JP5321067B2 - Context generation device, system, program, and method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem that when a context generation device generates the context of a certain entity from the context of the other entity, it is not possible to reflect the reliability of relationship between both the entities. <P>SOLUTION: The context generation device includes: a management part for storing a management table in which 0 or more contexts and 0 or more pieces of relevant information including relevant entity identification information and relevant reliability (R reliability) for each entity; and a generation part for inputting the identification information of the first entity, and for acquiring the management table of the second entity via one or more management tables of the other entities by tracing the relevant information by using the management table of the first entity as a start point, and for, when the comparison result of comprehensive reliability searched based on the R reliability of the relevant information traced until the management table is obtained and the obtained reliability satisfies the first prescribed conditions, and the context is stored in the management table, generating the context of the first entity from the context. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a context generation device, system, program, and method.

  Patent Document 1 describes a context providing device. The context is information representing the status of a certain target (person, thing, etc.). The context is, for example, the position of a person, the state of the person (during a telephone call / meeting, etc.), the inventory status of an object, and the temperature to be managed in a room or furnace. The apparatus of Patent Literature 1 generates a context of a target to be acquired from a target context different from a target for which a context is to be acquired using a relationship between the targets.

  The apparatus stores the relationship between objects as a link. For example, the person 1 and the context of the mobile phone 1 carried by the person 1 are linked semi-fixedly. The context of the automobile 1 and the RFID tag 1 mounted on the automobile 1 is fixedly linked. Further, when the person 1 gets into the car 1, the RFID reader included in the mobile phone 1 carried by the person 1 reads the RFID tag 1 mounted on the car 1, and between the context of the mobile phone 1 and the RFID tag 1. A link is temporarily established.

  When the position context of the person 1 is requested, the apparatus uses the link of the person 1 for the context of the mobile phone 1, uses the link of the mobile phone 1 for the context of the RFID tag 1, and the RFID tag 1 The link is used to sequentially search the context of the automobile 1. Then, the apparatus acquires position information from the context of the automobile 1 and outputs the position information as the position information of the person 1. The vehicle 1 is equipped with a GPS (Global Positioning System) position detection device, and position information acquired by the device is stored as a context of the vehicle 1.

  Patent Document 2 describes an electronic medical chart system that stores in advance a correlation value indicating a correlation between each family member and the patient himself according to the number of generations from the patient himself to each family member on the family tree. ing. The system calculates the degree of interest based on the correlation value, and obtains the family member's medical information for the degree of interest exceeding the threshold from the family member's electronic medical record.

  Patent Document 3 describes an information association apparatus that determines the degree of association between search terms based on a search execution time difference.

JP 2005-293012 A JP 2008-171133 A Japanese Patent Laid-Open No. 10-320419

  In the technology of the above patent document, when the context of an entity (subject that can generate situation information such as a person, thing, or part thereof) is generated from the context of another entity, the reliability of the relationship between the two entities is improved. There is a problem that cannot be reflected.

  The apparatus of Patent Document 1 creates a link based on the relationship between two entities, and generates the context of the other entity from the context of one entity using the link. However, links do not always represent a uniform relationship between entities. Further, the relationship detection method and the type / value of the detection information are not necessarily uniform. When links are diverse, it is expected that contexts obtained using links may have different degrees of trust. The device of Patent Document 1 cannot generate a context based on the degree of trust related to such a link.

  Since Patent Documents 2 and 3 do not acquire the context of an entity from another entity, they do not solve the above-described problem.

  The objective of this invention is providing the context production | generation apparatus, system, program, and method which solve the above-mentioned subject.

  The context generation device according to an embodiment of the present invention stores, for each entity, zero or more contexts and zero or more related information including related entity identification information and related reliability (R reliability). A management unit that stores a management table and identification information of a first entity are input, and the management of zero or more other entities is traced from the management table of the first entity as a starting point. The management table of the second entity is acquired via the table, and the total reliability obtained based on the R reliability of the related information traced until the management table is acquired is compared with the acquisition reliability If the result satisfies the first predetermined condition and the context is stored in the management table, a generation unit that generates the context of the first entity from the context is provided. .

  The context generation program according to the embodiment of the present invention stores, for each entity, zero or more contexts and zero or more related information including related entity identification information and related reliability (R reliability). The identification information of the first entity is input to a computer having a management unit that stores the management table, and the related information is traced to start from the management table of the first entity, and zero or more other entities The management table of the second entity is acquired via the management table of the second entity, and the total reliability and the acquisition reliability obtained based on the R reliability of the related information traced until the management table is acquired If the comparison result satisfies the first predetermined condition and the context is stored in the management table, the context of the first entity from the context is stored. To execute the generation process of generating and.

  The context generation method according to an embodiment of the present invention stores, for each entity, zero or more contexts and zero or more related information including related entity identification information and related reliability (R reliability). A storage step of storing a management table in the management unit and identification information of the first entity are input, and the related information is traced to zero or more other entities starting from the management table of the first entity The management table of the second entity is acquired via the management table of the second entity, and the total reliability and the acquisition reliability obtained based on the R reliability of the related information traced until the management table is acquired If the comparison result satisfies the first predetermined condition and the context is stored in the management table, the context for the first entity is generated from the context. A step.

  The context generation device according to the present invention can reflect the reliability of the relationship between two entities when the context of an entity is generated from the context of another entity.

FIG. 1 shows the configuration of a context generation system 10 according to the first embodiment of the present invention. FIG. 2 shows a configuration of the context generation device 11. FIG. 3 shows the configuration of the management table 30. FIG. 4 shows the configuration of the context request 28. FIG. 5 shows a configuration of data stored in the association rule holding unit 26. FIG. 6 shows a configuration of data stored in the search rule holding unit 24. FIG. 7 is an operation flowchart of the registration unit 27. FIG. 8 is an operation flowchart of the processing unit 23. FIG. 9 is an operation flowchart of the generation unit 20. FIG. 10 shows the configuration of the data list 21. FIG. 11 shows the configuration of the inspected list 22. FIG. 12 is a flowchart of the search process of the context 36 starting from the management table A by the generation unit 20. FIG. 13 shows a first application example (situation 1) of the context generation system 10. FIG. 14 shows a management table 30 corresponding to the first application example (situation 1). FIG. 15 shows a first application example (situation 2) of the context generation system 10. FIG. 16 shows the management table 30 corresponding to the first application example (situation 2). FIG. 17 shows a configuration of the context generation system 10 according to the third exemplary embodiment of the present invention. FIG. 18 shows the configuration of the R history table 50. FIG. 19 is an operation flowchart (part) of the processing unit 23 according to the third embodiment. FIG. 20 shows a second application example (situation 1) of the context generation system 10. FIG. 21 shows a management table 30 corresponding to the second application example (situation 1). FIG. 22 shows a second application example (situation 2) of the context generation system 10. FIG. 23 shows the management table 30 corresponding to the second application example (situation 2). FIG. 24 shows the management table 30 when the person 1 goes out of the truck 1 through the gate while holding the products 1, 2 and 3. FIG. 25 shows a configuration of the context generation system 10 according to the fourth exemplary embodiment of the present invention. FIG. 26 illustrates a configuration of data stored in the association rule holding unit 26 according to the fourth embodiment. FIG. 27 shows the structure of the S history table 70. FIG. 28 shows the configuration of the conversion table 80. FIG. 29 is an operation flowchart (part) 1/2 of the processing unit 23 according to the fourth embodiment. FIG. 30 is an operation flowchart (part) 2/2 of the processing unit 23 according to the fourth embodiment. FIG. 31 is a diagram for explaining the overall reliability 3c. FIG. 32 shows a third application example (situation 1) of the context generation system 10. FIG. 33 shows the management table 30 corresponding to the third application example (situation 1). FIG. 34 shows the management table 30 corresponding to the third application example (situation 2). FIG. 35 shows the configuration of the reliability list 90. FIG. 36 shows a configuration of data stored in the association rule holding unit 26 in the second embodiment. FIG. 37 shows a basic configuration of the context generation device 11 according to the present invention. FIG. 38 shows another storage format of the management table 30.

  FIG. 1 shows the configuration of a context generation system 10 according to the first embodiment of the present invention. The context generation system 10 includes a context generation device 11, a sensor 13, a tag reader 15, a management terminal 16, and a user terminal 17 connected to a network 18.

  The sensor 13 is a device that outputs measurement values of the outside world such as an infrared sensor, a position sensor, a thermal sensor, a vibration sensor, and a sound sensor. The sensor 13 is attached to the entity 12 and generates a context 36 of the entity 12. Here, “attach” refers to mounting, sticking, hanging, arrangement in the vicinity, and the like. Which form is taken is appropriately selected according to the properties of the entity 12 and the sensor 13 and the usage environment. For example, when it is desired to measure the temperature of the box, a heat sensor is attached to the outer surface of the box, but when it is desired to measure the environmental temperature, the heat sensor is placed on a shelf that houses the box (arranged in the vicinity of the box).

