A smelting simulation method based on digital twin technology

Abstract

The present invention discloses a smelting simulation method based on digital twin technology, which relates to the field of data processing technology. The method comprises the following steps: obtaining the size data and historical temperature data of a furnace, and constructing a temperature distribution model; obtaining the real-time temperature data of the furnace; identifying the smelting stage according to the real-time temperature data; selecting the corresponding temperature distribution model according to the smelting stage, and calculating the smelting temperature compensation value; adjusting the heating power of the furnace according to the smelting temperature compensation value; wherein the temperature distribution model comprises: a temperature distribution model of a preheating stage and a temperature distribution model of a melting stage. The present invention constructs a temperature distribution model by acquiring the size data of the furnace, the historical temperature data of the preheating stage, and the historical temperature data of the melting stage, selects the corresponding temperature distribution model according to the acquired real-time temperature data, and calculates the value that the furnace temperature needs to be adjusted, thereby improving the smelting efficiency and reducing energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a smelting simulation method based on digital twin technology. Background Art

[0002] Melting simulation is a computer simulation technology that can imitate and predict the physical behavior of the melting process. By simulating the melting process, the parameters of the melting process can be optimized, thereby improving the quality of the smelting products, reducing energy consumption and reducing production costs.

[0003] The existing smelting simulation simulates the overall smelting process, but lacks stage-by-stage simulation of the smelting process. When the real-time smelting data is obtained through digital twin technology to analyze the smelting process, due to the limitations of smelting simulation, only a small amount of optimization results can be obtained for the current smelting process, and the optimization effect of smelting process parameters is poor, resulting in the improvement of smelting efficiency and the reduction of energy consumption not achieving the expected goals. Summary of the invention

[0004] The purpose of the present invention is to provide a smelting simulation method based on digital twin technology. The present invention constructs a temperature distribution model by acquiring the size data of the furnace, the historical temperature data of the preheating stage and the historical temperature data of the melting stage. The corresponding temperature distribution model is selected according to the acquired real-time temperature data, and the value of the furnace temperature that needs to be adjusted is calculated, thereby improving the smelting efficiency and reducing energy consumption.

[0005] The purpose of the present invention is achieved by the following technical means:

[0006] In a first aspect, the present invention provides a smelting simulation method based on digital twin technology, comprising the following steps:

[0007] Obtain the furnace's dimension data and historical temperature data and build a temperature distribution model;

[0008] Get real-time temperature data of the furnace;

[0009] identifying a melting stage based on the real-time temperature data;

[0010] Selecting the corresponding temperature distribution model according to the smelting stage to calculate the smelting temperature compensation value;

[0011] adjusting the heating power of the furnace according to the smelting temperature compensation value;

[0012] Wherein, the temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage;

[0013] Constructing a temperature distribution model of the preheating stage according to the size data and the historical temperature data of the preheating stage;

[0014] A temperature distribution model of the melting stage is constructed according to the size data and the historical temperature data of the melting stage.

[0015] Preferably, the step of acquiring the dimension data and historical temperature data of the furnace and constructing the temperature distribution model comprises the following steps:

[0016] Obtain the dimension data of the furnace bottom, and construct a geometric model with the center of the furnace bottom as the origin;

[0017] Meshing the geometric model;

[0018] A plurality of temperature sensors are arranged at the bottom of the furnace, and coordinate data of the temperature sensors are recorded;

[0019] Acquiring the historical temperature data according to the temperature sensor;

[0020] According to the coordinate data and the historical temperature data, radial basis function interpolation is used to interpolate the grid nodes to obtain grid node temperature data;

[0021] According to the grid node temperature data, the grid units are interpolated by linear interpolation to obtain the temperature distribution model.

[0022] Preferably, obtaining the historical temperature data according to the temperature sensor comprises the following steps:

[0023] Get multiple sets of temperature data from the same temperature sensor;

[0024] Calculating the variance of each set of temperature data;

[0025] Calculating the weight of the temperature data according to the variance;

[0026] The historical temperature data is calculated according to the weight and the temperature data.

