KR102454774B1 - Apparatus and method for virtualization of apparatus - Google Patents

Abstract

Embodiments of the present invention provide technology for virtualizing a device. The apparatus for virtualizing a device according to one embodiment of the present invention comprises at least one processor, a memory, and a display. The processor can: receive an input file; extract first file level data including file level area data and file level command data from the input file; obtain optimization data including optimization area data and optimization command data by performing task optimization on the first file level data for each of one or more post-processing devices; obtain rendering level data including rendering area data and rendering command data by performing rendering for each of the one or more post-processing devices, based on the optimization data; convert the rendering level data to obtain standard data which can be applied to the one or more post-processing devices; convert the standard data in response to each of the one or more post-processing devices to obtain device level data including device level area data, device level command data and a device identifier; and transmit the device level data to the post-processing device corresponding to the device identifier among the one or more post-processing devices. Accordingly, different devices can be integrally operated.

Description

Device virtualization apparatus and method using a neural network

Embodiments of the present invention relate to a technology for virtualizing a device, and to a technology for integrally operating different types of post-processing devices.

A plotter is a large-scale output device for outputting an output result as a graph, figure, CAD, drawing, etc. on a plane such as paper, film, or sheet paper. The plotter can perform printing and post-processing. Plotters can be classified into various types such as flat bed type, drum type, belt bed type, linear motor type, personal plotter, ink jet type, electrostatic type, thermal transfer type, laser beam type, vinyl cutter, etc. depending on the printing or post-processing method. have.

Embodiments provide a device virtualization system capable of integrally operating different devices.

The technical problems to be achieved in the embodiments are not limited to those mentioned above, and other technical problems not mentioned may be considered by those of ordinary skill in the art from various embodiments to be described below. can

Device virtualization device according to an embodiment, at least one processor; Memory; and a display, wherein the processor receives an input file, extracts first file-level data including file-level area data and file-level command data from the input file, and the first file for each one or more post-processing devices. Task optimization is performed on level data to obtain optimization data including optimization region data and optimization instruction data, and rendering is performed for each of the one or more post-processing devices based on the optimization data to include rendering region data and rendering instruction data. to obtain rendering level data, convert the rendering level data to obtain standard data applicable to one or more post-processing apparatuses, and convert the standard data corresponding to each of the one or more post-processing apparatuses to obtain apparatus level area data, apparatus obtain device level data including level command data and device identifier, and transmit the device level data to a post-processing device corresponding to the device identifier among the one or more post-processing devices.

The display may display thumbnails and selected pages of a plurality of pages of the input file when the input file is an image integrated file, respectively, and display the input file when the input file is a device level file .

The processor estimates a complexity or an expected time for the first file level data based on the characteristic information of each of the one or more post-processing devices, and converts the first file level data in a direction in which the complexity or the expected time decreases. By converting, second file level data may be obtained as the optimization data.

The processor is configured to generate candidate file level data of a plurality of scenarios based on the first file level data, and a complexity or expected time for the candidate file level data of each scenario based on characteristics of each of the one or more post-processing devices. may be estimated, and candidate file level data of a scenario having the smallest complexity or the least expected time may be obtained as the optimization data.

The processor determines a weight for each of the components constituting the file level area data and the file level command data of the first file level data, and the weight determined for each component, the type of the component, and the component The complexity may be estimated based on the number of .

The processor determines the operating time of each component for each post-processing device in response to the component constituting the file level area data and the file level command data of the first file level data, and each component It is possible to estimate the estimated time for each post-processing apparatus by summing the operation times of the .

The processor converts coordinates of one or more objects included in the first file level data, enlarges/reduces the object, rotates the object, converts offset information included in the first file level data, or , converts the file-level area data of the first file-level data by adjusting the number of segments of the object, changing the type of the segment, or removing overlapping segments, thereby converting the file-level area data of the second file-level data may be obtained, and the file level command data of the second file level data may be obtained based on the file level area data of the second file level data and the characteristics of each post-processing apparatus.

The processor performs rendering by using the characteristics of the one or more post-processing apparatuses and the optimization data for each of the one or more post-processing apparatuses, the display displays a result of the rendering, and the processor includes: The rendering level data may be obtained by transforming the optimization data based on a user input input corresponding to the display.

The processor is configured to: store first rendering level data in the queue, then store second rendering level data in the queue, receive status information of each of the post-processing devices from the one or more post-processing devices, and the status information is normal In the case of indicating a state, after the first standard data is obtained by converting the first rendering level data, the second standard data may be obtained by converting the second rendering level data.

The processor may determine a group including one or more post-processing devices, determine a standard protocol corresponding to the group, and convert the rendering level data based on the determined standard protocol to obtain the standard data.

The processor estimates the complexity by using a complexity estimation model, wherein the complexity estimation model includes an input layer, one or more hidden layers, and an output layer, and characteristic information, file level data, and correct answer complexity of each post-processing device A plurality of training data consisting of , the loss function layer outputs a loss value using a loss function that compares the output vector with the correct vector for each training data, and the parameters of the spatial information extraction model are learned in a direction in which the loss value becomes smaller, ,

[Equation]

The loss function follows the above equation, where N is the number of the plurality of training data, n is a natural number identifying the training data, k is a natural number identifying the value of the nth training data, nk is the nth It may mean the k-th value of the training data, t may mean correct answer data, y may mean the output vector, and E may mean a loss value.

