KR20170059097A - Internet of Things big data active processing system - Google Patents

Abstract

An Internet Big Data Active Processing System is disclosed.
According to an embodiment of the present invention, there is provided a system for processing active Internet Big Data, comprising: a data collecting unit collecting data from an object Internet appliance; A data storage unit for storing the collected data through a network; A report generation unit for generating a report based on data stored in the data storage unit; A learning and reasoning unit for learning and reasoning on the Internet based on the report; A notification setting unit for determining whether to execute the notification type received from the report generation unit or the learning and inferencing unit;
An information notifying unit configured to configure a protocol according to the notification type and to transmit the protocol to the information presentation unit through a network, and an information presentation unit expressing information notified from the information notification unit.

Description

[0001] The present invention relates to a large data active processing system,

[0001] The present invention relates to an Internet Big Data Active Processing System, and more particularly, to a Internet Explorer Big Data Active Processing System that provides a personalized service model to a user who uses the Internet using the Big Data collected from the Internet .

Gartner estimates that the number of Internet of Things (IoT) devices will reach 375 million in 2014 and 25 billion in 2020, with more than half of the devices expected to come from consumers.

In addition to the increasing number of object Internet devices in recent years, the amount of digital data produced by devices will also increase, and the amount of data collected or stored is also increasing.

Therefore, methods are being developed to provide regularity and reproduce information as useful information in digital data stored without specific criteria. Also, it is possible to use digital data provider There is also a need for a business model that can satisfy each other.

And, if the users of Internet devices and devices are different, a suitable business model is needed. If the business model is different, the digital data types and processing forms need to be changed. Therefore, an information extraction pattern is needed to extract information according to the requirements of a specific business model .

In addition, since there may be variable factors depending on the application target in a specific business model, it is necessary to secure model extensibility by selectively selecting the model configuration information, and a company operating a business model using the collected digital data By pursuing profits from diverse information-based businesses that accumulate in the data base, digital data providers can provide instant information, periodic information, and reservation information that are reproduced as useful information in the form of value compensation for data provision, To be able to pursue.

Therefore, it is possible to provide information in a timely manner with information extraction patterns for machine learning, so that digital data can be collected, stored and processed from the object Internet device, Using the Information Internet Big Data Active Processing System, which is an information reduction system that needs an information feedback system and applicable to various business models, it can be linked with the business model of the vehicle that has the closest relationship with real life. You must configure your system to do so.

We propose a data manipulation system that provides a customized service model to users who use the Big Internet data collected from the Internet.

The solution of the present invention is not limited to the above-mentioned solutions, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description.

According to another aspect of the present invention, there is provided a system for processing active Internet Big Data, comprising: a data collecting unit collecting data from an object Internet appliance; A data storage unit for storing the collected data through a network; A report generation unit for generating a report based on data stored in the data storage unit; A learning and reasoning unit for learning and reasoning on the Internet based on the report; A notification setting unit for determining whether to execute the notification type received from the report generation unit or the learning and inferencing unit; An information notifying unit configured to configure a protocol according to the notification type and to transmit the protocol to the information presentation unit through a network, and an information presentation unit expressing information notified from the information notification unit.

In addition, the object Internet Big Data active processing system may further include a security system that prevents an access with an attack possibility except for a data collecting unit and an allowed access, and responds to predetermined rules upon detecting an attack.

According to the object Internet Big Data active processing system according to the embodiment of the present invention, a customized service model can be provided to a user using the object Internet by utilizing big data collected from the object Internet.

FIG. 1 is a block diagram of an Internet Big Data active processing system according to an embodiment of the present invention.
2 is a configuration diagram of a data collecting unit according to an embodiment of the present invention.
3 is a block diagram of a security system according to an embodiment of the present invention.
4 is a configuration diagram of a data storage unit according to an embodiment of the present invention.
5 is a configuration diagram of a report generating unit according to an embodiment of the present invention.
6 is a configuration diagram of a learning & reasoning unit according to an embodiment of the present invention.
7 is a configuration diagram of a notification setting unit according to an embodiment of the present invention.
8 is a configuration diagram of an information notification unit according to an embodiment of the present invention.
9 is a configuration diagram of an information expression unit according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Embodiments of the present invention are provided to more fully describe the present invention to those skilled in the art, and the following embodiments may be modified in various other forms, The present invention is not limited to the following embodiments. Rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the invention to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an," and "the" include plural forms unless the context clearly dictates otherwise. Also, " comprise "and / or" comprising "when used herein should be interpreted as specifying the presence of stated shapes, numbers, steps, operations, elements, elements, and / And does not preclude the presence or addition of one or more other features, integers, operations, elements, elements, and / or groups. As used herein, the term "and / or" includes any and all combinations of one or more of the listed items.

