CN117763948A - Dual-drive atmospheric and vacuum device modeling method, device, equipment and storage medium - Google Patents
Dual-drive atmospheric and vacuum device modeling method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a model construction technology, and discloses a modeling method and device of a dual-drive atmospheric and vacuum device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring operation data of the atmospheric and vacuum device, and performing outlier screening on the operation data to obtain standard data; performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types; performing network training according to multiple types of standard operation data sets and corresponding crude oil types to obtain data models of atmospheric and vacuum devices of different types; and acquiring a mechanism model of the atmospheric and vacuum device, and performing rule setting on the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device. The simulation accuracy of the atmospheric and vacuum device can be improved.
Description
Technical Field
The invention relates to the field of model construction, in particular to a modeling method, a modeling device, modeling equipment and modeling medium for a dual-drive atmospheric and vacuum device.
Background
The atmospheric and vacuum distillation device is used as a primary link of crude oil production, separates crude oil into various intermediate products for further processing by downstream devices or directly used as a final product, so that modeling the atmospheric and vacuum distillation device and using the atmospheric and vacuum distillation device in a production plan to improve profit benefits of a refinery is very necessary, and the production efficiency directly determines the utilization rate of crude oil. Modeling and simulation have become important means for understanding and reforming the world as important means for analyzing and researching system behaviors and structures and revealing system operation processes and laws.
In the prior art, the atmospheric and vacuum device can be simulated by establishing a mathematical model describing the process characteristics, and the model development time is long and the experience of an expert is enriched although the atmospheric and vacuum device has strict theoretical foundation support; the data modeling is performed by using a machine learning method, data information can be mined from the data without relying on system mechanism and priori knowledge, and a mapping relation between the data is constructed, but the model performance depends on the quality and the number of training samples, and the data model cannot predict the data which are not involved in the samples, so that the simulation accuracy is not high.
Therefore, a method for improving the accuracy of the simulation model of the atmospheric and vacuum device is needed to solve the problem of low accuracy of the simulation of the atmospheric and vacuum device in the prior art.
Disclosure of Invention
In view of the above problems, embodiments of the present invention provide a dual-drive atmospheric and vacuum device modeling method, apparatus, device, and storage medium.
In a first aspect, an embodiment of the present invention provides a modeling method for a dual-driven atmospheric and vacuum device, including:
acquiring operation data of the atmospheric and vacuum device, and performing outlier screening on the operation data to obtain standard data;
performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
performing network training according to multiple types of standard operation data sets and corresponding crude oil types to obtain data models of atmospheric and vacuum devices of different types;
and acquiring a mechanism model of the atmospheric and vacuum device, and performing rule setting on the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device.
According to an embodiment of the present invention, the performing outlier screening on the operation data to obtain standard data includes:
performing data smoothing on the operation data to obtain preprocessed data;
error elimination is carried out on the preprocessed data, and accurate data are obtained;
and supplementing the abnormal value of the accurate data to obtain standard data.
According to an embodiment of the present invention, the performing data smoothing on the operation data to obtain preprocessed data includes:
and carrying out data smoothing processing on the operation data by using the following steps:
F(t)＝(A t-1 +A t-2 +A t-3 +…+A t-n )/n
wherein t represents a period corresponding to the operation data; f (t) represents the preprocessing data for period t; n represents the number of preset periods; a is that t-n Expressed as actual values for n epochs before the t epoch; a is that t-1 An actual value expressed as a period immediately before the period t; a is that t-2 Expressed as the actual values of the first two epochs of the t epoch; a is that t-3 Expressed as actual values for the first three phases of the t phase.
According to an embodiment of the present invention, the performing error rejection on the preprocessed data to obtain accurate data includes:
carrying out parameter calculation on the preprocessed data to obtain a mean value and a standard deviation;
setting a probability interval according to the mean value and the standard deviation;
and carrying out probability screening on the preprocessed data according to the probability interval to obtain the accurate data.
According to an embodiment of the present invention, the performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types includes:
acquiring attribute key parameters of the crude oil data, and performing similarity calculation on the crude oil data according to the attribute parameters to obtain a similarity result;
and classifying the crude oil data according to the similarity result to obtain multiple types of the crude oil data and corresponding crude oil types.
