CN114840961A - Crystal pulling method, system, computer device and storage medium based on big data analysis - Google Patents

Crystal pulling method, system, computer device and storage medium based on big data analysis Download PDF

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Publication number
CN114840961A
CN114840961A CN202110142124.3A CN202110142124A CN114840961A CN 114840961 A CN114840961 A CN 114840961A CN 202110142124 A CN202110142124 A CN 202110142124A CN 114840961 A CN114840961 A CN 114840961A
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node
workpiece
model
crystal pulling
data
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Inventor
董恩慧
高润飞
李雪峰
景吉祥
王静
沈瑞川
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Inner Mongolia Zhonghuan Solar Material Co Ltd
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Inner Mongolia Zhonghuan Solar Material Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B15/00—Single-crystal growth by pulling from a melt, e.g. Czochralski method
    • C30B15/20—Controlling or regulating
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
    • C30B29/02—Elements
    • C30B29/06—Silicon
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/22—Yield analysis or yield optimisation

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Materials Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Crystals, And After-Treatments Of Crystals (AREA)

Abstract

Crystal pulling method, system, computer device and storage medium based on big data analysis, S1: acquiring basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process; s2: processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece; s3: establishing a model for each parameter in each node of each workpiece through deep learning; s4: the value of each parameter in each node of each workpiece in S2 is compared with the model in S3 to determine whether the value of each parameter in the node where the workpiece is located is reasonable. The invention can effectively carry out intelligent crystal pulling in big data and deep learning, utilizes big data analysis and execution optimization scheme, and then organically combines the big data and the deep learning, thereby improving the crystal pulling quality and the crystal pulling efficiency and reducing the crystal pulling cost.

