Industrial silicon smelting process prediction method and system based on data analysis

Abstract

The invention discloses an industrial silicon smelting process prediction method and system based on data analysis, and relates to the technical field of industrial silicon smelting management and control. The physical structure of the smelting furnace is analyzed, thermocouple nodes are positioned in the virtual three-dimensional model to serve as temperature mapping points, and a spatial topological relation is established; historical smelting data are collected, temperature monitoring parameters and process operation parameters are aligned according to time nodes, and a time-space associated smelting parameter set is constructed; generating a three-dimensional temperature performance state based on the temperature mapping point data, and classifying the temperature performance state through temperature change curve fitting and heat conduction correlation analysis; the current process parameters are matched with the reference historical process operation parameter sequence library in real time, and a three-dimensional temperature field prediction result corresponding to the optimal historical parameter group is output; according to the method, the problems of low efficiency and large error of traditional manual analysis are effectively solved, and reliable technical support is provided for intelligent control of industrial silicon smelting.

Description

Technical Field

[0001] The present invention relates to the field of industrial silicon smelting control technology, and in particular to an industrial silicon smelting process prediction method and system based on data analysis. Background Art

[0002] In the process of industrial silicon smelting, the real-time and accurate prediction of the three-dimensional temperature field in the furnace is the core requirement for optimizing process control, reducing energy consumption and ensuring production safety. At present, the industry generally uses thermocouple arrays to monitor the temperature of fixed points in the furnace, and relies on operators to manually analyze the temperature distribution trend by combining historical data and empirical rules. However, there are significant defects in the manual analysis mode: on the one hand, limited by the sparseness of thermocouple points (usually only covering 15%-20% of the key area of ​​the furnace body), the temperature of the unmonitored area needs to be estimated by linear interpolation or empirical formula, resulting in low spatial resolution (local area prediction error exceeds ±80℃), and it is difficult to capture transient temperature anomalies in key areas such as electrode periphery and charge junction in time (such as sudden overheating or cooling); on the other hand, manual analysis requires the integration of multiple sets of discrete data and relies on the subjective experience of the operator. When faced with dynamic working conditions such as raw material composition fluctuations and current phase adjustment, the analysis takes up to 15-30 minutes, which seriously lags behind the actual temperature change rate in the furnace (local temperature change can reach 5-10℃ / s), which is easy to cause the risk of process out of control. Summary of the invention

[0003] The object of the present invention is to provide an industrial silicon smelting process prediction method and system capable of predicting changes in the temperature field in a smelting furnace.

[0004] The present invention discloses a method for predicting an industrial silicon smelting process based on data analysis, comprising: Analyze the structure of the smelting furnace, build a virtual three-dimensional smelting furnace, and set the temperature mapping point in the virtual three-dimensional smelting furnace based on the location node of each thermocouple; Acquire a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group; Based on the historical temperature monitoring parameters in the historical smelting parameter group, the temperature performance of the virtual three-dimensional smelting furnace is constructed; The temperature performance states of the virtual three-dimensional smelting furnace are classified to obtain a number of virtual three-dimensional smelting furnace performance groups, and the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group are compared to determine the parameter mapping interval of the corresponding process factor parameter, and the parameter mapping interval is configured under the corresponding process factor parameter in the historical process operation parameter sequence, and the several historical process operation parameter sequences are constructed as a reference historical process operation parameter sequence library; The current process operating parameters of the smelting furnace are compared and analyzed using the reference historical process operating parameter sequence library to determine the most suitable historical process operating parameter sequence, and the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operating parameter sequence is identified as the predicted reference performance.

[0005] In some embodiments disclosed in the present invention, a method for constructing a temperature representation state of a virtual three-dimensional smelting furnace includes: Determine a mapping point temperature change curve of each temperature mapping point in the virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group; Based on the relative position characteristics between the temperature mapping points and the matching characteristics of the temperature change curves of the mapping points, the temperature mapping points are connected to each other to obtain the temperature performance line in the furnace.

[0006] In some embodiments disclosed in the present invention, a method for classifying the temperature performance of a virtual three-dimensional smelting furnace includes: The temperature performance states of the virtual three-dimensional smelting furnaces are compared with each other to determine the degree of temperature equivalence. If the degree of temperature equivalence is greater than or equal to a preset value, the temperature performance states of the corresponding virtual three-dimensional smelting furnaces are classified into one category to obtain several virtual three-dimensional smelting furnace performance groups.

