Urban management support method, urban management support device, and urban management support program

Abstract

To provide a system which is advantageous for all information providers who actively provide data.SOLUTION: A method for supporting an urban management includes the steps of: organizing service-related data and predicting the future supply of at least one service; organizing individual-related data and predicting the future demand of at least one service; relating the data of supply prediction and the data of demand prediction to each other by using at least one of time and a position as a parameter; generating feedback data for a business operator and feedback data for an individual on the basis of the aggregate of the related data; and sending the feedback data for a business operator to a terminal of a business operator and sending the feedback data for an individual to a terminal of an individual.SELECTED DRAWING: Figure 8

Description

Technical Field

[0001] The present disclosure relates to a method, apparatus, and program for assisting in the management of a city in which one or more services by one or more operators are provided to one or more individuals.

Background Art

[0002] Japanese Patent Application Laid-Open No. 2003-114135 discloses a method for providing geographical information. In this conventional method, map information of an area that a user plans to visit is provided to the user. In this conventional method, additional information provided to the user is generated together with the map information. The additional information is generated based on input information such as a destination that the user plans to visit, a time zone for visiting this destination, the user's preferences, and real-time information of an area including the destination.

[0003] As a document showing the technical level of the technical field related to the present disclosure, in addition to Japanese Patent Application Laid-Open No. 2003-114135, Japanese Patent Application Laid-Open No. 2021-093088 can be exemplified.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the conventional method, the information used for generating the additional information is various input information by the user, information obtained in real time (real-time data), and the like. Therefore, it can be said that the user has the merit of obtaining additional information as a consideration for various input information and / or as a consideration for the trouble of providing various input information.

[0006] Consider a case where real-time data is actively provided in a conventional method. In this case, the provider of real-time data only unilaterally provides data and has fewer benefits compared to users. Therefore, it is desirable to construct a system that benefits all providers who actively provide data.

[0007] One object of the present disclosure is to provide a system that benefits all providers who actively provide data.

Means for Solving the Problem

[0008] A first aspect of the present disclosure is a method for assisting in the management of a city in which one or more services by one or more operators are provided to one or more individuals, and has the following features. The method includes steps of obtaining service-related data indicating data generated in relation to the one or more services, sorting the service-related data based on at least one of position and time data included in the service-related data, making a future supply prediction for the one or more services by the one or more operators based on the sorted service-related data, obtaining individual-related data indicating data generated in relation to the one or more individuals, sorting the individual-related data based on at least one of position and time data included in the individual-related data, making a future demand prediction for the one or more services by the one or more individuals based on the sorted individual-related data, associating the supply prediction data and the demand prediction data using at least one of position and time data as a parameter, generating feedback data for operators and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data, and transmitting the feedback data for operators to terminals of the one or more operators and the feedback data for individuals to terminals of the one or more individuals, respectively. The process of generating the feedback data for the business operators and individuals includes a process of predicting future usage of the one or more services by a specific individual among the one or more individuals based on the data set, and a process of identifying, based on the data of the usage prediction, the service whose future usage by the specific individual is predicted, and at least one of the location and time at which the usage of the service is predicted. The feedback data for individuals includes data of the identified service and data of at least one of the location and time of using the identified service. In the process of transmitting the feedback data for the business operators and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual.

[0009] A second aspect of the present disclosure is an apparatus for assisting in the management of a city in which one or more services by one or more operators are provided to one or more individuals, and has the following features. The apparatus includes a data processing device. The data processing device performs a process of acquiring service-related data indicating data generated in relation to the one or more services, a process of arranging the service-related data based on at least one of position and time data included in the service-related data, a process of performing a future supply prediction for the one or more services by the one or more operators based on the arranged service-related data, a process of acquiring personal-related data indicating data generated in relation to the one or more individuals, a process of arranging the personal-related data based on at least one of position and time data included in the personal-related data, a process of performing a future demand prediction for the one or more services by the one or more individuals based on the arranged personal-related data, a process of associating the supply prediction data and the demand prediction data using at least one of position and time data as a parameter, a process of generating feedback data for operators and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data, and a process of transmitting the feedback data for operators to terminals of the one or more operators and transmitting the feedback data for individuals to terminals of the one or more individuals, respectively, and is configured to perform the above processes. 。 The process of generating the feedback data for the business operators and individuals includes a process of predicting future usage of the one or more services by a specific individual among the one or more individuals based on the data set, and a process of identifying, based on the data of the usage prediction, the service whose future usage by the specific individual is predicted, and at least one of the location and time at which the usage of the service is predicted. The feedback data for individuals includes data of the identified service and data of at least one of the location and time of using the identified service. In the process of transmitting the feedback data for the business operators and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual.

