CN114841484A - Equal-path self-decision method, system, equipment and storage medium based on big data - Google Patents
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Abstract
The method, the system, the equipment and the storage medium are based on the isodiametric self-decision-making method, the system, the equipment and the storage medium of big data, and the method comprises the steps of processing, screening and converting basic source data of isodiametric nodes in the isodiametric process of Czochralski single crystal into a plurality of data sets which are easy to identify and mark in the isodiametric nodes, establishing a model, cleaning multi-dimensional data and establishing a dimensional data warehouse; acquiring basic metadata of a current node, converting the basic metadata into process parameters, comparing the process parameters with models in a dimension data warehouse, performing data analysis on a judgment result, judging whether a current equal-diameter process link is abnormal or not, and performing self-decision according to the judgment result. According to the technical scheme, when the isodiametric abnormal condition occurs in the isodiametric process of single crystal pulling, the optimal assessment and decision can be automatically given in time, the intervention of workers is reduced, the automatic decision is realized, the isodiametric abnormal problem is processed more comprehensively, accurately and standardly, and the production efficiency and the product quality are improved.
Description
Technical Field
The invention belongs to the technical field of photovoltaic single crystal pulling production, and particularly relates to a constant-diameter self-decision method, a system, equipment and a storage medium based on big data.
Background
The growth process of the czochralski single crystal mainly comprises the working steps of temperature stabilization, seeding, shouldering, diameter equalization, ending and the like. In the constant diameter process of pulling the single crystal, workers are required to supervise on site, and if the abnormal condition of constant diameter occurs, the field workers are required to make evaluation decision. However, since the skills, experiences, habits, etc. of the field workers are different, the evaluation and decision are different, and the real-time, comprehensive, accurate and standard evaluation cannot be realized.
Therefore, in order to automatically give out optimal assessment and decision in time when the equal diameter abnormal condition occurs in the equal diameter process of single crystal pulling, reduce the intervention of workers, realize automatic decision, more timely, comprehensively, accurately and standardly process the equal diameter abnormal problem, and improve the production efficiency and the product quality, the method for assessing and deciding in the current equal diameter process needs to be improved.
Disclosure of Invention
The invention aims to provide a method, a system, equipment and a storage medium for setting a constant-diameter SOP (silicon on insulator) based on big data, which are particularly suitable for producing a solar Czochralski silicon single crystal and effectively solve the problems that in the prior art, if constant-diameter abnormality occurs in the Czochralski single crystal constant-diameter process, skills, experiences, habits and the like of field workers are different, evaluation and decision are different, and real-time, comprehensive, accurate and standard operation cannot be realized.
In order to solve the technical problems, the invention adopts the following technical scheme:
the equal-path self-decision method based on big data comprises the following steps:
s1: acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the isodiametric process of pulling the single crystal;
s2: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
s3: establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
s4: performing multi-dimensional data cleaning on each model in the step S3 through deep learning, and establishing a dimensional data warehouse of a Czochralski single crystal constant diameter process;
s5: analyzing and calculating each model in the step S3 through deep learning to obtain current basic source data of equal-diameter nodes of a current furnace type, a current series, a current furnace platform, a current raw material and current repeated casting times;
s6: processing the current basic source data acquired in the step S5, screening and converting the current basic source data into process parameters which are easy to identify and mark in equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated throwing times;
s7: comparing the easily identifiable and marked process parameters obtained in the step S6 with each model in the dimension data warehouse in the step S4 to judge whether the easily identifiable and marked process parameter values in the node where the single crystal is located are reasonable;
s8: and performing data analysis on the judgment result in the step S7 through deep learning, judging whether the current equal-diameter process link is abnormal or not, and performing self-decision according to the judgment result.
Further, each of the parameters in the isodiametric nodes of all different furnace types, different series, different furnace platforms, different raw materials, different repeated throws in the step S2 corresponds to all the process parameter types in the step S6.
Further, the parameters are established according to the production area, the duration of the equal-diameter nodes and the equal-diameter function.
Further, all the parameters are configured in a terminal display of the single crystal furnace for displaying.
Further, the basic source data of the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different multiple feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
A constant-path self-decision making system, the system comprising:
acquiring a source data unit: the method is used for acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the process of isodiametric Czochralski single crystal pulling;
processing the source data unit: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
establishing a model unit: the system is used for establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
a data cleaning unit: the dimensional data warehouse is used for carrying out multi-dimensional data cleaning on each model and establishing a Czochralski single crystal isodiametric process;
a data comparison unit: the system is used for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
big data platform unit: and the big data analysis is carried out on the judgment result for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw material and the current repeated casting times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, whether the current equal-diameter process link is abnormal or not is judged, and self-decision is carried out according to the judgment result.
Furthermore, each parameter in the equal-diameter nodes of all different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the source data acquisition unit corresponds to all process parameter types in the data processing unit;
the parameters are established according to the production area, the duration time of the equal-diameter nodes and the equal-diameter function;
all the parameters are configured in a terminal display of the single crystal furnace to be displayed.
