Information processing apparatus and method

Abstract

To control each electric apparatus used in each facility in time series on the basis of an estimation result of a human flow.SOLUTION: The electric device 11 is, for example, an air conditioner, a refrigerator / freezer, a fryer, a heat retaining device, or the like. The server device 30 includes an acquisition unit 31 configured to acquire a people-flow estimation result obtained by estimating future people-flow in time series in a target area including a plurality of facilities, a prediction unit 33 configured to perform prediction related to power demand in time series for each facility included in the target area on the basis of the acquired people-flow estimation result, and a control unit 34 configured to control each electric apparatus used in each facility in time series on the basis of a result predicted by the prediction unit.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a technique for controlling the power of an electrical device. [Background technology]

[0002] To reduce power consumption, it is necessary to optimize power consumption not only in individual homes but also in various facilities such as stores. For example, Patent Document 1 discloses a method for analyzing and predicting the power consumption and power generation of each facility with high accuracy and controlling the energy conservation of the equipment within the facility. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-22939 Summary of the Invention [Problem to be solved by the invention]

[0004] In stores and other facilities, there is a need to reduce power consumption by increasing the temperature of air conditioning and turning off backup electrical equipment when there are few people inside. However, there are issues such as the difficulty of controlling the large number of electrical devices used in the facility and the difficulty of predicting the number of customers who will visit the store.

[0005] The present invention has been made in view of the above background, and aims to control each electrical device used in each facility in a time series manner based on the results of pedestrian flow estimation. [Means for solving the problem]

[0006] In order to solve the above problem, the present invention provides an information processing device comprising: an acquisition unit that acquires people flow estimation results that estimate future people flow in a time series in a target area including a plurality of facilities; a prediction unit that performs predictions regarding power demand in a time series for each of the facilities included in the target area based on the acquired people flow estimation results; and a control unit that controls each electrical device used in each of the facilities in a time series based on the results predicted by the prediction unit. [Effects of the Invention]

[0007] According to the present invention, the electrical devices used in each facility are controlled in a time series manner based on the estimated people flow, thereby making it possible to reduce the power consumption in each facility, for example. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram showing an example of the overall configuration of an information processing system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing a hardware configuration of a server device 30 according to the embodiment. [Figure 3] FIG. 2 is a block diagram showing a functional configuration of a server device 30 according to the embodiment. [Figure 4] 10 is a diagram illustrating an example of data stored in the server device 30 according to the embodiment. FIG. [Figure 5] 10 is a diagram illustrating an example of data stored in the server device 30 according to the embodiment. FIG. [Figure 6] 10 is a diagram illustrating an example of data stored in the server device 30 according to the embodiment. FIG. [Figure 7] 10 is a diagram illustrating an example of data stored in the server device 30 according to the embodiment. FIG. [Figure 8] 10 is a diagram illustrating an example of data stored in the server device 30 according to the embodiment. FIG. [Figure 9] 10 is a graph illustrating the transition of the people flow estimation result and the power consumption of electrical appliances in the embodiment. [Figure 10]10 is a flowchart showing an example of the operation of the server device 30 according to the embodiment. [Figure 11] FIG. 10 is a block diagram showing a functional configuration of a server device 30 according to a modified example. [Figure 12] FIG. 10 is a diagram illustrating data stored in a server device 30 according to a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0009] [Embodiment] 1 is a diagram showing an example of an information processing system 1 according to an embodiment of the present invention. The information processing system 1 includes one or more electrical devices 11 installed in each of a plurality of facilities 10 corresponding to various types of stores such as convenience stores, drug stores, shops, supermarkets, etc., a people flow estimation system 20 that estimates the movement of people (hereinafter referred to as people flow) in a wide area, a server device 30 that functions as an example of the information processing device of the present invention, and power supply equipment 40 that supplies power to each of the electrical devices 11 via a power supply network (not shown).