[0010] A third aspect of the present disclosure is a program for assisting in the management of a city in which one or more services by one or more operators are provided to one or more individuals, and has the following features. The program includes a process of acquiring service-related data indicating data generated in relation to the one or more services, a process of arranging the service-related data based on at least one of position and time data included in the service-related data, a process of predicting a future supply of the one or more services by the one or more operators based on the arranged service-related data, a process of acquiring individual-related data indicating data generated in relation to the one or more individuals, a process of arranging the individual-related data based on at least one of position and time data included in the individual-related data, a process of predicting a future demand for the one or more services by the one or more individuals based on the arranged individual-related data, a process of using at least one of position and time data as a parameter to associate the supply prediction data with the demand prediction data, a process of generating feedback data for operators and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data, and a process of transmitting the feedback data for operators to terminals of the one or more operators and transmitting the feedback data for individuals to terminals of the one or more individuals, respectively, to be executed by a computer. The process of generating the feedback data for the business operators and individuals includes a process of predicting future usage of the one or more services by a specific individual among the one or more individuals based on the data set, and a process of identifying, based on the data of the usage prediction, the service whose future usage by the specific individual is predicted, and at least one of the location and time at which the usage of the service is predicted. The feedback data for individuals includes data of the identified service and data of at least one of the location and time of using the identified service. In the process of transmitting the feedback data for the business operators and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual.

Advantages of the Invention

[0011] According to the present disclosure, feedback data for one or more operators who provided service-related data is provided to the one or more operators. Also, feedback data for one or more individuals who provided individual-related data is provided to the one or more individuals. Therefore, all information providers who actively provided data in the city can enjoy the benefits of this information provision.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In each figure, the same or corresponding parts are denoted by the same reference numerals, and the description thereof is simplified or omitted.

[0014] 1. Overview of the City In the present disclosure, urban CT refers to the physical space where the lives of one or more individuals PS are conducted by providing one or more services SR. There is no limit to the scale of urban CT in the present disclosure. So-called smart cities are an example of large-scale urban CT, underground shopping streets are an example of medium-scale urban CT, and large buildings are an example of small-scale urban CT. The virtual space of the Internet, which is not a physical space, is not included in the urban CT in the present disclosure. In urban CT, one or more services SR are provided by one or more operators PR.

[0015] FIG. 1 is a diagram showing an overview of urban CT. In FIG. 1, operator PR1 that provides service SR1, operator PR2 that provides service SR2, and operator PR3 that provides service SR3 in urban CT are depicted. Operators PR1, PR2, and PR3 are an example of one or more operators PR and are natural persons or legal persons. Each business office (store) of these operators exists in at least one urban CT.

[0016] Service SR1, SR 2 and SR 3 are an example of one or more services SR. Service SR1, SR 2 and SR 3 are not particularly limited, and any service that can be provided in urban CT corresponds to these. Examples of arbitrary services include services directly or indirectly related to the operations included in major category H "Transportation and Postal Services", major category M "Accommodation and Food Services", and major category N "Life-Related Services and Entertainment" of the Japanese Standard Industrial Classification.

[0017] Figure 1 also depicts individual PS1, PS2, and PS3. Individual PS1, PS2, and PS3 are examples of one or more individual PSs, and all are natural persons. Individual PS1, PS2, and PS3 may or may not reside in urban area CT. Examples of individuals in the latter case include visitors temporarily staying in urban area CT and passers-by who temporarily enter and leave urban area CT on the way to destinations outside urban area CT. Individual PS1, PS2, and PS3 use at least one of services SR1, SR 2 and SR 3, or do not use any of the services.

[0018] Figure 1 also depicts management server 10 as an urban management support device according to an embodiment. Management server 10 actively or passively collects urban-related data CTY and individual-related data PSN in order to support management in urban area CT. Management server 10 also performs a process for supporting management in urban area CT (hereinafter, also referred to as "management support process") based on the collected urban-related data CTY and individual-related data PSN. Hereinafter, the urban-related data CTY, individual-related data PSN, and management support process will be described.

[0019] 2. Urban-related data CTY Urban-related data CTY is data generated in relation to urban area CT. Urban-related data CTY includes service-related data SRV and various other data VRS other than this service-related data SRV.

[0020] 2-1. Service-related data SRV Service-related data SRV indicates data generated in relation to one or more services SR. FIG. 2 is a diagram for explaining a configuration example of the service-related data SRV. In the example shown in FIG. 2, the service-related data SRV includes operator ID data PR_ID. The operator ID data PR_ID is assigned, for example, for each of one or more operators PR. Preferably, the operator ID data PR_ID is assigned for each office (store) of one or more operators PR. When the operator ID data PR_ID is assigned for each office, the location of the office where the service-related data SRV was generated (for example, latitude, longitude, altitude, floor information, etc.) can be specified.

[0021] In the example shown in FIG. 2, the service-related data SRV also includes point-of-sale data POS. The point-of-sale data POS is data for specifying services provided to one or more individuals PS, products sold to one or more individuals PS, and the like. In the example shown in FIG. 2, the point-of-sale data POS includes serial number data SSN, service ID data SR_ID, price data PRC, quantity data NUM, and time data TIM.

