A real-time intelligent measurement method and system for high-temperature melt flow in metallurgical chute

Abstract

The present invention relates to the field of flow measurement technology, and discloses a real-time intelligent measurement method and system for high-temperature melt flow in a metallurgical chute. The method includes inputting the chute inclination angle, chute diameter, cross-sectional shape and metal type information into a real-time flow calculation program module; measuring the temperature of characteristic points in the chute in real time through a high-temperature non-contact measurement module; measuring the melt depth and coking thickness, and the characteristic surface height in real time through a visual image processing module; and inputting the real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module into the real-time flow calculation program module to calculate the real-time flow. The system includes an information input module, a parameter calculation module, and a result output module. The present invention can measure the high-temperature melt flow in a metallurgical chute in real time. Through accurate measurement of the flow, it can help metallurgical engineers and operators understand the flow state of the melt in the chute, optimize process parameters, and improve production efficiency and quality control level.

Description

Technical Field

[0001] The present invention relates to the technical field of flow measurement, and in particular to a real-time intelligent measurement method and system for high-temperature melt flow in a metallurgical chute. Background Art

[0002] The chute is an important equipment in the smelting process. The high-temperature melt flows out of the smelting furnace and enters the next processing stage or equipment through the chute. The flow rate of the high-temperature melt in the chute directly affects the downstream smelting process and related operations. For example, when the high-temperature melt flows into the furnace along the chute, the flow rate of the high-temperature melt directly determines the feed rate and smelting cycle of the downstream furnace; when the high-temperature melt is directly cast, the chute flow determines the efficiency and precise control of the casting. It should be noted that although the flow measurement of the high-temperature melt in the chute is very important, in actual production, how to accurately measure the flow has always been a problem that has plagued the industry. This is because the chute is an open pipe and conventional methods such as Venturi flowmeters cannot be used for flow measurement. In addition, the high temperature of the melt also limits the measurement method to a certain extent. At present, companies often obtain the velocity of characteristic points in the chute through non-contact measurement, and estimate the flow rate by multiplying the velocity of the characteristic point by the flow area. However, due to the existence of the boundary layer, the velocity of the characteristic point is often greater than the average flow velocity of the flow surface, which makes the current measurement method produce large errors. In order to illustrate the source of the error vividly, Figure 1 Taking a circular tube as an example, the development of the boundary layer and the velocity distribution are demonstrated. First, the high-temperature melt needs to flow through a certain distance before the boundary layer can fully develop. Secondly, the thickness of the boundary layer will be affected by the viscosity of the fluid. The greater the viscosity of the fluid, the stronger the shear effect and the thicker the boundary layer. In addition, the average velocity of the fluid also depends on the boundary layer thickness and the flow channel diameter ratio. When the flow channel diameter is very small, only a small part of the fluid is in the mainstream area outside the boundary layer. At this time, the center area velocity will deviate significantly from the average velocity; when the flow channel diameter is very large, most of the pipeline is in the mainstream area, and the center area velocity is close to the average velocity. Finally, the chute flow rate will change in real time with the smelting cycle. In summary, the above-mentioned complex factors make the method of directly measuring the high-temperature melt flow rate only by the characteristic point velocity very limited.

[0003] Prior art 1, application number: CN202310605825.5 discloses a device for measuring the flow of high Reynolds number media in a metallurgical system, including a rectifier, a connecting pipe and a meter. The rectifier is connected to the meter through a connecting pipe to form a measuring pipeline structure. The two ends of the measuring pipeline are connected to the corresponding metallurgical system gas medium pipeline through flanges. A matrix through-hole structure is provided in the rectifier. The flowmeter in the meter adopts a matrix characteristic point sampling setting according to the rectifier cross section. Compared with the prior art, although the rectifier realizes the conversion from turbulent flow to laminar flow within a distance of 10cm to 25cm in the pipeline axis, it overcomes the difficulty of selecting a 10D straight pipe section on site, eliminates the influence of gas turbulence on measurement, and is suitable for online measurement of precise air volume values ​​in high dust, high turbulence, and high vibration environments, and completely solves the problem that the straight pipe section is insufficient and cannot be accurately measured. However, its structure is relatively simple and cannot effectively improve the accuracy of flow measurement.

[0004] Prior art 2, application number: CN 202311252279.8 discloses a chute device for liquid metal pouring. Through the setting of the hydraulic cylinder and the angle adjustment mechanism, when the pouring operation is carried out, the liquid metal will generate an impact force on the upper surface of the chute, and the impact force is transmitted to the hydraulic cylinder at the bottom of the angle adjustment mechanism. When the pouring flow rate increases, the elongation of the hydraulic cylinder increases, so that the inclination angle of the chute decreases. Through the above action, the liquid metal is minimized from impacting the ingot mold and the slag has sufficient crystallization time. When the pouring flow rate is very small, the elongation of the hydraulic cylinder decreases, so that the inclination angle of the chute increases, and the liquid metal is quickly flowed into the ingot mold, so that it will not solidify in the chute. Although the above action realizes the function of adjusting the inclination angle of the chute in real time. However, the lack of control over the flow rate affects the efficiency and precise control of casting.

[0005] Prior art three, application number: CN 202310298109.7 discloses a method for direct iron tapping during the smelting process of high-speed rail steel by pre-desulfurization of molten iron. According to the existing secondary model, before the end of blowing, the molten iron temperature is measured online and the actual consumption ratio of magnesium powder and lime is calculated; according to the deviation between the molten iron temperature, the actual consumption ratio of magnesium powder and lime and the calculated value of the secondary model, the type and amount of magnesium powder and lime added are controlled, and then the bottom blowing gas flow rate is controlled to ensure the hit of the S content at the end of the molten iron. Although iron can be tapped at the end of desulfurization blowing without reblowing, the production smelting cycle is reduced by an average of 3.5 minutes, and the production cost is reduced by 12.5 yuan / ton. Compared with the traditional molten iron pre-desulfurization process, it effectively improves the production efficiency of molten iron pre-desulfurization and reduces the production cost. However, the accuracy of flow monitoring is low, which affects the efficiency of iron tapping to a certain extent.

[0006] At present, the existing technologies 1, 2 and 3 have great limitations in the way of directly measuring the high-temperature melt flow rate only by the characteristic point velocity, resulting in the problem of low accuracy of the flow calculation results. Therefore, the present invention provides a real-time intelligent measurement method and system for the high-temperature melt flow rate of a metallurgical chute, which uses a method combining real-time measurement, computational fluid dynamics and multiple interpolation technology to establish a database of characteristic point velocity-cross-section average velocity under different working conditions, and obtains relevant correction coefficients and conversion formulas. By implanting the calculation formula into a computer, the high-temperature melt flow rate of a metallurgical furnace chute can be accurately monitored in real time. Summary of the invention

[0007] The main purpose of the present invention is to provide a real-time intelligent measurement method and system for high-temperature melt flow in a metallurgical chute, so as to solve the problem that the method of directly measuring the high-temperature melt flow by only using the characteristic point velocity in the prior art has great limitations, resulting in low accuracy of the flow calculation results.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute, the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute comprising:

[0010] Input chute inclination, chute diameter, cross-sectional shape and metal type information into the real-time flow calculation program module;

[0011] The temperature of characteristic points in the chute is measured in real time through the high-temperature non-contact measurement module; the melt depth, coking thickness, and characteristic surface height are measured in real time through the visual image processing module;

[0012] The real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module are input into the real-time flow calculation program module to calculate the real-time flow;

[0013] The specific principle of the real-time traffic calculation program module is:

[0014] The estimated flow rate Q1 is calculated based on the characteristic point velocity V and effective flow area S measured by non-contact measurement:

[0015] Q1=V*S;

[0016] Where S is calculated based on the characteristic surface height and the flow area shape, multiplied by a flow correction coefficient λ less than 1, the actual flow Q2 of the chute is calculated using the following formula:

[0017] Q2=λ*Q1.