According to an embodiment of the present invention, the performing network training according to the standard operation data sets and the corresponding crude oil types to obtain data models of atmospheric and vacuum devices in different categories includes:
acquiring parameter point location data of the atmospheric and vacuum device, and carrying out data correction on the parameter point location data to obtain a correction data set;
and performing simulation calculation on the correction data set and the standard operation data sets according to the crude oil types to obtain an atmospheric and vacuum device data model.
According to an embodiment of the present invention, the rule setting is performed on the atmospheric and vacuum device mechanism model according to the atmospheric and vacuum device data model to obtain an atmospheric and vacuum device simulation model, including:
adding a preset point location error range to the atmospheric and vacuum device data model;
and connecting the atmospheric and vacuum device mechanism model with the data model to obtain the atmospheric and vacuum device simulation model.
In a second aspect, an embodiment of the present invention provides a dual-driven modeling apparatus for an atmospheric and vacuum apparatus, including:
the abnormal value screening module is used for acquiring the operation data of the atmospheric and vacuum device, and performing abnormal value screening on the operation data to obtain standard data;
the type clustering module is used for carrying out type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
the data model generation module is used for carrying out network training according to a plurality of types of standard operation data sets and corresponding crude oil types to obtain data models of different types of atmospheric and vacuum devices;
and the simulation model generation module is used for carrying out rule setting on the atmospheric and vacuum device mechanism model according to the atmospheric and vacuum device data model to obtain an atmospheric and vacuum device simulation model.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement a dual-drive atmospheric and vacuum device modeling method as described in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a dual-drive atmospheric and vacuum device modeling method as described in the first aspect.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the embodiment of the invention provides a modeling method of a double-drive atmospheric and vacuum device, which can eliminate coarse errors in operation data by performing outlier screening on the operation data, and improve the data quality of the operation data, thereby improving the accuracy of a data model of the atmospheric and vacuum device; according to the multi-type standard operation data set and the corresponding crude oil types, the network training is carried out, the atmospheric and vacuum device data model corresponding to each type can be obtained aiming at the different types of crude oil data sets, the problem of insufficient generalization performance of a single atmospheric and vacuum device data model is solved, and therefore accuracy of the atmospheric and vacuum device simulation model is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing a method of modeling a dual-drive atmospheric and vacuum device according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of performing outlier screening on the operation data to obtain standard data according to the first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a first embodiment of the present invention for performing type clustering on crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
FIG. 4 shows a functional block diagram of a dual-drive atmospheric and vacuum device modeling apparatus according to a third embodiment of the present invention;
fig. 5 shows a schematic diagram of a composition structure of an electronic device implementing the modeling method of a dual-drive atmospheric-vacuum device according to the fourth embodiment of the present invention.
Detailed Description
The disclosure is further described below with reference to the embodiments shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
The invention provides a modeling method of a double-drive atmospheric and vacuum device, which can remove coarse errors in operation data by performing outlier screening on the operation data, and improve the data quality of the operation data, thereby improving the accuracy of a data model of the atmospheric and vacuum device; according to the multi-type standard operation data set and the corresponding crude oil types, network training is carried out, the atmospheric and vacuum device data model corresponding to each type can be obtained according to the different types of crude oil data, and compared with the traditional method, the problem of insufficient generalization performance of a single atmospheric and vacuum device data model is solved.
Example 1
As shown in fig. 1, the invention provides a modeling method of a dual-drive atmospheric and vacuum device, which comprises the following steps:
s1, acquiring operation data of the atmospheric and vacuum device, and performing outlier screening on the operation data to obtain standard data.
In the embodiment of the invention, the operation data of the atmospheric and vacuum device can be obtained by configuring a system software and hardware environment, wherein the software environment comprises various crude oil real-time databases and information systems, the information systems comprise but are not limited to a real-time database system, a LIMS (laboratory information management system Laboratory information management system, abbreviated as LIMS) and the like, and the hardware environment comprises the atmospheric and vacuum device, an informatization technical laboratory and the like; the operation data of the atmospheric and vacuum device comprise device operation data, crude oil quick evaluation data (crude oil property data obtained by analysis of the information system) and crude oil product quality index parameters.
In the embodiment of the present invention, referring to fig. 2, the performing outlier screening on the operation data to obtain standard data includes:
s21, carrying out data smoothing processing on the operation data to obtain preprocessing data;
s22, performing error elimination on the preprocessed data to obtain accurate data;
s23, carrying out abnormal value supplementation on the accurate data to obtain standard data.