Further, the basic source data of the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different multiple feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
A computer device comprising a memory and a processor; the memory stores a computer program; the processor is adapted to execute the computer program and, when executing the computer program, to cause the processor to perform the steps of the constant path self-decision method according to any of claims 1-7.
A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the constant path self-decision method of any one of claims 1-7.
Compared with the prior art, the isodiametric self-decision method, the system, the equipment and the storage medium based on the big data designed by the invention are adopted to process, screen and convert basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times in the isodiametric process of pulling the Czochralski single crystal into a plurality of data sets of parameter values corresponding to the model, which are easy to identify and mark in the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times; meanwhile, establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times through deep learning, carrying out multi-dimensional data cleaning on each model, establishing a dimensional data warehouse of the Czochralski single crystal isodiametric process, obtaining current basic source data of the isodiametric nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times through calculation, screening and converting the current basic source data into easily identified and marked process parameters in the isodiametric nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times, comparing the easily identified and marked process parameters with each model in the dimensional data warehouse to judge whether the easily identified and marked process parameter values in the nodes of the single crystal are reasonable or not, carrying out data analysis on a judgment result through deep learning to judge whether the current isodiametric process link is abnormal or not, and making a self-decision according to the judgment result.
According to the technical scheme, when the equal diameter abnormal condition occurs in the equal diameter process of single crystal pulling, the optimal evaluation and decision can be automatically given in time, the intervention of workers is reduced, the automatic decision is realized, the equal diameter abnormal problem is more comprehensively, accurately and standardly processed in real time, and the production efficiency and the product quality are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a big data-based equal-path self-decision method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a system of a constant-path self-decision method according to an embodiment of the present invention;
Detailed Description
The invention is further illustrated by the following examples and figures.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the accompanying drawings in combination with the embodiments. It is to be understood that these descriptions are only illustrative and are not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a big data-based equal-path self-decision method, including the following steps:
s1: acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the isodiametric process of pulling the single crystal;
specifically, in the constant diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated feeding times in the constant diameter process of pulling the single crystal, each single crystal furnace has individual characteristics, and the basic source data of the constant diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
The production process data comprises equipment name, start-stop time, batch number, process mode, formula name, diameter measurement value, thermal field temperature value, main heater power measurement, bottom heater power measurement, actual crystal pulling speed and the like.
The raw and auxiliary material data comprise the preparation date, the preparation serial number, the staff shift, the heat number, the workpiece specification, the crucible type, the crucible production area, the primary polycrystalline weight, the reclaimed 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.
S2: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
specifically, basic source data are processed, screened and converted into a plurality of parameters which are easy to identify and mark in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, so that a data set of all parameter values of the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times is obtained, namely, scattered, disordered and standard non-uniform source data in the input basic source data are integrated and then converted into a common parameter data set in a workpiece processing node, and a basis is provided for subsequent parameter comparison and judgment analysis.
Further, all parameters are established according to the production area, the duration of the equal-diameter nodes and the equal-diameter function.
Further, all the parameters are configured in a terminal display of the single crystal furnace for displaying.
S3: establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
specifically, a deep learning method is adopted to establish a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated feeding times so as to monitor the nodal analysis and judgment of all single crystals of all furnace types, series, furnace platforms, raw materials and repeated feeding times in the isodiametric process, and obtain the single crystals with quality meeting the standard.
S4: performing multi-dimensional data cleaning on each model in the step S3 through deep learning, and establishing a dimensional data warehouse in the straight-pull single crystal isodiametric process;
specifically, a deep learning method is adopted to carry out multi-dimensional data cleaning on each model in the step S3, and model data in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times are integrated to establish a dimensional data warehouse of the Czochralski single crystal isodiametric process.
S5: analyzing and calculating each model in the step S3 through deep learning to obtain current basic source data of equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw material and the current repeated casting times;
s6: processing the current basic source data acquired in the step S5, screening and converting the current basic source data into process parameters which are easy to identify and mark in equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw material and the current repeated throwing times;
s7: comparing the easily identified and marked process parameters obtained in the step S6 with each model in the dimension data warehouse in the step S4 to judge whether the easily identified and marked process parameter values in the node where the single crystal is located are reasonable or not;
s8: and (4) performing data analysis on the judgment result in the step S7 through deep learning, judging whether the current equal-diameter process link is abnormal or not, and performing self-decision according to the judgment result.
A constant-path self-decision making system, as shown in fig. 2, the system comprising:
acquiring a source data unit: the method is used for acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the process of isodiametric Czochralski single crystal pulling;
processing the source data unit: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
establishing a model unit: the method is used for establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
a data cleaning unit: the dimensional data warehouse is used for carrying out multi-dimensional data cleaning on each model and establishing a dimensional data warehouse in the Czochralski single crystal isodiametric process;
a data comparison unit: the system is used for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
big data platform unit: and the big data analysis is carried out on the judgment result for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw material and the current repeated feeding times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated feeding times, whether the current equal-diameter process link is abnormal or not is judged, and self decision is carried out according to the judgment result.
Further, each parameter of the equal-diameter nodes which are obtained from the source data unit and have different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times corresponds to all process parameter types in the data processing unit;
parameters are established according to the production area, the duration time of the equal-diameter nodes and the equal-diameter function;
all parameters are configured in a terminal display of the single crystal furnace to be displayed.