[0022] The serial number data SSN is assigned each time the point-of-sale data POS is generated. The service ID data SR_ID is data for identifying the types of services provided to one or more individuals PS, products sold to one or more individuals PS, and the like. The price data PRC and the quantity data NUM are data attached to the service ID data SR_ID. These data are assigned for each unit with the provided services, sold products, etc. as one unit. The time data TIM is data indicating the time when the point-of-sale data POS was generated. The time data TIM is also data for specifying the time when the service-related data SRV was generated.

[0023] An example of generating point-of-sale data POS described with reference to FIG. 2 will be described with reference to FIG. 3. In the example shown in FIG. 3, the business office of business operator PR is restaurant A, and this business operator PR provides food and beverage services. The width of the time shown in FIG. 3 represents the width of the stay time of each of customers CA1 to CA7. In this example, at each time (t11 to t16) immediately before customers CA1 to CA7 leave restaurant A, point-of-sale data POS_CA1 to POS_CA7 are each generated. As the time immediately before leaving restaurant A, the time when the consideration for the food and beverage service is paid is exemplified.

[0024] The width of the time shown in FIG. 3 can be grasped, for example, by specifying the time when each of customers CA1 to CA7 enters restaurant A and the time when each of customers CA1 to CA7 leaves restaurant A. For example, images outside and inside the store are acquired from cameras installed outside and inside restaurant A. Then, a person identification process using this camera image is performed. As a result, when a certain customer CA pays the consideration for the food and beverage service, the time when the same person as this customer CA enters restaurant A is specified. Also, according to the person identification process, after a certain customer CA pays the consideration for the food and beverage service, the time when the same person as this customer CA leaves restaurant A is specified.

[0025] When the width of the time shown in FIG. 3 is specified, data on the stay time of each of customers CA1 to CA7 may be added to the point-of-sale data POS. Incidentally, the data on the stay time of each of these customers CA1 to CA7 can be said to be data on each usage time zone of the food and beverage service by customers CA1 to CA7.

[0026] FIG. 4 is a diagram for explaining another configuration example of service-related data SRV. In the example shown in FIG. 4, the service-related data SRV includes operator ID data PR_ID and point-of-sale data POS. The operator ID data PR_ID is as described with reference to FIG. 2. In the example shown in FIG. 4, the point-of-sale data POS includes serial number data SSN, customer ID data CS_ID, service ID data SR_ID, and usage time zone data TZN. The serial number data SSN and the service ID data SR_ID are as described with reference to FIG. 2.

[0027] The customer ID data CS_ID is data included in the point-of-sale data POS when a customer such as a service recipient or a product sales target is identified. The customer ID data CS_ID is typically uniquely assigned to a customer by one or more operators PR. The customer ID data CS_ID is set, for example, for each office of one or more operators PR. The usage time zone data TZN is data on the time zone during which the customer identified by the customer ID data CS_ID uses the service SR provided by the operator PR identified by the operator ID data PR_ID.

[0028] An example of generating the point-of-sale data POS described with reference to FIG. 4 will be described with reference to FIG. 5. In the example shown in FIG. 5, the office of the operator PR is the sports facility B, and various services within the sports facility are provided by this operator PR. In this example, data acquired at each time (t21 to t24) when customers CB1 to CB4 entered the sports facility B and data acquired at each time (t25 to t28) when customers CB1 to CB4 left the sports facility B are combined to generate respective point-of-sale data POS_CB1 to POS_CB4. Note that the data at each time when customers CB1 to CB4 entered or left the sports facility B is used as data for specifying the time when the service-related data SRV was generated.

[0029] The width of the time shown in FIG. 5 represents the width of the usage time of various services by customers CB1 to CB4 (that is, the usage time zone). These time widths can be generated by specifying the respective times when customers CB1 to CB4 enter sports facility B and the respective times when customers CB1 to CB4 leave sports facility B. These times are specified, for example, by recognition processing using an IC card reader installed at the entrance of sports facility B. These times may also be specified by the above-described person identification processing.

[0030] 2-2. Various Data VRS FIG. 6 is a diagram for explaining a configuration example of various data VRS. In the example shown in FIG. 6, the various data VRS includes map data MAP and public data PUB.

[0031] The map data MAP includes data on specifications of artificial objects such as buildings, roads, and railways existing in city CT (for example, type, size, center position, or latitude, longitude, and height of one or more representative positions), and data on specifications of natural objects such as rivers and lakes existing in city CT.

[0032] The public data PUB is data (so-called open data) that is available to the public in relation to city CT. The public data PUB includes environmental data ENV such as weather, temperature, and humidity, traffic data TRA such as traffic volume and operation status of transportation agencies, and other data OTH such as administrative data and statistical data. If all of the data constituting the map data MAP corresponds to the above-described open data, the map data MAP may be included in the public data PUB.

