KR20140118925A

KR20140118925A - A method for IoT cloud service and the system thereof

Info

Publication number: KR20140118925A
Application number: KR1020140037337A
Authority: KR
Inventors: 이순호
Original assignee: 이순호
Priority date: 2013-03-29
Filing date: 2014-03-29
Publication date: 2014-10-08

Abstract

The present invention provides an IoT cloud service in which IoT service providers can provide their own IoT service at low cost, and also provides an environment in which an application can be developed to provide such service.

Description

A method and system for an Internet cloud service and a system thereof A system and a method for an application cloud cloud service and system thereof

The present invention relates to a system and IoT providing method for providing an integrated development system for IoT application development to IoT (Internet of Things) service providers and developers, and providing an IoT cloud environment for IoT services .

As IoT technology spreads in various fields such as medical, logistics, energy, social safety, and smart home, many IoT service providers want to develop and service IoT applications for their services. However, This is a stumbling block to the spread of IoT services.

In order to develop IoT service, not only a server application but also a device application development are required together, so a new development environment for reducing development cost is required. Recently, JavaScript has attracted attention as a programming language that can develop servers and devices together. In addition, discussions are actively being made on international standards for interfaces between IOT servers and devices.

Recently, as the amount of data generated in IoT devices has increased rapidly, there has been a growing interest in data analysis technology capable of efficiently analyzing the data. Big data technology capable of analyzing large amounts of data is attracting attention as a technical alternative for IoT data analysis, and real-time analysis technology is required due to characteristics of IoT data.

The present invention can provide an IoT application integrated development environment in which IoT service providers can easily develop their IoT applications and provide IoT service providers with an IoT service cloud capable of providing their IoT services at low cost .

A system for providing an IoT cloud service according to the present invention includes: a service platform configured to control and manage IoT terminals and manage rules for behaviors of the IoT terminals; And an analysis platform configured to communicate data with the service platform and to analyze data from the IoT terminals.

And a data collecting module configured to collectively collect data transmitted from the IoT terminals in real time and transmit the collected data to the service platform or the analysis platform,

It also includes a development platform that is designed to enable service providers who offer IoT cloud services to their customers to easily develop IoT applications.

The service platform may further include: a rule engine including a rule for the behavior of the IoT terminals; A rule engine management module configured to manage the rule engine; A device / application management module configured to manage devices connected to the IoT cloud service system and devices / apps used in the IoT cloud service system; A dashboard configured to display a status of the system; A dashboard management module configured to manage the dashboard; A data service management module configured to efficiently manage the data; And a European Telecommunications Standards Institute (ETSI) interface management module that provides international standard based server / terminal interface functionality.

The analysis platform may further include a data visualization module configured to visually display the analyzed data; A dynamic data handling module that dynamically manipulates data from the IoT terminals; A report generation module for reporting a result of analyzing the data; A data batch analysis module configured to collectively analyze the data; And a data stream analysis module configured to analyze the data in real time.

Meanwhile, a system capable of integrally developing an IoT application according to the present invention includes a dashboard module configured to provide a dashboard function indicating a system status; A dynamic data handling module configured to provide dynamic management of data; A rule management module configured to manage rules that can be provided in the IoT cloud service; An analysis API module that collects analysis APIs configured to enable analysis of data in IoT cloud service; And a sensor API module that collects APIs used in sensors or actuators mounted on IoT terminals that are targets of IoT cloud services.

According to another aspect of the present invention, there is provided a method of providing an IoT cloud service, comprising: receiving real-time sensor data generated from sensors installed in IoT terminals; Determining whether to process the received real-time sensor data in real-time event processing, real-time data analysis, or batch data analysis; Processing the event in the rule engine when the real-time event processing is determined in the determining step; And generating an event processing report after the processing step.

In addition, when the real-time data analysis is determined in the determining step, the data stream analyzing module further includes a real-time analyzing step of real-time analyzing the data.

In addition, when the batch data analysis is determined in the determining step, the data batch analysis module collectively analyzes the data.

