JP7308603B2 - economic indicator calculator - Google Patents

Description

The present invention relates to an economic indicator calculation device, an economic indicator calculation method, and an economic indicator calculation program for calculating numerical values relating to economic indicators.

2. Description of the Related Art Conventionally, computer systems that output economic indicators for evaluating economic conditions have been known. Patent Literature 1 listed below describes a technique for extracting a parking lot image from a remote sensing image and outputting an economic index based on the parking lot image.

JP 2016-170722 A

However, in the technique described in Patent Document 1, since economic indicators are output based on remote sensing images, there is a tendency for the subject of economic activity on which the economic indicators are based to be limited. Therefore, it is difficult to output an economic index for a subject of a desired economic activity as an index in line with the actual situation.

Accordingly, one aspect of the present invention has been made in view of such problems. The purpose is to provide a calculation method and an economic indicator calculation program.

In order to solve the above-described problems, an economic index calculation device according to one aspect of the present invention includes an acquisition unit that acquires position information regarding the positions of terminal devices of a plurality of users and attribute information regarding attributes of the plurality of users; A generating unit that generates qualification information for selecting a user to be calculated based on location information and attribute information, and selects a user to be calculated from among a plurality of users based on the qualification information of a plurality of users. A selection unit, a learning unit that builds a learning model for calculating numerical values relating to economic indicators using location information and attribute information corresponding to users to be calculated, and location information and attribute information that correspond to users to be calculated. to the learning model to calculate numerical values related to economic indicators.

Alternatively, an economic index calculation method according to another aspect of the present invention is an economic index calculation method executed by an economic index calculation device, and includes location information relating to the locations of terminal devices of a plurality of users and attributes of the plurality of users. an acquisition step of acquiring attribute information; a generating step of generating credential information for selecting users to be calculated based on the location information and the attribute information; A selection step of selecting users to be calculated from among them; a learning step of constructing a learning model for calculating numerical values related to economic indicators using location information and attribute information corresponding to the users to be calculated; a calculation step of applying the location information and attribute information corresponding to the user to the learning model to calculate numerical values related to economic indicators.

Alternatively, an economic index calculation program according to another aspect of the present invention comprises an acquisition unit that acquires location information relating to the locations of terminal devices of a plurality of users and attribute information relating to attributes of the plurality of users, location information and attributes, and A generation unit that generates qualification information for selecting a calculation target user based on information, a selection unit that selects a calculation target user from among a plurality of users based on the qualification information of a plurality of users, and a calculation target A learning unit that builds a learning model for calculating numerical values related to economic indicators using the location information and attribute information corresponding to the users of functions as a calculation unit that calculates numerical values related to economic indicators.

According to any one of the aspects described above, based on the location information about the terminal devices of the users and the attribute information about the users, the qualification information of the users is generated, and the users to be calculated based on the qualification information are generated. is selected, and based on the location information and attribute information corresponding to the user to be calculated, a numerical value related to the economic index is calculated using a learning model constructed in advance. As a result, it is possible to widely select users who are the subject of desired economic activities based on the qualification information. It becomes possible to calculate numerical values of indices. As a result, it is possible to output an economic index that is in line with the actual state of affairs for the subject of the desired economic activity.

ADVANTAGE OF THE INVENTION According to one aspect of the present invention, it is possible to output an economic index that is in line with the actual state of a subject of a desired economic activity.

1 is a block diagram showing the configuration of an economic index calculation device 1 according to a preferred embodiment of the present invention; FIG. 2 is a diagram showing an example of the data configuration of attribute information stored in an attribute information acquisition unit 101 of FIG. 1; FIG. 2 is a diagram showing an example of a data configuration of position information stored in a position information acquisition unit 102 of FIG. 1; FIG. FIG. 2 is a diagram showing an example of data structure of staying time counted by a qualification information generating unit 103 of FIG. 1; FIG. 2 is a diagram showing an example of the data configuration of position information processed by the qualification information generation unit 103 of FIG. 1; FIG. FIG. 2 is a diagram showing an example of a data configuration of qualification information generated by a qualification information generation unit 103 of FIG. 1; FIG. 2 is a diagram showing an example of a data configuration of qualification requirements generated by a qualification information generation unit 103 of FIG. 1; FIG. 2 is a diagram showing an example of a data structure of activity information generated by an activity information calculation unit 107 in FIG. 1; FIG. 2 is a diagram showing an example of numerical values of economic indicators calculated by a calculation unit 110 of FIG. 1; FIG. 4 is a flowchart showing an operation procedure in a learning phase for constructing a learning model by the economic index calculation device 1; 4 is a flowchart showing an operation procedure in an economic index calculation phase using a learning model by the economic index calculation device 1; It is a figure showing an example of hardware constitutions of economic index calculation device 1 concerning one embodiment of this indication. It is a figure which shows the structure of the economic index calculation program concerning one Embodiment of this invention.

Embodiments of the present invention will be described with reference to the accompanying drawings. Where possible, the same parts are denoted by the same reference numerals, and duplicate descriptions are omitted.

