Information processing apparatus and method for operating server apparatus

Abstract

To make it possible to obtain higher level information about an area based on GPS coordinates.SOLUTION: An information processing apparatus comprises: a storage unit for storing map image data corresponding to an area and including one or more symbolic representations; and a control unit for embedding spatial coordinates included in the area by machine learning using the map image data and generating a model that estimates first area information corresponding to first spatial coordinates.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing device and a method for operating a server device. [Background technology]

[0002] Models that embed GPS (Global Positioning System) coordinates using machine learning are known. For example, Non-Patent Document 1 discloses a technique for training an embedding model using additional semantic data sources such as geotags and geographical and statistical features at locations corresponding to GPS coordinates. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Joshi et al., “Inferring generic activities and events from image content and bags of geo-tags,” Association for Computing Machinery, July 7, 2008, p. 37-46. Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional GPS coordinate embedding models rely on additional semantic data and are therefore unable to capture higher-level information that indicates the complex relationships between different locations.

[0005] The present disclosure relates to an information processing device or the like that enables acquisition of higher level information of an area that includes a point based on GPS coordinates. [Means for solving the problem]

[0006] The information processing device of the present disclosure includes a memory unit that stores map image data that corresponds to a region and includes one or more symbolic expressions, and a control unit that uses the map image data to embed spatial coordinates contained in the region through machine learning and generate a model that estimates first region information corresponding to first spatial coordinates.

[0007] An operation method of a server device in the present disclosure includes a server device that stores map image data corresponding to a region and including one or more symbolic expressions, embedding spatial coordinates contained in the region using the map image data through machine learning, and generating a model that estimates first region information corresponding to first spatial coordinates. Effect of the Invention

[0008] According to the information processing device and the like of the present disclosure, it becomes possible to obtain higher level information about a region based on GPS coordinates. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 illustrates an example of a configuration of an information processing system. [Diagram 2] FIG. 11 is a flowchart illustrating an example of an operation procedure of the server device. [Diagram 3] FIG. 1 is a diagram for explaining machine learning by a server device. [Figure 4] FIG. 11 is a sequence diagram showing an example of an operation procedure of the information processing system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment will be described with reference to the drawings.

[0011] 1 is a diagram showing an example of the configuration of an information providing system according to an embodiment. The information providing system 1 includes one or more server devices 10 and one or more terminal devices 12, which are connected to each other via a network 11 so as to be able to communicate information with each other.

[0012] The server device 10 is, for example, a server computer that belongs to a cloud computing system or other computing system and functions as a server that implements various functions. The server device 10 may be composed of two or more server computers that are connected to each other so as to be able to communicate information and operate in cooperation with each other. The server device 10 corresponds to the "information processing device" in this embodiment, which executes information processing for providing various information related to a region, for example.

[0013] The terminal device 12 is an information processing device such as a personal computer, a smartphone, a tablet terminal, etc., that has a communication function and is configured to be able to execute various information processing. The terminal device 12 is used by a user to obtain various information related to a desired area from the server device 10.

[0014] The network 11 is, for example, the Internet, but may also include a mobile communication network, an ad-hoc network, a Local Area Network (LAN), a Metropolitan Area Network (MAN), or other networks, or any combination of these.

[0015] The server device 10 includes a storage unit 102 that stores map image data corresponding to a region and including one or more symbolic expressions, and a control unit 103 that embeds spatial coordinates included in the region by machine learning using the map image data to generate a model that estimates first region information corresponding to the first spatial coordinates. The map image data is image data in any format, and includes schematic expressions such as icons and symbols that represent points, facilities, etc. on a map, that is, image data including symbolic expressions. The region is an area having any shape and area, and can be arbitrarily defined based on geographical, economic, or policy factors. The spatial coordinates are coordinates identified by, for example, a GPS or other positioning system, and include, for example, latitude and longitude. The region information is a feature related to the region, and includes various high-level information such as representative land prices, land cover types, demographics, and water quality of the region. The server device 10 maps high-level region information corresponding to each spatial coordinate into a vector space by embedding using the map image data, and generates a model (hereinafter referred to as an estimation model) that can estimate region information based on the relevance, similarity, etc. between the spatial coordinates according to the spatial coordinates. Therefore, the server device 10 makes it possible to acquire higher level information about the area that includes any given spatial coordinate based on the spatial coordinate.

[0016] The configurations of the server device 10 and the terminal device 12 will now be described in detail.

[0017] The server device 10 includes a communication unit 101, a storage unit 102, and a control unit 103. When the server device 10 is configured with two or more server computers, these components are appropriately arranged in the two or more computers.