[0027] Preferably, the calculation formula of the weight is as follows:

[0028] ,

[0029] in, For the The weight of the group temperature data, For the The variance of the group temperature data;

[0030] The calculation formula of the historical temperature data is as follows:

[0031] ,

[0032] in, is the historical temperature data, For the The average value of the group temperature data, For the The weight of the group temperature data.

[0033] Preferably, identifying the smelting stage according to the real-time temperature data comprises the following steps:

[0034] Calculating a real-time temperature average according to the real-time temperature data;

[0035] Subtract the real-time temperature data from the real-time temperature mean to calculate the real-time temperature error;

[0036] Calculating temperature uniformity according to the real-time temperature error;

[0037] Calculating the similarity between the real-time temperature data and the historical temperature data according to the temperature uniformity;

[0038] Based on the similarity, the smelting stage is identified.

[0039] Preferably, the calculation formula for the temperature uniformity is as follows:

[0040] , ,

[0041] in, is the temperature uniformity, is the extreme value of real-time temperature data, is the number of real-time temperature data, For the The real-time temperature error of the real-time temperature data, is the maximum real-time temperature data, It is the minimum real-time temperature data;

[0042] The calculation formula of the similarity is as follows:

[0043] ,

[0044] in, is the similarity, For the temperature uniformity of real-time temperature data, is the temperature uniformity of historical temperature data.

[0045] Preferably, the selecting the corresponding temperature distribution model according to the smelting stage and calculating the smelting temperature compensation value comprises the following steps:

[0046] Calculating temperature volatility according to the real-time temperature data;

[0047] When the temperature volatility is less than the fluctuation threshold, subtract each of the real-time temperature data from the temperature distribution model to obtain a compensation value for each of the real-time temperature data;

[0048] Averaging the compensation values ​​to obtain the smelting temperature compensation value;

[0049] The calculation formula of the temperature fluctuation is as follows:

[0050] ,

[0051] in, is the temperature fluctuation, is the number of real-time temperature data, For the Temperature sensor No. Real-time temperature data measured by For the Temperature sensor No. Real-time temperature data of the measurement.

[0052] In a second aspect, the present invention provides a smelting simulation system based on digital twin technology, applying the above-mentioned smelting simulation method based on digital twin technology, including: a temperature distribution construction module, a temperature data acquisition module, a smelting stage identification module, a temperature compensation calculation module and a heating power control module;

[0053] The temperature distribution building module is used to obtain the size data and historical temperature data of the furnace and build a temperature distribution model;

[0054] The temperature data acquisition module is used to acquire real-time temperature data of the furnace;

[0055] The smelting stage identification module is used to identify the smelting stage according to the real-time temperature data;

[0056] The temperature compensation calculation module is used to select the corresponding temperature distribution model according to the smelting stage to calculate the smelting temperature compensation value;

[0057] The heating power control module is used to adjust the heating power of the furnace according to the smelting temperature compensation value;

[0058] Wherein, the temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage;

[0059] Constructing a temperature distribution model of the preheating stage according to the size data and the historical temperature data of the preheating stage;

[0060] A temperature distribution model of the melting stage is constructed according to the size data and the historical temperature data of the melting stage.

[0061] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the above-mentioned smelting simulation method based on digital twin technology.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the above-mentioned smelting simulation method based on digital twin technology.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention constructs a temperature distribution model by acquiring the size data of the furnace, the historical temperature data of the preheating stage and the historical temperature data of the melting stage, selects the corresponding temperature distribution model according to the acquired real-time temperature data, calculates the value that the furnace temperature needs to be adjusted, thereby improving the smelting efficiency and reducing energy consumption;

[0065] The present invention constructs a geometric model by acquiring the dimension data of the bottom of the furnace, meshes the geometric model, and interpolates the mesh nodes and mesh units respectively using radial basis function interpolation and linear interpolation to obtain a temperature distribution model, thereby improving the accuracy of the temperature distribution model construction.

