KR20220031501A - Fluid behavior analysis system based on artificial intelligent and method thereof - Google Patents

Abstract

The present invention relates to a system for analyzing pipe fluid flow based on artificial intelligence and a method thereof. The system comprises: a numerical analysis device outputting 3D flow pattern information data of each pipe by using a 3D computational fluid dynamics (CFD) analysis method; a data processing device receiving the 3D flow pattern information data of each pipe to extract and process characteristic data for fluid flow in each pipe; an artificial intelligence learning device receiving the characteristic data for fluid flow in each pipe and constructing a fluid flow intelligence learning model for each pipe through a pre-set machine learning method; and a fluid flow analysis device inferring and analyzing a fluid flow characteristic for each pipe by using the fluid flow intelligence learning model. Accordingly, the present invention can predict fluid flow status of each pipe provided in a piping system.

Description

Artificial intelligence-based pipeline fluid flow analysis system and method

The present invention relates to an artificial intelligence (AI)-based pipe fluid flow analysis system and a method therefor.

In general, analysis of the flow (or behavior) of air and fluids such as oil and water is considered very important for designing plants and solving problems in work processes in various industrial fields such as petrochemical plants and power plants.

This fluid flow analysis means to understand the interaction between the fluid, gas, etc., and the surface defined by boundary conditions around the parts to be analyzed (eg, pipes), and the change in flow and related characteristics. do.

On the other hand, Computational Fluid Dynamics (CFD) reproduces the flow of heat and fluid through computational operations. Reproducibility through effective numerical analysis resulted in significant savings in time and cost.

However, the computational fluid dynamics (CFD) analysis technique used to predict the flow of heat and fluid in the past increases the amount of computation exponentially as the size of the analysis target increases, requiring enormous computational resources and the analysis time There is a problem that it is difficult to be utilized in building a digital twin that takes a long time and requires real-time prediction.

Domestic Registered Patent No. 10-0957061 (Announced on May 13, 2010)

The present invention has been devised to solve the above-described problems, and an object of the present invention is to use a three-dimensional computational fluid dynamics (CFD) analysis method and an artificial intelligence (AI) machine learning method in a complex manner to solve each pipe provided in the piping system. By inferring and analyzing the fluid flow characteristics in the piping system, it is possible to quickly and accurately predict the fluid flow phenomenon in each pipe provided in the piping system, as well as significantly reduce computational resources and time, and in various industrial fields such as petrochemical plants and power plants. It is to provide an artificial intelligence-based pipeline fluid flow analysis system and its method that can be used in building a digital twin.

In order to achieve the above object, the first aspect of the present invention is based on the operating condition information of each pipe, including shape information, material property information, and specification information for a plurality of different pipes required for performing numerical analysis. Numerical analysis device that outputs 3D flow pattern information data in each pipe including 3D grid network information of each pipe using a computational fluid dynamics (CFD) analysis method; Characteristics of fluid flow in each pipe for inferring fluid flow characteristics in each pipe based on receiving the 3D flow pattern information data in each pipe including the 3D grid network information of each pipe output from the numerical analysis device a data processing device for extracting and processing data; By receiving the characteristic data of the fluid flow in each pipe extracted and processed from the data processing device, and based on this, it learns for each characteristic item about the fluid flow in each pipe through a preset artificial intelligence (AI)-based machine learning method. A fluid flow artificial intelligence learning device that builds a fluid flow artificial intelligence learning model for each pipe; and fluid for inferring and analyzing the fluid flow characteristics for each pipe for at least one pipe system designed with a plurality of different pipes using the fluid flow AI learning model for each pipe built from the fluid flow AI learning device. It is to provide an artificial intelligence-based pipe fluid flow analysis system including a flow analysis device.

Here, each of the pipes is preferably made of at least one of a straight pipe, an elbow, a T-tube, a reducer, and an orifice.

Preferably, the operating condition information of each pipe may include at least one condition information of flow rate, pressure, temperature, and dryness.

Preferably, the three-dimensional flow pattern information data in each pipe output from the numerical analysis device may consist of at least one information data of flow velocity, pressure, and temperature information.

Preferably, the artificial intelligence (AI)-based machine learning method applied to the fluid flow artificial intelligence learning device is a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and deep learning. ) of at least one artificial intelligence learning method.

Preferably, the fluid flow analysis device is, for each pipe, for at least one pipe system designed with a plurality of different pipes using the fluid flow AI learning model for each pipe built from the fluid flow AI learning device. Inferred and analyzed fluid flow characteristics, and based on the inferred and analyzed fluid flow characteristics information for each pipe of each pipe system, at least one evaluation information of thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe can create

Preferably, evaluation information of at least one of thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed from the fluid flow analysis device A storage device for storing and managing the database (DB) for each pipe system and each pipe may be further included.

Preferably, evaluation information of at least one of thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed from the fluid flow analysis device A communication device for transmitting the to an external terminal or server may be further included.

Preferably, the external terminal or server includes the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate of each pipe, the expected lifespan, At least one of the evaluation information of the inspection period and the replacement period is provided, and based on this, the user receives at least one of fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and the replacement cycle It is possible to provide a service so that the evaluation information of the user can be visually displayed on the display screen.

Preferably, the external terminal or server includes the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate of each pipe, the expected lifespan, At least one evaluation information of an inspection cycle and a replacement cycle is provided, and based on the evaluation information, at least one of fluid flow characteristic information for each pipe of each pipe system, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle is evaluated It is possible to provide a service so that the information is converted into a database (DB) for each piping system and for each piping, and stored and managed in a separate storage device.

Preferably, the server may provide a cloud computing service in response to a request from an external client terminal.

Preferably, the client terminal, using the client member login cloud web service of the server, fluid flow characteristic information for each piping system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle may be searched and displayed on the display screen in real time.

Preferably, when the server stores the evaluation information of at least one of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle into a database (DB) and stores it , using a symmetric or asymmetric encryption method to encrypt and store at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle, and the above A decryption key capable of decrypting at least one of the encrypted fluid flow characteristic information for each piping system and each pipe, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and the replacement cycle is transmitted to a preset client terminal. We can provide services as much as possible.

Preferably, the client terminal is encrypted and stored in the server using the decryption key transmitted from the server together with the client member login information, fluid flow characteristic information for each piping system and each pipe, the thickness of each pipe, It is possible to decode the evaluation information of at least one of the expected lifespan, the inspection period, and the replacement period, and search and display it on the display screen in real time.

Preferably, the server can download at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle through the client terminal. Cloud web service can be provided.

Preferably, the client terminal is, through the pipe fluid flow analysis related cloud application downloaded from the server, fluid flow characteristic information for each pipe system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle , and at least one evaluation information of a replacement cycle may be displayed on a real-time search and display screen.

Preferably, the operating condition information of each pipe, including shape information, material property information, and standard information for each pipe, may be input through an application service related to pipe fluid flow analysis pre-installed in an external terminal or server.

Preferably, the design information for each pipe designed for each pipe system may be input through a pipe fluid flow analysis related application service installed in advance in an external terminal or server.