[0018] As a further improvement of the present invention, information is input into the real-time flow calculation program module, including:

[0019] Obtain the chute inclination, chute diameter, cross-sectional shape and metal type information through the equipment database or measurement tools; write a script in the real-time flow calculation program module to parse the received information and extract the required inclination, diameter, cross-sectional shape and metal type parameters according to the data format and protocol;

[0020] Using the data interface of the input device, the collected information is transmitted to the real-time flow calculation program module;

[0021] The parameter data obtained by parsing is calculated according to the input parameter data through function calls.

[0022] As a further improvement of the present invention, calculation is performed according to input parameter data by function calling, including:

[0023] In the real-time flow calculation program module, a calculation function is defined to accept the chute inclination angle, chute diameter, cross-sectional shape and metal type parameters as input;

[0024] In the calculation function, the corresponding calculation is performed according to the input parameters, the flow rate of the chute is calculated according to the chute inclination angle and diameter, the cross-sectional area of ​​the chute is calculated according to the cross-sectional shape, and the friction coefficient of the chute is calculated according to the metal type;

[0025] In the calculation function, the calculated result is returned or stored in the corresponding variable for use in subsequent flow calculations.

[0026] As a further improvement of the present invention, the process of obtaining the velocity of the feature point includes:

[0027] The radar speed gun transmits microwave signals to the surface of the molten metal flow; the microwave signals are reflected back by the surface of the molten metal flow, and the radar speed gun receives the echo signals;

[0028] Based on the Doppler effect, the radar speed meter obtains the velocity information of the molten metal flow surface by calculating the frequency shift of the echo signal; by processing and analyzing the echo signal, the characteristic points, i.e., certain specific areas on the molten metal flow surface, are identified;

[0029] Among them, by measuring the frequency shift of the echo signal, that is, the magnitude of the frequency change, the radar speed meter obtains the speed information of the molten metal flow surface. The magnitude of the frequency shift is proportional to the speed of the object. Specifically, the radar speed meter will compare the received echo signal with the transmitted microwave signal, calculate the magnitude of the frequency shift, and infer the speed of the molten metal flow surface based on the parameters of the radar system and the frequency shift value.

[0030] For each feature point, the speed value corresponding to the feature point is calculated according to its frequency shift value and the parameters of the radar system; after obtaining the speed of the feature point, the data is output to the display device.

[0031] As a further improvement of the present invention, the radar speed meter transmits microwave signals, including:

[0032] Before starting the speed measurement, the microwave signal parameters need to be initialized; the radar speed meter sends a microwave signal to the metal flow surface and waits for the reception of the echo signal;

[0033] The radar speed meter receives the echo signal reflected from the surface of the metal flow, and obtains the target characteristics by analyzing the characteristics of the echo signal; based on the target characteristics and the preset evaluation indicators, the quality of the current signal parameters is evaluated;

[0034] Based on the evaluation results, an adaptive algorithm is used to adjust the frequency and waveform of the microwave signal and resend the signal. The echo signal is continuously received and analyzed, and the signal parameters are continuously adjusted based on the real-time target characteristics and evaluation results until the preset speed measurement requirements are met.

[0035] As a further improvement of the present invention, the velocity of the molten metal flow surface is calculated, including:

[0036] The radar speed meter receives the echo signal reflected from the surface of the metal flow;

[0037] By analyzing the characteristics of the echo signal, the target characteristics are obtained;

[0038] According to the parameters and frequency shift value of the radar system, the velocity of the molten metal flow surface is calculated using the Doppler effect formula; the Doppler effect formula is:

[0039] Speed ​​= (frequency shift × speed of light) / (2 × emission frequency × cosine θ)

[0040] Among them, frequency shift represents the frequency shift of the received echo signal, speed light speed is the value of the speed of light, transmission frequency is the frequency of microwave signals transmitted by the radar system, and θ is the incident angle of the transmitted signal.

[0041] As a further improvement of the present invention, the acquisition of the characteristic surface height includes:

[0042] Take a picture of the liquid level tank through a camera or other image acquisition device to obtain a liquid level image; pre-process the captured liquid level image, including image enhancement, filtering, and denoising;

[0043] Extracting liquid level features from the preprocessed image by region segmentation, separating the liquid level feature region from the entire image; analyzing the extracted liquid level features, including calculating the position, shape, and size information of the feature surface;

[0044] Through the pre-trained convolutional neural network image recognition model, by extracting the key feature points in the image and calculating the similarity between the feature points, the feature points that match the known marked scale are found and the height of the feature surface is determined.

[0045] As a further improvement of the present invention, the process of obtaining a database of different chute geometries, molten metal properties and coking conditions by the real-time flow calculation program module includes:

[0046] Taking the chute as the object, a three-dimensional computational domain model with geometric proportions is established according to the actual size; the chute model is meshed to obtain a hexahedral structure mesh for capturing the computational fluid domain. The part where the melt flows needs to be specially encrypted to accurately capture the free liquid surface shape and velocity;

[0047] The gas-liquid interface is set as a free interface, the solid wall is regarded as a no-slip boundary, and the boundary layer near the wall is treated with a standard wall function. The inlet and outlet boundary conditions and initial conditions are set according to the actual industrial situation.

[0048] Set the VOF model, the surface tension between the mixed gas and liquid phases and the ke turbulence model; extract and analyze the velocity and boundary layer flow of the chute under different working conditions.

[0049] As a further improvement of the present invention, computational fluid dynamics (CFD) solves the fluid dynamics control equations by mathematical calculation methods to obtain a discrete quantitative description of the flow field and the phase interface, and uses this to predict the fluid motion behavior in the calculation area; the VOF model assumes that there is no mutual penetration between multiphase fluids. For each additional phase in the model, it is necessary to introduce a volume fraction of one more phase. In each control volume, the sum of the volume fractions of all phases is 1; the phase interface between the multiphases is tracked by solving the continuity equation of the multiphase volume fraction. For the qth phase, this equation has the following form:

[0050]

[0051] Where α q is the volume fraction of the qth phase; ρ q is the density of the qth phase; is the velocity of the fluid; S αq is the source phase;

[0052] In the VOF model, the velocity field is obtained by solving a single momentum equation for the entire region. The velocity field is shared by all phases as a result of the calculation. The momentum equation depends on ρ and μ based on the volume fractions of all phases in the control volume:

[0053]

[0054] Where ρ is the density of the fluid; is the velocity of the fluid; μ is the viscosity of the fluid; is the volume force. In the VOF model, the energy equation is expressed as follows:

[0055]

[0056]

[0057] Where Eq is calculated by the specific heat capacity of the qth phase and the shared temperature T; k eff is effective heat conduction; source term S h Includes radiation and other volumetric heat sources;

[0058] Turbulence model equation, the k-ε model was proposed by Launder and Spalding. The model introduces two unknown quantities: turbulent kinetic energy k and turbulent dissipation rate ε. The transport equations corresponding to the two unknown quantities are:

[0059]

[0060] where ρ is the density of the fluid, The partial derivative of the turbulent kinetic energy k with respect to time, The turbulent kinetic energy k is multiplied by the velocity component u i For the space coordinate x i The partial derivative of The diffusion term of the turbulent kinetic energy k, where μ is the dynamic viscosity of the fluid, μ t is the turbulent viscosity, σ k is the turbulent viscosity proportional factor, G k The generation term of turbulent kinetic energy k includes pressure-velocity related term and pressure square term, G b The dissipation term of turbulent kinetic energy k includes the square term of velocity gradient and the product term of velocity gradient and pressure gradient, S k The source term of turbulent kinetic energy k, including body force terms and other source terms, Y M It represents the turbulent pressure dissipation term in the k-ε turbulence model;

[0061] The partial derivative of the turbulent dissipation rate ε with respect to time, σ ε is the turbulent viscosity proportional factor, C 1e The generation term coefficient of the turbulent dissipation rate ε, The ratio of the gradient term of the turbulent kinetic energy k to the turbulent kinetic energy k, C 2e The dissipation term coefficient of the turbulent dissipation rate ε, The ratio of the square of the turbulent dissipation rate ε to the turbulent kinetic energy k, S ε The source terms of the turbulent dissipation rate ε include body force terms and other source terms.