The IoT application development system and the IoT cloud service provision system and the service provision method provided by the present invention can contribute to the spread of the IoT service by providing the application development and the reduction of the service operation cost to the IoT service providers.

In addition, it provides analysis capability of large capacity IoT data, so it can be used as marketing data through various analysis related to service and customer, and it can be used as analysis data for business decision making.

Figure 1 illustrates the IoT cloud service system disclosed herein and a system for developing applications to provide such services.
Figure 2 shows the components of the service platform of the present invention.
Figure 3 shows the components of the analytical platform of the present invention.
4 shows an IoT application development environment system disclosed in the present invention.
Figure 5 shows the steps of the IoT cloud service according to the present invention.
Figure 6 shows the steps after real-time data analysis is selected among the steps of the IoT cloud service according to the present invention.
FIG. 7 shows the steps after batch data analysis is selected among the steps of the IoT cloud service according to the present invention.
FIG. 8 more clearly shows the present invention in correspondence with a flowchart in the system components used according to the data flow at the time of service according to the method of the present invention.
FIG. 9 illustrates a process of determining whether input data conforms to a certain pattern in order to generate an event according to the method of the present invention.
Fig. 10 illustrates, in accordance with the method of the present invention, enabling a new type of situational awareness and behavior through virtual data.

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

For reference, all M2M (Machine-to-machine) described in the drawings can be interpreted as IoT (Internet of Things) in the present specification, and an event can be interpreted as an action throughout the specification.

Meanwhile, the term 'platform' used in the present specification has the same or very similar meaning as 'module' or 'component'.

As shown in FIG. 1, the IoT cloud service system 100 according to the present invention comprises an analysis platform 110 for analyzing data and a service platform 120 for managing services. The analysis platform and the service platform may have a configuration capable of communicating data with each other.

The service platform (120)

1) a rule engine 221 'including a rule for the behavior of the IoT terminals, a rule engine management module 221 configured to manage the rule engine,

2) a device / application management module 222 configured to manage devices connected to the IoT cloud service system and devices / apps used in the IoT cloud service system,

3) a dashboard 223 'configured to display the status of the system and a dashboard management module 223 configured to manage the dashboard,

4) a data service management module 224 configured to efficiently manage the data, and

5) a European Telecommunications Standards Institute (ETSI) interface management module 225 that provides server / terminal interface functionality based on an international standard.

On the other hand, the rule engine 221 'and the rule engine management module 221 are parts responsible for the real-time sensor data when it is determined that real-time event processing is necessary.

Describing the functions performed by the rule engine 221 'in more detail, the rule engine divides the rule to be set by the user into three elements and constructs them. A rule consists of data (target data), condition (when agent) and target agent (what agent). Through the rule consisting of three elements rather than a simple rule, the condition and the target action used in each rule become independent from each other. In addition, various rules can be constructed through combinations.

For example, a plurality of thermometers (# 1, # 2...) Are installed in a certain place, and the rule here is that "when 13th thermometer is more than 20 degrees, send e-mail". In this case, if you look at the three elements of the rule, the '13th thermometer' is the target data, 'above 20 degrees temperature' is the when agent, and 'send email' . The goal action once used, 'Send e-mail', can be used again when creating other rules (ie, different temperature conditions of different thermometers, different temperature conditions of the same thermometer, etc.) What agent can be used different from 'mail transmission'.

One rule created with the above data, condition, and target behavior is extensible, which can be extended via virtual data. Virtual data is not physical and actual data coming from terminals (i.e., sensors), but is data that the user arbitrarily designates.

I will expand the example of the thermometer example above.

After defining the two-digit number as virtual data (A), if the temperature data transmitted from the thermometer is more than 30 degrees (Condition 1), change the tens digit of A to 1. If the humidity data transmitted by the hygrometer is more than 60% (Condition 2), change the number of digits of A to 1. In this case, a state of 11 based on the virtual data (A), that is, a discomfort index risk state, can be newly defined. Thus, by combining simple rules with virtual data, a new complex situation can be defined. The virtual data types utilized in this example can be changed depending on the problem to be solved. Such a process is illustrated in FIG. 10.

