System, method, and program

Abstract

To improve complementary accuracy using information of more appropriate areas.SOLUTION: A system 1 includes a specifying unit 101, a determination unit 102, and a complementary unit 103. The specifying unit 101 specifies an area which is closer from a predetermined area than other areas, out of areas of the same kind as the predetermined area. The determination unit 102 determines whether a value of an area attribute related to the predetermined area is similar to a value of an area attribute related to the specified area. When they are similar, the complementary unit 103 complements a value of a position attribute related to a predetermined position included in the predetermined area, on the basis of values of position attributes related to positions included in the specified area. When they are not similar, the specifying unit 101 specifies another area of the same kind, which is the closest from the predetermined area next to the specified area.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to systems and the like. [Background technology]

[0002] There is a technology in which devices such as sensors are placed at each location to obtain various information at each location. For example, Patent Document 1 describes a technology in which a sensor is placed at each sales floor of a store, and an attention level, which is an index showing the degree to which people staying in a target area are paying attention to that area, is calculated based on each piece of information obtained from the sensor. Patent Document 1 also describes a technology in which the attention level of a sales floor without a sensor or a sales floor with a broken sensor is predicted using the past attention levels of sales floors on other floors of the same store or sales floors in other stores. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2011 / 162119 Summary of the Invention [Problem to be solved by the invention]

[0004] When information in an area where there is no sensor or where the sensor has failed is complemented with information from other areas as in Patent Document 1, there is a problem that the accuracy of the complementation may be reduced depending on the information from the other areas.

[0005] One example of the objective of the present disclosure is to improve the accuracy of completion by using information on more appropriate regions. [Means for solving the problem]

[0006] A system in one aspect of the present disclosure includes an identification means for identifying an area of ​​the same type as a predetermined area that is closer to the predetermined area than other areas, a determination means for determining whether a value of an area attribute for the predetermined area is similar to a value of the area attribute for the identified area, and a complement means for complementing the value of the position attribute for a predetermined position included in the predetermined area based on the value of a position attribute for each position included in the identified area, if they are similar, and if they are not similar, the identification means identifies an area of ​​the same type that is next closest to the area whose distance from the predetermined area is identified.

[0007] A method in one aspect of the present disclosure identifies areas of the same type as a predetermined area that are closer to the predetermined area than other areas, determines whether the value of the area attribute for the predetermined area is similar to the value of the area attribute for the identified area, and if they are similar, complements the value of the position attribute for a predetermined position included in the predetermined area based on the value of the position attribute for each position included in the identified area, and if they are not similar, identifies the area of ​​the same type that is next closest to the area whose distance from the predetermined area is identified.

[0008] In one aspect of the present disclosure, a program causes a computer to perform the following processes: identify an area of ​​the same type as a predetermined area that is closer to the predetermined area than other areas; determine whether the value of an area attribute for the predetermined area is similar to the value of the area attribute for the identified area; and, if they are similar, complement the value of the position attribute for a predetermined position included in the predetermined area based on the value of the position attribute for each position included in the identified area; and, if the areas are not similar, identify an area of ​​the same type that is next closest to the area whose distance from the predetermined area is identified. Effect of the Invention

[0009] According to the present disclosure, the accuracy of complementation is improved by using information on a more appropriate region. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration example of a system according to a first embodiment. [Diagram 2] 4 is a flowchart showing an operation example of the system according to the first embodiment. [Diagram 3] FIG. 11 is a block diagram showing a configuration example of an information processing device included in a system according to a second embodiment. [Figure 4] 11A and 11B are explanatory diagrams showing examples of area attributes by area type, position attributes of complement targets, and areas to be identified. [Diagram 5] FIG. 13 is an explanatory diagram showing a first complement example. [Figure 6] FIG. 11 is an explanatory diagram showing a second complement example. [Figure 7] FIG. 11 is an explanatory diagram showing a third complement example. [Figure 8] FIG. 11 is an explanatory diagram showing a fourth complement example. [Figure 9] FIG. 11 is an explanatory diagram showing a fifth complement example. [Figure 10] 11 is a flowchart showing an operation example of an information processing device included in the system according to the second embodiment. [Figure 11] FIG. 2 is an explanatory diagram illustrating an example of a hardware configuration of a computer device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, with reference to the drawings, embodiments of a system, an information processing device, a method, a program, and a recording medium for recording a program according to the present disclosure will be described in detail. However, the drawings show the configuration of the embodiment of the present disclosure in a schematic manner. Furthermore, the embodiment of the present disclosure described below is an example, and can be appropriately modified within the scope of the same essence.

[0012] Here, we will briefly explain digital twins. Digital twins are a technology that collects information in the real world and reproduces the real world in a virtual space based on the collected information. More specifically, digital twins are a solution that collects information acquired in real time, reproduces situations that may actually occur in a virtual space such as a computer using technologies such as AI (Artificial Intelligence), performs simulations, and predicts what will happen in the real world. Time-series information is acquired through sensors installed in various locations and mobile objects equipped with sensors. When simulating in a virtual space, a dynamic map is created by overlaying the acquired time-series information and future prediction information on the map information, and the actual situation is reproduced as a twin in the virtual space. In order to express the events (World) of the real world on the dynamic map, the data used in the simulation is accumulated in layers classified by purpose.

[0013] In the system described in this embodiment, for example, data including attribute values ​​of each position attribute related to the position at each position of a device that emits information in real space is acquired. In the system, multiple attribute values ​​of the same position attribute at different positions may be managed as a layer. In other words, a layer is an information group consisting of multiple attribute values ​​of a certain position attribute. In the following description, the attribute value of a position attribute is also referred to as the value of a position attribute.