[0062] To achieve the above object, the present invention also provides the following technical solutions:

[0063] A real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute, which is applied to the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute. The real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute comprises:

[0064] An information input module, used to input the information of chute inclination angle, chute diameter, cross-sectional shape and metal type into the real-time flow calculation program module;

[0065] The parameter calculation module is used to measure the temperature of the characteristic points in the chute in real time through the high-temperature non-contact measurement module; the melt depth and coking thickness, as well as the characteristic surface height, are measured in real time through the visual image processing module;

[0066] The result output module is used to input the real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module into the real-time flow calculation program module to calculate the real-time flow.

[0067] The present invention provides basic data for subsequent flow calculation by inputting geometric parameters and material properties of the chute. Through high-temperature non-contact measurement and visual image technology, parameters such as temperature, melt depth and coke thickness of characteristic points in the chute are obtained in real time. Through the real-time flow calculation program module, combined with the measured parameters such as temperature, melt depth and coke thickness, the real-time flow in the metallurgical chute is accurately calculated. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the steps of an embodiment of a method for real-time intelligent measurement of high-temperature melt flow in a metallurgical chute according to the present invention;

[0069] Figure 2 It is a schematic diagram of the principle of an embodiment of a method for real-time intelligent measurement of high-temperature melt flow in a metallurgical chute of the present invention;

[0070] Figure 3 A schematic flow chart of the steps of inputting information into a real-time flow calculation program module in accordance with an embodiment of a real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to the present invention;

[0071] Figure 4 A schematic flow chart of the steps of obtaining the characteristic point velocity in one embodiment of the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute of the present invention;

[0072] Figure 5 A schematic flow chart of the steps of obtaining a database of different chute geometries, molten metal properties, and coking conditions by a real-time flow calculation program module in accordance with one embodiment of the present invention for real-time intelligent measurement of high-temperature melt flow in a metallurgical chute;

[0073] Figure 6 It is a functional module schematic diagram of an embodiment of a real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute of the present invention;

[0074] Figure 7 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0075] Figure 8 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0078] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0079] like Figure 1As shown, this embodiment provides an embodiment of a real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute. In this embodiment, the real-time intelligent measurement method and system for high-temperature melt flow in a metallurgical chute specifically include the following steps:

[0080] Step S1: inputting the chute inclination angle, chute diameter, cross-sectional shape and metal type information into the real-time flow calculation program module;

[0081] Step S2: measuring the temperature of the characteristic points in the chute in real time through the high-temperature non-contact measurement module; measuring the melt depth and coking thickness, as well as the characteristic surface height in real time through the visual image processing module;

[0082] Step S3: The real-time measurement parameters acquired by the high-temperature non-contact measurement module and the visual image processing module are input into the real-time flow calculation program module to calculate the real-time flow;

[0083] The specific working principle of the real-time traffic calculation program module is:

[0084] First, the estimated flow rate Q1 is calculated based on the non-contact measurement of the characteristic point velocity V and the effective flow area S:

[0085] Q1=V*S(1)

[0086] Where S can be calculated based on the characteristic surface height and the shape of the flow area. Due to the existence of the boundary layer, the characteristic point velocity is greater than the average velocity of the flow surface, so it needs to be multiplied by a flow correction coefficient λ that is less than 1. Therefore, the actual flow Q2 of the chute can be calculated using the following formula:

[0087] Q2=λ*Q1(2)

[0088] The method for obtaining the correction coefficient λ depends on the thickness of the boundary layer and the velocity distribution in the boundary layer. For each specific structure of the chute, flow velocity, melt type, and chute inclination angle, the velocity distribution of the boundary layer is different. Therefore, computational fluid dynamics (CFD) is used to obtain the relationship between the boundary layer velocity distribution, characteristic point velocity, and average velocity under each operating condition, and then the correction coefficient can be obtained. However, the parameters that affect the chute flow rate include geometric parameters, physical parameters, and coking conditions, which are about 7 to 10 variables. In a multidimensional variable database, the number of calculation conditions and the number of variables are exponentially related. For example, each variable contains 5 to 8 data, and the complete database contains 5 7 ~10 8 This means that there are at least 10 4 It is difficult to obtain all the data of this magnitude through CFD methods. Therefore, the strategy adopted is to calculate the core parameters and use multiple interpolation technology to obtain the flow rate of high-temperature melt in the chute under any working conditions.

[0089] Preferably, step S1 of this embodiment provides basic data for subsequent flow calculation by inputting geometric parameters and material properties of the chute. Step S2 obtains parameters such as temperature, melt depth and coke thickness of characteristic points in the chute in real time through high-temperature non-contact measurement and visual image technology. Step S3 uses a real-time flow calculation program module to accurately calculate the real-time flow in the metallurgical chute by combining the measured parameters such as temperature, melt depth and coke thickness. For specific principles, please refer to the attached Figure 2 .

[0090] In summary, this embodiment can measure the flow rate of high-temperature melt in a metallurgical chute in real time. By accurately measuring the flow rate, it can help metallurgical engineers and operators understand the flow state of the melt in the chute, optimize process parameters, and improve production efficiency and quality control level. At the same time, accurate measurement of the flow rate in the chute is also helpful in predicting and avoiding the occurrence of process safety problems such as tank overflow and overflow.

[0091] This embodiment can accurately measure the chute flow in real time, providing a new method and means for real-time measurement of the chute flow. The accurate flow information provided is conducive to the accurate control of the feed rate and smelting cycle of the downstream furnace. When the high-temperature melt is directly cast, the chute flow information also determines the efficiency and accurate control of the casting, which is of great significance to enterprises in improving metallurgical efficiency and quality.

[0092] Furthermore, if Figure 3 As shown, the process of inputting information into the real-time flow calculation program module in step S1 specifically includes the following steps:

[0093] Step S11: Obtain the information of the chute inclination angle, chute diameter, cross-sectional shape and metal type through the equipment database or measurement tool; write a script in the real-time flow calculation program module to parse the received information and extract the required parameters such as inclination angle, diameter, cross-sectional shape and metal type according to the format and protocol of the data;

[0094] Step S12: using the data interface of the input device to transmit the collected information to the real-time flow calculation program module;

[0095] Step S13: The parameter data obtained by parsing is calculated according to the input parameter data through function call.

[0096] Preferably, step S11 of this embodiment obtains the inclination angle, diameter, cross-sectional shape and metal type information of the chute through the equipment database or measurement tool, and obtains the actual parameter value or the data obtained through measurement; these parameters are the basic parameters necessary for calculating the flow rate, and by accurately obtaining these parameters, the accuracy and reliability of the subsequent flow calculation can be guaranteed. Step S12 uses the data interface of the input device to transmit the collected information to the real-time flow calculation program module, and transmits the parameter data collected in real time to the flow calculation program module through the data interface or communication protocol, which can realize the real-time update of the parameters, timely reflect the actual situation, and improve the real-time and accuracy of the flow calculation. Step S13 calculates the parameter data obtained by parsing according to the input parameter data through function calls, and in the real-time flow calculation program module, uses the corresponding algorithms and formulas to calculate the flow according to the input parameter data; the real-time flow of the chute can be accurately calculated according to the actual parameter data; the real-time monitoring and calculation of the chute flow is realized, which helps to control and manage the flow of the chute and improve production efficiency and resource utilization.

