JPWO2020105154A1 - Data analysis system and data analysis method - Google Patents

Abstract

A data analysis system that holds a storage unit that holds a mathematical model that shows the relationship between the objective index related to human productivity and the environmental index related to the environment of the person's activity location, and the data related to the environment of the person's activity location acquired from the sensor. Environmental data that is, and business goal data including goals related to the activity of the person entered by the person are accepted, and based on the business goal data and the mathematical model, the conditions of the environmental index suitable for increasing the productivity of the person. With a recommended processing unit that searches for information on activity locations suitable for increasing the productivity of people based on the conditions of environmental indicators and environmental data, and outputs information on activity locations obtained by the search. Have.

Description

The present invention relates to a technique for recommending an effective working space for a business purpose based on sensor data of people and the environment.

In recent years, the office space has diversified, and employees are becoming able to freely choose where to work. However, the only criteria for choosing a location was personal preference or availability. On the other hand, technologies for quantitatively measuring human behavior such as walking and sleeping with wearable sensors have been spreading so far. Further, Patent Document 1 discloses a technique for providing work style advice for increasing behavioral indicators related to work productivity by using such data.

Patent Document 1: Japanese Unexamined Patent Publication No. 2017-208005

Office workers have various business purposes depending on the situation, such as wanting to concentrate on writing, talking with colleagues and coming up with ideas. However, the suitable office space differs depending on the purpose. For example, a quiet space may be preferable to concentrate on writing, and a noisy space or outdoors may be preferable to come up with ideas. However, the office environment, specifically room temperature, illuminance, environmental noise, and the number of people, fluctuate in real time. In addition, the characteristics of the space in which the effect can be easily obtained differ depending on the individuality and condition of the person. Therefore, staying in a specific area does not always give the same productivity. Therefore, it is useful to dynamically match people and the office space by reflecting the situation at that time.

Based on the above, an object of the present invention is to provide a system that recommends an effective working space for business purposes based on sensor data of people and the environment.

A typical example of the means for solving the problem according to the present invention is a data analysis system, which is a mathematical system showing the relationship between a purpose index related to a person's productivity and an environmental index related to the environment of a person's activity place. The storage unit that holds the model, environmental data that is data related to the environment of the activity place of the person acquired from the sensor, and business goal data including the goal related to the activity of the person input by the person are received, and the business goal is received. Based on the data and the mathematical model, the condition of the environmental index suitable for increasing the productivity of the person is searched, and the productivity of the person is determined based on the condition of the environmental index and the environmental data. It has a recommended processing unit that searches for information on an activity location suitable for enhancement and outputs information on the activity location obtained by the search.

According to one aspect of the present invention, it is possible to analyze time-continuous sensor data relating to a person and the environment to match an effective workplace space in real time in order to improve the performance of the person in work. Issues, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

It is explanatory drawing which shows the outline of the real-time office space utilization recommendation system of embodiment of this invention. It is a block diagram which shows an example of the structure of the analysis server and the environment measuring machine in the learning phase of embodiment of this invention. It is a block diagram which shows an example of the structure of the wearable sensor and the surveillance camera in the learning phase of embodiment of this invention. It is a block diagram which shows an example of the configuration of the client and the application server in the operation phase of the Embodiment of this invention. It is a block diagram which shows an example of the structure of the environment measuring machine and the surveillance camera in the operation phase of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen displayed on the display of the client of embodiment of this invention. It is explanatory drawing which shows an example of the screen at the time of performing the questionnaire to the user by the wearable sensor of embodiment of this invention. It is a sequence diagram which shows the procedure of the sensing performed in the wearable sensor, the environment measuring machine and the surveillance camera in the learning phase of the embodiment of this invention, and the procedure until the data is stored in the analysis server. In the learning phase of the embodiment of the present invention, the procedure for generating a mathematical model performed on the analysis server and the procedure for updating the mathematical model DB of the application server by copying, excerpting, or integrating the mathematical model DB of the analysis server are performed. It is a sequence diagram which shows. It is a sequence diagram which shows the procedure of processing and updating the environment data measured in the application server in the operation phase of embodiment of this invention. FIG. 5 is a sequence diagram showing a procedure in which an application server generates a recommendation and presents it on a screen with respect to a business goal input by a user by operating a client in the operation phase of the embodiment of the present invention. It is explanatory drawing which shows an example of the structure of the objective variable table included in the mathematical model DB of the Embodiment of this invention. It is explanatory drawing which shows an example of the structure of the mathematical model table table included in the mathematical model DB of the Embodiment of this invention. It is explanatory drawing which shows an example of the structure of the explanatory text table included in the mathematical model DB of the Embodiment of this invention. It is explanatory drawing which shows an example of the structure of the office space information DB of embodiment of this invention. It is explanatory drawing which shows an example of the structure of the area definition information of embodiment of this invention. It is explanatory drawing which shows an example of the structure of the terminal management information which stores the information measured by the environment measuring instrument of embodiment of this invention. It is explanatory drawing which shows an example of the structure of the terminal management information which stores the information measured by the wearable sensor of embodiment of this invention. It is explanatory drawing which shows an example of the structure of the terminal management information which stores the information measured by the surveillance camera of embodiment of this invention.

The present invention is a system that recommends an effective work space for business purposes in real time, and is characterized by using sensor data that measures the conditions of people and the environment. Hereinafter, the description will be given with reference to the drawings.

First, embodiments of the present invention will be described with reference to the drawings.

<Fig. 1: System overview>
FIG. 1 is an explanatory diagram showing an outline of a real-time work space utilization recommendation system according to an embodiment of the present invention.

In the embodiment of the present invention, there are two types, a learning phase for creating a mathematical model and an operation phase for operating a work space utilization recommendation system using the mathematical model.

First, the learning phase will be described.

The learning phase consists of a wearable sensor (IR), an environment measuring device (EM), a surveillance camera (SC), and an analysis server (SS). However, the surveillance camera (SC) is not an indispensable configuration.

A user (US) wears a wearable sensor (TR). A sensor (not shown) that measures a physical quantity in a wearable sensor (TR) acquires sensing data regarding the movement of the wearer and sensing data regarding interaction with another wearer (for example, face-to-face or proximity state). In the following description, the wearable sensor (TR) has a 3-axis acceleration sensor (not shown), and the 3-axis acceleration data measured by the wearable sensor (TR) is used as sensing data related to the wearer's movement, but is measured by another type of sensor. Other physical quantities may be used.