FIG. 1 is a block diagram showing the configuration of an economic index calculation device 1 according to a preferred embodiment of the present invention. This economic index calculation device 1 is a server device that calculates numerical values related to economic indices that indicate the economic situation of the world by using information about terminal devices including smartphones, tablet terminals, feature phones, etc. used by a plurality of users. .

As shown in FIG. 1, the economic index calculation device 1 includes an attribute information acquisition unit 101, a location information acquisition unit 102, a qualification information generation unit 103, a subject selection unit 104, an external information acquisition unit 105, an external information storage unit 106, It includes an activity information calculation unit 107 , a training data storage unit 108 , a learning unit 109 and a calculation unit 110 . Each component of the economic index calculation device 1 will be described below.

The attribute information acquisition unit 101 acquires and stores attribute information regarding attributes of multiple users of the terminal device. This attribute information is acquired via a network from a predetermined server device or the like having a database for managing contract information relating to terminal devices, and is stored in advance. Also, the attribute information may be partially or wholly obtained from a terminal device or the like as answer information to a questionnaire sent from the economic index calculation device 1 and stored in advance. At this time, the attribute information may be actively acquired and stored by the economic index calculation device 1, or passively acquired in response to an instruction from the operator of the economic index calculation device 1 or another device. may be stored.

FIG. 2 shows an example of the data configuration of attribute information stored in the attribute information acquisition unit 101. As shown in FIG. As shown in the figure, each user's attribute information includes a terminal identifier (for example, "1"), which is user identification information for identifying the user of the terminal device, and information indicating the user's age (for example, " 25”), information indicating the gender of the user (for example, “male”), information indicating the family structure of the user (for example, “single”), and information indicating the type of occupation of the user (for example, , “A”) are associated with each other.

Returning to FIG. 1, the location information acquisition unit 102 acquires and stores location information regarding the locations of terminal devices of multiple users. This location information is information acquired by any positioning means such as GPS (Global Positioning System) positioning, positioning using beacons, etc. in the terminal device, or positioning using information of the base station to which the terminal device is connected for communication. Information obtained by The location information is acquired via a network from a terminal device or a predetermined server device having a database for managing location information and is stored in advance. At this time, the position information may be actively acquired and stored by the economic index calculation device 1, or passively acquired in response to an instruction from the operator of the economic index calculation device 1 or another device. may be stored.

FIG. 3 shows an example of the data structure of the position information stored in the position information acquisition unit 102. As shown in FIG. As shown in the figure, the location information of each user includes a terminal identifier (for example, "1") specifying the user of the terminal device and information indicating time (for example, "2018/07/12 8:00 ”) is associated with position information indicating the position of the user at that time (for example, “longitude 135.0”, “latitude 35.1”). The position information may be converted into area information in units of meshes, area information in units of administrative divisions, or the like.

Returning to FIG. 1 again, the qualification information generation unit 103 selects a target for calculating the value of the economic index based on the attribute information stored in the attribute information acquisition unit 101 and the location information stored in the location information acquisition unit 102. Generate credentials for electing users of That is, the qualification information generation unit 103 generates, as qualification information, the type of industry related to the user's occupation, the user's place of stay, the user's work situation, the means of transportation used by the user, and the like.

For example, when generating the type of business of the user, the qualification information generation unit 103 may generate the type of business based on the attribute information acquired and stored as the answer information to the questionnaire, or the location of each user. It may be generated by estimating from information. When estimating from location information, the qualification information generation unit 103 first aggregates the length of stay for each user, each place, and each date based on the location information stored in the location information acquisition unit 102 . FIG. 4 shows an example of stay time counted by the qualification information generation unit 103 . In this way, a plurality of locations (eg, "place A", "place B", " The length of stay (for example, “12 hours”, “1 hour”, “9 hours”, “1 hour”) for each location C”, “location D”) is aggregated. When summarizing, locations within a predetermined error range indicated by the position information are determined to be the same location. Then, the qualification information generation unit 103 uses a predetermined criterion (for example, the length of the length of stay within a predetermined range, the place of stay with the longest length of stay, etc.) based on the aggregated length of stay. , the work locations of each of the plurality of users are estimated, and the business types of each of the multiple users are estimated based on the work locations.

When generating the place of stay of the user, the qualification information generation unit 103 similarly aggregates the length of stay for each user, each place, and each date, estimates the place of stay for each user based on the tallied result, A frequently visited place or a place of stay for a long time is generated as qualification information for each user. Further, the qualification information generation unit 103 may generate the place of stay based on attribute information acquired and stored as answer information to a questionnaire.

When generating the user's actual working conditions, the qualification information generation unit 103 similarly aggregates the length of stay for each user, for each location, and for each date, estimates the location of stay for each user based on the aggregation results, Based on the estimation result, the residence and work place of each user are estimated. Then, the qualification information generation unit 103 generates qualification information by estimating the actual working conditions based on the status of stay at the place of residence and the place of work (time zone of stay, length of stay, etc.). The estimated working conditions include information indicating the degree of work frequency of the user, such as "full-time" and "part-time," information indicating the type of work hours, such as "day shift," "night shift," and "shift work." Information that indicates the type of work that mainly involves moving around, such as "truck driver"; information that indicates the type of work that mainly involves working within or outside the company, such as "inside work" or "outside work"; Examples include information representing a work style in which a worker is mainly stationed at the destination company. In addition, the qualification information generation unit 103 may generate the actual working conditions based on attribute information acquired and stored as answer information to a questionnaire.