[0033] Various types of data VRS include data of at least one of position and time (date and time). For example, in the map data MAP, the data of the center position included in the data regarding specifications corresponds to this position data. That is, the map data MAP includes position data. The map data MAP may include time data according to the content of the data constituting this data MAP. The environmental data ENV includes, in addition to the position data indicating which location the data constituting this data ENV is, time data indicating which time zone the said constituent data is. Similar to the environmental data ENV, the traffic data TRA also includes position data and time data. That is, the environmental data ENV includes position data and time data. Other data OTH includes at least position data. Other data OTH may include time data according to the content of the constituent data.

[0034] 3. Personal-related data PSN Personal-related data PSN is data generated in relation to the individual PS. FIG. 7 is a diagram for explaining a configuration example of the personal-related data PSN. In the example shown in FIG. 7, the personal-related data PSN includes personal ID data PS_ID, position data LCT, payment data PAY, service registration data RGS, schedule data SCH, image data IMG, text data MSG, and vital data VTL.

[0035] Personal ID data PS_ID is assigned for each one or more individuals PS. The personal ID data PS_ID includes the attribute data of the individual PS (e.g., name, gender, age, family composition, etc.). Location data LCT is data indicating the history of the location of the individual PS specified by the personal ID data PS_ID. This location history is associated with time. Settlement data PAY is data indicating the history of settlements when the individual PS specified by the personal ID data PS_ID pays the consideration for one or more services SR. The settlement data PAY is generated, for example, when the individual PS conducts an electronic settlement. Similar to the location data LCT, the settlement data PAY is associated with time. By associating the settlement data PAY with time, it becomes possible to associate the settlement data PAY with the location data LCT.

[0036] The personal ID data PS_ID, location data LCT, and settlement data PAY are essential data for the personal-related data PSN. On the other hand, the service registration data RGS, schedule data SCH, image data IMG, text data MSG, and vital data VTL are optionally included in the personal-related data PSN.

[0037] The service registration data RGS is data corresponding to the customer ID data CS_ID assigned to the individual PS specified by the personal ID data PS_ID. The service registration data RGS is set, for example, for each office of one or more operators PR. The schedule data SCH is data regarding the schedule of the individual PS specified by the personal ID data PS_ID. The image data IMG is data of an image (e.g., a camera image) owned by the individual PS specified by the personal ID data PS_ID. The text data MSG is data such as a message created by the individual PS specified by the personal ID data PS_ID. Any of these data can be obtained, for example, from the terminal of the individual PS specified by the personal ID data PS_ID.

[0038] Vital data VTL is biometric data such as the pulse, blood pressure, respiration, and body temperature of an individual PS identified by personal ID data PS_ID. Vital data VTL can be obtained, for example, from a wearable device of an individual PS identified by personal ID data PS_ID.

[0039] Any data in personal-related data PSN such as service registration data RGS and vital data VTL includes at least one of location and time data. For example, service registration data RGS is set for each office of one or more operators PR. Therefore, service registration data RGS includes the location data of this office. When the destination data of an individual PS is included in schedule data SCH, the location data of this destination is the location data included in schedule data SCH, and the data of the time staying at this destination can be said to be the time data included in schedule data SCH.

[0040] Image data IMG and text data MSG include at least time data. The time data included in image data IMG is, for example, the data of the time when the image was acquired. The time data included in text data MSG is, for example, the data of the time when the message was created or sent. Image data IMG and text data MSG may include location data. The location data of the location where the image was acquired can be said to be the location data included in image data IMG. When the destination of an individual PS is included in the message, the location data of this destination can be said to be the location data included in text data MSG.

[0041] Similar to image data IMG and text data MSG, vital data VTL includes at least time data. The time data included in vital data VTL is, for example, the data of the time when the biometric data was acquired. Vital data VTL may include location data. When the location data of the location where the biometric data was acquired is included, this location data can be said to be the location data included in vital data VTL.

[0042] 4. Management Support Processing by Management Server 10 FIG. 8 is a diagram showing an example of the flow of management support processing particularly related to the embodiment. In the example shown in FIG. 8, based on service-related data SRV and personal-related data PSN, processing is performed to generate recommendation data RCM_PR for one or more operators PR and recommendation data RCM_PS for one or more individuals PS. Note that the recommendation data RCM_PR is an example of the "feedback data for operators" in the present disclosure, and the recommendation data RCM_PS is an example of the "feedback data for individuals" in the present disclosure.

