CN113741362A - Method, system, medium, and computing device for optimizing operation of liquefied natural gas receiving station - Google Patents
Method, system, medium, and computing device for optimizing operation of liquefied natural gas receiving stationDownload PDFInfo
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Abstract
Description
Technical Field
The invention relates to the field of petroleum and natural gas chemical industry, in particular to an operation optimization method, system, medium and computing equipment for a liquefied natural gas receiving station.
Background
How to optimize the production energy consumption of the LNG receiving station, improve the operation efficiency and realize cost reduction and efficiency improvement is one of the main problems facing and concerned by the current LNG receiving station.
Conventionally, a system model is generally established according to a mechanism of a study object, such as a physical or chemical change rule, and is called a mechanism modeling method, a modeling process is called mechanism modeling, and the established model is called a mechanism model. The mechanism model, also called white box model, is an accurate mathematical model established based on certain assumed conditions according to the internal mechanism of the object, the production process or the transfer mechanism of the material flow. It is a mathematical model based on mass balance equations, energy balance equations, momentum balance equations, phase balance equations, and some physical property equations, chemical reaction laws, circuit fundamental laws, etc. to obtain an object or process. The mechanism model has the advantages that model parameters are easy to adjust and have very definite physical meanings, and has the defect that for some objects or micro processes, mathematical expressions are difficult to write at present, or certain coefficients in the expressions are difficult to determine, so that the applicability is poor. The mechanism model often requires a large number of parameters, which, if not accurately obtained, may affect the simulation effect of the model.
With the rapid development of the internet of things and the continuous progress of cloud technology, the existing data generation, collection and storage modes are more convenient than the past, and big data mining is used as a new analysis method to provide a hand grip for the utilization and analysis of massive historical data of the LNG receiving station. The data driving is to collect mass data by means of mobile internet or other related software, organize the data to form information, integrate and refine the related information, and form an automatic decision model through training and fitting on the basis of the data. Compared with a mechanism modeling method, the data driving method does not need to pay attention to the intrinsic physical law of the system, the modeling process is simple, and the calculation cost is lower than that of the mechanism modeling method. However, the modeling by adopting such a method only needs a large amount of training data, has high requirements on the quality of the data, and is usually a black box model, so that the interpretability of the model is weak. On the other hand, the running state of the research object or the value (such as running efficiency) of the key parameter often changes along with the increase of time, and the existing data model often lacks the perception capability of the change, thereby causing the reduction of modeling precision. At present, the application of big data technology in various industries is more and more popular, but the application in the liquefied natural gas industry is almost not.
The single mechanism modeling method and the big data analysis method have certain disadvantages and can not completely meet the requirements of operation optimization research of the receiving station. For a mechanism model method, the method has the disadvantages that the working conditions of the LNG receiving station are complex, multiple working conditions or intermittent or periodic discontinuous occurrences of unloading operation, liquid state output, gas state output and ship return are often preset in actual modeling, the boundary conditions and parameters of the mechanism modeling are changed more, and some parameters are difficult to measure and identify, so that great challenges are brought to the mechanism modeling.
For a big data analysis method, since LNG is liquid natural gas after being cooled to-160 ℃, on one hand, due to the low-temperature characteristic of LNG, a temperature and flow field in a receiving station system is complex, and a big data model is difficult to accurately represent all the conditions of low-temperature two-phase fluid changing along with the time sequence of physical parameters; on the other hand, for the working conditions which have not occurred in the history, the data limit in the whole plant and the whole life cycle can be expanded only by accurately simulating the two-phase flow modeling process under each working condition in the receiving station system by means of the mechanism model.
In summary, the mechanism modeling and data driving methods have respective advantages and disadvantages in representing complex objects or micro processes, modeling accuracy, calculation efficiency, model interpretability and the like, so that the optimization problem of the operation of the lng receiving station becomes a technical problem which needs to be solved at present.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an operation optimization method, system, medium, and computing device for a lng receiving station, which can improve operation efficiency, achieve cost reduction and efficiency improvement, and have the characteristics of high modeling accuracy and high computing efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme: a method of optimizing lng receiving station operations, comprising: acquiring historical data from multiple dimensions as a sample data set; preprocessing the sample set data set to obtain complete and available data and establish a data database; training a mechanism model by using historical data in the data formation library to obtain calculation working condition data, and adding the calculation working condition data into the data formation library; and performing big data analysis on the calculation working condition data in the database by combining the mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
Further, the preprocessing the sample set data set includes: and carrying out data cleaning, data integration, data reduction, data transformation and data filtering on the sample data set, eliminating abnormal data which are incomplete, inconsistent and extremely susceptible to noise intrusion, and acquiring complete and available data.