When generating means of transportation for a user, position information is processed for each user, and as a result, the status of stay such as the time of stay at multiple places of stay is estimated, and the travel time between multiple places of stay is calculated. By doing so, the means of transportation used by the user is estimated. FIG. 5 shows an example of location information processed by the qualification information generation unit 103. As shown in FIG. In this way, in correspondence with the terminal identifier (eg, "1") and the time (eg, "2018/7/12 20:00"), information indicating the location where the user stayed at that time (eg, “Tokyo”) is generated. Then, the qualification information generation unit 103 estimates the travel time by calculating the time difference of the stay times between the places where each user stayed, and based on the estimated travel time and the distance between the places of stay, each user Estimates the means of transportation used by (for example, “Shinkansen”) and adds it to the processed position information. Further, the qualification information generation unit 103 may generate the means of transportation based on the attribute information acquired and stored as the answer information to the questionnaire.

FIG. 6 shows an example of the data configuration of the credential information generated for each user by the credential information generation unit 103. As shown in FIG. In this way, the qualification information generation unit 103 associates the terminal identifier (eg, “1”) with information indicating the industry (eg, “A”), information indicating the work location (eg, “X”), Then, information (for example, "α") indicating the actual working conditions is generated as the qualification information. Here, the qualification information generation unit 103 refers to the location information to calculate the travel distance of each user during the day, and also uses the travel distance to estimate the type of business. For example, in the case of a user whose travel distance is relatively long and whose work location cannot be estimated, the business type can be estimated to be “transportation”, and if the travel distance is relatively short, the business type can be assumed to be “sales business” based on the work location. ” can be estimated.

Further, the qualification information generation unit 103 sets a plurality of qualification requirements for selecting users for which economic indices are to be calculated, based on the qualification information generated for each user. FIG. 7 shows the data configuration of the qualification requirements generated by the qualification information generation unit 103. As shown in FIG. In this way, the qualification information generation unit 103 sets a flag indicating necessary requirements (for example, a flag "1" corresponding to "qualification A" and a flag corresponding to "qualification B") as qualification requirements (eg, "requirement 1"). and a flag "1" corresponding to "qualification C"). In this case, "requirement A" and "requirement B" corresponding to the flag "1" are requirements for user selection. These “qualifications A” and “qualifications B” are set based on the qualification information generated in advance, and are set as requirements such as “working status is full-time”, “industry is sales”, etc. be.

Returning to FIG. 1, the subject selection unit 104 selects a user whose economic index is to be calculated from among a plurality of terminal device users based on the qualification requirements generated by the qualification information generation unit 103 . That is, the subject selection unit 104 selects one or more qualification requirements from among the plurality of qualification requirements set by the qualification information generation unit 103 based on the input of the operator or the like of the economic index calculation device 1, and selects one or more of the qualification requirements. Based on the credential information for each user generated by the credential information generation unit 103, the terminal identifier corresponding to the user who satisfies is specified. The target person selection unit 104 passes the specified terminal identifier to the external information acquisition unit 105 and the activity information calculation unit 107 .

Note that the subject selection unit 104 may select a user having qualification information similar to the qualification information corresponding to the user selected in the past as a user whose economic index is to be calculated. For example, a user of an industry similar to that of a user selected in the past may be selected, a user whose place of stay is close to the place of stay of a user selected in the past may be selected, or a user selected in the past may be selected. A user with a work situation similar to the current work situation may be selected, or a user with a means of transportation similar to the means of transportation of a user selected in the past may be selected. At this time, the degree of similarity between users may be calculated based on the degree of similarity of multiple types of qualification information, and users may be selected based on the degree of similarity. In addition, the target person selection unit 104 groups users based on a predetermined condition, aggregates position information and attribute information for each grouped user, and uses the qualification information generated based on the aggregation result to may be selected in groups.

The external information acquisition unit 105 acquires external information corresponding to the user selected by the target person selection unit 104 from the outside via the network. Specifically, the external information acquisition unit 105 acquires external information including information on the external situation around the user's stay position or information on the user's attributes from the user's terminal device or a server device on the Internet. do. For example, the external information includes social network information about the user (life stage information, friendship relationships, etc.), usage status information of the terminal device by the user (access location, usage status, usage purpose, usage frequency, usage time, access behavior, etc.). type, e-commerce payment information, etc.), information around the location indicated by the user's location information (surrounding temperature or weather, news information about the surrounding area, etc.), nearby competitors searched based on the user's location information and industry. and information related to an arbitrary area around the position indicated by the user's position information (average annual income of all users, public statistical information such as age structure of all users), and the like. The external information acquisition unit 105 stores the acquired external information for each user in the external information storage unit 106 .