[0043] In the example shown in FIG. 8, data arrangement of the service-related data SRV is performed (step S11). In the processing of step S11, specifically, the service-related data SRV is arranged based on at least one of the data of the location LC and the time (date and time) TM. As described in the explanation of FIG. 2, when the operator ID data PR_ID is assigned to each office of the operator PR, the location of the office where the service-related data SRV is generated is specified. Also, in the example of FIG. 2, the time data TIM is included in the service-related data SRV. Further, in the example of FIG. 4, the time when the service-related data SRV is generated is specified by the time data used for generating the usage time zone data TZN.

[0044] Therefore, the service-related data SRV can be arranged based on at least one of the data of the location LC and the time TM. Hereinafter, for convenience of explanation, the arranged service-related data SRV is referred to as "arranged data SRV(LC, TM)".

[0045] Following the process of step S11, analysis of the sorted data SRV(LC, TM) is performed (step S12). Examples of the analysis of the sorted data SRV(LC, TM) include past analysis, current analysis, and future analysis of the sorted data SRV(LC, TM). According to the past analysis, the past generation trend of the sorted data SRV(LC, TM) is grasped. According to the current analysis, the real-time generation trend of the sorted data SRV(LC, TM) is grasped.

[0046] In the past analysis or the current analysis, various data VRS in which at least one of the data of the sorted data SRV(LC, TM), the position LC, and the time TM are common can be appropriately used. This is because such various data VRS are considered external factors that can affect the generation of the service-related data SRV. For example, by using the map data MAP, geographical factors that affect the generation of the service-related data SRV can be considered. By using the environmental data ENV, environmental factors that affect the generation of the service-related data SRV can be considered. By using the traffic data TRA, traffic factors that affect the generation of the service-related data SRV can be considered.

[0047] The future analysis is performed, for example, using a prediction logic (prediction model) that takes the results of the past analysis or the current analysis as input. According to the future analysis, the future generation trend of the service-related data SRV when focusing on at least one of the data of the position LC and the time TM is predicted. That is, according to the future analysis, the future supply prospect of the service SR by the provider PR specified by the provider ID data PR_ID is predicted. When the provider ID data PR_ID is assigned to each office of the provider PR, the future supply prospect of the service SR provided at this office is predicted.

[0048] Such a prediction of the supply outlook becomes possible when a past analysis or a current analysis of the organized data SRV(LC, TM) organized by focusing on the data of the location LC is performed. On the other hand, when a past analysis or a current analysis of the organized data SRV(LC, TM) organized by focusing on the data of the time TM is performed, even if the above-described future analysis is performed, the business operator PR and its business office cannot be identified. However, according to the future analysis in this case, the future supply outlook of the service SR specified by the service ID data SR_ID is predicted. It is also possible to predict the time (date and time) when the supply of the service SR is expected.

[0049] When a past analysis or a current analysis of the organized data SRV(LC, TM) organized by focusing on the data of both the location LC and the time TM is performed, if the above-described future analysis is performed, among the services SR provided by the business operator PR specified by the business operator ID data PR_ID, the future supply outlook of the service SR specified by the service ID data SR_ID is predicted. It is also possible to predict the time when the supply of the service SR is expected.

[0050] In addition, when various data VRS in which at least one of the organized data SRV(LC, TM) and the data of the location LC and the time TM share data is used in the past analysis or the current analysis, it is desirable that the predicted values of the various data VRS be used as the input to the prediction logic (prediction model) used in the future analysis. Hereinafter, for convenience of explanation, the data regarding the supply outlook of the service SR obtained by the future analysis is referred to as "supply prediction data SRV*(LC, TM)".

[0051] In the example shown in FIG. 8, data arrangement of personal-related data PSN is also performed (step S13). Similar to the process of step S11, in the process of step S13, the personal-related data PSN is arranged based on at least one of the data of the position LC and the time TM. As described in the explanation of FIG. 7, the personal-related data PSN includes position data LCT and settlement data PAY respectively associated with the time. Therefore, the settlement data PAY at the time common to the time associated with the position data LCT is associated with the position data LCT. Therefore, the personal-related data PSN can be arranged based on both the data of the position LC and the time TM. Hereinafter, for convenience of explanation, the personal-related data PSN after arrangement is referred to as "arranged data PSN(LC, TM)".

[0052] Subsequent to the process of step S13, analysis of the arranged data PSN(LC, TM) is performed (step S14). The analysis of the arranged data PSN(LC, TM) focuses on a single personal ID data PS_ID and is performed using a feature analysis logic (feature analysis model). The feature analysis logic is set in advance according to the information to be output from the feature analysis logic (hereinafter, also referred to as "feature analysis result AN").

[0053] The feature analysis result AN includes the result of the future analysis of the individual PS. The future analysis of the individual PS is performed using the result of the past analysis or the current situation analysis focusing on a single personal ID data PS_ID. The result of the future analysis of the individual PS corresponds to the expected demand for the service SR in the future by this individual PS. The feature analysis result AN also includes the behavior tendency of the individual PS (for example, active, social, adventurous, impulsive, etc.), the judgment tendency of the individual PS with respect to the service SR (price emphasis, environmental emphasis, etc.), the health status of the individual PS, and the recommended nutrients.