A second aspect of the present invention is a method for analyzing the fluid flow of an artificial intelligence-based pipe using a system including a numerical analysis device, a data processing device, a fluid flow artificial intelligence learning device, and a fluid flow analysis device, (a ) 3D computational fluid dynamics (Computational Fluid Dynamics, CFD) outputting 3D flow pattern information data in each pipe including 3D grid network information of each pipe using an analysis method; (b) each for inferring the fluid flow characteristics in each pipe based on the 3D flow pattern information data in each pipe including the 3D grid network information of each pipe output in step (a) through the data processing device extracting and processing characteristic data on fluid flow in the pipe; (c) each using an artificial intelligence (AI)-based machine learning method that is preset based on the characteristic data for the fluid flow in each pipe extracted and processed in step (b) through the fluid flow artificial intelligence learning device Building a fluid flow artificial intelligence learning model for each pipe by learning for each feature item on the fluid flow in the pipe; and (d) using the fluid flow artificial intelligence learning model for each pipe constructed in step (c) through the fluid flow analysis device for at least one pipe system designed with a plurality of different pipes for each pipe. It is to provide an artificial intelligence-based pipe fluid flow analysis method comprising the step of inferring and analyzing flow characteristics.

Here, in step (a), each pipe is preferably made of at least one of a straight pipe, an elbow, a T-tube, a reducer, and an orifice.

Preferably, in the step (a), the operating condition information of each pipe may consist of at least one condition information of flow rate, pressure, temperature, and dryness.

Preferably, in the step (a), the output 3D flow pattern information data in each pipe may be composed of at least one information data of flow velocity, pressure, and temperature information.

Preferably, in the step (c), the preset artificial intelligence (AI)-based machine learning method is a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and deep learning (Deep). Learning) of at least one artificial intelligence learning method.

Preferably, in step (d), at least one pipe system designed with a plurality of different pipes using the fluid flow artificial intelligence learning model for each pipe built in step (c) through the fluid flow analysis device. After inferring and analyzing the fluid flow characteristics for each pipe for At least one piece of evaluation information may be generated.

Preferably, after step (d), the thickness reduction rate of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in step (d) through a separate storage device, is expected The method may further include the step of storing and managing the evaluation information of at least one of the life cycle, the inspection cycle, and the replacement cycle into a database (DB) for each pipe system and each pipe.

Preferably, after step (d), the thickness reduction rate of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in step (d) through a separate communication device, is expected The method may further include transmitting evaluation information of at least one of a lifespan, an inspection cycle, and a replacement cycle to an external terminal or server.

Preferably, the external terminal or server includes the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate of each pipe, the expected lifespan, At least one of the evaluation information of the inspection period and the replacement period is provided, and based on this, the user receives at least one of fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and the replacement cycle It is possible to provide a service so that the evaluation information of the user can be visually displayed on the display screen.

Preferably, the external terminal or server includes the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate of each pipe, the expected lifespan, At least one evaluation information of an inspection cycle and a replacement cycle is provided, and based on the evaluation information, at least one of fluid flow characteristic information for each pipe of each pipe system, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle is evaluated It is possible to provide a service so that the information is converted into a database (DB) for each piping system and for each piping, and stored and managed in a separate storage device.

Preferably, the server may provide a cloud computing service in response to a request from an external client terminal.

Preferably, the client terminal, using the client member login cloud web service of the server, fluid flow characteristic information for each piping system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle may be searched and displayed on the display screen in real time.

Preferably, when the server stores the evaluation information of at least one of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle into a database (DB) and stores it , using a symmetric or asymmetric encryption method to encrypt and store at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle, and the above A decryption key capable of decrypting at least one of the encrypted fluid flow characteristic information for each piping system and each pipe, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and the replacement cycle is transmitted to a preset client terminal. We can provide services as much as possible.

Preferably, the client terminal is encrypted and stored in the server using the decryption key transmitted from the server together with the client member login information, fluid flow characteristic information for each piping system and each pipe, the thickness of each pipe, It is possible to decode the evaluation information of at least one of the expected lifespan, the inspection period, and the replacement period, and search and display it on the display screen in real time.

Preferably, the server can download at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle through the client terminal. Cloud web service can be provided.

Preferably, the client terminal is, through the pipe fluid flow analysis related cloud application downloaded from the server, fluid flow characteristic information for each pipe system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle , and at least one evaluation information of a replacement cycle may be displayed on a real-time search and display screen.

Preferably, in step (a), the operating condition information of each pipe, including shape information, material property information, and specification information for each pipe, is through an external terminal or server pre-installed pipe fluid flow analysis related application service. can be entered.

Preferably, in step (d), the design information for each pipe designed in each pipe system may be input through an application service related to pipe fluid flow analysis pre-installed in an external terminal or server.

A third aspect of the present invention provides a computer-readable recording medium in which a program capable of executing the above-described artificial intelligence-based pipe fluid flow analysis method is recorded.

The artificial intelligence-based pipe fluid flow analysis method according to the present invention can be implemented as a computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored.

For example, computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, hard disk, floppy disk, removable storage device, and non-volatile memory (Flash Memory). , and optical data storage devices.

According to the artificial intelligence-based piping fluid flow analysis system and method of the present invention as described above, the three-dimensional computational fluid dynamics (CFD) analysis method and the artificial intelligence (AI) machine learning method are combined to be used in the piping system. By inferring and analyzing the fluid flow characteristics in each pipe provided, it is possible to quickly and accurately predict the fluid flow phenomenon in each pipe provided in the pipe system, as well as significantly reduce computational resources and time, and can be used in petrochemical plants and power plants. There are advantages that can be used to build a digital twin in various industries such as

1 is an overall block diagram for explaining an artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention.
2 is a detailed block diagram illustrating an external terminal applied to an embodiment of the present invention.
3 is an overall flowchart for explaining an artificial intelligence-based pipe fluid flow analysis method according to an embodiment of the present invention.

The above-described objects, features and advantages will be described below in detail with reference to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the present invention pertains will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise.

The terms used in the present invention have been selected as currently widely used general terms as possible while considering the functions in the present invention, but these may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, and the like. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than the name of a simple term.

In the entire specification, when a part "includes" a certain element, this means that other elements may be further included, rather than excluding other elements, unless otherwise stated. In addition, terms such as "...unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software, or a combination of hardware and software. .

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the embodiments of the present invention illustrated below may be modified in various other forms, and the scope of the present invention is not limited to the embodiments described below. The embodiments of the present invention are provided to more completely explain the present invention to those of ordinary skill in the art.

Each block in the accompanying block diagram and combinations of steps in the flowchart may be executed by computer program instructions (execution engine), which may be executed by a processor of a general-purpose computer, special-purpose computer, or other programmable data processing equipment. It may be mounted so that the instructions, which are executed by the processor of a computer or other programmable data processing equipment, create means for performing the functions described in each block of the block diagram or in each step of the flowchart. These computer program instructions may also be stored in a computer-usable or computer-readable memory that may direct a computer or other programmable data processing equipment to implement a function in a particular manner, and thus the computer-usable or computer-readable memory. It is also possible to produce an article of manufacture containing instruction means for performing the functions described in each block of the block diagram or each step of the flowchart, the instructions stored in the block diagram.