Interaction is detected, for example, by transmitting and receiving an infrared signal or other wireless signal between each wearable sensor (TR) when users (US) face each other. Further, by communicating between the infrared beacon (IB) installed on the desk or the like and the wearable sensor (TR), it is possible to acquire sensing data regarding the place and time when the user (US) stayed. These acquired sensing data are stored in the analysis server (SS) through a wireless or wired network (NW).

Further, an environment measuring machine (EM) that mainly measures an indoor environment (including outdoors) at a predetermined timing (for example, at regular intervals) is installed in the work space. The environmental measuring machine (EM) has a group of sensors (for example, a temperature sensor, a humidity sensor, a noise measuring machine, etc., but other means may be used) for measuring the state of the space, and using them. Acquire sensing data regarding the status of work space. The acquired sensing data is stored in the analysis server (SS) through a wireless or wired network (NW).

In addition, a surveillance camera (SC) installed on the wall surface or ceiling of the work space records a moving image or a still image showing the situation of the space, and stores the moving image or a still image in the analysis server (SS) through a wireless or wired network (NW).

The analysis server (SS) extracts behavioral features from the data from the wearable sensor (TR) and generates environmental features from the data from the environmental measuring machine (EM) and the surveillance camera (SC). , Combine and perform statistical analysis. In statistical analysis, the analysis server (SS) uses behavioral features related to human business performance, such as concentration duration or conversational bidirectionality, as objective variables, and other statistically relevant behavioral features and environmental features. Find out the amount. The analysis server (SS) stores the combination between these features in the mathematical model DB (SSME_MD).

Next, the operation phase will be described.

The operation phase consists of an application server (AS), a client (CL), an environment measuring machine (EM), and a surveillance camera (SC). However, the surveillance camera (SC) is not an indispensable configuration. Further, the operation phase may further include a wearable sensor (TR), but for the sake of simplicity, a configuration not included in the present invention will be described.

At a specific timing, the mathematical model DB (SSME_MD) in the analysis server (SS) is manually or automatically copied to the mathematical model DB (ASME_MD) in the application server (AS). Further, the sensing data of the environment measuring machine (EM) and the surveillance camera (SC) are stored in the office space information DB (ASME_SD) in the application server (AS) in real time.

When a user (US) who is currently searching for a place suitable for his / her business purpose operates a client (CL) and inputs a business purpose, the application server (AS) searches the mathematical model DB (ASME_MD) for the purpose. The environmental feature amount that matches the picked up environmental feature amount is picked up, and from the data of the latest time included in the office space information DB (ASME_SD), the area showing the state closest to the picked up environmental feature amount is displayed on the display (CLOSE) as a recommendation. do.

<Fig. 2A, Fig. 2B: System configuration diagram of learning phase>
2A and 2B are block diagrams showing an example of the configuration of an analysis server (SS), an environment measuring device (EM), a wearable sensor (TR), and a surveillance camera (SC) in the learning phase of the embodiment of the present invention. be.

An environmental measuring machine (EM) is a device that includes a group of sensors for measuring an indoor or outdoor environment, including a sensor unit (EMSE), a storage unit (EMME), a battery (EMBT), a control unit (EMCO), and a control unit (EMCO). It has a transmitter / receiver (EMSR).

Examples of the sensor included in the sensor unit (EMSE) include a temperature sensor (EMSE_T), a humidity sensor (EMSE_H), and a noise measuring device (EMSE_S) for measuring environmental sound. In addition, a sensor (not shown) for measuring the CO2 concentration in the air may be included. When the environment measuring device (EM) is installed outdoors, the sensor unit (EMSE) may include an outdoor environment measuring device group (EMSE_O) for measuring rainfall, sunshine, and the like.

Further, the storage unit (EMME) stores a clock (EMCK) for holding time information, terminal ID information (EMID) for identifying the environment measuring device (EM), and sensing data (EMME_S) which is a measurement result. In addition, the environment measuring machine (EM) has a battery (EMBT) for securing a power source.

Further, the control unit (EMCO) controls sensing and transmission / reception. Specifically, the control unit (EMCO) controls the time synchronization (EMCO_T) that synchronizes the time information in the clock (EMCK) with the standard time, and the sensor of the sensor unit (EMSE) to acquire data on the environment and obtain the time information. The sensing control (EMCO_S) to be applied, and the data transmission (EMSD) for transmitting the data to the analysis server (SS) via the transmission / reception unit (EMSR) are performed.

The wearable sensor (TR) is a sensor for measuring human behavior and state, and is a sensor unit (TRSE), an input / output unit (TRIO), a storage unit (TRME), a battery (TRBT), and a control unit (TRCO). And has a transmitter / receiver (TRSR). In the present invention, it is described as wearable, and it is assumed that the sensor is worn on the body. For example, a camera, a vibration sensor, a thermometer, or the like may be used.

As an example of the sensor included in the sensor unit (TRSE), the acceleration sensor (TRSE_A) for measuring the movement of the body and the wearable sensor (TR) communicate with each other to detect the face-to-face state and the proximity state between the wearers. There is an infrared transmitter / receiver (TRSE_IR), an illuminance sensor (TRSE_L) and a heart rate monitor (TRSE_H).

Further, the wearable sensor (TR) may have an input / output unit (TRIO) for presenting a questionnaire to a user (US) and receiving an answer, if necessary. The function for inputting the questionnaire may be provided in a terminal other than the wearable sensor (TR), for example, a PC or a smartphone (not shown).

The input / output unit (TRIO) includes a screen (TRIO_D) for displaying questionnaire questions and time, a button (TRIO_B) for the user (US) to input an answer and switch screens, and a specific one. It has a speaker (TRIO_S) for prompting the user (US) to answer at the time of.

The storage unit (TRME) includes a watch (TRCK) that holds time information, terminal ID information (TRID) that identifies the wearable sensor (TR), sensing data (TRME_S) that is the measurement result, and questionnaire response result data. (TRME_Q) is stored. Further, the wearable sensor (TR) has a battery (TRBT) for securing a power source.

The control unit (TRCO) controls sensing and transmission / reception. Specifically, the control unit (TRCO) controls the time synchronization (TRCO_T) that synchronizes the time information in the clock (TRCK) with the standard time, and the sensor of the sensor unit (EMSE) to acquire data on human behavior and state. Sensing control (TRCO_S) that gives time information, questionnaire control (TRCO_Q) that controls the display of questionnaires and input of answers, and data that transmits these data to the analysis server (SS) via the transmission / reception unit (TRSR). Perform transmission (TRSD).

A surveillance camera (SC) is a camera mounted on a wall, ceiling, or PC, and is for recording the number and movement of people staying in a space as a moving image or a still image. The surveillance camera (SC) includes a sensor unit (SCSE), a storage unit (SCME), a battery (not shown), a control unit (SCCO), and a transmission / reception unit (SCSR).