In the embodiment of the invention, a moving average method can be adopted for data smoothing, the moving average method can be divided into a simple moving average method and a weighted moving average method, and is a simple smoothing prediction technology, the basic idea is a method for sequentially calculating the operation data of a t period according to item-by-item transition of the operation data of the atmospheric and vacuum device in time sequence so as to reflect long-term trend, wherein t is represented as a predicted period; because the operation data is time sequence data, the influence of the periodic variation and the random fluctuation is large, and when the development trend of an event is not easy to display, the influence of the factors can be eliminated by using a moving average method, the development direction and trend (namely a trend line) of the event are displayed, and then the long-term trend of a predicted sequence is analyzed according to the trend line; the data smoothing process can reduce measurement errors generated during original data analysis, and improves the accuracy of data analysis of the atmospheric and vacuum device.
In the embodiment of the invention, the abnormal value supplementation can be realized by utilizing an interpolation method, firstly, linear fitting is carried out on the accurate data to obtain a fitting polynomial, the missing value is solved by utilizing the fitting polynomial, and the missing value is inserted into the accurate data to obtain the standard data.
In the embodiment of the present invention, the performing data smoothing on the operation data to obtain preprocessed data includes:
and carrying out data smoothing processing on the operation data by using the following steps:
F(t)＝(A t-1 +A t-2 +A t-3 +…+A t-n )/n
wherein t represents a period corresponding to the operation data; f (t) represents the preprocessing data for period t; n represents the number of preset periods; a is that t-n Expressed as actual values for n epochs before the t epoch; a is that t-1 An actual value expressed as a period immediately before the period t; a is that t-2 Expressed as the actual values of the first two epochs of the t epoch; a is that t-3 Expressed as actual values for the first three phases of the t phase.
In the embodiment of the invention, the error rejection is to reject coarse errors in the preprocessed data, the coarse errors are obviously deviated from the actual values of crude oil measurement results, the measured values containing the coarse errors are abnormal values, and the abnormal values can be rejected according to a coarse error judgment criterion; the coarse error judgment criterion is to establish a data standard as a judgment criterion for choosing or rejecting the abnormal value according to the principle of mathematical statistics under a preset assumption condition, and can adopt a 3 sigma criterion; the 3 sigma criterion is that a group of preprocessing data only contains random errors, standard calculation processing is carried out on the preprocessing data to obtain standard deviation, a section is determined according to preset probability, if the error exceeds the section, the error does not belong to random errors, and the data containing the coarse errors are removed.
In the embodiment of the present invention, the performing error rejection on the preprocessed data to obtain accurate data includes:
carrying out parameter calculation on the preprocessed data to obtain a mean value and a standard deviation;
setting a probability interval according to the mean value and the standard deviation;
and carrying out probability screening on the preprocessed data according to the probability interval to obtain the accurate data.
In the embodiment of the invention, the probability interval can be (mu-sigma, mu+sigma), (mu-2 sigma, mu+2 sigma), (mu-3 sigma, mu+3 sigma), wherein mu is expressed as the mean value of the preprocessing data, and sigma is expressed as the standard deviation of the preprocessing data; the probability screening is to reject error values exceeding the probability interval according to preset probabilities, and the preset probabilities can be: the probability of the pre-processed data being distributed in (μ - σ, μ+σ) is 0.652, the probability of the standard deviation being distributed in (μ -2σ, μ+2σ) is 0.9544, and the probability of the standard deviation value being distributed in (μ -3σ, μ+3σ) is 0.9974.
S2, performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types.
In the embodiment of the present invention, referring to fig. 3, the performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types includes:
s31, acquiring attribute key parameters of the crude oil data, and performing similarity calculation on the crude oil data according to the attribute parameters to obtain a similarity result;
s32, classifying the crude oil data according to the similarity result to obtain standard operation data sets corresponding to different crude oil types.