Real-time input data is transmitted through sensors (here, thermometers, hygrometers), and A is defined as virtual data (1010, 1020 and 1015).

Then, when the data for the situation is inputted and recognized, it is determined whether each criterion (temperature 30 degrees, humidity 60%) is satisfied (1030), and when the condition is satisfied, Change the position as defined above (Action, 1040).

Then, the virtual data A can be recognized as a situation in a behavior (What), and in this case, recognition and action by virtue of the virtual data 1050, rather than actual physical data 1010 and 1020 (1060, 1070 ).

On the other hand, application of virtual data enables sequential situation propagation for specific situations, which can be reflected in the work progress process definition.

For example, if the value of the virtual data (a) is changed, if a plurality of rules reflecting this are registered, a large number of rules will be triggered in this situation. If the rule triggered here changes the value of the virtual data (A) again, a 'workflow extension configuration with periodicity' using this becomes possible.

For example, virtual data (B) indicating the degree of congestion is defined, and the value of the congestion virtual data (B) is increased as the degree of congestion increases. On the other hand, independently of this rule, when the congestion degree is 10 or more, a logic flow different from the existing logic flow can be generated through the rule of changing the congestion virtual data (B) to 0.

That is, at 1050, when the value of the virtual data A becomes equal to or higher than a predetermined level, it can be set to 0 again to have a periodicity that restarts at 1010 and 1020 (temperature 30 degrees and humidity 60%).

In this case, we can define multiple actions (What), return A to zero, and continue to flow in parallel without recognizing flows and periodicities.

On the other hand, the analysis platform 310,

1) a data visualization module 311 configured to visually display analyzed data,

2) a dynamic data handling module 213 that dynamically manipulates the data from the IoT terminals,

3) a report generation module 313 for reporting a result of analyzing the data,

4) a data batch analysis module 314 configured to batch analyze the data, and

5) a data stream analysis module 315 configured to analyze the data in real time.

The data batch analysis module 314 analyzes the data based on the big data when it is determined that the batch data analysis is necessary for the real time sensor data of the sensor of the IoT terminal inputted into the system.

Big data can be used in the IoT cloud service of the present invention, although the DB and the equivalent storage for storing big data that store big data are not explicitly disclosed in the present specification and drawings.

In addition, the data stream analysis module 315 is a part for analyzing data when real-time sensor data of a sensor of the IoT terminal input to the system is determined to require real-time data analysis.

The real-time event processing by the rule engine and the real-time data analysis by the data stream analysis module occur in real-time, but the real-time data analysis by the data stream analysis module is similar to the real- As a further enhancement process, unlike real-time event processing, 'association analysis' makes it possible to perform much more complex analysis than event processing. Such a difference is obvious to a person skilled in the art of providing IoT service.

Specifically, there is a pattern-based normal / abnormal state judgment on time-series data as an example of real-time data analysis. There are cases where time series data has a certain pattern. For these patterns, we can derive clustering patterns. This derivation process is a 'supervised method' selected by the user and an 'unsupervised method' in which the system itself derives it by the clustering algorithm. Patterns derived from this process are classified into normal and abnormal patterns through screens provided by the system. In addition, regarding the abnormal pattern in terms of knowledge management, contents estimated as the cause of occurrence of each pattern are recorded by the user. This record is used to provide future analysis reports.

In order to determine the pattern of the real-time input data based on the pattern thus derived, the registered data table is referred to as a pattern map based on the time intervals at which the patterns are generated with respect to the previously derived patterns. The pattern matching with respect to the data to be input in real time based on the patterns registered in the pattern map is confirmed.

Specifically, a method of deriving a data set as a matching criterion is as follows. 2) derive inflection points for the moving average data; and 3) derive the data sets through the min / max values of the derived inflection point values.

The real-time pattern matching determination for the data sets thus derived proceeds according to the flowchart shown in FIG. The Kologorov-Smirnov test is used as an example to determine whether a match is made during the matching process, but other statistical methods may also be used.

Step 910 is a step of preparing a pattern list according to the length of time. In this list, as shown by reference numeral 915, the length of one column is 10 minutes and the length of 20 minutes. Pattern).