The sensor unit (SCSE) has a camera (SCSE_C). In addition, the storage unit (SCME) stores a clock (SCCK) that holds time information, terminal ID information (SCID) that identifies a surveillance camera (SC), and moving image data (SCME_M) that is a recording result. Further, the surveillance camera (SC) has a battery (not shown) for securing a power source.

Further, the control unit (SCCO) controls shooting and transmission / reception, time synchronization (SCCO_T) that synchronizes the time information in the clock (SCCK) with the standard time, and shooting control (SCSE_C) that controls the camera (SCSE_C) to shoot the space (SCCO). SCCO_R), and further data transmission (SCSD) is performed to transmit the captured data to the analysis server (SS) via the transmission / reception unit (SCSR).

The analysis server (SS) has the role of analyzing the collected data on human behavior and environment and generating a mathematical model on human business performance and environmental conditions. The analysis server (SS) has a transmission / reception unit (SSSR), a storage unit (SSME), and a control unit (SSCO).

The storage unit (SSME) stores terminal IDs (SSME_T) that manage terminal IDs and types of environmental measuring instruments (EM), wearable sensors (TR), surveillance cameras (SC), and the types of spaces in which they are installed. Area definition information (SSME_A) to be shown, program group (SSME_P) for performing feature amount extraction (SSCO_FE), sensing DB (SSME_SD) for storing collected sensing data, feature amount DB (SSME_FD) for storing the extracted feature amount. ), And a mathematical model DB (SSME_MD) for storing the calculated mathematical model.

The control unit (SSCO) receives various data from the transmission / reception unit (SSSR) via the network (NW) (SSRD), and uses the feature amount extraction program (SSME_P) to match the feature amount according to each data type. Is extracted (SSCO_FE). Specific examples of the feature amount include the number of steps, average heart rate, face-to-face time and number of face-to-face with others, bidirectionality during conversation, and acceleration as those calculated from the data obtained from the wearable sensor (TR). There is a degree of happiness that indicates the active state of the workplace or individual using sensor data, the place and time of stay by area, and the results of questionnaire responses. As an example of the feature amount calculated from the data obtained from the environmental measuring device (EM), there is a discomfort index calculated from the average room temperature or temperature and humidity in units of 5 minutes or 10 minutes. Examples of features calculated from data obtained from surveillance cameras (SC) include the number of people staying in the area where the camera is placed, the ratio of the number of people by attribute of the resident, and the activity status of the resident such as walking or stationary. There is.

In addition, since a series of terminals such as an environment measuring device (EM), a wearable sensor (TR), and a surveillance camera (SC) are time-synchronized, data of the same time zone acquired from a plurality of types of terminals can be combined. Then, for example, a combination feature amount such as "the number of steps is 60 steps / minute or more in an area of room temperature of 25 to 28 degrees" may be generated.

Next, feature quantities (including questionnaire response results) related to human behavior or state that can be used as at least one objective variable are defined in advance. The objective variables are specified one by one in order (SSCO_SO), and statistical analysis (SSCO_SA) and mathematical model storage (SSCO_MD) obtained from the objective variables are performed. Statistical analysis (SCSP_SA) uses simple regression analysis, multiple regression analysis, machine learning, etc., and is a set of explanatory variables (environmental features, behavioral features, or a combination thereof) that are statistically significantly related to a predetermined objective variable. It is to find out the feature amount). The combination group of the objective variable and the explanatory variable is called a mathematical model. The learning phase is completed by generating a mathematical model.

<Fig. 3A, Fig. 3B: System configuration diagram of operation phase>
3A and 3B are block diagrams showing an example configuration of a client (CL), an application server (AS), an environment measuring device (EM), and a surveillance camera (SC) in the operation phase of the embodiment of the present invention. ..

The operation phase is a phase for operating the office space utilization recommendation system using the mathematical model generated in the learning phase. The mathematical model DB (ASME_MD) is a copy or integration of the mathematical model DB (SSME_MD) created by the analysis server (SS) in the learning phase, but the workplace to which it is applied must be the same in the learning phase and the operation phase. There is no. In other words, the workplace that collects data for creating a mathematical model and the workplace that utilizes the office space utilization recommendation system using it may be different.

In the operation phase, the environment measuring machine (EM) and the surveillance camera (SC) have the same configuration as the learning phase, so the description thereof will be omitted. The type and model of the sensor do not have to be exactly the same as in the learning phase, but it is desirable that the performance is similar to that used in the learning phase in terms of error range and installation location criteria. Further, it differs from the learning phase in that the destination of the sensing data (EMME_S) and the moving image data (SCME_M) acquired by the environment measuring machine (EM) and the surveillance camera (SC) in the operation phase is the application server (AS).

The application server (AS) has a role of utilizing a mathematical model and performing a process for recommending an area in the office space suitable for the business purpose of the user (US). The application server (AS) has a transmission / reception unit (ASSR), a storage unit (ASME), an environment data processing unit (ASCE), and a recommendation processing unit (ASCO).

The storage unit (SSME) is a terminal management information (ASME_T) that manages terminal IDs and types of environment measuring machines (EM) and surveillance cameras (SC), and area definition information (ASME_A) that indicates the type of space in which they are installed. ), Program group for performing feature quantity extraction (ASCE_FE) (not shown), sensing DB for storing collected sensing data (not shown), working space information DB for managing the latest working space data that is updated sequentially. It has (ASME_SD) and a mathematical model DB (ASME_MD) copied or partially extracted from the analysis server (SS).

12A to 12C show an example of the configuration of the table included in the mathematical model DB (ASME_MD) or (SSME_MD).

Specifically, FIG. 12A is an explanatory diagram showing an example of the configuration of the objective variable tables (SSME_MD_O) and (ASME_MD_O) included in the mathematical model DB (ASME_MD) and (SSME_MD) according to the embodiment of the present invention. FIG. 12B is an explanatory diagram showing an example of the configuration of the mathematical model table (SSME_MD_T1) and (ASME_MD_T1) included in the mathematical model DB (ASME_MD) and (SSME_MD) according to the embodiment of the present invention. FIG. 12C is an explanatory diagram showing an example of the configuration of the explanatory text tables (SSME_MD_E) and (ASME_MD_E) included in the mathematical model DB (ASME_MD) and (SSME_MD) according to the embodiment of the present invention.