[0066] The present invention obtains temperature data of multiple smelting processes and calculates historical temperature data by weighting according to the variance of the temperature data, thereby improving the accuracy of obtaining historical temperature data;

[0067] The present invention calculates the similarity between the real-time temperature data and the historical temperature data according to the temperature uniformity of the real-time temperature data, identifies the smelting stage of the furnace, provides an accurate temperature distribution model for calculating the smelting temperature compensation value, and improves the accuracy of the calculation of the smelting temperature compensation value;

[0068] The present invention determines whether the furnace state is stable through the temperature volatility of real-time temperature data, and calculates the smelting temperature compensation value when the temperature volatility is less than the fluctuation threshold, thereby improving the accuracy of the calculation of the smelting temperature compensation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0071] Figure 1 A schematic diagram of a process flow of a smelting simulation method based on digital twin technology provided in this embodiment;

[0072] Figure 2 Step S1 provided in this embodiment is a flow chart of obtaining the dimension data and historical temperature data of the furnace and constructing a temperature distribution model;

[0073] Figure 3 Step S14 provided in this embodiment is a flow chart of obtaining historical temperature data according to the temperature sensor;

[0074] Figure 4 Step S3 provided in this embodiment is a flow chart of identifying the smelting stage according to the real-time temperature data;

[0075] Figure 5 Step S4 provided in this embodiment is a flow chart of selecting a corresponding temperature distribution model according to the smelting stage and calculating a smelting temperature compensation value;

[0076] Figure 6 A schematic diagram of the structure of a smelting simulation system based on digital twin technology provided in this embodiment;

[0077] Figure 7 A schematic diagram of the structure of an electronic device provided in this embodiment. DETAILED DESCRIPTION

[0078] 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.

[0079] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0080] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0081] Embodiment 1

[0082] This embodiment provides a smelting simulation method based on digital twin technology. Figure 1 As shown, the following steps are included:

[0083] S1, obtain the size data and historical temperature data of the furnace and build a temperature distribution model;

[0084] S2, obtaining real-time temperature data of the furnace;

[0085] S3, identifying the melting stage based on real-time temperature data;

[0086] S4, selecting a corresponding temperature distribution model according to the smelting stage and calculating a smelting temperature compensation value;

[0087] S5, adjusting the heating power of the furnace according to the smelting temperature compensation value;

[0088] The temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage;

[0089] Construct a temperature distribution model for the preheating stage based on the size data and the historical temperature data for the preheating stage;

[0090] Based on the size data and the historical temperature data of the melting stage, a temperature distribution model of the melting stage is constructed.

[0091] It should be noted that the smelting stage includes the preheating stage and the melting stage. The preheating stage is the stage of preheating the furnace at low power and gradually adding metal raw materials or alloys. The melting stage is the stage of heating the furnace at high power to melt and refine the metal raw materials or alloys. In these two stages, the appropriate temperature can improve the efficiency of smelting and reduce energy consumption. It is very important to control the temperature of the furnace and ensure that the furnace is fed and melted at the appropriate temperature. In these two stages, the material distribution in the furnace chamber and the state of the material will cause differences in the temperature distribution of the furnace. Therefore, by establishing a digital twin system, a temperature distribution model of the preheating stage of the smelting process and a temperature distribution model of the melting stage are constructed. The smelting temperature compensation value is calculated through the real-time acquired furnace temperature data, that is, the real-time temperature data, and the heating power of the furnace is adjusted according to the smelting temperature compensation value to adjust the furnace temperature. Specifically, the melting temperature compensation value is the value that needs to be adjusted from the furnace temperature to the expected temperature. When the melting temperature compensation value is greater than zero, it means that the furnace temperature needs to be increased by the value of the melting temperature compensation value. When the melting temperature compensation value is less than zero, it means that the furnace temperature needs to be reduced by the value of the melting temperature compensation value.

