KR100660031B1 - Apparatus and method for automatically generating rules in ubiquitous circumstance - Google Patents

Abstract

A device and a method for automatically generating rules in a ubiquitous environment are provided to reduce user effort for making an ECA(Event-Condition-Action) rule without respect to a quantity of rules and efficiently offering a personalized service in the ubiquitous environment by generating a personalized rule. A symbol storing part(220) receives and stores context including ECA information needed for generating the rule. A symbol analyzer(230) generates the primary rule by analyzing the context. A rule composer(246) stores the primary rule as a secondary rule by receiving universal rules related to the primary rule among the current provided universal rules and comparing the primary rule with the universal rules. A rule optimizer(250) updates the universal rules by registering the secondary rule as the universal rule in case that the secondary rule is not included in the universal rules by comparing the secondary rule with the universal rules. A local storing part(270) stores the context, and the primary and secondary rule.

Description

Apparatus and method for automatically generating rules in ubiquitous circumstance}

BRIEF DESCRIPTION OF THE DRAWINGS In order to better understand the drawings cited in the detailed description of the invention, a brief description of each drawing is provided.

1 is a block diagram of a system to which a rule generator according to an embodiment of the present invention is applied.

2 is a block diagram of the rule generator of FIG. 1.

3 is an example of a screen displayed on the control display of FIG. 2.

4 is a block diagram of the monitoring unit of FIG. 1.

FIG. 5 is a diagram for describing an operation of the rule generator of FIG. 1.

6 is a diagram for describing an operation of the monitoring unit of FIG. 1.

7 is a state diagram for explaining an operation of reflecting a previous history when a rule is generated according to an embodiment of the present invention.

The present invention relates to ubiquitous technology, and more particularly, to an apparatus and method for automatically generating rules in a ubiquitous environment.

In a ubiquitous environment in which a desired service can be provided without being aware of the existence of a computer or a network, it is desirable that a minimum Human Computer Interaction (HCI) is required or no HCI is required.

To this end, conventionally, as a method of using probability, a method of identifying a user's intention through inference and providing a service accordingly is used. However, this conventional method has a disadvantage in that it is substantially difficult to accurately determine the intention of the user and accurately judge it.

On the other hand, another conventional method is based on the ECA (Event Condition Action) rule (rule). This method provides a service by accurately detecting and using an event trigger and a situation condition when an event occurs. The method of providing a service using an ECA rule can be judged more accurately than the method of inferring using a probability and providing a service. However, there are disadvantages in that a lot of effort is required and even impossible to describe many rules that can occur in a complex environment where ubiquitous is used. In particular, it is difficult to provide a service by using general rules in a ubiquitous environment that provides a personalized service.

Therefore, in the ubiquitous environment, the individual user's actions and situations are tracked and stored, the ECA rules are automatically generated using the stored information, and the generated rules are used to provide services according to the same conditions or situations. There is a need.

An object of the present invention is to provide an apparatus for automatically generating a rule in a ubiquitous environment.

Another object of the present invention is to provide a method for automatically generating a rule in a ubiquitous environment.

The rule generating apparatus according to the embodiment of the present invention for achieving the above technical problem comprises a symbol storage unit, symbol analysis unit, rule synthesis unit, and rule optimization unit. The symbol storage unit receives and stores a context including event-condition-action (ECA) information required for rule generation. The symbol analyzer analyzes the context to generate a primary rule. The rule synthesis unit receives general rules related to the primary rule among general rules currently being serviced, compares the primary rule with the related general rules, and stores the primary rule as a new secondary rule. The rule optimizer compares the secondary rule with the universal rules and updates the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules.

The rule generating apparatus according to an embodiment of the present invention may further include a local storage that stores the context, the primary rule, and the secondary rule.

The symbol analysis unit includes a position search unit and a rule preparation unit. The location search unit searches for a location where an action is performed among the ECA information included in the context. The rule creator generates a primary rule by describing a situation and an action in the ECA information based on the position.

The rule synthesizing unit includes a rule searching unit, a rule comparing unit, and a rule determining unit. The rule search unit searches for and receives general rules related to the primary rule. A rule comparison unit compares the primary rule with the related general-purpose rules to generate a similarity between the two. The rule determining unit deletes the primary rule when the similarity is greater than a predetermined similarity range, and stores the primary rule as a new secondary rule by merging the related general rule when the similarity is within the similarity range, and If the similarity is less than the similarity range, the primary rule is stored as a new secondary rule.

The rule generating apparatus according to the embodiment of the present invention may further include a symbol conversion unit for converting the general rules into a context description markup language and a behavior description markup language.

