KR20230053276A - A global cloud system providing regional independence - Google Patents

Abstract

The present invention proposes a global cloud system that secures regional independence for the annotation process of artificial intelligence (AI) learning data. The system divides the cloud system into multiple global regions, where personal information within one region is isolated to prevent leakage to other regions unless there is a special exception. The regions can be defined by national boundaries or by the extent to which the export of personal information is restricted. The region is divided into at least a first region and a second region, wherein the first region and the second region cannot exchange data directly with each other. Instead, the regions can exchange data only via a sink connected to the regions at the center of a star-network structure. The present invention solves the problem that personal information contained in data collected in a country may be exported to other countries.

Description

A global cloud system providing regional independence}

The present invention relates to a global cloud system that secures regional independence. More specifically, it relates to a computer program recorded in a global cloud system that secures regional independence in annotation of data for artificial intelligence (AI) machine learning.

Artificial intelligence (AI) refers to a technology that artificially implements some or all of human learning abilities, reasoning abilities, and perception abilities using computer programs. In relation to artificial intelligence (AI), machine learning refers to learning to optimize parameters with given data using a model composed of multiple parameters. Such machine learning is classified into supervised learning, unsupervised learning, and reinforcement learning according to the form of learning data.

In general, designing data for artificial intelligence (AI) learning proceeds in the steps of data structure design, data collection, data refinement, data processing, data expansion, and data verification.

To describe each step in more detail, data structure design is performed through ontology definition, classification system definition, and the like. Data collection is performed by collecting data through direct filming, web crawling, or associations/professional organizations. Data purification is performed by removing redundant data from collected data and de-identifying personal information. Data processing is performed by performing annotation and inputting metadata. Data extension is performed by performing ontology mapping and supplementing or extending the ontology as needed. In addition, data verification is performed by verifying validity according to the set target quality using various verification tools.

The data collected by the collection device for artificial intelligence learning is composed of a very large number of images taken in real time, not just a few. There is a lot of burden for the data collection device to transmit a lot of raw data to the data collection server for the collected data in this way. In addition, since the collection device generally collects raw data through real-time shooting, among the collected raw data, similar data with low artificial intelligence learning efficiency may be included, thereby highlighting the efficiency of data management.

In the global area, it is possible to consider distributing a lot of raw data to the cloud and performing annotation in a specific global country. There is a problem that the personal information of the foreigner may be taken out to other countries, or personal information included in data collected in the country may be taken out to other countries. Since such transfer of personal information to other countries may be illegal under the Personal Information Protection Act of each country, there is a need to protect personal information from being processed only within one country and exported to other countries.

Korean Patent Laid-open Publication No. 10-2020-0042629, ‘Method and apparatus for generating touch-based annotations and images in mobile devices for artificial intelligence learning’, (published on April 24, 2020)

One object of the present invention is to provide a global cloud system that secures regional independence of annotation work of artificial intelligence (AI) learning data.

Another object of the present invention is to provide a global cloud system that solves the problem that personal information included in data collected in a corresponding country may be exported to other countries.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the technical problem as described above, the present invention proposes a global cloud system that secures regional independence of annotation work of artificial intelligence (AI) learning data.

A global cloud system that secures regional independence of annotation work of artificial intelligence (AI) learning data according to an embodiment of the present invention classifies the cloud system into a plurality of global regions, and personal information within one region It is isolated so that it cannot be leaked to other areas unless there is a special prior notice, and the area is a national unit or a regional unit subject to restrictions on the export of personal information. and the second region cannot directly exchange data with each other, and exchange data only through a sink connected to the regions at the center of the star-network structure.

The region may be set on a project-by-project basis, and it may be considered that the range and size of the region are set according to project attributes and annotator's writing ability.

Sink can consider securing independence by region, which is characterized by being set to allow access only to information in a certain area permitted to be disclosed without direct access to personal information in a single storage in the region.

When a user approaches, the sink identifies the country where the user is located based on information (location value, language, etc.) that can specify the user's country, and plays a role in connecting the identified country to an accessible area. can consider doing

When a user requests upload, the sink may consider allowing upload only to a region permitted to the user and blocking upload to other regions.

According to embodiments of the present invention, it is possible to consider performing annotation in a specific global country by distributing a lot of raw data in the cloud in the global area. When performing work abroad, solve the problem that the foreigner's personal information may be exported to another country, or personal information included in data collected in the country may be exported to another country, and only within one country It has the effect of protecting personal information from being processed and exported to other countries.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

It should be noted that the technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. In addition, technical terms used in this specification should be interpreted in terms commonly understood by those of ordinary skill in the art to which the present invention belongs, unless specifically defined otherwise in this specification, and are overly inclusive. It should not be interpreted in a positive sense or in an excessively reduced sense. In addition, when the technical terms used in this specification are erroneous technical terms that do not accurately express the spirit of the present invention, they should be replaced with technical terms that those skilled in the art can correctly understand. In addition, general terms used in the present invention should be interpreted as defined in advance or according to context, and should not be interpreted in an excessively reduced sense.