The structures such as the types of tables and the types of columns included in the mathematical model DB (ASME_MD) and (SSME_MD) are almost the same. FIG. 12A shows an example of the structure of the table (SSME_MD_O) and (ASME_MD_O) that define the objective variable. This is a table in which behavioral features and questionnaire items related to human business performance that can be used as objective variables are specified in advance. In statistical analysis (SSCO_SA), these are used as objective variables for analysis.

FIG. 12B shows an example of the structure of the mathematical model table (SSME_MD_T1) and (ASME_MD_T1). Here, the result narrowed down when # 1, that is, "concentration duration" is selected as the objective variable is displayed. Listed in the mathematical model tables (SSME_MD_T1) and (ASME_MD_T1) are the explanatory variables that have been confirmed to be statistically related to the selected objective variable.

For example, in the first row of the mathematical model table in FIG. 12B, when the environment is in the range of "temperature 23 to 25 ° C" and the environmental sound is "50 to 60 dB", "the speed of thinking by answering the questionnaire is 4 to 5". It shows that the concentration duration is high when "desk work_30 minutes or more" is performed in the "high" state. In this way, the combination feature quantity of the feature quantity related to the environment and the feature quantity related to human behavior or state is used as the explanatory variable, and the statistical relationship with the specific objective variable is clarified.

The statistic r in the table is a statistic indicating the strength of the association, and for example, a multiple regression coefficient is described. In addition, when performing statistical analysis, if the time of features is taken into consideration, the method of interpreting the analysis results will change. When the objective variable and the explanatory variable are in the same time zone, it is shown that the objective variable improves while the explanatory variable is in the state. This is described as During in the analysis type column.

In addition, when the objective variable is selected so that the time is later than the explanatory variable, it is shown that the objective variable improves after the state of the explanatory variable. This is described as After in the analysis type column. Further, when the time division is divided by one day and the data of the objective variable and the explanatory variable on the same date are used, it is shown that the objective variable rises on the day when the explanatory variable is in the state. This is described as Day in the analysis type column.

Further, since it is difficult for the user (US) to understand the table as it is as described above, as shown in FIG. 12C, the commentary table (SSME_MD_E) for storing the commentary automatically generated based on the values in the table and the commentary table (SSME_MD_E) (SSME_MD_E) may also be present.

FIG. 13 is an explanatory diagram showing an example of the configuration of the office space information DB (ASME_SD) according to the embodiment of the present invention.

The office space information DB (ASME_SD) is a database for managing data obtained by an environment measuring machine (EM) and a surveillance camera (SC) and their feature amounts in association with time data. Although multiple terminals may be installed in one room or area, the office space information DB (ASME_SD) is associated with the information contained in the terminal management information (ASME_T), and the Room ID indicating the room or area and the time. It is a table with the key. The office space information DB (ASME_SD) includes the temperature, humidity, volume, etc. obtained from the indoor environment measuring machine (EM), the amount of sunshine and rain obtained from the outdoor environment measuring machine (EM), and the surveillance camera (SC). ) Is a table that shows the data of features such as the number of people staying in each area and by time.

Although omitted in FIG. 13, the office space information DB (ASME_SD) may further include data indicating the reservation status of a room or area that needs to be reserved for use. When the reservation system for managing the reservation status of a room or area is operating independently of the real-time office space utilization recommendation system in the workplace to which the operation phase of the real-time office space utilization recommendation system of the present embodiment is applied. The data indicating the reservation status read from the reservation system may be stored in the office space information DB (ASME_SD), or the office space information DB (ASME_SD) does not hold the data indicating the reservation status. , The reservation system may be referred to if necessary. In addition, a room or area for which the time zone that the user wants to use is already reserved may be excluded from the target to be presented to the user.

FIG. 14 is an explanatory diagram showing an example of the configuration of the area definition information (SSME_A) and (ASME_A) according to the embodiment of the present invention.

The area definition information (SSME_A) is a table for managing information on the characteristics of the area and room in the workplace where the measurement is performed. In the present embodiment, the room and the area other than the room (for example, a roof garden) may be collectively referred to as an "area".

The area definition information (SSME_A) uses the RoomID indicating each area or room as a key, and stores the name, type, closing / opening, coordinates on the map, reservation necessity, and the like. The name and type indicate the name and type of each area or room. Closed / open indicates whether each area or room is an open space where people can freely enter and exit, or a closed space where it is not. In addition, the real-time work space utilization recommendation system holds an image of a map of the entire workplace, and the coordinates on the map indicate the coordinates of each area or room in the image. Reservation required indicates whether or not a reservation is required to use each area or room.

Terminal management information (SSME_T) and (ASME_T) are shown in FIGS. 15A to 15C. This includes an ID indicating the individual terminal being measured, its model, the Room ID of the installed area, the type of sensor included in the terminal, and information indicating the attribute of the installation location (for example, whether the installation location is outdoors). It manages.

FIG. 15A is an explanatory diagram showing an example of the configuration of terminal management information (SSME_T_EM) and (ASME_T_EM) that store information measured by the environmental measuring instrument (EM) according to the embodiment of the present invention.

In the terminal management information (SSME_T_EM) and (ASME_T_EM) shown in FIG. 15A, a terminal ID for identifying each environmental measuring instrument (EM), a model of each environmental measuring instrument (EM), and each environmental measuring instrument (EM) are installed. RoomID identifying a room or area, whether each environmental instrument (EM) includes a temperature sensor, humidity sensor and noise instrument, and whether each environmental instrument (EM) is installed outdoors or indoors. Includes information such as whether it is.

FIG. 15B is an explanatory diagram showing an example of a configuration of terminal management information (SSME_T_TR) that stores information measured by the wearable sensor (TR) according to the embodiment of the present invention.

The terminal management information (SSME_T_TR) shown in FIG. 15B includes a terminal ID that identifies each wearable sensor (TR), a type of each wearable sensor (TR) (for example, whether the name tag is another terminal or a bangle-type terminal, etc.). The model of each wearable sensor (TR), the user ID that identifies the user (US) wearing each wearable sensor (TR), and whether each wearable sensor (TR) includes an acceleration sensor, an infrared transmitter / receiver, and a heart rate monitor. Includes information such as whether or not.

Since the terminal management information only needs to be that of the type of terminal measured in the workplace, the terminal management information related to the wearable sensor (TR) is not required in the learning phase in the example of the embodiment of this embodiment. ..

FIG. 15C is an explanatory diagram showing an example of the configuration of terminal management information (SSME_T_SC) and (ASME_T_SC) that store information measured by the surveillance camera (SC) according to the embodiment of the present invention.