[0010] The electrical appliances 11 may be any electrical appliances, but if the facility 10 is a retail store, for example, they may be air conditioners, refrigerators / freezers, fryers, heat retention devices, lighting devices, etc. These electrical appliances 11 can be remotely controlled from the server device 30 via the communication network 2.

[0011] The people flow estimation system 20 is a computer system that calculates the geographic distribution of people's positions over time, and can be realized, for example, by using a system called Mobile Spatial Statistics (registered trademark) by NTT Docomo, Inc. The people flow estimation system 20 calculates the distribution of the positions of mobile devices carried by anonymized users as user positions for each predetermined time period in predetermined mesh units divided on a map, for example, through de-identification processing, aggregation processing, and anonymization processing. The calculated distribution of user positions for each predetermined time period is not limited to the distribution of user positions in the past, but also includes future estimates based on the distribution of user positions in the past.

[0012] The server device 30 performs time-series prediction of power demand for each facility 10 based on the people flow estimation results obtained by estimating future people flows over time by the people flow estimation system 20, and further controls each electrical device 11 used in each facility 10 over time based on the prediction results. The server device 30 is not limited to a single computer, and may be composed of multiple computers.

[0013] The communication network 2 includes a wireless communication network or a wired communication network that communicatively connects the electrical devices 11, the people flow estimation system 20, the server device 30, and the power supply facility 40. Note that the numbers of the electrical devices 11, the people flow estimation system 20, the server device 30, and the power supply facility 40 shown in Fig. 1 are merely examples and are not limited to the numbers shown in the figure.

[0014] 2 is a diagram illustrating an example of the hardware configuration of server device 30. Server device 30 is physically configured as a computer including a processor 3001, memory 3002, storage 3003, and a bus connecting these. In the following description, the term "device" can be interpreted as a circuit, device, unit, etc. The hardware configuration of server device 30 may be configured to include one or more of the devices shown in the diagram, or may be configured to exclude some of the devices.

[0015] Each function in the server device 30 is realized by loading predetermined software (programs) onto hardware such as the processor 3001 and memory 3002, causing the processor 3001 to perform calculations, control communication via the communication device 3004, and control at least one of reading and writing data in the memory 3002 and storage 3003.

[0016] The processor 3001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc.

[0017] The processor 3001 reads programs (program codes), software modules, data, etc. from at least one of the storage 3003 and the communication device 3004 into the memory 3002, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the server device 30 may be implemented by a control program stored in the memory 3002 and running on the processor 3001. Various processes may be executed by one processor 3001, or may be executed simultaneously or sequentially by two or more processors 3001. The processor 3001 may be implemented by one or more chips. The programs may be transmitted from the communication network 2 to the server device 30 via a telecommunications line.

[0018] The memory 3002 is a computer-readable recording medium and may be configured by, for example, at least one of a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), etc. The memory 3002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 3002 can store an executable program (program code), a software module, etc. for implementing the method according to this embodiment.

[0019] Storage 3003 is a computer-readable recording medium, and may be constituted by at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 3003 may also be referred to as an auxiliary storage device.

[0020] The communication device 3004 is hardware (transmission / reception device) for performing communication between computers via the communication network 2, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0021] Each device, such as the processor 3001 and the memory 3002, is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses between each device.

[0022] Server device 30 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, processor 3001 may be implemented using at least one of these pieces of hardware.

[0023] 3 is a block diagram illustrating an example of the functional configuration of the server device 30. In the server device 30, a processor 3001, a memory 3002, a storage 3003, and a communication device 3004 cooperate with each other to realize the functions of an acquisition unit 31, a storage unit 32, a prediction unit 33, and a control unit 34.