[0014] (Embodiment 1) FIG. 1 is a block diagram showing a configuration example of a system according to the first embodiment. The system 1 complements the value of a position attribute related to a predetermined position included in a predetermined area by using the value of a position attribute related to each position included in another area. The predetermined area is not particularly limited. For example, the predetermined area may be outdoors or indoors. Here, the predetermined area is called a target area. The predetermined position is not particularly limited. The predetermined position is, for example, a position in the target area where there is no sensor, a position where a sensor has failed, a specified position, a center position, or the like. There may be a plurality of predetermined positions. The system 1 includes an identifying unit 101, a determining unit 102, and a complementing unit 103.

[0015] The identification unit 101 identifies an area close to the target area among areas of the same type as the type of the target area. The target area is not particularly limited. Furthermore, the type of each area is not particularly limited. The types of each area are, for example, a conference room, a hospital room, a store, a data center, and the like. The types may be divided into more detailed categories. For example, in the case of a store, the type of store may be divided into more detailed categories such as a restaurant store, a clothing store, and the like.

[0016] A method for calculating the distance between a target region and a region of the same type as the target region is not particularly limited. For example, the distance may be a distance between a certain position included in the target region and another position included in a region of the same type as the target region. The certain position and the other position are not particularly limited. For example, at least one of the certain position and the other position may be a center position of the target region. At least one of the certain position and the other position may be a position selected randomly.

[0017] The determination unit 102 determines whether the value of the area attribute related to the target area is similar to the value of the area attribute of the specified area. The area attribute is, for example, an attribute related to the type of each area. The area attribute may differ depending on the type. For example, when the type of area is a conference room, the area attribute may be the area size including each position, the number of chairs in the area, the arrangement of desks in the area, and the like. The value of the area attribute related to the target area is a specific numerical value such as the area size, the number of chairs, the number of desks, or a value indicating the arrangement direction or the number of desks. Taking the area size as an example, the determination unit 102 may determine whether the area size of the specified area is within a predetermined range of the area size of the target area. The predetermined range is not particularly limited. If the area size of the specified area is within a predetermined range of the area size of the target area, the determination unit 102 determines that the area size of the target area is similar to the area size of the specified area.

[0018] If the determination unit 102 determines that the two locations are similar, the complement unit 103 complements the value of the location attribute of a predetermined location included in the target area based on the value of the location attribute of each location included in the specified area. The location attribute is not particularly limited. The location attribute may be, for example, an attribute that represents the environment at each location. Examples of the attributes that represent the environment include temperature, humidity, sound volume, and dirtiness. The value of the location attribute is a measurement value of a sensor that detects temperature, humidity, or sound volume, or a calculated value of the dirtiness level in the target area. The sensor may be a fixed sensor, or may be a sensor possessed by a mobile object. In addition, when there are multiple values ​​of the same location attribute related to the same location at the same time or time period, the statistical value of those values ​​may be used. The statistical value is, for example, the average value, median value, mode value, etc. When the temperature values ​​at a certain location around 10 o'clock are measured as 20°C, 21°C, and 22°C, if the average value or median value is used, the temperature value related to a certain location around 10 o'clock is 21°C.

[0019] The complementing method is not particularly limited. For example, the complementing unit 103 may set the value of the position attribute related to the position included in the identified area as the value of the position attribute related to the predetermined position. When a value of the position attribute is associated with each of a plurality of positions included in the identified area, the complementing unit 103 identifies a position that has a similar positional relationship with the predetermined position in the target area from the plurality of positions included in the identified area. Then, the complementing unit 103 may set the value of the position attribute related to the identified position as the value of the position attribute related to the predetermined position. In addition, the complementing unit 103 may set, for example, a statistical value of the values ​​of the position attribute related to each position included in the identified area as the value of the position attribute related to the predetermined position. The statistical value is, for example, an average value, a median value, a mode value, or the like.

[0020] Furthermore, when the value of the region attribute of the target region and the value of the region attribute of the identified region are not similar, the identifying unit 101 identifies the region that is the next closest to the identified region in terms of distance from the target region among the regions of the same type as the target region. This allows the identifying unit 101 to identify candidate regions in order of distance.

[0021] 2 is a flowchart showing an example of an operation of the system 1 according to the first embodiment. The identification unit 101 identifies an area close to an area of ​​the same type as the type of the target area (step S101). The determination unit 102 determines whether the value of the area attribute of the target area is similar to the value of the area attribute of the identified area (step S102).

[0022] If it is determined that they are not similar (step S102: No), the identifying unit 101 returns to step S101. As a result, the identifying unit 101 returns to step S101, and identifies the next closest region from among the regions of the same type.

[0023] On the other hand, if it is determined that the regions are similar (step S102: Yes), the complementing unit 103 complements the value of the position attribute of a predetermined position included in the target region based on the value of the position attribute of each position included in the region determined to be similar (step S103). After step S103, the system 1 ends the operation of the flow.

[0024] Next, the effect of the first embodiment will be described. The system 1 identifies an area that is close from areas of the same type as the type of the target area. The system 1 then complements the value of the position attribute of a predetermined position included in the target area based on the value of the position attribute of each position included in the identified area. For example, the closer an area is from the target area, the more likely it is to be in an environment similar to that of the target area. For this reason, the system 1 can complement more accurately by using the value of the position attribute of each position included in an area closer to the target area. Therefore, the system 1 can use the value of the position attribute of each position included in an appropriate area, thereby improving the complement accuracy.

[0025] The first embodiment is not limited to the above-mentioned example, and various modifications are possible. For example, each functional unit shown in Fig. 1 may be realized by one device (information processing device). Also, for example, each functional unit shown in Fig. 1 may be realized by multiple devices.

[0026] (Embodiment 2) Next, the second embodiment will be described in detail with reference to the drawings. In the second embodiment, a more specific complement example will be described in detail. Below, the description of the contents that overlap with the above description will be omitted to the extent that the description of the second embodiment is not unclear.