Further, the basic source data of the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different multiple feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
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 equal path self-decision method as described in any one of the above.
A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the constant path self-decision method as claimed in any one of the preceding claims.
The invention has the advantages and beneficial effects that:
1. the isodiametric self-decision method, the system, the equipment and the storage medium based on the big data are designed, and the basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times in the isodiametric process of pulling the monocrystal are processed, screened and converted into a plurality of data sets of parameter values corresponding to the model, which are easy to identify and mark in the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times; meanwhile, establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times through deep learning, carrying out multi-dimensional data cleaning on each model, establishing a dimensional data warehouse of the Czochralski single crystal isodiametric process, obtaining current basic source data of the isodiametric nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times through calculation, screening and converting the current basic source data into easily identified and marked process parameters in the isodiametric nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times, comparing the easily identified and marked process parameters with each model in the dimensional data warehouse to judge whether the easily identified and marked process parameter values in the nodes of the single crystal are reasonable or not, carrying out data analysis on a judgment result through deep learning to judge whether the current isodiametric process link is abnormal or not, and making a self-decision according to the judgment result.
2. According to the technical scheme, when the equal diameter abnormal condition occurs in the equal diameter process of single crystal pulling, the optimal evaluation and decision can be automatically given in time, the intervention of workers is reduced, the automatic decision is realized, the equal diameter abnormal problem is more comprehensively, accurately and standardly processed in real time, and the production efficiency and the product quality are improved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. The equal-path self-decision method based on big data is characterized by comprising the following steps:
s1: acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the isodiametric process of pulling the single crystal;
s2: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the equal-diameter nodes for different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
s3: establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
s4: performing multi-dimensional data cleaning on each model in the step S3 through deep learning, and establishing a dimensional data warehouse of a Czochralski single crystal constant diameter process;
s5: analyzing and calculating each model in the step S3 through deep learning to obtain current basic source data of equal-diameter nodes of a current furnace type, a current series, a current furnace platform, a current raw material and current repeated casting times;
s6: processing the current basic source data acquired in the step S5, screening and converting the current basic source data into process parameters which are easy to identify and mark in equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated throwing times;
s7: comparing the easily identifiable and marked process parameters obtained in the step S6 with each model in the dimension data warehouse in the step S4 to judge whether the easily identifiable and marked process parameter values in the node where the single crystal is located are reasonable;
s8: and performing data analysis on the judgment result in the step S7 through deep learning, judging whether the current equal-diameter process link is abnormal or not, and performing self-decision according to the judgment result.
2. The big-data-based constant-path self-decision method according to claim 1, wherein: each of the parameters in the isodiametric nodes of all different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the step S2 corresponds to all the process parameter types in the step S6.
3. The big-data-based constant-path self-decision method according to claim 2, wherein: the parameters are established according to the production area, the duration of the equal-diameter nodes and the equal-diameter function.
4. The big-data-based constant-path self-decision method according to claim 3, wherein:
all the parameters are configured in a terminal display of the single crystal furnace to be displayed.
5. The big-data-based constant-path self-decision method according to any one of claims 1 to 4, wherein: the basic source data of the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different multiple feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
6. A constant-path self-decision making system, the system comprising:
acquiring a source data unit: the method is used for acquiring basic source data of isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated feeding times in the process of Czochralski single crystal isodiametric drawing;
processing the source data unit: processing the acquired basic source data, screening and converting the basic source data into a plurality of parameters which are easy to identify and mark in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, and acquiring a data set of all parameter values of the isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
establishing a model unit: the system is used for establishing a model for each parameter in isodiametric nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times through deep learning;
a data cleaning unit: the dimensional data warehouse is used for carrying out multi-dimensional data cleaning on each model and establishing a Czochralski single crystal isodiametric process;
a data comparison unit: the system is used for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw materials and the current repeated casting times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times;
big data platform unit: and the big data analysis is carried out on the judgment result for comparing the process parameters in the equal-diameter nodes of the current furnace type, the current series, the current furnace platform, the current raw material and the current repeated casting times with each model in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated casting times, whether the current equal-diameter process link is abnormal or not is judged, and self-decision is carried out according to the judgment result.
7. The system of claim 6, wherein: each parameter in the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different repeated throwing times in the source data acquisition unit corresponds to all process parameter types in the data processing unit;
the parameters are established according to the production area, the duration time of the equal-diameter nodes and the equal-diameter function;
all the parameters are configured in a terminal display of the single crystal furnace to be displayed.
8. A constant-path self-decision making system according to claim 6 or 7, characterized in that: the basic source data of the equal-diameter nodes of different furnace types, different series, different furnace platforms, different raw materials and different multiple feeding times comprise production process data and/or raw and auxiliary material data and/or quality data.
9. A computer device, characterized by: comprising a memory and a processor; the memory stores a computer program; the processor is adapted to execute the computer program and, when executing the computer program, to cause the processor to perform the steps of the constant path self-decision method according to any of claims 1-7.
10. A computer-readable storage medium characterized by: a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the constant path self-decision method as defined in any one of claims 1-7.
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