[0092] In this embodiment, a temperature distribution model is constructed by acquiring the size data of the furnace, the historical temperature data of the preheating stage, and the historical temperature data of the melting stage. The corresponding temperature distribution model is selected according to the acquired real-time temperature data, and the value of the furnace temperature that needs to be adjusted is calculated, thereby improving the smelting efficiency and reducing energy consumption.

[0093] In some embodiments, step S1, obtains the size data and historical temperature data of the furnace and constructs a temperature distribution model, such as Figure 2 As shown, the following steps are included:

[0094] S11, obtaining the dimension data of the bottom of the furnace, and constructing a geometric model with the center of the bottom of the furnace as the origin;

[0095] S12, meshing the geometric model;

[0096] S13, setting a plurality of temperature sensors at the bottom of the furnace and recording coordinate data of the temperature sensors;

[0097] S14, obtaining historical temperature data according to the temperature sensor;

[0098] S15, interpolating the grid nodes using radial basis function interpolation according to the coordinate data and the historical temperature data to obtain grid node temperature data;

[0099] S16, interpolating the grid units using linear interpolation according to the grid node temperature data to obtain a temperature distribution model.

[0100] It should be noted that the temperature distribution model is the temperature distribution model of the bottom of the furnace. Therefore, the dimensional data of the bottom of the furnace is obtained to construct a virtual geometric model identical to the bottom of the furnace. After the geometric model is constructed, the geometric model is also meshed to obtain the coordinate data of the bottom of the furnace. The accuracy of the meshing is set according to the actual modeling process and is not limited here. By obtaining the temperature data of the temperature sensor, the temperature data is input into the calculation software, and the temperature of the grid nodes is interpolated by radial basis function interpolation to obtain the temperature of the grid nodes of the geometric model. The use of radial basis function interpolation to interpolate the grid nodes can be free from the limitation of the number and position of the temperature sensors, and a more accurate temperature distribution model can be established. After obtaining the temperature data of the grid nodes, the conditions for constructing the temperature distribution model are already met. In order to improve the accuracy of the temperature distribution model, linear interpolation is used to interpolate the grid units to obtain the grid unit temperature data, thereby obtaining a complete temperature distribution model of the bottom of the furnace.

[0101] In this embodiment, a geometric model is constructed by acquiring dimensional data of the bottom of the furnace, the geometric model is meshed, radial basis function interpolation and linear interpolation are used to interpolate mesh nodes and mesh cells respectively to obtain a temperature distribution model, thereby improving the accuracy of temperature distribution model construction.

[0102] In some embodiments, step S14, historical temperature data is obtained according to the temperature sensor, such as Figure 3 As shown, the following steps are included:

[0103] S141, acquiring multiple groups of temperature data from the same temperature sensor;

[0104] S142, calculating the variance of each set of temperature data;

[0105] S143, calculating the weight of the temperature data according to the variance;

[0106] S144, calculating historical temperature data according to the weight and the temperature data.

[0107] The weight is calculated as follows:

[0108] ,

[0109] in, For the The weight of the group temperature data, For the The variance of the group temperature data;

[0110] The calculation formula for historical temperature data is as follows:

[0111] ,

[0112] in, is the historical temperature data, For the The average value of the group temperature data, For the The weight of the group temperature data.

[0113] It should be noted that the historical temperature data is obtained by weighted calculation of the temperature data of multiple smelting processes. Temperature data of each melting process, each melting process setting temperature sensor. When measuring, abnormal conditions may occur in the temperature sensor, resulting in inaccurate temperature data. Therefore, calculate the temperature of each temperature sensor. The variance of the group temperature data is calculated, and the inverse of the variance is used as the weight of the temperature data of the temperature sensor. The average value of the temperature data of the group is used to calculate the historical temperature data of the temperature sensor. The number of temperature data of the temperature sensor is set according to actual needs and is not limited here.