[0097] Furthermore, the calculation according to the input parameter data by function calling in step S13 specifically includes the following steps:

[0098] Step S131: in the real-time flow calculation program module, a calculation function is defined to accept parameters such as the chute inclination angle, chute diameter, cross-sectional shape and metal type as input;

[0099] Step S132: in the calculation function, corresponding calculations are performed according to the input parameters, such as calculating the flow rate of the chute according to the inclination angle and diameter of the chute, calculating the cross-sectional area of ​​the chute according to the cross-sectional shape, and calculating the friction coefficient of the chute according to the metal type, etc.;

[0100] Step S133: In the calculation function, the calculated result is returned or stored in the corresponding variable for use in subsequent flow calculation.

[0101] Preferably, the technical effect of defining the calculation function in step S131 of this embodiment is to create a function in the real-time flow calculation program module that can accept parameter input and perform calculations, and the calculation logic can be encapsulated in the function for easy calling and reuse; improve the readability and maintainability of the code, and make the program module more modular and structured; by defining the calculation function, different calculation steps can be separated, the coupling degree of the code can be reduced, and the program can be easier to understand and modify. Step S132 performs corresponding calculations according to the input parameters. According to the given parameter values, specific calculation operations are performed, the flow rate of the chute is calculated according to the inclination angle and diameter of the chute, the cross-sectional area of ​​the chute is calculated according to the cross-sectional shape, and the friction coefficient of the chute is calculated according to the metal type, etc.; the corresponding calculation logic is executed according to the specific parameter values ​​to obtain accurate calculation results. In this way, different calculation operations can be performed according to different input parameters, making the calculation process more flexible and customizable. Step S133 returns or stores the calculated results and passes the calculation results to other processes or stores them in variables for subsequent flow calculations; saves the calculation results for the convenience of subsequent flow calculation modules. In this way, the calculation results can be directly obtained when needed, which improves the efficiency and performance of the program. At the same time, it also facilitates the transmission and sharing of results, allowing the calculation results to be interacted and used between different modules.

[0102] Furthermore, if Figure 4 As shown, the process of obtaining the feature point velocity in step S2 specifically includes the following steps:

[0103] Step S21: the radar speed meter transmits a microwave signal to the surface of the molten metal flow; the microwave signal is reflected back by the surface of the molten metal flow, and the radar speed meter receives an echo signal;

[0104] Step S22: According to the Doppler effect, the radar speed meter obtains the velocity information of the molten metal flow surface by calculating the frequency shift of the echo signal; and identifies the characteristic points, i.e., certain specific areas on the molten metal flow surface, by processing and analyzing the echo signal;

[0105] Among them, by measuring the frequency shift of the echo signal, that is, the magnitude of the frequency change, the radar speed meter obtains the speed information of the molten metal flow surface. The magnitude of the frequency shift is proportional to the speed of the object. Specifically, the radar speed meter will compare the received echo signal with the transmitted microwave signal, calculate the magnitude of the frequency shift, and according to the parameters of the radar system and the frequency shift value, the speed of the molten metal flow surface can be inferred;

[0106] Step S23: For each feature point, the speed value corresponding to the feature point is calculated according to its frequency shift value and the parameters of the radar system; after the speed of the feature point is obtained, the data is output to the display device.

[0107] Preferably, step S21 of this embodiment realizes interaction with the surface of the molten metal flow by emitting microwave signals and receiving echo signals, and transmits the signals to the subsequent processing steps, thereby obtaining physical contact with the surface of the metal flow, so that the subsequent speed measurement process can be carried out. Step S22 calculates the frequency shift of the echo signal according to the Doppler effect, and can obtain the speed information of the surface of the molten metal flow. By processing and analyzing the echo signal, the characteristic points, i.e., certain specific areas of the surface of the molten metal flow, are identified; the speed information and characteristic points of the surface of the metal flow are extracted, providing a basis for subsequent speed calculation. Step S23 calculates the speed value corresponding to each characteristic point according to the frequency shift value of the characteristic point and the parameters of the radar system, and can accurately obtain the speed information of the characteristic point, and output these data to the display device; the accurate calculation and visual display of the characteristic point speed are realized, providing accurate data support for subsequent analysis and control.

[0108] In summary, this embodiment uses a radar speed meter to obtain velocity information on the surface of the molten metal flow, which is very important for smelting, casting and other process processes in industrial production; by real-time monitoring and controlling the speed of the metal flow, the stability and quality of the production process can be guaranteed, and production efficiency and product quality can be improved. In addition, since the radar speed meter is not affected by environmental factors, it can work stably under high temperature and complex working conditions, and has high application value.

[0109] Furthermore, in step S21, the process of transmitting microwave signals by the radar speed gun specifically includes the following steps:

[0110] Step S211: before starting the speed measurement, it is necessary to initialize the microwave signal parameters, such as frequency, waveform, transmission power and the like; the radar speed meter sends the microwave signal to the metal flow surface and waits for the reception of the echo signal;

[0111] Step S212: The radar speed meter receives the echo signal reflected from the surface of the metal flow, and obtains the target characteristics, such as speed, position, etc., by analyzing the characteristics of the echo signal, such as amplitude, spectrum, phase, etc.; and evaluates the quality of the current signal parameters according to the target characteristics and preset evaluation indicators;

[0112] Step S213: According to the evaluation results, use an adaptive algorithm to adjust the frequency and waveform of the microwave signal and resend the signal; continue to receive and analyze the echo signal, and continuously adjust the signal parameters according to the real-time target characteristics and evaluation results until the preset speed measurement requirements are met.

[0113] Preferably, the purpose of initializing the microwave signal parameters in step S211 of this embodiment is to ensure that the transmitted signal has a suitable frequency, waveform and transmission power to adapt to different metal flow surface conditions; the quality and accuracy of the echo signal can be improved. Step S212 receives and analyzes the characteristics of the echo signal to obtain the characteristics of the target, such as speed and position; by analyzing the amplitude, spectrum, phase and other characteristics of the echo signal, information about the target can be extracted and further processed and evaluated. Step S213 The adaptive algorithm automatically adjusts the frequency and waveform of the microwave signal according to the real-time target characteristics and evaluation results. By continuously adjusting the signal parameters, more accurate speed information can be obtained, the speed measurement accuracy and reliability of the radar speed meter can be improved, and it can adapt to different metal flow surface conditions.

[0114] In summary, this embodiment improves the speed measurement precision and accuracy of the radar speed meter. Through the dynamic adjustment of the adaptive algorithm, the microwave signal can be better adapted to different metal flow surface conditions, thereby obtaining more accurate speed information; it is of great significance for many application fields, such as traffic management, industrial production, etc., and can improve safety and efficiency.

[0115] Furthermore, the process of calculating the velocity of the molten metal flow surface in step S22 specifically includes the following steps:

[0116] Step S221: the radar speed meter receives the echo signal reflected from the surface of the metal flow;

[0117] Step S222: Analyze the characteristics of the echo signal, such as amplitude, spectrum, phase, etc., to obtain target characteristics, such as the magnitude of the frequency shift;

[0118] Step S223: According to the parameters and frequency shift value of the radar system, the velocity of the molten metal flow surface can be calculated using the Doppler effect formula; the Doppler effect formula is:

[0119] Speed ​​= (frequency shift × speed of light) / (2 × emission frequency × cosine θ)

[0120] Among them, frequency shift represents the frequency shift of the received echo signal, speed light speed is the value of the speed of light, transmission frequency is the frequency of microwave signals transmitted by the radar system, and θ is the incident angle of the transmitted signal.