The context description markup language describes the situation when the universal rules are generated and includes ECA history information until the general rule is generated, and the behavior description markup language is used when the general rules are generated. Describe the behavior and include the ECA history information.

In accordance with another aspect of the present invention, there is provided a rule generating method comprising: receiving and storing a context including Event-Condition-Action (ECA) information required to generate a rule; analyzing the context to generate a primary rule; Receiving general rules related to the primary rule among the universal rules currently being serviced, comparing the primary rule with the related universal rules, and storing the primary rule as a new secondary rule; and Comparing the universal rules and updating the universal rules by registering the secondary rule as a universal rule if the secondary rule is not in the universal rules.

The generating of the primary rule may include: retrieving a location where an action is performed in the ECA information included in the context; and generating a primary rule by describing a situation and an action in the ECA information based on the location. It is provided.

The storing as the secondary rule may include: searching for and receiving universal rules associated with the primary rule, comparing the primary rule with the related universal rules, and generating a similarity between the two; If the similarity range is greater than a predetermined similarity range, the primary rule is deleted, and if the similarity is within the similarity range, the primary rule is merged and stored as a new secondary rule, and the similarity is greater than the similarity range. If small, storing the primary rule as a new secondary rule.

The rule generation method according to another embodiment of the present invention may further include converting the general rules into a context description markup language and a behavior description markup language.

The context description markup language describes the situation when the universal rules are generated and includes ECA history information until the general rule is generated, and the behavior description markup language is used when the general rules are generated. Describe the behavior and include the ECA history information.

The ubiquitous system according to another embodiment of the present invention includes a situation management unit that provides a user with a service suitable for a situation based on a rule, and also includes a general rule storage unit, a monitoring unit, and a rule generation unit. The universal rule storage unit stores a general rule that is the base of the service. The monitoring unit receives the context data including the ECA information according to the start command, and terminates the reception of the context data according to the end command and filters the received context data to generate a context including the ECA information required for rule generation. do. The rule generation unit generates a new secondary rule by comparing the primary rule generated by using the context with the general rules related to the primary rule, and updates the universal rules by comparing the secondary rule with the universal rules. .

The monitoring unit includes an event detector, a context data receiver, a filter, and a context transmitter. The event detector detects the start command and the end command. The context data receiving unit receives the context data including the ECA information in response to the detection of the start command, and stops receiving the context data in response to the detection of the end command. The filtering unit filters the context data unnecessary for rule generation in response to the detection of the end command, to generate the context. The context transmitter transmits the context to the rule generator.

The rule generation unit includes a control display unit, a symbol storage unit, a symbol analyzer, a rule synthesizer, and a rule optimizer. The control display unit provides a screen for inputting the start command or the end command and transmits the screen to the monitoring unit that receives the start command or the end command. The symbol storage unit receives and stores the context. The symbol analyzer analyzes the context to generate the first rule. The rule synthesizing unit receives the related general rules among the general rules, and compares the primary rule with the related general rules to generate the secondary rule. The rule optimizer compares the secondary rule with the universal rules and updates the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules.

The start command and the end command may be input from the user.

The rule generator may further include a symbol converter configured to convert the general rules into a context description markup language and a behavior description markup language.

The context description markup language describes the situation when the universal rules are generated and includes ECA history information until the general rule is generated, and the behavior description markup language is used when the general rules are generated. Describe the behavior and include the ECA history information.

The context management unit provides a context-sensitive service in response to the context description markup language and the behavior description markup language.

The method according to another embodiment of the present invention is a method for providing a user with a service suitable for a situation using a pre-stored general rule in a ubiquitous system, receiving context data including ECA information according to a start command, and ending Terminating the reception of the context data according to a command and filtering the received context data to generate a context including ECA information necessary for rule generation; a primary rule and the primary rule generated using the context; Generating a new secondary rule by comparing related universal rules, comparing the secondary rule with the universal rules to update the universal rules, and providing a contextual service using the updated universal rule. It is provided.

The generating of the context may include receiving context data including the ECA information in response to the detection of the start command, and stopping reception of the context data in response to the detection of the termination command, and the termination command. And generating the context by filtering context data unnecessary for rule generation in response to the detection of.

The updating of the general rules may include analyzing the context to generate the first rule, receiving the related general rules among the general rules, and comparing the primary rule with the related general rules. Generating a rule, and comparing the secondary rule with the universal rules and updating the universal rules by registering the secondary rule as a universal rule if the secondary rule is not in the universal rules.

The updating of the general rules may further comprise converting the general rules into a context description markup language and a behavior description markup language.

The context description markup language describes the situation when the universal rules are generated and includes ECA history information until the general rule is generated, and the behavior description markup language is used when the general rules are generated. Describe the behavior and include the ECA history information.