  The entity 12 is a subject that can generate situation information, such as a person, a thing, or a part thereof. The context 36 is status information of the entity 12.

  The entity 12 is, for example, an operator, a driver, a product, a car, or the like. One person or one thing may be associated with a plurality of entities 12. A person's right hand and left hand may be associated with separate entities 12, and the inside and outside of the box may be associated with separate entities 12. Some of the people or things that can generate different contexts 36 may be managed as separate entities 12. The context 36 is, for example, a person's position, a person's state (during a telephone call / meeting, etc.), an inventory status of an object, and a room or furnace management target temperature.

  An ID tag 14 or a tag reader 15 may be attached to the entity 12 and used to set related information 3b between the entities 12. The ID tag 14 is an RFID tag (Radio Frequency IDentification) or the like. The tag reader 15 is an RFID reader or the like. The ID tag 14 may be a barcode label, and the tag reader 15 may be a barcode reader.

  FIG. 2 shows a configuration of the context generation device 11. The context generation device 11 includes a generation unit 20, a processing unit 23, and a registration unit 27. The context generation device 11 holds a data list 21, a checked list 22, and a reliability list 90 on a memory (not shown). The context generation device 11 includes a search rule holding unit 24, a management unit 25, and an association rule holding unit 26 on a storage device such as a disk device. The management unit 25 stores a management table 30 for each entity 12.

  The generation unit 20 inputs a context request 28 from the user terminal 17 and outputs a context 36. The processing unit 23 inputs the information of the ID tag 14 attached to the entity 12 from the tag reader 15 or the measurement value from the sensor 13 to attach the related information 3b between the entities 12. The registration unit 27 inputs setting information and correction information from the management terminal 16 and registers and corrects the management table 30 of the management unit 25. The processing unit 23 and the registration unit 27 may be distributed and arranged in devices outside the context generation device 11. The processing unit 23 and the registration unit 27 do not need to exist when the entity 12 and the related information 3b are fixed.

  The generation unit 20, the processing unit 23, and the registration unit 27 are realized by hardware. The generation unit 20, the processing unit 23, and the registration unit 27 may be realized by executing a context generation program 29 on a memory (not shown) by a processor (not shown) of the context generation device 11 that is also the computer 19.

  FIG. 3 shows the configuration of the management table 30. The management table 30 stores ID 31 of entity A, type (E type 32), tag ID 33, reader ID 34, C information 3a, and related information 3b for each entity 12 (for example, entity A). A plurality of tag IDs 33, reader IDs 34, C information 3a, and related information 3b may be stored.

  The E type 32 indicates the type of the entity A (person, product, car, etc.). The tag ID 33 stores the identification information when the ID tag 14 is attached to the entity A. The reader ID 34 stores the identification information when the tag reader 15 is attached to the entity A. A plurality of ID tags 14 and tag readers 15 may be attached to the entity A. In this case, a plurality of tag IDs 33 and reader IDs 34 may be stored. When the ID tag 14 and the tag reader 15 are not attached to the entity 12, the tag ID 33 and the reader ID 34 store Null.

  A plurality of entities 12 (for example, the outside of the box and the inside of the box) may share the same ID tag 14 or tag reader 15. In this case, the same tag ID 33 and reader ID 34 are stored in the plurality of management tables 30.

  The C information 3a describes the status information of the entity A. The C information 3a includes a sensor ID 35, a context 36, and a C type 37. The context 36 stores the status information of the entity A. The C type 37 indicates the type of the context 36 (temperature, impact, enrollment / seating status, etc.). The sensor ID 35 is identification information of the sensor 13 for acquiring the context 36. When the entity 12 has a plurality of types of contexts 36, a plurality of C information 3 a exists for each entity A. When the context 36 is not acquired from the sensor 13, for example, when input from the management terminal 16, the sensor ID 35 stores Null. A plurality of entities 12 may share the same sensor 13. In this case, the same sensor ID 35 is stored in the plurality of management tables 30.

  The related information 3 b describes the relationship that the entity A has with respect to the other entities 12. The related information 3 b includes R reliability 38 and related entity identification information 39. The related entity identification information 39 is pointer information (for example, an identifier of the other entity 12 and an address of the management table 30 corresponding to the other entity 12) for the other entity 12.

  The R reliability 38 indicates the reliability of the relationship with respect to the other entity 12. The R reliability 38 is, for example, a value from 0 to 1, and a higher value indicates higher reliability. When the entity A has a plurality of relationships, a plurality of pieces of related information 3b exist for the entity A. When the entity A has no relationship with the other entity 12, the related information 3b stores Null.

  When the generation unit 20 is requested to generate a context 36 of a certain entity 12 (entity A), the generation unit 20 traces the related information 3b starting from the entity A, and extracts it from the context 36 of another entity 12 (entity B). May be generated.

  At this time, if the pointer information to the entity B is stored in the related entity identification information 39 of the management table 30 of the entity A, the generation unit 20 directly searches the management table 30 of the entity B by following this information. .

  In some cases, pointer information to the entity C is stored in the related entity identification information 39 of the management table 30 of the entity A, and pointer information to the entity B is stored in the related entity identification information 39 of the management table 30 of the entity C. . In this case, the generation unit 20 searches the entity B management table 30 from the entity A management table 30 via the entity C management table 30 by following the pointer information.

  Similarly, the generation unit 20 may search the management table 30 of the entity B via the management table 30 of the plurality of entities 12 starting from the management table 30 of the entity A.

  Any of the above-described searches is expressed as “follow related information 3b from entity A to entity B”.

  The storage format of the management table 30 is not limited to that shown in FIG. FIG. 38 shows another storage format of the management table 30. In this storage format, a plurality of tag IDs 33, reader IDs 34, C information 3a, and related information 3b that can be stored for each management table 30 are stored in different areas, and a pointer to the area is stored in the management table 30. You may do it. For example, a C information area 91 for storing a plurality of C information 3 a is provided in the management unit 25, and the address information of the C information area 91 is stored as a C information area pointer 92 in the management table 30.

  FIG. 4 shows the configuration of the context request 28. The context request 28 includes ID 31 and C type 37. This ID 31 indicates the target entity 12 of the context request 28. The C type 37 indicates the type of the context 36 to be acquired for the target entity 12.

  FIG. 5 shows a configuration of data stored in the association rule holding unit 26. The association rule holding unit 26 stores one or a plurality of first type association rules 40. The first type association rule 40 includes a type pair 41 and a setting reliability 42. The type pair 41 stores an E type 32 pair. The setting reliability 42 stores the reliability (R reliability 38) of the related information 3b set between the two entities 12 having the E type 32 of each type pair 41.

  According to FIG. 5, in the fourth type 1 association rule 40, the relationship information 3 b having the R reliability 38 of “1” is set between the two entities 12 whose E type 32 is a person and a heart rate monitor. It prescribes. This rule states that the heart rate meter attached to a person measures the person's context 36 (heart rate) itself, so that the heart rate context 36 may be referred to as the person's context 36. Reflects.

  The third first-type association rule 40 defines that related information 3b having an R reliability 38 of “0.95” is set between two entities 12 of which the E type 32 is a product and a product. This rule reflects that the reliability of the obtained context 36 is high when the context 36 of another entity 12 is referenced because the products are likely to be placed in the same state. For example, a truck includes a driver's seat and a loading platform, but the products are likely to be placed in close proximity within the loading platform, and are likely to be placed in the same state.

  The first type 1 association rule 40 stipulates that related information 3b having an R reliability 38 of “0.9” is set between two entities 12 of which the E type 32 is a track and a product. . This rule reflects that the entity 12 of different E type 32 is less likely to be in the same state than the products, and the reliability of the obtained context 36 is low when the context 36 is referred to. . For example, if a product is wrapped in an impact modifier, the impact of the truck is not affected, and if it is placed in a cooler box, the temperature of the truck is not affected. The second and fifth first type association rules 40 reflect the same situation.

  Note that the context generation device 11 may not include the association rule holding unit 26 and may hold data corresponding to the first type association rule 40 in the processing unit 23 in a fixed manner. The apparatus may input the first type association rule 40 from the user terminal 17.

  FIG. 6 shows a configuration of data stored in the search rule holding unit 24. The search rule holding unit 24 stores one or a plurality of search rules 43. The search rule 43 includes a request specification 44, a search reliability 45, and an acquisition reliability 46. The request specification 44 stores one or a plurality of C types 37. The search reliability 45 is used as a criterion for determining whether to continue or end the search (see FIG. 12 for details), and stores the overall reliability 3c. The acquisition reliability 46 is used as a criterion for determining whether or not the context 36 is acquired from a certain management table 30 (see FIG. 11 for details), and stores the total reliability 3c.

