WO2011055628A1 - Organization behavior analyzer and organization behavior analysis system - Google Patents

Abstract

An analytical model composed of the beneficial behavioral factors required to solve an organization's problems is generated from internal organization behavioral data and communication data. Organization dynamics indices are determined from sensor data and set as explanatory variables to serve as internal organization behavioral data. Objective data such as productivity and accidents/failures of the organization, and subjective data from questionnaire responses such as leadership/teamwork indices, employee desire to work/fulfillment indices, and stress/mental distress indices are determined and set as purpose functions. By selecting beneficial factors of purpose variables from the explanatory variables and creating a model, the behaviors affecting the problem given by the organization as purpose variables become clear.

Description

Organizational Behavior Analysis Device and Organizational Behavior Analysis System

The present invention relates to technology for visualizing the state of an organization from behavior data and communication data of members in the organization.

We have more or less problems in every organization. In order to solve this problem, the bookstore is lined with many business books, and while the manager reads these books, the manager is troubled to solve the problem. In addition, since organizational problems are universal problems, experienced managers have many experiences with the same scene, so they can solve problems with experience and feeling.

There are also attempts to solve organizational problems by having members of the organization answer questionnaires. Since the subjectivity of members is reflected, it is possible to discover factors that the members are concerned about.

Also, one way to detect the behavior of members in an organization is to utilize sensor nets. A sensor network is a system applied to the acquisition and control of the state by attaching a terminal equipped with a sensor and a wireless communication circuit to an environment, an object, a person, etc., and extracting various information obtained from the sensor via wireless. . The physical quantities acquired by the sensor to detect this communication include infrared rays for detecting a face-to-face state, voices for detecting speech and environment, and acceleration for detecting human action. By using these data, activities of members and teams in the organization can be obtained as feature quantities.

As an example using information obtained from a sensor, based on an analysis model prepared in advance, feature quantities and questionnaire responses obtained from position information obtained from infrared rays are analyzed to find tissue features (extract points of interest) Patent Document 1 discloses that.

Patent Document 2 discloses that life support is performed by viewing the relationship between stress and the behavior of the person by displaying the feature value obtained from the acceleration sensor in a time series and the stress questionnaire on one screen. There is.

JP, 2008-9595, A JP 2001-344352 A

When trying to find a solution to an organization's problem from the experience and intuition of books and managers, it is difficult to solve the problem because it does not interview members of the organization even though it is a problem that originates from that organization. Also, even if the organization is analyzed, the manager often can not make sufficient observation and interviews with members necessary for the problem due to business trips and the like.

Also, when trying to solve an organization's problem by having members of the organization answer in a questionnaire, it is difficult to put it into action even if it is known as words. Because we do not know what kind of action we should do. It is considered that the organization does not penetrate unless it is specified which organizational behavior should be improved.

In addition, it is possible to detect the relationship between members in an organization or the activity of a team from physical quantities obtained by sensors. For example, in Patent Document 1, questionnaire responses and position information are prepared based on an analysis model prepared in advance. And analyze the points of interest as an organization. However, it is difficult to obtain good results unless the appropriate model is selected in advance for that tissue. Furthermore, using only the questionnaire information and the position information, it is only to extract attention points such as a group with high satisfaction despite the few contacts, and specifically specify which organization behavior should be improved. It is not possible.

In addition, Patent Document 2 discloses a method of displaying stress level and behavior at one time, but does not describe specifying behavior that is a factor of stress or a countermeasure (stress elimination) method.

In view of such circumstances, an object of the present invention is to generate an analysis model composed of factors useful for problem solution of an organization, from behavior data and communication data in the organization.

Since problem solution of an organization needs to find out the cause and beneficial factor, behavior data in the organization obtained from a large amount of sensor data and questionnaire responses cause the problem of the organization Model and present multiple factors.

As behavior data in an organization, an organization dynamics index is obtained from sensor data and used as an explanatory variable. Objective data are obtained from questionnaire responses such as objective data such as organizational productivity and accidents / defects, leadership / teamwork indicators, employee's rewards / enhancement indicators, and stress / mental upset indicators. By selecting useful factors of the objective variable from the explanatory variables and creating a model, it is possible to clarify the behavior affecting the problem that the organization has identified as the objective variable.

In addition, it is an organization behavior analysis device which analyzes an organization constituted by a plurality of persons. A receiver for receiving sensor data acquired by an infrared transmitting / receiving unit and an acceleration sensor of a terminal attached to each of a plurality of persons, and data indicating a subjective evaluation or an objective evaluation of each of the plurality of persons; A control unit that analyzes data indicating a subjective evaluation or an objective evaluation, and a recording unit that records analysis conditions for the control unit to analyze and a result of analysis by the control unit. The control unit calculates, from the sensor data, an indicator indicating the relationship between persons in the organization and the action in the organization for each of the plurality of persons, and records the index in the recording unit. Of data showing subjective evaluation or objective evaluation in the organization by correlating data showing subjective evaluation or objective evaluation of the subject with indicators of relationship between persons in the organization and actions in the organization Identify

In addition, an infrared transmitting and receiving unit attached to each of a plurality of persons constituting a tissue and acquiring data indicating meeting, an acceleration sensor acquiring acceleration data, and a transmitting unit transmitting data indicating meeting and acceleration data as sensor data And a receiving unit that receives sensor data, and receives data indicating a subjective evaluation or an objective evaluation of each of a plurality of persons, sensor data, and data indicating the subjective evaluation or the objective evaluation The tissue behavior analysis system includes a tissue behavior analysis device including: a control unit that analyzes and a recording unit that records analysis conditions for the analysis by the control unit and the analysis result of the control unit. The control unit calculates, from the sensor data, an indicator indicating the relationship between persons in the organization and the action in the organization for each of the plurality of persons, and records the index in the recording unit. Of data showing subjective evaluation or objective evaluation in the organization by correlating data showing subjective evaluation or objective evaluation of the subject with indicators of relationship between persons in the organization and actions in the organization Identify

Furthermore, it is an organization behavior analysis device which analyzes an organization constituted by a plurality of persons.
A receiver for receiving data indicating subjective evaluation of each of a plurality of persons, a controller for analyzing data indicating the subjective evaluation, data for indicating a seat position in a tissue, and an analysis for the controller to analyze And a recording unit that records the condition and the analysis result of the control unit. The control unit calculates, for each of a plurality of persons, an index calculation unit that calculates an index related to stress from data indicating a subjective evaluation based on analysis conditions, data indicating a seat position, and an index related to stress. And a seat arrangement determining unit that determines the arrangement of seats in the organization of each of the plurality of persons.

The useful action factors necessary for problem solution in an organization are clarified, and appropriate measures can be made for the organization.

Example of system configuration of embodiment 1 Example of system configuration of embodiment 1 Example of system configuration of embodiment 1 Example of system configuration of embodiment 1 Example of system configuration of embodiment 1 The example of the figure which shows the whole processing flow of Example 1 The example of the figure which shows the whole processing flow of Example 1 The example of the figure which shows the whole processing flow of Example 1 Example of Meeting Table in Example 1 Example of Body Rhythm Table of Example 1 Example of the facing matrix of Example 1 Example of Network Indicator of Example 1 and Body Rhythm Indicator Example of network diagram (Part 1) Example of face-to-face index of Example 1 and organization activity index Example of Personality Index in Example 1 Example of productivity index of example 1, accident failure index Examples of Leadership / Teamwork Index, Employee's Demanding / Fullness Index, Stress / Mental Disorder Index, and Organization Activation Index in Example 1 Example of factor coefficient of Example 1 The example of the figure which shows the whole processing flow of Example 2 Example of personality coefficient of Example 2 Example of user-specific personality factor in Example 2 Example of estimated personality index of Example 2 Example of Big Five theory of Example 1 Example of Personality Questionnaire in Example 1 Example of stress / mental disorder questionnaire in Example 1 Example of Happiness Questionnaire of Example 1 Example of Teamwork Questionnaire of Example 1 Example of tissue activation questionnaire of Example 1 Example of the figure which shows the whole processing flow of Example 3 Example of the face-to-face grouping table of the third embodiment Example of User ID Table, Example of Location ID Table of Example 3 The example of the figure which shows the whole processing flow of Example 4 Example of Place Table of Example 4 Example of user / place table according to the fourth embodiment Example of the figure which shows the whole processing flow of Example 5 Example of Field List (IC) of Example 5 Map according to the number of people in the field of Example 5, an example of the map inside and outside the team in the field Example of the figure which shows the whole processing flow of Example 6 Example of the meeting set map of the place of Example 6, an example of the user map of the place Example of the figure which shows the whole processing flow of Example 7 Example of Temperature Table of Example 7 Example of site use situation map of Example 7 Example of the figure which shows the whole processing flow of Example 8 Example of the figure which shows the whole processing flow of Example 9 Example of the meeting matrix according to the number of people facing in the ninth embodiment Example of Maximum Meeting Time Matrix of Example 9, Example of Maximum Meeting Time People Matrix Example of the maximum meeting time number of people network diagram of the ninth embodiment Example of the figure which shows the whole processing flow of Example 10 Example of frequency main component of Example 10, tissue frequency Example of tissue frequency graph of Example 10 Example of the figure which shows the whole processing flow of Example 11 Example of network diagram (Part 2) Example of the number of reaching steps on the network diagram of each person in the eleventh embodiment Example of the facing distance matrix of Example 11 Example of the facing distance network diagram of the eleventh embodiment An example of seat arrangement using the adaptability of Example 11 One Example of Adaptability of Each Person of Example 11 Example of the seat arrangement to the place of Example 11 A figure showing an example of whole processing flow of Example 12 A diagram showing an example of a processing flow of intra- and intra-hierarchical tissue frequency analysis of the twelfth embodiment FIG. 26 is a diagram showing an example of a process flow of job position hierarchical network diagram coordinate identification according to the twelfth embodiment; A diagram showing an example of a job position hierarchical network diagram coordinate list of the twelfth embodiment The figure which shows an example of the post hierarchy hierarchy structure of Example 12

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

In order to clarify the position and function of the analysis system in the present invention, first, a business microscope system will be described. Here, a business microscope is used to monitor the behavior and behavior of a human being with a sensor node worn by a human, and to help improve the organization by illustrating the relationship between persons and the image of the current organization as an organization activity. System. In addition, data on face-to-face detection, action, voice, etc. acquired by the sensor node is generically referred to as tissue dynamics data.