Further, the establishing a database includes: and organizing the complete and available data according to a preset data structure to form a database.
Furthermore, a key parameter database is arranged in the data forming library, so that the key parameters can be conveniently inquired; the key parameters are key measuring points which are daily concerned by field operators.
Further, the training of the mechanism model by using the historical data in the database includes: the mechanism model reads and calls parameters required by calculation in the data forming library; and the mechanism model calculates the called parameters, compares the calculated result with the historical data of the corresponding measuring points, and corrects the mechanism model according to error analysis until the accuracy of the mechanism model meets the preset requirement.
Further, the performing big data analysis on the calculated working condition data in the database by combining the mechanism model includes: analyzing history, predicting early warning and optimizing operation; the analysis history: finding out the characteristics of the past events according to the calculated working condition data in the data database; selecting corresponding analysis methods according to different working conditions, and finding out rules among different measuring points; the prediction early warning comprises the following steps: forecasting and early warning production according to the characteristics of the past events; analyzing the production plan, the production process and the corresponding result of the liquefied natural gas receiving station according to the process rule and the historical data of the past events, setting a predicted value, and calculating the working condition parameter corresponding to the predicted value; if the deviation between the predicted value and the current value exceeds a preset range, pre-alarming the production; the predicted values include: a downstream export plan, an upstream resource supply plan; the operation optimization comprises the following steps: and setting at least one optimization target, and modeling and optimizing according to the optimization target to obtain an optimization result.
Further, the directing production comprises: off-line guidance and on-line guidance; the off-line type guidance means that after the big data analysis result is provided for an operator, the operator manually operates in a receiving station DCS system according to the suggestion; the online guidance means that the optimization suggestion obtained by the big data analysis is directly accessed to a receiving station DCS system.
A lng receiving station operation optimization system, comprising: the system comprises an acquisition module, a database formation module, a training module and a big data analysis module; the acquisition module acquires historical data from multiple dimensions as a sample data set; the database formation module is used for preprocessing the sample set data set, acquiring complete and available data and establishing a database formation; the training module is used for training a mechanism model by using historical data in the data formation library to obtain calculation working condition data, and adding the calculation working condition data into the data formation library; and the big data analysis module is used for carrying out big data analysis on the calculation working condition data in the data formation base by combining the mechanism model to obtain a big data analysis result and an operation optimization result, and guiding the production.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the above methods.
A computing device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured for execution by the one or more processors, the one or more programs including instructions for performing any of the above-described methods.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention combines big data analysis and process mechanism, and provides accurate and effective calculation parameters for a mechanism model.
2. According to the invention, through the combination of big data analysis and a process mechanism, real historical data is utilized to provide accurate calculation parameters for a mechanism model, the mechanism model is utilized to expand big data boundaries, the training precision of the data model is improved, and the modeling precision and the calculation efficiency are improved.
3. The invention combines big data analysis and process mechanism, combines the advantages of the two schemes, mutually adopts advantages and makes up for the disadvantages, can explain the specific mechanism of the process by using the model in a relatively fitting way, the calculation trend of the model is often identical with the actual process, and can directly analyze the interrelation between historical process data, thereby solving the problems brought by a single scheme, improving the operation efficiency, realizing cost reduction and efficiency improvement, and providing a set of complete technical route for the exploration and utilization of industrial data.
Drawings
FIG. 1 is a schematic flow diagram illustrating an overall process for optimizing the operation of a LNG receiving station according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart of a method for optimizing the operation of a LNG receiving station according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a computing device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and it should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of features, steps, operations, devices, components, and/or combinations thereof.