In step 920, a minimum interval of time that has not yet been verified is selected. By selecting 10 minutes, for example, observation is made for 10 minutes with the passage of time in step 930.

In step 1040, it is determined in step 940 whether the pattern of the input data matches the A or B pattern (step 960). If the matching pattern is found, the process is completed, (Step 970).

If there is no matching pattern in step 960 (i.e., the real time input data observed for 10 minutes is neither A pattern nor B pattern), then in step 950 a larger time period is selected than the current time , 20 minutes, a time period greater than 10 minutes, is selected for the next verification).

If the current time period is 20 minutes and the large time period is not in the list, the verification process is terminated. Otherwise, the verification process is continued according to the elapse of time (step 930).

After the event processing by the rule engine, an event processing report is generated. After analyzing the data batch analysis module 314 or the data stream analysis module 315, analysis reports are generated. (313).

Although not explicitly shown in the figure, in each of the service platforms or the analysis platform, input data or processed data is stored in each of the configuration modules, that is, the service platform 220, 223 ', and it is evident that the analysis platform 310 can move between 311 and 315 as needed from time to time.

Meanwhile, in order to collect real-time sensor data input from the IoT terminals equipped with the IoT sensor and to use the real-time sensor data in the IoT service system 100, the data collection module 130 may be further included in the service system 100, It is also clear that data can be freely moved on demand between the analysis platform 110, the service platform 120 and the data collection module 130.

The data collection module 130 may exist independently of the analysis platform 110 and the service platform 120 in the service system 100 or may be an analysis platform 110 or a service platform 120, And serves as an interface between the IoT terminal (reference numeral 170 in FIG. 1) and the originating service system 100, which is evident on the system.

The development platform 140 may be referred to herein as a development system (or an IoT application development system), although the configurations are the same entity, even if the names are different.

IoT applications are under development because they do not have an integrated development / operation system, which is a burdensome cost. As a result of this economic reason, the spread of IoT services is slow.

Accordingly, the present disclosure discloses a development system that provides an integrated and convenient IoT application development environment to service providers 150 for spreading IoT services.

The development system 400 includes a dynamic data handling module 402 for handling real-time data, a dashboard module 401 for enabling development of a dashboard for displaying status, A rule management module 403 for managing a rule for designating a predetermined behavior according to a certain pattern of data, and an analysis API module 403 for collecting an application program interface (API) for data batch analysis or data stream analysis 404), and a sensor API module 405 that can define functions for sensors or actuators mounted on IoT terminals.

Although such a development platform is disclosed in the service system 100 in FIG. 1, it may be configured independently of the service system 100, and if it is configured independently, as shown by the dotted arrow in FIG. 1, It is possible to apply the developed environment to the analysis platform and the service platform.

Hereinafter, the IoT cloud service method according to the present invention will be described with reference to the drawings.

As shown in FIGS. 5 to 7, the service system 100 receives data (real-time sensor data) generated by a sensor installed in the IOT terminal 170 wirelessly, Service provision is started.

In step 510, although the service platform is disclosed as receiving data, the data collecting module 130 serving as an interface with the outside receives the data.

Then, it is determined which process is required for the data, which is determined as one of real time event processing, real time data analysis, or batch data analysis (step 520). The above decision is determined according to the type of data to be input, After that, the data is sent to the necessary place.

If it is determined that the real-time event processing is required data, the real-time event is input to the rule engine 221 '(step 530), and an event processing report is generated in the report generation module 313 (step 540)

If it is determined that real-time data analysis is required, the data is input to the data stream analysis module 315 and real-time analysis is performed (630). Thereafter, an analysis report is generated in the report generation module 313 (step 640 )

In addition, if it is determined that the data to be subjected to the batch analysis is the data, the data is input to the data batch analysis module 314 and the real time analysis is performed (730), and then the analysis report is generated in the report generation module 313 740) At this point, big data may be used in step 730 for more efficient data analysis.

Figure 8 is a view of the components used in the method of the present invention, with reference to the drawings in the system.