Although the terms first, second, etc. are used herein to describe various elements, regions and / or regions, it should be understood that these elements, components, regions, layers and / Do. These terms do not imply any particular order, top, bottom, or top row, and are used only to distinguish one member, region, or region from another member, region, or region. Thus, the first member, region or region described below may refer to a second member, region or region without departing from the teachings of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings schematically showing embodiments of the present invention. In the figures, for example, variations in the shape shown may be expected, depending on manufacturing techniques and / or tolerances. Accordingly, embodiments of the present invention should not be construed as limited to any particular shape of the regions illustrated herein, including, for example, variations in shape resulting from manufacturing.

FIG. 1 is a block diagram of an Internet Big Data active processing system according to an embodiment of the present invention.

Referring to FIG. 1, the object Internet Big Data active processing system according to an embodiment of the present invention includes a data collecting unit, a security system, a data storing unit, a report generating unit, a learning & speculating unit, a notification setting unit, Expression unit.

2 is a configuration diagram of a data collecting unit according to an embodiment of the present invention.

Referring to FIG. 2, the data collecting unit according to the embodiment of the present invention includes an IoT device, a communication terminal, a video input device, and a security module.

The role of the data collecting unit is to transmit GPS-based traffic and road information to the server through navigation, and to transmit real-time traffic information (congestion, delay, smoothness) and safe driving road information (road speed limit, ) In order to provide a navigation function.

In addition, the data collector supports ADAS (Advanced Driver Assistance System). The main supporting items include a system for extracting safe driving information through the image processing algorithm and an LDWS (Lane Departure Warning System) lane departure warning system and a left or right lane Forward Collision Warning System (FCWS): recognizes the distance between the forward collision warning system and the forward vehicle based on the speed of the vehicle, detects the collision risk, and displays the danger warning and FVSA Vehicle Start Alarm): It informs the departure of the front car and the departure of the front car from the stop and signal dash.

Pedestrian Collision Warning System (PCWS): recognizes the pedestrian collision warning system and pedestrians on the road while driving, detects danger of collision, and displays danger warning and signal light violation warning system (SVWS) If it is not a signal that can be traveled on the basis of the stop signal notification.

The data collecting unit transmits to the server a phone call prohibition history, a driving route, and a real-time traffic information report through the app installed in the smartphone. It also provides GPS (Travel Route) information, Call Status, Road hazards such as falling objects / road damage, attention to a faulty vehicle, etc.).

The data collecting unit collects and stores the RPM, the battery voltage, the fuel gauge, the travel distance, the brake / accelerator operation state, the gear position, and the like, and transmits the specific sensor value among 100 kinds of sensors to the server. (heart rate, blood sugar level, sweat discharge, body temperature, etc.) to the server by using various sensors attached to the sensor, band, Smart watch, Smart shoes, and Smart glasses.

In addition, the data collecting unit transmits CO2 change and temperature / humidity change values to the server using a carbon dioxide sensor and a temperature / humidity sensor, and inputs a video input device such as a Smart Dash Cam (Multi-channel) And transmits the vehicle driving image and the vehicle interior image data to the server.

The data collecting unit may include a wireless communication device, a data modem, a hotspot (Smart Phone), and a digital camera for transmitting the digital data of the IoT device and the video streaming of the video input device to the server, And the like.

3 is a block diagram of a security system according to an embodiment of the present invention.

Referring to FIG. 3, a security system according to an embodiment of the present invention includes an IPS / IDS, an encryption module, and a decryption module.

 The IPS / IDS is an intrusion prevention system, an intrusion detection system that prevents access with the possibility of attack except for the data collection unit and the permitted access, and responds according to predetermined rules when an attack is detected.

The encryption module and the decryption module convert plain text into cipher text and ciphertext into plain text using an encryption algorithm, and transmit and receive the data to prevent direct data exposure on the network.

4 is a configuration diagram of a data storage unit according to an embodiment of the present invention.

Referring to FIG. 4, a data storage unit according to an embodiment of the present invention includes a data classification module, an image data storage management module, and a sensor data storage management module.

The data classification module divides the received data into digital data and image data collected from the object Internet device, and transmits the data to a module capable of storing and managing each data. The data classification module uses a data set having a specific protocol .

The image data storage management module changes the data control flow to the streaming server when the image data is transferred, and transfers the image data requiring storage to the database server by specifying the storage and managing the information. Also, the storage rule is applied through the identifier for each image when stored.

