machine readable storage medium, which stores instructions, and the instructions make the processor be configured to execute the above-mentioned heating furnace coking prediction method when the processor executes the instructions.

[0127] Machine-readable storage media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for information storage. 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0128] The embodiment of the present application also provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned coke formation prediction method of the heating furnace when executing the computer program.

[0129] As shown in Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 3 , the electronic device 10 of the embodiment comprises a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. The processor 100 implements the steps in the above-mentioned method embodiments when executing the computer program 102. Alternatively, the processor 100 implements the functions of the modules / units in the above-mentioned device embodiments when executing the computer program 102.

[0130] For example, the computer program 102 can be divided into one or more modules / units, which are stored in the memory 101 and executed by the processor 100 to complete the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 102 in the electronic device 10. For example, the computer program 102 can be divided into a real-time monitoring module, a current furnace tube hot zone determination module, a temperature change calculation module, and a prediction module.

[0131] The electronic device 10 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices. The electronic device 10 can include, but is not limited to, the processor 100, the memory 101. Those skilled in the art can understand that Figure 3The electronic device 10 is merely an example and does not limit the electronic device 10, and can include more or less components than illustrated, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0132] The processor 100 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0133] The memory 101 can be an internal storage unit of the electronic device 10, for example, a hard disk or a memory of the electronic device 10. The memory 101 can also be an external storage device of the electronic device 10, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 10. Further, the memory 101 can include both the internal storage unit and the external storage device of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0134] It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0135] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, a system or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0136] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0137] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0138] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0139] It should also be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0140] The above embodiments are only used to illustrate the present application, but not to limit it. Instead of the above, various modifications and changes can be made to the application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall into the scope of the claims of the application.

Claims

1. A method of heating furnace coking prediction, characterized by, The method comprises the following steps: monitoring the temperature of the furnace tube of the target heating furnace in real time based on video monitoring information of the furnace tube of the target heating furnace; determining the current furnace tube hot zone and a temperature variation range value within a preset time period according to the temperature of the furnace tube; calculating the temperature variation value δT and the temperature variation rate of the current furnace tube hot zone within the preset time period according to the temperature variation range value; predicting the coking condition and the rupture condition of the furnace tube according to the video monitoring information, the temperature variation value δT and the temperature variation rate; wherein the coking condition of the furnace tube comprises a coking risk level, and the rupture condition of the furnace tube comprises a furnace tube rupture risk level; the step of predicting the coking condition of the furnace tube according to the video monitoring information, the temperature variation value δT and the temperature variation rate comprises: analyzing and processing the video monitoring information to obtain a furnace tube hot zone image analysis result; inputting the furnace tube hot zone image analysis result, the temperature variation value δT and the temperature variation rate into a preset database for matching to obtain a corresponding coking risk level, wherein the preset database stores the corresponding relationship among the furnace tube hot zone image analysis result, the temperature variation value δT and the temperature variation rate and the coking risk level; the furnace tube hot zone image analysis result comprises a furnace tube deformation parameter and a furnace tube wall thickness; the step of analyzing and processing the video monitoring information to obtain a furnace tube hot zone image analysis result comprises: decompressing the video monitoring information into continuous image frames; comparing the current furnace tube hot zone image in any image frame with a factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result; obtaining a furnace tube deformation parameter based on the comparison result; measuring a furnace tube wall thickness based on the furnace tube deformation parameter; the furnace tube deformation parameter comprises a tube wall deformation position and a tube wall deformation range; the step of measuring a furnace tube wall thickness based on the furnace tube deformation parameter comprises: measuring the tube wall thickness corresponding to the tube wall deformation position to obtain a first thickness value; measuring the tube wall thickness within the tube wall deformation range in sequence with the tube wall deformation position as the center to obtain at least one second thickness value; combining the first thickness value and the second thickness value to obtain the furnace tube wall thickness; the step of predicting the rupture condition of the furnace tube according to the temperature variation value δT comprises: When the condition is satisfied, the furnace tube rupture risk level is predicted as the highest level, where N represents a time length N seconds of the collected temperature, i represents the i-th second in the N seconds, represents a furnace tube failure temperature, represents a furnace tube outer wall normal temperature.