[0027] FIG. 3 is a block diagram showing a configuration example of an information processing device included in the system according to the second embodiment. The system 2 includes, for example, an information processing device 20. The information processing device 20 may be a moving object such as a robot or a drone, a PC (Personal Computer), a smartphone, a tablet-type device, a server, or the like, but is not limited thereto. The information processing device 20 is connected to other devices via a communication network 21. Although not shown, the system 2 may have a simulation device in a digital twin. The information processing device 20 may be a simulation device. The system 2 may also have a moving object that uses a complemented value. The moving object may be, for example, a robot, a drone, or the like, but is not limited thereto. In the case of a robot, the robot may be, for example, a service robot that performs tasks such as guidance and cleaning. For example, the robot may perform an assigned task based on the value of a position attribute related to each position included in the target area. For example, a cleaning robot may perform cleaning according to the degree of dirt at each position included in the target area. The information processing device 20 may be a robot as described above. The information processing device 20 may be a parent robot among a plurality of robots. The system 2 may also have other devices that use the values ​​of location attributes associated with each location included in the target area. For example, a device that manages air conditioning may set the temperature of the air conditioning based on the temperature values ​​at each location included in the target area.

[0028] 3, the information processing device 20 has an identifying unit 201, a determining unit 202, a complementing unit 203, a predicting unit 204, and a storage unit 205. The information processing device 20 is an example in which a predicting unit 204 and a storage unit 205 are newly added to the functional units of the system 2 described in the first embodiment. The identifying unit 201, the determining unit 202, and the complementing unit 203 each have the functions of the identifying unit 101, the determining unit 102, and the complementing unit 103 described in the first embodiment as basic configurations.

[0029] The storage unit 205 stores, for example, the processing results of each unit of the information processing device 20. Examples of the storage unit 205 include a read only memory (ROM), a random access memory (RAM), a semiconductor memory, a hard disk drive (HDD), and a solid state drive (SSD). The storage unit 205 may be a combination of these.

[0030] The storage unit 205 may store the type of area for each area. Furthermore, the storage unit 205 may store the position of each area. Note that this information may be stored not only in the storage unit 205 but also in a storage device accessible by the information processing device 20.

[0031] The storage unit 205 may also store the value of each position attribute in association with each position included in each region. The storage unit 205 may also store the value of each position attribute in association with each position included in each region in chronological order.

[0032] The identifying unit 201 identifies an area that is closer to the target area than other areas among areas of the same type as the target area. The types of areas used in the detailed description of the second embodiment will be described later with reference to FIG.

[0033] Furthermore, the identification unit 201 may identify an area that is the same type as the target area and is close to the target area from areas included in the same building or facility as the target area. Furthermore, the identification unit 201 identifies an area that is the same type as the target area and is close to the target area from areas included in the same building or facility as the target area. The identification unit 201 identifies an area that is the same type as the target area and is close to the target area from areas included in a building within a predetermined range from the building including the target area.

[0034] Furthermore, the identification unit 201 may identify an area that is the same type as the target area and has a short distance from the target area, from among areas with a high predetermined priority. Priority may be determined for each type of area. Priority by type will be described later with reference to FIG. 4.

[0035] Next, the determination unit 202 determines whether or not the value of the area attribute related to the target area is similar to the value of the area attribute related to the specified area. In this way, the determination unit 202 determines whether or not the target area is similar to the specified area. The area attribute is an attribute of an area related to the type of the area. There may be multiple area attributes. The area attribute may be a dynamically changing attribute or a static attribute. An example of the area attribute according to the type of the area will be described later with reference to FIG. 4. The storage unit 205 may store the value of the area attribute corresponding to the type of the area for each area.

[0036] For example, the determination unit 202 may determine whether or not the similarity between the value of the region attribute related to the target region and the value of the region attribute related to the specified region satisfies a predetermined condition. More specifically, for example, the determination unit 202 may determine that the two regions are similar if the similarity is equal to or greater than a predetermined similarity. The method of calculating the similarity is not particularly limited. Also, for example, when there are multiple region attributes, the determination unit 202 may obtain the similarity for each region attribute and determine the similarity based on the average value of the obtained similarities. The determination unit 202 may determine that the two regions are similar if the average value of the similarities is equal to or greater than a predetermined similarity.

[0037] Alternatively, for example, the determination unit 202 may determine the similarity depending on whether or not the value of the region attribute related to the specified region is included within a predetermined range from the value of the region attribute related to the target region. The predetermined range may be specified by a user or the like, or may be a fixed value. For example, when the region attribute is the area size, the determination unit 202 may determine the similarity of the values ​​of the two region attributes depending on whether or not the area size of the specified region is included within a predetermined range from the area size in the target region. The determination unit 202 may determine that the two areas are similar if the area size of the specified region is included within a predetermined range from the area size in the target region.

[0038] The determination unit 202 may determine whether or not the value of the region attribute of the target region and the value of the region attribute of the specified region match. For example, when the region attribute is floor material, the determination unit 202 may determine whether or not the floor material of the target region and the floor material of the specified region are the same.

[0039] If the determining unit 202 determines that the regions are not similar, the identifying unit 201 identifies, from among regions of the same type as the region of interest, a region that is next closest in distance from the region of interest to the region that has already been identified.

[0040] 4 is an explanatory diagram showing examples of area attributes by area type, position attributes of complementation targets, and areas to be identified. In FIG. 4, the types of areas are conference rooms, hospital rooms, stores, data centers, and the like.

[0041] The area attribute is an attribute of the area related to the type of the area. The position attribute of the completion target is an attribute that represents the environment of each position included in the area. The position attribute of the completion target may be specified by the user. For example, a receiving unit (not shown) may receive the specification of the position attribute of the completion target. In addition, at least one of the areas to be searched and the priority of the areas to be searched may be determined when identifying areas close to the target area, depending on the type of area.