FIGS. 1A, 1B, 1C, 1D and 1E are explanatory diagrams showing components of one embodiment of a business microscope system, which are shown separately for convenience of illustration. The respective processes are executed in cooperation with each other.

1A to 1E show a sensor net server (SS) that stores organization dynamics data from a name tag type sensor node (TR), a base station (GW), and an application server (AS) that analyzes organization dynamics data, It shows a series of flows up to the client (CL) that outputs the analysis result to the viewer.

This system comprises a name tag type sensor node (TR), a base station (GW), an organization sensor network server (SS), an application server (AS), and a client (CL). In the present embodiment, the sensor network server and the application server are described as separate devices, but it is also possible to realize the functions of these servers with one server.

An application server (AS), shown in FIG. 1A, analyzes and processes tissue dynamics data. At the request from the client (CL) shown in FIG. 1B or automatically at the set time, the analysis application is started. The analysis application requests the sensor network server (SS) shown in FIG. 1C to acquire necessary tissue dynamics data. Furthermore, the analysis application analyzes the acquired tissue dynamics data, and returns the analysis result to the client (CL) shown in FIG. 1B. Alternatively, the analysis application may record the analysis result as it is in the analysis result database (F).

The application used for analysis is stored in the analysis algorithm (D) and executed by the control unit (ASCO). The processing executed by the present embodiment is modeling analysis (CA), personality index extraction analysis (CA1), and personality index conversion analysis (CA2).

The application server (AS) includes a transmission / reception unit (ASSR), a storage unit (ASME), and a control unit (ASCO).

The transceiver unit (ASSR) transmits and receives tissue dynamics data between the sensor network server (SS) shown in FIG. 1C and the client (CL) shown in FIG. 1B. Specifically, the transmission / reception unit (ASSR) receives a command sent from the client (CL), and transmits a tissue dynamics data acquisition request to the sensor network server (SS). Further, the transmission / reception unit (ASSR) receives tissue dynamics data from the sensor network server (SS), and transmits an analysis result to the client (CL).

The storage unit (ASME) is configured of an external recording device such as a hard disk, a memory, or an SD card. The storage unit (ASME) stores setting conditions for analysis and analysis results. Specifically, the storage unit (ASME) includes user / place information table (I), organization information table (H), questionnaire (G), analysis result table (F), analysis condition period table (E), analysis algorithm Store (D).

The user / place information table (I) is a table in which personal information such as the user's name, job title, and user ID, and information on places are described.

The organizational information table (H) contains data necessary for organizational modeling, such as productivity (HA) and accident defects (HB), and data necessary for organizational activities such as climate and stock prices as general information. It is a stored table.

The questionnaire (G) is a table in which the questionnaires the user is asked to perform and the responses are stored.

The analysis result table (F) is a table in which results of analysis of tissue dynamics data (tissue dynamics index) and results of analysis of questionnaire results are stored.

The analysis condition period table (E) is a table for temporarily storing analysis conditions for display requested from the client (CL).

The analysis algorithm (D) stores a program used for analysis. At the request of the client (CL), an appropriate program is selected, sent to the control unit (ASCO), and analysis is performed.

The control unit (ASCO) includes a central processing unit CPU (not shown) and executes control of data transmission / reception and analysis of sensing data. Specifically, the CPU (not shown) realizes various functions by reading and executing various programs stored in the storage unit (ASME). Specifically, communication control (ASCC), modeling analysis (CA), personality index extraction analysis (CA1), and personality index conversion analysis (CA2) are executed.

Communication control (ASCC) controls the timing of communication with the sensor network server (SS) and client data (CL) by wire or wireless. Furthermore, communication control (ASCC) executes format conversion of data and distribution of destinations according to data types.

Modeling analysis (CA) is a process of modeling the main factor of the problem which the organization has from tissue dynamics data and questionnaire results. Modeling analysis (CA) includes meeting table creation (C1A), body rhythm table creation (C1B), meeting matrix creation (C1C), network index extraction (CAA), body rhythm index extraction (CAB), meeting index extraction (CAC) Organization activity index extraction (CAD), correlation analysis (CAE), and factor selection (CAF).

Face-to-face table creation (C1A) is a process in which organization dynamics data is rearranged in chronological order for each user, and is a process for creating a table on face-to-face.

Body rhythm table creation (C1B) is a process in which tissue dynamics data is rearranged in chronological order for each user, and is a process of creating a table related to body rhythm.

Face-to-face matrix creation (C1C) is a process of creating a table in which the faces of each user are summarized in a matrix from the result of the face-to-face table creation (C1A).

Network metric extraction (CAA) analyzes metrics on networks in organizational dynamics metric from face-to-face table.

Body rhythm index extraction (CAB) analyzes indices related to body rhythm in tissue dynamics indices from a body rhythm table.

Face-to-face index extraction (CAC) analyzes the face-to-face index in the tissue dynamics index from the face-to-face table and the body rhythm table.

Activity index extraction (CAD) analyzes indexes related to the tissue in the tissue dynamics index from the face-to-face table and the body rhythm table.

Correlation analysis (CAE) is an analysis that finds correlation between tissue dynamics indicators and questionnaire results.

Factor selection (CAF) is a process of selecting useful factors as a result of correlation analysis.

Conventionally, personality index extraction analysis (CA1) and personality index conversion analysis (CA2) have acquired user's subjective data from questionnaires, but even if this questionnaire is not used, personality indexes can be obtained from tissue dynamics data. It is a process.

The personality index extraction analysis (CA1) is to obtain the contribution coefficient of the tissue dynamics index for each questionnaire item. This is a process performed by personality index coefficient extraction (CA1A).

Personality index conversion analysis (CA2) is a process for obtaining an index to be used as a substitute for a questionnaire from tissue dynamics indexes and contribution coefficients obtained by personality index extraction analysis (CA1). This is a process performed by personality index conversion (CA2A).

The analysis result is transmitted from the analysis result table (F) or the transmission / reception unit (ASSR) to the display (J) of the client (CL) shown in FIG. 1B.

The client (CL) shown in FIG. 1B is a contact point with the user, and performs data input / output. The client (CL) includes an input / output unit (CLIO), a transmission / reception unit (CLSR), a storage unit (CLME), and a control unit (CLCO).

The input / output unit (CLIO) is a part serving as an interface with the user. The input / output unit (CLIO) includes a display (CLOD), a keyboard (CLIK), a mouse (CLIM) and the like. Other input / output devices can also be connected to the external input / output (CLIU) as required.

The display (CLOD) is an image display device such as a CRT (CATHODE-RAY TUBE) or a liquid crystal display. The display (CLOD) may include a printer or the like.

The transmission / reception unit (CLSR) transmits / receives data to / from the application server (AS) shown in FIG. 1A or the sensor network server (SS) shown in FIG. 1C. Specifically, the transmission / reception unit (CLSR) transmits the analysis condition (CLMP) to the application server (AS), and receives the analysis result.

The storage unit (CLME) is configured by an external recording device such as a hard disk, a memory or an SD card. The storage unit (CLME) records information necessary for drawing such as analysis conditions (CLMP) and drawing setting information (CLMT). The analysis condition (CLMP) records conditions such as the number of analysis target members set by the user and the selection of the analysis method. The drawing setting information (CLMT) records information on the drawing position of what part of the drawing to plot. Furthermore, the storage unit (CLME) may store a program executed by a CPU (not shown) of the control unit (CLCO).

The control unit (CLCO) has a CPU (not shown) and executes control of communication, input of analysis conditions from the client user (US), and drawing for presenting the analysis result to the client user (US). Do. Specifically, the CPU executes the program stored in the storage unit (CLME) to execute the processing of communication control (CLCC), analysis condition setting (CLIS), drawing setting (CLTS), and display (J). Run.

Communication control (CLCC) controls the timing of communication with a wired or wireless application server (AS) or sensor network server (SS). Also, communication control (CLCC) converts the format of data and distributes destinations according to the type of data.

The analysis condition setting (CLIS) receives an analysis condition designated from the user via the input / output unit (CLIO), and records it in the analysis condition (CLMP) of the storage unit (CLME). Here, a period of data used for analysis, a member, a type of analysis, parameters for analysis, and the like are set. The client (CL) sends these settings to the application server (AS) to request analysis, and in parallel with this, executes drawing settings (CLTS).