And, since the computer program instructions may be mounted on a computer or other programmable data processing equipment, a series of operating steps are performed on the computer or other programmable data processing equipment to create a computer-executed process to create a computer or other program It is also possible that instructions for performing the possible data processing equipment provide steps for carrying out the functions described in each block of the block diagram and in each step of the flowchart.

In addition, each block or step may represent a module, segment, or portion of code comprising one or more executable instructions for executing specified logical functions, and in some alternative embodiments the blocks or steps referred to in some alternative embodiments. It should be noted that it is also possible for functions to occur out of sequence. For example, it is possible that two blocks or steps shown one after another may be performed substantially simultaneously, and also the blocks or steps may be performed in the reverse order of the corresponding functions, if necessary.

1 is an overall block diagram for explaining an artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention, and FIG. 2 is a specific block for explaining an external terminal applied to an embodiment of the present invention. It is a configuration diagram.

1 and 2, the artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention is largely a numerical analysis device 100, a data processing device 200, a fluid flow artificial intelligence learning device ( 300), and a fluid flow analysis device 400, and the like. In addition, the artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention includes a storage device 500 , a communication device 600 , an external terminal (or client terminal) 20 , a server 30 , and the like. may include more. On the other hand, since the components shown in FIGS. 1 and 2 are not essential, the artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention may have more or fewer components than that. there is.

Hereinafter, the components of the artificial intelligence-based pipe fluid flow analysis system according to an embodiment of the present invention will be described in detail as follows.

Numerical analysis device 100 is a three-dimensional computational fluid dynamics (Computational Fluid Mechanics) based on the operating condition information of each pipe, including shape information, material property information, and/or standard information for a plurality of different pipes required for the execution of the numerical analysis Fluid Dynamics (CFD) analysis method is used to output 3D flow pattern information data in each pipe including 3D grid network information of each pipe.

At this time, it is preferable that each of the pipes is made of, for example, at least one of a straight pipe, an elbow, a T-tube, a reducer, and/or an orifice.

It is preferable that the operation condition information of each pipe includes, for example, at least one condition information of flow rate, pressure, temperature, and/or dryness.

In addition, it is preferable that the three-dimensional flow pattern information data in each pipe output from the numerical analysis device 100 consists of, for example, at least one information data of flow velocity, pressure, and/or temperature information.

On the other hand, the operating condition information of each pipe, including shape information, material property information and/or standard information for each pipe, is a pipe installed in advance in the external terminal 20 and/or the server 30 to be described later. It can be input through an application service related to fluid flow analysis.

The data processing device 200 receives the three-dimensional flow pattern information data in each pipe including the three-dimensional grid network information of each pipe output from the numerical analysis device 100, and infers the fluid flow characteristics in each pipe based on this data It performs the function of extracting and processing the characteristic data of the fluid flow in each pipe for

At this time, it is preferable that the characteristic data for the fluid flow in each pipe is composed of, for example, a curve (ie, a valve characteristic curve) of an input value that influences the characteristic of the fluid flow and an output value that expresses the characteristic flow.

The fluid flow artificial intelligence learning device 300 receives the characteristic data for the fluid flow in each pipe extracted and processed from the data processing device 200, and based on this, a preset artificial intelligence (AI)-based machine learning method is used. It performs a function of building a fluid flow artificial intelligence learning model for each pipe by learning for each feature item about the fluid flow in each pipe.

At this time, the artificial intelligence (AI)-based machine learning method applied to the fluid flow artificial intelligence learning apparatus 300 is, for example, a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and/or a deep It is preferable to use at least one artificial intelligence learning method among deep learning.

And, the fluid flow analysis device 400 uses the fluid flow artificial intelligence learning model for each pipe built from the fluid flow artificial intelligence learning device 300 for at least one pipe system designed with a plurality of different pipes, each It performs the function of inferring and analyzing the fluid flow characteristics for each pipe.

In addition, the fluid flow analysis device 400 uses a fluid flow AI learning model for each pipe built from the fluid flow AI learning device 300, for example, a plurality of different Inferring and analyzing the fluid flow characteristics for each pipe for at least one pipe system designed as a pipe of , an inspection cycle, and/or a replacement cycle may perform a function of generating at least one evaluation information.

In this case, the design information for each pipe designed for each pipe system may be input through an application service related to pipe fluid flow analysis pre-installed in the external terminal 20 and/or the server 30 .

Additionally, the storage device 500 includes fluid flow characteristic information for each pipe inferred and analyzed from the fluid flow analysis device 400, as well as the thickness reduction rate, expected lifespan, inspection cycle, and / Alternatively, at least one evaluation information during the replacement cycle is converted into a database (DB) for each piping system and/or each pipe, and stores and manages the information.

The storage device 500 includes, for example, a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (eg, SD or XD memory). etc.), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), It may include at least one type of storage medium among a magnetic memory, a magnetic disk, and an optical disk.

In addition, the communication device 600 includes the fluid flow characteristic information for each pipe of each pipe system inferred and analyzed from the fluid flow analysis device 400, as well as the thickness reduction rate, expected lifespan, inspection cycle, and / Alternatively, it performs a function of transmitting at least one piece of evaluation information during the replacement cycle to the external terminal 20 and/or the server 30 through the communication network 10 .

At this time, the communication network 10 is a communication network that is a high-speed backbone network of a large-scale communication network capable of large-capacity, long-distance voice and data services, and includes Wi-Fi, WiGig, It may be a next-generation wireless communication network including WiBro (Wireless Broadband Internet, Wibro) and Wimax (World Interoperability for Microwave Access, Wimax).

The Internet includes the TCP/IP protocol and various services existing in its upper layers, namely HTTP (Hyper Text Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), It means a worldwide open computer network structure that provides SNMP (Simple Network Management Protocol), NFS (Network File Service), NIS (Network Information Service), etc., and the communication device 600 is an external terminal 20 and/or An environment that allows access to the server 30 is provided. Meanwhile, the Internet may be a wired or wireless Internet, or may be a core network integrated with a wired public network, a wireless mobile communication network, or a portable Internet.

If the communication network 10 is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of the asynchronous mobile communication network, a wideband code division multiple access (WCDMA) type communication network may be used. In this case, although not shown in the drawings, the mobile communication network may include, for example, a Radio Network Controller (RNC). Meanwhile, although the WCDMA network is taken as an example, it may be a next-generation communication network such as a cellular-based 3G network, an LTE network, a 4G network, or a 5G network, and other IP-based IP networks. The communication network 10 serves to mutually transmit signals and data between the communication device 600 and the external terminal 20 and/or the server 30 .