  When the related information 3b is traced from the entity A to the entity B as a starting point, the total reliability 3c is determined by the R reliability 38 of each related information 3b on the simple path traced until the management table 30 of the entity B is acquired. Is the product. Here, the simple path refers to a series (path) of related information 3b that does not include a cycle from the entity A to the entity B.

  FIG. 31 is a diagram for explaining the overall reliability 3c. In the figure, entity A is entity 12 that is the starting point. Between entities A and B, there is related information 3b (related AB) having an R reliability 38 of 0.9. Between entities B and C, there is related information 3b (related BC) having an R reliability 38 of 0.8. Between entities B and D, there is related information 3b (related BD) with an R reliability 38 of 0.7.

  At this time, the overall reliability 3c of the entity B is 0.9. This is because the related information 3b on the simple path starting from the entity A is only the related AB. The overall reliability 3c of entity C is 0.92 and 0.72. This is because the related information 3b on the simple path that follows the entity A is the related AB and the related BC. The overall reliability 3c of the entity D is 0.63 at 0.9x0.7. This is because the related information 3b on the simple path starting from the entity A is the related AB and the related BD.

  Here, the simple path from the entity A to the entity D is a column of two related information 3b, that is, a related AB and a related CD. Entity D can also be reached from entity A via entities B, C, B, but the path from entity B back to entity B via entity C forms a cycle, so the total reliability 3c This part is ignored for calculation.

  The context generation device 11 may not include the search rule holding unit 24. In this case, the context generation device 11 may input the search rule 43 from the user terminal 17 or may hold data equivalent to the search rule 43 inside.

  FIG. 7 is an operation flowchart of the registration unit 27. The registration unit 27 inputs a request to create or modify the management table 30 from the management terminal 16 (S1). If the input is a creation request (Y in S2), the same unit reserves a new management table 30 area in the management unit 25 and performs initial setting (setting of a null value) (S3). Subsequently, the registration unit 27 stores the ID 31 and E type 32 input from the management terminal 16 in the created management table 30 (S4). If any one of the tag ID 33, reader ID 34, C information 3a, and related information 3b is input from the management terminal 16, the same unit stores the information in the created management table 30 (S5) and performs processing. finish. Here, the information of the tag ID 33, the reader ID 34, the C information 3a, and the related information 3b input from the management terminal 16 is fixed information about the entity 12, for example.

  When the input is a correction request (N in S2), the registration unit 27 uses the ID 31 input from the management terminal 16 to acquire the management table 30 that stores the ID 31 (S6). Thereafter, the same part modifies the contents of the retrieved management table 30 based on the correction information input from the management terminal 16 (S7), and ends the process.

  For example, when the ID tag 14, the tag reader 15, and the sensor 13 are attached to or removed from the entity 12, the tag ID 33, the reader ID 34, and the sensor ID 35 are corrected. Further, for example, when a semi-fixed relationship with another entity 12 is set, the related information 3b is corrected. Furthermore, for example, when the situation information that does not depend on the sensor 13 occurs, the context 36 is corrected. The situation information that does not depend on the sensor 13 occurs when the delivery person inputs from the portable terminal as the management terminal 16 that the delivery of the parcel delivery package as the entity 12 has been completed.

  FIG. 8 is an operation flowchart of the processing unit 23. The processing unit 23 starts operation when data is input from the sensor 13 or the tag reader 15. For example, the sensor 13 periodically transmits the sensor ID 35 and the measurement value to the processing unit 23. For example, when the transmission information of the ID tag 14 is detected (when it appears) or when the transmission information of the ID tag 14 is no longer detected (when it disappears), the tag reader 15 notifies the processing unit 23 of the appearance or disappearance. The tag reader 15 notifies the processing unit 23 of the tag ID 33 and the reader ID 34 as appearance data or disappearance data.

  When data is input from the sensor 13 (N in S10), the processing unit 23 acquires all the management tables 30 in which the sensor ID 35 that matches the input sensor ID 35 is stored (S12). The same unit stores the input measurement values in the contexts 36 of all the acquired management tables 30 (S12). More precisely, the same unit stores the measurement value in the context 36 of the C information 3 a having the sensor ID 35 that matches the input sensor ID 35 in the management table 30.

  When appearance data is input from the tag reader 15 (Y in S10 and Y in S13), the processing unit 23 stores all the management table 30 (management table A) in which the tag IDs 33 matching the input tag IDs 33 are stored. Obtain (S17). Further, the same unit acquires all the management table 30 (management table B) in which the reader ID 34 that matches the input reader ID 34 is stored (S18).

  The same section sets the related information 3b for all combinations of the acquired management table A and management table B (S19 to S1C). Specifically, the same unit compares the two E types 32 acquired from the sequentially selected management tables A and B with the type pair 41 of the association rule holding unit 26 and acquires the first type association rule 40 that matches. (S19). The processing unit 23 acquires the R reliability 38 to be set from the setting reliability 42 of the acquired first type association rule 40 (S1A). The same part sets the R reliability 38 acquired in the R reliability 38 of the management table A and the address of the management table B in the related entity identification information 39 (S1B). The same part sets the R reliability 38 acquired in the R reliability 38 of the management table B and the address of the management table A in the related entity identification information 39 (S1C).

  When erasure data is input from the tag reader 15 (Y in S10 and N in S13), the processing unit 23 stores all of the management table 30 (management table C) in which the tag IDs 33 matching the input tag IDs 33 are stored. Obtain (S14). Further, the same unit acquires all the management table 30 (management table D) in which the reader ID 34 that matches the input reader ID 34 is stored (S15). The same part deletes the mutual related information 3b (sets a null value) for all combinations of the acquired management table C and management table D (S16).

  For example, when the tag reader 15 detects the transmission information of the ID tag 14 (when it appears), the tag reader 15 notifies the processing unit 23 of the appearance, but when it does not detect the transmission information of the ID tag 14 (when it disappears). May be configured not to notify the disappearance. In this configuration, the processing unit 23 may process the received data as having been received when the appearance data is received in a state of being related between the entities 12. For example, a tag reader 15 is installed at the entrance of a truck or a warehouse, and when the ID tag 14 attached to the product is read at the time of carrying in, it is interpreted as appearance data, and when it is read again at the time of carrying out, it is interpreted as lost data. It means to cancel the association.

  In this case, for example, after acquiring the R reliability 38 to be set (S1A), the processing unit 23 checks whether the related information 3b is set between the management tables A and B, and has already been set. If so (Y in S1Z), the related information 3b may be deleted (after S14).

  FIG. 9 is an operation flowchart of the generation unit 20. First, the generation unit 20 inputs a context request 28 from the user terminal 17 (S20), and initializes the data list 21, the reliability list 90, and the inspected list 22 as work areas (sets Null values) (S21). .

  FIG. 10 shows the configuration of the data list 21. The data list 21 is an area for storing one or a plurality of contexts 36 found in the search process in association with the overall reliability 3c when the management table 30 is searched by tracing the related information 3b.

  FIG. 35 shows the configuration of the reliability list 90. The reliability list 90 is a stack area for storing the reliability 38 of the related information 3b on the simple path from the starting entity 12 to the currently searched entity 12 during the search of the entity 12. The total reliability 3c is calculated based on the data of the reliability list 90.

  FIG. 11 shows the configuration of the inspected list 22. The inspected list 22 is a stack area that can include the IDs 31 of one or a plurality of management tables 30 that have already been searched when the management table 30 is searched by tracing the related information 3b. The inspected list 22 is provided to prevent an endless loop of searching for the related information 3b.

  Next, the generation unit 20 acquires the management table 30 (management table A) in which the ID 31 input by the context request 28 is stored (S22). The same part searches the search rule holding unit 24 and acquires the search rule 43 (search rule A) having the C type 37 input by the context request 28 in the request specification 44 (S23).

  The generation unit 20 searches the context 36 starting from the management table A (S24). That is, the same part adds the inputted context 36 of the C type 37 to the data list 21 from the management table 30 searched by tracing the related information 3b starting from the management table A (see FIG. 12 for details). See).

  When the search is completed and the context 36 is stored in the data list 21 (Y in S25), the same part checks whether the total reliability 3c of the context 36 is equal to or higher than the acquisition reliability 46 of the search rule A. Inspect (S26). If the inspection is positive (Y in S26), the same unit outputs the context 36 (S27) and ends the process.

  When the search is completed and the context 36 is not stored in the data list 21 (N in S25), the same unit outputs a notification that “there is no requested context” (S28) and ends the process. To do. Further, if the total reliability 3c of the context 36 stored in the data list 21 is not equal to or higher than the acquisition reliability 46 of the search rule A (N in S26), the same section indicates that “there is no requested context”. Is output (S28).

  If a plurality of contexts 36 having an overall reliability 3c of the acquisition reliability 46 of the search rule A are stored in the data list 21, the same section outputs the context 36 having the highest overall reliability 3c. Also good. The same unit may output a weighted average value obtained by weighting the plurality of contexts 36 with the total reliability 3 c as the context 36.