[0018] The communication unit 101 includes one or more communication interfaces. The communication interface is, for example, a LAN interface. The communication unit 101 receives information used in the operation of the server device 10, and transmits information obtained by the operation of the server device 10. The server device 10 is connected to a network 11 by the communication unit 101, and communicates information with a terminal device 12 via the network 11.

[0019] The storage unit 102 includes, for example, one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of them, functioning as a main storage device, an auxiliary storage device, or a cache memory. The semiconductor memory is, for example, a random access memory (RAM) or a read only memory (ROM). The RAM is, for example, a static RAM (SRAM) or a dynamic RAM (DRAM). The ROM is, for example, an electrically erasable programmable ROM (EEPROM). The storage unit 102 stores information used in the operation of the server device 10 and information obtained by the operation of the server device 10.

[0020] The control unit 103 includes one or more processors, one or more dedicated circuits, or a combination of these. The processor is, for example, a general-purpose processor such as a CPU (Central Processing Unit), or a dedicated processor such as a GPU (Graphics Processing Unit) specialized for a specific process. The dedicated circuit is, for example, an FPGA (Field-Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), etc. The control unit 103 executes information processing related to the operation of the server device 10 while controlling each unit of the server device 10.

[0021] The functions of the server device 10 are realized by a processor included in the control unit 103 executing a control program. The control program is a program for causing a computer to function as the server device 10. Also, some or all of the functions of the server device 10 may be realized by a dedicated circuit included in the control unit 103. Also, the control program may be stored in a non-transitory recording / storage medium readable by the server device 10, and read by the server device 10 from the medium.

[0022] The storage unit 102 stores map image data corresponding to an area and including one or more symbolic expressions. The map image data is in any format, such as JPG (Joint Photographic Experts Group) or PNG (Portable Network Graphics). Such map image data is provided from a server that provides map information, or is generated based on satellite images or the like. The storage unit 102 may also store various types of area information. For example, land price information, demographics, land cover type, water quality, and the like are acquired from a server that provides each piece of information and stored.

[0023] The terminal device 12 includes a communication unit 121 , a storage unit 122 , a control unit 123 , a positioning unit 124 , an input unit 125 , and an output unit 126 .

[0024] The communication unit 121 has a communication module compatible with wired or wireless LAN standards, a module compatible with mobile communication standards such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation), etc. The terminal device 13 is connected to the network 11 by the communication unit 121 via a nearby router device or a mobile communication base station, and communicates information with other devices via the network 11.

[0025] The memory unit 122 includes one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of these. The semiconductor memories are, for example, RAM or ROM. The RAM is, for example, SRAM or DRAM. The ROM is, for example, EEPROM. The memory unit 122 functions as, for example, a main memory device, an auxiliary memory device, or a cache memory. The memory unit 122 stores information used in the operation of the control unit 123 and information obtained by the operation of the control unit 123.

[0026] The control unit 123 has, for example, one or more general-purpose processors such as a CPU, an MPU (Micro Processing Unit), or one or more dedicated processors specialized for a specific process. Alternatively, the control unit 123 may have one or more dedicated circuits such as an FPGA, an ASIC, or the like. The control unit 123 generally controls the operation of the terminal device 13 by operating according to a control / processing program, or operating according to an operating procedure implemented as a circuit. The control unit 123 transmits and receives various information to and from the server device 10, etc. via the communication unit 121, and executes the operation according to this embodiment.

[0027] The positioning unit 124 includes one or more GNSS (Global Navigation Satellite System) receivers. The GNSS includes at least one of GPS, QZSS (Quasi-Zenith Satellite System), BeiDou, GLONASS (Global Navigation Satellite System), and Galileo, for example. The positioning unit 124 acquires location information of the terminal device 12 and transmits it to the control unit 123.

[0028] The input unit 125 includes one or more input interfaces. The input interfaces include, for example, physical keys, capacitive keys, a pointing device, a touch screen integrated with a display, a camera for capturing a captured image or an image code, or an IC card reader. The input interface may include a microphone for receiving voice input. The input unit 125 receives input of information used for the operation of the control unit 123 and sends the input information to the control unit 123.

[0029] The output unit 126 includes one or more output interfaces. The output interface is, for example, a display or a speaker. The display is, for example, an LCD or an organic EL display. The output unit 126 outputs information obtained by the operation of the control unit 123.

[0030] The functions of control unit 123 are realized by a processor included in control unit 123 executing a control program. The control program is a program for causing the processor to function as control unit 123. In addition, some or all of the functions of control unit 123 may be realized by a dedicated circuit included in control unit 123.