And, the external terminal (or client terminal) 20 is a sense of each pipe, including the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service. At least one of evaluation information of meat rate, expected lifespan, inspection cycle, and/or replacement cycle is provided, and based on this, the user receives information about the fluid flow characteristics of each pipe of each pipe system, thinning rate of each pipe, expected lifespan, and inspection cycle , and/or may perform a function of providing a service to be displayed on the display screen to visually see at least one evaluation information of the replacement cycle.

In addition, the external terminal 20 includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate of each pipe, expected lifespan, Receive at least one evaluation information of the inspection cycle and/or replacement cycle, and based on it, the fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and/or the replacement cycle At least one piece of evaluation information may be converted into a database (DB) for each piping system and/or for each pipe, and may perform a function of providing a service to be stored and managed in a separate storage device (not shown).

In this case, the storage device is, for example, a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.). ), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), Magnetic It may include at least one type of storage medium among a memory, a magnetic disk, and an optical disk.

In addition, the external terminal 20 (preferably, the client terminal) uses the client member login cloud web service of the server 30, and the fluid flow characteristics for each piping system and/or each pipe stored in the server 30 It is possible to perform a function of searching and displaying evaluation information of at least one of information, a thinning rate of each pipe, an expected lifespan, an inspection period, and/or a replacement period in real time on the display screen.

In addition, the external terminal 20 (preferably, the client terminal) is encrypted and stored in the server 30 using the decryption key transmitted from the server 30 together with the corresponding client member login information for each piping system and / or It is possible to perform a function of decoding the evaluation information of at least one of the fluid flow characteristic information for each pipe, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and/or the replacement cycle, and searching and displaying it on the display screen in real time. .

In addition, the external terminal 20 (preferably, the client terminal) is stored in the server 30 through the pipe fluid flow analysis related cloud application downloaded from the server 30 for each pipe system and/or each pipe fluid flow. It is possible to perform a function of real-time search and display of evaluation information of at least one of characteristic information, thinning rate of each pipe, expected lifespan, inspection period, and/or replacement period on the display screen.

On the other hand, the external terminal (or client terminal) 20 applied to an embodiment of the present invention is a smart phone (Smart Phone), a smart pad (Smart Pad) or a smart note (Smart Note) that communicates through the wireless Internet or portable Internet (Smart Note) ) is preferably composed of at least one mobile terminal device, and in addition to personal PCs, notebook PCs, Palm PCs, mobile play-stations, DMB (Digital Multimedia Broadcasting) phones with communication functions, tablets It may comprehensively mean all wired and wireless home appliances/communication devices having a user interface for accessing the communication network 10 and/or the server 30, such as a PC, an iPad, and the like.

As shown in FIG. 2, the external terminal 20 includes a wireless communication module 21, an A/V (Audio/Video) input module 22, a user input module 23, a sensing module 24, It may include an output module 25 , a storage module 26 , an interface module 27 , a terminal control module 28 , and a power supply module 29 . Meanwhile, since the components shown in FIG. 2 are not essential, the external terminal 20 may have more components or fewer components.

Hereinafter, the components of the external terminal 20 will be described in detail as follows.

The wireless communication module 21 may include one or more modules that enable wireless communication between the external terminal 20 and the communication device 600 and/or the server 30 . For example, the wireless communication module 21 may include a broadcast reception module 21a, a mobile communication module 21b, a wireless Internet module 21c, a short-range communication module 21d, and a location information module 21e.

The broadcast reception module 21a transmits a broadcast signal (for example, a TV broadcast signal, a radio broadcast signal, a data broadcast signal, etc.) and/or broadcast from an external broadcast management server through various broadcast channels (eg, a satellite channel, a terrestrial channel, etc.) Receive relevant information.

The mobile communication module 21b transmits/receives wireless signals to and from at least one of a base station, an external terminal 20, and a server on a mobile communication network. The wireless signal may include various types of data according to transmission and reception of a voice call signal, a video call call signal, or a text/multimedia message.

The wireless Internet module 21c is a module for wireless Internet access, and may be built-in or external to the external terminal 20 . As the wireless Internet technology, for example, WLAN (Wi-Fi), Wibro, Wimax, HSDPA, LTE, etc. may be used.

The short-range communication module 21d is a module for short-range communication, for example, Bluetooth communication, ZigBee communication, Ultra Wideband (UWB) communication, Radio Frequency Identification (RFID) communication, or infrared Data Association (IrDA) communication. ) communication, etc. may be used.

The location information module 21e is a module for confirming or obtaining the location of the external terminal 20 , and may obtain current location information of the external terminal 20 using a global positioning system (GPS) or the like.

On the other hand, according to the control of the terminal control module 28, the server 30 and /or data transmission/reception with the communication device 600 may be performed.

The A/V input module 22 is a module for inputting an audio signal or a video signal, and may basically include a camera unit 22a, a microphone unit 22b, and the like. The camera unit 22a processes an image frame such as a still image or a moving image obtained by an image sensor in a video call mode or a photographing mode. The microphone unit 22b receives an external sound signal by a microphone in a call mode, a recording mode, a voice recognition mode, and the like, and processes it as electrical voice data.

The user input module 23 is a module that generates input data for operation control of the external terminal 20 , in particular, to any one of the data management information displayed through the display unit 25a of the output module 25 . Performs a function of inputting a selection signal for the user, for example, using a touch panel (static pressure/capacitance) type input by the user's touch or a separate input device (eg, keypad dome switch, jog wheel, jog switch, etc.) can be entered.

The sensing module 24 includes the opening/closing state of the external terminal 20 , the position of the external terminal 20 , the presence or absence of user contact, the user's touch action on a specific part, the orientation of the external terminal 20 , and the external terminal A sensing signal for controlling the operation of the external terminal 20 is generated by detecting the current state of the external terminal 20, such as acceleration/deceleration of (20). This sensing signal may be transmitted to the terminal control module 28, and may become a basis for the terminal control module 28 to perform a specific function.

The output module 25 is a module for generating an output related to visual, auditory or tactile sense, and basically includes a display unit 25a, a sound output unit 25b, an alarm unit 25c, and a haptic unit 25d. can

The display unit 25a is for displaying and outputting information processed by the external terminal 20, for example, when the external terminal 20 is in a call mode, a user interface (UI) or a graphical user interface (GUI) related to a call ) and, in the case of a video call mode or a shooting mode, a captured and/or received image, UI, or GUI is displayed.

The sound output unit 25b may output audio data received from the wireless communication module 21 or stored in the storage module 26 in, for example, call signal reception, call mode or recording mode, voice recognition mode, broadcast reception mode, etc. .

The alarm unit 25c may output a signal for notifying the occurrence of an event of the external terminal 20 . Examples of events occurring in the external terminal 20 include call signal reception, message reception, key signal input, touch input, and the like.

The haptic unit 25d generates various tactile effects that the user can feel. A representative example of the tactile effect generated by the haptic unit 25d is vibration. The intensity and pattern of vibration generated by the haptic unit 25d are controllable.

The storage module 26 may store a program for the operation of the terminal control module 28 and may temporarily store input/output data (eg, phonebook, message, still image, video, etc.).

In addition, the storage module 26 may store data related to vibration and sound of various patterns output when a touch input on the touch screen is input, and may store an application program related to pipe fluid flow analysis.