Device virtualization device according to an embodiment, the input unit; parser; leader; pass engine; integrated viewer; renderer; spooling layer; standard protocol layer; vendor protocol layer; driver layer; and a communication unit, wherein the input unit receives an input file, and the parser or the reader extracts first file level data including file level area data and file level command data from the input file, and the path The engine performs task optimization on the first file level data for each one or more post-processing devices to obtain optimization data including optimization region data and optimization instruction data, and the renderer performs the task optimization on the one or more file level data based on the optimization data. Rendering is performed for each post-processing device to obtain rendering level data including rendering area data and rendering command data, and the standard protocol layer converts the rendering level data to obtain standard data that can be applied to one or more post-processing devices, , the vendor protocol layer converts the standard data corresponding to each of the one or more post-processing devices to obtain device-level data including device-level region data, device-level command data, and device identifier, and the driver layer includes: The device level data may be transferred to a finishing device corresponding to the device identifier among one or more finishing devices.

Device virtualization system according to an embodiment, a virtualization device; and one or more post-processing devices, wherein the virtualization device includes: at least one processor; Memory; and a display, wherein the processor receives an input file, extracts first file-level data including file-level area data and file-level command data from the input file, and the first file for each one or more post-processing devices. Task optimization is performed on level data to obtain optimization data including optimization region data and optimization instruction data, and rendering is performed for each of the one or more post-processing devices based on the optimization data to include rendering region data and rendering instruction data. to obtain rendering level data, convert the rendering level data to obtain standard data applicable to one or more post-processing apparatuses, and convert the standard data corresponding to each of the one or more post-processing apparatuses to obtain apparatus level area data, apparatus Acquire device level data including level command data and device identifier, and transmit the device level data to a post-processing device corresponding to the device identifier among one or more post-processing devices, each of the one or more post-processing devices comprising: Post-processing may be performed based on the device level data corresponding to the device identifier corresponding to .

A device virtualization method according to an embodiment includes, by a processor, extracting, by a processor, first file-level data including file-level area data and file-level command data from an input file; obtaining, by the processor, optimization data including optimization region data and optimization instruction data by performing task optimization on the first file level data for each one or more post-processing devices; performing, by the processor, rendering for each of the one or more post-processing devices based on the optimization data to obtain rendering level data including rendering area data and rendering command data; converting, by the processor, the rendering level data to obtain standard data that can be applied to one or more post-processing devices; converting, by the processor, the standard data corresponding to each of the one or more post-processing devices to obtain device-level data including device-level domain data, device-level command data, and device identifiers; and transferring, by the processor, the device-level data to a post-processing device corresponding to the device identifier among the one or more post-processing devices.

A device virtualization method according to an embodiment includes, by an input unit, receiving an input file; extracting, by a parser or a reader, first file-level data including file-level area data and file-level command data from the input file; performing task optimization on the first file-level data for each one or more post-processing devices by a path engine to obtain optimization data including optimization region data and optimization instruction data; performing rendering by the renderer for each of the one or more post-processing devices based on the optimization data to obtain rendering level data including rendering area data and rendering command data; converting the rendering level data by a standard protocol layer to obtain standard data that can be applied to one or more post-processing devices; converting the standard data corresponding to each of the one or more post-processing devices by a vendor protocol layer to obtain device-level data including device-level domain data, device-level command data and device identifiers; and transferring, by a driver layer, the device-level data to a post-processing device corresponding to the device identifier among the one or more post-processing devices.

According to embodiments, a plurality of devices may be integratedly operated through device virtualization.

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned are clearly derived and understood by those of ordinary skill in the art based on the following detailed description. can be

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and, together with the detailed description, explain technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram illustrating a configuration of a program according to an exemplary embodiment.
3 is a diagram illustrating an overall configuration of a device virtualization system according to an embodiment.
4 is a flowchart illustrating an operation of a device virtualization method by a device virtualization apparatus according to an embodiment.
5 is a flowchart illustrating an operation of a device virtualization method by a device virtualization system according to an embodiment.
6 is a block diagram of a device virtualization system according to an embodiment.
7 is a diagram illustrating an example of a device virtualization system according to an embodiment.
8 is a diagram illustrating an example in which coordinates are rotated or converted by offset adjustment by a device virtualization system according to an embodiment.
9 is a diagram illustrating an example in which a relative size of coordinates is converted by a device virtualization system according to an embodiment.
10 is a diagram illustrating a configuration of a device virtualization device according to an embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood at the level of those of ordinary skill in the art are also not described. did.

Throughout the specification, when a part is said to "comprising or including" a certain component, it does not exclude other components unless otherwise stated, meaning that other components may be further included. do. In addition, terms such as "... unit", "... group", and "module" described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and like related terms are used herein in the context of describing various embodiments (especially in the context of the claims that follow). Unless otherwise indicated or clearly contradicted by context, it may be used in a sense including both the singular and the plural.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent the only embodiments.

In addition, specific terms used in various embodiments are provided to help the understanding of various embodiments, and the use of these specific terms may be changed to other forms without departing from the technical spirit of various embodiments. .

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100 , an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or a second network 199 . It may communicate with at least one of the electronic device 104 and the server 108 through (eg, a long-distance wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120 , a memory 130 , an input module 150 , a sound output module 155 , a display module 160 , an audio module 170 , and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or an antenna module 197 . In some embodiments, at least one of these components (eg, the connection terminal 178 ) may be omitted or one or more other components may be added to the electronic device 101 . In some embodiments, some of these components (eg, sensor module 176 , camera module 180 , or antenna module 197 ) are integrated into one component (eg, display module 160 ). can be The electronic device 101 may also be referred to as a client, a terminal, or a peer.

The processor 120, for example, executes software (eg, a program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in , process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 . According to an embodiment, the processor 120 is the main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit (eg, a graphic processing unit, a neural network processing unit) a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, when the electronic device 101 includes the main processor 121 and the sub-processor 123 , the sub-processor 123 uses less power than the main processor 121 or is set to be specialized for a specified function. can The auxiliary processor 123 may be implemented separately from or as a part of the main processor 121 .