[0042] First, a conference room will be taken as an example. In FIG. 4, for example, in the case of a conference room, the area attributes are the size of the conference room, the number of chairs in the conference room, the number of desks in the conference room, the arrangement of the desks in the conference room, and at least one of the materials of the floor and walls of the conference room. For example, the material of the floor may be carpet, wood, etc. For example, the material of the wall may be concrete, wood, etc. Note that the area attributes may be at least one of these, or may be specified from these. For example, the position attributes of the complement target are temperature and volume. The areas to be searched are areas on the same floor, areas in the same building, and areas in neighboring buildings. The neighboring buildings are, for example, buildings within a predetermined range from the target area. The buildings are also referred to as buildings hereinafter. In FIG. 4, the smaller the numerical value, the higher the priority used at the time of identification by the identification unit 201. For example, the priority of the area on the same floor is the highest, followed by the priority of the area in the same building, and finally the priority of the area in the neighboring building is the lowest.

[0043] For example, when the type of the target area is a conference room, the identification unit 201 identifies an area on the same floor that is the closest to the target area among areas whose type is a conference room. Then, the determination unit 202 determines whether or not the value of the area attribute of the target area is similar to the value of the area attribute of the identified area based on at least one of the size of the conference room, the number of chairs in the conference room, the number of desks in the conference room, the arrangement of the desks in the conference room, and the material of the walls and floor of the conference room. If it is determined that they are not similar, the identification unit 201 identifies an area on the same floor that is the next closest to the target area among areas whose type is a conference room after the area already identified. For example, when there is no area whose type is the next closest, the identification unit 201 identifies an area in the same building that is the closest to the target area among areas whose type is a conference room. In this way, the identification unit 201 may sequentially identify areas based on the priority.

[0044] Next, a hospital room will be taken as an example for explanation. In FIG. 4, for example, in the case of a hospital room, the area attributes are the number of patients in the hospital room, the number of vacant beds in the hospital room, the orientation of the windows in the hospital room, and the like. The area attributes may be at least one of these, or may be specified from among these. For example, the position attributes to be complemented are temperature and humidity. Areas to be searched are areas on the same floor, areas in other wards, and areas in hospitals of the same chain. Also, for example, areas on the same floor have the highest priority, areas in other wards have the next highest priority, and finally areas in hospitals of the same chain have the lowest priority.

[0045] Next, a store will be taken as an example for explanation. In FIG. 4, for example, in the case of a store, the area attributes are the size of the store, the material of the walls inside the store, the items (products) sold in the store, etc. The area attributes may be at least one of these, or may be specified from among these. For example, the position attribute to be complemented is temperature. Areas to be searched are areas on the same floor, areas in the same facility, and areas of nearby stores outside the facility. In addition, when identifying by the identifying unit 201, for example, the priority of areas on the same floor is the highest, followed by areas in the same facility, and finally the priority of areas of nearby stores outside the facility is the lowest.

[0046] Next, a data center will be taken as an example for explanation. In FIG. 4, for example, in the case of a data center, the area attributes are the size of the data center, the number of racks in the data center, the material of at least one of the floors and walls in the data sensor, and the position of the ventilation holes in the data center. The area attributes may be at least one of these, or may be specified from these. For example, the position attributes to be complemented are temperature, humidity, and volume. Areas to be searched are areas on the same floor and areas in the same facility. In addition, when the identification unit 201 identifies, for example, areas on the same floor have a high priority, and areas in the same facility have a low priority.

[0047] Return to the description of FIG. 3. When the determination unit 202 determines that the two regions are similar, the complement unit 203 complements the value of the position attribute of a predetermined position included in the target region based on the value of the position attribute of each position included in the specified region. The predetermined position is not particularly limited as described in the first embodiment. Here, the predetermined position is called a target position. As described above, the position attribute is an attribute that represents the environment at each position. The position attribute may be determined in advance according to the type of the target region, as shown in FIG. 4. To explain using the example of FIG. 4, in the case of a conference room, the complement unit 203 complements the temperature value of the target position based on the temperature value of each position included in the specified region. Then, in the case of a conference room, the complement unit 203 complements the humidity value of the target position based on the humidity value of each position included in the specified region. Alternatively, the position attribute may be specified.

[0048] In addition, the complementing method is not particularly limited. For example, the complementing unit 203 may set the value of the position attribute related to a position included in the identified region as the value of the position attribute related to the target position. Alternatively, the complementing unit 203 may specify a position having a similar positional relationship with the target position in the target region from among a plurality of positions included in the identified region. Then, the complementing unit 203 may set the value of the position attribute related to the specified position as the value of the position attribute related to the target position.

[0049] FIG. 5 is an explanatory diagram showing a first complementation example. In FIG. 5, there is no sensor in conference room X on the first floor of S building. Meanwhile, there is one sensor at position PN1 in conference room Y on the second floor of N building. Here, a case is taken as an example where the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. For example, complementation unit 203 sets the temperature value related to position PN1 included in conference room Y to the temperature value related to position PS1 included in conference room X. As a result, the temperature value related to position PS1 included in conference room X in FIG. 5 is complemented.

[0050] Returning to the description of Fig. 3, the complementing unit 203 may set, for example, a statistical value of the values ​​of the position attribute related to each position included in the identified area as the value of the position attribute related to the target position. The statistical value may be, for example, an average value, a median value, a mode value, or the like.