In addition, the storage module 26 may store source data for the formation of pipe fluid flow analysis related information, and the pipe fluid flow analysis related data may be formed in the form of images and sounds, and may be used for pipe fluid flow analysis. The process and results of generation of related data may also be stored together.

The storage module 26 is a flash memory type, hard disk type, multimedia card micro type, card type memory (eg, SD or XD memory, etc.), RAM (RAM), SRAM, ROM (ROM), EEPROM, PROM , a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium.

The interface module 27 serves as a passage with all external devices connected to the external terminal 20 . The interface module 27 receives data from an external device or receives power and transmits it to each component inside the external terminal 20 , or allows data inside the external terminal 20 to be transmitted to an external device.

The terminal control module 28 generally controls the overall operation of the external terminal 20, and performs related control and processing for, for example, voice call, data communication, video call, and execution of various applications.

That is, the terminal control module 28 controls the execution of the pipe fluid flow analysis related application program stored in the storage module 26, and generates pipe fluid flow analysis related data through the execution of the pipe fluid flow analysis related application program. It performs the function of controlling so that it can request and receive pipe fluid flow analysis related data.

In addition, the terminal control module 28 displays auxiliary elements including at least one of an image, a voice, and a sound in the process of generating the pipe fluid flow analysis related data desired by the user through the execution of the pipe fluid flow analysis related application program. (25a) and other output devices (eg, the sound output unit 25b, the alarm unit 25c, the haptic unit 25d, etc.) performs a control function to be output through at least one.

Also, the terminal control module 28 may constantly monitor the charging current and the charging voltage of the battery unit 29a and temporarily store the monitoring values in the storage module 26 . In this case, the storage module 26 preferably stores battery specification information (product code, rating, etc.) as well as battery charge state information such as monitored charging current and charging voltage.

The power module 29 receives external power and internal power under the control of the terminal control module 28 to supply power necessary for the operation of each component. The power module 29 supplies power from the built-in battery unit 29a to each component to operate, and the battery can be charged using a charging terminal (not shown).

Various embodiments described herein may be implemented in a computer-readable recording medium using, for example, software, hardware, or a combination thereof.

According to the hardware implementation, the embodiments described herein are ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays) , processors, controllers, micro-controllers, microprocessors, and may be implemented using at least one of an electrical unit for performing a function. In some cases, such embodiments may be implemented by the terminal control module 28 .

According to the software implementation, embodiments such as a procedure or function may be implemented together with a separate software module for performing at least one function or operation. The software code may be implemented by a software application written in a suitable programming language. In addition, the software code may be stored in the storage module 26 and executed by the terminal control module 28 .

If the external terminal 20 is made of a smart phone, the smart phone, unlike a general mobile phone (aka feature phone), downloads various application programs desired by the user, uses it freely, and deletes it. As a phone based on an open operating system that is possible, not only commonly used functions such as voice/video calls and Internet data communication, but also all mobile phones with mobile office functions or any Internet access capable of Internet access It is preferable to understand it as a communication device including a phone or a tablet PC (Tablet PC).

Such a smart phone may be implemented as a smart phone equipped with various open operating systems, and the open operating systems include, for example, Symbian of Nokia, BlackBerry of RIMS, iPhone of Apple, iPhone of Microsoft. (MS) Windows Mobile, Google's Android, Samsung Electronics' sea, etc. can be made.

As described above, since a smartphone uses an open operating system, a user can install and manage various application programs arbitrarily, unlike a mobile phone with a closed operating system.

In addition, the external server 30 includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate, expected lifespan, Upon receiving the evaluation information of at least one of the inspection period and/or replacement period, the user receives information about the fluid flow characteristics of each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and/or replacement based on this information. It is possible to perform a function of providing a service to be displayed on the display screen to visually see at least one piece of evaluation information during the cycle.

In addition, the external server 30 includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service, as well as the thinning rate, expected lifespan, Receive at least one evaluation information of the inspection cycle and/or replacement cycle, and based on it, the fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and/or the replacement cycle At least one piece of evaluation information may be converted into a database (DB) for each piping system and/or for each pipe, and may perform a function of providing a service to be stored and managed in a separate storage device (not shown).

In addition, the external server 30 may perform a function of providing a cloud computing service in response to a request of the external terminal 20 (preferably, a client terminal).

That is, the external server 30 is connected to the plurality of terminals 20 possessed by the administrator on the network through the communication network 10 and provides system resources (such as OS, CPU, memory, storage, etc.) to the plurality of terminals 20 . In a cloud service environment, a plurality of servers are connected to a plurality of terminals 20 on a network, and in this case, the external server 30 is a concept including a plurality of servers. As illustrated, for example, the system resources are distributed so that the plurality of terminals 20 can use the system resources through a guest machine in a virtual space created through virtualization technology. These matters can be understood as well-known general concepts.

In this case, the cloud service environment refers to Internet-based (cloud) computing technology. This cloud computing (Cloud Computing) represents the Internet as a cloud in the computer network diagram, has a hidden complex infrastructure structure, and has a computing style in which IT-related functions are provided in the form of services. Users can use services provided by cloud computing by using the Internet.

In addition, the cloud computing is a mixture of various computing concepts and communication technologies, such as virtualized computing, utility computing, and on-demand computing, and is a virtualization technology for a plurality of data centers composed of a plurality of computers. It refers to a technology that implements a single virtual computer or service by integrating the

That is, the cloud computing is an 'on-demand outsourcing service of IT resources through the Internet', in which programs or documents that were individually stored in a personal computer or a corporate server are stored in an Internet-based virtual server (Server) or storage (Storage). It is a method in which a user can perform a desired task by driving a cloud application such as a web browser using various terminals including a personal computer.

In this case, users can select and use computing resources such as cloud applications, storage, OS, and security as much as they want at a desired time and pay a price based on usage.

That is, the external server 30 is a server that provides a cloud computing service to the external terminal 20 , and provides the computing resource requested by the external terminal 20 through the communication network 10 . The external server 30 may provide a computing service for enabling the external terminal 20 to use a requested device.

The external server 30 has, for example, application program files, game program files, text data files, document files, picture files, music files, video files, barcode files, etc. A plurality of storage devices for storing files, that is, storage (Storage) are provided.

In addition, the external server 30 stores at least one evaluation information of fluid flow characteristic information for each pipe system and/or each pipe, thinning rate of each pipe, life expectancy, inspection cycle, and replacement cycle database (DB) When stored in a symmetrical or asymmetrical encryption method, at least one evaluation of fluid flow characteristic information for each pipe system and/or each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and/or replacement cycle The information is encrypted and stored, and at least one evaluation information of the encrypted fluid flow characteristic information for each piping system and/or each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and/or replacement cycle is decrypted. A function of providing a service may be performed so that the decryption key capable of being transmitted is transmitted to the external terminal 20 (preferably, a preset client terminal).