In an embodiment of the present invention, as shown in fig. 1, an optimization method for an operation of a lng receiving station is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented by interaction between the terminal and the server. The method for optimizing the operation of the lng receiving station provided by the embodiment may be used for optimizing the operation of the lng receiving station, and may also be applied to other fields. In this embodiment, the method includes the steps of:
step 1, acquiring historical data from multiple dimensions as a sample data set;
step 2, preprocessing a sample set data set to obtain complete and available data and establish a data database;
step 3, training the mechanism model by using historical data in the data formation library to obtain calculation condition data, and adding the calculation condition data into the data formation library;
and 4, performing big data analysis on the calculation working condition data in the data formation library by combining a mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
As shown in fig. 2, in step 1, obtaining history data from multiple dimensions as a sample data set includes:
(1) reading directly from a factory decentralized control system;
(2) reading directly from a third-party software platform connected with factory data;
(3) importing from csv files, txt files and files;
(4) and (4) manually and directly entering.
In the step 2, the sample set data set is preprocessed, because in the historical real data, the data is incomplete (lacks some attribute values), inconsistent (such as differences in names), and extremely vulnerable to noise (errors or abnormal values), and due to the wide data sources, the data set often comes from a plurality of heterogeneous data sources, and the number of the data sets is too large, and the data preprocessing is necessary to improve the integrity and usability of the data, which specifically includes:
and (3) carrying out data cleaning, data integration, data reduction, data transformation and data filtering on the sample data set, eliminating abnormal data which are incomplete, inconsistent and extremely easy to be invaded by noise, and acquiring complete and available data.
In the present embodiment, the data preprocessing process includes, but is not limited to, the above-described processing.
In the step 2, establishing a database:
and organizing the acquired complete and available data according to a preset data structure to form a database. The form of the database is not limited, and the database comprises: hierarchical databases, network databases, and relational databases.
The database has the advantages of storing a large amount of data, occupying less space, being convenient and quick to manage and operate, being accurate, quick and efficient in retrieval statistics, being good in data application sharing performance and reducing the redundancy of the data.
The database is internally provided with a key parameter database, so that the key parameters can be conveniently inquired. The key parameters are key measuring points which are paid attention to by field operators in daily life.
When the system is used, key measuring points which are daily concerned by field operators are combed to form a key parameter database; the database can realize the basic functions of the database such as data query, screening, sorting, comparison, unit conversion, import, export and the like.
Taking the lng receiving station as an example, the operator may choose to view the level, pressure, flow rate of the recondenser during a certain month or at a certain time of the year, or may query the maximum pressure that the recondenser has reached during historical operations, etc.
In the step 3, the training of the mechanism model by using the historical data in the data database includes the following steps:
3.1, the data formation library can carry out data communication with the mechanism model, and the mechanism model reads and calls parameters required by calculation in the data formation library;
and 3.2, calculating the called parameters by the mechanism model, comparing the calculated result with the historical data of the corresponding measuring point, and correcting the mechanism model according to error analysis until the accuracy of the mechanism model meets the preset requirement.
After the training of the mechanism model is finished, preset parameters are input according to expert experience, and a result is calculated through the mechanism model and is added into a database as calculation working condition data. In the embodiment, the boundary of the underlying analysis data is expanded through the mechanism model, and the problem of limitation that only historical working condition data can be analyzed in the conventional big data analysis is solved.
In the step 3, the database formation includes: and calculating different data such as working condition data, historical data, expert data, industrial experience and the like, and carrying out field identification according to different sources.
In the step 4, the big data analysis of the calculation condition data in the data library is performed by combining the mechanism model, and the big data analysis comprises the following steps: analyzing history, predicting early warning and optimizing operation. Wherein:
analyzing the history: finding out the characteristics of the past events through the calculated working condition data in the data library; selecting corresponding analysis methods according to different working conditions, and finding out rules among different measuring points;
predicting and early warning: predicting and early warning production through the characteristics of past events; analyzing the production plan, the process and the corresponding result rule of the liquefied natural gas receiving station according to the process rule and the historical data of the passing event, setting a predicted value (comprising a downstream export plan, an upstream resource supply plan and the like), calculating a working condition parameter corresponding to the predicted value and informing an operator; if the deviation between the predicted value and the current value exceeds a preset range, early warning is carried out on production;
operation optimization: and setting at least one optimization target, and modeling and optimizing according to the optimization target to obtain an optimized result.