The terminal management information (SSME_T_SC) and (ASME_T_SC) shown in FIG. 15C are a terminal ID that identifies each surveillance camera (SC), a model of each surveillance camera (SC), and a room or area in which each surveillance camera (SC) is installed. RoomID, resolution of each surveillance camera (SC), frame rate of video captured by each surveillance camera (SC), angle of view of each surveillance camera (SC) (for example, wide angle or not), and each surveillance Includes information such as whether the camera (SC) is installed outdoors or indoors.

Next, the application server (AS) will be described with reference to FIG. 3 again. The application server (AS) has an environment data processing unit (ASCE), and periodically updates the data obtained from the environment measuring machine (EM) and the surveillance camera (SC). The environmental data processing unit (ASCE) receives data (ASRD) through the network (NW) through the transmission / reception unit (ASSR), extracts features (ASCE_FE) from the data by the same method as the analysis server (SS), and then performs feature quantity extraction (ASCE_FE). The extracted features are added to the office space information DB (ASME_SD) together with the time information (ASCE_SO).

On the other hand, the recommendation processing unit (ASCO) is a process of providing a recommendation of the office space at that time, starting from an inquiry from a user (US) received via the input / output function of the client (CL). When the user (US) inputs a business goal using the input function of the client (CL), for example, the keyboard (CLIK) or the touch panel (CLIT) (CLCO_I), the client (CL) sends it to the application server (AS). (CLCO_S).

The recommendation processing unit (ASCO) receives the business goal via the transmission / reception unit (ASSR) (ASCO_R), searches for a record whose objective variable is the business goal selected from the mathematical model DB (ASME_MD), and describes the corresponding description. Get a list of variables (ASCO_SM). Next, the recommendation processing unit (ASCO) searches the office space information DB (ASME_SD) and acquires a list of rooms / areas in a state similar to the list of explanatory variables with the latest data (ASCO_SS).

Furthermore, the recommendation processing unit (ASCO) calculates the priority based on the value indicating the difference between the range of the explanatory variables and the state of the actual office space (ASCO_CP), ranks the recommendations, and determines the recommendation item (ASCO). ASCO_DR), a screen to be displayed together with a map image, etc. is generated and sent to a client (CL) (ASCO_OD).

The client (CL) receives the screen (CLCO_R) and displays it on an output device such as a display (CLOD) (CLCO_D). The input / output control (CLCC) in the control unit (CLCO) of the client (CL) displays the screen sent from the application server (AS) and accepts the input based on the operation of the user (US). Control the scene such as sending to (AS).

In addition, the accuracy of mathematical models improves as data is accumulated. Therefore, if the learning phase is being performed in parallel at another workplace or in the same workplace, the mathematical model that reflects the new data obtained in the learning phase is updated manually or automatically on a regular basis, and the mathematical model is used. It is also possible to add or change to the DB (ASME_MD) (ASCO_RM). In addition to recommending the optimal area extracted from the office space information as a recommendation, it also has a function (ASCO_IU) that links with an external system such as a conference room reservation system, and continues after the user (US) receives the recommendation. The client (CL) may be used to make a reservation for the recommended area.

Here, as a feature of the present invention, a mathematical model DB (ASME_MD) for storing statistical analysis results using past accumulated data and an office space information DB (ASME_SD) are separately configured. The mathematical model DB (ASME_MD) and (SSME_MD) handle only general-purpose behavioral features and environmental features common to multiple workplaces, and the office space information DB (ASME_SD) is used for specific workplaces that are implementing the operation phase. Sorted to handle information about individual rooms and areas that are only useful. By distinguishing these, it becomes possible to divert the mathematical model DB generated from the data of another workplace to the operation of another workplace. It is also possible to create a mathematical model DB by integrating the data obtained in multiple workplaces.

On the other hand, the findings obtained from them are general theory, which is difficult for users (US) to understand and has low value. Therefore, by using the office space information DB (ASME_SD) to convert the information to reflect the real-time data of the workplace to which the user (US) belongs and provide the recommendation, the user (US) should specifically go. Can understand the location. In this way, the data accumulated with general knowledge is searched in the mathematical model DB (ASME_MD), and then the information for a specific workplace is searched in the office space information DB (ASME_SD). To answer. As a result, the reliability of the knowledge can be improved by increasing the population parameter of the data that serves as the backbone, and at the same time, the convenience of the user (US) can be improved at the same time.

<Fig. 4A to Fig. 6B: Example of client display screen>
4A to 6B are explanatory views showing an example of a screen displayed on the display (CLOD) of the client (CL) according to the embodiment of the present invention.

These screens are generated in the application server (AS) (ASCO_OD) and displayed by the input / output control (CLCC) of the client, but the screens may be generated in the client (CL). Further, in FIGS. 4A to 6B, the client (CL) is shown assuming a large touch panel or a Web browser of a PC, but another means such as a smartphone or a tablet may be used.

Screen example 1 (CLOD_1) of FIG. 4A is an example of a screen that is automatically displayed during a time zone when the user (US) is not operating. This screen visualizes the latest time-of-day environmental indicators obtained by the environmental measuring instrument (EM). Specifically, the screen example 1 (CLOD_1) displays the date and time (D11), the type of index to be visualized (D12), and the visualization data (D13) associated with the map of the workplace. This is a screen generated by combining the information of the office space information DB (ASME_SD), the terminal management information (ASME_T), and the area definition information (ASME_A). When the user (US) presses the button 1 (CLIO_B1), the search mode of the screen example 2 (CLOSED_2) is entered.

FIG. 4 (shows a screen example 2 (CLOD_2) for allowing the user (US) to input a business goal (CLCO_I). The notification column (D21) is a column for describing a question to the user (US). For example, "How do you want to work from now on?" Is displayed. Also, as an example of the items to be input, the choices are narrowed down according to the number of people, how many people will work and what kind of business goals they have. The business goal options such as "concentrated desk work" and "idea generation" correspond to the items in the objective variable table (ASME_MD_O). The user (US) sets the number of people and business goals. When the button 2 (CLIO_B2) is selected and the button 2 (CLIO_B2) is pressed, the screen transitions to the next screen example 3 (CLOSED_3).

Screen example 3 (CLOD_3) of FIG. 5A is an example of a screen asking the state of the user (US). This screen may be skipped if the state is not used for analysis. For example, for items such as "mood", "physical condition", and "thinking", the degree (for example, how good the mood is, how good the physical condition is, how fast the thinking is) can be set in the range of 1 to 5. Let (US) select. When the user (US) presses the button 3 (CLIO_B3), the screen example 4 (CLID_4) is displayed.