The secondary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or when the main processor 121 is active (eg, executing an application). ), together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to an embodiment, the coprocessor 123 (eg, an image signal processor or a communication processor) may be implemented as part of another functionally related component (eg, the camera module 180 or the communication module 190 ). have. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176 ) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, the program 140 ) and instructions related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 . The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output a sound signal to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from or as part of the speaker.

The display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 . The display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device. According to an embodiment, the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.

The audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 , or an external electronic device (eg, a sound output module 155 ) connected directly or wirelessly with the electronic device 101 . The electronic device 102) (eg, a speaker or headphones) may output a sound.

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the sensed state. can do. According to an embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more specified protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, the electronic device 102 ). According to an embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102 ). According to an embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication performance through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : It may include a local area network (LAN) communication module, or a power line communication module). A corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a telecommunication network such as a LAN or a WAN). These various types of communication modules may be integrated into one component (eg, a single chip) or may be implemented as a plurality of components (eg, multiple chips) separate from each other. The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 . The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, a new radio access technology (NR). NR access technology includes high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency) -latency communications)). The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various techniques for securing performance in a high-frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), all-dimensional multiplexing. It may support technologies such as full dimensional MIMO (FD-MIMO), an array antenna, analog beam-forming, or a large scale antenna. The wireless communication module 192 may support various requirements defined in the electronic device 101 , an external electronic device (eg, the electronic device 104 ), or a network system (eg, the second network 199 ). According to an embodiment, the wireless communication module 192 may include a peak data rate (eg, 20 Gbps or more) for realizing eMBB, loss coverage (eg, 164 dB or less) for realizing mMTC, or U-plane latency for realizing URLLC ( Example: Downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive a signal or power to the outside (eg, an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a conductor formed on a substrate (eg, a PCB) or a radiator formed of a conductive pattern. According to an embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected from the plurality of antennas by, for example, the communication module 190 . can be selected. A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) other than the radiator may be additionally formed as a part of the antenna module 197 .

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and a signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or a part of operations executed in the electronic device 101 may be executed in one or more external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a function or service automatically or in response to a request from a user or other device, the electronic device 101 may perform the function or service itself instead of executing the function or service itself. Alternatively or additionally, one or more external electronic devices may be requested to perform at least a part of the function or the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 . The electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of things (IoT) device. The server 108 may be an intelligent server using machine learning and/or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199 . The electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and may provide a service to the connected electronic device 101 . In addition, the server 108 may store and manage various types of information of users who have joined as members by performing a membership registration procedure, and may provide various purchase and payment functions related to services. Also, the server 108 may share execution data of a service application executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. The server 108 may have the same configuration as a typical web server (Web Server) or WAP server (WAP Server) in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Visual Basic, or Visual C and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, receives a request to perform a task from a client or other server, and derives and provides the work result. It means a computer system and the computer software (server program) installed therefor. In addition, the server 108, in addition to the above-described server program, a series of application programs (Application Program) operating on the server 108 and in some cases, various databases (DB: Database, hereinafter " DB") should be understood as a broad concept including. Accordingly, the server 108 classifies, stores and manages member registration information, various information and data about the game in a DB, and this DB may be implemented inside or outside the server 108 . In addition, the server 108 uses a server program that is provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment may be used. In addition, the server 108 may cooperate with an authentication system and a payment system for user authentication of a service or a purchase payment related to a service.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between respective nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104 . (Network). The first network 198 and the second network 199 are Internet (Internet), LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G , 4G, LTE, 5G, Wi-Fi, and the like. The first network 198 and the second network 199 may be the closed first network 198 and the second network 199 such as a LAN or a WAN, but preferably an open type such as the Internet. The Internet is a TCP/IP protocol and several services existing in its upper layers, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service) means a worldwide open computer first network 198 and second network 199 structure.

The database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched (extracted), deleted, edited, added, and the like. Database is a relational database management system (RDBMS) such as Oracle, Infomix, Sybase, DB2, or object-oriented database management such as Gemston, Orion, O2, etc. It can be implemented for the purpose of an embodiment of the present disclosure by using a system (OODBMS) and an XML-only database (XML Native Database) such as Excelon, Tamino, and Sekaiju, and its function It may have an appropriate field or elements to achieve .

2 is a diagram illustrating a configuration of a program according to an exemplary embodiment.

2 is a block diagram 200 illustrating a program 140 in accordance with various embodiments. According to an embodiment, the program 140 executes an operating system 142 , middleware 144 , or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101 . may include Operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 are, for example, preloaded into the electronic device 101 at the time of manufacture, or an external electronic device (eg, the electronic device 102 or 104 ), or a server (eg, the electronic device 102 or 104 ) when used by a user ( 108)), or may be updated. All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, a process, memory, or power) of the electronic device 101 . The operating system 142 may additionally or alternatively include other hardware devices of the electronic device 101 , for example, the input module 150 , the sound output module 155 , the display module 160 , and the audio module 170 . , sensor module 176 , interface 177 , haptic module 179 , camera module 180 , power management module 188 , battery 189 , communication module 190 , subscriber identification module 196 , or It may include one or more driver programs for driving the antenna module 197 .