[0007] In some embodiments disclosed in the present invention, the method of comparing the temperature performance states of virtual three-dimensional smelting furnaces includes: The temperature expression states between the virtual three-dimensional smelting furnaces are aligned in time, and the relative temperature expression lines in the furnaces are compared, and based on the comparison results, a first temperature equivalent expression operator is constructed; Comparing the temperature change curves of each relative mapping point, and based on the comparison result, correcting the first temperature equivalent expression operator to obtain the temperature equivalent expression degree; Among them, the expression for calculating the degree of temperature equivalent performance is: ; in, For temperature equivalent performance, is the temperature equivalent parameter of the furnace temperature expression line at the t-th time node, The adjustment coefficient of the furnace temperature performance line is equivalent to the influence of the adjustment coefficient. The temperature performance line in the furnace is equivalent to the adjustment constant. is the number of all time nodes involved in the comparison, is the temperature equivalent parameter of the temperature change curve of the hth mapping point at the tth time node, is the total number of temperature change curves of the mapping points involved in the comparison, The temperature change curve of the mapping point is equivalent to the impact adjustment coefficient, Adjust the constants for the equivalent impact of the temperature change curve at the mapping point.

[0008] In some embodiments disclosed in the present invention, the method for determining the temperature equivalent parameter of the temperature performance line in the furnace includes: Determine the relative distance between each relative temperature mapping point. If the relative distance is less than or equal to a preset value, the corresponding temperature mapping points are deemed equal, and the mapping points are recorded equal once. Based on the recorded number of times the mapping points are equal, determine the temperature equalization parameter of the temperature expression line in the furnace. Among them, the expression for calculating the temperature equivalent parameter of each furnace temperature expression line is: ; in, is the temperature mapping point equality judgment function. If the x1th temperature mapping point is recorded as the mapping point equality, then Output 1, y1 is the total number of temperature mapping points involved in the comparison; The method for determining the temperature equivalent parameter of the temperature change curve of the mapping point includes: Determine a time node for comparison on the temperature change curve, record it as the comparison time node, and intercept a comparison curve segment on the temperature change curve according to a preset time length with the comparison time node as the center; Comparing the relative comparison curve segments, the comparison method includes evenly setting a number of curve comparison points on the comparison curve segment, determining the curve vertical axis difference between the relative curve comparison points, if the curve vertical axis difference is less than or equal to a preset value, then identifying the corresponding curve comparison point as an equivalent curve comparison point, determining the equivalent comparison point ratio of the equivalent curve comparison point to all curve comparison points, and determining the corresponding temperature equivalent parameter based on the comparison point ratio interval to which the equivalent comparison point ratio belongs.

[0009] In some embodiments disclosed in the present invention, the method for determining the variation sensitivity index between different process factor parameters includes: Construct a parameter mapping axis for each type of process factor parameter, and map the corresponding process factor parameter on the parameter mapping axis in the form of factor parameter mapping points; The factor parameter mapping point distribution of the parameter mapping axis is determined, a factor parameter mapping point distribution interval having a distribution density greater than or equal to a preset value is determined, and based on the length of the factor parameter mapping point distribution interval, a parameter mapping interval of the corresponding process factor parameter is determined.

[0010] In some embodiments disclosed in the present invention, a method for comparing and analyzing the current process operation parameters of a smelting furnace by referring to a historical process operation parameter sequence library is as follows: Comparing the current process operation parameters with different reference historical process operation parameter sequences in the reference historical process operation parameter sequence library in a time-series corresponding manner, wherein the comparison method includes comparing the process factor parameters in the current process operation parameters with the parameter mapping intervals in the reference historical process operation parameter sequences, and if the process factor parameters belong to the corresponding parameter mapping intervals, then the corresponding process factor parameters are identified as matching process factor parameters; The matching number of the matching process factor parameters of each parameter type is recorded in real time. If the matching number within the preset time period is greater than or equal to the preset value, the parameter type is determined to be a matching parameter type. If each parameter type is a matching parameter type, the corresponding reference historical process operation parameter sequence is determined to be the most suitable historical process operation parameter sequence.