In addition, the external server 30 through the external terminal 20 (preferably, a preset client terminal) for each pipe system and / or each pipe fluid flow characteristic information, thinning rate of each pipe, expected life, It may perform a function of providing a cloud web service so that evaluation information of at least one of an inspection cycle and/or a replacement cycle can be downloaded.

Hereinafter, an artificial intelligence-based pipe fluid flow analysis method according to an embodiment of the present invention will be described in detail.

3 is an overall flowchart for explaining an artificial intelligence-based pipe fluid flow analysis method according to an embodiment of the present invention.

1 to 3 , the artificial intelligence-based pipe fluid flow analysis method according to an embodiment of the present invention is first performed in a plurality of different pipes required for performing numerical analysis through the numerical analysis device 100 . Each pipe including the 3D grid information of each pipe using the 3D computational fluid dynamics (CFD) analysis method based on the operating condition information of each pipe, including shape information, material property information, and specification information for each pipe Outputs the three-dimensional flow pattern information data in the (S100).

In this case, in step S100, each pipe is preferably made of at least one of, for example, a straight pipe, an elbow, a T-tube, a reducer, and/or an orifice.

In addition, in step S100, it is preferable that the operation condition information of each pipe consists of, for example, at least one condition information of flow rate, pressure, temperature, and/or dryness.

In addition, in the step S100, the output 3D flow pattern information data in each pipe is, for example, it is preferable to consist of at least one information data of flow velocity, pressure, and/or temperature information.

In addition, in the step S100, the operating condition information of each pipe, including shape information, material property information, and standard information for each pipe, is, for example, the pipe fluid flow installed in the external terminal 20 and/or the server 30 in advance. It may be input through an analysis-related application service.

Thereafter, each for inferring the fluid flow characteristics in each pipe based on the 3D flow pattern information data in each pipe including the 3D grid network information of each pipe output in step S100 through the data processing device 200 Extracts and processes characteristic data for fluid flow in the pipe (S200).

Then, each using an artificial intelligence (AI)-based machine learning method that is preset based on the characteristic data for the fluid flow in each pipe extracted and processed in step S200 through the fluid flow artificial intelligence learning device 300 A fluid flow artificial intelligence learning model for each pipe is built by learning for each feature item on the fluid flow in the pipe (S300).

In this case, in the step S300, the preset artificial intelligence (AI)-based machine learning method is, for example, a neural network, a support vector machine (SVM), a multi layer perception (MLP), and/or deep learning (Deep). Learning), it is preferable to use at least one artificial intelligence learning method.

Next, the fluid flow for each pipe for at least one pipe system designed with a plurality of different pipes using the fluid flow artificial intelligence learning model for each pipe built in step S300 through the fluid flow analysis device 400 Characteristics are inferred and analyzed (S400).

At this time, in step S400, using the fluid flow artificial intelligence learning model for each pipe built in step S300 through the fluid flow analysis device 400, each for at least one pipe system designed with a plurality of different pipes After inferring and analyzing the fluid flow characteristics for each pipe, based on the inferred and analyzed fluid flow characteristics information for each pipe of each pipe system, at least one of thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe is selected. It is preferable to generate evaluation information.

In addition, in step S400, design information for each pipe designed in each pipe system may be input through, for example, an external terminal 20 and/or a pipe fluid flow analysis related application service installed in the server 30 in advance. .

Additionally, although not shown in the drawing, after the step S400, the generated pipe including the fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in the step S400 through a separate storage device 500 The method may further include the step of storing and managing the evaluation information of at least one of the thinning rate, the expected lifespan, the inspection period, and/or the replacement period into a database (DB) for each piping system and each pipe.

Furthermore, although not shown in the drawing, after the step S400, the generated each pipe including the fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in the step S400 through the separate communication device 600 The method may further include transmitting evaluation information of at least one of thinning rate, expected lifespan, inspection period, and/or replacement period to the external terminal 20 and/or the server 30 .

At this time, the external terminal 20 and/or the server 30 includes the fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service, as well as each pipe. of at least one evaluation information of thinning rate, expected lifespan, inspection cycle, and/or replacement cycle of A service may be provided so that evaluation information of at least one of an inspection period and/or a replacement period is displayed on a display screen to be visually viewed.

In addition, the external terminal 20 and/or the server 30 includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device 600 through the pre-installed pipe fluid flow analysis related application service, as well as each pipe. of at least one evaluation information of thinning rate, expected lifespan, inspection cycle, and/or replacement cycle of , and/or a service can be provided so that the evaluation information of at least one of the replacement cycle is stored and managed in a separate storage device 500 by forming a database (DB) for each pipe system and/or each pipe.

Also, the server 30 may provide a cloud computing service in response to a request from an external terminal (or client terminal) 20 .

In addition, the external terminal (or client terminal) 20 uses the client member login cloud web service of the server 30, fluid flow characteristic information for each piping system and/or each pipe stored in the server 30, At least one evaluation information of the thinning rate, expected lifespan, inspection period, and/or replacement period of each pipe may be searched and displayed on the display screen in real time.

In addition, the server 30 stores at least one evaluation information of fluid flow characteristic information for each piping system and/or each pipe, thinning rate of each pipe, life expectancy, inspection cycle, and/or replacement cycle database (DB) When stored in a symmetrical or asymmetrical encryption method, at least one evaluation of fluid flow characteristic information for each pipe system and/or each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and/or replacement cycle The information is encrypted and stored, and at least one evaluation information of the encrypted fluid flow characteristic information for each piping system and/or each pipe, thinning rate of each pipe, expected lifespan, inspection cycle, and/or replacement cycle is decrypted. A service may be provided so that a decryption key capable of being performed is transmitted to a preset external terminal (or client terminal) 20 .

In addition, the external terminal (or client terminal) 20 is encrypted and stored in the server 30 using the decryption key transmitted from the server 30 together with the corresponding client member login information for each piping system and/or each pipe At least one of the fluid flow characteristic information, each pipe's thinning rate, expected lifespan, inspection period, and/or replacement period may be decoded and searched and displayed on the display screen in real time.

In addition, the server 30 through the external terminal (or client terminal) 30 for each piping system and / or each pipe fluid flow characteristic information, each pipe thinning rate, life expectancy, inspection cycle, and / or A cloud web service may be provided so that at least one piece of evaluation information can be downloaded during the replacement cycle.

In addition, the external terminal (or client terminal) 20 is stored in the server 30 through the pipe fluid flow analysis related cloud application downloaded from the server 30 and/or fluid flow characteristic information for each pipe. , it is possible to display the evaluation information of at least one of the thinning rate of each pipe, the expected lifespan, the inspection period, and/or the replacement period on the real-time search and display screen.

On the other hand, the artificial intelligence-based pipe fluid flow analysis method according to an embodiment of the present invention can also be implemented as a computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices in which data readable by a computer system is stored.

For example, computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, hard disk, floppy disk, removable storage device, and non-volatile memory (Flash Memory). , and optical data storage devices.

In addition, the computer-readable recording medium may be distributed in a computer system connected through a computer communication network, and stored and executed as readable code in a distributed manner.