[0051] FIG. 6 is an explanatory diagram showing a second complement example. In FIG. 6, there is no sensor in conference room X on the first floor of S building. On the other hand, there are three sensors in conference room Y on the second floor of N building, located at positions PN1, PN2, and PN3. In FIG. 6, the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. For example, the complement unit 203 sets the statistical value of the temperature values ​​related to the positions PN1, PN2, and PN3 included in conference room Y as the temperature value related to position PS1 included in conference room X. As a result, the temperature value related to position PS1 included in conference room X in FIG. 6 is complemented.

[0052] Returning to the description of FIG. 3, the complementing unit 203, for example, identifies a correspondence between each of a plurality of positions included in the target area and each of a plurality of positions included in the specified area. For example, the complementing unit 203 may identify the correspondence based on the positions of a window, a door, and a desk. For example, taking the position of a door as an example, the complementing unit 203 identifies the correspondence based on the similarity between the distance from the position of the door in the target area to each position included in the target area and the distance from the position of the door in the specified area to each position included in the specified area. Then, the complementing unit 203 calculates the difference between the value of the position attribute related to each position other than the target position included in the target area and the value of the position attribute related to each position corresponding to each of these positions. The complementing unit 203 obtains the value of the position attribute related to the target position by adding or subtracting a statistical value of the difference from the value of the position attribute related to the position corresponding to the target position.

[0053] FIG. 7 is an explanatory diagram showing complement example 3. In FIG. 7, in conference room X on the first floor of S building, there are a total of two sensors, located at positions PS1 and PS4. Meanwhile, in conference room Y on the second floor of N building, there are a total of four sensors, located at positions PN1 to PN4. In FIG. 7, the value of the area attribute of conference room Y is similar to the value of the area attribute of conference room X. In FIG. 7, complement unit 203 complements temperature values ​​related to two positions (PS3 and PS4) in conference room X on the first floor of S building.

[0054] Therefore, the complementing unit 203, for example, identifies the correspondence between each of the multiple positions included in the conference room X and each of the multiple positions included in the conference room Y. For example, the complementing unit 203 may identify the correspondence based on the distance from the window position, the distance from the door position, the arrangement of desks, etc. In Fig. 7, for example, the position PS1 corresponds to the position PN1, the position PS2 corresponds to the position PN2, the position PS3 corresponds to the position PN3, and the position PS4 corresponds to the position PN4.

[0055] Here, an example of complementing temperature values ​​will be given. The complementing unit 203 subtracts the temperature value associated with position PS1 corresponding to position PN1 from the temperature value associated with position PN1. The complementing unit 203 also calculates a value by subtracting the temperature value associated with position PS2 from the temperature value associated with position PN2. The complementing unit 203 calculates the average value of these subtracted values. The complementing unit 203 sets a value by subtracting the average value from the temperature value associated with position PN3 as the temperature value associated with position PS3 corresponding to position PN3. The complementing unit 203 sets a value by subtracting the average value from the temperature value associated with position PN4 as the temperature value associated with position PS4 corresponding to position PN4. In this way, the temperature value associated with position PS3 and the temperature value associated with position PS4 are complemented.

[0056] In the case of a hospital room, the complement unit 203 may specify the positional correspondence between areas based on the arrangement of beds, etc. In the case of a data center, the complement unit 203 may specify the positional correspondence between areas based on the positions of racks, the arrangement of racks, etc.

[0057] Furthermore, the complementing unit 203 may complement the value of the electric field intensity using radio wave propagation characteristics or the like.

[0058] FIG. 8 is an explanatory diagram showing a fourth supplementary example. In FIG. 8, the target position is position PS1. As an example of a position attribute related to the target position PS1, electric field strength is taken as an example. In a conference room X on the first floor of the S building, a router 2001 is installed, but there is no terminal device. On the other hand, in a conference room Y on the second floor of the N building, a router 2002 is installed, and there is a terminal device 2003. Note that the terminal device 2003 is not particularly limited as long as it is a device capable of receiving radio waves from the router 2002, such as a PC, a smartphone, or a tablet device.

[0059] The complementing unit 203 obtains radio wave propagation characteristics (radio wave propagation model) for the router 2002 based on the transmission power of the router 2002, the value of the electric field strength when the terminal device 2003 communicates with the router 2002, and the distance from the position of the router 2002 to the position of the terminal device 2003. For example, the radio wave propagation model may be a function that returns the value of the electric field strength with the transmission power and distance as parameters. Then, the complementing unit 203 complements the value of the electric field strength for the target position PS1 based on the calculated radio wave propagation characteristics with the transmission power of the router 2002 and the distance from the position of the router 2001 to the target position PS1 as parameters.

[0060] Also, for example, there may be cases where the performance (e.g., transmission power) of router 2001 is the same as that of router 2002, and the positional relationship between router 2002 and terminal device 2003 is the same as the positional relationship between router 2001 and target position PS1. In such a case, complementing unit 203 may set the value of the electric field strength at terminal device 2003 as the value of the electric field strength at target position PS1. As a result, the value of the electric field strength related to position PS1 included in conference room X in FIG. 8 is complemented.

[0061] In the above example, an example of complementing the value of the position attribute related to the target position has been described. The complementing unit 203 may complement the value of the future position attribute instead of the value of the current position attribute. In addition, the prediction unit 204 may predict the value of the position attribute related to the same position at a different time from the value of the position attribute related to each position included in the target area. For example, the prediction unit 204 may predict the value of the position attribute related to the same position tomorrow from the value of the current position attribute related to each position included in the target area. At this time, the prediction unit 204 may further predict the value of the position attribute related to the same position tomorrow using other information. Examples of the other information include schedule information for the target area and weather information around the target area. For example, the prediction unit 204 may predict the value of the position attribute related to the same position tomorrow from the value of the current position attribute related to each position included in the target area using current weather information and tomorrow's weather forecast information. For example, if the current weather and tomorrow's weather are the same, the prediction unit 204 may set the current temperature value related to each position included in the target area as the temperature value related to the same position tomorrow. Furthermore, the prediction unit 204 may predict the future value of the location attribute for each location from the time-series value of the location attribute at the same location. For example, the prediction unit 204 may predict the temperature value of each location tomorrow from the temperature values ​​of each location for the past two days. In this way, the prediction method by the prediction unit 204 is not particularly limited.