[0031] Fig. 2 is a flow chart showing a procedure by which the server device 10 generates an estimation model. The steps in Fig. 2 are executed by the control unit 103. The procedure in Fig. 2 is executed in response to, for example, an instruction from the terminal device 12. For example, when an operator uses the terminal device 12 to send an instruction to generate an estimation model to the server device 10, the control unit 103 of the server device 10 starts the procedure in Fig. 2 in response to the instruction.

[0032] In step S21, the control unit 103 acquires spatial coordinates. The control unit 103 reads a set of spatial coordinates stored in advance in the storage unit 102. The set of spatial coordinates is acquired and stored in the storage unit 102, for example, from another server that provides map information or by extracting the set of spatial coordinates from the provided map information.

[0033] In step S22, the control unit 103 acquires map image data. The control unit 103 reads out map image data stored in advance in the storage unit 102. The map image data is acquired, for example, from a server that provides map information, or by extracting it from the provided map information, and stored in the storage unit 102. One piece of map image data is, for example, image data that shows a map of an area having an arbitrary shape, such as a rectangle, and corresponding to an arbitrary area, and includes a symbolic expression of the area. The control unit 103 acquires an arbitrary number of pieces of map image data.

[0034] In step S23, the control unit 103 filters the map image data. The control unit 103 filters the map image data according to an arbitrary criterion. The arbitrary criterion is, for example, a criterion for the magnitude of entropy of the map image, and the control unit 103 removes map image data whose relevance to the set of spatial coordinates is lower than the criterion.

[0035] In step S24, the control unit 103 extracts spatial coordinates to be used for machine learning from the set of spatial coordinates. For example, the control unit 103 extracts spatial coordinates whose coordinate distance from spatial coordinates already included in the map image data is equal to or smaller than an arbitrary reference. If an estimation model has not already been generated, step S24 may be omitted.

[0036] In step S25, the control unit 103 executes machine learning using the filtered map image data, and embeds spatial coordinates.

[0037] FIG. 3 is a diagram for explaining the machine learning in step S25. The control unit 103 inputs map image data 31 corresponding to a set 30 of spatial coordinates to the teacher network 34 as teacher input data 32, and causes the teacher network 34 to generate teacher embedding data 36. The control unit 103 also inputs a subset of the teacher input data 32 to the learning network 35 as learning input data 33, and causes the learning network 35 to generate learning embedding data 37. The control unit 103 then causes the learning network 35 to perform self-supervised learning so that the loss 39 between the teacher embedding data 37 and the learning embedding data 27 is minimized. The spatial coordinates 30 used in the self-supervised learning are assigned characteristics of the corresponding area, such as land price, land cover type, demographics, and water quality, as area information 38. Thus, the learning embedding data 37 includes area information 300 corresponding to the spatial coordinates 30. In this way, an estimation model capable of estimating the area information 300 corresponding to any spatial coordinate 30 is generated.

[0038] In a variation of step S25, the control unit 103 causes the learning network 35 to generate a map image corresponding to the spatial coordinates 30 as reconstructed map image data 331, thereby generating the learning-side embedded data 37. Furthermore, the control unit 103 causes the teacher network 34 to cluster the teacher-side embedded data 36 into clusters corresponding to the spatial coordinates 30, and causes the learning network 35 to cluster the learning-side embedded data 37 into clusters corresponding to the spatial coordinates 30. Then, the control unit 103 causes the learning network 35 to perform self-supervised learning so that the loss 39 between the clusters of the teacher-side embedded data 37 and the clusters of the learning-side embedded data 27 is minimized.

[0039] Fig. 4 is a sequence diagram illustrating the cooperative operation between the server device 10 and the terminal device 12 in the embodiment of the information providing system 1. The server device 10 here is a server computer having the estimation model shown in Fig. 3, or a server computer that can communicate with a server computer having the estimation model and use the estimation model.

[0040] In step S40, the terminal device 12 sends information specifying the spatial coordinates and the type of regional information to the server device 10. The control unit 123 accepts an input operation by the input unit 125 in which the user specifies the desired spatial coordinates and the type of acquaintance information. For example, the control unit 123 displays a map image acquired from the server device 10 or the like by the output unit 126 and accepts an operation in which the user specifies a desired spot and an operation in which the user specifies the type of regional information. The type of regional information is one or more of the characteristics of the area corresponding to the specified spatial coordinates, such as land price, land cover type, demographics, water quality, etc. The control unit 123 sends the information specifying the spatial coordinates to the server device 10 by the communication unit 121. In the server device 10, the control unit 103 receives the information sent from the terminal device 12 by the communication unit 101.