Here, the external information acquisition unit 105 may generate information about the external situation corresponding to the user by estimating it based on position information or attribute information. For example, when generating information on the number of customers at a store where the user works as external information on the external situation around the user's place of stay, the number of terminal devices located within a predetermined area centering on the store is calculated. Aggregate in chronological order using location information. Then, the amount of increase or decrease is calculated by subtracting a reference value (such as the average value of the number of terminal devices in the past year) from the aggregated value, and an evaluation value obtained by evaluating this amount of increase or decrease in stages is generated as external information.

The activity information calculation unit 107 generates activity information related to activities such as labor of the user selected by the target person selection unit 104 based on the position information, attribute information, and external information corresponding to the user. That is, the activity information calculation unit 107 generates activity information for each user based on the payment information corresponding to the user, and the work situation and movement range obtained by processing the position information. More specifically, the activity information calculation unit 107 uses user attribute information or social network information to generate information about the user's life stage. Further, the activity information calculation unit 107 determines whether or not the length of stay at the location estimated as the place of residence has exceeded a predetermined time based on the location of stay estimated by processing the location information of the user in the qualification information generation unit 103. By determining , information on working conditions indicating whether or not there is work is generated. FIG. 8 shows an example of the data structure of activity information generated by the activity information calculation unit 107. As shown in FIG. In this way, the activity information calculation unit 107 associates the terminal identifier (eg, “1”) that identifies the user with life stage information (eg, “student”) and information on work status (eg, “none”). ”) is generated.

At this time, the activity information calculation unit 107 may generate the activity information as information indicating a change or rate of change from an arbitrary point in time such as the previous day or the previous month, or may generate a change or rate of change from an average value in the past. You may generate it as information to show. Further, the activity information calculation unit 107 may generate information about the user's hobbies and preferences as activity information based on external information, the estimated place of stay, and the like. Further, the activity information calculation unit 107 may generate activity information based on attribute information acquired and stored as answer information to a questionnaire.

Returning to FIG. 1 again, the learning unit 109 creates a learning model for calculating numerical values related to economic indicators based on location information, attribute information, external information, and activity information corresponding to users whose economic indicators are to be calculated. To construct. This learning model is a supervised machine for calculating numerical values of economic indicators to be calculated as objective variables from explanatory variables based on location information, attribute information, external information, and activity information corresponding to the user to be calculated. It is a learning model for learning, and non-linear learning models such as linear models using linear functions and random forests are used. The learning unit 109 builds a learning model by machine learning using training data acquired from information on economic indicators published by institutions such as the government and stored in the training data storage unit 108 and explanatory variables. As explanatory variables applied to the learning model, public information such as various economic indicators obtained from the outside, stock price information of each company, financial statement information of each company, and job information of each company can be used together. good. The training data used to construct the learning model may be obtained in advance via a network based on information published on the Internet, or may be input in advance by the operator of the economic index calculation device 1. good.

The calculation unit 110 applies explanatory variables based on location information, attribute information, external information, and activity information corresponding to the user whose economic index is to be calculated, to the learning model constructed by the learning unit 109. Calculate numerical values for economic indicators. FIG. 9 shows an example of the data configuration of the numerical values of the economic indicators calculated by the calculation unit 110. As shown in FIG. In this way, the numerical value of the economic index calculated at an arbitrary time interval (e.g., the numerical value of economic index A, “25”, etc.) is converted to the information on the calculation time (e.g., “2018/9/12 10:00”). is generated in association with The generated numerical value of the economic index may be stored in an internal data storage unit such as a memory, or may be actively or passively transmitted to an external terminal device, server device, or the like.

In addition, in order to improve the calculation accuracy, the learning unit 109 and the calculation unit 110 estimate the information of the user's judgment state or psychological state based on the external information and activity information corresponding to the user, may be used as an explanatory variable. That is, based on the information about the number of customers at the store where the user works, which is obtained by the external information obtaining unit 105, information about the psychological state related to events that the user perceives, such as whether the economy is good or bad, is generated as a feature amount. At this time, the learning unit 109 and the calculation unit 110 construct a learning model using quantitative data such as customer traffic as an explanatory variable and a numerical value of the economy as an objective variable, and then use the learning model. Generating psychological state information. This learning model is constructed, for example, by using numerical values of economic conditions included in response information obtained from users with similar attributes in the past through questionnaires. Based on the information about the external situation acquired by the external information acquisition unit 105, information of the judgment state caused by the external situation (for example, "Refrain from going out because it is raining", "Because of a terrorist incident") It is also possible to generate "Refrain from traveling abroad" etc.). In this case as well, the learning unit 109 and the calculation unit 110 use the quantified information about the external situation as an explanatory variable and the quantified judgment state as an objective variable to construct a learning model, and then use the learning model. to generate decision state information.

Examples of economic index values calculated by the economic index calculation device 1 are shown below.