In the providing of the service, a context-sensitive service may be provided in response to the context description markup language and the behavior description markup language.

DETAILED DESCRIPTION In order to fully understand the present invention, the operational advantages of the present invention, and the objects achieved by the practice of the present invention, reference should be made to the accompanying drawings which illustrate preferred embodiments of the present invention and the contents described in the drawings.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Like reference numerals in the drawings denote like elements.

In the present invention, in order to provide a service using a rule in a ubiquitous environment, an accurate service can be provided through an action described by a rule by automatically generating a rule for an action according to an explicit action of a user and a situation condition. do. Such actions and actions based on situational conditions are important elements in generating rules, and are commonly referred to as event-condition-action (ECA).

ECAs can be described in many forms, but they are generally common in that they describe what type of behavior is taken into account in the occurrence of an event and the conditions under which the event occurs. An example of one of several rules for turning on a living room light is as follows.

[Example 1]

Event: User enters the living room

Condition 1: The current time is 8 pm.

Condition 2: The living room light is not on.

Action: Turn on the living room light.

Such research on ECA rules is actively conducted in various fields, and if a system providing a service is thoroughly operated on a rule basis, a rule for providing a service should be described in all possible cases.

In the case of providing a service in a specific area, a rule related to the specific area may be predicted to some extent, and the number of rules may be limited. Thus, the user will be able to artificially create rules that can be run on the system.

However, in a ubiquitous environment, efficient service should be provided in a wide range of areas by minimizing user interaction. Therefore, in the case of artificially or forcibly writing rule information, the number of rules will be numerous and it will be almost impossible to forcibly or artificially create a large amount of rules. The service will not be applicable to the user. For example, if the current time is 9:00, the example of the rule presented above would not be applicable.

To solve this problem, we can think of how to write a more efficient rule by changing the condition such as "Condition 1: The current living room is not turned on", but this method requires artificially writing a large amount of rules. I still have the problem.

Therefore, it is important to write rules efficiently, but there is a need for a fundamental solution for automatically writing rules, not forcibly or artificially. By automatically writing rules, users can be free from the burden of complex rules. Hereinafter, a method of automatically generating a rule and operating the generated rule in order to smoothly realize a rule-based service in a ubiquitous environment will be described.

1 is a block diagram of a system to which a rule generator according to an embodiment of the present invention is applied. System 100 is preferably a ubiquitous system. The system 100 may include a situation management unit (not shown) that provides a user with a context-sensitive service based on a rule, and the situation management unit may include a situation-behavior conversion manager 140 and a situation-behavior converter 150. And a symbol codec unit 160, an external communication unit 170, a support unit 120, and the like.

The rule generator 110 generates a rule by using the ECA information obtained from the situation manager, and the situation manager provides a service suitable for a situation by using the generated rule and pre-stored general rules.

In the following embodiment of the present invention, it will be described that the rule generator 110 and the general rule storage 130 are installed outside the situation manager to generate a rule, but may be installed inside the situation manager.

In addition, ECA information is required to generate a rule, and in the embodiment of the present invention, the ECA information is included in the context from the monitoring unit in the support unit 120 and transmitted to the rule generation unit 110. Accordingly, the system 100 according to an embodiment of the present invention for generating a rule may include a general rule storing unit 130, a monitoring unit, and a rule generating unit 110.

Hereinafter, an operation of generating a rule in the ubiquitous system 100 including the general rule storing unit 130, the monitoring unit, and the rule generating unit 110 will be described.

The universal rule storage unit 130 stores the general rules on which the service is based.

The monitoring unit receives the context data including the ECA information according to the start command START, and terminates the reception of the context data according to the end command and filters the received context data to include a context including the ECA information necessary for generating a rule. Occurs. The detailed structure and operation of the monitoring unit will be described later in detail with reference to FIGS. 4 and 6.

The rule generation unit 110 generates a new secondary rule by comparing a primary rule generated by using a context with a universal rule related to the primary rule, and compares the secondary rule with the universal rules to generate the universal rule. Update them. A detailed structure and operation of the rule generator 110 will be described later in detail with reference to FIGS. 2, 3, and 5.

2 is a block diagram of the rule generation unit of FIG. 1, FIG. 3 is an example of a screen displayed on the control display unit of FIG. 2, FIG. 4 is a block diagram of the monitoring unit of FIG. 1, and FIG. 5 is a rule of FIG. 1. Diagram for explaining the operation of the generator.

 The rule generator 110 includes a control display unit 210, a symbol storage unit 220, a symbol analyzer 230, a rule synthesizer 240, and a rule optimizer 250, and a rule converter 260. It may be further provided.