[0011] In some embodiments disclosed in the present invention, an industrial silicon smelting process prediction system based on data analysis is also disclosed, which is characterized by comprising: The first module is used to analyze the structure of the smelting furnace, construct a virtual three-dimensional smelting furnace, and set the temperature mapping point in the virtual three-dimensional smelting furnace based on the position node of each thermocouple; The second module is used to obtain a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group; The third module is used to construct the temperature performance state of the virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group; The fourth module is used to classify the temperature performance states of the virtual three-dimensional smelting furnace to obtain a number of virtual three-dimensional smelting furnace performance groups, compare the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group, determine the parameter mapping interval of the corresponding process factor parameter, and configure the parameter mapping interval under the corresponding process factor parameter in the historical process operation parameter sequence, and construct a number of historical process operation parameter sequences as a reference historical process operation parameter sequence library; The fifth module is used to compare and analyze the current process operation parameters of the smelting furnace using the reference historical process operation parameter sequence library, determine the most suitable historical process operation parameter sequence, and identify the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operation parameter sequence as the predicted reference performance.

[0012] The present invention discloses an industrial silicon smelting process prediction method and system based on data analysis, which relates to the technical field of industrial silicon smelting control and management; the physical structure of the smelting furnace is analyzed, thermocouple nodes are located as temperature mapping points in a virtual three-dimensional model, and a spatial topological relationship is established; historical smelting data is collected, temperature monitoring parameters and process operation parameters are aligned according to time nodes, and a smelting parameter group associated with time and space is constructed; three-dimensional temperature manifestations are generated based on temperature mapping point data, and temperature manifestations are classified through temperature change curve fitting and heat conduction correlation analysis; current process parameters are matched with a reference historical process operation parameter sequence library in real time, and a three-dimensional temperature field prediction result corresponding to the optimal historical parameter group is output; the present invention effectively solves the problems of low efficiency and large errors in traditional manual analysis, and provides reliable technical support for intelligent control of industrial silicon smelting.

[0013] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The invention discloses an industrial silicon smelting process prediction method and method steps based on data analysis. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0016] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention.

[0017] Example:

[0018] The present invention discloses a method for predicting industrial silicon smelting process based on data analysis, see Figure 1 ,include: Step S100 , analyzing the structure of the smelting furnace, constructing a virtual three-dimensional smelting furnace, and setting a temperature mapping point in the virtual three-dimensional smelting furnace based on the location node to which each thermocouple belongs.

[0019] The core of this step is to establish a three-dimensional model consistent with the actual smelting furnace structure through digital means, and accurately map the location of the temperature monitoring point. First, based on the design drawings or actual measurement data of the smelting furnace (such as furnace size, electrode layout, refractory material distribution, etc.), extract key geometric parameters (such as furnace diameter, electrode insertion depth), and use computer-aided design (CAD) software (such as SolidWorks) to build a parametric three-dimensional model to ensure that the model can dynamically simulate changes in the furnace structure (such as electrode lifting). Subsequently, according to the actual installation position of the thermocouple (such as layered arrangement of the furnace wall, annular distribution around the electrode), mark the corresponding temperature mapping point in the virtual model. The spatial coordinates of each mapping point must be strictly aligned with the actual position, which is usually achieved through coordinate system conversion (such as local coordinate system to global coordinate system). In the model verification stage, by comparing the actual temperature measurement data with the model simulation results (such as temperature distribution in steady state), adjust the mesh division or material property parameters to ensure that the model error is controlled within a reasonable range (such as ±5℃). The technical significance of this step is to provide a reference framework for the spatial positioning of subsequent temperature data. For example, by using temperature mapping points at different heights to reflect the influence of the molten pool depth on the thermal field distribution, this can lay a physical consistency foundation for multi-source data fusion.

[0020] Step S200, obtain a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group.