[0054] When the feature analysis result AN is obtained, data of the feature analysis result AN is added to the organized data PSN(LC, TM) input to the feature analysis logic when outputting the feature analysis result AN. The data of the feature analysis result AN includes data such as the future demand forecast of the service SR by the individual PS in addition to data such as the behavior tendency of the individual PS. Therefore, hereinafter, for convenience of explanation, the organized data PSN(LC, TM) to which the data of the feature analysis result AN is added is also referred to as "demand prediction data PSN*(LC, TM, AN)".

[0055] The processes of steps S11 to S14 are repeatedly executed at a predetermined processing cycle. Therefore, the supply prediction data SRV*(LC, TM) and the demand prediction data PSN*(LC, TM, AN) are continuously updated at this processing cycle.

[0056] Following the process of step S12 or S14, an association process of data is performed (step S15). The process of step S15 is performed when the management server 10 receives an association command INS. The association command INS is automatically transmitted to the management server 10, for example, every time a predetermined period (for example, 12 hours, 24 hours) elapses. The association command INS may be transmitted to the management server 10 based on an association request from the operator PR or the individual PS.

[0057] In the process of step S15, an association is made between the latest supply prediction data SRV*(LC, TM) and the latest demand prediction data PSN*(LC, TM, AN). This association is made using at least one of the data of the location LC and the time TM as a parameter.

[0058] The association using the data of the location LC as a parameter is performed, for example, when the data of the location LC that matches the data of the location LC included in the demand prediction data PSN*(LC, TM, AN) is included in the supply prediction data SRV*(LC, TM). In this case, it is predicted that the individual PS specified by the individual ID data PS_ID used for generating the demand prediction data PSN*(LC, TM, AN) will use the service SR provided by the business operator PR included in the supply prediction data SRV*(LC, TM) at the location LC.

[0059] The association using the data of the time TM as a parameter is performed, for example, when the data of the time TM that matches the data of the time TM included in the demand prediction data PSN*(LC, TM, AN) is included in the supply prediction data SRV*(LC, TM). In this case, it is predicted that the individual PS specified by the individual ID data PS_ID used for generating the demand prediction data PSN*(LC, TM, AN) will use the service SR provided by the business operator PR included in the supply prediction data SRV*(LC, TM) at the time TM.

[0060] The association using the data of both the location LC and the time TM as parameters is a combination of the above two types of associations. In this case, it is predicted that the individual PS specified by the individual ID data PS_ID used for generating the demand prediction data PSN*(LC, TM, AN) will use the service SR provided by the business operator PR included in the supply prediction data SRV*(LC, TM) at the location LC and the time TM.

[0061] In addition, when the service registration data RGS is included in the individual-related data PSN of the individual PS specified by the individual ID data PS_ID used for generating the demand prediction data PSN*(LC, TM, AN), and the customer ID data CS_ID corresponding to this service registration data RGS is included in the service-related data SRV of the business operator PR specified by the business operator ID data PR_ID included in the supply prediction data SRV*(LC, TM), the accuracy of the above prediction is improved.

[0062] Following the process of step S15, recommendation data RCM_PR and RCM_PS are generated (step S16). In the process of step S16, recommendation data RCM_PR for the operator PR corresponding to the prediction result in the process of step S15 is generated. In the process of step S16, also, recommendation data RCM_PS for the individual PS corresponding to the prediction result in the process of step S15 is generated.

[0063] In the process of step S16, recommendation data RCM_PR for "another individual PS" different from the individual PS corresponding to the prediction result in the process of step S15 may be generated. This "another individual PS" is assumed to be an individual PS whose characteristic analysis result AN coincides with or is similar to that of the individual PS corresponding to the prediction result in the process of step S15. Such another individual PS is likely to have similar health conditions and recommended nutrients to those of the individual PS corresponding to the prediction result in the process of step S15, and thus is expected to accept the recommendation data RCM_PR.

[0064] In the process of step S16, also, when the recommendation data RCM_ PS for "another individual PS" is generated, recommendation data RCM_PR for "another operator PR" may be generated based on the recommendation data RCM_ PS for "another individual PS". This is because the information of the service SR expected to be used by "another individual PS" at the location LC or time TM is likely to be useful information for "another operator PR". Here, the "another operator PR" mentioned is different from the operator PR corresponding to the prediction result in the process of step S15.

[0065] Thus, according to the embodiment, recommendation data RCM_PR for the provider PR who provided the service-related data SRV to the management server 10 is generated. Also, recommendation data RCM_PS for the individual PS who provided the personal-related data PSN to the management server 10 is generated. Therefore, all information providers who actively provided data to the management server 10 can enjoy the benefits brought by this information provision.