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, an application manager 201 , a window manager 203 , a multimedia manager 205 , a resource manager 207 , a power manager 209 , a database manager 211 , and a package manager 213 . ), a connectivity manager 215 , a notification manager 217 , a location manager 219 , a graphics manager 221 , a security manager 223 , a call manager 225 , or a voice recognition manager 227 . can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in the screen, for example. The multimedia manager 205, for example, identifies one or more formats required for playback of media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage the space of the source code of the application 146 or the memory of the memory 130 , for example. The power manager 209 may, for example, manage the capacity, temperature, or power of the battery 189 , and determine or provide related information required for the operation of the electronic device 101 by using the corresponding information. . According to an embodiment, the power manager 209 may interwork with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, retrieve, or change a database to be used by the application 146 , for example. The package manager 213 may manage installation or update of an application distributed in the form of a package file, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide, for example, a function for notifying the user of the occurrence of a specified event (eg, an incoming call, a message, or an alarm). The location manager 219 may manage location information of the electronic device 101 , for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227, for example, transmits the user's voice data to the server 108, and based at least in part on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, the converted text data may be received from the server 108 based at least in part on the voice data. According to an embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to an embodiment, at least a portion of the middleware 144 may be included as a part of the operating system 142 or implemented as software separate from the operating system 142 .

Application 146 includes, for example, home 251 , dialer 253 , SMS/MMS 255 , instant message (IM) 257 , browser 259 , camera 261 , alarm 263 . , contacts 265, voice recognition 267, email 269, calendar 271, media player 273, album 275, watch 277, health 279 (such as exercise or blood sugar) measuring biometric information), or environmental information 281 (eg, measuring atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit specified information (eg, call, message, or alarm) to an external electronic device, or a device management application configured to manage the external electronic device. have. The notification relay application, for example, transmits notification information corresponding to a specified event (eg, mail reception) generated in another application (eg, the email application 269 ) of the electronic device 101 to the external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from the external electronic device and provide it to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some components thereof (eg, a display module or a camera module of the external electronic device). ) or a function (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network network, and a network function may be used interchangeably. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network is configured to include at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and the links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two neural network networks having different weight values between the links, the two neural network networks may be recognized as different from each other.

3 is a diagram illustrating an overall configuration of a device virtualization system according to an embodiment.

The plotter may have a unique coordinate system and operation command according to the characteristics of each device, and may correspond to a file having a unique format capable of expressing the unique coordinate system and operation command. Since a file of a unique format is required for each device, there is a problem in that it is difficult to control different devices in an integrated manner. In addition, there is a problem that files and devices of incompatible formats are not compatible with each other. In addition, since physical characteristics such as an output method, paper size, and speed are different for each device, it is difficult to perform an integrated operation.

According to an embodiment, the device virtualization system 300 may collectively control different types of devices. The device virtualization system 300 may standardize data to be transmitted to different types of devices and then convert data for each desired device, thereby controlling different types of devices in an integrated manner.

The device virtualization system 300 may receive order data, convert it into data suitable for each device, and transmit the converted data to each device. The device virtualization system 300 may convert the received order data into logical data and then convert the logical data into data suitable for each device. The logical data may be standardized data that can be converted into data for each device.

The device virtualization system 300 may load the order data as a pre-work at the same time as the input of the input file, analyze each order data, convert it into metadata, and convert the metadata directly into equipment level data for use. The device virtualization system 300 can convert metadata into all coordinate systems used in post-processing at once. Through this, the device virtualization system 300 can maintain high throughput even when a lot of order data is received at the same time.

The device virtualization system 300 may perform rendering by virtualizing the device to simultaneously support devices having various characteristics, and may establish consistency of the output system based on the execution result. The device virtualization device 310 may operate a virtualization system for output system consistency and operational efficiency. The device virtualization device 310 may operate different types of post-processing devices through a single API through the virtualization system.

Hereinafter, before describing the device virtualization system 300 in detail, terms will be described.

The input file is a file input to the device virtualization system 300 and may include an image integration file or a device level file. The image integration file consists of a plurality of layers and may include a print area layer and a post-processing area layer. The print area layer refers to a layer used for image printing. The post-processing area layer means a layer used for post-processing. Post-processing refers to processing that is subsequently applied to a printed work, and may include, for example, cutting, heat treatment, chemical treatment, etc., but is not limited thereto. The image integration file may include, for example, a PDF file or an AI (Adobe Illustrator) file, but this is only an example and may include various types of files.

The device level file refers to a file of a format that can be input to each device. For example, the device level file may include, but is not limited to, a PLT file, and may include various types of files. The device level file may contain a post-processing area layer. The post-processing region layer may include operation region data and operation instruction data. The operation area data refers to data indicating an object on which the device operates in response to an operation command. For example, the motion region data may include coordinates of an object, and lengths and widths of line segments or curves constituting the object. The operation command data represents a command for controlling the operation of the device.

The file level data may refer to data loaded from an input file. File level data may be referred to as physical data or order data. The file level data may include file level area data and file level command data. The optimization data may mean a result of performing an optimization operation on the file level data. The minimization data may include optimization region data and optimization instruction data. The rendering level data may mean data generated by the renderer based on the optimization level data. The rendering level data may include rendering area data and rendering command data. The standard data may mean data standardized based on rendering level data. Standard data may be referred to as logical data or meta data. The standard data may include standard area data and standard command data. The device level data may refer to data obtained by converting standard data for each device. The device level data may include device level domain data and device level command data.

The device virtualization system 300 includes a device virtualization device 310 (eg, the electronic device 100 of FIG. 1 ), a database 311 , a network 340 (eg, the second network 199 of FIG. 1 ), a device The layer 320 may include an external server 331 (eg, the server 108 of FIG. 1 ). The device layer 320 may include a plurality of devices 321 , 322 , 323 . Here, the plurality of devices 321 , 322 , 323 may include a post-processing device. The post-processing device may refer to a device for performing post-processing operations. Hereinafter, the apparatus may be referred to as a post-processing apparatus.

The device virtualization system 300 may generate standard data that can be applied to various devices through a preprocessing process. The device virtualization system 300 may convert the standard data into device-level data that can be decoded by each device through an output process and transmit it to a corresponding device.