Screen example 4 (CLOD_4) of FIG. 5B is an example of a screen displaying a recommendation calculated based on the business goal and status information input so far. Areas with high priority (3 cases in this example) extracted by the recommendation item determination (ASCO_DR) are displayed. The notification column (D41) indicates that this page is a recommended result, and the commentary column (D42) is inserted with the corresponding sentence selected from the commentary table (ASME_MD_E).

In addition, for the three recommended areas, the name of each area registered in the area definition information (ASME_A) and the (for example, real-time) image taken by the surveillance camera (SC) of that area are displayed. Is also good. In the example of FIG. 5B, such an image is displayed in the image display column (D43). The reason for displaying the image is for the user (US) to grasp the situation such as the degree of congestion in the area, so the process of blurring or converting to CG so that the person in the image cannot be identified. May be added.

The recommendation processing unit estimates the current number of people staying in each area from the real-time video obtained from the surveillance camera (SC), and as a result, for example, it is already full and cannot be used by the user, or the degree of congestion is determined. Areas that are judged to be inappropriate for the user's business (for example, productivity is expected to decrease) may be excluded from the targets recommended to the user.

In addition, the basis display column (D44) illustrates the information in the mathematical model table (ASME_MD) so that the user (US) can understand the basis of the recommendation and the characteristics of each area. Here, the information that "environmental sound is 50 to 60 dB" and "temperature is 23 to 25 ° C", which are effective explanatory variables for the objective variable of concentration, is displayed in two stages, and the current environmental sound of each area is displayed. And how close the temperature is to that range is expressed by the number of stars. When the user (US) selects one of the options presented by pressing the button 4 (CLIO_B4), the screen example 6 (CLID_6) is displayed.

In the screen example 6 (CLOD_6) of FIG. 6B, the route (D61) from the position of the display (CLOD) displaying the screen referred to when the user (US) selects the area to the selected area. Is displayed. When the user confirms the displayed contents and operates the button 6 (CLIO_B6) for closing the screen, the screen example 6 (CLOSED_6) is closed and the screen transitions to the screen example 1 (CLOSED_1). This completes the process of recommending the office to the user (US).

However, when the user (US) selects an area that requires reservation, the client (CL) connects to the conference room reservation system (not shown) through the external system linkage control (ASCO_IU) of the application server (AS). , The screen example 5 (CLOD_5) shown in FIG. 6A may be continuously transitioned to make a reservation. At this time, the client (CL) causes the user (US) to input, for example, the reservation end time (D51) and the employee number (D52), and secures a conference room from the current time to the desired end time.

<Fig. 7: Example of terminal display screen>
FIG. 7 is an explanatory diagram showing an example of a screen (TRIO_D) when a user (US) is surveyed by the wearable sensor (TR) according to the embodiment of the present invention.

The wearable sensor (TR) sounds an alarm at a predetermined or random time on the speaker (TRIO_S) to prompt the user (US) to answer. The question (TRIO_D1) and the answer number and the corresponding legend (TRIO_D2) are displayed on the screen (TRIO_D). The user (US) completes the answer by pressing one of the plurality of buttons (TRIO_B) corresponding to the legend.

<Fig. 8: Learning phase sensing sequence>
FIG. 8 shows the sensing procedure performed by the wearable sensor (TR), the environment measuring device (EM), and the surveillance camera (SC) in the learning phase of the embodiment of the present invention, and the data thereof is transferred to the analysis server (SS). It is a sequence diagram which shows the procedure to store.

The wearable sensor (TR) is activated (TR81) when the user (US) switches it on or picks it up from the charger (US81), and synchronizes the time (TRCO_T). Subsequently, the wearable sensor (TR) starts sensing (TR82) and transmits sensing data (TRSD) at regular time intervals. Further, the environment measuring machine (EM) synchronizes the time (EMCO_T) after starting, performs sensing (EM82), and transmits sensing data (EMSD) at regular time intervals. Similarly, after the surveillance camera (SC) is activated (SC81), time synchronization (SCCO_T) is performed, shooting is performed (SC82), and moving image data is transmitted at regular time intervals (SCSD).

When conducting a questionnaire with a wearable sensor (TR), a timer activates the questionnaire at a predetermined or random time (TR83), and a speaker (TRIO_S) notifies the user (TR84). Then, the wearable sensor (TR) displays the questionnaire item on the screen (TRIO_D) (TR85), and when the user (US) answers (US82), the answer data is transmitted (TR86) to the analysis server (SS). Although this process is controlled by questionnaire control (TRCO_Q), it may be carried out using another terminal such as a smartphone or PC instead of the wearable sensor (TR).

Finally, the analysis server (SS) receives the sensing data and the questionnaire data (SSRD), and stores the data in the sensing DB (SSME_SD) (SS81).

<Fig. 9: Learning phase Mathematical model generation sequence>
FIG. 9 shows a copy, excerpt, or integration of a procedure for generating a mathematical model performed on an analysis server (SS) and a mathematical model DB (SSME_MD) of the analysis server (SS) in the learning phase of the embodiment of the present invention. It is a sequence diagram which shows the procedure of updating the mathematical model DB (ASME_MD) of the application server (AS).

The analysis server (SS) starts at a predetermined time at a predetermined frequency, for example, once a week (SS81), issues a request to the storage unit (SSME), and outputs a predetermined date and target sensing data in the sensing DB. Acquire (SS82). Next, the analysis server (SS) extracts the feature amount (SSCO_FE) from the data and stores it in the feature amount DB. Since the method of performing feature extraction differs depending on the type of data, the analysis server (SS) repeats the processing until the calculation of all types of sensing data is completed (SS83).

Next, the analysis server (SS) specifies one objective variable (SSCO_SO) from a predetermined objective variable table (SSME_MD_O), performs statistical analysis (SSCO_SA), and extracts explanatory variables related to the objective variable. Then, the result is stored in the mathematical model DB as a mathematical model (SSCO_MD). The analysis server (SS) repeats this until the calculation is completed for all the objective variables in the objective variable table (SS84), and ends (SS85).

Furthermore, when starting the operation phase, or when the learning phase and the operation phase are being carried out in parallel at different workplaces, the analysis server (SS) periodically applies the mathematical model DB (SSME_MD) to the application server. It is manually or automatically copied to the mathematical model DB (ASME_MD) of (AS) and updated (ASCO_RM). At this time, the analysis server (SS) may send a part extracted from the mathematical model DB (SSME_MD) to the application server (AS), or integrate a plurality of types of mathematical model DBs (SSME_MD) by a plurality of workplaces. Then, it may be used as a mathematical model DB (ASME_MD) of the application server (AS). In addition, every time the mathematical model DB (SSME_MD) is updated, only the difference is reflected in the mathematical model DB (ASME_MD) so that the contents of the mathematical model DB (SSME_MD) and the mathematical model DB (ASME_MD) always match. Is also good.