The drawing setting (CLTS) calculates the method of displaying the analysis result based on the analysis condition (CLMP) and the position at which the drawing is plotted. The result of this process is recorded in drawing setting information (CLMT) of the storage unit (CLME).

The display (J) generates a display screen based on the format described in the drawing setting information (CLMT), the analysis result acquired from the application server (AS). For example, model drawing (JA) and the like shown in FIG. 2C are stored in the drawing setting information (CLMT). At this time, if necessary, the display (J) also displays an attribute such as the name of the person being displayed. The created display result is presented to the user via an output device such as a display (CLOD). For example, the display (CLOD) displays a screen such as a scientific management knowledge model (KA) shown in FIG. 2C. The user can finely adjust the display position by an operation such as drag and drop.

The sensor net server (SS) shown in FIG. 1C manages data collected from name tag type sensor nodes (TR) shown in FIG. 1E. Specifically, the sensor net server (SS) stores data sent from the base station (GW) shown in FIG. 1D in a database, and also shows the application server (AS) shown in FIG. 1A and FIG. 1B. Send sensing data based on the request from the client (CL). Furthermore, the sensor net server (SS) receives a control command from the base station (GW), and sends the result obtained from the control command back to the base station (GW).

The sensor net server (SS) includes a transmission / reception unit (SSSR), a storage unit (SSME), and a control unit (SSCO). If time synchronization management (GWCD) is performed on the sensor net server (SS), the sensor net server (SS) also needs a clock.

The transmission / reception unit (SSSR) performs data transmission and reception with the base station (GW), the application server (AS) and the client (CL). Specifically, the transmission / reception unit (SSSR) receives the sensing data sent from the base station (GW), and sends the sensing data to the application server (AS) or the client (CL).

The storage unit (SSME) is configured by a non-volatile storage device such as a hard disk or flash memory, and at least a data table (BA), a performance table (BB), data format information (SSMF), a terminal management table (SSTT), and a terminal Stores firmware (SSTF). Furthermore, the storage unit (SSME) may store a program executed by a CPU (not shown) of the control unit (SSCO).

In the data table (BA), tissue dynamics data acquired by name tag type sensor node (TR), information on name tag type sensor node (TR), and tissue dynamics data transmitted from name tag type sensor node (TR) passed It is a database for recording information of a base station (GW) and the like. A column is created for each element of data, such as acceleration and temperature, and data is managed. Also, a table may be created for each element of data. In either case, all data are stored in the data table (BA) by associating the terminal information (TRMT), which is the ID of the acquired nameplate type sensor node (TR), with the information related to the acquired time. Ru. The data table (BA) is the same as the data table (BA) of FIG. 2B.

The performance table (BB) is a database for recording evaluations (performances) related to organizations and individuals, which are input from name tag type sensor nodes (TR) or from existing data, together with time data. The performance table (BB) is the same as the performance table (BB) of FIG. 2B.

Data format information (SSMF) includes a data format for communication, a method of separating sensing data tagged by a base station (GW) and recording it in a database, a response method to a request for data, and the like. There is. As described later, after data reception, before data transmission, this data format information (SSMF) is always referred to by the communication control unit (SSCC), and data format conversion and data management (SSDA) are performed.

The terminal management table (SSTT) is a table which records which name tag type sensor node (TR) is currently under management of which base station (GW). When a name tag type sensor node (TR) is newly added under the management of the base station (GW), the terminal management table (SSTT) is updated.

The terminal firmware (SSTF) temporarily stores the updated terminal firmware (GWTF) of the name tag type sensor node stored in the terminal firmware registration unit (TFI).

The control unit (SSCO) includes a central processing unit CPU (not shown), and controls transmission / reception of sensing data and recording / extraction to a database. Specifically, various functions are realized by the CPU reading and executing various programs stored in the storage unit (SSME). Specifically, processing such as communication control (SSCC), terminal management information correction (SSTM) and data management (SSDA) is executed.

A communication control unit (SSCC) controls the timing of communication with a wired or wireless base station (GW), application server (AS) and client (CL). Also, as described above, the communication control unit (SSCC) uses the data format in the sensor network server (SS) based on the data format information (SSMF) recorded in the storage unit (SSME) as the format of data to be transmitted and received. Convert to a format or data format specialized for each communication partner. Furthermore, communication control (SSCC) reads a header portion indicating the type of data and distributes the data to the corresponding processing unit. Specifically, received data is distributed to data management (SSDA), and a command for correcting terminal management information is distributed to terminal management information correction (SSTM). The destination of the data to be transmitted is determined to a base station (GW), an application server (AS) or a client (CL).

The terminal management information correction (SSTM) updates the terminal management table (SSTT) when a command to correct terminal management information is received from the base station (GW).

Data Management (SSDA) manages correction / acquisition and addition of data in the storage unit (SSME). For example, according to data management (SSDA), sensing data is recorded in appropriate columns of a database by element of data based on tag information. Even when the sensing data is read out from the database, processing such as sorting necessary data according to time information and terminal information and sorting in time order is performed.

In FIG. 2B, the sensor net server (SS) organizes and records the data received via the base station (GW) by the data management (SSDA) in the performance table (BB) and the data table (BA). This corresponds to tissue dynamics data collection (B).

Performance input (C) is a process of inputting a value indicating performance. Here, the performance is a subjective or objective evaluation that is determined based on some criteria. For example, a person who wears a nameplate type sensor node (TR) at a predetermined timing has a subjective evaluation (performance) value based on some criteria such as the degree of business achievement, degree of contribution to organization, and degree of satisfaction at that time. Enter The predetermined timing may be, for example, once every several hours, once a day, or when an event such as a meeting is over. A person wearing the nameplate type sensor node (TR) operates the nameplate type sensor node (TR) or operates a personal computer (PC) such as a client (CL) to input a performance value. can do. Alternatively, handwritten values may be input later by the PC. In the present embodiment, an example is shown in which the name tag type sensor node can input the performance of a person (SOCIAL), a row (INTELLECTUAL), a mind (SPIRITUAL), a body (PHYSICAL), and an intelligence (EXECUTIVE) as a rating. The input performance value is used for analysis processing. The meaning of each question is: "Can people make rich human relationships (cooperation, sympathy)", "Can you do what you have to do?", "Do you feel rewarding and fulfilling your work?" ", Did the body take care of the body (rest, nutrition, exercise)?", The knowledge "have new knowledge (notice, knowledge)".

The performance on the organization may be calculated from the performance of the individual. Objective data such as sales or cost, and already quantified data such as customer questionnaire results may be periodically input as performance. When a numerical value is automatically obtained, such as an error occurrence rate in production control or the like, the obtained numerical value may be automatically input as a performance value. Furthermore, economic indicators such as gross national product (GNP) may be input. These are stored in the organization information table (H).

The base station (GW) shown in FIG. 1D has a role of mediating the name tag type sensor node (TR) shown in FIG. 1E and the sensor network server (SS) shown in FIG. 1C. A plurality of base stations (GWs) are arranged to cover an area such as a living room or work area in consideration of the reach of wireless. The base station (GW) includes a transceiver unit (GWSR), a storage unit (GWME), a clock (GWCK), and a control unit (GWCO).

The transmission / reception unit (GWSR) receives the radio from the nameplate type sensor node (TR), and performs wired or wireless transmission to the base station (GW). Furthermore, the transceiver unit (GWSR) comprises an antenna for receiving radio.

The storage unit (GWME) is configured of a non-volatile storage device such as a hard disk or a flash memory. The storage unit (GWME) stores at least operation setting (GWMA), data format information (GWMF), a terminal management table (GWTT), and base station information (GWMG). The operation setting (GWMA) includes information indicating the operation method of the base station (GW). Data format information (GWMF) includes information indicating a data format for communication, and information necessary to tag sensing data. The terminal management table (GWTT) is terminal information (TRMT) of name tag type sensor nodes (TR) under the current association and locals distributed for managing those name tag type sensor nodes (TR) Contains the ID. Base station information (GWMG) includes information such as the address of the base station (GW) itself. The storage unit (GWME) temporarily stores updated terminal firmware (GWTF) of the nameplate type sensor node.

The storage unit (GWME) may further store a program executed by a central processing unit CPU (not shown) in the control unit (GWCO).

The clock (GWCK) holds time information. The time information is updated at regular intervals. Specifically, the time information of the clock (GWCK) is corrected by the time information acquired from the NTP (NETWORK TIME PROTOCOL) server (TS) at regular intervals.

The control unit (GWCO) includes a CPU (not shown). When the CPU executes a program stored in the storage unit (GWME), acquisition timing of sensing data sensor information, processing of sensing data, transmission / reception to name tag type sensor node (TR) or sensor net server (SS) Manage timing and timing of time synchronization. Specifically, when the CPU executes a program stored in the storage unit (GWME), the communication control unit (GWCC), associate (GWTA), time synchronization management (GWCD), time synchronization (GWCS), etc. Execute the process

The communication control unit (GWCC) controls the timing of communication with the name tag type sensor node (TR) and the sensor network server (SS) by wireless or wired. Also, the communication control unit (GWCC) distinguishes the type of received data. Specifically, the communication control unit (GWCC) identifies from the header portion of the data whether the received data is general sensing data, data for association, or a time synchronization response. Pass those data to the appropriate functions.