Description

Crystal pulling method, system, computer device and storage medium based on big data analysis
Technical Field
The invention belongs to the technical field of photovoltaic single crystal pulling production, and particularly relates to a crystal pulling method and system based on big data analysis, computer equipment and a storage medium.
Background
The growth process of the czochralski single crystal mainly comprises other steps of temperature stabilization, seeding, shouldering, diameter equalization, ending and the like. At present, the single crystal pulling process is mainly controlled by a manually operated centralized control system, the control mode can realize automatic operation within a standard range through the centralized control system, but a decision part still needs a professional engineer to carry out operation control, and the crystal pulling mode has unstable production, low crystal pulling production efficiency and too many human factors, thus leading to higher crystal pulling production cost.
Disclosure of Invention
The invention provides a crystal pulling method, a crystal pulling system, computer equipment and a storage medium based on big data analysis, which are particularly suitable for solar Czochralski silicon single crystal production and solve the technical problems of unstable crystal pulling quality and high production cost caused by manual operation of a centralized control system in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that:
the crystal pulling method based on big data analysis comprises the following steps:
s1: acquiring basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process;
s2: processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece;
s3: establishing a model for each parameter in each node of each workpiece through deep learning;
s4: comparing the value of each of the parameters in each node of each workpiece in the S2 with the model in the S3 to determine whether the value of each of the parameters in the node of the workpiece is reasonable.
Further, in S4, if all the parameter values in the node where the workpiece is located are within the model range, continuing the operation of the next node;
if some of the parameter values in the node where the workpiece is located are not within the model range, the relevant parameters in the corresponding node are paused or adjusted.
Further, all the parameter types of each node of each workpiece in the S2 correspond to all the parameter types of each node of each workpiece in the S3;
preferably, the parameters are established according to a production area, head and tail time of each node, associated data between an upper node and a lower node, different functions in each node and the like;
preferably, all of the parameters of each node in each workpiece are configured to be displayed on a terminal display of a single crystal furnace in which the workpiece is being pulled.
Further, the source data of each node in each workpiece comprises production process data and/or raw and auxiliary material data and/or quality data;
preferably, the nodes in each workpiece at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged;
preferably, the model at least comprises a temperature-stabilizing node model, a seeding node model, a shouldering node model, an equal-diameter node model and a ending node model.
A crystal pulling system, the system comprising:
acquiring a source data unit: the method comprises the steps of obtaining basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process;
processing the source data unit: the data acquisition module is used for processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece;
establishing a model unit: the model is established for each parameter in each node of each workpiece through deep learning;
a determination parameter unit: and the model establishing unit is used for comparing the value of each parameter in each node of each workpiece in the processing source data unit with the model in the model establishing unit so as to judge whether the value of each parameter in the node where the workpiece is positioned is reasonable.
Further, in the parameter judgment unit, when all the parameter values in the node where the workpiece is located are within the model range, the operation of the next node is continued;
if some of the parameter values in the node where the workpiece is located are not within the model range, the related parameters in the corresponding node are paused or adjusted.
Further, all of the parameter types of each node of each workpiece in the processing source data unit correspond to all of the parameter types of each node of each workpiece in the modeling unit;
preferably, the parameters are established according to a production area, head and tail time of each node, associated data between an upper node and a lower node, different functions in each node and the like;
preferably, all of the parameters of each node in each workpiece are configured to be displayed on a terminal display of a single crystal furnace in which the workpiece is being pulled.
Further, the source data of each node in each workpiece comprises production process data and/or raw and auxiliary material data and/or quality data;
preferably, the nodes in each workpiece at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged;
preferably, the model at least comprises a temperature-stabilizing node model, a seeding node model, a shouldering node model, an equal-diameter node model and a ending node model.
A computer device comprising a memory and a processor; the memory stores a computer program; the processor is configured to execute the computer program and, when executing the computer program, to cause the processor to perform the steps of the crystal pulling method as described in any one of the above.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of a crystal pulling method as defined in any one of the preceding claims.
Compared with the prior art, the crystal pulling method, the crystal pulling system, the computer equipment and the storage medium based on big data analysis are adopted to collect, screen and convert basic source data into a plurality of data sets of parameter values corresponding to the model, wherein the parameter values are easy to identify and mark in a plurality of nodes of each workpiece; simultaneously establishing a model for each parameter in a plurality of nodes of each workpiece through deep learning; and comparing the value of each parameter in the plurality of nodes of each processed workpiece with the parameter range in the model, and judging whether the parameter value in the node where the workpiece is located is reasonable.