  Further, the same part may calculate an average value by excluding a particularly large or small value from the plurality of contexts 36. The same unit may output the maximum value or the minimum value as the context 36 from the plurality of contexts 36. If the variance of the values of the plurality of contexts 36 is greater than a predetermined value, the same unit may output a notification that “there is no requested context”.

  FIG. 12 is a flowchart of the search process of the context 36 starting from the management table A by the generation unit 20. If the input C type 37 context 36 exists in the management table A, the generation unit 20 adds the context 36 and its total reliability 3c in pairs to the data list 21 (S30). The same part calculates the product of all the R reliability levels 38 included in the reliability level list 90 to obtain the total reliability level 3c. The same section sets the total reliability 3c to 1 when the reliability list 90 is Null. The same part registers the ID 31 of the management table A in the searched list 22 (S31).

  Thereafter, the generation unit 20 searches the management table 30 by tracing all the related information 3b of the management table A (S32 to S37).

  Specifically, the generation unit 20 extracts the R reliability 38 (R reliability A) of the sequentially selected related information 3b (relation A), and all the R reliability included in the R reliability A and the reliability list 90. Find the product of degree 38. The same part checks whether or not the obtained integrated value is the search reliability 45 or more of the search rule A (S32). If the inspection result is affirmative (Y in S32), the management table 30 (new management table A) of the other entity 12 related by following the related entity identification information 39 of the related A is acquired (S33). ).

  Thereafter, the same part checks whether or not the new management table A has already been searched (S34). The same part checks this by checking whether the ID 31 of the new management table A is registered in the searched list 22.

  If not already searched (Y in S34), the same part adds the R reliability A to the reliability list 90 (S35), and searches the context 36 starting from the new management table A (S36). After that, the R reliability A is deleted from the reliability list 90 (S37). The same unit may perform addition / deletion of R reliability A to / from reliability list 90 as addition / deletion of data to / from the stack. In addition, the same part may realize the search process of the context 36 starting from the new management table A by calling the process of this figure recursively.

  When all the related information 3b of the management table A is traced and the search of the management table 30 (S32 to S37) is completed, the generation unit 20 deletes the ID 31 of the management table A from the searched list 22 (S38) Exit. The same unit may perform addition / deletion of the ID 31 to / from the searched list 22 as addition / deletion of data to / from the stack.

[First application example]
FIG. 13 shows a first application example (situation 1) of the context generation system 10. In this example, the entity 12 is a product 1 and a product 2 (E type 32 is a product), person 1 (E type 32 is a person), heart rate monitor 1 (E type 32 is a heart rate monitor), track 1 (E Type 32 is a track). The product 1 includes an RFID tag 1 (ID tag 14) and a temperature sensor 1 (sensor 13). The product 2 includes an RFID tag 2. The person 1 includes an RFID tag 3. The heart rate monitor 1 includes a heart rate sensor (sensor 13). The truck 1 has a gate provided with RFID readers 1 and 2 (tag reader 15) at the load / unload port. A heart rate monitor 1 is in close contact with the person 1. The person 1 holds the products 1 and 2 and stands in front of the loading / unloading exit of the truck 1.

  FIG. 14 shows a management table 30 corresponding to the first application example (situation 1). In the management unit 25, a management table 30 is created for each entity 12. Then, between the person 1 and the heart rate monitor 1, related information 3b (F-a, b) having an R reliability 38 of 1 is set. For example, the administrator of the context generation system 10 registers these pieces of information in the management unit 25 from the management terminal 16 via the registration unit 27.

  FIG. 15 shows a first application example (situation 2) of the context generation system 10. In this situation, the person 1 gets into the truck through the gate while holding the goods 1 and 2 and places the goods 1 and 2 on the truck 1. The person 1 stands with his / her foot capable of absorbing vibration in the track 1 and is in a state different from the products 1 and 2 from the viewpoint of the context 36 such as vibration.

  FIG. 16 shows the management table 30 corresponding to the first application example (situation 2). When a person 1 wears a heart rate monitor 1 and passes through the gate while holding the products 1 and 2, the RFID reader 1 or 2 of the gate reads the identification information of the RFID tags 1, 2, and 3, and reads the read data. Output to the processing unit 23. In response to this, the processing unit 23 sets new related information 3b. It is assumed that the RFID readers 1 and 2 at the gate are arranged at a distance where the information of the same ID tag 14 is not read repeatedly.

  When the processing unit 23 inputs the identification information of the RFID tag 1 from the tag reader 1 or 2, according to the first type association rule 40, the related information 3 b having the R reliability 38 of 0.9 is set between the track 1 and the product 1. Set (r1-a, b). When the processing unit 23 inputs the identification information of the RFID tag 2 from the tag reader 1 or 2, according to the first type association rule 40, the related information 3 b having the R reliability 38 of 0.9 is set between the track 1 and the product 2. Set (r2-a, b). When the processing unit 23 inputs the identification information of the RFID tag 3 from the tag reader 1 or 2, according to the first type association rule 40, the related information 3 b having the R reliability 38 of 0.8 between the track 1 and the person 1 is obtained. Set (r3-a, b).

  Further, the temperature sensor 1 and the heart rate sensor 1 regularly output measurement values to the processing unit 23. When these measurement values are input, the processing unit 23 stores a context 36 corresponding to each sensor ID 35 (r4, r5).

  In this state, the processing unit 23 obtains the context 36 of the entity 12 as follows. For example, the same part obtains the heart rate of the person 1 as follows.

  The generation unit 20 inputs a context request 28 for designating person 1 (ID 31) and heart rate (C type 37) (S20). The generation unit 20 accesses the management table 30 of the person 1 using the ID 31 as a key (S22). The same unit obtains a search rule A (third search rule 43 in FIG. 6) for searching for a heart rate (S23), and starts a search starting from the person 1 (S24).

  The person 1 has R reliability 38 of 1 and 0.8 as the related information 3b (F-a and r3-b). The generation unit 20 performs a search process for the both related information 3b (S32 to S37). However, according to the search rule A, the same part does not follow the related information 3b (r3-b) with the R reliability 38 of 0.8 (N in S32), and the related information 3b (F with the R reliability 38 of 1) Follow -a) (Y in S32). This is because the search reliability 45 of the search rule A is 0.9 or more.

  The generation unit 20 registers the R reliability 38 (= 1) of the related information 3b (Fa) in the reliability list 90 (S35), and starts again from the management table 30 of the heart rate monitor 1 that has been traced. The search is started (S36).

  The same unit obtains a context 36 (r5) in which the C type 37 is a heart rate from the management table 30 and registers it in the data list 21 (S30 during recursive call). At this time, the total reliability 3c of the heart rate registered in the data list 21 is 1. The reason is that the R reliability 38 registered in the reliability list 90 is only that of the related information 3b (F-a).

  Since the heart rate monitor 1 has only other related information 3b (F-b) to be returned to the person 1, the same section does not follow the related information 3b (Y in S34 at the time of the recursive call) and starts the heart rate monitor 1. End the search.

  The same part resumes the search for person 1 (after the return of S36), but since person 1 has no other relevant information 3b (other than Fa and r3-b), the search starting from person 1 finish.

  After completing the search starting from the person 1 (after returning from S24), the same unit extracts and outputs the heart rate in the data list 21 (S27). The reason is that the total reliability 3c of the heart rate monitor 1 starting from the person 1 is 1, and the acquisition reliability 46 of the search rule A is 0.98 or more (Y in S26).

  On the other hand, the production | generation part 20 calculates | requires the temperature of the person 1 as follows. The generation unit 20 inputs a context request 28 for designating person 1 (ID 31) and temperature (C type 37). The generation unit 20 accesses the management table 30 of the person 1 using the ID 31 as a key. The same part acquires search rule B (first search rule 43 in FIG. 6) for searching for temperature, and starts a search starting from person 1.

  The person 1 has R reliability 38 of 1 and 0.8 as the related information 3b (F-a and r3-b). The generation unit 20 performs a search process for the both related information 3b according to the search rule B (S32 to S37).

  The same section cannot obtain the context 36 in which the C type 37 is temperature from the heart rate monitor 1 that has followed the related information 3b (F-a) having the R reliability 38 of 1.

  On the other hand, the generation unit 20 starts from the previous track 1 that has tracked the related information 3b (r3-b) with the R reliability 38 of 0.8, and further relates to the related information 3b (r1-a) with the R reliability 38 of 0.9. ) Is accessed to the previous product 1. Then, the same unit can obtain the context 36 (r4) in which the C type 37 is the temperature from the product 1 and store it in the data list 21 together with the total reliability 3c (S30). The reason is that the total reliability 3c of the product 1 starting from the person 1 is 0.8 × 0.9 = 0.72, and the search reliability 45 of the search rule B is 0.6 or more. .