In the embodiment of the invention, the attribute key parameters of the crude oil data comprise: crude oil density, crude oil sulfur content, crude oil pour point, crude oil IBP-45 ℃ light end fraction, crude oil 45-165 ℃ naphtha fraction, crude oil 165-250 ℃ aviation kerosene fraction, crude oil 250-350 ℃ diesel fraction, crude oil 350-500 ℃ wax oil fraction, crude oil 500 ℃ fraction and the like; the similarity calculation may employ a clustering algorithm, which is an unsupervised machine learning method that groups the crude data into a plurality of similar groups, each group referred to as a "class", one class containing a subset of observations from the crude data, all observations in the same class being considered similar, the observations in each class being close to each other, the observations in two different classes being distant from each other.
In the embodiment of the invention, classification can be distinguished according to the similarity result according to crude oil relative density (API), crude oil sulfur content and crude oil composition, for example, light crude oil and heavy crude oil can be classified according to the crude oil relative density, wherein the API of the light crude oil is (20, 34), and the API of the heavy crude oil is (10, 20); the crude oil types include paraffinic crude oil, naphthenic crude oil, intermediate crude oil, and the like.
And S3, performing network training according to the standard operation data sets and the corresponding crude oil types to obtain data models of the atmospheric and vacuum devices of different types.
In the embodiment of the present invention, the network training is performed according to a plurality of types of the standard operation data sets and corresponding crude oil types to obtain different types of atmospheric and vacuum device data models, including:
acquiring parameter point location data of the atmospheric and vacuum device, and carrying out data correction on the parameter point location data to obtain a correction data set;
and performing simulation calculation on the correction data set and the standard operation data sets according to the crude oil types to obtain an atmospheric and vacuum device data model.
In the embodiment of the invention, the parameter point location data is input point location and output point location data of the atmospheric and vacuum device data model, wherein the input point location data comprises: the primary distillation tower feeding flow, primary distillation tower feeding temperature, primary distillation tower top circulation flow, primary distillation tower top circulation return tower temperature and the like, and the output point position data comprises: primary top oil flow, primary top gas flow, normal top oil flow, etc.; the data correction is carried out by adopting a preset mechanism model of the atmospheric and vacuum device, the mechanism model of the atmospheric and vacuum device is built based on mechanism knowledge and expert experience, the Aspen Hysys software can be utilized to carry out process simulation on the atmospheric and vacuum device, a dynamic link library generated by the software is linked with a control system of the atmospheric and vacuum device, the operation data of the atmospheric and vacuum device can enter the software, and meanwhile, the parameter point position data of the software can also be transmitted back to the atmospheric and vacuum device, and the technology can realize the following steps: on-line optimization control; and (3) production guidance and training, and improving the accuracy of the crude oil data, thereby improving the accuracy of the atmospheric and vacuum device data model.
In the embodiment of the invention, the simulation calculation can be neural network training on the correction data set and the crude oil data set corresponding to each type of crude oil, wherein the neural network is obtained by pre-training according to the crude oil data set and comprises an input layer, a hidden layer and an output layer; firstly, inputting the crude oil data set into the input layer for feature extraction to obtain feature vectors, training and calculating the feature vectors in the hidden layer, obtaining an effective neural network through training of a large amount of crude oil data, and inputting the correction data into the effective neural network to obtain an atmospheric and vacuum device data model corresponding to each type of crude oil. The method and the device can improve the accuracy of the data model of the atmospheric and vacuum device, and can greatly improve the accuracy of the data model of the atmospheric and vacuum device due to a large amount of data training.
S4, acquiring a mechanism model of the atmospheric and vacuum device, and setting rules of the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device.
In the embodiment of the invention, the mechanism model of the atmospheric and vacuum device is built based on mechanism knowledge and expert experience, and the Aspen Hysys software can be utilized to simulate the process of the atmospheric and vacuum device.
In the embodiment of the present invention, the rule setting is performed on the atmospheric and vacuum device mechanism model according to the atmospheric and vacuum device data model to obtain an atmospheric and vacuum device simulation model, which includes:
adding a preset point location error range to the atmospheric and vacuum device data model;
and connecting the atmospheric and vacuum device mechanism model with the data model to obtain the atmospheric and vacuum device simulation model.