The technical scheme of the invention can effectively carry out intelligent crystal pulling in big data and deep learning, utilizes big data analysis and execution optimization scheme, and then organically combines the big data and the deep learning, thereby improving the crystal pulling quality and the crystal pulling efficiency and reducing the crystal pulling cost.
Drawings
FIG. 1 is a flow chart of a crystal pulling method based on big data analysis according to an embodiment of the present invention;
FIG. 2 is a flow chart of a crystal pulling process in accordance with one embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a crystal pulling system in accordance with one embodiment of the present invention.
In the figure:
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The embodiment provides a crystal pulling method based on big data analysis, as shown in fig. 1, the steps include:
s1: the method includes acquiring basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process.
Specifically, each single crystal workpiece is drawn by each single crystal furnace, the plurality of nodes are involved, the nodes at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged. The basic source data of each node comprises production process data and/or raw material and auxiliary material data and/or quality data.
The production process data includes equipment name, start-stop time in each node, batch number, process model, recipe name, diameter measurement value, thermal field temperature value, main heater power measurement, bottom heater power measurement, actual crystal pulling rate, etc.
The raw and auxiliary material data comprise the preparation date, the batching serial number, the shift of personnel in each node, the heat number, the workpiece specification, the crucible type, the crucible production area, the primary polycrystal weight, the recycled material proportion, the overall weight and the like.
Quality data includes single crystal number, length, weight, diameter, resistivity, lifetime, oxygen content, carbon content, defects, etc. for each workpiece.
S2: and processing the source data acquired in the step S1, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece.
Specifically, the basic data of each node is extracted, screened and converted into a plurality of parameters which are easy to identify and mark in the node, so as to obtain a data set of parameter values which are easy to compare with the parameters in the standard module, namely, the data set of the scattered, messy and non-uniform source data in the input basic source data is integrated and then converted into a common parameter data set in the node of the workpiece manufacturing process, so that a basis is provided for the subsequent parameter judgment and analysis.
Further, all parameters are established according to the production area, the head and tail time of each node, the associated data between the upper node and the lower node, different functions in each node and the like. And all the parameters of each node in each workpiece are configured and displayed in a terminal display of the single crystal furnace in which the workpiece is drawn, so that personnel can monitor the change condition of each single crystal furnace platform in real time.
S3: and establishing a model for each parameter in each node of each workpiece through deep learning.
Specifically, a deep learning method is adopted to establish a model for all parameters of each node of each workpiece so as to monitor node analysis and judgment of workpieces of all furnace platforms in the manufacturing process, and thus single crystal workpieces with quality meeting the standard can be obtained.
Further, each parameter type of each node of each workpiece in the model corresponds to each parameter type of each node of each workpiece in step S2.
Further, the model at least comprises a model of a temperature-stabilizing node, a model of a seeding node, a model of a shouldering node, a model of an equal-diameter node and a model of a ending node.
S4: the value of each parameter in each node of each workpiece in step S2 is compared with the model in step S3 to determine whether the value of each parameter in the node of the workpiece is reasonable.
Specifically, if all the parameter values in the node where the workpiece is located are within the model range, the operation of the next node is continued.
If some parameter values in the node where the workpiece is located are not in the model range, the related parameters in the corresponding node are paused or adjusted.
FIG. 2 shows a flow chart of a crystal pulling process:
and during drawing, matching data in workpiece temperature stabilization acquired from the data set in the processing source data unit with data in the temperature stabilization node model, and starting temperature stabilization when the real-time data is within the range in the temperature stabilization node model. In the whole temperature stabilizing process, the temperature stabilizing node model can self-adjust the relevant parameter values to enable the parameter values to reach the qualified range until the temperature stabilization is finished. The temperature-stabilizing node model can automatically judge whether the data at the end of the temperature-stabilizing node can be seeded; if the process can continue to the seeding node process; if not, in the process step before temperature stabilization, the adjustment is carried out from the temperature stabilization node.
And in a seeding node manufacturing process, matching data in workpiece seeding acquired from the data set with data in a seeding node model, and starting seeding when the real-time data is within the range of the seeding node model. In the whole seeding process, the seeding node model can self-adjust the relevant parameter values to enable the relevant parameter values to reach a qualified range until seeding is finished. Seeding node models and automatically judging whether data can be shouldered or not when the seeding nodes are finished; if the process can be continued to the shoulder node process; if not, backtracking and adjusting to the step before temperature stabilization, and gradually proceeding from the temperature stabilization node until the shoulder-off node can be processed.
And in the shoulder node process, matching data in the workpiece shoulder acquired from the data set with data in the shoulder node model, and when the real-time data is within the range of the shoulder node model, beginning shoulder. In the whole shouldering process, the shouldering node model can self-adjust the relevant parameter values to enable the relevant parameter values to reach the qualified range until the shouldering is finished. The shouldering node model can automatically judge whether the data after the shouldering node is finished can be turned to the shoulder; if the process can be continued to the shoulder-turning node process; if not, backtracking and adjusting to the step before temperature stabilization, and gradually proceeding from the temperature stabilization node until the shoulder-turning node can be processed.