  When the related information 3b (r3-b) and the related information 3b (r1-a) are traced from the person 1 to the product 1, the same part displays the related information 3b (r3-b) and the related information 3b ( The R reliability 38 (0.8 and 0.9) of r1-a) is registered (S35). The same part calculates the total reliability 3c based on the value. After the calculation, the same part deletes the R reliability 38 (0.9) of the last related information 3b (r1-a) from the reliability list 90 (S37).

  Further, the generation unit 20 does not follow the related information 3b (r1-b) of the product 1. When tracing the related information 3b (r3-b) and the related information 3b (r1-a) from the person 1 to the product 1, the generation unit 20 registers the ID 31 of the person 1 and the track 2 in the searched list 22 ( S31). Therefore, the same part determines that the track 1 that has followed r1-b has already been searched based on the searched list 22 (Y in S34).

  Thereafter, the generation unit 20 returns to the search for the track 1 from the product 1 (the recursive call is terminated with N in S34), but before that, the ID 31 of the product 1 is deleted from the searched list 22 (S38). ).

  At this time, the ID 31 of the track 1 remains in the searched list 22 and the R reliability 38 (0.8) of the related information 3b (r3-b) remains in the reliability list 90.

  The generation unit 20 also retrieves the previous product 2 from the track 1 that has traced the related information 3b (r2-a) having an R reliability 38 of 0.9. This is because the total reliability 3c of the product 2 is 0.8 × 0.9 = 0.72, which is 0.6 or more which is the search reliability 45 of the search rule B.

  When tracing the related information 3b (r2-a) from the truck 1 to the product 2, the same part additionally registers the R reliability 38 (0.9) of the related information 3b (r2-a) in the reliability list 90. (S35). At this time, the R reliability 38 (0.8) of the related information 3b (r3-b) and the R reliability 38 (0.9) of the related information 3b (r2-a) are registered in the reliability list 90. Has been. The same part calculates the total reliability 3c based on the value.

  However, the management table 30 of the product 2 does not store the context 36 in which the C type 37 is temperature, and the generation unit 20 cannot obtain the temperature. Further, the generation unit 20 does not follow the related information 3b (r2-b) of the product 2. This is because the track 31 ID 31 remains in the searched list 22 and it is determined that the track 1 that has followed r2-b has already been searched (Y in S34).

  Finally, the generation unit 20 outputs the context 36 (20 degrees) stored in the data list 21 as the context 36 of the person 1 (S27). The reason is that the total reliability 3c of the product 1 is 0.8 × 0.9 = 0.72, which is 0.7 or more, which is the acquisition reliability 46 of the search rule B.

  The first effect of the context generation system 10 of this embodiment is that when the context 36 of one entity 12 is generated from the context 36 of another entity 12, the reliability of the relationship between the two entities 12 can be reflected. . The system generates the context 36 of the entity 12 from the context 36 of another entity 12 having a reliability relationship equal to or higher than a predetermined value, but the context of the other entity 12 having a reliability relationship smaller than the predetermined value. It is not generated from 36.

  The reason why this effect is obtained is that the related information 3b having the R reliability 38 is set between the entities 12, and the context 36 of the other entity 12 having the predetermined total reliability 3c is accessed by following the related information 3b. Because.

  The second effect is that a highly reliable context 36 can be acquired. The reason for this is that firstly, by setting the acquisition reliability 46 high, it is possible to eliminate the context 36 having a low overall reliability 3c. The second reason is that the context generation device 11 acquires the context 36 of the C type 37 to be obtained from the plurality of entities 12 and calculates an average value after excluding a particularly large or small value. This is because 36 is output. The apparatus can improve the reliability of the acquired context 36 by using a plurality of information sources.

  A third effect is that the entity 12 can be flexibly defined (corresponding to an object in the real world). The reason is that the entity 12 can have and share the ID tag 14 and the sensor 13 separately. For example, the ID tag 14 can be shared between the inner surface and the outer surface while the box has the sensors 13 (for example, thermometers) separately on the outer surface and the inner surface. In this way, the context generation device 11 can manage the inside and outside of the box as separate entities 12. The apparatus can separately manage the internal temperature and the external temperature of the box while grasping the movement of both entities 12 at the same time.

  The fourth effect is that the relevant information 3b with the appropriate R reliability 38 can be set between the entities 12. The reason is that the relevant information 3b of the R reliability 38 appropriate for the type of entity 12 (E type 32) is set.

  In the context generation device 11 of this embodiment, the management table 30 does not necessarily include the ID 31 of the entity 12. For example, the apparatus may be configured to have a correspondence table between the ID 31 and the address of the management table 30 separately. Further, if the entity 12 has only one type of context 36, the C type 37 is unnecessary.

  Further, the R reliability 38 may be defined so that a lower value represents a higher reliability. In this case, the total reliability 3c may be an added value of the R reliability 38 of each related information 3b that has been traced. If the total reliability 3c is smaller than the acquisition reliability 46, the context generation device 11 may acquire the context 36 from the management table 30 (S26 in FIG. 9).

  The apparatus may store the data accuracy of the sensor 13 in the C information 3a of the management table 30. The apparatus may calculate the total reliability 3c reflecting the accuracy, for example, by adding the accuracy to the R reliability 38 of the related information 3b.

  Further, the apparatus describes the time when the related information 3b is generated in the related information 3b of the management table 30, and updates the R reliability 38 according to the difference between the generation time and the search time (decreases with time). Etc.). This prevents the apparatus from following the related information 3b having an old generation time, and avoids deterioration in accuracy of the generated context.

[Second Embodiment]
The context generation device 11 according to the second exemplary embodiment of the present invention generates a context 36 with higher reliability from a plurality of contexts 36 having a relatively low overall reliability 3c.

  Even if the total reliability 3c of the context 36 stored in the data list 21 is less than the acquisition reliability 46 of the search rule A (N in S26 in FIG. 12), the generation unit 20 of the present embodiment 36 may be output (S27 in the figure).

  The total reliability 3c of the context 36 stored in the data list 21 has a value of at least the search reliability 45 or more. In the data generation unit 20 of the present embodiment, a predetermined number or more of contexts 36 are stored in the data list 21, and the context 36 can be output as long as the width between the maximum value and the minimum value is within a certain value. to decide. In that case, the same unit outputs, for example, the context 36 having the maximum total reliability 3c. The same unit may output an average value of the plurality of contexts 36. Further, the same unit may determine whether or not the context 36 can be output by calculating the variance of the plurality of contexts 36 instead of the width between the maximum value and the minimum value.

  The context generation device 11 of the present embodiment is the same as the context generation device 11 of the first embodiment in other points.

  The context generation device 11 according to the present embodiment generates a context 36 having higher reliability from the contexts 36 of the plurality of entities 12 each having the related information 3b for which the R reliability 38 is not sufficient. The reason is that the generation unit 20 collects the context 36 in the data list 21 and uses them collectively if the search reliability 45 or higher even if the total reliability 3c is less than the acquisition reliability 46. is there.

[Third Embodiment]
FIG. 17 shows a configuration of the context generation system 10 according to the third exemplary embodiment of the present invention. In the context generation system 10 of the first embodiment, the tag reader 15 of the entity A detects the approach between the entity A and the entity B by inputting information on the ID tag 14 of the entity B. In the context generation system 10 of this embodiment, the tag reader 15 inputs information on the ID tag 14 of the entity A and the ID tag 14 of the entity B at the same time (within the first time), so that the entity A and the entity B Detect approach.

  Except as described below, the context generation system 10 of the present embodiment is the same as the context generation system 10 of the first embodiment. The context generation device 11 of this embodiment holds an R history table 50 in a memory (not shown).

  FIG. 18 shows the configuration of the R history table 50. The R history table 50 stores a time series of input data (reader history 51) from the tag reader 15. The reader history 51 includes an input time 52, a reader ID 34, and a tag ID 33.

  FIG. 36 shows a configuration of data stored in the association rule holding unit 26 in the third embodiment. The association rule holding unit 26 stores one or a plurality of second type association rules 47. The second type association rule 47 includes a time difference 48 and a setting reliability 42.

  The second type association rule 47 is based on the relationship information 3b between the two entities 12 according to the time difference (time difference 48) when the tag reader 15 inputs the information of the ID tag 14 of the entity A and the ID tag 14 of the entity B. The R reliability 38 to be assigned is defined.

  The time difference 48 is expressed in a range between a lower limit value and an upper limit value (for example, greater than the lower limit value and less than the upper limit value). For example, the reliability (setting reliability 42) given between both entities 12 becomes higher as the time difference 48 is smaller. The reason is that, as detected at close times, both entities 12 are physically close and can be trusted as information sources for generating the context 36. The maximum value among the upper limit values is called the first time.

  The association rule holding unit 26 may store both the first type association rule 40 and the second type association rule 47.

  FIG. 19 is an operation flowchart (part) of the processing unit 23 according to the third embodiment. The processing in this figure is inserted between a and b in FIG. At this time, the process between b and c in FIG. 8 (the process according to the first embodiment) may or may not be present. That is, this embodiment may be mounted in parallel with the first embodiment, or may be mounted independently.