A first example is the unemployment rate. In this example, the place of work and the place of residence are estimated based on the place of stay estimated by processing the position information corresponding to the user to be calculated. Then, based on the processed positional information, the unemployed person is selected as a user for which the numerical value of the economic index is to be calculated by detecting the stay state at the estimated place of work and place of residence. Furthermore, using the number of selected unemployed users and the number of employed users who are staying at the user's place of work as explanatory variables, the unemployment rate, which is a numerical value of the economic index, is calculated. .

A second example is the figures of economic indicators corresponding to the economic watchers survey (current situation DI) released by the Cabinet Office. In this example, based on the type of industry for each user generated by the qualification information generation unit 103, users whose economic index values are to be calculated are selected. Then, based on the activity information corresponding to the selected user and the information on the external situation such as the number of customers, a learning model is used to generate the user's state of mind regarding business sentiment. This learning model is built in advance using, as training data, numerical values relating to business sentiment included in response information obtained from users with similar attributes in past questionnaires. Furthermore, by applying the generated state of mind to the learning model, the present condition judgment DI, which is a numerical value of an economic index, is calculated. This learning model is constructed in advance by using, as training data, numerical values of the current situation judgment DI published by the Cabinet Office in the past.

Next, the operation of the economic index calculation device 1 configured as described above will be described, and the economic index calculation method according to the present embodiment will be described in detail. FIG. 10 is a flowchart showing the operation procedure in the learning phase for constructing the learning model by the economic index calculation device 1. FIG. 11 shows the operation procedure in the economic index calculation phase using the learning model by the economic index calculation device 1. It is a flow chart showing.

The learning phase by the economic index calculation device 1 shown in FIG. 10 is executed at an arbitrary timing (regular or fixed timing) before the calculation phase of FIG. 11 is started. When this learning phase is started, first, attribute information and location information of each user are acquired from the attribute information acquisition unit 101 and the location information acquisition unit 102 by the qualification information generation unit 103 (step S1). Next, after the qualification information generation unit 103 generates qualification information corresponding to each user based on the acquired attribute information and location information, qualification requirements for selecting users for whom economic indices are to be calculated are set. (step S2).

After that, the target person selection unit 104 selects a user whose economic index is to be calculated from among the plurality of users using the qualification requirements (step S3). Then, after the external information acquisition unit 105 acquires the external information corresponding to the selected user (step S4), the activity information calculation unit 107 generates activity information corresponding to the selected user (step S5).

Furthermore, training data is acquired from external public information by the learning unit 109, and the training data is stored in the training data storage unit 108 (step S6). Finally, the learning unit 109 uses the explanatory variables based on the location information, attribute information, external information, and activity information corresponding to the user whose economic index is to be calculated, and the training data stored in the training data storage unit 108. is used to construct a learning model for calculating numerical values of economic indicators (step S7).

The calculation phase by the economic index calculation device 1 shown in FIG. 11 is executed at an arbitrary timing (regular or fixed timing) after the learning phase of FIG. 10 is executed. When this learning phase is started, first, attribute information and location information of each user are acquired from the attribute information acquisition unit 101 and the location information acquisition unit 102 by the qualification information generation unit 103 (step S101). Next, after the qualification information generation unit 103 generates qualification information corresponding to each user based on the acquired attribute information and location information, qualification requirements for selecting users for whom economic indices are to be calculated are set. (step S102).

After that, the target person selection unit 104 selects a user whose economic index is to be calculated from among the plurality of users using the qualification requirements (step S103). After the external information acquisition unit 105 acquires the external information corresponding to the selected user (step S104), the activity information calculation unit 107 generates activity information corresponding to the selected user (step S104). S105).

Furthermore, the calculation unit 110 applies the explanatory variables based on the location information, attribute information, external information, and activity information corresponding to the user whose economic index is to be calculated to the learning model constructed in the learning phase. The numerical value of the economic index is calculated by (step S106). Finally, the numerical value of the economic index calculated by the calculator 110 is output (step S107).

Next, the effect of the economic index calculation device 1 of this embodiment will be described. In this economic index calculation device 1, the qualification information of a plurality of users is generated based on the position information regarding the terminal devices of the plurality of users and the attribute information regarding the plurality of users. Based on the location information and attribute information corresponding to the selected user to be calculated, a numerical value related to the economic index is calculated using a learning model constructed in advance. As a result, it is possible to widely select users who are the subject of desired economic activities based on the qualification information. It becomes possible to calculate numerical values of indices. As a result, it is possible to output an economic index that is in line with the actual state of affairs for the subject of the desired economic activity. Furthermore, since the economic index is calculated using the information linked to the terminal device, it is possible to obtain the economic index in real time, and the promptness can be improved.

Further, in the above-described embodiment, the qualification information generation unit 103 estimates the place of stay of the user based on the position information, and generates information about the user's work as qualification information based on the place of stay. In this way, by using the work-related qualification information generated based on the location of the user's stay, it is possible to calculate the economic index for the subject of the desired work activity.