[0021] The goal of this step is to extract structured data from historical smelting records and establish a temporal correlation between temperature and process parameters. The specific process includes: obtaining temperature monitoring data (such as thermocouple second-level sampling values) and process operation parameters (such as current, voltage, and feed amount) from industrial control systems (such as DCS, SCADA), and integrating manual records (such as fault logs). Arrange the data of the same smelting batch in chronological order to form a temperature monitoring parameter sequence (such as T1(t), T2(t), ..., Tn(t)) and a process operation parameter sequence (such as current I(t), voltage V(t)), and bind the two into a "historical smelting parameter group" to ensure that the temperature at each time point corresponds to the process parameter one by one. The technical significance of this step is to build a highly reliable structured data set. For example, through time alignment, the dynamic correlation between current fluctuations and temperature changes in a specific area can be analyzed, providing an input basis for subsequent pattern mining.

[0022] Step S300, constructing a temperature representation state of a virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group.

[0023] This step aims to convert discrete temperature monitoring data into a characteristic representation that reflects the overall state of the temperature field in the furnace. The specific method includes: drawing a curve of temperature change over time for each temperature mapping point (such as the typical trend of heating in the early stage of smelting, stability in the middle stage, and cooling in the later stage), and calculating the morphological similarity of the curves of adjacent nodes through dynamic time warping (DTW) or Pearson correlation coefficient. If the curve similarity of adjacent nodes exceeds the threshold (such as correlation coefficient>0.8) and the spatial spacing meets the requirements (such as less than 0.5 meters), they are connected as a "temperature expression line" to represent the coordinated change area of ​​the local temperature field. For example, the dense connection of multiple expression lines in the molten pool area may reflect the uniformity of molten silicon. Finally, by integrating all the expression lines, a dynamic change pattern of the three-dimensional temperature field is constructed. The technical significance of this step is to upgrade the discrete point data to a continuous field representation. For example, the expression line can identify the hot spot area or temperature gradient anomaly in the furnace, breaking through the limitations of traditional single-point analysis.

[0024] Step S400, classify the temperature performance states of the virtual three-dimensional smelting furnace to obtain several virtual three-dimensional smelting furnace performance groups, compare the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group, determine the parameter mapping interval of the corresponding process factor parameters, and configure the parameter mapping interval under the corresponding process factor parameters in the historical process operation parameter sequence, and construct several historical process operation parameter sequences into a reference historical process operation parameter sequence library.

[0025] Temperature performance classification: First, the three-dimensional temperature expressions of different smelting batches are standardized to ensure that the time span is aligned with the smelting stage (for example, the data of the first 2 hours of the smelting period are uniformly intercepted). By calculating the similarity score between the temperature expressions (comprehensive spatial distribution consistency, curve morphology matching and other indicators), the expressions with high similarity are classified into the same category. For example, if the temperature gradient change trends of two temperature fields in the molten pool area are consistent, and the overlap of the hot spot position distribution exceeds the set threshold, they are judged to be of the same type. The classification process uses an automated clustering algorithm (such as density-based clustering) to eventually form a number of "virtual three-dimensional smelting furnace expression groups", each of which represents a typical temperature field evolution pattern.

[0026] Parameter mapping interval determination: Perform statistical analysis on the historical process parameters (such as current, voltage, and feeding frequency) corresponding to each performance group to extract their distribution patterns. For example, if the current values ​​of a performance group are mainly concentrated in the range of 2000-2200A, and the temperature field stability in this range is relatively high, this range is defined as the "mapping interval" of the current parameter. The delineation of interval boundaries needs to take into account process constraints (such as equipment safety thresholds) and data distribution density to avoid intervals that are too wide or too narrow due to extreme values. Ultimately, the mapping intervals of all process parameters are bound to the corresponding historical sequences to form a reference historical process operation parameter sequence library.

[0027] Step S500, compare and analyze the current process operation parameters of the smelting furnace using the reference historical process operation parameter sequence library, determine the most suitable historical process operation parameter sequence, and identify the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operation parameter sequence as the predicted reference performance.

[0028] This step realizes the prediction of the smelting process by matching real-time data with the historical database. The specific logic is: collect the current process parameters (such as current and voltage) from the DCS system in real time, and smooth them according to a fixed time window (such as 10 seconds) to eliminate instantaneous noise. Then compare the current parameters with the historical parameter mapping intervals one by one, for example, determine whether the current current is within the range of 2000-2200A of a certain historical group, and count the number of consecutive matches of each parameter type. If a parameter falls into the same interval in N consecutive time windows (such as N=5), it is marked as "stable match"; only when all process parameters meet the stable matching conditions, the corresponding historical sequence is judged as "most suitable". If multiple sequences meet the conditions at the same time, the sequence with the highest D value is selected as the prediction reference. Finally, the temperature performance state corresponding to the most suitable sequence (such as performance line distribution, hot spot location) is used as a prediction reference for the current smelting process, such as warning of the risk of abnormal temperature rise in the electrode area. The technical significance of this step is to achieve rapid diagnosis of process status through real-time data drive. For example, the matching results can directly guide the operator to adjust the current or feeding strategy to avoid temperature runaway and improve the stability and energy efficiency of the smelting process.