The control display unit 210 provides a screen for inputting the start command START or the stop command STOP, and receives the start command or the end command and transmits it to the monitoring unit. In the embodiment of the present invention, the control display unit 210 may be used to designate the start and end of rule generation according to the user's intention.

That is, the control display unit 210 provides a screen for inputting a start command START or a stop command STOP indicating the start and end of rule generation. The user may designate the start and end of rule generation by inputting a start command START and a stop command STOP using a user interface (not shown) such as a keyboard, a mouse, or the like.

3 is an example of a screen displayed on the control display of FIG. 2.

The user can start the rule generation operation by selecting the START button (that is, the start command START), and the start command START is transmitted to the monitoring unit of the situation manager. On the other hand, the user can designate the termination of the rule generation operation by selecting the STOP button (that is, the termination command STOP), and the termination command STOP is also transmitted to the monitoring unit of the situation manager.

As will be described later, the monitoring unit acquires information (detected information, environmental information, etc.) according to the start command START, and includes information that is not necessary for rule generation among information acquired according to the stop command STOP. The filtered information is transmitted to the rule generation unit 110 as a context for the rule generation (601).

The symbol storage unit 220 receives and stores the context (603), and informs the symbol analysis unit 230 when the storage is completed. At this time, the symbol storage unit 220 receives the context from the monitoring unit, and stores the same context in the local storage unit 270. In addition, the symbol storage unit 220 notifies the symbol analyzer 230 that the storage is completed so that analysis of the context may be started.

The symbol analyzer 230 generates a first rule by analyzing the context. Primary rules are the basic rules for events, situations, and actions that are included in the context. The symbol analyzer 230 includes a location searcher 231 and a rule generator 233.

The location search unit 231 searches for a location where an action is performed among the ECA information included in the context. The context received from the monitoring unit is stored in the form of a table in the local storage unit 270. The location retrieval unit 231 searches the context table stored in the local storage to search for the location where the user's action corresponding to the context occurred. do.

As a result of searching the context table, if the action is happening in the same place, it can ultimately be described as a rule. On the other hand, if the action is occurring in two or more places, the rule can be described as many as the number of locations found. The searched position is transmitted to the rule preparation unit 233.

The rule writing unit 233 generates the first rule by describing the situation and the action in the ECA information based on the searched position and stores it in the local storage unit 270. That is, the rule preparing unit 233 extracts the relevant context value from the context table of the local storage unit 270 based on the searched position and stores the context value as an action list table and a situation list table.

In this way, the rules described in the table according to the position, behavior, and situation can form a basic unit of rule based on the occurrence position. For example, if the user has stored actions leading from the living room to the bedroom, these actions are stored separately in different rules at two different locations. Thereafter, two consecutive rules are used to provide a service by applying a causal relationship between each of the two rules.

The rule synthesizing unit 240 receives the general rules related to the primary rule among the general rules, and generates the secondary rule by comparing the general rules related to the primary rule and the primary rule. That is, the rule synthesizing unit 241 compares the general rule stored in the local storage unit 270 with the general rule stored in the general rule storage unit 130 to exclude the primary rule or determine whether it is a completely new rule. Then, it decides whether to update with the new rule by synthesizing with similar general rule already stored. The rule synthesizing unit 240 stores the new rule in the local storage unit 270 as a secondary rule. The rule synthesizing unit 240 includes a rule searching unit 241, a rule comparing unit 243, and a rule determining unit 245.

The rule search unit 241 searches for and receives general rules related to the primary rule. The general rule related to the primary rule is a general rule at the position corresponding to the position where the primary rule is occurring.

Accordingly, the rule retrieval unit 241 receives a position where the primary rule is generated from the rule analyzer 230, searches for and receives a general rule corresponding to the position where the primary rule is generated. In addition, the rule retrieval unit 241 transmits the rule related to the received primary rule to the rule comparison unit 243.

The rule comparison unit 243 compares the general rules related to the primary rule and the primary rule, generates similarities between the two, and transmits the similarities to the rule determination unit 245.

If the similarity is greater than a predetermined similarity range, the first rule and the general rule may be determined to be very similar to each other, and thus the rule determining unit 245 excludes the first rule (ie, deletes it from the local storage unit 270). ). On the other hand, if the similarity is smaller than the similarity range, the first rule and the general rule may be determined to be different from each other. Thus, the rule determiner 245 may determine the next number of the last rule number among the general rules retrieved by the rule search unit 241. Assign and store the primary rule in the local storage 270 as a new secondary rule.

If the similarity is within the range of similarity, it can be determined that the primary rule has only simple similarity with the general rule, so that the primary rule and the universal rule similar to the primary rule are merged and stored in the local storage unit 270 as a new secondary rule. do. At this time, the merged rule will be in a state where the general rule before the merge is updated.