In addition, the qualification information generation unit 103 generates information about the user's place of work and residence as qualification information based on the place of stay, and the target person selection unit 104 calculates unemployed people based on the information about the place of work and residence. , and the calculation unit 110 calculates the unemployment rate as a numerical value related to the economic index. With such a configuration, it is possible to select unemployed users based on the information on the user's place of work and place of residence, and by using the number of such users, it is possible to calculate the unemployment rate as an economic indicator that is in line with the actual situation. can.

Further, the qualification information generation unit 103 generates information about the user's industry as qualification information based on the place of stay, the target person selection unit 104 selects the user to be calculated based on the information about the industry, and the calculation unit 110 calculates Based on the information acquired in correspondence with the target user's position information, a numerical value related to economic conditions is calculated as a numerical value related to the economic index. With such a configuration, it is possible to select a user based on the qualification information related to the industry generated based on the user's place of stay. It is possible to calculate numerical values related to the economy that are in line with the actual working conditions in Japan as economic indicators.

Further, the qualification information generation unit 103 estimates the place of stay and the time of stay of the user based on the position information, and generates information on the means of transportation used by the user as qualification information based on the place of stay and the time of stay. With such a configuration, by using the qualification information related to the means of transportation generated based on the place of stay and the time of stay of the user, it is possible to calculate the economic index for the subject of economic activity who uses the desired means of transportation. can.

In addition, the target person selection unit 104 selects users corresponding to qualification information similar to the qualification information related to previously selected users as calculation target users. With such a configuration, by selecting users whose credentials are similar to those of users selected in the past, it is possible to efficiently calculate economic indicators that are in line with the actual situation, targeting a wide range of subjects of desired economic activities. be able to.

It should be noted that the block diagrams used in the description of the above embodiments show blocks in units of functions. These functional blocks (components) are implemented by any combination of at least one of hardware and software. Also, the method of realizing each functional block is not particularly limited. That is, each functional block may be implemented using one device physically or logically coupled, or directly or indirectly using two or more physically or logically separated devices (e.g. , wired, wireless, etc.) and may be implemented using these multiple devices. A functional block may be implemented by combining software in the one device or the plurality of devices.

Functions include judging, determining, determining, calculating, calculating, processing, deriving, examining, searching, checking, receiving, transmitting, outputting, accessing, resolving, selecting, choosing, establishing, comparing, assuming, expecting, assuming, Broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc. can't For example, a functional block (component) that makes transmission work is called a transmitting unit or a transmitter. In either case, as described above, the implementation method is not particularly limited.

For example, the economic indicator calculation device 1 according to the embodiment of the present disclosure may function as a computer that performs the processing of the economic indicator calculation method of the present disclosure. FIG. 12 is a diagram showing an example of the hardware configuration of the economic index calculation device 1 according to one embodiment of the present disclosure. The economic index calculation device 1 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

Note that in the following description, the term "apparatus" can be read as a circuit, device, unit, or the like. The hardware configuration of the economic index calculation device 1 may be configured to include one or more of each device shown in the figure, or may be configured without including some of the devices.

Each function of the economic index calculation device 1 is performed by the processor 1001 performing calculations, controlling communication by the communication device 1004, It is realized by controlling at least one of data reading and writing in the memory 1002 and the storage 1003 .

The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, registers, and the like. For example, the qualification information generation unit 103 , the subject selection unit 104 , the external information acquisition unit 105 , the activity information calculation unit 107 , the learning unit 109 , the calculation unit 110 and the like may be realized by the processor 1001 .

The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 to the memory 1002, and executes various processes according to these. As the program, a program that causes a computer to execute at least part of the operations described in the above embodiments is used. For example, the qualification information generation unit 103 of the economic index calculation device 1 may be implemented by a control program stored in the memory 1002 and operating on the processor 1001, and other functional blocks may be similarly implemented. Although it has been explained that the above-described various processes are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. FIG. Processor 1001 may be implemented by one or more chips. Note that the program may be transmitted from a network via an electric communication line.

The memory 1002 is a computer-readable recording medium, and is composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and RAM (Random Access Memory). may be The memory 1002 may also be called a register, cache, main memory (main storage device), or the like. The memory 1002 can store executable programs (program code), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

The storage 1003 is a computer-readable recording medium, for example, an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disc, a magneto-optical disc (for example, a compact disc, a digital versatile disc, a Blu-ray disk), smart card, flash memory (eg, card, stick, key drive), floppy disk, magnetic strip, and/or the like. Storage 1003 may also be called an auxiliary storage device. The storage medium described above may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003 . For example, the attribute information acquisition unit 101, the position information acquisition unit 102, the external information storage unit 106, the training data storage unit 108, etc. may be realized by the storage 1003. FIG.

The communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called a network device, a network controller, a network card, a communication module, or the like. The communication device 1004 includes a high-frequency switch, a duplexer, a filter, a frequency synthesizer, and the like, for example, in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). may consist of For example, the external information acquisition unit 105 and the calculation unit 110 described above may be implemented by the communication device 1004 .

The input device 1005 is an input device (for example, keyboard, mouse, microphone, switch, button, sensor, etc.) that receives input from the outside. The output device 1006 is an output device (eg, display, speaker, LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated (for example, a touch panel).