The sensor data storage management module transmits the manageable information to the database server according to the sensor data type.

5 is a configuration diagram of a report generating unit according to an embodiment of the present invention.

Referring to FIG. 5, a report generator according to an embodiment of the present invention includes a report setting module, a report generating module, and a report managing module.

The report setting module sets pattern selection and driver calibration pattern reports to be used in the vehicle-related business model based on vehicle-related data. For example, pattern definitions and lane changes to select data collected and stored from IoT devices that are dependent on the vehicle and the driver so that the driver can assess the incorrect driving pattern that the driver is unaware of or is not aware of, (Green -> yellow, yellow -> red), whether or not the direction indicator is used, whether the safety distance is maintained, whether the speed is in compliance with the specified speed, rapid acceleration, rapid start, rapid deceleration, The driver's response behavior according to the forward driving vehicle behavior, the position of the gear during signal standby and main / stop (except the manual transmission), fuel economy, sleepiness driving, whether the seatbelt is worn, Whether the accelerator pedal is used or not.

Also, the report setting module The driver habit analysis pattern (car insurance company) report is also set. For example, a pattern definition to select data collected and stored from IoT devices that are dependent on the vehicle and the driver so that the insurer can identify the driver's driving habits and car care habits to determine the likely risk of an accident. , Whether the turn signal is used for lane change, whether the feed route is changed or not, whether the lane change frequency is maintained, whether the safe distance is maintained, whether the speed is maintained, rapid acceleration, rapid deceleration, rapid start, (Green -> yellow, yellow -> red), the driver 's response behavior according to forward driving vehicle behavior, P position of gear during main / stop (except manual gear) or parking brake use, sleepiness and driver Whether or not driving according to the health condition (excited state, hypoglycemia, hypotension, etc.), driving time, whether the seat belt is worn, Actions, such as whether the actions of the vehicles checked messages.

The report setting module also sets up a car sales pattern (car sales) report. For example, in car dealerships and secondhand car dealerships, it is necessary to predict when a vehicle owner should change their vehicle, and in case of vehicle repair shops, periodic vehicle management and forecasting measures should be taken before the failure occurs. Therefore, it is necessary to define a pattern for selecting data collected and stored from a vehicle-dependent IoT device, vehicle fuel consumption information, engine oil change timing, travel distance (total distance, distance by period), running speed, engine load, , Battery temperature, aging progress, etc.), tire information (air pressure, internal temperature: TPMS supported vehicle), engine failure information, lockup clutch status (operating as a physical clutch condition) Driving state).

The report setting module also sets up a driver management pattern (protector) report. For example, it is necessary to define the rules to be applied to novice drivers or drivers who need to be protected and managed, and to define the criteria for controlling whether or not the specified rules are adhered to by the driver, and to designate the operating area (Seoul, Or within 10km from home, limited to the limited area within the designated area)

Designation of driving time (limitation of driving time to specific period for the purpose of restricting driving time or dawn time), designation of driving route (restriction of the route for vehicles to be used repeatedly such as school, commute, etc.) The limitation behavior designation can be optional depending on the range of digital data that can be collected by the object Internet sensor.

The passenger restraint is judged based on the image information transmitted through the indoor camera.

The report setting module also sets up a vehicle monitoring pattern (delivery, back / forth vehicle, tour bus, car rental) report. For example, a pattern definition, a vehicle ' s parking position information (GPS & Beacon), a vehicle location information (GPS / Wi-Fi), departure / arrival information (boarding / departing), detection of anomalies due to impact (detection of accidents due to impact during stopping / stopping) (In the case of a vehicle having a certain route of travel according to time, a vehicle that moves a designated route along schedule, such as a school trip or a sightseeing trip), the theft suspicion information , Accelerator pedal operation, movement of the place, etc.) is detected.

The report generation module expresses the information that can be produced based on the information extracted in each pattern as numerical information and generates the report by presenting evaluation criteria.

The report generation module also generates a running calibration report. For example, when a lane departure (change of lane without a turn signal) occurs, the probability of drowsiness is expressed as a stochastic numerical value UI through carbon dioxide measurement value and internal humidity measurement value, and rapid acceleration, rapid departure, rapid deceleration, It is possible to express the defensive operation index by numerical UI by expressing numerical UI by the numerical UI by frequency of observing the speed and keeping the safety distance when driving, analyzing the left and right deflection driving habits by numerical UI, , The numerical user interface (UI) of the defensive driving index through the action of the traffic signal change (green -> yellow, yellow -> red), the fuel consumption in relation to the position of the gear and the mileage during the signal standby and main / , Expressing the economic driving index as a numerical UI, wearing a safety belt, and using an accelerator pedal during vehicle warm-up time to express the accident safety index as a numerical UI.