  In the present embodiment, when the processing unit 23 inputs the tag ID 33 and the reader ID 34 as appearance data or disappearance data from the tag reader 15, the management table 30 (management table 30 storing the tag ID 33 that matches the input tag ID 33 is stored. All of Table P) is acquired (S40). The same unit acquires all the reader histories 51 in which the difference between the current time and the input time 52 is smaller than the first time and the reader ID 34 matches the input reader ID 34 from the R history table 50 (S41). The processing unit 23 inputs the current time from a timer or the like (not shown).

  The processing unit 23 performs the following processes (S42 to S49) for all acquired reader histories 51. First, the processing unit 23 acquires the tag ID 33 from the sequentially selected reader history 51 (S42), and then stores all the management tables 30 (management table Q) in which the tag IDs 33 matching the acquired tag IDs 33 are stored. Obtain (S43). The same section sets or deletes the related information 3b for all combinations of the acquired management table P and management table Q (S44 to S49). Specifically, the same unit acquires the second type association rule 47 in which the difference between the current time and the input time 52 of the selected reader history 51 falls within the range of the time difference 48 (S44). The processing unit 23 acquires the R reliability 38 to be set from the setting reliability 42 of the acquired second type association rule 47 (S45). The same part checks whether the related information 3b is set between the management tables P and Q (S46).

  If not already set (N in S46), the same part sets the R reliability 38 acquired in the R reliability 38 of the management table P and the address of the management table Q in the related entity identification information 39 ( S47). The same part sets the R reliability 38 acquired in the R reliability 38 of the management table Q and the address of the management table P in the related entity identification information 39 (S48).

  If already set (Y in S46), the same part deletes the related information 3b between the management tables P and Q (S49).

[Second application example]
FIG. 20 shows a second application example (situation 1) of the context generation system 10. The person 1 holds the product 3 in addition to the products 1 and 2 and stands in front of the loading / unloading exit of the truck 1. Person 1 does not have a heart rate monitor 1. Others are the same as the first application example (situation 1).

  FIG. 21 shows a management table 30 corresponding to the second application example (situation 1). In the management unit 25, a management table 30 corresponding to each entity 12 is created.

  FIG. 22 shows a second application example (situation 2) of the context generation system 10. In this situation, the person 1 gets into the truck through the gate while holding the goods 1, 2 and 3, and places the goods 1, 2 and 3 on the truck 1. The person 1 stands with his / her foot capable of absorbing vibration in the track 1 and is in a state different from the products 1 and 2 from the viewpoint of the context 36 such as vibration.

  FIG. 23 shows the management table 30 corresponding to the second application example (situation 2). In the following figures, the two related information 3b in the opposite directions set between the two management tables 30 are represented by a single bidirectional arrow.

  When a person 1 passes through the gate with goods 1, 2 and 3, the RFID reader 1 or 2 of the gate reads the identification information of the RFID tags 1, 2, 3, and 4, and processes the read data To the unit 23. In response to this, the processing unit 23 sets new related information 3b.

  For example, when the identification information of the RFID tag 2 is received within 0.5 seconds following the identification information of the RFID tag 1 from the tag reader 1 or 2, the same unit detects the approach of the products 1 and 2, and the second type association rule 47, the related information 3b having the R reliability 38 of 1 is set between them (r10). Subsequently, when receiving the identification information of the RFID tag 4 within 0.5 seconds, the processing unit 23 detects the approach of the products 1, 2 and 3, and in accordance with the second type association rule 47, the products 3 and 2 and The related information 3b having the R reliability 38 of 1 is set between the product 3 and the product 1 (r11, r12). Subsequently, when the identification information of the RFID tag 3 is received with a delay of 1 second, the processing unit 23 detects the approach of the person 1 and the products 1, 2 and 3. The same section sets related information 3b (r13, r14, r15) having an R reliability 38 of 0.9 between the person 1 and the products 1, 2 and 3 according to the second type association rule 47.

  If the process between b and c in FIG. 8 (the process according to the first embodiment) is also executed, the processing unit 23 also sets related information 3b (r1 to r4) with the track 1.

  FIG. 24 shows the management table 30 when the person 1 goes out of the truck 1 through the gate while holding the products 1, 2 and 3. The processing unit 23 deletes the related information 3b of r10 to r15 according to the operation flowchart of FIG. For example, when the processing unit 23 receives the identification information of the RFID tag 2 within 0.5 seconds following the identification information of the RFID tag 1 from the tag reader 1 or 2, the processing unit 23 performs the following. First, the processing unit 23 attempts to set related information 3b having an R reliability 38 of 1 between the two according to the second type association rule 47. However, when it is detected that the related information 3b has already been set, the same part determines that the products 1 and 2 are far away, and deletes the related information 3b (r10) between them.

  Further, when the process between b and c in FIG. 8 (the process according to the first embodiment) is also executed, the processing unit 23 also deletes the related information 3b (r1 to r4) with the track 1.

  When detecting the disappearance of the entity 12 (after S14 in FIG. 8), the processing unit 23 deletes all the related information 3b (excluding the related information 3b set by the registration unit 27) possessed by the entity 12. You may implement as follows. That is, the processing unit 23 may delete all the related information 3b set at the time of carry-in at the time of carry-out. In this case, a plurality of entities 12 may be detected simultaneously at the time of carry-out, and in that case, a relationship may be established between these entities 12. In this way, when the products 1, 2 and 3 are carried in at the same time and only the products 1 and 2 are carried out at the same time, the products 1 and 2 are deleted while deleting the related information 3b with the product 3 for the products 1 and 2. The related information 3b can be held.

  The context generation system 10 of the present embodiment can set various related information 3b between the entities 12. The reason is that when the tag reader 15 inputs the information of the ID tag 14 of the entity A and the ID tag 14 of the entity B at the same time, the system detects that the entity A and the entity B are approaching and far away, and the related information This is because 3b is set and deleted.

  The R reliability 38 may be determined based on a combination of a pair of E type 32 of both entities 12 in which the related information 3b is set and a difference in time when the identification information is received. Specifically, it is assumed that the difference in identification information reception time between the ID tags 14 of the products 1 and 2 is 1.5 seconds. In this case, the generation unit 20 calculates the product of the setting reliability 42 “0.95” obtained from the first type association rule 40 and the setting reliability 42 “0.9” obtained from the second type association rule 47. This may be obtained and used as the R reliability 38 of the related information 3b between the product 1 and the product 2. That is, the same part may set the R reliability 38 to 0.95 × 0.9 = 0.855.

[Fourth Embodiment]
FIG. 25 shows a configuration of the context generation system 10 according to the fourth exemplary embodiment of the present invention. The context generation system 10 of the present embodiment detects the approach between the entities A and B when the sensors 13 of the entities A and B detect the measured values within the predetermined measured value difference at the same time (within the second time). In addition, the system detects that the entities A and B are far away when both sensors 13 simultaneously detect a measured value greater than or equal to a predetermined measured value difference.

  Except as described below, the context generation system 10 of this embodiment is the same as that of the first embodiment. The context generation device 11 of the present embodiment holds an S history table 70 and a conversion table 80 in a memory (not shown).

  FIG. 26 illustrates a configuration of data stored in the association rule holding unit 26 according to the fourth embodiment. The association rule holding unit 26 of the present embodiment stores one or a plurality of third type association rules 49. The third type association rule 49 includes a sensor type 61, a measured value difference 4a, and a setting reliability 42.

  The measured value difference 4a indicates the range of the difference between the two measured values obtained from the two sensors 13 of the same sensor type 61. The measured value difference 4a is, for example, a difference between temperature data obtained from two temperature sensors. The measured value difference 4a is, for example, a distance between positions obtained from two position degree sensors. The measured value difference 4a is expressed in a range between a lower limit value and an upper limit value (for example, greater than the lower limit value and less than the upper limit value).

  When the third type association rule 49 inputs a measured value within the range of the measured value difference 4 a from the two sensors 13 of the two entities 12, the R reliability 38 is set between the entities 12. It is defined that related information 3b which is 42 is set.

  For example, the reliability (setting reliability 42) given between the two entities 12 increases as the measurement value difference 4a decreases. The reason is that the smaller the measured value difference of the sensor 13, the closer the both entities 12 are physically close to each other and the more reliable the information source for generating the context 36.

  The association rule holding unit 26 may store a third type association rule 49 (disconnection rule) in which the value of the setting reliability 42 is 0. When a measurement value having a difference equal to or greater than the lower limit value (predetermined measurement value difference) of the measurement value difference 4a of the rule is input, the rule is related information between the entities 12 of the sensor 13 that measured the measurement value. 3b is not set, and if it is already set, disconnection is specified. The predetermined measurement value difference may be a setting parameter given to the processing unit 23 instead of as a cutting rule.