In this embodiment, the method for analyzing big data is not limited, and includes but is not limited to: a comparative analysis method, a group analysis method, a cross analysis method, a structural analysis method, a funnel graph analysis method, a comprehensive evaluation analysis method, a factor analysis method, a matrix correlation analysis method, a regression analysis method, a cluster analysis method, a discriminant analysis method, a principal component analysis method, a factor analysis method, a correspondence analysis method, a time series, and the like.
Taking the lng receiving station as an example, the optimization objectives include: the method comprises the following steps of optimizing the pressure of a storage tank during discharging, optimizing the cold insulation circulation amount during non-discharging, optimizing the energy consumption of equipment (such as a low-pressure pump, a high-pressure pump, a sea water pump, a low-pressure compressor and a high-pressure compressor), optimizing the starting and stopping times of the equipment, optimizing the operation stability of the equipment (such as a recondenser), optimizing the evaporation gas amount of a whole plant and the like. The data analysis can flexibly perform modeling optimization according to an optimization target set by an operator, the model gives an optimization result, and the result can be a specific value of a process measuring point (such as pressure, flow, temperature and liquid level), can also be the opening degree of a valve, can also be a start-stop suggestion of equipment, can also be the number of starting equipment and the like.
In the step 4, the guidance for production includes: off-line guidance and on-line guidance. Wherein:
off-line guidance means that after the big data analysis result is provided for an operator, the operator operates in a receiving station DCS system according to a suggested manual work;
and the online guidance means that the optimization suggestion obtained by big data analysis is directly accessed to the receiving station DCS.
In conclusion, the method solves the problem that the traditional single mechanism model calculation method is difficult to represent some complex objects or micro processes, and can provide a large number of effective parameters for the mechanism model through big data. Furthermore, the invention solves the problem that the existing data model in the single big data analysis method has insufficient perception capability on the real change along with the time. The two methods with advantages are combined with each other, data boundaries are expanded for a big data bottom layer through a mechanism model, the range of data analysis is expanded, a large number of parameters are provided for the mechanism model through the big data model, and the calculation efficiency is improved. The invention combines the flow of big data analysis and the process mechanism completely and applies the flow to the industry of the liquefied natural gas receiving station, and provides a set of complete technical route for the exploration and utilization of industry data.
In one embodiment of the present invention, there is provided a lng receiving station operation optimizing system, including: the system comprises an acquisition module, a database formation module, a training module and a big data analysis module;
the acquisition module acquires historical data from multiple dimensions as a sample data set;
the database formation module is used for preprocessing the sample set data set, acquiring complete and available data and establishing a database formation;
the training module is used for training the mechanism model by using historical data in the data formation library to obtain calculation working condition data, and adding the calculation working condition data into the data formation library;
and the big data analysis module is used for carrying out big data analysis on the calculation working condition data in the data formation library by combining the mechanism model to obtain a big data analysis result and an operation optimization result, and guiding the production.