[0024] The acquisition unit 31 acquires various data from external sources. The acquisition unit 31 acquires information indicating the distribution of user locations for each predetermined time period calculated for a target area including multiple facilities 10 by the people flow estimation system 20 for the future, i.e., people flow estimation results that estimate future people flows in the target area in a time series, from the people flow estimation system 20 via the communication network 2. The acquired people flow estimation results are stored in the storage unit 32 as shown in FIG. 4. FIG. 4 illustrates an example in which, for example, at 10:00 on May 1, 2024, a mesh code for identifying each mesh, the position of the mesh (e.g., the center of gravity of the mesh or the latitude and longitude indicating the positions of each vertex of the mesh), and the number of users estimated to be present in the mesh are stored in association with each other. The people flow estimation results are calculated for each predetermined time period, such as 30 minutes or 1 hour, and are stored in the storage unit 32 in chronological order for each time period.

[0025] The acquisition unit 31 also acquires information indicating the amount of power supplied to each mesh from the power supply facility 40. As a result, as shown in Fig. 5, a mesh code for identifying each mesh and the amount of power supplied to the houses and facilities 10 included in that mesh are stored in association with each other.

[0026] Furthermore, the storage unit 32 stores information about each facility 10 in response to an application from each facility 10. Specifically, as shown in FIG. 6, the storage unit 32 stores a facility ID for identifying each facility, the location of the facility (e.g., latitude and longitude), and electrical equipment information about the electrical equipment used in the facility, in association with each other. The electrical equipment information includes the device code, type, specifications, rated power consumption, etc. of each electrical equipment. When an administrator or the like of each facility 10 starts using power control by this information processing system 1, this information is registered in the server device 30 in response to a request from a communication terminal (not shown) of the facility 10.

[0027] Based on the people flow estimation results acquired by the acquisition unit 31, the prediction unit 33 makes a time-series prediction of the power demand for each facility 10 included in the target area that is the target of power control.

[0028] Generally, it is believed that there is a correlation between the size of people flow and power demand, such that an increase in people flow increases power demand and a decrease in people flow decreases power demand. Therefore, when the prediction unit 33 acquires a time-series people flow estimation result for a certain mesh, it defines a power load curve g2, which is similar to a curve g1 indicating the people flow estimation result, as the power load curve corresponding to the mesh, as shown in FIG. 7. Furthermore, as shown in FIG. 8, the prediction unit 33 calculates a power load curve g3 corresponding to a certain facility 10 included in the mesh by multiplying the power load curve g2 by a coefficient corresponding to the maximum power consumption estimated from the electrical appliance information of the electrical appliances 11 included in the facility 10. This power load curve is calculated for each facility 10, and its maximum and minimum values ​​vary depending on the number, specifications, or rated power consumption of the electrical appliances 11 included in each facility 10.

[0029] Returning to the explanation of FIG. 3, the control unit 34 controls the electrical appliances 11 used in each facility 10 in a time series manner based on the results predicted by the prediction unit 33.

[0030] First, the control unit 34 refers to the power supply amount for each mesh stored in the memory unit 32 (FIG. 5) and identifies the power supply amount corresponding to a mesh that includes a certain facility 10. Next, the control unit 34 predicts a facility 10 that meets the condition for a tight power supply and demand based on the power supply amount identified for the certain mesh and the history of past power supply and demand at each facility 10 included in the mesh. A possible condition for a tight power supply and demand is, for example, that the past power consumption at each facility 10 included in a certain mesh is X% or more (X is an arbitrary numerical value) of the power supply amount identified for that mesh.

[0031] Next, the control unit 34 calculates a target value for power consumption for the facility 10 that is predicted to meet the condition of tight power supply and demand based on the history of past power supply and demand. This target value may be, for example, Y% or more (Y is an arbitrary value) of the power consumption consumed in the facility 10.

[0032] Based on this target value, the control unit 34 controls the output level and power management of each electrical appliance 11 so that the total amount of power consumed by the electrical appliances 11 used in the facility 10 that meets the condition of tight power supply and demand does not exceed the target value. Possible examples of output level control include control to change the set temperature of the electrical appliance 11 if the electrical appliance 11 is an air conditioner or a refrigerator / freezer, or control to instruct the timing of turning the power on and off if the electrical appliance is a fryer.