[0062] Then, the complementing unit 203 may complement the value of the position attribute related to the target position based on the value of the position attribute related to each position related to the specified area and the predicted value of the position attribute related to each position included in the target area. A specific example will be described with reference to FIG.

[0063] Fig. 9 is an explanatory diagram showing supplementary example 5. In Fig. 9, in conference room X on the first floor of S building, there are two sensors in total, located at positions PS1 and PS4. Meanwhile, in conference room Y on the second floor of N building, there are four sensors in total, located at positions PN1 to PN4.

[0064] Here, an example will be described in which the prediction unit 204 and the complement unit 203 obtain tomorrow's temperature values ​​for each of positions PS1 to PS4 included in the conference room X on the first floor of the S building. First, the prediction unit 204 obtains tomorrow's temperature value for position PS1 from the current temperature value for position PS1. The prediction unit 204 obtains tomorrow's temperature value for position PS2 from the current temperature value for position PS2. For example, when the current weather and the weather forecast for tomorrow are the same, the prediction unit 204 sets the current temperature value for position PS1 to the tomorrow's temperature value for position PS1. Then, the prediction unit 204 sets the current temperature value for position PS2 to the tomorrow's temperature value for position PS2. In this way, the temperature value for position PS1 and the temperature value for position PS2 are predicted.

[0065] The complementing unit 203 obtains a correspondence relationship between each position included in the target region and each position included in the specified region, as described in Fig. 7. In Fig. 9, for example, the position PS1 corresponds to the position PN1, the position PS2 corresponds to the position PN2, the position PS3 corresponds to the position PN3, and the position PS4 corresponds to the position PN4.

[0066] Then, as described in FIG. 7, the complementing unit 203 subtracts tomorrow's temperature value (predicted value) for the position PS1 corresponding to the position PN1 from the current temperature value for the position PN1. The complementing unit 203 also calculates a value by subtracting tomorrow's temperature value (predicted value) for the position PS2 from the current temperature value for the position PN2. The complementing unit 203 calculates the average value of these subtracted values. The complementing unit 203 sets the value by subtracting the average value from the current temperature value for the position PN3 as tomorrow's temperature value for the position PS3 corresponding to the position PN3. The complementing unit 203 sets the value by subtracting the average value from the temperature value for the position PN4 as tomorrow's temperature value for the position PS4 corresponding to the position PN4. In this way, the temperature value for the position PS3 and the temperature value for the position PS4 are complemented.

[0067] 10 is a flowchart showing an example of an operation of the information processing device 20 included in the system 2 according to the second embodiment. The identification unit 201 acquires the type of the target area (step S201). For example, when the storage unit 205 stores information on the type of area by area, the identification unit 201 reads out the information on the type of the target area from the storage unit 205.

[0068] Then, for example, the identifying unit 201 identifies an area of ​​the same type (step S202). In step S202, for example, the identifying unit 201 searches the storage unit 205 for an area of ​​the same type as the type of the target area. Also, in step S202, the identifying unit 201 searches the storage unit 205 for an area that is within a predetermined distance from the target area and is of the same type as the type of the target area.

[0069] The identification unit 201 arranges the regions of the same type in order of proximity to the target region (step S203). In step S203, the identification unit 201 may arrange the regions of the same type in order of priority and proximity. The priority may be an example as described in FIG. 4.

[0070] The identification unit 201 determines whether or not there is an unselected area among the arranged areas (step S204). If there is an unselected area (step S204: Yes), the identification unit 201 selects an area in order of proximity (step S205). The determination unit 202 determines whether or not the value of the area attribute of the selected area is similar to the value of the area attribute of the target area (step S206). If it is determined that they are not similar (step S206: No), the complementation unit 203 returns to step S204.

[0071] On the other hand, if it is determined that the selected region is similar (step S206: Yes), the complement unit 203 judges whether or not there is a value of the position attribute of the complement target related to each position included in the selected region (step S207). That is, in step S207, the complement unit 203 judges whether or not each position is associated with a value of the position attribute of the complement target. Here, the position attribute of the complement target may be predetermined as shown in FIG. 4. Also, the position attribute of the complement target may be designated by the user. If there is no value (step S207: No), the complement unit 203 returns to step S204. If there is a value (step S207: Yes), the complement unit 203 complements the value of the position attribute of the target position with the value of the position attribute related to each position included in the selected region (step S208). After step S208, the information processing device 20 ends the operation of the flow.

[0072] If there is no unselected area (step S204: No), the information processing device 20 ends the operation of the flow. Note that the information processing device 20 may end the operation by outputting an error.

[0073] Next, the effect of the second embodiment will be described. The information processing device 20 may determine whether the similarity between the value of the region attribute related to the target region and the value of the region attribute related to the specified region satisfies a predetermined condition. This allows the information processing device 20 to relatively determine whether they are similar or not.

[0074] Also, for example, the items (desks, beds, etc.) arranged in the room differ depending on the type of room, such as a conference room or a hospital room. Therefore, the area attribute is an attribute related to the type of each area. This allows the information processing device 20 to specify an area similar to the target area based on the value of the area attribute related to the type of area. Therefore, the information processing device 20 can specify an area that is more similar to the target area, thereby improving the accuracy of completion.

[0075] Furthermore, the closer the region is to the target region, the more similar the environment is. Therefore, if the position attribute of the complement target is an attribute that represents the environment of each position, the information processing device 20 can improve the complement accuracy.