The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
As shown in fig. 3, which is a schematic structural diagram of a computing device provided in an embodiment of the present invention, the computing device may be a terminal, and may include: a processor (processor), a communication Interface (communication Interface), a memory (memory), a display screen and an input device. The processor, the communication interface and the memory are communicated with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium, an internal memory, the non-volatile storage medium storing an operating system and a computer program that when executed by the processor implements an optimized computation method; the internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computing equipment, an external keyboard, a touch pad or a mouse and the like. The processor may call logic instructions in memory to perform the following method:
acquiring historical data from multiple dimensions as a sample data set; preprocessing a sample set data set to obtain complete and available data and establish a data database; training the mechanism model by using historical data in the data formation library to obtain calculation condition data, and adding the calculation condition data into the data formation library; and (4) carrying out big data analysis on the calculation working condition data in the data formation library by combining a mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing one or more computer devices (which may be personal computers, servers, or network devices) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above method embodiments, for example, comprising: acquiring historical data from multiple dimensions as a sample data set; preprocessing a sample set data set to obtain complete and available data and establish a data database; training the mechanism model by using history data in the data formation library to obtain calculation condition data, and adding the calculation condition data into the data formation library; and (4) carrying out big data analysis on the calculation working condition data in the data formation library by combining a mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
In one embodiment of the invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided by the above embodiments, for example, including: acquiring historical data from multiple dimensions as a sample data set; preprocessing a sample set data set to obtain complete and available data and establish a data database; training the mechanism model by using history data in the data formation library to obtain calculation condition data, and adding the calculation condition data into the data formation library; and (4) carrying out big data analysis on the calculation working condition data in the data formation library by combining a mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for optimizing the operation of a lng receiving station, comprising:
acquiring historical data from multiple dimensions as a sample data set;
preprocessing the sample set data set to obtain complete and available data and establish a data database;
training a mechanism model by using historical data in the data formation library to obtain calculation working condition data, and adding the calculation working condition data into the data formation library;
and performing big data analysis on the calculation working condition data in the database by combining the mechanism model to obtain a big data analysis result and an operation optimization result, and guiding production.
2. The lng receiving station operation optimization method of claim 1, wherein the preprocessing the sample set data set comprises: and (3) carrying out data cleaning, data integration, data reduction, data transformation and data filtering on the sample data set, eliminating abnormal data which are incomplete, inconsistent and extremely easy to be invaded by noise, and acquiring complete and available data.
3. The lng receiving station operation optimizing method according to claim 1 or 2, wherein the creating of the database includes: and organizing the complete and available data according to a preset data structure to form a database.
4. The liquefied natural gas receiving station operation optimization method according to claim 1, 2 or 3, wherein a key parameter database is provided in the database, so as to facilitate query of key parameters; the key parameters are key measuring points which are daily concerned by field operators.
5. The method for optimizing lng receiving station operation according to claim 1, wherein the training of the mechanism model using the historical data in the data base includes:
the mechanism model reads and calls parameters required by calculation in the data forming library;
and the mechanism model calculates the called parameters, compares the calculated result with the historical data of the corresponding measuring points, and corrects the mechanism model according to error analysis until the accuracy of the mechanism model meets the preset requirement.
6. The method for optimizing lng receiving station operation according to claim 1, wherein the performing big data analysis on the calculated operating condition data in the data base in combination with the mechanism model includes: analyzing history, predicting early warning and optimizing operation;
the analysis history: finding out the characteristics of the past events according to the calculated working condition data in the data database; selecting corresponding analysis methods according to different working conditions, and finding out rules among different measuring points;
the prediction early warning comprises the following steps: forecasting and early warning production according to the characteristics of the past events; analyzing the production plan, the production process and the corresponding result of the liquefied natural gas receiving station according to the process rule and the historical data of the past events, setting a predicted value, and calculating the working condition parameter corresponding to the predicted value; if the deviation between the predicted value and the current value exceeds a preset range, early warning is carried out on production; the predicted values include: a downstream export plan, an upstream resource supply plan;
the operation optimization comprises the following steps: and setting at least one optimization target, and modeling and optimizing according to the optimization target to obtain an optimization result.
7. The lng receiving station operation optimizing method of claim 1, wherein the directing production comprises: off-line guidance and on-line guidance;
the off-line guidance means that after the big data analysis result is provided for an operator, the operator manually operates in a receiving station DCS system according to the suggestion;
the online guidance means that the optimization suggestion obtained by the big data analysis is directly accessed to a receiving station DCS system.
8. A lng receiving station operation optimization system, comprising: the system comprises an acquisition module, a database formation module, a training module and a big data analysis module;
the acquisition module acquires historical data from multiple dimensions as a sample data set;
the database formation module is used for preprocessing the sample set data set, acquiring complete and available data and establishing a database formation;
the training module is used for training a mechanism model by using historical data in the data formation library to obtain calculation working condition data, and adding the calculation working condition data into the data formation library;
and the big data analysis module is used for carrying out big data analysis on the calculation working condition data in the data formation base by combining the mechanism model to obtain a big data analysis result and an operation optimization result, and guiding the production.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
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