[0121] Preferably, in step S221 of this embodiment, the radar speed meter receives the echo signal reflected from the surface of the metal flow, and obtains the signal reflected from the surface of the metal flow; step S222 obtains the target characteristics, such as the size of the frequency shift, by analyzing the characteristics of the echo signal, such as amplitude, spectrum, phase, etc.; analyzes the characteristics of the echo signal, and extracts information related to the target characteristics, such as the size of the frequency shift. Step S223, according to the parameters of the radar system and the frequency shift value, the speed of the molten metal flow surface can be deduced using the formula of the Doppler effect; according to the parameters of the radar system and the frequency shift value, the speed of the metal flow surface is calculated using the Doppler effect formula; by deducing the speed of the metal flow surface, the motion state of the metal flow can be monitored and controlled in real time. This is very important for the metallurgical process of the molten metal flow, which can help optimize the fluidity and stability of the metal flow, improve the smelting efficiency and product quality. In addition, for metal flow pipelines or equipment in industrial production, monitoring the changes in the surface speed of the metal flow can timely discover abnormal conditions, perform fault diagnosis and maintenance, and ensure the safety and stability of production.

[0122] Furthermore, the process of acquiring the characteristic surface height in step S2 specifically includes the following steps:

[0123] Step S24: photographing the liquid level tank through a camera or other image acquisition device to obtain a liquid level image; preprocessing the photographed liquid level image, including image enhancement, filtering, denoising and other operations;

[0124] Step S25: extracting liquid level features from the preprocessed image by region segmentation, separating the liquid level feature region from the entire image; analyzing the extracted liquid level features, including calculating information such as the position, shape, and size of the feature surface;

[0125] Step S26: By using a pre-trained convolutional neural network image recognition model, key feature points in the image are extracted and the similarity between the feature points is calculated to find feature points that match the known marking scale and determine the height of the feature surface.

[0126] Preferably, step S24 of this embodiment can enhance the quality of the image, reduce noise and interference, and make subsequent processing more accurate and reliable by taking an image of the liquid level tank and performing preprocessing. Step S25 extracts the liquid level features by regional segmentation, and can separate the liquid level feature area from the entire image, which is convenient for subsequent analysis and processing, and at the same time obtains information such as the position, shape and size of the feature surface, providing a basis for subsequent identification. Step S26 matches the extracted liquid level features with the known marking scale through an image recognition model or algorithm, and can determine the height of the feature surface. Through the identified liquid level feature height, the information of the liquid level can be fed back in real time, and automated liquid level monitoring and control can be achieved, thereby improving production efficiency and safety.

[0127] In summary, this embodiment realizes automatic acquisition of the height of the liquid level feature surface through image acquisition, preprocessing, feature extraction and recognition. Through automated liquid level monitoring and control, the efficiency and safety of the production process can be improved, manual intervention can be reduced, the incidence of human errors can be reduced, and work efficiency and accuracy can be improved.

[0128] Furthermore, if Figure 5 As shown, the process of obtaining the database of different chute geometries, molten metal properties and coking conditions by the real-time flow calculation program module in step S3 specifically includes the following steps:

[0129] Step S31: Taking the chute as the object, a three-dimensional computational domain model is established in accordance with the actual size; the chute model is meshed to obtain a hexahedral structure mesh for capturing the computational fluid domain, and the melt flow portion needs to be specially encrypted to accurately capture the free liquid surface morphology and velocity;

[0130] Step S32: The gas-liquid interface is set as a free interface, the solid wall is regarded as a no-slip boundary, and the boundary layer near the wall is processed by a standard wall function; the inlet and outlet boundary conditions and initial conditions are set according to the actual industrial situation;

[0131] Step S33: setting the VOF model, the mixed gas-liquid surface tension and the ke turbulence model; extracting and analyzing the velocity and boundary layer flow of the chute under different working conditions.

[0132] Preferably, step S31 of this embodiment can capture the hexahedral structure grid of the computational fluid domain by establishing a chute model and dividing the grid, thereby accurately simulating the flow of molten metal; it can provide accurate fluid dynamics information, including velocity, pressure, etc., for subsequent flow calculation and analysis. Step S32 can simulate the interaction between gas and liquid phases and the flow of fluid on the chute wall by setting the gas-liquid interface as a free interface and the solid wall as a no-slip boundary. At the same time, setting the inlet and outlet boundary conditions and initial conditions can provide the starting state and boundary conditions for the simulation calculation. Step S33 can simulate the surface tension and turbulence characteristics of the molten metal by setting the VOF model mixed gas-liquid phase surface tension and ke turbulence model; it can more accurately predict the flow of molten metal in the chute, including the shape of the liquid surface and the velocity distribution.

[0133] In summary, this embodiment includes providing accurate fluid dynamics information, simulating the interaction between gas and liquid phases, and simulating the surface tension and turbulence characteristics of molten metal. By obtaining a database of different chute geometries, molten metal properties, and coking conditions, accurate input parameters can be provided for the real-time flow calculation program, thereby improving the accuracy and reliability of flow calculation; it is of great significance for flow control and optimization in industrial production processes.

[0134] In this embodiment, computational fluid dynamics (CFD) is a discipline that uses mathematical calculations to solve fluid dynamics control equations to obtain discrete quantitative descriptions of flow fields and phase interfaces, and to predict the fluid motion behavior in the calculation area. It is necessary to consider the shape and height of the free liquid surface in the slotted state. In addition to solving conventional mass, momentum and energy conservation equations, the VOF method is also required to capture and reconstruct the gas-liquid interface.

[0135] The VOF model is suitable for simulating a variety of immiscible fluids, including stratified flow and free surface flow. The VOF model assumes that there is no interpenetration between multiphase fluids. For each additional phase in the model, it is necessary to introduce an additional phase volume fraction. In each control volume, the sum of the volume fractions of all phases is 1. The phase interface between the multiphases is tracked by solving the continuity equation of the multiphase volume fraction. For the qth phase, this equation has the following form:

[0136]

[0137] Where α q is the volume fraction of the qth phase; ρ q is the density of the qth phase; is the velocity of the fluid; S αq For the source phase.

[0138] In the VOF model, the velocity field is obtained by solving a single momentum equation for the entire region. The velocity field is shared by all phases as a result of the calculation. The momentum equation depends on ρ and μ based on the volume fractions of all phases in the control volume:

[0139]

[0140] Where ρ is the density of the fluid; is the velocity of the fluid; μ is the viscosity of the fluid; is the volume force.

[0141] In the VOF model, the energy equation is expressed as follows:

[0142]

[0143]

[0144] Where Eq is calculated by the specific heat capacity of the qth phase and the shared temperature T; k eff is effective heat conduction; source term S h Includes radiation and other volumetric heat sources.

[0145] Turbulence model equation, the k-ε model was proposed by Launder and Spalding. The model introduces two unknown quantities: turbulent kinetic energy k and turbulent dissipation rate ε. The transport equations corresponding to the two unknown quantities are:

[0146]

[0147] where ρ is the density of the fluid, The partial derivative of the turbulent kinetic energy k with respect to time, The turbulent kinetic energy k is multiplied by the velocity component u i For the space coordinate x i The partial derivative of The diffusion term of the turbulent kinetic energy k, where μ is the dynamic viscosity of the fluid, μ t is the turbulent viscosity, σ k is the turbulent viscosity proportional factor, G k The generation term of turbulent kinetic energy k includes pressure-velocity related term and pressure square term, G b The dissipation term of turbulent kinetic energy k includes the square term of velocity gradient and the product term of velocity gradient and pressure gradient, S k The source term of turbulent kinetic energy k, including body force terms and other source terms, Y M It represents the turbulent pressure dissipation term in the k-ε turbulence model;

[0148] The partial derivative of the turbulent dissipation rate ε with respect to time, σ ε is the turbulent viscosity proportional factor, C 1e The generation term coefficient of the turbulent dissipation rate ε, The ratio of the gradient term of the turbulent kinetic energy k to the turbulent kinetic energy k, C 2e The dissipation term coefficient of the turbulent dissipation rate ε, The ratio of the square of the turbulent dissipation rate ε to the turbulent kinetic energy k, S ε The source terms of the turbulent dissipation rate ε include body force terms and other source terms.