[0033] Furthermore, the control unit 34 performs different controls for each electrical appliance 11 before and after the time point corresponding to the peak of the people flow estimation result. For example, since it takes a certain amount of time for a refrigerator / freezer to refrigerate / freeze products to a predetermined temperature, it is desirable to increase the output level to refrigerate / freeze the products before the peak of people flow arrives. On the other hand, it is thought that sufficient cooking can be performed by increasing the output level of a fryer, for example, in accordance with the peak of people flow.

[0034] Therefore, as shown in Fig. 9, for example, the control unit 34 controls a refrigerator / freezer in a certain facility 10 to obtain a power load curve g4 whose peak arrives a predetermined time before the peak of a power load curve g3 that is similar to the result of the people flow estimation in the facility 10. The control unit 34 also controls the refrigerator / freezer to start lowering its output level just before the peak of the power load curve g3 that is similar to the result of the people flow estimation in the facility 10 arrives. The control unit 34 also controls the fryer to obtain a power load curve g5 whose peak arrives at approximately the same time as the peak of the power load curve g3. This makes it possible to control the electrical appliances 11 according to the result of the people flow estimation.

[0035] Next, the operation of the server device 30 will be described with reference to Fig. 10. In Fig. 10, when a group of facilities to be subject to power control is designated, the acquisition unit 31 acquires various data for the target area including the group of facilities (step S11). Specifically, the acquisition unit 31 acquires the people flow estimation results calculated for the target area by the people flow estimation system 20. The acquisition unit 31 also acquires information indicating the amount of power supplied from the power supply facility 40 to one or more meshes corresponding to the target area.

[0036] The prediction unit 33 makes a time-series prediction of the power demand for each facility 10 included in the target area based on the people flow estimation results acquired by the acquisition unit 31 and information indicating the amount of power supplied to each mesh (step S12).

[0037] Based on the results predicted by the prediction unit 33, the control unit 34 controls each electrical device 11 used in each facility 10 in a time series manner using the target values ​​and peaks of people flow as described above (step S13).

[0038] According to the embodiment described above, the electrical devices used in each facility are controlled in a time series manner based on the estimated results of people flow. This makes it possible to reduce the power consumption in each facility, especially when the number of people is low, compared to when the electrical devices are not controlled according to the number of people. On the other hand, when the number of people is high, the quality of the services provided to users in each facility can be maintained without compromising the quality of the services, such as by maintaining a comfortable temperature inside the facility, refrigerating / freezing products sold in the facility at an appropriate temperature, or quickly cooking and serving products in a fryer, thereby improving the satisfaction of users when using the facility.

[0039] [Variations] The present invention is not limited to the above-described embodiment. The above-described embodiment may be modified as follows. Furthermore, two or more of the following modifications may be combined and implemented.

[0040] [Variation 1] The number of users who will visit each facility 10 may be predicted, and each electrical appliance 11 used in each facility 10 may be controlled based on the predicted number of users.

[0041] For example, the prediction unit 33 performs machine learning using training data in which the people flow estimation results acquired for each mesh are used as explanatory variables and the number of users who actually visited each facility 10 included in that mesh is used as a target variable, to generate a trained model, which is then stored in the storage unit 32. The number of users who actually visited the facility 10 can be calculated, for example, by a POS system or an electronic payment system. When the prediction unit 33 acquires a time-series people flow estimation result for a certain mesh, the prediction unit 33 inputs the people flow estimation result into the trained model to obtain the number of users estimated to visit each facility 10. Note that the prediction unit 33 is not limited to such machine learning, and may predict the number of users visiting each facility 10 based on the difference between a people flow estimation result acquired in the past and the number of users who actually visited each facility 10 in the past.