  The association rule holding unit 26 may store one or both of the first type association rule 40 and the second type association rule 47 in addition to the third type association rule 49.

  FIG. 27 shows the structure of the S history table 70. The S history table 70 stores a time series of input data (sensor history 71) from the sensor 13. The sensor history 71 includes an input time 52, a sensor ID 35, a sensor type 61, and a history measurement value 72.

  FIG. 28 shows the configuration of the conversion table 80. The conversion table 80 stores a sensor type 61 for each sensor ID 35.

  FIG. 29 is an operation flowchart (part) 1/2 of the processing unit 23 according to the fourth embodiment. The processing in this figure is inserted between d and e in FIG. At this time, there may or may not be a process (a process according to the first or third embodiment) between a and c in FIG. That is, this embodiment may be mounted in parallel with the first or third embodiment, or may be mounted independently.

  In this embodiment, when the processing unit 23 inputs the sensor ID 35 and the measurement value from the sensor 13, the processing unit 23 acquires all the management table 30 (management table E) in which the sensor ID 35 that matches the input sensor ID 35 is stored. (S50). The same unit acquires the sensor type 61 corresponding to the input sensor ID 35 from the conversion table 80 (S51). The same unit acquires from the S history table 70 all sensor histories 71 that match the sensor type 61 acquired by the sensor type 61 within the second time difference between the current time and the input time 52 (S52).

  If it can be obtained (Y in S53), the processing unit 23 sets, updates, and deletes the related information 3b (FIG. 30). When the information cannot be obtained (N in S53), or after setting, updating, and deleting the related information 3b, the processing unit 23 adds the sensor history 71 based on the sensor ID 35 and the measurement value input from the sensor 13 (S54). ), This process is terminated.

  FIG. 30 is an operation flowchart (part) 2/2 of the processing unit 23 according to the fourth embodiment. The processing unit 23 performs the processing from S60 to S69 on the acquired all sensor history 71.

  The processing unit 23 acquires the third type association rule 49 that satisfies the following conditions from the association rule holding unit 26 (S60). The first condition is that the sensor type 61 of the rule matches the sensor type 61 acquired from the conversion table 80. The second condition is that the difference between the measurement value acquired from the sensor 13 and the history measurement value 72 in the sensor history 71 is within the range of the measurement value difference 4a of the rule.

  The same unit acquires the setting reliability 42 from the rule (S61), and acquires all the management table 30 (management table F) in which the sensor ID 35 that matches the sensor ID 35 acquired from the sensor history 71 is stored. (S62). The same unit performs the processing of S63 to S69 for all combinations of the acquired management tables E and F.

  If the related information 3b is already set between the management tables E and F (Y in S63) and the setting reliability 42 of the rule is not 0 (N in S64), The R reliability 38 of the management tables E and F is updated with the setting reliability 42 of the rule (S65).

  If the related information 3b has already been set between the management tables E and F (Y in S63) and the setting reliability 42 of the rule is 0 (Y in S64), the same part The related information 3b is deleted from the tables E and F (S66).

  If the related information 3b is not set between the management tables E and F (N in S63) and the setting reliability 42 of the rule is not 0 (N in S67), the same part manages Related information 3b is set between tables E and F. Specifically, the same part sets the setting reliability 42 in the R reliability 38 of the management table E and the address of the management table F in the related entity identification information 39 (S68). The same part sets the setting reliability 42 in the R reliability 38 of the management table F and the address of the management table E in the related entity identification information 39 (S69).

  If the related information 3b is not set between the management tables E and F (N in S63) and the setting reliability 42 of the rule is 0 (Y in S67), what is the same part? If not.

[Third application example]
FIG. 32 shows a third application example (situation 1) of the context generation system 10. In this example, the entity 12 is the product 1 and the product 2 (both E type 32 is a product). The products 1 and 2 are provided with position sensors 1 and 2, respectively. Assume that products 1 and 2 are both in the warehouse.

  The position sensors 1 and 2 are RFID tags 1 and 2, for example. A position determination unit (not shown) measures the position information of the RFID tags 1 and 2 by three-point surveying based on the radio field intensity received by the three tag readers installed in the warehouse, and sends the information to the processing unit 23 of the context generation device 11. It is transmitted as position information of the position sensor 1 or 2.

  The position information is, for example, a plane coordinate value based on the illustrated orthogonal axis with one corner of the warehouse bottom as the origin. For example, the products 1 and 2 are placed at the positions of the coordinates (10, 11) and the coordinates (11, 11), respectively.

  FIG. 33 shows the management table 30 corresponding to the third application example (situation 1). In the management unit 25, a management table 30 corresponding to each entity 12 (product 1 and product 2) is created. For example, the administrator of the context generation system 10 registers these pieces of information in the management unit 25 from the management terminal 16 via the registration unit 27.

  Here, in the third application example (situation 2), the processing unit 23 receives the position information (10, 11) of the position sensor 1 from the position determination unit, and then within the second time, the position sensor Assume that position information (11, 11) of 2 is received. The same part calculates the difference (1) between the two measured values, and based on the second type 3 association rule 49 in FIG. 26, the R reliability 38 is 0.9 between the products 1 and 2. The related information 3b is set (r13). FIG. 34 shows the management table 30 corresponding to the third application example (situation 2).

  The context generation system 10 of the present embodiment can set various related information 3b between the entities 12. The reason is that the context generation system 10 of this embodiment sets the related information 3b of the R reliability 38 according to the measurement value difference between the sensors 13 of the entities A and B.

  FIG. 37 shows a basic configuration of the context generation device 11 according to the present invention. The context generation device 11 includes a management unit 25 and a generation unit 20. The management unit 25 stores, for each entity 12, zero or more contexts 36 and zero or more related information 3b including related entity identification information 39 and related reliability (R reliability 38). 30 is memorized.

  The generation unit 20 inputs the identification information of the first entity 12, starts the management table 30 of the first entity 12, and traces the management table 30 of zero or more other entities 12 by tracing the related information 3 b. Via which the management table 30 of the second entity 12 is acquired, and the total reliability 3c and the acquisition reliability 46 obtained based on the R reliability 38 of the related information 3b traced until the management table 30 is acquired, If the comparison result satisfies the first predetermined condition and the context 36 is stored in the management table 30, the context 36 of the first entity 12 is generated from the context 36.

DESCRIPTION OF SYMBOLS 10 Context production | generation system 11 Context production | generation apparatus 12 Entity 13 Sensor 14 ID tag 15 Tag reader 16 Management terminal 17 User terminal 18 Network 20 Generation part 21 Data list 22 Tested list 23 Processing part 24 Search rule holding part 25 Management part 26 Association rule holding Unit 27 registration unit 28 context request 29 context generation program 30 management table 31 ID
32 E type 33 Tag ID
34 Reader ID
35 Sensor ID
36 Context 37 C type 38 R reliability 39 Related entity identification information 3a C information 3b Related information 3c Total reliability 40 First type association rule 41 Type pair 42 Setting reliability 43 Search rule 44 Request specification 45 Search reliability 46 Acquisition reliability Degree 47 Second type association rule 48 Time difference 49 Third type association rule 4a Measurement value difference 50 R history table 51 Reader history 52 Input time 61 Sensor type 70 S history table 71 Sensor history 72 History measurement value 80 Conversion table 90 Reliability list 91 C information area 92 C information area pointer

Claims (28)