[0066] 5. Configuration Example of Urban Management Support System FIG. 9 is a diagram for explaining an overall configuration example of an urban management support system including the management server 10. In the example shown in FIG. 9, the urban management support system includes, in addition to the management server 10, a terminal of the provider PR (hereinafter also referred to as the "PR terminal") 20 and a terminal of the individual PS (hereinafter also referred to as the "PS terminal") 30. The PR terminal 20 and the PS terminal 30 communicate with the management server 10 via the communication line network 40. Note that the communication line network 40 is not particularly limited, and wired and wireless networks can be used.

[0067] The management server 10 includes a data processing device 11 and databases (DBs) 12 and 13. The data processing device 11 includes at least one processor 14 and at least one memory 15. The processor 14 includes a CPU (Central Processing Unit). The memory 15 is a volatile memory such as a DDR memory, and performs the deployment of various programs used by the processor 14 and the temporary storage of various data. The various programs used by the processor 14 include the urban management support program according to the embodiment. The various data used by the processor 14 include the data stored in the databases 12 and 13.

[0068] Database 12 stores data related to urban CT. Examples of data related to urban CT include urban-related data CTY and various types of data derived therefrom. Examples of various types of data derived from urban-related data CTY include data sorted based on at least one of position LC and time TM (i.e., sorted data SRV(LC, TM) and supply prediction data SRV*(LC, TM)).

[0069] Database 13 stores data related to individual PS. Examples of data related to individual PS include individual-related data PSN and various types of data derived therefrom. Examples of various types of data derived from individual-related data PSN include data sorted based on at least one of position LC and time TM (i.e., sorted data PSN(LC, TM) and demand prediction data PSN*(LC, TM, AN)).

[0070] The PR terminal 20 is installed, for example, for each business office of the business operator PR. The total number of PR terminals 20 is at least 1. In communication with the management server 10, the PR terminal 20 transmits service-related data SRV to the management server 10. The PR terminal 20 also receives recommendation data RCM_PR from the management server 10.

[0071] The PS terminal 30 is a portable terminal such as a smartphone or a tablet owned by the individual PS. The total number of PS terminals 30 is at least 1. In communication with the management server 10, the PS terminal 30 transmits individual-related data PSN to the management server 10. The PS terminal 30 also receives recommendation data RCM_PS from the management server 10.

[0072] 6. Data processing example FIG. 10 is a flowchart showing the flow of processing particularly related to the embodiment, which is executed in the data processing device 11 (processor 14). The routine shown in FIG. 10 is repeatedly executed, for example, at a predetermined processing interval.

[0073] In the routine shown in FIG. 10, first, it is determined whether or not an association command INS has been received (step S21). As already explained, the association command INS is automatically transmitted to the management server 10 every time a predetermined period elapses. The association command INS may be transmitted to the management server 10 based on an association request from the business operator PR or the individual PS.

[0074] If the determination result in step S21 is affirmative, the latest supply prediction data SRV*(LC, TM) and the latest demand prediction data PSN*(LC, TM, AN) are extracted (step S22). The extraction of the latest supply prediction data SRV*(LC, TM) is performed with reference to the database 12. The extraction of the latest demand prediction data PSN*(LC, TM, AN) is performed with reference to the database 13.

[0075] Following the process of step S22, an association process is performed (step S23). The target of the association process is the data extracted in step S22. The details of the association process are as described in step S15 of FIG. 8.

[0076] Following the process of step S23, recommendation data RCM_PR and RCM_PS are generated (step S24). The details of the generation process of the recommendation data RCM_PR and RCM_PS are as described in step S16 of FIG. 8.

[0077] Following the process of step S24, the recommendation data RCM_PR is transmitted to the PR terminal 20, and the recommendation data RCM_PS is transmitted to the PS terminal 30 (step S25). The recommendation data RCM_PR transmitted to the PR terminal 20 is the one generated in the process of step S24. The recommendation data RCM_PS transmitted to the PS terminal 30 is the one generated in the process of step S24.

Explanation of Signs

[0078] 10 Management server 11 Data processing device 12, 13 Database 14 Processor 15 Memory CT City PR1~PR3 Operators PS1~PS3 Individuals CTY City-related Data PSN Individual-related Data SRV Service-related Data VRS Various Data

Claims

1. A method for assisting in the management of a city in which one or more services provided by one or more service providers are provided to one or more individuals, comprising: obtaining service-related data indicating data generated in relation to the one or more services; sorting the service-related data based on at least one of location and time data included in the service-related data; performing a future supply prediction for the one or more services by the one or more service providers based on the analysis result of the sorted service-related data; obtaining individual-related data indicating data generated in relation to the one or more individuals; sorting the individual-related data based on at least one of location and time data included in the individual-related data; performing a future demand prediction for the one or more services by the one or more individuals based on the analysis result of the sorted individual-related data; associating the supply prediction data and the demand prediction data using at least one of location and time data as a parameter; generating feedback data for service providers and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data; transmitting the feedback data for service providers to the terminals of the one or more service providers and transmitting the feedback data for individuals to the terminals of the one or more individuals respectively; including the step of generating the feedback data for service providers and individuals performing a future usage prediction for the one or more services by a specific individual among the one or more individuals based on the data set; identifying, based on the usage prediction data, at least one of the service whose future usage is predicted by the specific individual and the location and time at which the usage of the service is predicted; including the feedback data for individuals includes data of the identified service and at least one of location and time data of the location and time at which the identified service is used; in the step of transmitting the feedback data for service providers and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual An urban management support method characterized by the above.