The communication control unit (GWCC) converts data into a format suitable for transmission and reception with reference to data format information (GWMF) recorded in the storage unit (GWME), and indicates the type of data. Perform data format conversion to add tag information.

The associate (GWTA) transmits a response (TRTAR) to the associate request (TRTAQ) sent from the name tag type sensor node (TR), and transmits a local ID assigned to the name tag type sensor node (TR). When the associate is established, the associate (GWTA) corrects the terminal management information using the terminal management table (GWTT) and the terminal firmware (GWTF).

The time synchronization management (GWCD) controls the interval and timing for performing time synchronization, and issues an instruction to perform time synchronization. Alternatively, the sensor network server (SS) may collectively send an instruction to the base station (GW) of the entire system by executing time synchronous management (GWCD) by the sensor network server (SS) described later. .

Time synchronization (GWCS) connects to an NTP server (TS) on the network to request and acquire time information. The time synchronization (GWCS) corrects the clock (GWCK) based on the acquired time information. The time synchronization (GWCS) transmits a time synchronization instruction and time information (GWCSD) to the nameplate type sensor node (TR).

FIG. 1E shows a configuration of a name tag type sensor node (TR) which is an embodiment of a sensor node, and the name tag type sensor node (TR) is a plurality of infrared ray transmitting / receiving units (AB) for detecting a human facing situation. ), A three-axis acceleration sensor (AC) for detecting the wearer's movement, a microphone (AD) for detecting the wearer's speech and surrounding sounds, and an illumination sensor (for detecting the front and back of the nameplate type sensor node) Various sensors of LS1F, LS1B) and temperature sensor (AE) are mounted. The mounted sensor is an example, and other sensors may be used to detect the wearer's facing situation and movement.

In the present embodiment, four infrared transmitting and receiving units are mounted. The infrared transmitting / receiving unit (AB) continuously transmits terminal information (TRMT), which is unique identification information of the nameplate type sensor node (TR), toward the front direction periodically. When a person wearing another name tag type sensor node (TR) is located substantially in front (for example, front or diagonal front), the name tag type sensor node (TR) and the other name tag type sensor node (TR) are Exchange terminal information (TRMT) with each other by infrared rays. By doing this, it is possible to record who and who are facing each other.

Each infrared transmitting and receiving unit is generally constituted by a combination of an infrared light emitting diode for infrared transmission and an infrared phototransistor. The infrared ID transmission unit (IRID) generates terminal information (TRMT), which is its own ID, and transfers it to the infrared light emitting diode of the infrared transmitting / receiving module. In this embodiment, all the infrared light emitting diodes are lighted at the same time by transmitting the same data to a plurality of infrared transmitting and receiving modules. Of course, separate data may be output independently of each other.

In addition, data received by the infrared phototransistor of the infrared transmitting / receiving unit (AB) is logically ORed by an OR circuit (IROR). That is, if at least one infrared light receiving unit receives an ID, it is recognized as an ID by the name tag type sensor node. Of course, it may be configured to have a plurality of ID receiving circuits independently. In this case, since the transmission / reception state can be grasped with respect to each infrared transmission / reception module, it is also possible to obtain additional information such as, for example, in which direction the other name tag type sensor node facing is located.

Sensor data (SENSD) detected by the sensor is stored in the storage unit (STRG) by the sensor data storage control unit (SDCNT). The sensor data (SENSD) is processed into a transmission packet by the communication control unit (TRCC), and is transmitted to the base station (GW) by the transmission / reception unit (TRSR).

At this time, it is the communication timing control unit (TRTMG) that takes out sensor data (SENSD) from the storage unit (STRG) and generates a timing for wireless transmission. The communication timing control unit (TRTMG) has a plurality of time bases for generating a plurality of timings.

For data stored in the storage unit, in addition to sensor data (SENSD) currently detected by a sensor, for giving up gift data (CMBD) accumulated in the past, and firmware that is an operation program of a name tag type sensor node There is firmware update data (FMUD).

The name tag type sensor node (TR) of this embodiment detects that the external power supply (EPOW) is connected by the external power supply connection detection circuit (PDET), and generates an external power supply detection signal (PDETS). The time base switching unit (TMGSEL) that switches the transmission timing generated by the communication timing control unit (TRTMG) by the external power supply detection signal (PDETS) or the data switching unit (TRDSEL) that switches data to be communicated wirelessly It has a unique configuration. FIG. 1E shows, as an example, a configuration in which the time base switching unit (TMGSEL) switches the transmission timing by two time bases, time base 1 (TB1) and time base (TB2), by the external power detection signal (PDETS). ing. Also, the data switching unit is based on the external power supply detection signal (PDETS) from sensor data (SENSD) obtained from the sensor, the data to be collected (CMBD) accumulated in the past, and firmware update data (FMUD) It illustrates a configuration in which (TRDSEL) switches.

The illuminance sensors (LS1F, LS1B) are mounted on the front and back of the nameplate type sensor node (TR), respectively. The data acquired by the illuminance sensors (LS1F, LS1B) are stored in the storage unit (STRG) by the sensor data storage control unit (SDCNT) and simultaneously compared by the reverse detection (FBDET). When the name tag is properly attached, the illuminance sensor (front) (LS1F) mounted on the front receives external light, and the illuminance sensor (back) mounted on the back (LS1B) is the nameplate type sensor node Since the positional relationship between the main body and the wearer is obtained, no extraneous light is received. At this time, the illuminance detected by the illuminance sensor (front) (LS1F) takes a larger value than the illuminance detected by the illuminance sensor (back) (LS1B). On the other hand, when the name tag type sensor node (TR) is turned over, the illuminance sensor (back) (LS1B) receives foreign light, and the illuminance sensor (front) (LS1F) faces the wearer, so the illuminance sensor (front) The illuminance detected by the illuminance sensor (back) (LS1B) is larger than the illuminance detected by (LS1F).

Here, the name tag node is turned over by comparing the illuminance detected by the illuminance sensor (front) (LS1F) with the illuminance detected by the illuminance sensor (back) (LS1B) by the reverse detection (FBDET). It can detect that it is not attached. When turning over is detected by turning over detection (FBDET), a warning sound is generated by the speaker (SP) to notify the wearer.

The microphone (AD) acquires audio information. Voice information can reveal the surrounding environment such as "noisy" or "quiet". Furthermore, by acquiring and analyzing the voice of the person, whether the communication is active or stagnant, whether they are exchanging conversations on an equal basis, are talking unilaterally, are they angry or laughing, Face-to-face communication such as Furthermore, the facing state in which the infrared transmitter-receiver (AB) can not be detected due to the relationship of the standing position of the person can be compensated by the audio information and the acceleration information.

The voice acquired by the microphone (AD) acquires both a voice waveform and a signal obtained by integrating it with an integrating circuit (AVG). The integrated signal represents the energy of the acquired speech.

A three-axis acceleration sensor (AC) detects the acceleration of the node, ie the movement of the node. For this reason, from the acceleration data, it is possible to analyze the movement of the person wearing the name tag type sensor node (TR), the behavior such as walking, and the like. Furthermore, by comparing the values of acceleration detected by a plurality of name tag type sensor nodes (TR), the degree of communication activity, mutual rhythm, and correlation between persons wearing those name tag type sensor nodes (TR) Etc. can be analyzed.

In the name tag type sensor node (TR) of this embodiment, data acquired by the three-axis acceleration sensor (AC) is stored in the storage unit (STRG) by the sensor data storage control unit (SDCNT), and at the same time upper and lower detection The direction of the name tag is detected by (UDDET). This is because the acceleration detected by the three-axis acceleration sensor (AC) utilizes two types of observation of dynamic acceleration change due to the movement of the wearer and static acceleration due to the gravitational acceleration of the earth .

When the name tag type sensor node (TR) is attached to the chest, the display device (LCDD) displays personal information such as the affiliation and name of the wearer. In other words, it acts as a name tag. On the other hand, when the wearer holds the nameplate type sensor node (TR) in hand and directs the display device (LCDD) toward him, the location of the nameplate type sensor node (TR) is reversed. At this time, the content displayed on the display device (LCDD) and the function of the button are switched by the vertical detection signal (UDDETS) generated by the vertical detection (UDDET). In this embodiment, the information displayed on the display device (LCDD) by the value of the upper and lower detection signal (UDDETS) is an analysis result by the infrared activity analysis (ANA) generated by the display control (DISP) and the name tag display (DNM) An example of switching to and) is shown.

Whether the name tag type sensor node (TR) has faced another name tag type sensor node (TR) by the infrared transceiver (AB) exchanging infrared rays between the nodes, ie, the name tag type sensor node (TR) It is detected whether or not the person wearing the has faced the person wearing the other nameplate type sensor node (TR). For this reason, it is desirable that the name tag type sensor node (TR) be mounted on the front of the person. As described above, the name tag type sensor node (TR) further includes a sensor such as a three-axis acceleration sensor (AC). The process of sensing in the name tag type sensor node (TR) corresponds to tissue dynamics data acquisition (A) in FIG. 2A.

In many cases, there are a plurality of name tag type sensor nodes (TRs), each of which is connected with a nearby base station (GW) to form a personal area network (PAN).

The temperature sensor (AE) of the nameplate type sensor node (TR) is the temperature of the place where the nameplate type sensor node (TR) is located, and the illuminance sensor (front) (LS1F) is the illuminance of the name tag type sensor node (TR) To get This allows the surrounding environment to be recorded. For example, based on the temperature and the illuminance, it can be known that the nameplate type sensor node (TR) has moved from one place to another.