Although a preferred embodiment of the artificial intelligence-based pipe fluid flow analysis system and method according to the present invention has been described above, the present invention is not limited thereto, and the scope of the claims and the detailed description of the invention and the accompanying drawings It is possible to carry out various modifications within, and this also belongs to the present invention.

100: numerical analysis device,
200: data processing device,
300: fluid flow artificial intelligence learning device,
400: fluid flow analysis device,
500: storage,
600: communication device

Claims (33)

Using the three-dimensional computational fluid dynamics (CFD) analysis method based on the operating condition information of each pipe, including shape information, material property information, and specification information for a plurality of different pipes required for numerical analysis, a numerical analysis device for outputting three-dimensional flow pattern information data in each pipe including the three-dimensional grid network information of each pipe;
Characteristics of fluid flow in each pipe for inferring fluid flow characteristics within each pipe based on receiving the 3D flow pattern information data in each pipe including the 3D grid network information of each pipe output from the numerical analysis device a data processing device for extracting and processing data;
By receiving the characteristic data of the fluid flow in each pipe extracted and processed from the data processing device, based on this, it learns for each characteristic item about the fluid flow in each pipe through a preset artificial intelligence (AI)-based machine learning method. A fluid flow artificial intelligence learning device that builds a fluid flow artificial intelligence learning model for each pipe; and
Fluid flow for inferring and analyzing fluid flow characteristics for each pipe for at least one pipe system designed with a plurality of different pipes using the fluid flow AI learning model for each pipe built from the fluid flow AI learning device An artificial intelligence-based pipeline fluid flow analysis system including an analysis device.
According to claim 1,
Each pipe, an artificial intelligence-based pipe fluid flow analysis system, characterized in that made of at least one of a straight pipe, an elbow, a T-tube, a reducer, and an orifice.
According to claim 1,
The operating condition information of each pipe is an artificial intelligence-based pipe fluid flow analysis system, characterized in that it consists of condition information of at least one of flow rate, pressure, temperature, and dryness.
According to claim 1,
The three-dimensional flow pattern information data in each pipe output from the numerical analysis device is an artificial intelligence-based pipe fluid flow analysis system, characterized in that it consists of information data of at least one of flow velocity, pressure, and temperature information.
According to claim 1,
The artificial intelligence (AI)-based machine learning method applied to the fluid flow artificial intelligence learning apparatus is at least one of a Neural Network, a Support Vector Machine (SVM), a Multi Layer Perception (MLP), and Deep Learning. An artificial intelligence-based pipe fluid flow analysis system, characterized in that it consists of one artificial intelligence learning method.
According to claim 1,
The fluid flow analysis device, using the fluid flow artificial intelligence learning model for each pipe built from the fluid flow artificial intelligence learning device, fluid flow characteristics for each pipe for at least one pipe system designed with a plurality of different pipes to infer and analyze, and based on the inferred and analyzed fluid flow characteristic information for each pipe of each pipe system, generating at least one evaluation information of the thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe An artificial intelligence-based pipeline fluid flow analysis system.
7. The method of claim 6,
In addition to the fluid flow characteristic information for each pipe of each pipe system inferred and analyzed from the fluid flow analysis device, evaluation information of at least one of the thickness reduction rate, expected lifespan, inspection cycle, and replacement cycle of each pipe generated is provided to each pipe. An artificial intelligence-based pipe fluid flow analysis system, characterized in that it further includes a storage device for storing and managing a database (DB) for each system and each pipe.
8. The method of claim 7,
In addition to the fluid flow characteristic information for each pipe of each pipe system inferred and analyzed from the fluid flow analysis device, at least one of the generated pipe thinning rate, expected lifespan, inspection cycle, and replacement cycle evaluation information Artificial intelligence-based pipe fluid flow analysis system, characterized in that it further comprises a communication device for transmitting to a terminal or a server.
9. The method of claim 8,
The external terminal or server includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through a pre-installed pipe fluid flow analysis related application service, as well as thinning rate, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle, and based on this, the user evaluates at least one of fluid flow characteristic information for each pipe of each pipe system, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle An artificial intelligence-based pipeline fluid flow analysis system, characterized in that it provides a service to be displayed on the display screen to visually see.
9. The method of claim 8,
The external terminal or server includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through a pre-installed pipe fluid flow analysis related application service, as well as thinning rate, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle is provided, and based on this, at least one evaluation information of fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle An artificial intelligence-based pipe fluid flow analysis system, characterized in that it provides a service to be stored and managed in a separate storage device by converting it into a database (DB) for each pipe system and each pipe.
11. The method of claim 10,
The server provides a cloud computing service in response to a request from an external client terminal,
The client terminal, using the client member login cloud web service of the server, fluid flow characteristic information for each piping system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle An artificial intelligence-based pipe fluid flow analysis system, characterized in that at least one of the evaluation information is searched for and displayed on a display screen in real time.
12. The method of claim 11,
The server, when storing the evaluation information of at least one of the fluid flow characteristic information for each piping system and for each pipe, the thinning rate of each pipe, the expected lifespan, the inspection period, and the replacement period into a database (DB) and stores it, symmetric or Using an asymmetric encryption method, at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe is encrypted and stored, and the encrypted data is stored. A service is provided so that a decryption key capable of decrypting at least one of the evaluation information of each piping system and each pipe fluid flow characteristic information, each pipe's thinning rate, expected lifespan, inspection cycle, and replacement cycle is transmitted to a preset client terminal. provide,
The client terminal is encrypted and stored in the server using the decryption key transmitted from the server together with the client member login information, fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, An artificial intelligence-based pipe fluid flow analysis system, characterized in that it decodes the evaluation information of at least one of the inspection period and the replacement period, and searches and displays it on the display screen in real time.
13. The method of claim 12,
The server, through the client terminal, each pipe system and each pipe fluid flow characteristic information, each pipe thinning rate, expected life, inspection cycle, and the cloud web web so that at least one evaluation information of the replacement cycle can be downloaded An artificial intelligence-based pipeline fluid flow analysis system, characterized in that it provides a service.
12. The method of claim 11,
The client terminal is, through the pipe fluid flow analysis related cloud application downloaded from the server, fluid flow characteristic information for each pipe system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement An artificial intelligence-based pipe fluid flow analysis system, characterized in that at least one evaluation information of a cycle is searched in real time and displayed on a display screen.
According to claim 1,
The operation condition information of each pipe, including shape information, material property information, and specification information for each pipe, is artificial intelligence, characterized in that it is input through an application service related to pipe fluid flow analysis pre-installed in an external terminal or server based pipeline fluid flow analysis system.
According to claim 1,
The design information for each pipe designed in each pipe system is an artificial intelligence-based pipe fluid flow analysis system, characterized in that it is input through an application service related to pipe fluid flow analysis that is pre-installed in an external terminal or server.