2. The method according to claim 1, wherein when the parameter is a location, the feedback data for individuals includes data on the location where the specified service is available, and when the parameter is time, the feedback data for individuals includes data on the time when the specified service is available. An urban management support method characterized by the above.

3. The method according to claim 1, wherein the step of generating the feedback data for the service providers and individuals includes performing a future usage prediction for the one or more services by a specific individual among the one or more individuals based on the data set, identifying, based on the data of the usage prediction, at least one of the service that future usage by the specific individual is predicted for, and the location and time when the usage of the service is predicted, identifying, from among the one or more service providers, the service provider that provides the service for which future usage by the specific individual is predicted, including wherein the feedback data for the service providers includes data on at least one of the location and time when the usage of the specified service is predicted, and in the step of transmitting the feedback data for the service providers and individuals, the feedback data for the service providers is transmitted to the terminal of the specified service provider. An urban management support method characterized by the above.

4. The method according to claim 3, wherein when the parameter is a location, the feedback data for the service providers includes data on the location where the usage of the specified service is predicted and data on the specific individual for whom the usage of the specified service is predicted, and when the parameter is time, the feedback data for the service providers includes data on the time when the usage of the specified service is predicted and data on the specific individual for whom the usage of the specified service is predicted. An urban management support method characterized by the above.

5. An apparatus for supporting the management of a city in which one or more services provided by one or more service providers are provided to one or more individuals, comprising a data processing device, wherein the data processing device performs a process of acquiring service-related data indicating data generated in relation to the one or more services, A process of sorting the service-related data based on at least one of the location and time data included in the service-related data; A process of making a future supply prediction for the one or more services by the one or more operators based on the analysis result of the sorted service-related data; A process of obtaining personal-related data indicating data generated in relation to the one or more individuals; A process of sorting the personal-related data based on at least one of the location and time data included in the personal-related data; A process of making a future demand prediction for the one or more services by the one or more individuals based on the analysis result of the sorted personal-related data; A process of associating the supply prediction data with the demand prediction data using at least one of the location and time data as a parameter; A process of generating feedback data for operators and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data; A process of transmitting the feedback data for operators to the terminals of the one or more operators and the feedback data for individuals to the terminals of the one or more individuals respectively; configured to perform; The process of generating the feedback data for operators and individuals A process of making a future usage prediction for the one or more services by a specific individual among the one or more individuals based on the data set; A process of identifying the service for which future usage is predicted by the specific individual and at least one of the location and time when the usage of the service is predicted based on the usage prediction data; includes; The feedback data for individuals includes data of the identified service and at least one of the location and time when the identified service is used; In the process of transmitting the feedback data for operators and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual A city management support device characterized by this.

6. A program for supporting the management of a city in which one or more services provided by one or more operators are provided to one or more individuals, A process of obtaining service-related data indicating data generated in relation to the one or more services; A process of organizing the service-related data based on at least one of the location and time data included in the service-related data; A process of making a future supply prediction for the one or more services by the one or more service providers based on the analysis result of the organized service-related data; A process of obtaining personal-related data indicating data generated in relation to the one or more individuals; A process of organizing the personal-related data based on at least one of the location and time data included in the personal-related data; A process of making a future demand prediction for the one or more services by the one or more individuals based on the analysis result of the organized personal-related data; A process of associating the supply prediction data with the demand prediction data, using at least one of the location and time data as a parameter; A process of generating feedback data for service providers and feedback data for individuals based on a data set including the associated supply prediction data and demand prediction data; A process of transmitting the feedback data for service providers to the terminals of the one or more service providers and the feedback data for individuals to the terminals of the one or more individuals respectively; To cause a computer to execute, The process of generating the feedback data for service providers and individuals, A process of making a future usage prediction for the one or more services by a specific individual among the one or more individuals based on the data set; A process of specifying, based on the usage prediction data, the service whose future usage is predicted by the specific individual and at least one of the location and time when the usage of the service is predicted; Including, The feedback data for individuals includes data of the specified service and at least one of the location and time when the specified service is used; In the process of transmitting the feedback data for service providers and individuals, the feedback data for individuals is transmitted to the terminal of the specific individual A city management support program characterized by the above.

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