The buttons 1 to 3 (BTNs 1 to 3), the display device (LCDD), the speaker (SP), and the like are provided as input / output devices corresponding to the person who has been worn.

Specifically, the storage unit (STRG) is configured by a non-volatile storage device such as a hard disk or a flash memory, and terminal information (TRMT) which is a unique identification number of a name tag type sensor node (TR), sensing interval, and display The operation settings (TRMA) such as output contents to the are recorded. Besides, the storage unit (STRG) can temporarily record data, and is used to record sensed data.

The communication timing control unit (TRTMG) holds time information (GWCSD), updates the time information (GWCSD) at fixed intervals, and records it as a clock (TRCK). The time information is periodically corrected by the time information (GWCSD) transmitted from the base station (GW) in order to prevent the time information (GWCSD) from shifting with another nameplate type sensor node (TR).

The sensor data storage control unit (SDCNT) controls the sensing interval and the like of each sensor according to the operation setting (TRMA) recorded in the storage unit (STRG), and manages the acquired data.

Time synchronization corrects a clock by acquiring time information from a base station (GW). The time synchronization may be performed immediately after association, which will be described later, or may be performed according to a time synchronization command transmitted from the base station (GW).

When transmitting and receiving data, the radio communication control unit (TRCC) performs control of a transmission interval and conversion to a data format corresponding to transmission and reception. The wireless communication control unit (TRCC) may have a wired communication function, not wireless, if necessary. The radio communication control unit (TRCC) may perform congestion control so that the transmission timing does not overlap with another nameplate type sensor node (TR).

An associate (TRTA) transmits / receives an associate request (TRTAQ) and an associate response (TRTAR) to form a personal area network (PAN) with the base station (GW) shown in FIG. Determine (GW). The associate (TRTA) is activated when the name tag type sensor node (TR) is powered on, and when the name tag type sensor node (TR) moves and transmission / reception with the base station (GW) is interrupted as a result To be executed. As a result of associate (TRTA), a nameplate type sensor node (TR) is associated with one base station (GW) in a close range to which a radio signal from the nameplate type sensor node (TR) can reach.

The transmission / reception unit (TRSR) includes an antenna and performs transmission and reception of a radio signal. If necessary, the transceiver unit (TRSR) can also perform transmission and reception using a connector for wired communication. Transmission / reception data (TRSRD) transmitted / received by the transmission / reception unit (TRSR) is transferred to / from the base station (GW) via the personal area network (PAN).

FIGS. 2A, 2B, and 2C show the overall flow of processing performed in one embodiment of the business microscope system, and are shown separately for convenience of illustration, but each of the illustrated Processing is performed in cooperation with each other. From acquisition (A) of tissue dynamics data by a plurality of nameplate type sensor nodes (TRa, TRb,..., TRi, TRj) shown in FIG. 2A, modeling analysis (CA) which is analysis of sensor data shown in FIG. The analysis result is visualized by model drawing (JA), and the visualization result shows a series of flows called scientific management knowledge model (KA).

Tissue dynamics data acquisition (A) will be described using FIG. 2A. Name tag type sensor node A (TRa) includes sensors such as infrared transceiver (AB), acceleration sensor (AC), microphone (AD), temperature (AE), net (AFA), notice (AFB), thanks AFC) buttons (AF) are composed of buttons.

It has a screen (AG) for displaying the face-to-face information obtained from the infrared transmitter / receiver, a user interface (AA) for inputting a rating, and a microcomputer and a wireless transmission function although illustration is omitted.

The acceleration sensor (AC) detects the acceleration of the name tag type sensor node A (TRa) (that is, the acceleration of the person A (not shown) wearing the name tag type sensor node A (TRa)). The infrared transceiver (AB) detects the facing state of the name tag type sensor node A (TRa) (that is, the state where the name tag type sensor node A (TRa) is facing another name tag type sensor node). The name tag type sensor node A (TRa) facing another name tag type sensor node means that the person A who wears the name tag type sensor node A (TRa) is the person who wears the other name tag sensor node Show that you are facing. The microphone (AD) detects the sound around the name tag type sensor node A (TRa), and the temperature sensor (AE) detects the temperature around the name tag type sensor node A (TRa).

The button (AF) is for performing input from a subjective viewpoint of a person A (not shown) wearing the name tag type sensor node A (TRa). If you are doing the main work net (AFA), if you find a new idea, etc., you notice (AFB), if you thank the members, the button of thanks (AFC) person A Let me press.

The system according to the present embodiment includes a plurality of name tag type sensor nodes (name tag type sensor node A (TRa) to name tag type sensor node J (TRj) in FIG. 2A). Each name tag type sensor node is attached to one person. For example, name tag type sensor node A (TRa) is attached to person A, and name tag type sensor node B (TRb) is attached to person B (not shown). The purpose is to analyze relationships between people and further illustrate the performance of the organization.

The name tag type sensor node B (TRb) to the name tag type sensor node J (TRj) also have sensors, a microcomputer, and a wireless transmission function, similarly to the name tag type sensor node A (TRa). In the following description, the name tag is used when the description applies to any of the name tag type sensor node A (TRa) to the name tag type sensor node J (TRj), and when it is not necessary to distinguish between the name tag type sensor nodes. Described as a sensor node.

Each name tag type sensor node performs sensing by sensors constantly (or repeatedly at short intervals). Then, each name tag type sensor node wirelessly transmits the acquired data (sensing data) at a predetermined interval. The interval for transmitting data may be the same as the sensing interval or may be larger than the sensing interval. The data transmitted at this time is assigned a sensing time and a unique identifier (ID) of the sensed nameplate type sensor node. The wireless transmission of data is collectively performed in order to maintain the usable state of the name tag type sensor node (TR) for a long time while being worn by a person by suppressing the power consumption by the transmission. In addition, it is desirable for the later analysis that the same sensing interval is set in all name tag type sensor nodes.
Data transmitted by wireless from the nameplate type sensor node is collected in tissue dynamics data collection (B) shown in FIG. 2B and stored in the database.

The data table (BA) stores sensor data obtained from name tag type sensor nodes.

The user ID (BAA) is the identifier of the user, the acquisition time (BAB) is the time when the name tag type sensor node (TR) receives, the base station (BAC) is the base station that received sensor data from the name tag type sensor node (TR), Acceleration sensor (BAD) is sensor data of acceleration sensor (AC), IR sensor (BAE) is sensor data of infrared transceiver (AB), sound sensor (BAF) is sensor data of microphone (AD), temperature (BAG) is Temperature sensor (AE) sensor data, notice (BAH) notice (AFB) button pressed, thanks (BAI) thanks (AFC) button pressed, net (BAJ) net (AFA) button The presence or absence of pressing, the terminal (BAI) is information for identifying the terminal.

The performance table (BB) stores performance values input at performance input (C) and rating input (AA).

The user ID (BBA) is the identifier of the user, and the acquisition time (BBB) is the time when the rating input (AA) is made by the name tag type sensor node (TR) or the time when the performance is input (C). SOCIAL (BBC), INTELLECTUAL (BBD), SPIRITUAL (BBE), PHYSICAL (BBF), and EXECUTIVE (BBG) are information for rating, and terminal (BBH) is information for identifying a terminal.

Further, in the dynamics data collection (B), an example is shown in which the data are stored in the order of arrival, and therefore, they are not necessarily arranged in time order. Further, the data table (BA) and the data table (BA) are an example, and a table may be created for each sensor data.

The tissue dynamics data collected by the tissue dynamics data collection (B) is generated by the modeling analysis (CA) shown in FIG. 2C to generate a model with a beneficial factor, visualized by model drawing (JA), and the visualization result is scientific It becomes a management knowledge model (KA).

Modeling analysis (CA) is a process to clarify which tissue activity is a beneficial factor, such as stress and productivity. Specifically, stress, productivity, etc. are used as objective variables, and tissue dynamics indexes, which are tissue activities, as explanatory variables, and correlation processing is performed to select useful factors based on the correlation results. This makes it possible to identify which organization activity affects stress, productivity, etc., and to identify the organization activity to be improved.

Describe the overall flow of modeling analysis (CA). The modeling analysis (CA) converts the tissue dynamics data into a time-series table for each user by creating a facing table (C1A) and creating a body rhythm table (C1B). Then, based on this result, the user's face-to-face situation is summarized in a matrix form (face-to-face matrix creation (C1C)). A variety of tissue dynamics covering tissue activity by processing network index extraction (CAA), body rhythm index extraction processing (CAB), face-to-face index extraction processing (CAC), and tissue activity index extraction (CAD) from these data Determine the index.

As questionnaires (G), personality questionnaires (GA), leadership / teamwork questionnaires (GB), employee satisfaction / satisfaction questionnaires (GC), stress / mental disorder questionnaires (GD), tissue activation questionnaires (GE) This is used as subjective data by having the user answer. The questionnaire results are stored in each of the analysis result tables (F). At that time, the personality index (FAAE) is used as an explanatory variable (FAA). The reason is based on the hypothesis that the personality of the user is born and does not change. In addition, the productivity index (HA) and the accident failure index (HB) in the organization are used as objective variables.