Also, singular expressions used in this specification include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "consisting of" or "having" should not be construed as necessarily including all of the various components or steps described in the specification, and some of the components or steps are included. It should be construed that it may not be, or may further include additional components or steps.

Also, terms including ordinal numbers such as first and second used in this specification may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but other components may exist in the middle. On the other hand, when a component is referred to as “directly connected” or “directly connected” to another component, it should be understood that no other component exists in the middle.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings, but the same or similar components are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. In addition, in describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description will be omitted. In addition, it should be noted that the accompanying drawings are only for easily understanding the spirit of the present invention, and should not be construed as limiting the spirit of the present invention by the accompanying drawings. The spirit of the present invention should be construed as extending to all changes, equivalents or substitutes other than the accompanying drawings.

The cloud system according to an embodiment of the present invention may consider performing annotation in a specific global country by distributing a lot of raw data to the cloud in the global area.

If the worker performing the annotation does not work in Korea as a Korean national and works abroad as a foreigner, the foreigner's personal information is taken out to another country, or the personal information included in the data collected in that country There is a problem that can be exported to other countries. Since such transfer of personal information to other countries may be illegal under the Personal Information Protection Act of each country, there is a need to protect personal information from being processed only within one country and exported to other countries.

Consider classifying cloud systems into multiple global regions. Personal information within one area is isolated so that it cannot be leaked to other areas unless there is a special prior notice. Here, the region may be a country unit, and may be applied to a regional unit subject to restrictions on export of personal information.

The first region and the second region cannot directly exchange data with each other, but can exchange data only through a sink connected to the regions at the center of the star-network structure.

Such an area may be set on a project basis, and the scope and size of the area may be determined according to project attributes, annotator's writing ability, the number of annotation workers, laws related to personal information, and the like.

Sink cannot directly access personal information in a local single storage, and can only access information in a certain area that is allowed to be disclosed. Each region can access synchronized non-personal information through Sync.

When a user approaches, Sink identifies the country where the user is located based on information (location value, language, etc.) that can specify the user's country, and plays a role in connecting the identified country to an accessible area. .

On the other hand, when a user requests upload, the sink allows uploading only to areas permitted to the user and blocks uploading to other areas.

An artificial intelligence learning system according to an embodiment of the present invention may include a plurality of learning data collection devices, a learning data generating device, a plurality of annotation devices, and an artificial intelligence learning device.

Since the components of the artificial intelligence learning system according to an embodiment are merely functionally distinct elements, two or more components are integrated and implemented in an actual physical environment, or one component is implemented in an actual physical environment. may be implemented separately from each other.

The learning data generating device may receive attributes of a project related to artificial intelligence (AI) learning from the artificial intelligence learning device. The learning data generation device performs data structure design, collected data refinement, data processing, data expansion, and data verification for artificial intelligence (AI) learning, based on the user's control and the attributes of the received project. can do.

First of all, the learning data generating device may design a data structure for artificial intelligence (AI) learning. For example, the learning data generating device may define an ontology for artificial intelligence (AI) learning and a classification system of data for artificial intelligence (AI) learning based on the properties of the user's control and the received project. can

The learning data generating device may collect data for artificial intelligence (AI) learning based on the designed data structure. To this end, the learning data generating device may receive sensing data, 3D point cloud data, 2D images, and distance information from the learning data collecting device. However, it is not limited thereto, and the learning data generating device may perform web crawling or download data from an external organization's device.

The learning data generating apparatus may remove redundant or extremely similar data from among the collected sensing data, 3D point cloud data, 2D images, and distance information. The learning data generating device may verify whether the personal information automatically de-identified for the 2D image by the learning data collecting device is correct.

On the other hand, in order to support the automation of object detection performed by the learning data collection device, the learning data generating device uses object detection artificial intelligence (AI) machine-learned based on the result of an existing annotation task targeting a plurality of 2D images. can be provided In addition, the learning data generating device may generate an object detection rule using object detection artificial intelligence (AI) and distribute the generated object detection rule to a plurality of learning data collection devices.

Meanwhile, the learning data generating device may distribute and transmit collected and refined sensing data, 3D point cloud data, 2D images, and distance information to a plurality of annotation devices. In this case, the learning data generating device may distribute sensing data, 3D point cloud data, 2D images, and distance information corresponding to a pre-allocated amount for an operator (ie, labeler) of the annotation device.