[0042] Then, the control unit 34 uses the number of users predicted in a time series by the prediction unit 33 instead of the power load curve g3 similar to the above-mentioned people flow estimation result, and performs different control for each electrical appliance 11 before and after the time point corresponding to the peak.

[0043] In this way, the prediction unit 33 may predict the number of users visiting each facility 10 included in the target area based on the people flow estimation result acquired by the acquisition unit 31, and the control unit 34 may time-series control each electrical appliance 11 used in each facility 10 based on the number of users predicted by the prediction unit 33. In the above embodiment, the electrical appliances 11 are controlled according to the people flow estimation result for each mesh. However, since this people flow estimation result includes users who do not visit the facility 10, it does not match the number of users visiting each facility 10. According to this modification, the electrical appliances 11 are controlled using the number of users predicted for each facility 10, and therefore it is possible to more accurately control the electrical appliances 11 according to the number of users in each facility 10.

[0044] [Variation 2] Furthermore, the prediction unit 33 may predict the number of users who will visit each facility 10 for each attribute of the users, and the control unit 34 may control each electrical appliance 11 based on the predicted number of users for each attribute.

[0045] For example, the prediction unit 33 performs machine learning using training data in which the people flow estimation results obtained for each mesh are used as explanatory variables, and the attributes of users who actually visited each facility 10 included in that mesh and the number of those users are used as objective variables, to generate a trained model, and stores the trained model in the storage unit 32. In this case, the attributes of users who actually visited the facility 10 can be identified by using user attributes registered in advance in an electronic payment system, for example.

[0046] Then, when the prediction unit 33 obtains the time series people flow estimation results for a certain mesh, it inputs the people flow estimation results into the above-mentioned trained model to obtain the attributes of users who are predicted to visit each facility 10 and the number of users.

[0047] The control unit 34 then controls each electrical appliance 11 based on the predicted number of users by attribute. For example, under the assumption that young users are more likely to purchase refrigerated drinks and frozen snacks, a conceivable example would be to control the refrigerator / freezer so that its power load curve peaks before a predetermined time during which many young users visit the facility 10. Another conceivable example would be to control the fryer so that its power load curve peaks during a time during which many single users visit the facility 10, under the assumption that single users are more likely to purchase cooked foods. In this way, by controlling the electrical appliances 11 according to the user attributes and the number of users, it is possible to further improve the satisfaction of users when they use the facility 10.

[0048] [Variation 3] The server device 30 may acquire and analyze evaluations and reviews about the facility 10 using, for example, a service called a social networking service (SNS) or a review site.

[0049] In this case, the server device 30 further includes functions of a rating information acquisition unit 35 and an analysis unit 36, as shown in FIG. 11. The rating information acquisition unit 35 acquires rating information, including ratings of products managed by the electrical appliance 11 by users who have visited the facility 10 where the electrical appliance 11 is controlled by the control unit 34, via services called SNS or word-of-mouth sites. In this case, a facility ID (see FIG. 6) assigned to each facility is used to identify each facility 10 and acquire rating information. The acquired rating information is stored in the storage unit 32 in association with the facility ID, as shown in FIG. 12.

[0050] The analysis unit 36 ​​performs various analyses using the acquired evaluation information. The analysis unit 36 ​​performs the analysis using large language models (LLMs) and generates and outputs evaluation results from any viewpoint, such as whether the product purchased at the facility 10 was sufficiently cold if it was refrigerated / frozen, whether the time it took for the product purchased at the facility 10 to be cooked and served was within an acceptable range, whether the heating and cooling in the store was comfortable, and whether the lighting in the store was sufficiently bright.