[0029] In some embodiments disclosed in the present invention, a method for constructing a temperature representation state of a virtual three-dimensional smelting furnace includes: Step S301, based on the historical temperature monitoring parameters in the historical smelting parameter group, determining a mapping point temperature change curve of each temperature mapping point in the virtual three-dimensional smelting furnace.

[0030] Based on the monitoring data of each temperature mapping point in the historical smelting parameter group, the temperature values ​​in the entire smelting cycle are extracted in chronological order to form an independent time series curve. For example, the curve of a furnace wall mapping point can reflect the rapid heating in the early stage of smelting (0-30 minutes), the steady-state maintenance in the middle stage (30-120 minutes), and the cooling process in the late stage (120-180 minutes). After the curve is drawn, key feature points (such as peak temperature and heating rate mutation point) need to be marked for subsequent analysis of the regularity and abnormality of local temperature behavior.

[0031] Step S302, based on the relative position characteristics between the temperature mapping points and the matching characteristics of the temperature change curves of the mapping points, the temperature mapping points are connected to obtain the furnace temperature performance line.

[0032] On the basis of generating a single-point curve, the discrete nodes are connected into continuous temperature performance lines by combining the spatial position relationship of the mapping points with the similarity of the curve shape. First, the physical distance between adjacent mapping points is calculated (for example, a spacing of less than 0.5 meters is defined as "spatial proximity") to screen out potential connection node pairs; secondly, the curve shapes of adjacent nodes are compared (such as consistency of fluctuation trends, alignment of key feature points). If the similarity exceeds the threshold (such as trend overlap > 80%), it is determined to be a "connectable node pair". Finally, a three-dimensional temperature performance line network is formed by cross-connecting node pairs that meet the conditions. For example, multiple adjacent nodes in the molten pool area form a dense cluster of performance lines due to the synchronous heating trend, which can intuitively reflect the spatial conduction path of heat; and the radial distribution of performance lines around the electrode may indicate uneven current distribution.

[0033] In some embodiments disclosed in the present invention, a method for classifying the temperature performance of a virtual three-dimensional smelting furnace includes: Step S401, compare the temperature performance states of the virtual three-dimensional smelting furnaces with each other to determine the degree of temperature equivalence. If the degree of temperature equivalence is greater than or equal to a preset value, classify the corresponding temperature performance states of the virtual three-dimensional smelting furnaces into one category to obtain several virtual three-dimensional smelting furnace performance groups.

[0034] In some embodiments disclosed in the present invention, the method of comparing the temperature performance states of virtual three-dimensional smelting furnaces includes: Step S402, aligning the temperature states between the virtual three-dimensional smelting furnaces in time, comparing the relative temperature expression lines in the furnaces, and constructing a first temperature equivalent expression operator based on the comparison results.

[0035] Step S403 : comparing the temperature change curves of each relative mapping point, and based on the comparison result, correcting the first temperature equivalent expression operator to obtain the temperature equivalent expression degree.

[0036] Among them, the expression for calculating the degree of temperature equivalent performance is: .

[0037] in, For temperature equivalent performance, is the temperature equivalent parameter of the furnace temperature expression line at the t-th time node, The adjustment coefficient of the furnace temperature performance line is equivalent to the influence of the adjustment coefficient. The temperature performance line in the furnace is equivalent to the adjustment constant. is the number of all time nodes involved in the comparison, is the temperature equivalent parameter of the temperature change curve of the hth mapping point at the tth time node, is the total number of temperature change curves of the mapping points involved in the comparison, The temperature change curve of the mapping point is equivalent to the impact adjustment coefficient, Adjust the constants for the equivalent impact of the temperature change curve at the mapping point.