And in the shoulder-turning node manufacturing process, matching data in the workpiece shoulder-turning acquired from the data set with data in the shoulder-turning node model, and starting shoulder-turning when the real-time data is within the range of the shoulder-turning node model. In the whole shoulder turning process, the shoulder turning node model can self-adjust the relevant parameter values to enable the shoulder turning node model to reach the qualified range until the shoulder turning is finished. The shoulder turning node model can automatically judge whether the data after the shoulder turning node is finished can be in equal diameter or not; if the process can be continued to the constant diameter node process; if not, backtracking and adjusting to the step before temperature stabilization, and gradually proceeding from the temperature stabilization node until the constant diameter node can be processed.
And (4) entering a constant-diameter node process, matching data in the constant diameter of the workpiece acquired from the data set with data in the constant-diameter node model, and starting the constant diameter when the real-time data is within the range of the constant-diameter node model. In the whole process of equal diameter, the equal diameter node model can self-adjust the value of the relevant parameter to enable the value to reach the qualified range until the equal diameter is finished. The equal-diameter node model can automatically judge whether the data can be terminated when the equal-diameter node is finished; if the process can continue to enter the end node process; if not, backtracking and adjusting to the step before temperature stabilization, and gradually proceeding from the temperature stabilization node until the ending node can be processed.
And in the ending node process, matching data in workpiece ending acquired from the data set with data in an ending node model, and ending when the real-time data is within the range of the ending node model. In the whole ending process, the ending node model can self-adjust the relevant parameter value to enable the relevant parameter value to reach the qualified range until ending. The ending node model can automatically judge whether the data at the ending of the ending node can be repeatedly cast or blown out; if the process can be continued to the process of the re-casting or blowing-out node; if not, backtracking and adjusting to the step before temperature stabilization, and gradually proceeding from the temperature stabilization node until the process of re-casting or blowing-out node can be entered.
A crystal pulling system, as shown in fig. 3, the system comprising:
acquiring a source data unit: the method is used for acquiring basic source data of a plurality of nodes of each workpiece of each single crystal furnace in the crystal pulling process.
Processing the source data unit: the data acquisition device is used for processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values in each node of the workpiece.
Establishing a model unit: for modeling each parameter in each node of each workpiece through deep learning.
A determination parameter unit: and the model establishing unit is used for comparing the value of each parameter in each node of each workpiece in the processing source data unit with the model in the model establishing unit so as to judge whether the value of each parameter in the node where the workpiece is positioned is reasonable.
And the parameter judging unit is used for continuing the operation of the next node when all the parameter values in the node where the workpiece is located are in the model range. And when the values of some parameters in the nodes where the workpiece is positioned are not in the range of the model, pausing or adjusting the relevant parameters in the corresponding nodes.
Further, all parameter types of each node of each workpiece in the processing source data unit correspond to all parameter types of each node of each workpiece in the modeling unit.
Further, the parameters are established according to the production area, the head and tail time of each node, the associated data between the upper and lower nodes, different functions in each node, and the like.
Further, all parameters of each node in each workpiece are configured and displayed in a terminal display of the single crystal furnace in which the workpiece is drawn.
Further, the source data of each node in each workpiece includes production process data and/or raw material and auxiliary material data and/or quality data.
Furthermore, the nodes in each workpiece at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged.
Furthermore, the model at least comprises a model of a temperature-stabilizing node, a model of a seeding node, a model of a shouldering node, a model of an equal-diameter node and a model of a ending node.
A computer device comprising a memory and a processor; wherein the memory stores a computer program; the processor is adapted to execute the computer program and to cause the processor to perform the steps of the crystal pulling method as described in any one of the above when the computer program is executed.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the crystal pulling method as defined in any one of the preceding claims.
1. The crystal pulling method, the crystal pulling system, the computer equipment and the storage medium based on big data analysis are characterized in that basic source data are collected, screened and converted into a plurality of data sets of parameter values corresponding to a model, wherein the parameter values are easy to identify and mark in a plurality of nodes of each workpiece; simultaneously establishing a model for each parameter in a plurality of nodes of each workpiece through deep learning; and comparing the value of each parameter in the plurality of nodes of each processed workpiece with the parameter range in the model, and judging whether the parameter value in the node where the workpiece is located is reasonable.
2. The technical scheme of the invention can effectively carry out intelligent crystal pulling in big data and deep learning, utilizes big data analysis and execution optimization scheme, and then organically combines the big data and the deep learning, thereby improving the crystal pulling quality and the crystal pulling efficiency and reducing the crystal pulling cost.
The embodiments of the present invention have been described in detail, and the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.