The rule optimizer 250 compares the secondary rule with the universal rules stored in the universal rule storage unit 130 and updates the universal rules by registering the secondary rule as a universal rule when the secondary rule is different from the universal rules.

If the secondary rules stored in the local storage unit 270 are merged and updated, the rule optimizer 250 compares the merged and updated secondary rules with the general rules stored in the universal rule storage unit 130. Check if it exists. If there is no identical rule, the rule optimizer allocates a new ID to the secondary rule and registers and stores it in the universal rule storage 130.

If the secondary rules are completely new, the new rules are assigned to the secondary rules without registering with the general rules stored in the rule optimizer 250 and the general rule storage unit 130 to register and store them in the general rule storage unit 130. .

Meanwhile, in the embodiment of the present invention, the rule generation unit 110 converts the general rules into a situation description markup language (SDML) and an action description markup language (ADML). It may be further provided with a portion 260. Context description markup language and behavior description markup language are XML-based languages. Finally, if the service is used, the service will be provided by interpreting SDML and ADML to determine the current situation and perform the corresponding action.

A context description markup language describes the situation when the general purpose rules are generated and includes ECA history information until the general rule is generated, and the behavior description markup language acts when the general purpose rules are generated. It describes and contains the ECA history information.

Hereinafter, the structure and operation of the monitoring unit will be described in detail with reference to FIGS. 4 and 6. 4 is a block diagram of the monitoring unit of FIG. 1, and FIG. 6 is a diagram for describing an operation of the monitoring unit of FIG. 1.

The monitoring unit 400 receives the context data including the ECA information according to the start command START. The context data may be provided from a context provider (not shown) in the context manager (not shown). Meanwhile, the monitoring unit 400 terminates the reception of the context data according to the end command STOP and filters the received context data to generate a context including the ECA information necessary for generating the rule. The monitoring unit 400 transmits the context to the rule generating unit 110.

The monitoring unit 400 includes an event detector 410, a context data receiver 420, a filter 450, and a context transmitter 460, and includes a context data storage unit 430 and a context storage unit 440. It may be further provided.

The event detector 410 detects a start command START and a stop command STOP. The start command START and the end command STOP are transmitted by the control display unit 210 of the rule generator 110. The start command START and the stop command STOP may be received from a user using a user interface (not shown).

The start command START is input with the intention of the user to save his or her behavior and pattern, and the event detector 410 transmits the start command START to the context data receiver 420.

The stop command STOP is input with the intention that the user stops storing his or her own behavioral patterns and patterns. The event detector 410 sends the stop command STOP to the context data receiver 420 and the filter 450. To send.

The context data receiver 420 receives the context data including the ECA information in response to the detection of the start command START and transmits the context data to the context data storage. The context data includes data such as a sensor, a DAS, and UpnP. The context data receiver 420 stops receiving the context data in response to the detection of the end command STOP.

The context data storage unit 430 receives the context data from the context data receiver 420 and stores the context data in the context storage unit 440.

The context storage unit 440 stores the context data and the context. The context is a filter of data that is not necessary for rule generation among context data. The context storage unit 440 stores a value of a sensor, a state of an electric device operated by a user, and information of UPnP in the form of a data table. The value of each field of the data table is used as information used in a situation management unit (not shown) or information for rule generation.

The following is an example of the message format and the possible values of the storage unit to be used when interworking with the situation management unit.

Plugins: "Sensor", "DAS", "UPnP"

ID: Sensor: "LocationX", "LocationY", "Location Z", "Humi", "TEMP", "Light"

DAS: "Command", "Device", "Property"

UPnP: "" Command "," UPnPDevice "," UPnPProperty "

Value: Boolean, Interger

Timestamp

The filtering unit 450 generates a context by filtering context data unnecessary for rule generation in response to the detection of the end command STOP. The filtering unit 450 searches for the context stored in the context storage unit 440 to remove unnecessary or duplicate contexts when generating the rule and to clean up only the contexts required for generating the rule. After the filtering is completed, the filtering unit 450 notifies the context transmitter 460 of the completion of the filtering.

The context transmitter 460 transmits the context stored in the context storage 440 to the symbol storage 220 of the rule generator 110 in response to the completion of the filtering. Meanwhile, after all data is transmitted to all symbol storage units 220, the data stored in the context storage unit 440 is deleted.

In the above, the rule generation operation in the rule generation unit 110 and the context transfer operation in the monitoring unit 400 have been described. As described above, the rule generator 110 generates a rule using a context, and converts the generated rule into a context description markup language and a behavior description markup language. The context management unit (not shown) provides a context-sensitive service in response to the context description markup language and the behavior description markup language.