In correlation analysis (CAE), the correlation between the explanatory variable (FAA) and the objective variable (FAB) in the analysis result table (F), and the correlation between the explanatory variable (FAA) and the objective variable (HA) in the tissue information table (H) Ask for At that time, not only the correlation between the explanatory variable of the member and the objective variable of the user may be determined, but also the value of the surrounding member facing the member may be used as the objective variable. That is, the surrounding members facing the members are identified by the facing matrix (FC1C), and the average or the variance of the objective variables of the plurality of surrounding members identified is used.

The results are stored in factor coefficients (FAC), and only factor that is beneficial by factor selection (CAF) is selected. In that case, it is possible not only to determine that the correlation value is high but also to select one that has a good test result (for example, P value) or one that is covered as tissue activity.

The factor selected by factor selection (CAF) is drawn by model drawing (JA). This result is a scientific management knowledge model (KA).

Face-to-face table creation (C1A) is a process of putting together the face-to-face situation between members from infrared data of tissue dynamics data in time series in a certain constant period. The extracted result is stored in the meeting table (FC1A) of the analysis result table (F). One example of the facing table (FC1A) is shown in FIG. This stores one day (24 hours) in chronological order with a time resolution of one minute (FC1A3) with the user as one record.

In the face-to-face table (July 1, 2009), the vertical axis is a user ID (FC1A1) for identifying a member individual, and the horizontal axis is a resolution time (FC1A2) indicating time by time resolution. The user's face-to-face situation at a certain time only needs to read the correspondence between the user ID (FC1A1) and the resolution time (FC1A2). For example, the meeting situation of 2009/7/1 10:02 of the user ID 001 is facing two people, and the members who were facing are the user IDs 002 and 003. NULL is stored in the facing table (FC1A) when there is no infrared data of tissue dynamics data of the corresponding user at that time.

The meeting table (FC1A) is generated for each day and each time resolution, so even if the same date, the time resolution is different. For example, although (FC1A4) and (FC1A5) are the same (July 2, 2009), they have different tables because they have different time resolutions. In addition, it is important to store the meeting table (FC1A) as a user ID facing the meeting number of people, so if this is satisfied, the table configuration used in the meeting table (FC1A) may be different. .

Body rhythm table creation (C1B) is a process of putting together the body rhythm status in time series in a certain constant period by indicating the movement of the member's body from the acceleration data of the tissue dynamics data as Hz.

The extracted result is stored in the body rhythm table (FC1B) of the analysis result table (F). One example of the body rhythm table (FC1B) is shown in FIG. Assuming that the user is one record, one minute (24 hours) is stored in chronological order, with a time resolution of one minute (FC1B3).

In the body rhythm table (July 1, 2009) (FC1B3), the vertical axis is a user ID (FC1B1) for identifying a member individual, and the horizontal axis is a resolution time (FC1B2) indicating time by time resolution . The physical rhythm situation of the user at a certain time only needs to read the correspondence between the user ID (FC1B1) and the resolution time (FC1B2). For example, the body rhythm situation of 2009/7/11 10:02 of user ID 001 is 2.1 Hz. NULL is stored in the body rhythm table (FC1B) when there is no acceleration data of tissue dynamics data of the corresponding user at that time.

Since the body rhythm table (FC1B) is generated for each day and for each time resolution, the same date may be another table if the time resolution is different. For example, although (FC 1 B 4) and (FC 1 B 5) are the same (July 2, 2009), they have different tables because they have different time resolutions. Further, since it is important to store the user's body rhythm, the body rhythm table (FC1B) may be different from the table configuration used in the body rhythm table (FC1B) if it is satisfied.

Face-to-face matrix creation (C1C) is a process of removing time-series information from the face-to-face table (FC1A) arranged in time-series and putting together in a two-dimensional matrix how much face-to-face contact is performed for each user.

The extracted result is stored in the facing matrix (FC1C) of the analysis result table (F). One example of the facing matrix (FC1C) is shown in FIG. FIG. 5 is a summary of one month's meeting results. In addition, since the time resolution in the facing table (FC1A) is taken as a unit, when 1 is stored in the facing matrix (FC1C), the time resolution is 1 minute if it is 1 minute, and 5 minutes if the time resolution is 5 minutes. It will be.

In the face-to-face matrix (FC1C), the vertical axis is a user ID (FC1C1) for identifying a member individual, and the horizontal axis is a user ID (FC1C2) indicating a partner who has met. For example, the meeting time of the user 002 with the user 003 is 33 minutes.

In creating this face-to-face matrix (FC1C), it is necessary to describe the original information because a lot of information is aggregated into one matrix. Period: 2009/7 / 1-July 31 (FC1C3) is the period used for the facing matrix (FC1C). Days: 31 days (FC1C4) is the number of days in the period (FC1C3). Actual days: 21 days (FC1C5) is the number of business days in the period (FC1C3). Temporal resolution: 1 minute (FC1C6) is the temporal resolution in the facing table (FC1A). Meeting determination time: 3 minutes / 1 day (FC1C7) is a threshold value for determining that meeting has occurred. If the infrared transmitting and receiving unit receives an infrared ray in the case of passing each other, it is determined that they have met, but such a threshold is introduced because the reaction is likely to be noise several times. In addition, since it is important to store the user's face-to-face situation, the face-to-face matrix (FC1C) may be different from the table configuration used in the face-to-face matrix (FC1C) if it is satisfied.

The network index extraction process (CAA) is a process of obtaining an index from a network diagram created from a face-to-face matrix (FC1C). Then, an example of a table storing the index obtained by the network index extraction process (CAA) is the network index (FAAA) of FIG. The network index (FAAA) is a table in which an index is stored for each user. The network index is an index indicating the connection between each of a plurality of persons and other persons in the organization.

The table identifies the user: user ID (FAAA1) and network index (order (FAAA2), cohesion (FAAA3), two-step reach (FAAA4), mediation centrality (FAAA5), meeting time (total) (FAAA6)) It consists of Period: 2009/7 / 1-July 31 (FAAA6) shows the period used for the analysis. Temporal resolution: 1 minute (FAAA 7) is analysis time resolution.

The network diagram (ZA) of FIG. 7 is an example of a network diagram created from the facing matrix. In this network diagram (ZA), (ZA1) to (ZA5) are made up of nodes representing persons, and (ZA6) to (ZA11) are made up of lines (edges) connecting members facing each other. Use a spring model for placement. The spring model (Hook's law) calculates the force (inward or outward) assuming that there is a spring when two nodes (points) are connected, and further calculates the distance from all nodes not connected with itself. It is a method to make it an optimal arrangement by repeating movement of a position as receiving repulsive force (repulsive force) according to. The network indicator (FAAA) will be described with an example of this network diagram (ZA).

The order (FAAA2) is the number of edges connected to the node. In the example of the network diagram (ZA), Takahashi (ZA1) is 2 because it is connected with Tanaka (ZA2) and Ito (ZA4).

The cohesion degree (FAAA3) is a density of nodes around oneself, and is an index indicating a degree of cooperation with one another around a person. Referring to Ito (ZA4) in the network diagram (ZA), ito (ZA4) has three opponents, Takahashi (ZA1), Yamamoto (ZA5), and Tanaka (ZA2). The density of the three persons should be examined, and as a result, the number of edges in three persons / 3, the maximum number of edges in persons = 2/3 = 0.67.

The two-step reach (FAAA4) is the total number of nodes within a range of two steps. In the example of the network diagram (ZA), all the nodes that can be covered by the two steps in the case of Watanabe (ZA3) are (ZA1) to (ZA5), which is 4.

Mediation centrality (FAAA4) is a value representing how much a node contributes to the connectivity of the entire network diagram. Mediation centrality is the number of cases where a person is present on a route that arrives at the shortest step on the network diagram in the combination of all the persons in an organization. When there are n types of shortest routes between the person A and the person B, it is counted and calculated as 1 / n.

Meeting time (total) (FAAA5) is the total of meeting time during the period. This is a value obtained from the facing matrix (FC1C). The sum of the rows (horizontal rows) of each user in the facing matrix (FC1C) is the facing time.

Although the network indicator (FAAA) has been described, the indicator is not limited to this, and another indicator may be created from the face-to-face matrix (FC1C) and used for analysis.

Body rhythm index extraction processing (CAB) is processing for obtaining an index from a body rhythm table (FC1B). Then, one example of a table storing the index obtained by the body rhythm index extraction process (CAB) is the body rhythm index (FAAB) of FIG. The body rhythm index (FAAB) is a table in which the index is stored for each user.

The table identifies the user: user ID (FAAB1) and body rhythm index (0 to 1 Hz appearance frequency (FAAB2), 1 to 2 Hz appearance frequency (FAAB3), 2 to 3 Hz appearance frequency (FAAB4), 0 to 1 Hz It consists of continuity (FAAB5), 1 to 2 Hz continuity (FAAB6), and 2 to 3 Hz continuity (FAAB7)).

Period: July 1-July 31, 2009 (FAAB 8) indicate the period used for analysis. Time resolution: 1 minute (FAAB 9) is analysis time resolution. Time interval: 1 day (FAAB10) is a range designation when obtaining an average etc. in a period (FAAB8).

The body rhythm table (FC1B) stores a body rhythm in terms of Hz for each time resolution, so that a histogram for each 1 Hz section is created. And the histogram value from 0 Hz to 1 Hz is the appearance frequency of 0 to 1 Hz (FAAB2), the histogram value from 1 Hz to 2 Hz is the appearance frequency of 1 to 2 Hz (FAAB3), the histogram value from 2 Hz to 3 Hz is 2 to 3 Hz Find the frequency of occurrence (FAAB4).