2. The heating furnace coking prediction method according to claim 1, characterized by, before the step of monitoring the temperature of the furnace tube of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace, the method further comprises the following steps: determining the configuration number and installation position of the preset camera based on the volume data of the furnace tube and the shooting angle of the preset camera; after completing the installation of each preset camera based on the determined installation position, performing camera initialization correction, and when the actual shooting angle matches the expected shooting angle, completing the configuration of the preset camera.

3. The heating furnace coking prediction method according to claim 1, characterized by, the coking condition of the furnace tube comprises whether the furnace tube is continuously coking; the determination rule of whether the furnace tube is continuously coking is that when δT* > 1, it is determined that the furnace tube is continuously coking, otherwise, it is determined that the furnace tube will not continuously coke, wherein δT* represents the temperature variation rate. the method further comprises the following steps:

4. The heating furnace coking prediction method according to claim 1, characterized by, when the temperature of the furnace tube of the target heating furnace changes, updating the current furnace tube hot zone according to the changed temperature of the furnace tube. ​ Re-perform coking prediction based on the updated hot zone of the furnace tube.

5. The heating furnace coking prediction method according to claim 1, characterized by, Also comprising: inputting the service life and use environment information of the target heating furnace into the preset danger index model to obtain corresponding danger index data; obtain historical prediction results of the coking and rupture of the furnace tube of the target heating furnace; matching the danger index data and the historical prediction results to obtain a matching result; determine whether to stop using the target heating furnace according to the matching result, wherein if it is determined to stop using the target heating furnace, a warning signal is sent to stop using.

6. A furnace coking prediction system characterized by, Comprise: a real-time monitoring module for monitoring the temperature of the furnace tube of the target heating furnace in real time based on video monitoring information of the furnace tube of the target heating furnace; a current furnace tube hot zone determination module for determining a current furnace tube hot zone and a temperature change range value within a preset time period according to the furnace tube temperature; a temperature change calculation module for calculating a temperature change value δT and a temperature change rate of the current furnace tube hot zone within a preset time period according to the temperature change range value; a prediction module for predicting the coking and rupture of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate; wherein the coking of the furnace tube includes a coking risk level, and the rupture of the furnace tube includes a furnace tube rupture risk level; the prediction of the coking of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate comprises: analyzing and processing the video monitoring information to obtain a furnace tube hot zone image analysis result; inputting the furnace tube hot zone image analysis result, the temperature change value δT and the temperature change rate into a preset database for matching to obtain a corresponding coking risk level, wherein the preset database stores the corresponding relationship between the furnace tube hot zone image analysis result, the temperature change value δT and the temperature change rate and the coking risk level; the furnace tube hot zone image analysis result includes a furnace tube deformation parameter and a furnace tube wall thickness; the analysis and processing of the video monitoring information to obtain a furnace tube hot zone image analysis result comprises: decompressing the video monitoring information into continuous image frames; comparing a current furnace tube hot zone image in any image frame with a factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result; obtaining a furnace tube deformation parameter based on the comparison result; measuring a furnace tube wall thickness based on the furnace tube deformation parameter; the furnace tube deformation parameter includes a tube wall deformation position and a tube wall deformation range; the measurement of the furnace tube wall thickness based on the furnace tube deformation parameter comprises: measuring a tube wall thickness corresponding to the tube wall deformation position to obtain a first thickness value; measuring tube wall thicknesses within the tube wall deformation range successively from the tube wall deformation position as the center to obtain at least one second thickness value; combining the first thickness value and the second thickness value to obtain the furnace tube wall thickness; the prediction of the rupture of the furnace tube according to the temperature change value δT comprises: When the condition is satisfied, the furnace tube rupture risk level is predicted as the highest level, where N represents a time length N seconds of the collected temperature, i represents the i-th second in the N seconds, represents a furnace tube failure temperature, represents a furnace tube outer wall normal temperature.

7. A machine-readable storage medium having stored thereon instructions, the instructions being executable by a machine to cause the machine to: The instructions, when executed by a processor, cause the processor to be configured to perform the heating furnace coking prediction method of any one of claims 1 to 5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the heating furnace coking prediction method in any one of claims 1 to 5 when executing the computer program.

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