[0038] In some embodiments disclosed in the present invention, the method for determining the temperature equivalent parameter of the temperature performance line in the furnace includes: Step S4031, determine the relative distance between each relative temperature mapping point. If it is less than or equal to a preset value, the corresponding temperature mapping points are deemed to be equal, and the mapping points are recorded as equal once. Based on the recorded number of times the mapping points are equal, the temperature equivalent parameters of the temperature expression line in the furnace are determined.

[0039] Among them, the expression for calculating the temperature equivalent parameter of each furnace temperature expression line is: .

[0040] in, is the temperature mapping point equality judgment function. If the x1th temperature mapping point is recorded as the mapping point equality, then Output 1, y1 is the total number of temperature mapping points involved in the comparison.

[0041] The method for determining the temperature equivalent parameter of the temperature change curve of the mapping point includes: Step S4032, determine the time node for comparison on the temperature change curve, record it as the comparison time node, and intercept the comparison curve segment on the temperature change curve according to the preset time length with the comparison time node as the center.

[0042] Step S4033, comparing the relative comparison curve segments, the comparison method includes evenly setting a number of curve comparison points on the comparison curve segment, determining the curve vertical axis difference between the relative curve comparison points, if the curve vertical axis difference is less than or equal to a preset value, then identifying the corresponding curve comparison point as an equivalent curve comparison point, determining the equivalent comparison point ratio of the equivalent curve comparison point to all curve comparison points, and determining the corresponding temperature equivalent parameter based on the comparison point ratio interval to which the equivalent comparison point ratio belongs .

[0043] In some embodiments disclosed in the present invention, the method for determining the variation sensitivity index between different process factor parameters includes: Step S404: construct a parameter mapping axis for each type of process factor parameter, and map the corresponding process factor parameter onto the parameter mapping axis in the form of factor parameter mapping points.

[0044] Step S405, determining the factor parameter mapping point distribution of the parameter mapping axis, determining the factor parameter mapping point distribution interval with a distribution density greater than or equal to a preset value, and determining the corresponding process factor parameter parameter parameter mapping interval based on the length of the factor parameter mapping point distribution interval.

[0045] In some embodiments disclosed in the present invention, a method for comparing and analyzing the current process operation parameters of a smelting furnace by referring to a historical process operation parameter sequence library is as follows: Step S501, compare the current process operation parameters with different reference historical process operation parameter sequences in the reference historical process operation parameter sequence library in a time-series manner. The comparison method includes comparing the process factor parameters in the current process operation parameters with the parameter mapping intervals in the reference historical process operation parameter sequences. If the process factor parameters belong to the corresponding parameter mapping intervals, the corresponding process factor parameters are identified as matching process factor parameters.

[0046] Step S502, record the number of matching process factor parameters of each parameter type in real time. If the number of matching times within a preset time period is greater than or equal to a preset value, determine that the parameter type is a matching parameter type. If each parameter type is a matching parameter type, determine that the corresponding reference historical process operation parameter sequence is the most suitable historical process operation parameter sequence.

[0047] In some embodiments disclosed in the present invention, an industrial silicon smelting process prediction system based on data analysis is also disclosed, which is characterized by comprising: The first module is used to analyze the structure of the smelting furnace, construct a virtual three-dimensional smelting furnace, and set the temperature mapping point in the virtual three-dimensional smelting furnace based on the position node of each thermocouple; The second module is used to obtain a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group; The third module is used to construct the temperature performance state of the virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group; The fourth module is used to classify the temperature performance states of the virtual three-dimensional smelting furnace to obtain a number of virtual three-dimensional smelting furnace performance groups, compare the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group, determine the parameter mapping interval of the corresponding process factor parameter, and configure the parameter mapping interval under the corresponding process factor parameter in the historical process operation parameter sequence, and construct a number of historical process operation parameter sequences as a reference historical process operation parameter sequence library; The fifth module is used to compare and analyze the current process operation parameters of the smelting furnace using the reference historical process operation parameter sequence library, determine the most suitable historical process operation parameter sequence, and identify the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operation parameter sequence as the predicted reference performance.