The learning data generating device may receive annotation work results from each annotation device. The learning data generating device may generate artificial intelligence (AI) learning data by packaging the received annotation work result. And, the learning data generating device may transmit the generated artificial intelligence (AI) learning data to the artificial intelligence learning device.

Any device capable of transmitting and receiving data to and from a learning data collection device, an annotation device, and an artificial intelligence learning device, and performing calculations based on the transmitted and received data may be accepted as the learning data generation device having such characteristics. . For example, the learning data generating device may be any one of a fixed computing device such as a desktop, workstation, or server, but is not limited thereto.

The annotation device may receive coordinates according to the selected tool through an input/output device. In addition, the annotation device may specify an object included in sensing data, 3D point cloud data, 2D image, or distance information by setting a bounding box based on the input coordinates.

Here, the bounding box is an area for specifying an object to be learned by artificial intelligence (AI) among objects included in sensing data, 3D point cloud data, 2D image, or distance information. Such a bounding box may have a rectangle or cube shape, but is not limited thereto.

For example, the annotation device receives two coordinates through an input/output device, and based on the inputted two coordinates, the coordinates of the upper left vertex and the lower right vertex within detection data, 3D point cloud data, 2D image, or distance information. An object can be specified by setting a bounding box based on a rectangle with coordinates of . In this case, the two coordinates may be set by the user inputting one type of input signal twice (eg, mouse click) or by the user inputting two types of input signal once (eg, mouse drag). It may, but is not limited thereto.

Alternatively, the annotation device may output information about the 2D image received from the learning data generating device and the object automatically detected by the learning data generating device for the corresponding 2D image to a display. In addition, the annotation device may receive input from the user as a result of verifying whether information on the automatically detected object is correct. If there is an error in the information about the automatically detected object, the annotation device may re-specify the object included in the 2D image according to a signal input from the user through the input/output device.

As the annotation device having such characteristics, any device may be accepted as long as it is capable of transmitting and receiving data to and from the learning data generating device and performing calculations based on the transmitted and received data. For example, the annotation device may be a stationary computing device such as a desktop, workstation, or server, or a smartphone, laptop, tablet, or phablet. ), a portable multimedia player (PMP), a personal digital assistant (PDA), or an e-book reader.

As described above, the plurality of learning data collection devices and learning data generating devices may transmit and receive data using a mobile communication network. For example, in a mobile communication network, Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), High Speed Packet Access (HSPA), Long Term Evolution (Long Term Evolution) Evolution, LTE) and 5th generation mobile telecommunication may be included, but is not limited thereto.

In addition, the learning data generating device, the plurality of annotation devices, and the artificial intelligence learning device may transmit and receive data using a network in which one or more of a security line, a common wired communication network, or a mobile communication network directly connects devices. For example, public wired communication networks may include Ethernet, x Digital Subscriber Line (xDSL), Hybrid Fiber Coax (HFC), and Fiber To The Home (FTTH). It may be, but is not limited thereto.

An apparatus for collecting learning data according to an embodiment of the present invention may include a communication unit, an input/output unit, a multi-sensor control unit, an object identification unit, a de-identification processing unit, a data providing unit, and a storage unit.

Since the components of the learning data collection device are merely functionally distinct elements, two or more components are integrated and implemented in an actual physical environment, or one component is implemented separately from each other in an actual physical environment. It could be.

Describing each component, the communication unit may transmit/receive data between multiple sensors installed in the vehicle and the learning data generating device.

Specifically, the communication unit may receive sensing data, 3D point cloud data, 2D image, and distance information from a radar, lidar, camera, and ultrasonic sensor fixedly installed in a vehicle.

The communication unit may measure communication quality of mobile communication with the learning data generating device at predetermined intervals. For example, the communication unit may transmit an Internet Control Message Protocol (ICMP) ping packet (echo request) to the learning data generating device at predetermined intervals and receive a response packet (echo reply) from the learning data generating device. .

The communication unit may transmit sensing data, 3D point cloud data, 2D image, and distance information to the learning data generating device under the control of the data providing unit. In this case, the 2D image may include information about a region occupied by an object identified from the 2D image by the object identification unit and information about de-identified personal information.

The communication unit may transmit an update request message of an object detection rule or an update request message of a de-identification processing rule to the learning data generating device under the control of the object identification unit and the de-identification processing unit.

Basically, the data providing unit may measure the communication quality of mobile communication with the learning data generating device at predetermined intervals and adjust the size of the buffer in response to the measured communication quality.