Context description markup language and behavior description markup language is different from the conventional ECA form in which the action by event and condition is performed. That is, the context description markup language and the behavior description markup language according to an embodiment of the present invention are implementations of finite automata and include a history until the current state is reached. Therefore, the context description markup language and the behavior description markup language according to an embodiment of the present invention can provide a service more efficiently than the conventional simple ECA form.

For example, in Example 1, only the simple condition that the living room is turned on when the user enters the living room at 8 pm is considered. That is, in the system to which the rule of [Example 1] is applied, no situation is considered at all by the user. In addition, in the system to which the rule of [Example 1] is applied, if the previous situation is considered, all related situations should be described in the condition part.

However, using SDML and ADML in accordance with embodiments of the present invention, state diagrams can be used to simply describe the relevant situations. In other words, by performing a state transition when a situation occurs, certain situations are already reflected before the situation.

7 is a state diagram for explaining an operation of reflecting a previous history when a rule is generated according to an embodiment of the present invention. Assuming that the currently applied rule is state C of FIG. 7, since the current state is reached through the previous state A and state B, the currently applied rule C may be an effective rule considering both the previous state A and state B.

As shown in [Example 2] below, it is possible to describe such a condition in the condition part, but it is almost impossible to describe by considering all the numerous rules artificially.

[Example 2]

Event: The user enters the living room.

Condition 1: The current time is 8 pm.

Condition 2: The living room light is not on.

Condition 3: The previous situation was B.

Condition 4: The situation before Condition 3 was A.

Action: Turn on the living room light.

As described above, the rule-based service provision may be made more rational by describing a rule by implementing a finite automata. In particular, the automatic generation of these rules will provide a smoother service in the ubiquitous environment.

Table 1 below shows the DTD of the SDML.

<? xml version = "1.0" encoding = "euc-kr"?> <! ELEMENT rule (header, fa)> <! ELEMENT header (name, comment ?, workingtime?)> <! ELEMENT name (#PCDATA)> <! ELEMENT comment (#PCDATA)> <! ELEMENT workingtime (wtime *)> <! ELEMENT wtime (wstart, wstop)> <! ELEMENT wstart (#PCDATA)> <! ELEMENT wstop (#PCDATA)> <! ELEMENT fa (init, transition)> <! ELEMENT init (#PCDATA)> <! ELEMENT transition (state +)> <! ELEMENT state (num, group *, final ?, stimeout?)> <! ELEMENT num (#PCDATA)> < ! ELEMENT group (delay ?, timeout ?, goto, input +)> <! ELEMENT delay (#PCDATA)> <! ELEMENT timeout (#PCDATA)> <! ELEMENT goto (#PCDATA)> <! ELEMENT input (plugin, id , type ?, value, value2?)> <! ELEMENT plugin (#PCDATA)> <! ELEMENT id (#PCDATA)> <! ELEMENT type (#PCDATA)> <! ELEMENT value (#PCDATA)> <! ELEMENT value2 (#PCDATA)> <! ELEMENT final (fid, fvalue)> <! ELEMENT fid (#PCDATA)> <! ELEMENT fvalue (#PCDATA) > <! ELEMENT stimeout (tid, tvalue)> <! ELEMENT tid (#PCDATA)> <! ELEMENT tvalue (#PCDATA)>

Table 2 below shows the DTD of ADML.

<? xml version = "1.0" encoding = "euc-kr"?> <! ELEMENT rule (header, fa)> <! ELEMENT header (name, comment ?, workingtime?)> <! ELEMENT name (#PCDATA)> <! ELEMENT comment (#PCDATA)> <! ELEMENT workingtime (wtime *)> <! ELEMENT wtime (wstart, wstop)> <! ELEMENT wstart (#PCDATA)> <! ELEMENT wstop (#PCDATA)> <! ELEMENT fa (init, transition)> <! ELEMENT init (#PCDATA)> <! ELEMENT transition (state +)> <! ELEMENT state (num, group *, action *, final ?, stimeout?)> <! ELEMENT num (#PCDATA <! ELEMENT group (delay ?, timeout ?, goto, input +)> <! ELEMENT delay (#PCDATA)> <! ELEMENT timeout (#PCDATA)> <! ELEMENT goto (#PCDATA)> <! ELEMENT input ( id, type ?, value, value2?)> <! ELEMENT id (#PCDATA)> <! ELEMENT type (#PCDATA)> <! ELEMENT value (#PCDATA)> <! ELEMENT value2 (#PCDATA)> <! ELEMENT action (signal *)> <! ELEMENT signal (sdelay ?, splugin, sid, svalue)> <! ELEMENT sdelay (#PCDATA)> <! ELEMENT splugin (#PCDATA)> <! ELEMENT sid (#PCDATA)> <! ELEMENT svalue (#PCDATA)> <! ELEMENT final EMPTY> <! ELEMENT stimeout EMPTY>

The description of each component of [Table 1] and [Table 2] may be described by a predetermined uSDML / uADML language specification.