[0114] In this embodiment, by acquiring temperature data of multiple smelting processes and weighting the historical temperature data according to the variance of the temperature data, the accuracy of acquiring the historical temperature data is improved.

[0115] In some embodiments, step S3, based on the real-time temperature data, identifies the melting stage, such as Figure 4 As shown, the following steps are included:

[0116] S31, calculating the real-time temperature average according to the real-time temperature data;

[0117] S32, subtracting the real-time temperature data from the real-time temperature mean to calculate the real-time temperature error;

[0118] S33, calculating temperature uniformity according to the real-time temperature error;

[0119] S34, calculating the similarity between the real-time temperature data and the historical temperature data according to the temperature uniformity;

[0120] S35, identifying the smelting stage according to the similarity.

[0121] The temperature uniformity is calculated as follows:

[0122] , ,

[0123] in, is the temperature uniformity, is the extreme value of real-time temperature data, is the number of real-time temperature data, For the The real-time temperature error of the real-time temperature data, is the maximum real-time temperature data, is the minimum real-time temperature data;

[0124] The similarity calculation formula is as follows:

[0125] ,

[0126] in, is the similarity, For the temperature uniformity of real-time temperature data, is the temperature uniformity of historical temperature data.

[0127] It should be noted that before calculating the melting temperature compensation value, it is necessary to judge the melting stage of the furnace at this time according to the real-time temperature data, so as to select the temperature distribution model that conforms to the melting stage. Temperature uniformity is used to measure whether the temperature distribution of the melting furnace is uniform. The smaller the temperature uniformity, the more even the temperature distribution of the melting furnace. There is a clear difference between the temperature uniformity in the preheating stage and the melting stage, which is manifested in that the temperature uniformity in the preheating stage is greater than that in the melting stage. Therefore, the melting stage is judged according to the similarity between the temperature uniformity of the real-time temperature data and the temperature uniformity of the temperature distribution model. Specifically, the temperature uniformity of the real-time temperature data is calculated by calculating the difference between the real-time temperature data of each temperature sensor and the average value of the real-time temperature data. After calculating the temperature uniformity, the similarity with the temperature uniformity of the preheating stage and the temperature uniformity of the melting stage is calculated respectively, and the melting stage with higher similarity is selected for temperature compensation value calculation. When calculating the temperature uniformity, the range of the real-time temperature data is increased to further amplify the difference of the temperature data, and the calculated similarity makes it easier to judge the melting stage.

[0128] In this embodiment, based on the temperature uniformity of the real-time temperature data, the similarity between the real-time temperature data and the historical temperature data is calculated, the smelting stage of the furnace is identified, and an accurate temperature distribution model is provided for the calculation of the smelting temperature compensation value, thereby improving the accuracy of the calculation of the smelting temperature compensation value.

[0129] In some embodiments, step S4, a corresponding temperature distribution model is selected according to the smelting stage to calculate the smelting temperature compensation value, such as Figure 5 As shown, the following steps are included:

[0130] S41, calculating temperature fluctuation according to real-time temperature data;

[0131] S42, when the temperature fluctuation is less than the fluctuation threshold, subtract each real-time temperature data from the temperature distribution model to obtain a compensation value for each real-time temperature data;

[0132] S43, averaging the compensation values ​​to obtain a smelting temperature compensation value.

[0133] The temperature fluctuation is calculated as follows:

[0134] ,

[0135] in, is the temperature fluctuation, is the number of real-time temperature data, For the Temperature sensor No. Real-time temperature data measured by For the Temperature sensor No. Real-time temperature data of the measurement.