[0076] The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included in the same building or facility as the target area. This allows the information processing device 20 to identify an area that is closer to the target area from areas that are close to the target area and are estimated to have a similar environment. As described above, the closer the area, the more likely the environment is similar, so the information processing device 20 can complement the value of the location attribute related to the target position with higher accuracy.

[0077] The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included in the same floor of the same building as the target area. This allows the information processing device 20 to identify an area that is closer to the target area from areas that are close to the target area and are estimated to have a similar environment. As described above, the closer the area, the more likely the environment is similar, so the information processing device 20 can complement the value of the location attribute related to the target position with higher accuracy.

[0078] The information processing device 20 identifies an area that is close to the target area from areas that are the same type as the target area and are included in buildings within a predetermined range from the building that includes the target area. This allows the information processing device 20 to identify an area that is close to the target area from areas that are estimated to be close. As described above, the closer the area, the more likely it is that the environment is similar, so the information processing device 20 can more accurately complement the value of the location attribute related to the target position.

[0079] The second embodiment is not limited to the above-mentioned examples, and various modifications are possible. Each functional unit of the information processing device 20 may be realized by a plurality of devices.

[0080] This concludes the description of each embodiment.

[0081] (Computer Equipment) Next, a hardware configuration example in the case where each device such as the information processing device 20 described in the second embodiment is realized by a computer device will be described. Fig. 11 is an explanatory diagram showing a hardware configuration example of a computer device. For example, a part or all of the devices such as the information processing device 20 described in the second embodiment can be realized by using any combination of a computer device 30 and a program as shown in Fig. 11.

[0082] The computer device 30 includes, for example, a processor 301, a ROM 302, a RAM 303, a storage device 304, a communication interface 305, and an input / output interface 306. Each component is connected to each other via a bus 307.

[0083] The processor 301 controls the entire computer device 30. The processor 301 may be, for example, a central processing unit (CPU) or a digital signal processor (DSP). The computer device 30 has a ROM 302, a RAM 303, and a storage device 304 as a storage unit. The storage device 304 may be, for example, a semiconductor memory such as a flash memory, a HDD, or an SSD. For example, the storage device 304 stores a program such as an operating system (OS) program, an application program, or a program according to each embodiment. Alternatively, the ROM 302 stores a program such as an application program or a program according to each embodiment. The RAM 303 is used as a work area for the processor 301.

[0084] Moreover, the processor 301 loads a program stored in the storage device 304, the ROM 302, etc. Then, the processor 301 executes each process coded in the program. The processor 301 may also download various programs via the communication network 31. The processor 301 functions as a part or the whole of the computer device 30. Then, the processor 301 may execute processes or instructions in the illustrated flowchart based on the program.

[0085] The communication interface 305 is connected to a communication network 31, such as a local area network (LAN) or a wide area network (WAN), via a wireless or wired communication line. This allows the computer device 30 to be connected to an external device or computer via the communication network 31. The communication interface 305 serves as an interface between the communication network 31 and the inside of the computer device 30. The communication interface 305 also controls the input and output of data from the external device or computer.

[0086] Furthermore, the input / output interface 306 is connected to at least one of an input device, an output device, and an input / output device. The connection method may be wireless or wired. Examples of the input device include a keyboard, a mouse, and a microphone. Examples of the output device include a display device, a lighting device, and a speaker that outputs sound. Examples of the input / output device include a touch panel display. The input device, the output device, and the input / output device may be built into the computer device or may be external.

[0087] The hardware configuration of the computer device 30 shown in FIG. 11 is an example. The computer device 30 may not have some of the components shown in FIG. 11. The computer device 30 may have components other than those shown in FIG. 11. The computer device 30 may have devices not shown. For example, the computer device 30 may have a camera as an imaging device. The computer device 30 may also have various sensors. For example, the computer device 30 may have a drive device. The processor 301 may read programs and data from a recording medium attached to the drive device or the like into the RAM 303. Examples of the recording medium include an optical disk, a flexible disk, a magneto-optical disk, and a USB (Universal Serial Bus) memory.

[0088] This concludes the description of the hardware configuration example of each device. There are various variations in the method of realizing each device. For example, the system may be realized by any combination of a computer and a program that are different for each component. Furthermore, multiple components included in each device may be realized by any combination of a single computer and a program.

[0089] Also, some or all of the components of each device may be realized by circuits for specific applications. Also, some or all of the system may be realized by general-purpose circuits including a processor such as an FPGA (Field Programmable Gate Array). Also, some or all of the system may be realized by a combination of circuits for specific applications and general-purpose circuits. Also, these circuits may be a single integrated circuit. Or, these circuits may be divided into multiple integrated circuits. And, the multiple integrated circuits may be configured by being connected via a bus or the like.

[0090] Furthermore, when some or all of the components of each device are realized by a plurality of computers, circuits, etc., the plurality of computers, circuits, etc. may be arranged in a centralized manner or in a distributed manner.

[0091] The method described in each embodiment is realized by a computer device executing the method. Also, the method is realized by a computer device executing a program prepared in advance. The program described in each embodiment is recorded on a computer-readable recording medium such as an HDD, SSD, flexible disk, optical disk, flexible disk, magneto-optical disk, USB memory, etc. Then, the program is executed by reading it from the recording medium by the computer. Also, the program may be distributed via a communication network 31.

[0092] The functions of each of the components of the system in each embodiment described above may be realized by hardware, such as computer device 30 shown in Fig. 11. Alternatively, each component may be realized by a computer device or firmware based on program control.