<Fig. 10: Operation phase Environmental data update sequence>
FIG. 10 is a sequence diagram showing a procedure for processing and updating environmental data measured by an application server (AS) in the operation phase of the embodiment of the present invention.

The flow from the activation of the environment measuring machine (EM) and the surveillance camera (SC) to the transmission of the sensing data is the same as the learning phase of FIG.

The application server (AS) receives sensing data (ASRD), extracts features from them (ASCO_FE), inputs the latest environmental features into the office space information DB (ASME_SD), and updates them (ASCO_SO).

<Fig. 11: Sequence of operation phase recommendation provision>
FIG. 11 shows, in the operation phase of the embodiment of the present invention, the application server (AS) generates a recommendation and presents it on the screen with respect to the business goal input by the user (US) by operating the client (CL). It is a sequence diagram which shows the procedure to perform.

When the client (CL) is started (CL11) by turning on the power, etc., it acquires the latest time office space information from the application server (AS) through the network (NW), and the initial screen (an example is shown in FIG. 4A). Screen example 1 (CLOD_1)) is automatically displayed (CL12). Next, when the search screen is started (US11) to determine the place where the user (US) works, the search screen (screen example 2 (CLOD_2) whose example is shown in FIG. 4B) is displayed (CL13). When the user (US) inputs the business goal from now on to improve the performance by answering the question on the screen (US12) (CLCO_I), the client (CL) sends it to the application server (AS). Send (COCO_S).

The application server (AS) receives the business goal (ASCO_R), searches the mathematical model DB (ASME_MD) for a record with the goal as the objective variable (ASCO_SM), and then the environment is close to the condition of the explanatory variable. The area is searched from the office space information DB (ASME_SD) (ASCO_SS). The application server (AS) calculates the priority based on the similarity and statistical reliability among the candidates narrowed down by the conditions (ASCO_CP), determines the top three recommended items (ASCO_DR), and data. And generate a screen integrated with the commentary (ASCO_OD).

The client (CL) displays the received recommendation screen (screen example 5 (CLOD_4) whose example is shown in FIG. 5B) (CL14), and when the user selects one of the recommendation items (US13), Finally, a map of the route from the current location to the selected area (an example of which is screen example 6 (CLOD_6) shown in FIG. 6B) is displayed, and the process is completed (CL15).

The typical embodiments of the present invention described above can be summarized as follows.

That is, the data analysis system (for example, the system shown in FIGS. 1 to 3B) according to the embodiment of the present invention has an objective index related to the productivity of a person (for example, an objective variable shown in FIG. 12A) and an activity place (working space) of the person. A storage unit (for example, a storage unit (ASME) of an application server (AS)) that holds a mathematical model (for example, the mathematical model table (ASME_MD_T1) shown in FIG. 12B) showing the relationship with the environmental index related to the environment of) and a sensor (for example,). Environmental data, which is data related to the environment of the place of activity of the person acquired from the sensor part (EMSE) of the environmental measuring machine (EM) or the sensor part (SCSE) of the surveillance camera (SC), and the activity of the person input by the person. Environmental index conditions suitable for increasing the productivity of a person based on the business target data and the mathematical model by accepting business target data (for example, data input via the screen of FIG. 4B) including the target related to (For example, mathematical model search (ASCO_SM)), and based on the conditions of environmental indicators and environmental data, search for information on activity locations suitable for increasing the productivity of people (for example, office space information search (ASCO_SS)). )), It has a recommended processing unit (for example, a recommendation processing unit (ASCO)) that outputs information about the activity location obtained by the search (for example, screen generation (ASCO_OD)).

This makes it possible to analyze time-continuous sensor data on people and the environment and match effective workplace spaces in real time to improve human performance in work.

Here, the storage unit holds a first database (for example, a mathematical model DB (ASME_MD)) for storing a mathematical model and a second database (for example, an office space information DB (ASME_SD)) for storing environmental data. , The recommended processing unit refers to the first database to search for the conditions of environmental indicators suitable for increasing the productivity of the person (for example, mathematical model search (ASCO_SM)), and then refers to the second database. Search for information on activity locations suitable for increasing the productivity of a person (eg, office space information search (ASCO_SS)).

In this way, by separating the general-purpose first database that can be applied without limiting the organization and the second database dedicated to each organization, the knowledge obtained from the data acquired by other organizations is used to use the area of the own organization. It will be possible to present recommendations in a specific way that reflects the name and characteristics.

Further, the environmental index in the mathematical model includes at least one of the temperature, humidity and noise of the activity place (for example, at least one of the temperature, humidity and sound included in the mathematical model table (ASME_MD_T1) of FIG. 12B), and environmental data. Is the value of an index included in the mathematical model (for example, the value of temperature measured by a temperature sensor (EMSE_T)) obtained from the sensors installed in a plurality of activity locations where a person may perform activities in the future. The humidity value measured by the humidity sensor (EMSE_H) and the noise value measured by the noise measuring device (EMSE_S)) may be included.

In this way, by using a general environmental index that can be measured at any place, it becomes possible to utilize the knowledge obtained from the data acquired by one organization in another organization.

In addition, the recommended processing unit searches for the conditions of the environmental index suitable for increasing the productivity of the person based on the relationship between the objective index corresponding to the business target data and the environmental index (for example, shown in the screen of FIG. 4B). When "concentrated desk work" is selected as described above, a mathematical model showing the relationship between the corresponding objective variable "concentrated duration" (FIG. 12A) and the environmental index (for example, the mathematical model table of FIG. 12B (for example, FIG. 12B)). From ASME_MD_T1)), search for the conditions of the environmental index suitable for increasing productivity), and the relationship between the condition of the environmental index and the environmental data satisfies the predetermined condition (for example, the degree of similarity between the two is high). The information may be searched for as information about a suitable place of activity to increase the productivity of the person.

As a result, an activity place suitable for the purpose of the business to be performed can be presented as a specific place (for example, a room or area) existing in the organization.

Further, in the mathematical model, the objective index, the environmental index, and the state of the person (for example, the "state" in the mathematical model table (ASME_MD_T1) of FIG. 12B) are associated with each other, and the recommended processing unit is the state of the person input by the person (for example, the state of the person (ASME_MD_T1)). For example, the user's mood, physical condition, thinking state, etc. input via the screen of FIG. 5A are further accepted, and the production of the person is based on the input person's state, business goal data, and mathematical model. You may search for the conditions of the environmental index suitable for enhancing the sex.