Table 3 below is a part of a rule automatically generated according to an embodiment of the present invention. The following rules were created by simulation. The user inputs a start command to instruct the start of rule generation. The situation management unit received the start command and arbitrarily designated the light state as sample data, and the context was provided to the rule generation unit through the monitoring unit according to this condition. The rule generator generates the rules of SDML and ADML according to the method according to the embodiment of the present invention. One rule was created by the start and end commands. One rule was converted to SDML (eg SDML_2) and ADML (eg ADML_2). Both SDML (eg SDML_2) and ADML (eg ADML_2) were interpreted simultaneously to provide a service.

According to Table 3, rule 2 starts from State 0 of ADML_2. At the same time as applying rule, ADML takes action

<action>

<signal>

<splugin> DAS </ splugin>

<sid> light_0 </ sid>

<svalue> 33 </ svalue>

</ signal>

</ action>

Use this to adjust the value of light_0 in the DAS to 33. At this time, SDML_2 that controls the current situation checks that the value of light_0 is 33 and changes the current situation to State 1 of the situation. The part applied at this time

<goto> 1 </ goto>

<input>

<plugin> DAS </ plugin>

<id> light_0 </ id>

<value> 33 </ value>

</ input>

Corresponds to Similar to ADML_2 in Action Control

<goto> 1 </ goto>

<input>

<id> SDML_2 </ id>

<value> 1 </ value>

</ input>

Part of the current state, state 0 changes the state of SDML_2 to State 1, and then moves to State 1 for the action.

<action>

<signal>

<splugin> DAS </ splugin>

<sid> light_1 </ sid>

<svalue> 33 </ svalue>

</ signal>

</ action>

Action to control the light of light_1 to 33 by the action control of.

If you go through this series of steps, you can automatically create a rule for the intended service, and the system can provide the stored service through the appropriate interface.

As described above, optimal embodiments have been disclosed in the drawings and the specification. Although specific terms have been used herein, they are used only for the purpose of describing the present invention and are not intended to limit the scope of the invention as defined in the claims or the claims. Therefore, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention will be defined by the technical spirit of the appended claims.

According to the present invention, in providing and operating an ECA rule-based service, a sensor network is used to obtain information by tracking a user's behavior and situation using a sensor network for automatically generating a rule, and automatically obtains a rule using the obtained information. Create In addition, the present invention converts the generated rules into rules in the form of XML-based Situation Description Markup Language (SDML) and Action Description Markup Language (ADML).

Therefore, the present invention can reduce the user's effort for ECA rule creation regardless of the amount of rules, and has the advantage of efficiently providing personalized services in the ubiquitous environment by generating user-specific rules.

In addition, the present invention distinguishes and applies the current progress according to states according to the XML format finite automata format, so that a more accurate judgment can be made than a simple method of taking action in consideration of the current situation and conditions. There are advantages to it.

Claims (24)