The report generation module also generates a driver habit analysis report (accident inducing index, safe driving index). For example, lane departure habits, maintenance of safety distance, adherence to prescribed speed, emergency start, emergency stop, response to changing signal, parking habit, wearing seat belt, response to pedestrian warning, And evaluating the accident inducing index and the safe driving index, and expressing them in a numerical UI.

The report generation module The operator management report (management index: concentration / caution / relief) is also generated. For example, the management index can be expressed in a numeric UI, from the stage where maintenance is continuously required to the stage where the automobile and the driver are required to follow the rule-conforming result to the stage when the management is almost unnecessary.

The report generation module It also generates automobile management reports (maintenance index, aging index, vehicle management index). For example, the vehicle aging index such as the operating distance, the number of inspection and trouble alarm frequencies, the fuel consumption degradation engine load, and the battery aging rate are evaluated and expressed in numerical UI, the travel distance, the inspection and trouble alarm, A vehicle index such as automobile fuel consumption, running speed, engine load, battery information, tire information, leave time for failure information, lock-up clutch, and frequency of fuel cut operation Evaluating the management index and expressing it as a numerical UI.

The report generation module also generates a vehicle monitoring report (observation information, deviation index). For example, it can be used to detect and monitor information such as burglary, contact accidents during parking, damage to vehicles, etc. Observation information expression, whether or not the place, section, route, Expression.

The report generation module also generates other reports. For example, a combination of patterns or a separate evaluation item according to the optional information is applied to the UI.

The report management module sets intuitive elements that can be reported based on reports without machine learning in the notification setting section. For example, warning notices such as lane departure, forward vehicle collision, specification speed violation, driving habit correction, traffic signal change, warning notices such as not wearing a seat belt, violation of rules, pedestrian collision, tire pressure check, , Vehicle location information, vehicle departure / arrival information, accident detection information by an impact, and information provision notification such as a designated route violation.

The report management module Create, modify, and delete patterns and evaluation indexes required for report setup and generation Combine management and patterns and select optional elements within patterns to manage complex patterns and report generation.

6 is a configuration diagram of a learning & reasoning unit according to an embodiment of the present invention.

Referring to FIG. 6, the learning and inferring unit according to the embodiment of the present invention includes a notification pattern definition module, a notification pattern learning module, a notification information reasoning module, and a notification information generation module.

remind The Notification Pattern Definition module defines a notification pattern for machine learning of digital Internet data collected based on the report pattern.

The notification pattern definition module also defines a security alarm notification pattern. For example, by learning the vehicle-related behaviors outside the specified time, it can detect and inform security risks, specify a time when the vehicle can not be operated such as bedtime, long-term business trip, working hours, Position Parking A pattern for a series of actions such as unlocking, accelerator pedal operation, and parking position change. It detects and informs the danger through change information that occurs within the set time range set at the designated main / stop position. Sensor operation detection, side brake release, gear position parking release, accelerator pedal operation, parking position change, etc., occurring in a predetermined time range (after 5 minutes or less) A pattern for a series of operations (such as opening a door), and the like. In addition, it detects and informs the danger about the behavior that deviates from the specified range in the designated route. In the situation that the designated route is set and operated such as the route from the specific place such as the substitute driving, It also defines patterns of dangerous driving behaviors of driving drivers, etc.

The notification pattern definition module detects and informs the risk of a certain driving behavior that occurs according to the behavioral alert notification pattern, for example, the changing peripheral factors, and displays the response of the driver in accordance with the speed and the distance to the traffic light The drowsiness driving is determined according to the patterns of the driver's response pattern, the indoor temperature, the humidity, the density of carbon dioxide, the lane departure having a certain pattern, and the response to the front collision according to the pattern, the rapid braking, Patterns, patterns that overtake overturns, patterns to respond after a pedestrian alert, and patterns to judge a dangerous driving according to the type of response to a vehicle inspection message.

The notification pattern definition module predicts the accident risk by applying a probabilistic factor to the predictive alarm notification pattern, for example, aggressive driving behavior. When the notification and the lane change are made, the direction indicator is not used, the feed route is changed and frequent advance, It also defines a pattern of risk prediction by identifying a series of precursors that can cause an accident through speed non-compliance, rapid acceleration, rapid braking, battery aging and engine failure, tire pressure change, travel distance, fuel efficiency information.