[0136] It should be noted that temperature volatility is used to measure the change in the real-time temperature data compared to the temperature data at the last measurement. The smaller the temperature volatility, the smaller the temperature change of the furnace. When the temperature volatility is less than the set fluctuation threshold, it means that the state of the furnace has stabilized. At this time, the expected temperature of each real-time temperature data in the temperature distribution model is obtained, the expected temperature is subtracted from the real-time temperature data, and the compensation value of each real-time temperature data is calculated respectively. By averaging the compensation values, the smelting temperature compensation value is obtained.

[0137] In this embodiment, whether the furnace state is stable is determined by the temperature volatility of the real-time temperature data. When the temperature volatility is less than the fluctuation threshold, the melting temperature compensation value is calculated, thereby improving the accuracy of the calculation of the melting temperature compensation value.

[0138] Embodiment 2

[0139] This embodiment provides a smelting simulation system based on digital twin technology, and applies the above-mentioned smelting simulation method based on digital twin technology, such as Figure 6 As shown, it includes: a temperature distribution building module, a temperature data acquisition module, a melting stage identification module, a temperature compensation calculation module and a heating power control module;

[0140] A temperature distribution building module is used to obtain the size data and historical temperature data of the furnace and build a temperature distribution model;

[0141] A temperature data acquisition module is used to acquire real-time temperature data of the furnace;

[0142] A smelting stage identification module is used to identify the smelting stage based on real-time temperature data;

[0143] A temperature compensation calculation module is used to select a corresponding temperature distribution model according to the smelting stage and calculate the smelting temperature compensation value;

[0144] A heating power control module is used to adjust the heating power of the furnace according to the smelting temperature compensation value;

[0145] The temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage;

[0146] Construct a temperature distribution model for the preheating stage based on the size data and the historical temperature data for the preheating stage;

[0147] Based on the size data and the historical temperature data of the melting stage, a temperature distribution model of the melting stage is constructed.

[0148] In this embodiment, a temperature distribution model is constructed by acquiring the size data of the furnace, the historical temperature data of the preheating stage, and the historical temperature data of the melting stage. The corresponding temperature distribution model is selected according to the acquired real-time temperature data, and the value of the furnace temperature that needs to be adjusted is calculated, thereby improving the smelting efficiency and reducing energy consumption.

[0149] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, each functional module can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0150] Embodiment 3

[0151] This embodiment provides an electronic device 2, such as Figure 7 As shown, a processor 21 and a memory 22, the memory 22 is used to store computer program codes, the computer program codes include computer instructions, and when the processor 21 executes the computer instructions, the electronic device executes the above-mentioned smelting simulation method based on digital twin technology.

[0152] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, the memory 22, the output device 23, and the input device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present invention. It should be understood that in various embodiments of the present invention, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0153] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor 21 may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0154] The memory 22 can be used to store computer program instructions and various computer program codes including program codes for executing the scheme of the present invention. Optionally, the memory 22 includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), and the memory 22 is used for related instructions and data.

[0155] The input device 24 is used to input data and / or signals, and the output device 23 is used to output data and / or signals. The output device 23 and the input device 24 can be independent devices or an integrated device.

[0156] The present embodiment provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the above-mentioned smelting simulation method based on digital twin technology.