[0048] The present invention discloses an industrial silicon smelting process prediction method and system based on data analysis, which relates to the technical field of industrial silicon smelting control and management; the physical structure of the smelting furnace is analyzed, thermocouple nodes are located as temperature mapping points in a virtual three-dimensional model, and a spatial topological relationship is established; historical smelting data is collected, temperature monitoring parameters and process operation parameters are aligned according to time nodes, and a smelting parameter group associated with time and space is constructed; three-dimensional temperature manifestations are generated based on temperature mapping point data, and temperature manifestations are classified through temperature change curve fitting and heat conduction correlation analysis; current process parameters are matched with a reference historical process operation parameter sequence library in real time, and a three-dimensional temperature field prediction result corresponding to the optimal historical parameter group is output; the present invention effectively solves the problems of low efficiency and large errors in traditional manual analysis, and provides reliable technical support for intelligent control of industrial silicon smelting.

[0049] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for predicting industrial silicon smelting process based on data analysis, characterized in that: include: Analyze the structure of the smelting furnace, build a virtual three-dimensional smelting furnace, and set the temperature mapping point in the virtual three-dimensional smelting furnace based on the location node of each thermocouple; Acquire a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group; Based on the historical temperature monitoring parameters in the historical smelting parameter group, the temperature performance of the virtual three-dimensional smelting furnace is constructed; The temperature performance states of the virtual three-dimensional smelting furnace are classified to obtain a number of virtual three-dimensional smelting furnace performance groups, and the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group are compared to determine the parameter mapping interval of the corresponding process factor parameter, and the parameter mapping interval is configured under the corresponding process factor parameter in the historical process operation parameter sequence, and the several historical process operation parameter sequences are constructed as a reference historical process operation parameter sequence library; The current process operating parameters of the smelting furnace are compared and analyzed using the reference historical process operating parameter sequence library to determine the most suitable historical process operating parameter sequence, and the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operating parameter sequence is identified as the predicted reference performance.

2. The method for predicting industrial silicon smelting process based on data analysis according to claim 1, characterized in that: The method for constructing the temperature representation state of a virtual three-dimensional smelting furnace includes: Determine a mapping point temperature change curve of each temperature mapping point in the virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group; Based on the relative position characteristics between the temperature mapping points and the matching characteristics of the temperature change curves of the mapping points, the temperature mapping points are connected to each other to obtain the temperature performance line in the furnace.

3. The method for predicting industrial silicon smelting process based on data analysis according to claim 2, characterized in that: Methods for classifying the temperature performance of a virtual three-dimensional smelting furnace include: The temperature performance states of the virtual three-dimensional smelting furnaces are compared with each other to determine the degree of temperature equivalence. If the degree of temperature equivalence is greater than or equal to a preset value, the temperature performance states of the corresponding virtual three-dimensional smelting furnaces are classified into one category to obtain several virtual three-dimensional smelting furnace performance groups.

4. The method for predicting industrial silicon smelting process based on data analysis according to claim 3, characterized in that: The method for comparing the temperature performance of the virtual three-dimensional smelting furnace with each other includes: The temperature expression states between the virtual three-dimensional smelting furnaces are aligned in time, and the relative temperature expression lines in the furnaces are compared, and based on the comparison results, a first temperature equivalent expression operator is constructed; Comparing the temperature change curves of each relative mapping point, and based on the comparison result, correcting the first temperature equivalent expression operator to obtain the temperature equivalent expression degree; Among them, the expression for calculating the degree of temperature equivalent performance is: ； in, For temperature equivalent performance, is the temperature equivalent parameter of the furnace temperature expression line at the t-th time node, The adjustment coefficient of the furnace temperature performance line is equivalent to the influence of the adjustment coefficient. The temperature performance line in the furnace is equivalent to the adjustment constant. is the number of all time nodes involved in the comparison, is the temperature equivalent parameter of the temperature change curve of the hth mapping point at the tth time node, is the total number of temperature change curves of the mapping points involved in the comparison, The temperature change curve of the mapping point is equivalent to the impact adjustment coefficient, Adjust the constants for the equivalent impact of the temperature change curve at the mapping point.