The notification pattern learning module also performs a threshold value determination process for determining a notification pattern, and performs security alarm notification learning. For example, a series of machine learning that can determine the range of thresholds that can be judged as a notification pattern in the case of security through Internet digital data collected and stored considering the statistical distribution and stochastic factors.

Also, the alert pattern learning module performs behavior based alert notification learning. For example, a series of machine learning that can determine the range of thresholds that can be judged as a notification pattern in the case of security through Internet digital data collected and stored considering the statistical distribution and stochastic factors.

Also, the notification pattern learning module performs predictive alarm notification learning. For example, a series of machine learning and forecasting that can determine the range of thresholds that can be judged as a notification pattern by statistical distribution and probabilistic factors through the Internet digital data that is collected and stored. Obtain and model actual data for the phenomenon and perform machine learning based on the predicted learning model.

The notification information reasoning module judges by judging which input type of the accumulated input data is a notification type having a certain threshold value.

The notification information generation module The inferred input is divided into a security alarm, an action-based alarm, and a predictive alarm, and then transmitted to the information notification unit.

7 is a configuration diagram of a notification setting unit according to an embodiment of the present invention.

Referring to FIG. 7, a notification setting unit according to an embodiment of the present invention includes a security alarm setting module, an action-based alarm setting module, a prediction alarm setting module, and a notification generating module.

The notification setting unit determines whether or not the notification type received from the report generation unit or the learning and reasoning unit is executed, and transmits the notification type to the information notification unit. The security alarm setting module performs the function of alarm activation / deactivation and detailed setting.

The behavior-based alarm setting module performs the function of alarm activation / deactivation and detailed setting.

The behavior prediction alarm setting module performs alarm setting / non-activation, instant / periodic / schedule information setting, and detailed setting.

The notification generation module delivers the notification to the information notification unit according to the time set in each setting module.

8 is a configuration diagram of an information notification unit according to an embodiment of the present invention.

Referring to FIG. 8, an information notification unit according to an embodiment of the present invention includes a video notification module, a sound information notification module, and another information notification module.

The video notification module configures a protocol by URL, GPS information, etc. of the streaming server of the image to be set in the notification.

The sound information notification module constructs a protocol based on a playback sound type, a playback sound priority, a playback constraint, and the like specified in the notification.

The other information notification module constructs a protocol by taking into account conditions for items to be expressed by information such as a vibration image or voice.

9 is a configuration diagram of an information expression unit according to an embodiment of the present invention.

Referring to FIG. 9, an information presentation unit according to an embodiment of the present invention includes a sound output device, a video output device, an output device, a communication terminal, and an output control module.

The sound output apparatus is a device (Dash Cam Speaker, Smart Phone Speaker, wireless connection speaker) for reproducing a sound signal among the notification information received from the object Internet Big Data active processing system.

The video output device includes an LCD display (Smart Phone Display) for visually displaying an analysis report, an automobile live stream / vod, and warnings or information necessary for the operation.

The other output device is a device that transmits information necessary for driving in a special visual information and sensory form. For example, HUD (Head Up Display, ADAS information at night, safe driving information in a simple visual configuration on a window or a separate transparent reflector), Vibrate Belt, Vibrate Clip, etc, drowsiness driving, A device for giving warning by using a safety belt or a vibration device attached to the body as a warning method during operation).

The output control module analyzes the type of data transmitted through the communication terminal, distributes and controls the data to an expressible device, decrypts the received data in a form that can be understood.

The communication terminal A wireless communication device (data modem, hotspot (Smart Phone), portable AP) for transmitting digital data of the IoT device and video streaming of the video input device to the server.

The present invention has been described above with reference to the embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. Therefore, the scope of the present invention is not limited to the above-described embodiments, but should be construed to include various embodiments within the scope of the claims and equivalents thereof.

Claims (2)

A data collecting unit for collecting data from the object Internet device;
A data storage unit for storing the collected data through a network;
A report generation unit for generating a report based on data stored in the data storage unit;
A learning and reasoning unit for learning and reasoning on the Internet based on the report;
A notification setting unit for determining whether to execute the notification type received from the report generation unit or the learning and inferencing unit;
An information notifying unit configured to configure a protocol according to the notification type and transmit the protocol to an information presentation unit through a network;
And an information expressing unit for expressing information notified from the information notifying unit.
The method according to claim 1,
The object Internet Big Data active processing system comprises:
Further comprising a security system for preventing an access having an attack possibility except for a data collecting unit and an allowed access, and responding to predetermined rules upon detecting an attack.

Images