Furthermore, since the body rhythms are stored in chronological order in the body rhythm table (FC1B), the continuation of each rhythm can be obtained. Specifically, it is sufficient to check the degree of continuation, compare the physical rhythm at a certain time with the physical rhythm at the next time, count cases where the two physical rhythms are from 0 Hz to 1 Hz, The continuity from 0 Hz to 1 Hz in the time interval is obtained by dividing by FAAB10). Similarly, the continuity from 1 Hz to 2 Hz is determined as the continuity of 1 to 2 Hz (FAAB 6), and the continuity from 2 Hz to 3 Hz is determined as the continuity of 2 to 3 Hz (FAAB 7).

Furthermore, since this is a daily value which is a time interval (FAAB10), when storing in the body rhythm index (FAAB), the average of the period (FAAB8) becomes a value stored in each value.

Although the body rhythm index (FAAB) has been described, the index is not limited to this, and another index may be created from the body rhythm table (FC1B) and used for analysis. Furthermore, in the body rhythm index extraction process (CAB), the average in the period (FAAB 8) is stored, but a variance or the like may be used.

The face-to-face index extraction process (CAC) is a process for obtaining an index from the face-to-face table (FC1A) and the body rhythm table (FC1B). Then, an example of a table storing the index obtained by the facing index extraction process (CAC) is the facing index (FAAC) of FIG. The face-to-face indicator (FAAC) is a table in which the indicator is stored for each user.

The table identifies the user: user ID (FAAC1) and face-to-face index (face-to-face time (FAAC2), non-face-to-face time (FAAC3), active face-to-face time (FAAC4), passive face-to-face time (FAAC5), two-person face-to-face time (FAAC6), It is composed of 3 to 5 face-to-face hours (FAAC 7) and 6 to 6 face-to-face times (FAAC 8).

Period: July 1-July 31, 2009 (FAAC 9) indicates the period used for analysis. Time resolution: 1 minute (FAAC 10) is analysis time resolution. Time interval: 1 day (FAAC 11) is a range designation when obtaining an average or the like in the period (FAAC 9).

From the face-to-face table (FC1A), find face-to-face time and non-face-to-face time when acquiring tissue dynamics data. If the value stored in the meeting table (FC1A) is 1 or more, the meeting time is counted, and if the value is 0, it is counted as the non-facing time. When the stored value is NULL, the facing time and the non-facing time are not counted. The facing time (FAAC2) is the time when counting the facing, and the non-facing time (FAAC3) is the time when the non-facing is counted. Since the analysis time resolution is one minute, the counted value itself becomes time.

By examining the body rhythm table (FC1B) at the time between facing members at the time when it is determined to be facing by the facing table (FC1A), it is determined whether facing positiveness, that is, active facing or passive facing. As a threshold of this judgment, the body rhythm in meeting is 2 Hz or more as active facing (active facing) and less than 2 Hz as passive facing (passive facing). This is because, when we focus on the relationship between the user's behavior and the movement rhythm at the time of meeting, in the case of a meeting that is considered to be positive like a meeting that includes not only words but gestures, the movement rhythm at the time of meeting is 2 Hz It is because it is based on the knowledge that it is more than. Active facing time (FAAC4) is time when counting active facing, and passive facing time (FAAC5) is time when counting passive facing. Since the analysis time resolution is one minute, the counted value itself becomes time.

Find out how many people were meeting from the meeting table (FC1A). In the face-to-face table (FC1A), the face-to-face number is described for each analysis time resolution, so the value is obtained by counting the number. The analysis range was three for two, three to five, and six. Two-person face-to-face time (FAAC 6) is the time when counting the two-person face-to-face. The 3 to 5 face-to-face time (FAAC 7) is the time when counting from 3 to 5 face-to-face meetings. Six people to face-to-face time (FAAC 8) is the time when counting six or more face-to-face meetings. Since the analysis time resolution is one minute, the counted value itself becomes time.

Furthermore, since these are values for each day which is a time interval (FAAC11), the average of the period (FAAC9) becomes each value.

Although the face-to-face index (FAAC) has been described, the index is not limited thereto, and another index may be created from the face-to-face table (FC1A) and the body rhythm table (FC1B) and used for analysis. Furthermore, in the face-to-face index extraction process (CAC), the average in the period (FAAC 9) is stored, but a variance or the like may be used.

The tissue activity index extraction process (CAD) is a process of obtaining an index from a face-to-face table (FC1A) and a body rhythm table (FC1B). And an example of a table which stores an index obtained by organization activity index extraction processing (CAD) is organization activity index (FAAD) of FIG. The organizational activity indicator (FAAD) is a table in which the indicator is stored for each user.

The table identifies the user user ID (FAAD1) and organization activity indicator (working hours average (FAAD2), average attendance time (FAAD3), average return time (FAAD4), standard deviation of working hours (FAAD5), standard deviation of attendance time (FAAD5) It consists of FAAD6) and return time standard deviation (FAAD7)). Period: July 1-July 31, 2009 (FAAD 8) indicate the period used for analysis. Time resolution: 1 minute (FAAD 9) is analysis time resolution. Time interval: 1 day (FAAD 10) is a range designation at the time of obtaining an average or the like in a period (FAAD 8).

By obtaining the tissue dynamics data acquisition start address and end address from the meeting table (FC1A) and the body rhythm table (FC1B), the working time, the time of coming to work, and the time of returning to work are determined from this. The start address means an address when data is stored (zero or more) from the time when tissue dynamics data can not be obtained (NULL). Further, the end address means an address when data can not be obtained (NULL) from the time when tissue dynamics data is obtained (0 or more). In the present embodiment, it is assumed that the name tag type sensor node is attached during working hours and the name tag type sensor node is removed when returning home.

Although time is not stored in the meeting table (FC1A) and the body rhythm table (FC1B), time is stored in chronological order, so time can be determined from the acquired address and time resolution (FAAD 9).

Working time becomes the working time by subtracting the start address from the end address. The working hours average (FAAD2) is an average of time intervals (FAAD10) in the working hours period (FAAD8). The working time standard deviation (FAAD5) is the average of the time interval (FAAD10) in the working time period (FAAD8).

The time of arrival at work (FAAD3) is the average of the time interval (FAAD10) in the period (FAAD8) of the start address. The attendance time standard deviation (FAAD6) is an average of the time interval (FAAD10) in the period (FAAD8) of the start address.

The leaving time average (FAAD4) is the average of the time interval (FAAD10) in the end address period (FAAD8). The return time standard deviation (FAAD7) is an average of the time interval (FAAD10) in the end address period (FAAD8).

From the meeting table (FC1A) and the body rhythm table (FC1B), it can be determined not to use tissue dynamic data in an error state. For example, it is assumed that when the name tag type sensor node (TR) is left and returned to the office, the meeting with the nearby node is reacted. It does not actually meet, but it can not judge from infrared rays. In order to improve the accuracy, it is necessary to eliminate such misjudgment. As a countermeasure, it is determined whether the facing of the facing table (FC1A) is correct by comparing with the physical rhythm table (FC1B). That is, if a rhythm (body rhythm is 0 Hz, for a long time) which is not correctly attached by a human being is detected, the value of the facing table at that time is not used.

Although the organization activity index (FAAD) has been described, the index is not limited to this, and another index may be created from the face-to-face table (FC1A) and the body rhythm table (FC1B) and used for analysis. Furthermore, in the body rhythm index extraction process (CAB), the mean and the standard deviation in the period (FAAD 10) are stored, but a variance or the like may be used.

Next, indices required from various questionnaires (GA to GE) and objective organizational indices (productivity index, accident defect index) will be described. These are obtained based on the values input by the performance input (C). Personality Questionnaire (GA) is a questionnaire that examines the characteristics of thinking and behavior. The following documents may be referred to as an example of the personality questionnaire (GA). V. Benet-Martinez and O. P. John, “Los Cinco Grandes across cultures and ethnic groups: Multitrait method analyzes of the Big Five in Spanish and English,” Journal of Personality and Social Psychology, 75, pp. 729-750, 1998. .

An example of the questionnaire is shown in FIG. The user answers this questionnaire and stores the result as a personality index. The personality index (FAAE) table of FIG. 9 will be described as an example of the personality index. The user ID (FAAE1) and personality (Extroversion (FAAE2), harmony (FAAE3), integrity (FAAE4), nervousness (FAAE5), openness (FAAE6)) for identifying the user. Answer date: July 15, 2009 (FAAE7) contains the date of response.

For the user, personality values are stored in each of the extroversion (FAAE2), the harmony (FAAE3), the integrity (FAAE4), the nervousness (FAAE5), and the openness (FAAE6).

The higher the value of the extroversion (FAAE2), the more the outward trend is meant. The higher the value of harmony (FAAE3), the more it tends to fit others. The higher the value of integrity (FAAE 4), the more it tends to be honest. The higher the value of nervousness (FAAE5), the more nervous the tendency. The higher the value of openness (FAAE6), the more open the trend towards new knowledge and experience. Furthermore, FIG. 17 is a table showing the effect of this value. In addition, it is sufficient if the user's thinking and behavior show the degree of adaptation to society, and another questionnaire may be used. Also, the table configuration used in the personality index (FAAE) may be changed accordingly.