[0149] Furthermore, in step S3, the process of using the multiple interpolation technique (MICE) to expand the database and perform the difference calculation in the real-time flow calculation program module specifically includes the following steps:

[0150] Step S33: fill all missing values ​​using some method (such as mean, median, random sampling, etc.) to obtain a complete initial data set;

[0151] Step S34: Perform multiple iterations, and each iteration will interpolate one or more variables containing missing values. In each iteration, for each variable Y containing missing values i , use a prediction model to estimate the missing value Y based on the other complete variables Xi i. This prediction model can be linear regression, logistic regression, decision tree, etc.;

[0152] Step S35: Repeat the iteration until a convergence condition is met, for example, when the interpolation results of several consecutive iterations do not change much, or a preset maximum number of iterations is reached;

[0153] Step S36: After all iterations are completed, the interpolation results obtained in each iteration are integrated. A common integration method is to take the average value of multiple interpolation results for each missing value.

[0154] Preferably, step S33 of this embodiment fills all missing values ​​with a certain method to obtain a complete initial data set; filling missing values ​​can ensure the integrity of the data set, so that subsequent data analysis and calculation can be carried out; obtaining a complete data set is the basis for real-time traffic calculation, which can reduce the analysis error caused by missing data. Step S34 performs multiple iterations, and each iteration interpolates one or more variables containing missing values; the missing values ​​are estimated based on other complete variables through the prediction model, and the correlation between data is maintained while filling the missing values; by interpolating missing values, real-time traffic can be analyzed and calculated more accurately, and the reliability and accuracy of the data can be improved. Step S35: Repeat the iteration until the convergence condition is met; through the iterative process, the interpolation result is continuously improved until the convergence condition is met, that is, the interpolation result no longer changes significantly; ensure the stability and reliability of the interpolation result, reduce the interpolation error, and improve the accuracy of data analysis. After all iterations are completed, step S36 integrates the interpolation results obtained in each iteration; takes the average of multiple interpolation results for each missing value to reduce interpolation errors and improve the accuracy of data analysis; obtains the final interpolation result, making the data set more complete and accurate, and providing a reliable data basis for subsequent real-time traffic calculations.

[0155] In this embodiment, multiple interpolation technology (MICE for short) is used to expand the database and perform difference. MICE is a commonly used data processing method in statistics. Its principle is based on the multivariate interpolation method. Its core idea is to use the pattern and association information of existing data to infer missing values. Specifically, MICE regards the variable containing missing values ​​as the predicted variable, and the other fully observed variables as the predictor variables. Then, MICE processes each variable containing missing values ​​in an iterative manner, and each iteration processes one variable in turn, and uses the values ​​of other variables as predictors to estimate the missing values ​​of the variable. In each iteration, MICE uses a model (such as linear regression, logistic regression or decision tree, etc.) to estimate the value of the missing variable. This model predicts missing values ​​based on other fully observed data, and in the prediction process, the correlation between other variables is taken into account. After multiple iterations, MICE will generate multiple complete data sets, each of which contains a reasonable estimate of the missing values. Finally, by combining the results of these complete data sets, usually simply taking the average or weighted average, the final estimation result is obtained. This method of combining multiple estimation results can better reflect the uncertainty of the data and improve the accuracy of the estimation of missing values. Therefore, the MICE method can effectively handle various types of missing data and has been widely used in data analysis and research.

[0156] The estimation process of MICE is represented by mathematical symbols:

[0157] For the imputation of the i-th variable, assume that the variable with missing values ​​is Y i , the complete set of variables is X, where Xi represents the removal of Y i A collection of variables.

[0158] The prediction model can be expressed as follows: i =f(Xi)+ε i Among them, f is a prediction function and εi is the error term;

[0159] For each iteration, the prediction model is used to estimate the missing values, and then the missing values ​​in the data set are updated. After multiple iterations, multiple complete data sets are obtained, and the final estimated values ​​are finally obtained by integrating the results of these data sets. In this way, the MICE method uses multiple prediction models and multiple iterations to estimate missing values, making full use of the information of the data and improving the accuracy and robustness of interpolation.

[0160] like Figure 6As shown, this embodiment also provides an embodiment of a real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute. In this embodiment, the real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute is applied to the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute in the above embodiment. The real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute includes an information input module 1, a parameter calculation module 2, and a result output module 3 which are electrically connected in sequence;

[0161] Among them, the information input module 1 is used to input the chute inclination angle, chute diameter, cross-sectional shape and metal type information into the real-time flow calculation program module; the parameter calculation module 2 is used to measure the temperature of the characteristic points in the chute in real time through the high-temperature non-contact measurement module; the melt depth and coking thickness, as well as the characteristic surface height are measured in real time through the visual image processing module; the result output module 3 is used to input the real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module into the real-time flow calculation program module to calculate the real-time flow;

[0162] Preferably, the information input module 1 of this embodiment is used to input the chute inclination angle, chute diameter, cross-sectional shape and metal type information into the real-time flow calculation program module; provide the necessary input information to the calculation module for flow calculation; ensure the accuracy and reliability of real-time flow calculation. The parameter calculation module measures the temperature of the characteristic points in the chute in real time through the high-temperature non-contact measurement module, and measures the melt depth, coking thickness and characteristic surface height in real time through the visual image processing module; obtains key parameter information in the chute; provides real-time measurement data to provide necessary input for flow calculation. The result output module 3 inputs the real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module into the real-time flow calculation program module to calculate the real-time flow; connects and transmits the measurement results to the calculation module to complete the flow calculation; realizes real-time monitoring and control of the melt flow in the chute, and improves production efficiency and quality.

[0163] In summary, each submodule in the system of this embodiment obtains necessary information and parameters through different technical means, and inputs them into the real-time flow calculation program module to achieve real-time measurement and control of the high-temperature melt flow of the metallurgical chute. This is of great significance for ensuring the stability of the production process, improving production efficiency and product quantity.

[0164] Furthermore, the information input module 1 specifically includes:

[0165] The program editing submodule is used to obtain the information of the chute inclination, chute diameter, cross-sectional shape and metal type through the equipment database or measurement tools; in the real-time flow calculation program module, a script is written to parse the received information and extract the required parameters such as inclination, diameter, cross-sectional shape and metal type according to the format and protocol of the data;

[0166] The information transmission submodule is used to transmit the collected information to the real-time flow calculation program module using the data interface of the input device;

[0167] The data parsing submodule is used to calculate the parameter data obtained by parsing according to the input parameter data through function calls.

[0168] Preferably, the program editing submodule of this embodiment realizes obtaining chute-related parameter information from the equipment database or measurement tool; ensuring that the real-time flow calculation program module can obtain accurate chute parameters and provide necessary input for subsequent flow calculation. The information transmission submodule realizes transmitting the collected chute parameter information to the real-time flow calculation program module; ensuring that the real-time flow calculation program module can obtain the latest chute parameter information in a timely manner, ensuring the accuracy and real-time nature of flow calculation. The data analysis submodule realizes passing the analyzed parameter data to the real-time flow calculation program module and performing calculations; ensuring that the real-time flow calculation program module can perform accurate flow calculations based on the input parameter data, and provide accurate flow data for subsequent result output.