Devices such as the processor 1001 and the memory 1002 are connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between devices.

The economic index calculation device 1 includes hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). part or all of each functional block may be implemented by the hardware. For example, processor 1001 may be implemented using at least one of these pieces of hardware.

FIG. 13 shows the configuration of an economic index calculation program for causing the computer 20 to function as the economic index calculation device 1. As shown in FIG.

The economic index calculation program PR1 includes a main module PR10, an attribute information acquisition module PR11, a location information acquisition module PR12, a qualification information generation module PR13, a candidate selection module PR14, an external information acquisition module PR15, an external information storage module PR16, and an activity information calculation. It comprises a module PR17, a training data storage module PR18, a learning module PR19, and a calculation module PR20.

The main module PR10 is a part that comprehensively controls the economic index calculation process. Attribute information acquisition module PR11, location information acquisition module PR12, qualification information generation module PR13, subject selection module PR14, external information acquisition module PR15, external information storage module PR16, activity information calculation module PR17, training data storage module PR18, learning module Functions realized by executing PR19 and calculation module PR20 are attribute information acquisition unit 101, location information acquisition unit 102, qualification information generation unit 103, subject selection unit 104, external The functions are the same as those of the information acquisition unit 105 , the external information storage unit 106 , the activity information calculation unit 107 , the training data storage unit 108 , the learning unit 109 , and the calculation unit 110 .

The economic index calculation program PR1 is provided by a recording medium such as a CD-ROM, DVD, or ROM, or a semiconductor memory, for example. The economic index calculation program PR1 may also be provided via a network as a computer data signal superimposed on a carrier wave.

Although the present disclosure has been described in detail above, it should be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described in this disclosure. The present disclosure can be practiced with modifications and variations without departing from the spirit and scope of the present disclosure as defined by the claims. Accordingly, the description of the present disclosure is for illustrative purposes and is not meant to be limiting in any way.

For example, the economic indicator calculation device 1 of the above-described embodiment is not limited to calculating numerical values related to economic indicators using a learning model constructed by machine learning. A learning model based on a simple calculation rule using, for example, may be used.

Notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods. For example, notification of information includes physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), higher layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, It may be implemented by broadcast information (MIB (Master Information Block), SIB (System Information Block)), other signals, or a combination thereof. RRC signaling may also be called an RRC message, and may be, for example, an RRC connection setup message, an RRC connection reconfiguration message, or the like.

Each aspect/embodiment described in the present disclosure includes LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark) )), IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, other suitable systems, and extended It may be applied to at least one of the next generation systems. Also, a plurality of systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A and 5G, etc.).

The processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be rearranged as long as there is no contradiction. For example, the methods described in this disclosure present elements of the various steps using a sample order, and are not limited to the specific order presented.

Certain operations that are described in this disclosure as being performed by a base station may also be performed by its upper node in some cases. In a network consisting of one or more network nodes with a base station, various operations performed for communication with a terminal may be performed by the base station and other network nodes other than the base station (e.g. MME or S-GW, etc. (including but not limited to). Although the case where there is one network node other than the base station is exemplified above, it may be a combination of a plurality of other network nodes (for example, MME and S-GW).

Information and the like (*see the item “information, signal”) can be output from the upper layer (or lower layer) to the lower layer (or higher layer). It may be input and output via multiple network nodes.

Input/output information and the like may be stored in a specific location (for example, memory), or may be managed using a management table. Input/output information and the like can be overwritten, updated, or appended. The output information and the like may be deleted. The entered information and the like may be transmitted to another device.

The determination may be made by a value represented by one bit (0 or 1), by a true/false value (Boolean: true or false), or by numerical comparison (for example, a predetermined value).

Each aspect/embodiment described in the present disclosure may be used alone, may be used in combination, or may be used by switching according to execution. In addition, the notification of predetermined information (for example, notification of “being X”) is not limited to being performed explicitly, but may be performed implicitly (for example, not notifying the predetermined information). good too.

Software, whether referred to as software, firmware, middleware, microcode, hardware description language or otherwise, includes instructions, instruction sets, code, code segments, program code, programs, subprograms, and software modules. , applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, and the like.

Software, instructions, information, etc. may also be sent and received over a transmission medium. For example, the software uses wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and/or wireless technology (infrared, microwave, etc.) to create websites, Wired and/or wireless technologies are included within the definition of transmission medium when sent from a server or other remote source.

Information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. may be represented by a combination of

The terms explained in this disclosure and the terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, the channel and/or symbols may be signaling. A signal may also be a message. A component carrier (CC) may also be called a carrier frequency, a cell, a frequency carrier, or the like.

As used in this disclosure, the terms "system" and "network" are used interchangeably.

In addition, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values from a predetermined value, or may be expressed using other corresponding information. may be represented. For example, radio resources may be indexed.

The names used for the parameters described above are not limiting names in any way. Further, the formulas, etc., using these parameters may differ from those expressly disclosed in this disclosure. Since the various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable names, the various names assigned to these various channels and information elements are in no way restrictive names. isn't it.