[0041] In step S41, the server device 10 estimates regional information corresponding to the designated spatial coordinates and the type of regional information. The control unit 103 estimates regional information of the region corresponding to the spatial coordinates by using an estimation model.

[0042] In step S42, the server device 10 transmits the estimated area information to the terminal device 12. The control unit 103 transmits the estimated area information to the terminal device 12 via the communication unit 101. In the terminal device 12, the control unit 123 receives the information transmitted from the server device 10 via the communication unit 121.

[0043] In step S43, the terminal device 12 outputs the area information. The control unit 123 displays or outputs the area information to the user by voice via the output unit 126. This allows the user to obtain the desired area information.

[0044] The above-described procedure allows a user to obtain higher level information about the area that contains any given spatial coordinate based on that spatial coordinate.

[0045] According to the information providing system 1 of this embodiment, it is possible to support decision-making for urban development, construction, and real estate development by estimating regional information such as land prices, demographics, etc. Also, it is possible to support decision-making for agricultural monitoring, precision agriculture, water supply management during natural disasters, etc. by estimating regional information such as land cover type and water quality.

[0046] The server device 10 may also generate an estimation model by collecting location information, action logs, comments, and life logs provided by users of the terminal devices 12 via SNS (Social Network Service) or the like, and using these as area information to be added to the map image data. According to such an estimation model, for example, it is possible to estimate the cost performance of restaurants and the like in an area including a spatial coordinate specified by the user, such as a current location or a destination. It is also possible to estimate the flow of people at any spatial coordinate in a smart city or the like. Furthermore, this embodiment can be applied to a service that estimates advertising information suitable for an area including a user's current location and travel and tourism information suitable for an area including a user's destination, and provides the estimated information to the user.

[0047] In the above-described embodiment, the processing / control program that specifies the operation of the control unit 123 of the terminal device 12 may be stored in a memory unit of the server device 10 or another server device and downloaded to each terminal device 12 via the network 11, or may be stored in a recording / storage medium readable by each terminal device 12 and read from the medium by each terminal device 12.

[0048] In the above, the embodiment has been described based on the drawings and examples, but it should be noted that those skilled in the art can easily make various modifications and corrections based on the present disclosure. Therefore, it should be noted that these modifications and corrections are included in the scope of the present disclosure. For example, the functions included in each means, each step, etc. can be rearranged so as not to be logically inconsistent, and multiple means, steps, etc. can be combined into one or divided into one. [Explanation of symbols]

[0049] 1. Information Processing Systems 10. Server device 11 Network 12 Terminal Equipment 101, 121 Communications Department 102, 122 Storage section 103, 123 Control section 124 Positioning unit 125 Input section 126 Output section

Claims

1. a storage unit for storing map image data corresponding to a region and including one or more symbolic representations; a control unit that embeds spatial coordinates included in the area by machine learning using the map image data to generate a model that estimates first area information corresponding to first spatial coordinates; An information processing device having the above configuration.

2. In claim 1, The control unit, as the machine learning, causes a teacher network to generate teacher-side embedding data using teacher-side input data generated based on the map image data, causes a learning network to generate learning-side embedding data using a subset of the teacher-side input data as learning-side input data, and causes self-supervised learning based on the teacher-side embedding data and a loss of the learning-side embedding data to be performed, thereby generating a model for pre-estimation. Information processing device.

3. In claim 2, the control unit causes the learning-side network to generate the learning-side embedding data using features corresponding to the spatial coordinates as the area information, and to execute the self-supervised learning. Information processing device.

4. In claim 3, The features are at least one of land prices, land cover types, demographic trends, water quality indexes, and the spatial coordinates themselves, corresponding to the spatial coordinates; Information processing device.

5. In claim 2, The control unit causes the learning-side network to reconstruct map image data corresponding to the spatial coordinates based on the learning-side embedded data, and executes the self-supervised learning. Information processing device.

6. In claim 2, the control unit causes the teacher network to determine a first cluster corresponding to the map image data based on the teacher embedded data, and the learning network to determine a second cluster corresponding to the map image data based on the learning embedded data, and performs the self-supervised learning based on losses derived from the first and second clusters. Information processing device.

7. An information processing device capable of using the model according to claim 3, A communication unit that communicates with a terminal device; a control unit that estimates the features of the position designated by the user of the terminal device using the model based on spatial coordinates of the position designated by the user, and sends information indicating the features to the terminal device. Information processing device.

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