[0169] In summary, the various submodules of the information input module 1 of this embodiment ensure that the real-time flow calculation program module can obtain accurate parameter data and perform flow calculation by obtaining chute parameter information, transmitting parameter data and analyzing data, thereby realizing real-time measurement and monitoring of the high-temperature melt flow of the metallurgical chute. This is of great significance for flow control and optimization in the metallurgical production process.

[0170] Furthermore, the parameter calculation module 2 specifically includes:

[0171] The signal transmitting submodule is used for the radar speed gun to transmit the signal to the surface of the molten metal flow by transmitting the microwave signal; the microwave signal is reflected back by the surface of the molten metal flow, and the radar speed gun receives the echo signal;

[0172] The signal analysis submodule is used to obtain the velocity information of the molten metal flow surface by calculating the frequency shift of the echo signal based on the Doppler effect; and to identify the characteristic points, i.e., certain specific areas on the molten metal flow surface, by processing and analyzing the echo signal;

[0173] Among them, by measuring the frequency shift of the echo signal, that is, the magnitude of the frequency change, the radar speed meter obtains the speed information of the molten metal flow surface. The magnitude of the frequency shift is proportional to the speed of the object. Specifically, the radar speed meter will compare the received echo signal with the transmitted microwave signal, calculate the magnitude of the frequency shift, and according to the parameters of the radar system and the frequency shift value, the speed of the molten metal flow surface can be inferred;

[0174] The speed calculation submodule is used to calculate the speed value corresponding to each feature point according to its frequency shift value and the parameters of the radar system; after obtaining the speed of the feature point, the data is output to the display device;

[0175] The image acquisition submodule is used to take a picture of the liquid level tank through a camera or other image acquisition device to obtain a liquid level image; pre-process the captured liquid level image, including image enhancement, filtering, denoising and other operations;

[0176] The image separation submodule is used to extract the liquid level features from the preprocessed image through region segmentation, and separate the liquid level feature area from the entire image; analyze the extracted liquid level features, including calculating the position, shape, size and other information of the feature surface;

[0177] The model training submodule is used to find feature points that match known marking scales and determine the height of the feature surface by extracting key feature points in the image and calculating the similarity between the feature points through a pre-trained convolutional neural network image recognition model.

[0178] Preferably, the signal transmitting submodule of this embodiment transmits microwave signals to the surface of the molten metal flow for subsequent signal analysis and speed calculation; the signal transmission on the surface of the molten metal flow is realized, and the necessary data for subsequent analysis and calculation is provided. The signal analysis submodule obtains the speed information of the surface of the molten metal flow and identifies the characteristic points by calculating the frequency shift of the echo signal according to the Doppler effect; the echo signal is analyzed and processed, thereby obtaining the speed information and characteristic points of the surface of the molten metal flow, providing a basis for subsequent speed calculation. The speed calculation submodule calculates the speed value corresponding to each characteristic point according to the frequency shift value of the characteristic point and the parameters of the radar system, and outputs the data to the display device; the calculation of the characteristic point speed is realized, and accurate speed data is provided for real-time monitoring and display. The image acquisition submodule takes a picture of the liquid level tank through a camera or other image acquisition device, obtains the liquid level image, and pre-processes the image; the liquid level image is obtained, and the image is enhanced and denoised, etc., to provide clear image data for subsequent image separation. The image separation submodule extracts liquid level features through regional segmentation, separates the liquid level feature area from the entire image, and analyzes the extracted liquid level features; it realizes the extraction and analysis of liquid level features, including calculating the position, shape, size and other information of the feature surface, providing accurate feature data for subsequent model training. The model training submodule extracts key feature points in the image through a pre-trained convolutional neural network image recognition model, and calculates the similarity between feature points to determine the height of the feature surface; it realizes the extraction and matching of feature points, thereby determining the height of the feature surface, providing accurate liquid level data for real-time monitoring and control.

[0179] In summary, this embodiment realizes real-time monitoring and control of the liquid level tank, provides accurate liquid level data and speed information, and can help operators make timely judgments and adjustments, thereby improving production efficiency and safety.

[0180] Furthermore, the result output module 3 includes:

[0181] The model building submodule is used to build a three-dimensional computational domain model with the chute as the object according to the actual size; the chute model is meshed to obtain a hexahedral structure grid for capturing the computational fluid domain. The melt flow part needs special encryption to accurately capture the free liquid surface shape and velocity;

[0182] The function processing submodule is used to set the gas-liquid interface as a free interface, the solid wall as a no-slip boundary, and the boundary layer near the wall is processed by a standard wall function; the import and export boundary conditions and initial conditions are set according to the actual industrial situation;

[0183] The parameter extraction submodule is used to set the VOF model, the surface tension between the mixed gas and liquid phases, and the ke turbulence model; extract and analyze the velocity and boundary layer flow of the chute under different working conditions.

[0184] Preferably, the model building submodule of this embodiment establishes a three-dimensional computational domain model of the chute according to the actual size, and divides the grid structure to accurately capture the free liquid surface morphology and velocity; provides accurate computational domain and grid for subsequent fluid simulation, so as to simulate and analyze fluid behavior. The function processing submodule sets the gas-liquid interface as a free interface, regards the solid wall as a no-slip boundary, and sets the inlet and outlet boundary conditions and initial conditions according to actual conditions; provides correct boundary conditions and initial conditions for fluid simulation, so as to accurately simulate the behavior of the fluid in the chute. The parameter extraction submodule sets the VOF model mixed gas-liquid interphase surface tension and ke turbulence model, and extracts and analyzes the velocity and boundary layer flow of the chute under different working conditions; obtains relevant parameters of the fluid inside the chute according to the simulation results, such as velocity and boundary layer flow, so as to evaluate and optimize the design and performance of the chute.

[0185] In summary, the various sub-modules of the result output module simulate, analyze and optimize the fluid behavior of the chute by establishing an accurate calculation domain model, setting appropriate boundary conditions and parameters, and extracting relevant parameters from the fluid simulation results, thereby improving the performance and efficiency of the chute.

[0186] like Figure 7 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0187] The memory 42 stores program instructions for implementing the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to any of the above-mentioned embodiments.

[0188] The processor 41 is used to execute program instructions stored in the memory 42 to perform real-time intelligent measurement of the high-temperature melt flow rate of the metallurgical chute.

[0189] The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0190] Further, Figure 8 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all the above methods, wherein the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0191] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0193] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute, characterized in that: The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute comprises: Input chute inclination, chute diameter, cross-sectional shape and metal type information into the real-time flow calculation program module; The temperature of characteristic points in the chute is measured in real time through the high-temperature non-contact measurement module; the melt depth, coking thickness, and characteristic surface height are measured in real time through the visual image processing module; The real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module are input into the real-time flow calculation program module to calculate the real-time flow; The specific principle of the real-time traffic calculation program module is: The estimated flow rate Q1 is calculated based on the characteristic point velocity V and effective flow area S measured by non-contact measurement: Q1=V*S; Where S is calculated based on the characteristic surface height and the flow area shape, multiplied by a flow correction coefficient λ less than 1, the actual flow Q2 of the chute is calculated using the following formula: Q2=λ*Q1.

2. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 1 is characterized in that: Information input to the real-time traffic calculation program module includes: Obtain the chute inclination, chute diameter, cross-sectional shape and metal type information through the equipment database or measurement tools; write a script in the real-time flow calculation program module to parse the received information and extract the required inclination, diameter, cross-sectional shape and metal type parameters according to the data format and protocol; Using the data interface of the input device, the collected information is transmitted to the real-time flow calculation program module; The parameter data obtained by parsing is calculated by accepting the input parameter data through function calls.

3. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 2 is characterized in that: The function calls accept the input parameter data for calculation, including: In the real-time flow calculation program module, a calculation function is defined to accept the chute inclination angle, chute diameter, cross-sectional shape and metal type parameters as input; In the calculation function, the corresponding calculation is performed according to the input parameters, the cross-sectional area of ​​the chute is calculated according to the chute inclination, diameter and cross-sectional shape, and the friction coefficient of the chute is calculated according to the metal type; In the calculation function, the calculated result is returned or stored in the corresponding variable for use in subsequent flow calculations.

4. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 1 is characterized in that: The process of obtaining the feature point velocity includes: The radar speed gun transmits microwave signals to the surface of the molten metal flow; the microwave signals are reflected back by the surface of the molten metal flow, and the radar speed gun receives the echo signals; According to the Doppler effect, the radar speed meter obtains the velocity information of the molten metal flow surface by calculating the frequency shift of the echo signal; by processing and analyzing the echo signal, the characteristic points, that is, certain specific areas on the molten metal flow surface, are identified; Among them, by measuring the frequency shift of the echo signal, that is, the magnitude of the frequency change, the radar speed meter obtains the speed information of the molten metal flow surface. The magnitude of the frequency shift is proportional to the speed of the object. Specifically, the radar speed meter will compare the received echo signal with the transmitted microwave signal, calculate the magnitude of the frequency shift, and infer the speed of the molten metal flow surface based on the parameters of the radar system and the frequency shift value. For each feature point, the speed value corresponding to the feature point is calculated according to its frequency shift value and the parameters of the radar system; after obtaining the speed of the feature point, the data is output to the display device.

5. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 4 is characterized in that: Radar speed guns emit microwave signals, including: Before starting the speed measurement, the microwave signal parameters need to be initialized; the radar speed meter sends a microwave signal to the metal flow surface and waits for the reception of the echo signal; The radar speed meter receives the echo signal reflected from the surface of the metal flow, and obtains the target characteristics by analyzing the characteristics of the echo signal; based on the target characteristics and the preset evaluation indicators, the quality of the current signal parameters is evaluated; Based on the evaluation results, an adaptive algorithm is used to adjust the frequency and waveform of the microwave signal and resend the signal. The echo signal is continuously received and analyzed, and the signal parameters are continuously adjusted based on the real-time target characteristics and evaluation results until the preset speed measurement requirements are met.

6. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 4 is characterized in that: Calculate the velocity of the molten metal flow surface, including: The radar speed meter receives the echo signal reflected from the surface of the metal flow; By analyzing the characteristics of the echo signal, the target characteristics are obtained; According to the parameters and frequency shift value of the radar system, the velocity of the molten metal flow surface is calculated using the Doppler effect formula; the Doppler effect formula is: Speed ​​= (frequency shift × speed of light) / (2 × emission frequency × cosine θ) Among them, frequency shift represents the frequency shift of the received echo signal, speed light speed is the value of the speed of light, transmission frequency is the frequency of microwave signals transmitted by the radar system, and θ is the incident angle of the transmitted signal.

7. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 1 is characterized in that: Acquisition of characteristic surface height, including: Take a picture of the liquid level tank through a camera or other image acquisition device to obtain a liquid level image; pre-process the captured liquid level image, including image enhancement, filtering, and denoising; Extracting liquid level features from the preprocessed image by region segmentation, separating the liquid level feature region from the entire image; analyzing the extracted liquid level features, including calculating the position, shape, and size information of the feature surface; Through the pre-trained convolutional neural network image recognition model, by extracting the key feature points in the image and calculating the similarity between the feature points, the feature points that match the known marked scale are found and the height of the feature surface is determined.

8. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 3 is characterized in that: The real-time flow calculation program module combines data on different chute geometries, molten metal properties and coking conditions to calculate the chute flow rate, including: Taking the chute as the object, a three-dimensional computational domain model with geometric proportions is established according to the actual size; the chute model is meshed to obtain a hexahedral structure mesh for capturing the computational fluid domain. The part where the melt flows needs to be specially encrypted to accurately capture the free liquid surface shape and velocity; The gas-liquid interface is set as a free interface, the solid wall is regarded as a no-slip boundary, and the boundary layer near the wall is treated with a standard wall function. The import and export boundary conditions and initial conditions are set according to the actual industrial situation. Set the VOF model, the surface tension between the mixed gas and liquid phases and the ke turbulence model; extract and analyze the velocity and boundary layer flow of the chute under different working conditions.

9. The real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute according to claim 8, characterized in that: The VOF model assumes that there is no interpenetration between multiphase fluids. For each additional phase in the model, the volume fraction of one more phase needs to be introduced. In each control volume, the sum of the volume fractions of all phases is 1. The phase interface between the multiphases is tracked by solving the continuity equation of the multiphase volume fraction. For the qth phase, this equation has the following form: Where α q is the volume fraction of the qth phase; ρ q is the density of the qth phase; is the velocity of the fluid; S αq is the source phase; In the VOF model, the velocity field is obtained by solving a single momentum equation for the entire region. The velocity field is shared by all phases as a result of the calculation. The momentum equation depends on ρ and μ based on the volume fractions of all phases in the control volume: Where ρ is the density of the fluid; is the velocity of the fluid; μ is the viscosity of the fluid; is the volume force. In the VOF model, the energy equation is expressed as follows: Where Eq is calculated by the specific heat capacity of the qth phase and the shared temperature T; k eff is effective heat conduction; source term S h Includes radiation and other volumetric heat sources; Turbulence model equation, the k-ε model was proposed by Launder and Spalding. The model introduces two unknown quantities: turbulent kinetic energy k and turbulent dissipation rate ε. The transport equations corresponding to the two unknown quantities are: where ρ is the density of the fluid, The partial derivative of the turbulent kinetic energy k with respect to time, The turbulent kinetic energy k is multiplied by the velocity component u i For the space coordinate x i The partial derivative of The diffusion term of the turbulent kinetic energy k, where μ is the viscosity of the fluid, μ t is the turbulent viscosity, σ k is the turbulent viscosity proportional factor, G k The generation term of turbulent kinetic energy k includes pressure-velocity related term and pressure square term, G K The dissipation term of turbulent kinetic energy k includes the square term of velocity gradient and the product term of velocity gradient and pressure gradient, S k The source term of turbulent kinetic energy k, including body force terms and other source terms, Y M It represents the turbulent pressure dissipation term in the k-ε turbulence model; The partial derivative of the turbulent dissipation rate ε with respect to time, σ ε is the turbulent viscosity proportional factor, C 1e The generation term coefficient of the turbulent dissipation rate ε, The ratio of the gradient term of the turbulent kinetic energy k to the turbulent kinetic energy k, C 2e The dissipation term coefficient of the turbulent dissipation rate ε, The ratio of the square of the turbulent dissipation rate ε to the turbulent kinetic energy k, S ε The source terms of the turbulent dissipation rate ε include body force terms and other source terms.

10. A real-time intelligent measurement system for high-temperature melt flow in a metallurgical chute, which is applied to the real-time intelligent measurement method for high-temperature melt flow in a metallurgical chute as claimed in any one of claims 1 to 9, characterized in that: The real-time intelligent measurement system for high-temperature melt flow in metallurgical chute includes: An information input module, used to input the information of chute inclination angle, chute diameter, cross-sectional shape and metal type into the real-time flow calculation program module; The parameter calculation module is used to measure the temperature of the characteristic points in the chute in real time through the high-temperature non-contact measurement module; the melt depth and coking thickness, as well as the characteristic surface height, are measured in real time through the visual image processing module; The result output module is used to input the real-time measurement parameters obtained by the high-temperature non-contact measurement module and the visual image processing module into the real-time flow calculation program module to calculate the real-time flow.

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