For each entity, the identification information of an entity, context or null, and, related information or the related entity identity and the composed of the relevant reliability related entity identification information is pointer information to other entities (R reliability) A management unit that stores a management table storing null for each of the R reliability levels ;
Enter identification information for the first entity
Starting from the management table of the first entity, tracing the related entity identification information stored in the management table of the first entity , directly, or the management of one or more other entities Obtain the management table of the second entity via the table,
The comparison result between the total reliability obtained based on the R reliability of the related information traced until the management table is acquired and the acquisition reliability satisfies the first predetermined condition, and the context exists in the management table If it is stored, a context generation device including a generation unit that generates the context of the first entity from the context. The generation unit that does not acquire the management table of the second entity when a comparison result between the total reliability and the search reliability until the second entity is acquired satisfies a second predetermined condition. The context generation device according to claim 1. Based on the context stored in each of the management tables of the plurality of second entities, and the overall reliability of the related information traced until obtaining each of the plurality of management tables, The context generation device according to claim 2, further comprising the generation unit including the generation unit that generates a context of a first entity. The product of the R reliability of each of the related information on the simple path traced until the management table of the second entity is acquired is set as the total reliability, and the first predetermined condition is the total reliability The generation unit includes: the generation unit being equal to or higher than the acquisition reliability, and the second predetermined condition being that the total reliability is lower than the search reliability which is a value equal to or lower than the acquisition reliability. Item 4. The context generation device according to any one of Items 2 to 3. The minimum or average value of the R reliability of the related information on each simple path that is traced until the management table of the second entity is acquired is defined as the total reliability, and the first predetermined condition is: The generating unit , wherein the total reliability is equal to or higher than the acquisition reliability, and the second predetermined condition is that the total reliability is smaller than the search reliability that is a value equal to or less than the acquisition reliability. The context generation device according to claim 2, further comprising: The management unit storing the management table storing the type (C type) for each context;
A search rule holding unit for storing the search reliability and the acquisition reliability for each C type;
If the C type is input, the search reliability and the acquisition reliability corresponding to the C type are acquired and used, and the context of the C type is stored in the management table of the second entity, The context generation device according to claim 2, further comprising the generation unit that generates a context of the first entity from the context. The management unit for storing the management table for storing the corresponding entity type (E type);
Related information for one of the entities having the R reliability determined based on the two E types stored in each of the management tables of the two entities by detecting the proximity of the two entities Is stored in the management table of the other entity, or
The context generation device according to claim 1, further comprising: a processing unit that detects that the two entities are far away and deletes the stored related information. The identification information of the ID tag attached to each of the two entities is read from one tag reader with a time difference shorter than the first time difference to detect the approach of the two entities, and the ID tag is read. Storing relevant information for one of the entities having an R confidence determined based on a time difference in the management table of the other entity, or
The context generation device according to claim 1, further comprising: a processing unit that detects that the two entities are far and deletes the stored related information. When measurement data having a difference smaller than a predetermined measurement difference is input within a second time from sensors attached to each of the two entities, the approach of the two entities is detected, and the measurement value difference between the sensors is detected. The related information for one of the entities having the R reliability determined based on the above is stored in the management table of the other entity, or the R reliability of the related information is determined based on the measured value difference Updated to R confidence level,
The processing unit according to claim 1, further comprising: a processing unit that detects that the two entities are far if the measurement data greater than the predetermined measurement difference is input within the second time period, and deletes the stored related information. Any context generator. The registration unit for creating or modifying the management table by inputting data from a management terminal, and the generation unit for inputting the identification information of the first entity and the C type from a user terminal. A context generator,
The management terminal;
The user terminal;
A context generation system including the sensor. For each entity, the identification information of an entity, context or null, and, related information or the related entity identity and the composed of the relevant reliability related entity identification information is pointer information to other entities (R reliability) In a computer having a management unit for storing a management table storing null for each of the R reliability levels ,
Enter identification information for the first entity
Starting from the management table of the first entity, tracing the related entity identification information stored in the management table of the first entity , directly, or the one or more other entities Obtain the management table of the second entity via the management table,
The comparison result between the total reliability obtained based on the R reliability of the related information traced until the management table is acquired and the acquisition reliability satisfies the first predetermined condition, and the context exists in the management table If stored, a context generation program that executes a generation process for generating the context of the first entity from the context. In the computer,
The generation process that does not acquire the management table of the second entity when a comparison result between the total reliability and the search reliability until the second entity is acquired satisfies a second predetermined condition. The context generation program according to claim 11, wherein: In the computer,
Based on the context stored in each of the management tables of the plurality of second entities, and the overall reliability of the related information traced until obtaining each of the plurality of management tables, The context generation program according to claim 12, wherein the generation process for generating the context of the first entity is executed. In the computer,
The product of the R reliability of each of the related information on the simple path traced until the management table of the second entity is acquired is set as the total reliability, and the first predetermined condition is the total reliability The generation process is executed, wherein the second predetermined condition is that the total reliability is smaller than the search reliability, which is a value equal to or less than the acquisition reliability , The context generation program according to claim 12. In the computer,
The minimum or average value of the R reliability of the related information on each simple path that is traced until the management table of the second entity is acquired is defined as the total reliability, and the first predetermined condition is: The generation process in which the total reliability is equal to or higher than the acquisition reliability, and the second predetermined condition is that the total reliability is lower than the search reliability that is a value equal to or lower than the acquisition reliability. The context generation program according to claim 12, wherein the context generation program is executed. The computer including the management unit that stores the management table that stores the type (C type) for each context, and the search rule holding unit that stores the search reliability and the acquisition reliability for each C type,
If the C type is input, the search reliability and the acquisition reliability corresponding to the C type are acquired and used, and the context of the C type is stored in the management table of the second entity, The context generation program according to any one of claims 12 to 15, wherein the generation processing for generating the context of the first entity from the context is executed. In the computer comprising the management unit for storing the management table storing the corresponding entity type (E type),
Related information for one of the entities having the R reliability determined based on the two E types stored in each of the management tables of the two entities by detecting the proximity of the two entities Is stored in the management table of the other entity, or
The context generation program according to any one of claims 11 to 16, which detects that the two entities are far from each other and executes related processing for deleting the stored related information. In the computer,
The identification information of the ID tag attached to each of the two entities is read from one tag reader with a time difference shorter than the first time difference to detect the approach of the two entities, and the ID tag is read. Storing relevant information for one of the entities having an R confidence determined based on a time difference in the management table of the other entity, or
The context generation program according to any one of claims 11 to 16, wherein when the two entities are far from each other, a related process for deleting the stored related information is executed. In the computer,
When measurement data having a difference smaller than a predetermined measurement difference is input within a second time from sensors attached to each of the two entities, the approach of the two entities is detected, and the measurement value difference between the sensors is detected. The related information for one of the entities having the R reliability determined based on the above is stored in the management table of the other entity, or the R reliability of the related information is determined based on the measured value difference Updated to R confidence level,
17. When measurement data greater than or equal to the predetermined measurement difference is input within the second time, it is detected that the two entities are far away, and related processing for deleting the stored related information is executed. Any context generator. For each entity, the identification information of an entity, context or null, and, related information or the related entity identity and the composed of the relevant reliability related entity identification information is pointer information to other entities (R reliability) A storage step of storing a management table storing null for each of the R reliability levels in the management unit;
Enter identification information for the first entity
Starting from the management table of the first entity, tracing the related entity identification information stored in the management table of the first entity , directly, or the management of one or more other entities Obtain the management table of the second entity via the table,
The comparison result between the total reliability obtained based on the R reliability of the related information traced until the management table is acquired and the acquisition reliability satisfies the first predetermined condition, and the context exists in the management table If it is stored, a context generation method including a generation step of generating a context of the first entity from the context. The generation step that does not acquire the management table of the second entity when a comparison result between the total reliability and the search reliability until the second entity is acquired satisfies a second predetermined condition. 21. The context generation method according to claim 20, further comprising: Based on the context stored in each of the management tables of the plurality of second entities, and the overall reliability of the related information traced until obtaining each of the plurality of management tables, The context generation method according to claim 21, further comprising the generation step of generating a context of a first entity. The product of the R reliability of each of the related information on the simple path traced until the management table of the second entity is acquired is set as the total reliability, and the first predetermined condition is the total reliability The generating step is that the second predetermined condition is that the total reliability is smaller than the search reliability that is a value equal to or less than the acquisition reliability. Item 23. The context generation method according to any one of Items 21 to 22. The minimum or average value of the R reliability of the related information on each simple path that is traced until the management table of the second entity is acquired is defined as the total reliability, and the first predetermined condition is: The generating step in which the total reliability is equal to or higher than the acquisition reliability, and the second predetermined condition is that the total reliability is smaller than the search reliability that is a value equal to or less than the acquisition reliability. The context generation method according to claim 21, comprising: The storage step of storing the management table storing the type (C type) for each context;
For each C type, a storage step of storing the search reliability and the acquisition reliability in a search rule holding unit;
If the C type is input, the search reliability and the acquisition reliability corresponding to the C type are acquired and used, and the context of the C type is stored in the management table of the second entity, The context generation method according to any one of claims 21 to 24, further comprising the generation step of generating a context of the first entity from the context. The storage step of storing the management table storing the corresponding entity type (E type) in a war record management unit;
Related information for one of the entities having the R reliability determined based on the two E types stored in each of the management tables of the two entities by detecting the proximity of the two entities Is stored in the management table of the other entity, or
The context generation method according to claim 20, further comprising a related step of detecting that the two entities are far from each other and deleting the stored related information. The identification information of the ID tag attached to each of the two entities is read from one tag reader with a time difference shorter than the first time difference to detect the approach of the two entities, and the ID tag is read. Storing relevant information for one of the entities having an R confidence determined based on a time difference in the management table of the other entity, or
26. The context generation method according to claim 20, further comprising a related step of detecting that the two entities are far and deleting the stored related information. When measurement data having a difference smaller than a predetermined measurement difference is input within a second time from sensors attached to each of the two entities, the approach of the two entities is detected, and the measurement value difference between the sensors is detected. The related information for one of the entities having the R reliability determined based on the above is stored in the management table of the other entity, or the R reliability of the related information is determined based on the measured value difference Updated to R confidence level,
26. The method according to any one of claims 20 to 25, further comprising a related step of detecting that the two entities are far if the measurement data greater than the predetermined measurement difference is input within the second time and deleting the stored related information. Any context generation method.

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