[0051] Furthermore, the analysis unit 36 ​​may use the acquired evaluation information to perform analysis to modify the control by the control unit 34. Specifically, the analysis unit 36 ​​performs analysis using a large-scale language model, and generates and outputs analysis results such as, for example, increasing the output level of the refrigerator / freezer because the refrigerated / frozen product purchased at the facility 10 was not sufficiently cooled; increasing the output level of the fryer because the time it took for the product purchased at the facility 10 to be cooked and served was long; increasing / decreasing the output level of the air conditioner because the heating and cooling in the store was uncomfortable; or increasing the output level because the lighting in the store was dim. The control unit 34 changes the rules for controlling the electrical appliances 11 based on these output results and performs the control accordingly. In this way, controlling the electrical appliances 11 based on the evaluations of users who visited the facility can further improve the user's satisfaction when using the facility 10.

[0052] [Other variations] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (for example, by wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or the multiple devices with software.

[0053] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0054] For example, the server device 30 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0055] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), 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 (registered trademark), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other appropriate systems, and next-generation systems extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0056] The present invention may also be an information processing method comprising the steps of: acquiring a people flow estimation result that estimates future people flow over time in a target area including a plurality of facilities; predicting power demand over time for each of the facilities included in the target area based on the acquired people flow estimation result; and controlling electrical equipment used in each of the facilities over time based on the predicted result. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure present various step elements using an exemplary order and are not limited to the specific order presented.

[0057] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0058] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0059] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0060] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0061] The 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 be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0062] Furthermore, the information, parameters, etc. described in this 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.

[0063] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0064] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0065] The "unit" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," etc.

[0066] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0067] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0068] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0069] 1: Information processing system, 2: Communication network, 10: Facility, 11: Electrical equipment, 20: People flow estimation system, 30: Server device, 31: Acquisition unit, 32: Memory unit, 33: Prediction unit, 34: Control unit, 35: Evaluation information acquisition unit, 36: Analysis unit, 3001: Processor, 3002: Memory, 3003: Storage, 3004: Communication device, 40: Power supply equipment.

Claims

1. an acquisition unit that acquires people flow estimation results that estimate future people flows in a time series in a target area including a plurality of facilities; a prediction unit that performs a time-series prediction of power demand for each of the facilities included in the target area based on the acquired people flow estimation result; a control unit that controls each electrical device used in each of the facilities in a time series manner based on the results of the prediction by the prediction unit; An information processing device comprising:

2. the prediction unit predicts facilities that meet the conditions for a tight power supply and demand situation based on the amount of power supplied to the target area, and calculates a target value for power consumption for the predicted facilities; The control unit controls each electrical device used in the facility that meets the condition of tight power supply and demand based on the target value.

2. The information processing apparatus according to claim 1, wherein:

3. The control unit performs different controls for each of the electrical devices before and after a time point corresponding to a peak of the pedestrian flow based on the pedestrian flow estimation result.

2. The information processing apparatus according to claim 1, wherein:

4. the prediction unit predicts the number of users who will visit each of the facilities included in the target area based on the acquired people flow estimation result; The control unit controls each electrical device used in each of the facilities in a time series manner based on the number of users predicted by the prediction unit.

2. The information processing apparatus according to claim 1, wherein:

5. the prediction unit predicts the number of users who will visit each of the facilities for each attribute of the users; The control unit controls each of the electrical devices based on the predicted number of users by attribute.

5. The information processing apparatus according to claim 4.

6. an evaluation information acquisition unit that acquires evaluation information including evaluations of products managed by the electrical device by users who have visited the facility where the electrical device is controlled by the control unit; an analysis unit that analyzes the acquired evaluation information; 2. The information processing apparatus according to claim 1, further comprising:

7. The analysis unit uses the acquired evaluation information to perform an analysis to correct the control by the control unit.

7. The information processing apparatus according to claim 6,

8. acquiring a people flow estimation result that estimates future people flow in a time series in a target area including a plurality of facilities; a step of predicting power demand for each of the facilities included in the target area in time series based on the acquired people flow estimation result; a step of controlling each electrical device used in each of the facilities in a time series manner based on the predicted results; An information processing method comprising:

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