5. The method for predicting industrial silicon smelting process based on data analysis according to claim 4, characterized in that: Methods for determining temperature equivalent parameters of the temperature performance line in the furnace include: Determine the relative distance between each relative temperature mapping point. If the relative distance is less than or equal to a preset value, the corresponding temperature mapping points are deemed equal, and the mapping points are recorded equal once. Based on the recorded number of times the mapping points are equal, determine the temperature equalization parameter of the temperature expression line in the furnace. Among them, the expression for calculating the temperature equivalent parameter of each furnace temperature expression line is: ； in, is the temperature mapping point equality judgment function. If the x1th temperature mapping point is recorded as the mapping point equality, then Output 1, y1 is the total number of temperature mapping points involved in the comparison; The method for determining the temperature equivalent parameter of the temperature change curve of the mapping point includes: Determine a time node for comparison on the temperature change curve, record it as the comparison time node, and intercept a comparison curve segment on the temperature change curve according to a preset time length with the comparison time node as the center; Comparing the relative comparison curve segments, the comparison method includes evenly setting a number of curve comparison points on the comparison curve segment, determining the curve vertical axis difference between the relative curve comparison points, if the curve vertical axis difference is less than or equal to a preset value, then identifying the corresponding curve comparison point as an equivalent curve comparison point, determining the equivalent comparison point ratio of the equivalent curve comparison point to all the curve comparison points, and determining the corresponding temperature equivalent parameter based on the comparison point ratio interval to which the equivalent comparison point ratio belongs .

6. The method for predicting industrial silicon smelting process based on data analysis according to claim 1, characterized in that: Methods for determining the variation sensitivity between different process factor parameters include: Construct a parameter mapping axis for each type of process factor parameter, and map the corresponding process factor parameter on the parameter mapping axis in the form of factor parameter mapping points; The factor parameter mapping point distribution of the parameter mapping axis is determined, a factor parameter mapping point distribution interval having a distribution density greater than or equal to a preset value is determined, and based on the length of the factor parameter mapping point distribution interval, a parameter mapping interval of the corresponding process factor parameter is determined.

7. The method for predicting industrial silicon smelting process based on data analysis according to claim 1, characterized in that: Method for comparing and analyzing the current process operation parameters of the smelting furnace by using the reference historical process operation parameter sequence library: Comparing the current process operation parameters with different reference historical process operation parameter sequences in the reference historical process operation parameter sequence library in a time-series corresponding manner, wherein the comparison method includes comparing the process factor parameters in the current process operation parameters with the parameter mapping intervals in the reference historical process operation parameter sequences, and if the process factor parameters belong to the corresponding parameter mapping intervals, then the corresponding process factor parameters are identified as matching process factor parameters; The matching number of the matching process factor parameters of each parameter type is recorded in real time. If the matching number within the preset time period is greater than or equal to the preset value, the parameter type is determined to be a matching parameter type. If each parameter type is a matching parameter type, the corresponding reference historical process operation parameter sequence is determined to be the most suitable historical process operation parameter sequence.

8. The industrial silicon smelting process prediction system based on data analysis is characterized by: include: The first module is used to analyze the structure of the smelting furnace, construct a virtual three-dimensional smelting furnace, and set the temperature mapping point in the virtual three-dimensional smelting furnace based on the position node of each thermocouple; The second module is used to obtain a number of historical smelting process data, determine the historical temperature monitoring parameters and historical process operation parameters at different time nodes, and construct a historical temperature monitoring parameter sequence and a historical process operation parameter sequence, and record the combination of the historical temperature monitoring parameter sequence and the historical process operation parameter sequence belonging to the same historical smelting process data as a historical smelting parameter group; The third module is used to construct the temperature performance state of the virtual three-dimensional smelting furnace based on the historical temperature monitoring parameters in the historical smelting parameter group; The fourth module is used to classify the temperature performance states of the virtual three-dimensional smelting furnace to obtain a number of virtual three-dimensional smelting furnace performance groups, compare the historical process operation parameter sequences corresponding to each virtual three-dimensional smelting furnace performance group, determine the parameter mapping interval of the corresponding process factor parameter, and configure the parameter mapping interval under the corresponding process factor parameter in the historical process operation parameter sequence, and construct a number of historical process operation parameter sequences as a reference historical process operation parameter sequence library; The fifth module is used to compare and analyze the current process operation parameters of the smelting furnace using the reference historical process operation parameter sequence library, determine the most suitable historical process operation parameter sequence, and identify the performance of the virtual three-dimensional smelting furnace corresponding to the historical process operation parameter sequence as the predicted reference performance.

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