A method for analyzing the fluid flow of an artificial intelligence-based pipe using a system including a numerical analysis device, a data processing device, a fluid flow artificial intelligence learning device, and a fluid flow analysis device,
(a) Three-dimensional computational fluid dynamics (Computational Fluid) based on the operating condition information of each pipe, including shape information, material property information, and specification information for a plurality of different pipes required for numerical analysis through the numerical analysis device using a Dynamics, CFD) analysis method to output 3D flow pattern information data in each pipe including 3D grid network information of each pipe;
(b) each for inferring the fluid flow characteristics in each pipe based on the 3D flow pattern information data in each pipe including the 3D grid network information of each pipe output in step (a) through the data processing device extracting and processing characteristic data on fluid flow in the pipe;
(c) each using an artificial intelligence (AI)-based machine learning method that is preset based on the characteristic data for the fluid flow in each pipe extracted and processed in step (b) through the fluid flow artificial intelligence learning device Building a fluid flow artificial intelligence learning model for each pipe by learning for each feature item on the fluid flow in the pipe; and
(d) fluid flow for each pipe for at least one pipe system designed with a plurality of different pipes using the fluid flow artificial intelligence learning model for each pipe built in step (c) through the fluid flow analysis device An artificial intelligence-based pipe fluid flow analysis method comprising the step of inferring and analyzing characteristics.
18. The method of claim 17,
In the step (a), each pipe, an artificial intelligence-based pipe fluid flow analysis method, characterized in that it consists of at least one of a straight pipe, an elbow, a T-tube, a reducer, and an orifice.
18. The method of claim 17,
In the step (a), the operating condition information of each pipe is an artificial intelligence-based pipe fluid flow analysis method, characterized in that it consists of at least one condition information of flow rate, pressure, temperature, and dryness.
18. The method of claim 17,
In the step (a), the outputted three-dimensional flow pattern information data in each pipe is an artificial intelligence-based pipe fluid flow analysis method, characterized in that it consists of information data of at least one of flow velocity, pressure, and temperature information.
18. The method of claim 17,
In the step (c), the preset artificial intelligence (AI)-based machine learning method, a neural network (Neural Network), SVM (Support Vector Machine), MLP (Multi Layer Perception), and deep learning (Deep Learning) An artificial intelligence-based pipe fluid flow analysis method, characterized in that it consists of at least one artificial intelligence learning method.
18. The method of claim 17,
In step (d), each for at least one pipe system designed with a plurality of different pipes using the fluid flow artificial intelligence learning model for each pipe built in step (c) through the fluid flow analysis device. After inferring and analyzing the fluid flow characteristics for each pipe, based on the inferred and analyzed fluid flow characteristics information for each pipe of each pipe system, at least one of thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe is selected. An artificial intelligence-based pipe fluid flow analysis method, characterized in that it generates evaluation information.
23. The method of claim 22,
After step (d), thinning rate, expected lifespan, and inspection of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in step (d) through a separate storage device An artificial intelligence-based pipe fluid flow analysis method, further comprising the step of storing and managing at least one evaluation information of a cycle and a replacement cycle into a database (DB) for each pipe system and for each pipe.
23. The method of claim 22,
After step (d), the thickness reduction rate, expected lifespan, and inspection of each pipe generated, including fluid flow characteristic information for each pipe of each pipe system inferred and analyzed in step (d) through a separate communication device Artificial intelligence-based pipe fluid flow analysis method, characterized in that it further comprises the step of transmitting the evaluation information of at least one of the cycle and the replacement cycle to an external terminal or server.
25. The method of claim 24,
The external terminal or server includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through a pre-installed pipe fluid flow analysis related application service, as well as thinning rate, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle, and based on this, the user evaluates at least one of fluid flow characteristic information for each pipe of each pipe system, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle An artificial intelligence-based pipe fluid flow analysis method, characterized in that it provides a service to be displayed on the display screen so that the
25. The method of claim 24,
The external terminal or server includes fluid flow characteristic information for each pipe of each pipe system transmitted from the communication device through a pre-installed pipe fluid flow analysis related application service, as well as thinning rate, expected lifespan, inspection cycle, and at least one evaluation information of the replacement cycle is provided, and based on this, at least one evaluation information of the fluid flow characteristic information for each pipe of each pipe system, the thinning rate of each pipe, the expected lifespan, the inspection cycle, and the replacement cycle An artificial intelligence-based pipe fluid flow analysis method, characterized in that it provides a service to be stored and managed in a separate storage device by converting it into a database (DB) for each pipe system and each pipe.
27. The method of claim 26,
The server provides a cloud computing service in response to a request from an external client terminal,
The client terminal, using the client member login cloud web service of the server, fluid flow characteristic information for each piping system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement cycle An artificial intelligence-based pipe fluid flow analysis method, characterized in that at least one of the evaluation information is searched for in real time and displayed on a display screen.
28. The method of claim 27,
The server, when storing the evaluation information of at least one of the fluid flow characteristic information for each piping system and for each pipe, the thinning rate of each pipe, the expected lifespan, the inspection period, and the replacement period into a database (DB) and stores it, symmetric or Using an asymmetric encryption method, at least one evaluation information of fluid flow characteristic information for each piping system and each pipe, thinning rate, expected lifespan, inspection cycle, and replacement cycle of each pipe is encrypted and stored, and the encrypted data is stored. A service is provided so that a decryption key capable of decrypting at least one of the evaluation information of each piping system and each pipe fluid flow characteristic information, each pipe's thinning rate, expected lifespan, inspection cycle, and replacement cycle is transmitted to a preset client terminal. provide,
The client terminal is encrypted and stored in the server using the decryption key transmitted from the server together with the client member login information, fluid flow characteristic information for each piping system and each pipe, thinning rate of each pipe, expected lifespan, An artificial intelligence-based pipe fluid flow analysis method, comprising decoding at least one evaluation information of an inspection cycle and a replacement cycle, searching for it in real time and displaying it on a display screen.
29. The method of claim 28,
The server, through the client terminal, each pipe system and each pipe fluid flow characteristic information, each pipe thinning rate, expected life, inspection cycle, and the cloud web web so that at least one evaluation information of the replacement cycle can be downloaded An artificial intelligence-based pipe fluid flow analysis method, characterized in that it provides a service.
28. The method of claim 27,
The client terminal is, through the pipe fluid flow analysis related cloud application downloaded from the server, fluid flow characteristic information for each pipe system and each pipe stored in the server, thinning rate of each pipe, expected lifespan, inspection cycle, and replacement An artificial intelligence-based pipe fluid flow analysis method, characterized in that at least one evaluation information of a cycle is searched in real time and displayed on a display screen.
18. The method of claim 17,
In the step (a), the operating condition information of each pipe, including shape information, material property information, and standard information for each pipe, is input through an external terminal or a pipe fluid flow analysis related application service installed in advance in the server. An artificial intelligence-based pipeline fluid flow analysis method.
18. The method of claim 17,
In the step (d), the design information for each pipe designed in each pipe system is artificial intelligence-based pipe fluid flow, characterized in that it is input through a pipe fluid flow analysis related application service installed in advance in an external terminal or server. analysis method.
A computer-readable recording medium in which a program capable of executing the method of any one of claims 17 to 32 by a computer is recorded.

Images