In the pre-processing process, the device virtualization system 300 may read the input file and load the order data. The device virtualization system 300 may classify objects included in the order data. The device virtualization system 300 may analyze each object to obtain information about coordinates of the object, types of lines constituting the object, and coordinates and widths of each line. The device virtualization system 300 may generate logical data by arranging analysis results of objects and converting the analysis results into coordinates of a normalized coordinate system.

In the output process, the device virtualization system 300 may convert logical data to suit the characteristics of each device. The device virtualization system 300 may convert logical data into a format suitable for each device. For example, the device virtualization system 300 may convert logical data into a coordinate system suitable for a device, a resolution suitable for a device, or a line type. However, this conversion is only an example, and various conversion operations may be included.

The device virtualization device 310 may receive an input file. The device virtualization device 310 may extract first file level data including file level area data and file level command data from the input file.

The device virtualization device 310 may display the first file level data through a display. When the input file is an image integrated file, the device virtualization device 310 may display thumbnails and selected pages of a plurality of pages of the input file, respectively. If the input file is a device level file, the device virtualization device 310 may display the input file.

For example, when the PDF file is an input file, the device virtualization device 310 may display an image thumbnail of the PDF file and a single screen of the selected page through the display. For example, when the PLF file is an input file, the device virtualization device 310 may display a single screen of the selected page through the display. The device virtualization device 310 may display rendering level data to be described later together through a display.

The device virtualization apparatus 310 may obtain optimization data including optimization area data and optimization instruction data by performing task optimization on the first file level data for each one or more post-processing apparatuses. According to one embodiment, the device virtualization device 310 may estimate the complexity or expected time for the first file level data based on the characteristic information of each of one or more post-processing devices. The device virtualization apparatus 310 may obtain the second file level data as optimization data by converting the first file level data in a direction in which complexity or expected time is reduced. Here, the first file level data means file level data before conversion, and the second file level data means file level data after conversion.

According to another embodiment, the device virtualization apparatus 310 may generate candidate file level data of a plurality of scenarios based on the first file level data. The device virtualization device 310 may estimate complexity or expected time for candidate file-level data of each scenario based on the characteristics of each of one or more post-processing devices. The device virtualization device 310 may acquire candidate file-level data of a scenario having the smallest complexity or expected time as optimization data.

According to an embodiment, the device virtualization apparatus 310 may determine a weight for each of the components constituting the file level area data and the file level command data of the first file level data. The device virtualization device 310 may estimate the complexity based on the weight determined for each component, the type of component, the number of components, the positional relationship of the components, or the precedence relationship of the components.

The device virtualization apparatus 310 may determine the operating time of each component for each post-processing device in response to the components constituting the file level area data and the file level command data of the first file level data. For example, the device virtualization device 310 may estimate pen up/down time, pen acceleration, speed, and the like. The device virtualization device 310 may estimate the estimated time for each post-processing device by summing the operating times of each component.

The device virtualization device 310 may estimate complexity using a complexity estimation model. For example, the complexity estimation model may consist of a neural network. The complexity estimation model may include an input layer, one or more hidden layers, and an output layer. The complexity estimation model may receive characteristic information and file level data of the post-processing apparatus and may output complexity.

To this end, the complexity estimation model may be pre-trained through a plurality of training data. A plurality of training data composed of characteristic information, file level data, and correct answer complexity of each post-processing device may be input to the input layer of the complexity estimation model, pass through one or more hidden layers and output layers, and output vectors may be output. The output vector is input to a loss function layer connected to the output layer, and the loss function layer may output a loss value using a loss function that compares the output vector with the correct vector for each training data. The parameters of the spatial information extraction model may be learned in a direction in which the loss value becomes smaller.

The loss function may follow Equation (1). In Equation 1, N is the number of a plurality of training data, n is a natural number identifying the training data, k is a natural number identifying the value of the n-th training data, nk is the k-th value of the n-th training data, t may indicate correct answer data, y may indicate an output vector, and E may indicate a loss value.

The device virtualization device 310 transforms the coordinates of one or more objects included in the first file-level data, enlarges/reduces the object, rotates the object, converts the offset information included in the first file-level data, The file-level area data of the second file-level data can be obtained by converting the file-level area data of the first file-level data by adjusting the number of segments of the object, changing the type of segment, or removing overlapping segments. . The device virtualization device 310 may acquire the file level command data of the second file level data based on the file level area data of the second file level data and the characteristics of each post-processing device.

The device virtualization apparatus 310 may obtain rendering level data including rendering area data and rendering command data by performing rendering for each one or more post-processing apparatuses based on the optimization data. The device virtualization device 310 may perform rendering by using characteristics and optimization data of one or more post-processing devices for each one or more post-processing devices. The device virtualization device 310 may display rendering level data on a display. The user can view the display result and edit the rendering level data, and the edited result can be immediately displayed on the display. The device virtualization apparatus 310 may obtain rendering level data by converting optimization data based on a user input input in response to display of a rendering result.

As such, the device virtualization apparatus 310 may simplify the identification and editing of various types of post-processing area layers by displaying the rendering level data on the display or directly displaying the editing result on the display.

The device virtualization device 310 may control a queue for a plurality of tasks. The device virtualization device 310 may store one or more rendering level data in a queue. The device virtualization device 310 may control the queue based on the state information delivered from each device. The device virtualization device 310 may store the second rendering level data in the queue after storing the first rendering level data in the queue. The device virtualization device 310 may receive status information of each post-processing device from one or more post-processing devices. When the state information indicates a normal state, the device virtualization device 310 converts the first rendering level data to obtain the first standard data, and then converts the second rendering level data to obtain the second standard data. .