Specifically, the data providing unit transmits ICMP ping packets to the learning data generating device at set intervals through the communication unit, and RTT (Round Trip Time) and TTL (Time To Live) included in the response packet received from the learning data generating device ), the size of the buffer can be determined based on In this case, the data providing unit may adjust a period for transmitting ICMP ping packets in inverse proportion to a moving speed of a vehicle equipped with a radar, lidar, camera, and ultrasonic sensor.

On the other hand, when it is determined that all packets are lost based on the TTL included in the response packet, the data providing unit does not directly perform mobile communication with the learning data generating device, but communicates with the learning data generating device via another learning data collecting device. It can also be controlled to perform mobile communication.

The learning data collection device may include a processor, memory, transceiver, input/output device 165, data bus 170, and storage. .

The processor may implement the operation and function of the learning data collection device based on instructions according to software in which the method according to the embodiments of the present invention is implemented, which resides in the memory. Software in which a method according to embodiments of the present invention is implemented may be loaded in the memory. The transceiver may transmit and receive data to and from radar, lidar, cameras, ultrasonic sensors, and learning data generating devices. The input/output device may receive data necessary for the operation of the learning data collection device and output collected sensing data, 3D point cloud data, 2D image, and distance information. The data bus is connected to a processor, a memory, a transceiver input/output device, and a storage, and may serve as a movement path for transferring data between respective components.

The storage may store an application programming interface (API), a library file, a resource file, etc. necessary for the execution of the software 180a in which the method according to the embodiments of the present invention is implemented. . The storage may store software and a database in which a method according to embodiments of the present invention is implemented. Object detection rules, non-identification processing rules, and 3D models for each type of object may be stored in the database.

More specifically, the processor may include an Application-Specific Integrated Circuit (ASIC), other chipsets, logic circuits, and/or data processing devices. The memory may include read-only memory (ROM), random access memory (RAM), flash memory, memory cards, storage media, and/or other storage devices. The transceiver 160 may include a baseband circuit for processing wired/wireless signals. The input/output device 165 includes an input device such as a keyboard, a mouse, and/or a joystick, and a Liquid Crystal Display (LCD), an Organic LED (OLED), and/or a liquid crystal display (LCD). Alternatively, an image output device such as an active matrix OLED (AMOLED) may include a printing device such as a printer or a plotter.

When the embodiments included in this specification are implemented as software, the above-described method may be implemented as a module (process, function, etc.) that performs the above-described functions. A module resides in memory and can be executed by a processor. The memory may be internal or external to the processor and may be coupled with the processor in a variety of well-known means.

In the case of implementation by firmware or software, an embodiment of the present invention is implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above, and is recorded on a recording medium readable through various computer means. can Here, the recording medium may include program commands, data files, data structures, etc. alone or in combination. Program instructions recorded on the recording medium may be those specially designed and configured for the present invention, or those known and usable to those skilled in computer software. For example, recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks), floptical It includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, such as a floptical disk, and ROM, RAM, flash memory, and the like. Examples of program instructions may include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes generated by a compiler. These hardware devices may be configured to operate as one or more pieces of software to perform the operations of the present invention, and vice versa.

As described above, although preferred embodiments of the present invention have been disclosed in the present specification and drawings, it is in the technical field to which the present invention belongs that other modified examples based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein. It is self-evident to those skilled in the art. In addition, although specific terms have been used in the present specification and drawings, they are only used in a general sense to easily explain the technical content of the present invention and help understanding of the present invention, but are not intended to limit the scope of the present invention. Accordingly, the foregoing detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be selected by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Claims (5)

In a global cloud system that secures regional independence of annotation work of artificial intelligence (AI) learning data,
The cloud system is classified into multiple global regions, and personal information within one region is isolated so that it cannot be leaked to other regions unless there is a special dictionary.
The above region is a national unit or a regional unit subject to restrictions on the export of personal information,
The area is divided into at least a first area and a second area, but the first area and the second area cannot directly exchange data with each other, and data is exchanged only through a sink connected to the areas in the center of the star-network structure. Characterized by a global cloud system that secures regional independence. According to claim 1,
The region can be set for each project, and the scope and size of the region are set according to the attributes of the project and the writing ability of the annotation worker. According to claim 1,
Sink is a global cloud system that secures regional independence, characterized in that it is set to allow access only to information in a certain area that is allowed to be disclosed without direct access to personal information in a single storage in the region. According to claim 3,
When a user approaches, the sink identifies the country where the user is located based on information (location value, language, etc.) that can specify the user's country, and plays a role in connecting the identified country to an accessible area. A global cloud system that secures regional independence. According to claim 3,
The sink is a global cloud system that secures regional independence, characterized in that when a user requests an upload, it allows upload only to the region permitted to the user and blocks upload to other regions.