[0157] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A smelting simulation method based on digital twin technology, characterized in that: The following steps are involved: Obtain the furnace's dimension data and historical temperature data and build a temperature distribution model; Get real-time temperature data of the furnace; identifying a melting stage based on the real-time temperature data; Selecting the corresponding temperature distribution model according to the smelting stage to calculate the smelting temperature compensation value; adjusting the heating power of the furnace according to the smelting temperature compensation value; Wherein, the temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage; Constructing a temperature distribution model of the preheating stage according to the size data and the historical temperature data of the preheating stage; Constructing a temperature distribution model of the melting stage according to the size data and the historical temperature data of the melting stage; The step of obtaining the dimension data and historical temperature data of the furnace and constructing a temperature distribution model comprises: Obtain the dimension data of the furnace bottom, and construct a geometric model with the center of the furnace bottom as the origin; Meshing the geometric model; A plurality of temperature sensors are arranged at the bottom of the furnace, and coordinate data of the temperature sensors are recorded; Acquiring the historical temperature data according to the temperature sensor; According to the coordinate data and the historical temperature data, radial basis function interpolation is used to interpolate the grid nodes to obtain grid node temperature data; According to the grid node temperature data, the grid units are interpolated by linear interpolation to obtain the temperature distribution model; The step of identifying the smelting stage according to the real-time temperature data comprises: Calculating a real-time temperature average according to the real-time temperature data; Subtract the real-time temperature data from the real-time temperature mean to calculate the real-time temperature error; Calculating temperature uniformity according to the real-time temperature error; Calculating the similarity between the real-time temperature data and the historical temperature data according to the temperature uniformity; identifying the smelting stage according to the similarity; The calculation formula for the temperature uniformity is as follows: ， ， in, is the temperature uniformity, is the extreme value of real-time temperature data, is the number of real-time temperature data, For the The real-time temperature error of the real-time temperature data, is the maximum real-time temperature data, is the minimum real-time temperature data; The calculation formula of the similarity is as follows: ， in, is the similarity, For the temperature uniformity of real-time temperature data, The temperature uniformity of the historical temperature data; The step of selecting the corresponding temperature distribution model according to the smelting stage and calculating the smelting temperature compensation value comprises: Calculating temperature volatility according to the real-time temperature data; When the temperature volatility is less than the fluctuation threshold, subtract each of the real-time temperature data from the temperature distribution model to obtain a compensation value for each of the real-time temperature data; Averaging the compensation values ​​to obtain the smelting temperature compensation value; The calculation formula of the temperature fluctuation is as follows: ， in, is the temperature fluctuation, is the number of real-time temperature data, For the Temperature sensor No. Real-time temperature data measured by For the Temperature sensor No. Real-time temperature data of the measurement.

2. According to a smelting simulation method based on digital twin technology according to claim 1, it is characterized in that: The step of obtaining the historical temperature data according to the temperature sensor comprises the following steps: Get multiple sets of temperature data from the same temperature sensor; Calculating the variance of each set of temperature data; Calculating the weight of the temperature data according to the variance; The historical temperature data is calculated according to the weight and the temperature data.

3. The smelting simulation method based on digital twin technology according to claim 2 is characterized in that: The weight calculation formula is as follows: ， in, For the The weight of the group temperature data, For the The variance of the group temperature data; The calculation formula of the historical temperature data is as follows: ， in, is the historical temperature data, For the The average value of the group temperature data, For the The weight of the group temperature data.

4. A smelting simulation system based on digital twin technology, using a smelting simulation method based on digital twin technology as described in any one of claims 1 to 3, characterized in that: include: Temperature distribution building module, temperature data acquisition module, melting stage identification module, temperature compensation calculation module and heating power control module; The temperature distribution building module is used to obtain the size data and historical temperature data of the furnace and build a temperature distribution model; The temperature data acquisition module is used to acquire real-time temperature data of the furnace; The smelting stage identification module is used to identify the smelting stage according to the real-time temperature data; The temperature compensation calculation module is used to select the corresponding temperature distribution model according to the smelting stage to calculate the smelting temperature compensation value; The heating power control module is used to adjust the heating power of the furnace according to the smelting temperature compensation value; Wherein, the temperature distribution model includes: a temperature distribution model in the preheating stage and a temperature distribution model in the melting stage; Constructing a temperature distribution model of the preheating stage according to the size data and the historical temperature data of the preheating stage; A temperature distribution model of the melting stage is constructed according to the size data and the historical temperature data of the melting stage.

5. An electronic device, characterized in that: It includes a processor and a memory, the memory is used to store computer program code, the computer program code includes computer instructions, and when the processor executes the computer instructions, the electronic device executes a smelting simulation method based on digital twin technology as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: A computer program is stored in the computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a smelting simulation method based on digital twin technology as described in any one of claims 1 to 3.

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