The device virtualization device 310 may adjust the order of subsequent processes of the rendering level data based on the device state information of the device identifier corresponding to the rendering level data. For example, the device virtualization device 310 may adjust the order of rendering level data corresponding to the device identifier of the device corresponding to the state information indicating the abnormal state to a lower priority. As such, the device virtualization device 310 may increase the overall operating speed and efficiency of the system by adjusting the order of operations based on the device state information.

The device virtualization device 310 may obtain standard data that can be applied to one or more post-processing devices by converting the rendering level data. The device virtualization system 300 may determine one or more groups including two or more devices. Standard protocols for all devices included in each group can be predefined. Each standard protocol may have a hierarchy according to the inclusion relationship of the group. The device virtualization device 310 may determine a standard protocol corresponding to the group. The device virtualization device 310 may select a standard protocol including the devices selected to perform post-processing. The device virtualization device 310 may obtain standard data by standardizing rendering level data based on a standard protocol. In general, a simpler standard protocol is defined as the number of devices included in the group is smaller, and the device virtualization device 310 performs standardization using the standard protocol of the smallest group including the selected device, thereby reducing the required resources. can be minimized

The device virtualization device 310 may obtain device-level data including device-level region data, device-level command data, and device identifier by converting standard data corresponding to each of one or more post-processing devices. The device identifier may be input by the user. The user may select a specific device or a specific group. Here, the group may be a group to which the same standard protocol is applied, or a group arbitrarily configured by a user. For example, the user may set a plurality of devices operated in the same company or adjacent region as one group, select a corresponding group, and obtain device level data through the device virtualization device 310 . Through this, processes such as transport after post-processing can be performed more efficiently.

The device virtualization device 310 may transmit device-level data to a post-processing device corresponding to a device identifier among one or more post-processing devices. Each device may use its own means of communication. The device virtualization device 310 may preset a hierarchical key of a communication protocol used by each device. The device virtualization device 310 may select a communication protocol used by a corresponding device based on the device identifier, and transmit device-level data to the corresponding device using the communication protocol.

In the above, the operation of the device virtualization device 310 has been described in terms of data, and below, the operation of the device virtualization device 310 is described in terms of the physical state information of the device.

Each of the devices 321 , 322 , and 323 may generate state information indicating a physical state. The status information may include various types of information indicating whether a job is being performed, whether it is in a normal state, and the like. For example, the status information may include status information that the device is working. The status information may include status information indicating that several jobs are waiting in the future. The status information may include status information indicating that the paper is exhausted. The status information may include status information indicating that a pen is jammed in the paper. The status information may include status information indicating that consumables for driving a laser should be replaced. Such state information is an example, and all information indicating the physical state of the device may be included in the state information.

The state information may be transmitted from each device and delivered to the device virtualization device 310 . State information may be delivered to the device virtualization device 310 through a unique communication means of each device. The device virtualization device 310 may adjust the queue based on the state information. The device virtualization device 310 may adjust the task order of the rendering level data stored in the queue based on the state information.

Through the above process, each device level data may be transmitted to a corresponding device. Each post-processing apparatus may perform post-processing based on device-level data corresponding to a device identifier corresponding to each of the post-processing apparatuses.

4 is a flowchart illustrating an operation of a device virtualization method by a device virtualization apparatus according to an embodiment.

According to an embodiment, in operation 401 , the device virtualization device 310 may extract first file-level data including file-level area data and file-level command data from the input file.

According to an embodiment, in operation 403 , the device virtualization device 310 performs task optimization on the first file level data for each one or more post-processing devices to obtain optimization data including optimization region data and optimization instruction data. can do.

According to an embodiment, in operation 405, the device virtualization device 310 may perform rendering for each one or more post-processing devices based on the optimization data to obtain rendering level data including rendering area data and rendering command data. have.

According to an embodiment, in operation 407 , the device virtualization device 310 may obtain standard data that can be applied to one or more post-processing devices by converting the rendering level data.

According to one embodiment, in operation 409 , the device virtualization device 310 converts standard data corresponding to each of the one or more post-processing devices to device level data including device level domain data, device level command data, and device identifier. can be obtained.

According to an embodiment, in operation 411 , the device virtualization device 310 may transmit device-level data to a post-processing device corresponding to a device identifier among one or more post-processing devices.

5 is a flowchart illustrating an operation of a device virtualization method by a device virtualization system according to an embodiment.

According to an embodiment, the input unit of the device virtualization device 310 may receive an input file.

According to an embodiment, in operation 501 , the first file-level data including the file-level area data and the file-level command data may be extracted from the input file by the parser or reader of the device virtualization device 310 . .

According to an embodiment, in operation 503 , task optimization is performed on the first file level data for each one or more post-processing devices by the pass engine of the device virtualization device 310 to include optimization region data and optimization instruction data. optimization data can be obtained.

According to an embodiment, in operation 505 , rendering is performed by the renderer of the device virtualization device 310 for each of the one or more post-processing devices based on optimization data, and a rendering level including rendering area data and rendering command data. data can be obtained.

According to an embodiment, in operation 507 , the standard protocol layer of the device virtualization device 310 may convert rendering level data to obtain standard data that can be applied to one or more post-processing devices.

According to an embodiment, in operation 509 , the standard data corresponding to each of the one or more post-processing devices is converted by the vendor protocol layer of the device virtualization device 310 to the device level domain data, device level command data and device identifier. It is possible to obtain device level data including

According to an embodiment, in operation 511 , the device-level data may be transferred to the post-processing device corresponding to the device identifier among one or more post-processing devices by the driver layer of the device virtualization device 310 .

6 is a block diagram of a device virtualization system according to an embodiment.

According to one embodiment, the device virtualization system 300 may include a device virtualization device 310 and a device layer 320 . The device layer 320 may include a plurality of devices 321 , 322 , 324 .