A symbol storage unit for receiving and storing a context including event-condition-action (ECA) information necessary for generating a rule; A symbol analyzer configured to analyze the context to generate a primary rule; A rule synthesizing unit which receives general rules related to the primary rule among general rules currently being serviced, compares the primary rule with the related general rules, and stores the primary rule as a new secondary rule; And And a rule optimizer configured to compare the secondary rule with the universal rules and update the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules. The method of claim 1, And a local storage for storing the context, the primary rule, and the secondary rule. The method of claim 1, wherein the symbol analysis unit, A location search unit for searching for a location where an action is performed among the ECA information included in the context; And And a rule preparing unit for generating a primary rule by describing a situation and an action in the ECA information based on the position. The method of claim 1, wherein the rule synthesizing unit, A rule search unit for searching for and receiving general rules related to the primary rule; A rule comparison unit for comparing the first rule with the related general-purpose rules and generating a similarity between the two; And If the similarity is greater than a predetermined similarity range, the primary rule is deleted, and if the similarity is within the similarity range, the primary rule is merged and stored as a new secondary rule, and the similarity is the And a rule determination unit for storing the primary rule as a new secondary rule if it is smaller than the similarity range.  The method of claim 1, And a symbol converter converting the general rules into a context description markup language and a behavior description markup language. The method of claim 5, The context description markup language describes a situation when the general rules are generated and includes ECA history information until the general rules are generated, And the behavior description markup language describes the behavior when the universal rules are generated and includes the ECA history information. Receiving and storing a context including Event-Condition-Action information required for rule generation; Analyzing the context to generate a primary rule; Receiving general rules associated with the primary rule among general rules currently being serviced, comparing the primary rule with the related general rules, and storing the primary rule as a new secondary rule; And And comparing the secondary rule with the universal rules and updating the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules. The method of claim 7, wherein generating the primary rule, Searching for a location where an action is performed among the ECA information included in the context; And And generating a primary rule by describing a situation and an action in the ECA information based on the position. The method of claim 7, wherein the storing as the secondary rule, Searching for and receiving universal rules related to the primary rule; Comparing the primary rule with the related general-purpose rules to generate a similarity between the two; And If the similarity is greater than a predetermined similarity range, the primary rule is deleted, and if the similarity is within the similarity range, the primary rule is merged and stored as a new secondary rule, and the similarity is the If less than a similarity range, storing the primary rule as a new secondary rule.  The method of claim 7, wherein And converting the general rules into a context description markup language and a behavior description markup language.  11. The method of claim 10 wherein the context description markup language describes a situation when the general rules are generated and includes ECA history information until the general rules are generated, The behavior description markup language describes a behavior when the universal rules are generated and includes the ECA history information. In the ubiquitous system having a situation management unit for providing a user service according to the situation based on rules, A general rule rule storage unit for storing a general rule which is the base of the service; Receives context data including the ECA information according to a start command, and terminates the reception of the context data according to a termination command and filters the received context data to generate a context including the ECA information required for rule generation. part; And A rule generation unit for generating a new secondary rule by comparing the primary rule generated by using the context with the general rules related to the primary rule, and updating the universal rules by comparing the secondary rule with the universal rules. Ubiquitous system characterized in that it comprises. The method of claim 12, wherein the monitoring unit, An event detector for detecting the start command and the end command; A context data receiver configured to receive context data including the ECA information in response to the detection of the start command and to stop receiving the context data in response to the detection of the end command; A filtering unit generating the context by filtering context data unnecessary for rule generation in response to the detecting of the termination command; And A ubiquitous system comprising a context transmitter for transmitting the context to the rule generator. The method of claim 12, wherein the rule generation unit, A control display unit for providing a screen for inputting the start command or the end command and transmitting the start command or the end command to the monitoring unit; A symbol storage unit for receiving and storing the context; A symbol analyzer configured to analyze the context to generate the first rule; A rule synthesizing unit which receives the related general rules among the general rules, and compares the primary rule with the related general rules to generate the secondary rule; And And a rule optimizer for comparing the secondary rule with the universal rules and updating the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules. The method of claim 14, wherein the start command and the end command, Ubiquitous system, characterized in that input from the user. 15. The method of claim 14, wherein the rule generation unit, And a symbol conversion unit for converting the general rules into a context description markup language and a behavior description markup language. The method of claim 16, The context description markup language describes the situation when the general rules are generated and includes ECA history information until the general rules are generated, And the behavior description markup language describes the behavior when the universal rules are generated and includes the ECA history information. The method of claim 17, wherein the situation management unit, A ubiquitous system, characterized in that to provide a context-sensitive service in response to the context description markup language and the behavior description markup language. In the ubiquitous system using a pre-stored general-purpose rule to provide a service to the user in a situation, Receiving context data including ECA information according to a start command, and terminating reception of the context data according to a termination command and filtering the received context data to generate a context including ECA information necessary for generating a rule; Generating a new secondary rule by comparing a primary rule generated by using the context with general rules associated with the primary rule, and updating the universal rules by comparing the secondary rule with the universal rules; And And providing a service in a context using the updated universal rule. 20. The method of claim 19, wherein generating the context comprises: Receiving context data including the ECA information in response to the detection of the start command, and stopping reception of the context data in response to the detection of the end command; And And generating the context by filtering context data unnecessary for rule generation in response to detecting the termination command. 20. The method of claim 19, wherein updating the general rules: Analyzing the context to generate the first rule; Receiving the related general rules among the general rules, and comparing the primary rule with the related general rules to generate the secondary rule; And And comparing the secondary rule with the universal rules to update the universal rules by registering the secondary rule as a universal rule when the secondary rule is not in the universal rules. The method of claim 21, wherein updating the general rules: Converting the general rules into a context description markup language and a behavior description markup language. The method of claim 22, The context description markup language describes the situation when the general rules are generated and includes ECA history information until the general rules are generated, And the behavior description markup language describes the behavior when the universal rules are generated and includes the ECA history information. The method of claim 23, wherein providing the service comprises: And providing a contextual service in response to the context description markup language and the behavior description markup language.

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