In the present disclosure, "base station (BS)", "radio base station", "fixed station", "NodeB", "eNodeB (eNB)", "gNodeB (gNB)", " "access point", "transmission point", "reception point", "transmission/reception point", "cell", "sector", "cell group", " Terms such as "carrier", "component carrier" may be used interchangeably. A base station may also be referred to by terms such as macrocell, small cell, femtocell, picocell, and the like.

A base station may serve one or more (eg, three) cells. When a base station serves multiple cells, the overall coverage area of the base station can be partitioned into multiple smaller areas, each smaller area being a base station subsystem (e.g., an indoor small base station (RRH: The term "cell" or "sector" refers to part or all of the coverage area of a base station and/or base station subsystem serving communication in this coverage. point to

In this disclosure, terms such as “Mobile Station (MS),” “user terminal,” “User Equipment (UE),” “terminal,” etc. may be used interchangeably. .

A mobile station is defined by those skilled in the art as subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless It may also be called a terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.

At least one of a base station and a mobile station may be called a transmitter, a receiver, a communication device, and the like. At least one of the base station and the mobile station may be a device mounted on a mobile object, the mobile object itself, or the like. The mobile object may be a vehicle (e.g., car, airplane, etc.), an unmanned mobile object (e.g., drone, self-driving car, etc.), or a robot (manned or unmanned ). Note that at least one of the base station and the mobile station includes devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an IoT (Internet of Things) device such as a sensor.

Also, the base station in the present disclosure may be read as a user terminal. For example, communication between a base station and a user terminal is replaced with communication between multiple user terminals (for example, D2D (Device-to-Device), V2X (Vehicle-to-Everything), etc.) Regarding the configuration, each aspect/embodiment of the present disclosure may be applied. In this case, the user terminal may have the functions that the above base station has. Also, words such as "up" and "down" may be replaced with words corresponding to inter-terminal communication (for example, "side"). For example, uplink channels, downlink channels, etc. may be read as side channels.

Similarly, a terminal device in the present disclosure may be read as a base station. In this case, the base station may have the functions of the terminal device described above.

As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Judgement", "determining" are, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (eg, lookup in a table, database, or other data structure); Also, "judgment" and "determination" are used for receiving (e.g., receiving information), transmitting (e.g., transmitting information), input, output, access (accessing) (for example, accessing data in memory) may include deeming that a "judgment" or "decision" has been made. In addition, "judgment" and "decision" are considered to be "judgment" and "decision" by resolving, selecting, choosing, establishing, comparing, etc. can contain. In other words, "judgment" and "decision" may include considering that some action is "judgment" and "decision". Also, "judgment (decision)" may be read as "assuming", "expecting", "considering", or the like.

The terms "connected", "coupled", or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, It can include the presence of one or more intermediate elements between two elements being "connected" or "coupled." Couplings or connections between elements may be physical, logical, or a combination thereof. For example, "connection" may be read as "access". As used in this disclosure, two elements are defined using at least one of one or more wires, cables, and printed electrical connections and, as some non-limiting and non-exhaustive examples, in the radio frequency domain. , electromagnetic energy having wavelengths in the microwave and optical (both visible and invisible) regions, and the like.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly specified otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

“Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms, like the term “comprising,” are inclusive. It is intended that there be Furthermore, the term "or" as used in this disclosure is not intended to be an exclusive OR.

In this specification, plural devices are also included unless the context or technicality clearly dictates that there is only one.

Throughout this disclosure, the plural shall be included unless the context clearly indicates the singular.

Reference Signs List 1 Economic index calculation device 101 Attribute information acquisition unit 102 Location information acquisition unit 103 Qualification information generation unit 104 Subject selection unit 105 External information acquisition unit 106 External information storage unit 107 ... activity information calculation unit, 108 ... training data storage unit, 109 ... learning unit, 110 ... calculation unit.

Claims (1)

an acquisition unit that acquires position information regarding the positions of terminal devices of a plurality of users and attribute information regarding attributes of the plurality of users;
a generation unit that generates qualification information for selecting a calculation target user based on the location information and the attribute information;
A selection unit that selects a calculation target user from among the plurality of users based on the qualification information of the plurality of users;
a learning unit that builds a learning model for calculating numerical values relating to economic indicators using the location information and the attribute information corresponding to the calculation target user;
a calculation unit that applies the location information and the attribute information corresponding to the calculation target user to the learning model to calculate numerical values related to economic indicators;
with
The generation unit aggregates the length of stay at each place of stay for each of the plurality of users based on the position information, estimates the work location of each of the plurality of users using the aggregated length of stay at each place of stay, and calculating the distance traveled for each of the plurality of users based on the location information, and generating, as the qualification information, information related to the type of industry for each of the plurality of users based on the work location and the travel distance ;
The selection unit selects the user to be calculated based on the information on the industry,
The calculation unit calculates a numerical value related to economic conditions as a numerical value related to the economic index based on the information acquired corresponding to the location information of the user to be calculated.
Economic indicator calculator.

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