The device virtualization device 310 includes an input unit (not shown), a parser 621, a reader 622, a pass engine 630, an integrated viewer 640, a renderer 650, a spooling layer 660, a standard protocol layer ( 680 ), a vendor protocol layer 680 , a driver layer 690 , and a communication unit (not shown).

The input unit may receive an input file.

The parser 621 or the reader 622 may extract the first file level data including the file level area data and the file level command data from the input file.

The pass engine 630 may obtain optimization data including optimization region data and optimization instruction data by performing task optimization on the first file level data for each one or more post-processing devices.

The renderer 650 may obtain rendering level data including rendering area data and rendering command data by performing rendering for each one or more post-processing apparatuses based on the optimization data.

The standard protocol layer 670 may obtain standard data that can be applied to one or more post-processing devices by converting the rendering level data.

The vendor protocol layer 680 may convert standard data corresponding to each of one or more post-processing devices to obtain device-level data including device-level domain data, device-level command data, and device identifiers.

The driver layer 690 may pass device-level data to a post-processing device corresponding to the device identifier among one or more post-processing devices.

7 is a diagram illustrating an example of a device virtualization system according to an embodiment.

The device virtualization device 310 may convert the physical data 701 loaded from the log file into logical data 703 . Here, the physical data 701 is device-specific data and is not compatible with other devices. The logical data 703 is standardized standard data, which is converted according to the characteristics of the device and can be used in other devices.

The device virtualization device 310 may generate device-level data by converting logical data based on each device identifier. The device virtualization device 310 may deliver each device level data to a device corresponding to the device identifier. Each of the post-processing devices 721 , 723 , 725 , and 727 may perform a post-processing operation according to the characteristics of each device based on the received device level data.

8 is a diagram illustrating an example in which coordinates are rotated or converted by offset adjustment by a device virtualization system according to an embodiment.

In the case of the post-processing apparatus, various coordinate systems are used for each apparatus, and the output direction, the paper insertion direction, or the extraction direction may be different from each other. In the case of a complex coordinate system, a situation in which the same data must be rotated for each device may occur. As such, when the coordinate systems are different or the resolutions are different, the probability of an error may increase.

In order to avoid this drawback, the device virtualization system 300 may internally use a standard coordinate system called a relative coordinate system in the system. The device virtualization system 300 may integrate different types of devices by standardizing rendering level data as standard data.

8 shows various types of coordinate systems. Referring to Figure 8 (a), a computer screen coordinate system is shown. Referring to FIG. 8B , PDF and AI coordinate systems are shown. Referring to FIG. 8C , a plotter coordinate system 1 is shown. Referring to FIG. 8D , plotter coordinate system 2 is shown. Referring to FIG. 8E , a plotter coordinate system 3 is shown. The plotter coordinate system 3 is a central coordinate system. Referring to FIG. 8(f) , plotter coordinate system 4 is shown. The plotter coordinate system 4 is an offset coordinate system. Referring to FIG. 8G , a plotter composite coordinate system is illustrated.

9 is a diagram illustrating an example in which a relative size of coordinates is converted by a device virtualization system according to an embodiment.

The device virtualization system 300 may convert the received order data into logical data and then convert the logical data into data suitable for each device. The device virtualization system 300 may convert a coordinate system of a straight line or a curve in the classified object into a relative coordinate system. For example, referring to FIG. 9 , each of x and y coordinates in the original coordinate system may have an unrestricted real value, and in the transformed coordinate system, x and y may be normalized to a real value between 0 and 1, respectively. For example, if the document input to a specific post-processing device is A4 (210x297mm), the original coordinates may have coordinates between (0,0) - (210,297), and the transformation coordinates are (0,0) - It can have coordinates between (1,1). In the case of transformation coordinates, since the document's horizontal and vertical information and the relative coordinate system range from 0 to 1, the resolution, rotation, enlargement, and reduction for a specific device can be calculated simply.

10 is a diagram illustrating a configuration of a device virtualization device according to an embodiment.

According to one embodiment, the device virtualization device 310 may include a processor 1001 and a memory 1003 . The device virtualization device 310 may further include a display.

According to an embodiment, the processor 1001 may extract first file level data including file level area data and file level command data from the input file.

According to an embodiment, the processor 1001 may obtain optimization data including optimization area data and optimization instruction data by performing task optimization on the first file level data for each one or more post-processing apparatuses.

According to an embodiment, the processor 1001 may obtain rendering level data including rendering area data and rendering command data by performing rendering for each one or more post-processing apparatuses based on the optimization data.

According to an embodiment, the processor 1001 may obtain standard data that can be applied to one or more post-processing apparatuses by converting the rendering level data.

According to an embodiment, the processor 1001 may obtain device-level data including device-level region data, device-level command data, and device identifiers by converting standard data corresponding to each of one or more post-processing devices.

According to an embodiment, the processor 1001 may transmit device-level data to a post-processing device corresponding to a device identifier among one or more post-processing devices.

The memory 1003 is a ROM, a RAM, a non-volatile memory, a volatile memory, a hard disk drive (HDD), a solid state drive (SSD), a USB memory, a flash memory card, and various removable and portable devices corresponding thereto. Storage media (eg, Secure Digital (SD) memory card, microSD memory card, ISO 7816 standard storage device, etc.), but is not limited thereto, and may include any type of storage device for storing data. The memory may be referred to as a storage device.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (12)