In the embodiment of the invention, the point location error range is an error range of an analysis result output by the atmospheric and vacuum device data model after crude oil analysis is performed, for example, the error range of a 95% distillation temperature point location of mixed diesel cannot exceed 0.1, and when the point location error range is exceeded, the atmospheric and vacuum device mechanism model is selected to be invoked for online simulation calculation; the accuracy judgment point location comprises: model accuracy evaluation point location: the initial distillation point of the normal first-line aviation kerosene, the 10% distillation temperature of the normal first-line aviation kerosene, the 50% distillation temperature of the normal first-line aviation kerosene, the final distillation point of the normal first-line aviation kerosene, the flash point of the normal first-line aviation kerosene, the initial distillation point of the mixed diesel oil, the 95% distillation temperature of the mixed diesel oil, the initial distillation point of naphtha and the final distillation point of naphtha.
Example two
In order to more clearly understand the present invention, the case that the type clustering is performed on the crude oil data in the standard data according to the embodiment of the present invention to obtain multiple crude oil data sets and corresponding crude oil types is further explained by a second embodiment.
In the embodiment of the present invention, the performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types includes:
extracting the characteristics of the crude oil data to obtain crude oil characteristic vectors;
and carrying out feature classification calculation on the crude oil feature vectors, and obtaining multiple types of crude oil data and corresponding crude oil types according to feature classification calculation results.
In the embodiment of the invention, the characteristic extraction can adopt a fully connected neural network algorithm to perform linear transformation function calculation on the crude oil data, and an activation function in the fully connected neural network is utilized to perform activation processing on the linear transformation function so as to obtain the crude oil characteristic vector; the classification calculation can utilize a preset crude oil attribute classifier, wherein the crude oil attribute classifier comprises a softmax function, and the function can convert crude oil feature vectors into element vectors of 0 to 1 and map the element vectors into a (0, 1) interval to obtain the probability of each crude oil feature vector, so that feature classification is realized.
Example III
As shown in fig. 4, the present embodiment also provides a functional block diagram of a dual-drive atmospheric and vacuum device modeling apparatus.
The dual-drive atmospheric and vacuum device modeling apparatus 100 according to the present embodiment may be mounted in an electronic device. Depending on the functions implemented, the dual-driven atmospheric and vacuum device modeling apparatus 100 may include a outlier screening module 101, a type clustering module 102, a data model generation module 103, and a simulation model generation module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the outlier screening module 101 is configured to obtain operation data of the atmospheric and vacuum device, and perform outlier screening on the operation data to obtain standard data;
the type clustering module 102 is configured to perform type clustering on crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
the data model generating module 103 is configured to perform network training according to multiple types of the standard operation data sets and corresponding crude oil types, so as to obtain data models of atmospheric and vacuum devices in different types;
the simulation model generation module 104 is configured to set rules for the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device, so as to obtain a simulation model of the atmospheric and vacuum device.
In detail, each module in the dual-drive atmospheric and vacuum device modeling apparatus 100 in the embodiment of the present invention adopts the same technical means as the dual-drive atmospheric and vacuum device modeling method in the first embodiment and the second embodiment, and can produce the same technical effects, which are not described herein.
Example IV
As shown in fig. 5, the present embodiment further provides a computer electronic device, which may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a dual-driven atmospheric and vacuum device modeling program, stored in the memory 11 and executable on the processor 10.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, abbreviated as CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., an atmospheric and vacuum device modeling program or the like that performs dual driving) stored in the memory 11, and calls data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, for example, a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and so on. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a dual-drive atmospheric and vacuum device modeling program, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Only an electronic device having components is shown, and it will be understood by those skilled in the art that the structures shown in the figures do not limit the electronic device, and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The dual-driven atmospheric and vacuum device modeling program stored in the memory 11 of the electronic device is a combination of instructions that, when executed in the processor 10, may implement:
acquiring operation data of the atmospheric and vacuum device, and performing outlier screening on the operation data to obtain standard data;
performing type clustering on the crude oil data in the standard data to obtain multiple types of standard operation data sets and corresponding crude oil types;
performing network training according to the multi-class standard operation data set and the corresponding crude oil types to obtain an atmospheric and vacuum device data model;
and acquiring a mechanism model of the atmospheric and vacuum device, and performing rule setting on the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device. In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Example five
The present embodiment provides a storage medium storing a computer program which, when executed by a processor, implements the steps of a dual-drive atmospheric and vacuum device modeling method as described above.
These program code 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.