As a result, it is possible to present an activity place suitable for work in consideration of the state of the person.

In addition, the recommended processing unit acquires information indicating the current or future state of the activity location obtained by the search, and determines the information regarding the activity location to be output based on the current or future state of the activity location. good.

Here, the information indicating the current or future state of the activity place is the information indicating the number of people staying in the activity place based on the information acquired from the sensor (for example, the image of the surveillance camera (SC)), or the activity place reservation system. It may be the reservation status of the activity place obtained from.

This makes it possible to present the activity locations that are actually available.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments, and various modifications can be made, and it is possible to appropriately combine the above-described embodiments. It will be understood by the trader.

Specifically, the present invention is not limited to the above-described examples, and includes various modifications. For example, the above-mentioned examples have been described in detail for a better understanding of the present invention, and are not necessarily limited to those having all the configurations of the description.

Further, each of the above configurations, functions, processing units, processing means and the like may be realized by hardware by designing a part or all of them by, for example, an integrated circuit. Further, each of the above configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be stored in non-volatile semiconductor memories, hard disk drives, storage devices such as SSDs (Solid State Drives), or computer-readable non-computers such as IC cards, SD cards, and DVDs. It can be stored in a temporary data storage medium.

In addition, the control lines and information lines are shown as necessary for explanation, and not all control lines and information lines are necessarily shown in the product. In practice, it can be considered that almost all configurations are interconnected.

Claims (14)

A storage unit that holds a mathematical model that shows the relationship between the objective index related to the productivity of a person and the environmental index related to the environment of the place where the person is active.
It accepts environmental data, which is data related to the environment of the person's activity location acquired from the sensor, and business goal data including the target related to the person's activity input by the person.
Based on the business goal data and the mathematical model, the conditions of the environmental index suitable for increasing the productivity of the person are searched for.
Based on the conditions of the environmental index and the environmental data, information on an activity place suitable for increasing the productivity of the person is searched for.
A data analysis system characterized by having a recommended processing unit that outputs information about the activity location obtained by the search. The data analysis system according to claim 1.
The storage unit holds a first database for storing the mathematical model and a second database for storing the environment data.
The recommended processing unit searches for the conditions of the environmental index suitable for increasing the productivity of the person by referring to the first database, and then refers to the second database of the person. A data analysis system characterized by searching for information about suitable activity locations to increase productivity. The data analysis system according to claim 2.
The environmental index in the mathematical model includes at least one of the temperature, humidity and noise of the activity site.
The environmental data includes data of the environmental index included in the mathematical model acquired from the sensors installed at a plurality of activity locations where the person may perform activities in the future. Analysis system. The data analysis system according to claim 1.
The recommended processing unit is
Based on the relationship between the target index and the environmental index corresponding to the business target data, the conditions of the environmental index suitable for increasing the productivity of the person are searched for.
Data analysis characterized by searching for information on the activity place where the relationship between the condition of the environmental index and the environmental data satisfies a predetermined condition as information on the activity place suitable for increasing the productivity of the person. system. The data analysis system according to claim 1.
In the mathematical model, the objective index, the environmental index, and the state of the person are associated with each other.
The recommended processing unit is
Further accepting the state of the person entered by the person,
Data characterized by searching for conditions of the environmental index suitable for increasing the productivity of the person based on the input state of the person, the business target data, and the mathematical model. Analysis system. The data analysis system according to claim 1.
The recommended processing unit is
Obtain the information indicating the current or future state of the activity place obtained by the search, and obtain the information.
A data analysis system characterized in determining information about the activity location to be output based on the current or future state of the activity location. The data analysis system according to claim 6.
The information indicating the current or future state of the activity place is information indicating the number of people staying at the activity place based on the information acquired from the sensor, or the reservation of the activity place obtained from the reservation system of the activity place. A data analysis system characterized by being a situation. A data analysis method executed by a data analysis system having a storage unit and a recommended processing unit.
The storage unit holds a mathematical model showing the relationship between the objective index regarding the productivity of the person and the environmental index regarding the environment of the place of activity of the person.
The data analysis method is
The first procedure in which the recommended processing unit receives environmental data that is data related to the environment of the activity place of the person acquired from the sensor and business goal data including the target related to the activity of the person input by the person.
A second step in which the recommended processing unit searches for conditions of the environmental index suitable for increasing the productivity of the person based on the business target data and the mathematical model.
A third step in which the recommended processing unit searches for information on an activity location suitable for increasing the productivity of the person based on the conditions of the environmental index and the environmental data.
A data analysis method comprising the fourth step of outputting information about the activity location obtained by the search by the recommended processing unit. The data analysis method according to claim 8.
The storage unit holds a first database for storing the mathematical model and a second database for storing the environment data.
In the second procedure, the recommended processing unit searches for the conditions of the environmental index suitable for increasing the productivity of the person by referring to the first database, and then in the third procedure, the recommended processing unit searches for the conditions of the environmental index. A data analysis method comprising referring to a second database to search for information about an activity location suitable for increasing the productivity of the person. The data analysis method according to claim 9.
The environmental index in the mathematical model includes at least one of the temperature, humidity and noise of the activity site.
The environmental data includes data of the environmental index included in the mathematical model acquired from the sensors installed at a plurality of activity locations where the person may perform activities in the future. Analysis method. The data analysis method according to claim 8.
In the second procedure, the recommended processing unit sets the conditions of the environmental index suitable for increasing the productivity of the person based on the relationship between the target index corresponding to the business target data and the environmental index. Explore and
In the third procedure, the recommended processing unit provides information on the activity location where the relationship between the condition of the environmental index and the environmental data satisfies a predetermined condition, and the activity location suitable for increasing the productivity of the person. A data analysis method characterized by searching for information about. The data analysis method according to claim 8.
In the mathematical model, the objective index, the environmental index, and the state of the person are associated with each other.
In the first procedure, the recommended processing unit further accepts the state of the person input by the person.
In the second procedure, the recommended processing unit is the environment suitable for increasing the productivity of the person based on the input state of the person, the business target data, and the mathematical model. A data analysis method characterized by searching for index conditions. The data analysis method according to claim 8.
In the third procedure, the recommended processing unit
Obtain the information indicating the current or future state of the activity place obtained by the search, and obtain the information.
A data analysis method comprising determining information about the activity location to be output based on the current or future state of the activity location. The data analysis method according to claim 13.
The information indicating the current or future state of the activity place is information indicating the number of people staying at the activity place based on the information acquired from the sensor, or the reservation of the activity place obtained from the reservation system of the activity place. A data analysis method characterized by being a situation.

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