The data collecting module 130 determines to send the data from the IoT terminal 170 to one of the rule engine 221 ', the data batch analysis module 314 or the data real time analysis module 315 . Where the determination may be made at any component located within the service system 100.

Thereafter, the data to be processed by the real-time event is transmitted to the rule engine 221 ', the data requiring batch data analysis is transmitted to the data batch analysis module 314, and the data requiring real-time data analysis is transmitted to the data real- Analysis report 640 and 740 are generated at the time of event processing after the event processing report 540 and at the time of batch data analysis or real time data analysis, respectively.

These reports are reviewed by the administrator and used for future services, or used as feedback data to add additional services.

As described above, the IoT cloud service providing method, the system for the IoT cloud service, and the IoT application developing system according to the present invention have been described, and the idea of the invention disclosed in the present application extends to a range obvious to a person skilled in the art , Items obvious to a person skilled in the art should be regarded as being described, although not described in the specification.

100: IoT cloud service system
110, 310: Analysis platform
120, 220: Service platform
130: Data collection module
140, 400: Development platform

Claims

In a system for providing IoT cloud service,
A service platform configured to control and manage IoT terminals and manage rules for the behavior of the IoT terminals; And
And an analysis platform configured to communicate data from the IoT terminals in communication with the service platform.

The method according to claim 1,
Further comprising a data collection module configured to collectively aggregate data transmitted in real time from the IoT terminals and transmit the collected data to the service platform or the analysis platform.

The method according to claim 1 or 2,
The IoT cloud service system, which further includes a development platform that is designed to enable service providers who offer IoT cloud services to their customers to easily develop IoT applications.

4. The compound according to any one of claims 1 to 3,
The service platform includes:
A rule engine including a rule for the behavior of the IoT terminals;
A rule engine management module configured to manage the rule engine;
A device / application management module configured to manage devices connected to the IoT cloud service system and devices / apps used in the IoT cloud service system;
A dashboard configured to display a status of the system;
A dashboard management module configured to manage the dashboard;
A data service management module configured to efficiently manage the data; And
An IoT cloud service system, comprising a European Telecommunications Standards Institute (ETSI) interface management module that provides server / terminal interface functionality based on international standards.

The method according to any one of claims 1 to 4,
The analysis platform includes:
A data visualization module configured to visually display the analyzed data;
A dynamic data handling module that dynamically manipulates data from the IoT terminals;
A report generation module for reporting a result of analyzing the data;
A data batch analysis module configured to collectively analyze the data; And
And a data stream analysis module configured to analyze the data in real time.

In a system where service providers providing IoT cloud services can develop IoT applications in an integrated manner,
A dashboard module configured to provide dashboard functionality indicative of system status;
A dynamic data handling module configured to provide dynamic management of data;
A rule management module configured to manage rules that can be provided in the IoT cloud service;
An analysis API module that collects analysis APIs configured to enable analysis of data in IoT cloud service; And
And a sensor API module for collecting APIs used in sensors or actuators mounted on IoT terminals that are targets of IoT cloud services.

In a method for providing an IoT cloud service,
Receiving real-time sensor data generated from sensors mounted on IoT terminals;
Determining whether to process the received real-time sensor data in real-time event processing, real-time data analysis, or batch data analysis;
Processing the event in the rule engine when the real-time event processing is determined in the determining step; And
And generating an event handling report after said processing step.

8. The method of claim 7,
And a real-time analysis step of analyzing data in real time in the data stream analysis module when the real-time data analysis is determined in the determining step.

9. The method according to claim 7 or 8,
Further comprising a batch analyzing step of collectively analyzing data in the data collective analysis module when the batch data analysis is determined in the determining step.

9. The method according to claim 7 or 8,
Wherein the event processing in the rule engine comprises:
Preparing a list according to a pattern by time length;
Selecting a minimum time interval that has not yet been verified;
Initiating an observation for verification over time;
Comparing the input real-time data with a pattern of the list;
If the real-time input data and the pattern are matched in the comparison, confirming the real-time input data and initializing the process;
If the comparison does not match the real-time input data and the pattern, selecting a time period longer than the current time period in the list; And
And initiating observations along the selected longer time span.