[0093] Although the present disclosure has been described above with reference to each embodiment, the present disclosure is not limited to the above embodiment. The configuration and details of each disclosure may include embodiments to which various modifications that a person skilled in the art may understand within the scope of the present disclosure are applied. The present disclosure may include embodiments in which the matters described in this specification are appropriately combined or replaced as necessary. For example, matters described using a specific embodiment may also be applied to other embodiments to the extent that no contradiction occurs. For example, although multiple operations are described in order in the form of a flowchart, the order of description does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations can be changed to the extent that the content is not impaired.

[0094] A part or all of the above-described embodiments can be described as follows: However, a part or all of the above-described embodiments is not limited to the following.

[0095] (Appendix 1) A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; a determination means for determining whether or not a value of an area attribute related to the predetermined area is similar to a value of the area attribute related to the identified area; a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values ​​are similar; Equipped with When the regions are not similar, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified. system.

[0096] (Appendix 2) The determining means determines whether or not a similarity between a value of the region attribute related to the predetermined region and a value of the region attribute related to the identified region satisfies a predetermined condition; the complementing means complements a value of the position attribute related to the predetermined position based on a value of the position attribute related to each position included in the identified area when the similarity satisfies the predetermined condition; When the similarity does not satisfy the predetermined condition, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified. 2. The system of claim 1.

[0097] (Appendix 3) The region attributes are attributes associated with each region type. 3. The system of claim 1 or 2.

[0098] (Appendix 4) The location attribute is an attribute that represents the environment of each location. 4. The system of claim 1.

[0099] (Appendix 5) The identification means identifies an area that is close to the predetermined area from among areas of the same type that are included in the same building or facility as the predetermined area. 5. The system of claim 1.

[0100] (Appendix 6) the specifying means specifies an area close to the predetermined area from among areas of the same type that are included in the same floor of the same building as the predetermined area, and 5. The system of claim 1.

[0101] (Appendix 7) The identification means identifies an area close to the predetermined area from areas of the same type that are included in buildings within a predetermined range from the building including the predetermined area. 5. The system of claim 1.

[0102] (Appendix 8) A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; a determination means for determining whether or not a value of an area attribute related to the predetermined area is similar to a value of the area attribute related to the identified area; a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values ​​are similar; Equipped with When the regions are not similar, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified. Information processing device.

[0103] (Appendix 9) Identifying an area that is closer to the predetermined area than other areas among areas of the same type as the predetermined area; determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; If the values ​​are similar, the value of the position attribute related to the predetermined position included in the specified area is complemented based on the value of the position attribute related to each position included in the identified area. If the regions are not similar, a region of the same type that is next closest to the region whose distance from the predetermined region is specified is specified. method.

[0104] (Appendix 10) On the computer, A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and Run the command, In the process of identifying, if the regions are not similar, an area that is next closest to the region whose distance from the predetermined region is identified is identified from among the regions of the same type. program.

[0105] (Appendix 11) On the computer, A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and Run the command, In the process of identifying, if the regions are not similar, an area that is next closest to the region whose distance from the predetermined region is identified is identified from among the regions of the same type. A non-transitory recording medium readable by the computer, on which a program is recorded. [Explanation of symbols]

[0106] 1 System 2. System 20 Information processing device 21 Communication Networks 30 Computer Equipment 31 Communication Networks 101 Specific section 102 Judgment section 103 Complementary Division 201 Specific section 202 Judgment section 203 Complementary Department 204 Prediction Department 205 Storage section 301 Processor 302 ROM 303 RAM 304 Storage device 305 Communication Interface 306 Output Interface 307 Bus

Claims

1. A specifying means for specifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; a determination means for determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; a complementing means for complementing a value of a position attribute related to a predetermined position included in the specified area based on a value of a position attribute related to each position included in the specified area when the values ​​are similar; Equipped with When the regions are not similar, the specifying means specifies, among the regions of the same type, a region next closest to the region whose distance from the predetermined region is specified, The region attributes are attributes associated with each region type. system.

2. The determining means determines whether or not a similarity between a value of the region attribute related to the predetermined region and a value of the region attribute related to the identified region satisfies a predetermined condition; the complementing means complements a value of the position attribute related to the predetermined position based on a value of the position attribute related to each position included in the identified area when the similarity satisfies the predetermined condition; When the similarity does not satisfy the predetermined condition, the specifying means specifies, from among the regions of the same type, a region next closest to the region whose distance from the predetermined region has been specified. The system of claim 1 .

3. The location attribute is an attribute that represents the environment of each location.

3. A system according to claim 1 or 2.

4. The identification means identifies an area that is close to the predetermined area from among areas of the same type that are included in the same building or facility as the predetermined area. A system according to any one of claims 1 to 3.

5. the specifying means specifies an area close to the predetermined area from among areas of the same type that are included in the same floor of the same building as the predetermined area, and A system according to any one of claims 1 to 3.

6. The identification means identifies an area close to the predetermined area from areas of the same type that are included in buildings within a predetermined range from the building including the predetermined area. A system according to any one of claims 1 to 3.

7. Identifying an area that is closer to the predetermined area than other areas among areas of the same type as the predetermined area; determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; If the values ​​are similar, the value of the position attribute related to the predetermined position included in the specified region is complemented based on the value of the position attribute related to each position included in the identified region. If the regions are not similar, a region of the same type that is next closest to the region whose distance from the predetermined region is specified is specified; The region attributes are attributes associated with each region type. method.

8. On the computer, A process of identifying an area that is closer to a predetermined area than other areas among areas of the same type as a predetermined area; A process of determining whether a value of a region attribute related to the predetermined region is similar to a value of the region attribute related to the identified region; If the two regions are similar, a process of complementing the value of the position attribute related to a predetermined position included in the specified region based on the value of the position attribute related to each position included in the specified region; and Run the command, In the process of identifying, if the regions are not similar, an area of ​​the same type is identified that is next closest to the region whose distance from the predetermined region is identified; The region attributes are attributes associated with each region type. program.

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