Storage media includes both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media may include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
It is noted that the terms used herein are used merely to describe particular embodiments and are not intended to limit exemplary embodiments in accordance with the present application and when the terms "comprises" and/or "comprising" are used in this specification they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among them, artificial intelligence (Artificial Intelligence, abbreviated as AI) is a theory, method, technique and application system that simulates, extends and expands human intelligence, senses environment, acquires knowledge and uses knowledge to obtain an optimal result using a digital computer or a machine controlled by a digital computer.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of modeling a dual-drive atmospheric and vacuum device, the method comprising:
acquiring operation data of the atmospheric and vacuum device, and performing outlier screening on the operation data to obtain standard data;
performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
performing network training according to multiple types of standard operation data sets and corresponding crude oil types to obtain data models of atmospheric and vacuum devices of different types;
and acquiring a mechanism model of the atmospheric and vacuum device, and performing rule setting on the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device.
2. The modeling method of the dual-drive atmospheric and vacuum device according to claim 1, wherein the performing outlier screening on the operation data to obtain standard data comprises:
performing data smoothing on the operation data to obtain preprocessed data;
error elimination is carried out on the preprocessed data, and accurate data are obtained;
and supplementing the abnormal value of the accurate data to obtain the standard data.
3. The modeling method of the dual-drive atmospheric and vacuum device according to claim 2, wherein the performing data smoothing on the operation data to obtain pre-processed data comprises:
and carrying out data smoothing processing on the operation data by using the following steps:
F(t)＝(A t-1 +A t-2 +A t-3 +…+A t-n )/n
wherein t represents a period corresponding to the operation data; f (t) represents the preprocessing data for period t; n represents the number of preset periods; a is that t-n Expressed as actual values for n epochs before the t epoch; a is that t-1 An actual value expressed as a period immediately before the period t; a is that t-2 Expressed as the actual values of the first two epochs of the t epoch; a is that t-3 Expressed as actual values for the first three phases of the t phase.
4. The modeling method of the dual-drive atmospheric and vacuum device according to claim 2, wherein the performing error rejection on the preprocessed data to obtain accurate data comprises:
carrying out parameter calculation on the preprocessed data to obtain a mean value and a standard deviation;
setting a probability interval according to the mean value and the standard deviation;
and carrying out probability screening on the preprocessed data according to the probability interval to obtain the accurate data.
5. The modeling method of the dual-drive atmospheric and vacuum device according to claim 1, wherein the performing type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types comprises:
acquiring attribute key parameters of the crude oil data, and performing similarity calculation on the crude oil data according to the attribute parameters to obtain a similarity result;
and classifying the crude oil data according to the similarity result to obtain standard operation data sets corresponding to different crude oil types.
6. The dual-driven atmospheric and vacuum device modeling method according to claim 1, wherein the performing network training according to the standard operation data sets and the corresponding crude oil types to obtain the atmospheric and vacuum device data models of different types comprises:
acquiring parameter point location data of the atmospheric and vacuum device, and carrying out data correction on the parameter point location data to obtain a correction data set;
and performing simulation calculation on the correction data set and the standard operation data sets according to the crude oil types to obtain an atmospheric and vacuum device data model.
7. The modeling method of the dual-driven atmospheric and vacuum device according to claim 1, wherein the rule setting is performed on the mechanism model of the atmospheric and vacuum device according to the data model of the atmospheric and vacuum device to obtain a simulation model of the atmospheric and vacuum device, and the method comprises the following steps:
adding a preset point location error range to the atmospheric and vacuum device data model;
and connecting the atmospheric and vacuum device mechanism model with the data model to obtain the atmospheric and vacuum device simulation model.
8. A dual-drive atmospheric and vacuum device modeling apparatus, the apparatus comprising:
the abnormal value screening module is used for acquiring the operation data of the atmospheric and vacuum device, and performing abnormal value screening on the operation data to obtain standard data;
the type clustering module is used for carrying out type clustering on the crude oil data in the standard data to obtain standard operation data sets corresponding to different crude oil types;
the data model generation module is used for carrying out network training according to a plurality of types of standard operation data sets and corresponding crude oil types to obtain data models of different types of atmospheric and vacuum devices;
and the simulation model generation module is used for carrying out rule setting on the atmospheric and vacuum device mechanism model according to the atmospheric and vacuum device data model to obtain an atmospheric and vacuum device simulation model.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the dual-drive atmospheric and vacuum device modeling method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, implements the dual-drive atmospheric and vacuum device modeling method of any of claims 1 to 7.
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