at least one processor;
Memory; and
including a display;
The processor is
receive an input file,
extracting first file level data including file level area data and file level command data from the input file;
performing task optimization on the first file level data for each of one or more post-processing devices to obtain optimization data including optimization area data and optimization instruction data for each post-processing device;
Rendering is performed for each of the one or more post-processing apparatuses based on the optimization data to obtain rendering level data including rendering area data and rendering instruction data for each post-processing apparatus,
Converting the rendering level data to obtain standard data that can be applied to two or more post-processing devices,
Transform the standard data corresponding to each of one or more post-processing devices to obtain device-level data including device-level area data, device-level command data and device identifiers for each post-processing device,
passing the device level data to a post-processing device corresponding to the device identifier;
Device virtualization device. According to claim 1,
The display is
When the input file is an image integrated file, thumbnails and selected pages for a plurality of pages of the input file are displayed, respectively;
displaying the input file if the input file is a device level file;
Device virtualization device. According to claim 1,
The processor is
estimating complexity or expected time for the first file-level data based on the characteristic information of each of the one or more post-processing devices;
converting the first file-level data in a direction in which the complexity or the expected time decreases to obtain second file-level data as the optimization data;
Device virtualization device. According to claim 1,
The processor is
generating candidate file level data of a plurality of scenarios based on the first file level data;
estimating complexity or expected time for the candidate file level data of each scenario based on the characteristics of each of the one or more post-processing devices,
Obtaining candidate file-level data of a scenario having the smallest complexity or expected time as the optimization data,
Device virtualization device. 4. The method of claim 3,
The processor is
Transform coordinates of one or more objects included in the first file-level data, enlarge/reduce the object, rotate the object, transform offset information included in the first file-level data, or converting the file level area data of the first file level data by adjusting the number of segments, changing the type of the segment, or removing overlapping segments to obtain the file level area data of the second file level data;
obtaining the file level command data of the second file level data based on the file level area data of the second file level data and the characteristics of each post-processing apparatus;
Device virtualization device. 4. The method of claim 3,
The processor is
Rendering is performed using the characteristics of the one or more post-processing devices and the optimization data for each of the one or more post-processing devices,
The display is
displaying the result of the rendering;
The processor is
Transforming the optimization data based on a user input input corresponding to the display of the rendering result to obtain the rendering level data,
Device virtualization device. According to claim 1,
The processor is
storing the second rendering level data in the queue after storing the first rendering level data in the queue;
receiving status information of each of the post-processing devices from the one or more post-processing devices,
When the state information indicates a normal state, converting the first rendering level data to obtain first standard data, then converting the second rendering level data to obtain second standard data,
Device virtualization device. delete input unit;
parser;
leader;
pass engine;
integrated viewer;
renderer;
spooling layer;
standard protocol layer;
vendor protocol layer;
driver layer; and
including a communication department;
The input unit receives an input file,
the parser or the reader extracts first file level data including file level area data and file level command data from the input file;
the pass engine performs task optimization on the first file-level data for each of one or more post-processing devices to obtain optimization data including optimization region data and optimization instruction data for each post-processing device;
The renderer performs rendering for each of the one or more post-processing devices based on the optimization data to obtain rendering level data including rendering area data and rendering command data for each post-processing device,
The standard protocol layer converts the rendering level data to obtain standard data that can be applied to one or more post-processing devices,
The vendor protocol layer converts the standard data corresponding to each of the two or more post-processing devices to obtain device-level data including device-level region data, device-level command data and device identifiers for each post-processing device,
the driver layer passes the device level data to a post-processing device corresponding to the device identifier;
Device virtualization device. virtualization device; and
one or more post-processing devices,
The virtualization device,
at least one processor;
Memory; and
including a display;
The processor is
receive an input file,
extracting first file level data including file level area data and file level command data from the input file;
performing task optimization on the first file level data for each of one or more post-processing devices to obtain optimization data including optimization area data and optimization instruction data for each post-processing device;
Rendering is performed for each of the one or more post-processing apparatuses based on the optimization data to obtain rendering level data including rendering area data and rendering instruction data for each post-processing apparatus,
Converting the rendering level data to obtain standard data that can be applied to two or more post-processing devices,
Transform the standard data corresponding to each of the one or more post-processing devices to obtain device-level data including device-level region data, device-level command data and device identifiers for each post-processing device,
passing the device level data to a post-processing device corresponding to the device identifier;
Each of the one or more post-processing devices,
performing post-processing on the basis of device-level data corresponding to device identifiers corresponding to each of the post-processing devices;
Device virtualization system. extracting, by the processor, first file level data including file level area data and file level command data from the input file;
performing, by the processor, task optimization on the first file-level data for each of the one or more post-processing devices to obtain optimization data including optimization region data and optimization instruction data for each post-processing device;
performing, by the processor, rendering on each of the one or more post-processing apparatuses based on the optimization data to obtain rendering level data including rendering area data and rendering instruction data for each post-processing apparatus;
converting, by the processor, the rendering level data to obtain standard data that can be applied to two or more post-processing devices;
converting, by the processor, the standard data corresponding to each of the one or more post-processing devices to obtain device-level data including device-level area data, device-level command data, and device identifiers for each post-processing device; and
passing, by the processor, the device level data to a post-processing device corresponding to the device identifier;
A device virtualization method comprising: receiving, by the input unit, an input file;
extracting, by a parser or a reader, first file-level data including file-level area data and file-level command data from the input file;
performing task optimization on the first file-level data for each of the one or more post-processing apparatuses, by the pass engine, to obtain optimization data including optimization area data and optimization instruction data for each post-processing apparatus;
performing, by the renderer, rendering on each of the one or more post-processing apparatuses based on the optimization data to obtain rendering level data including rendering area data and rendering instruction data for each post-processing apparatus;
converting the rendering level data by a standard protocol layer to obtain standard data that can be applied to two or more post-processing devices;
converting, by the vendor protocol layer, the standard data corresponding to each of one or more post-processing devices to obtain device-level data including device-level region data, device-level command data and device identifiers for each post-processing device; and
passing the device level data by the driver layer to the post-processing device corresponding to the device identifier
A device virtualization method comprising:

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