Claims (10)

1. A crystal pulling method based on big data analysis is characterized by comprising the following steps:
s1: acquiring basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process;
s2: processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece;
s3: establishing a model for each parameter in each node of each workpiece through deep learning;
s4: comparing the value of each of the parameters in each node of each workpiece in the S2 with the model in the S3 to determine whether the value of each of the parameters in the node of the workpiece is reasonable.
2. A crystal pulling method as set forth in claim 1 wherein in S4, if all of the parameter values in the node where the workpiece is located are within the model range, the operation of the next node is continued;
if some of the parameter values in the node where the workpiece is located are not within the model range, the related parameters in the corresponding node are paused or adjusted.
3. A crystal pulling method as claimed in claim 1 or 2, wherein all the parameter types of each node of each workpiece in the S2 correspond to all the parameter types of each node of each workpiece in the S3;
preferably, the parameters are established according to a production area, head and tail time of each node, associated data between an upper node and a lower node, different functions in each node and the like;
preferably, all of the parameters of each node in each workpiece are configured to be displayed on a terminal display of a single crystal furnace in which the workpiece is being pulled.
4. A crystal pulling method as defined in claim 1, wherein the source data for each node in each workpiece includes process data and/or raw material and/or quality data;
preferably, the nodes in each workpiece at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged;
preferably, the model at least comprises a temperature-stabilizing node model, a seeding node model, a shouldering node model, an equal-diameter node model and a ending node model.
5. A crystal pulling system, comprising:
acquiring a source data unit: the method comprises the steps of obtaining basic source data of a plurality of nodes of each workpiece of each single crystal furnace in a crystal pulling process;
processing the source data unit: the data acquisition module is used for processing the acquired source data, screening and converting the source data into a plurality of parameters which are easy to identify and mark in each node of each workpiece, and acquiring a data set of all parameter values of each node of the workpiece;
establishing a model unit: the model is established for each parameter in each node of each workpiece through deep learning;
a determination parameter unit: and the model establishing unit is used for comparing the value of each parameter in each node of each workpiece in the processing source data unit with the model in the model establishing unit so as to judge whether the value of each parameter in the node where the workpiece is positioned is reasonable.
6. A crystal pulling system as set forth in claim 5 wherein in the decision parameter unit, when all of the parameter values in the node in which the workpiece is located are within the model range, operation of the next node continues;
if some of the parameter values in the node where the workpiece is located are not within the model range, the related parameters in the corresponding node are paused or adjusted.
7. A crystal pulling system as set forth in claim 5 or claim 6 wherein all of the parameter types for each node of each workpiece in the process source data unit correspond to all of the parameter types for each node of each workpiece in the build model unit;
preferably, the parameters are established according to a production area, head and tail time of each node, associated data between an upper node and a lower node, different functions in each node and the like;
preferably, all of the parameters of each node in each workpiece are configured to be displayed on a terminal display of a single crystal furnace in which the workpiece is being pulled.
8. A crystal pulling system as set forth in claim 1 wherein the source data for each node in each workpiece comprises process data and/or raw material data and/or quality data;
preferably, the nodes in each workpiece at least comprise a temperature stabilizing node, a seeding node, a shouldering node, an equal-diameter node and a ending node, and the temperature stabilizing node, the seeding node, the shouldering node, the equal-diameter node and the ending node are sequentially arranged;
preferably, the model at least comprises a temperature-stabilizing node model, a seeding node model, a shouldering node model, an equal-diameter node model and a ending node model.
9. A computer device comprising a memory and a processor; the memory stores a computer program; the processor for executing the computer program and causing the processor to carry out the steps of the crystal pulling method as claimed in one of claims 1 to 9 when the computer program is executed.
10. A computer-readable storage medium, characterized in that a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the crystal pulling method as claimed in one of claims 1 to 9.
CN202110142124.3A 2021-02-02 2021-02-02 Crystal pulling method, system, computer device and storage medium based on big data analysis Pending CN114840961A (en)

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CN202110142124.3A CN114840961A (en) 2021-02-02 2021-02-02 Crystal pulling method, system, computer device and storage medium based on big data analysis

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