The organization information table (H) will be described with reference to FIG. The organization information table (H) stores indexes related to the organization and members.

Store productivity indicators in Productivity Indicators (HA). The table consists of the user ID (HA1) that identifies the user and the productivity index (performance (HA2), contribution (HA3), number of program steps (HA4), number of sales (HA5), sales (HA6)) . The period is from July 1, 2009 to July 15, 2009 (HA7).

If it is alphabetic notation like contribution (HA3), convert good results into large values. In addition, if it is an index for each team, members belonging to that team substitute the same value. Other indicators may be used as long as they relate to productivity.

Stores indicators related to accidents and defects in accident defect indicators (HB). The table consists of a user ID (HB1) for identifying the user and an accident defect index (days closed (HB2), number of bugs (HB3), number of incidents (HB4), number of defects (HB5), number of complaints (HB6)) There is. The period is from 1 July 2009 to 15 July 2009 (HB7).

If it is an index for each team, members belonging to that team substitute the same value.
In addition, other indicators may be used as long as they are indicators relating to an accident failure.

A Leadership / Teamwork Questionnaire (GB) is a questionnaire that examines the work, cooperation, awareness, and behavior that members of a group perform to achieve the same goal. The following documents may be referred to as an example of the Leadership / Teamwork Questionnaire (GB). Ryo Misawa, Kunihide Sashio, Hiroyuki Yamaguchi, making a teamwork measurement scale for nurse teams, social psychology research, 24 (3) pp. 219-232, 20090227. .

An example of the questionnaire is shown in FIG. The user answers this questionnaire and stores the result in the leadership / teamwork index. The Leadership / Teamwork Index (FABC) table of FIG. 11 will be described as an example of the Leadership / Teamwork Index.

The table is composed of a user ID (FABC1) for identifying a user and indicators (team orientation (FABC2), team leadership (FABC5), team process (FABC8)). Answer date: July 15, 2009 (FABC13) contains the date of response. From the Leadership / Teamwork Questionnaire (GB), we seek from three perspectives: Team Orientation (FABC2), Team Leadership (FABC5), and Team Process (FABC8).

In the team orientation (FABC2), the job orientation (FABC3) showing attitudes and values regarding duties and the interpersonal orientation (FABC4) showing good interpersonal relationships in the team are sought.

In Team Leadership (FABC5), the Team Process (FABC8) asks for instructions on job performance (FABC6) that show appropriate instructions and guidance to members, and interpersonal considerations (FABC7) for maintaining and strengthening interpersonal relationships. ) Monitor each other's work progress mutually, monitor and show mutual coordination actions as needed (FABC 9), and analyze duties that indicate action content that is clarified by agreement among members Clarification (FABC10), sharing knowledge and information (FABC11) representing an action to disseminate knowledge and information thoroughly, and asking for feedback (FABC12) representing feedback on mistakes and problems.

Also, it is sufficient if the members belonging to the group know the work, cooperation, awareness, and actions they perform to achieve the same goal, and other questionnaires may be used. Also, the table configuration used in the Leadership / Teamwork Index (FABC) may be changed accordingly.

The employee satisfaction / satisfaction questionnaire (GC) is a questionnaire that examines the level of health, happiness and prosperity in the presence of human beings. The following documents may be referred to as an example of the employee satisfaction / fullness questionnaire (GC).
Hills, P .; , And Argyle, M. The Oxford Happiness Questionnaire: a compact scale for the measurement of psychological well-being. Personality and Individual Differences, 33, 1073-1082, 2002. .

An example of the questionnaire is shown in FIG. The user is asked to answer this questionnaire, and the result is stored in the employee's satisfaction / satisfaction index (FABD). As an example of the employee satisfaction / severity index, the employee satisfaction / satisfaction index (FABD) table shown in FIG. 11 will be described. The table is composed of a user ID (FABD1) for specifying a user and a happiness (FABD2) as an index. Answer date: July 15, 2009 (FABD3) contains the date of response. The higher the happiness (FABD2), the higher the degree of health, happiness and prosperity.

Also, in the presence of human beings, it is sufficient to know the level of health, happiness and prosperity, and another questionnaire may be used. In addition, it is also possible to change the table configuration used in the employee satisfaction / sufficiency index (FABD) accordingly.

The stress / mental disorder questionnaire (GD) is a questionnaire for examining the degree of the psychological state of depression. The following documents may be referred to as an example of a stress / mental disorder questionnaire (GD). Radloff, L .; S. (1977) 'The CES-D scale: A self report depression scale for research in the general population'. Applied Psychological Measurement 1: 385-401. .

An example of the questionnaire is shown in FIG. The user answers this questionnaire and stores the result in stress / mental disorder index (FABE).

The stress / mental malfunction indicator (FABE) table in FIG. 11 will be described as an example of the stress / mental malfunction indicator. The table is composed of a user ID (FABE1) for identifying a user and depression (FABE2) as an index. Date of answer: July 15, 2009 (FABE3) contains the date of response. The higher the depression (FABE2), the higher the psychological state of depression.

In addition, it is sufficient to know the degree of the mental state of stress or depression, and another questionnaire may be used. Also, the table configuration used in the stress / mental disorder index (FABE) may be changed accordingly.

Tissue Activation Questionnaire (GE) is a questionnaire for examining the degree of subjective effects after measures of activation. An example of the questionnaire is shown in FIG. The user answers this questionnaire and stores the result in the organization activation index (FABF). The tissue activation indicator (FABF) table of FIG. 11 will be described as an example of the tissue activation indicator. The table is made up of a number of indicators such as a user ID (FABF1) for identifying a user, communication increase (FABE2), and feeling that it is easy to speak (FABE3). This indicator is present as much as the questionnaire item of the tissue activation questionnaire (GE). Answer date: July 15, 2009 (FABF5) contains the date of response.

In addition, it is sufficient if the degree of subjective effect is known after the activation measure, and another questionnaire may be used. Also, the table configuration used in the tissue activation index (FABF) may be changed accordingly.

The correlation analysis (CAE) shown in FIG. 2C is an analysis unit in which the stress and productivity of each member of an organization are taken as an objective variable and the tissue dynamics index which is an organization activity is taken as an explanatory variable. It is. The feature of this correlation analysis is that not only the variables of the person but also the variables of members around the person are analyzed.

The objective variable (CAE1) of the person is a variable stored in the record of the user ID of the user in the objective variable (FAB) of the analysis result table (F) or the objective variable (HA) of the organization information table (H). is there.

The principal explanatory variable (CAE2) is a variable stored in the record of the principal user ID in the explanatory variable (FAA) of the analysis result table (F).

The surrounding of the surrounding explanatory variable (CAE3) means a surrounding member connected to the person by facing. The surrounding explanatory variable (CAE3) is an explanatory variable to be obtained from surrounding members.

A method of processing surrounding explanatory variables (CAE3) will be described. Select surrounding members by surrounding member selection (CAE 31). Select a member connected to you from the facing matrix (FC1C). In this embodiment, an example is shown in which one step member and two step members are selected. Here, a member of one step is a member connected to oneself. The two-step member is a one-step member and a member connected to it.

The feature value calculation (CAE 32) is a process of obtaining surrounding explanatory variables from explanatory variables of members selected by surrounding member selection (CAE 31). As a calculation method for determining surrounding explanatory variables from the explanatory variables of members selected in the one-step member, the average or variance of the explanatory variables of the selected surrounding members is determined. The same calculation is performed in the case of two steps.

The correlation (CAE4) is a correlation between the objective variable (CAE1) of the user and the explanatory variable (CAE2) of the user and the explanatory variables (CAE3) of the surroundings. The correlated result is stored in the factor coefficient (FAC) of the analysis result table (F). Period: 2009/7 / 1-July 31 (FAC1) is the period of the tissue dynamics index used for analysis.

The factor coefficient (FAC) is part of a table that stores the correlation coefficient obtained by the correlation (CAE4). An example is shown in FIG. The person (FACA) is the result of the correlation between the object variable (CAE1) of the person and the explanatory variable (CAE2) of the person. The one-step average (FACB) is the result of the correlation between the objective variable (CAE1) of the user and the surrounding explanatory variables (CAE3) (of which the average value of the explanatory variables of the members of one step). The two-step variance (FACC) is the result of the correlation between the objective variable of the user (CAE1) and the surrounding explanatory variables (CAE3) (of which the variance of the explanatory variables of the two-step members).

The factor coefficient (FAC) can be obtained by substituting the correlation result between the user and the surrounding members, so store the result of the dispersion value of the explanatory variable of one step member and the average value of the explanatory variable of two step members. I don't care.

The table configuration of the person (FACA) will be described. The objective variable (FACA1) on the vertical axis is a variable stored as the objective variable (FAB) of the analysis result table (F) or the objective variable (HA) of the organization information table (H). Therefore, items such as the performance (FACA2) and the degree of contribution (FACA3) that were the productivity index (HA) of the objective variable (HA) of the organization information table (H) are substituted. The horizontal axis is a variable stored as an explanatory variable (FAA) of the analysis result table (F). Therefore, the items of the order (FACA6), the cohesion degree (FACA7), and the two-step attainment degree (FACA8), which are the network index (FAAA) of the explanatory variable (FAA) of the analysis result table (F), are substituted. Further, as described above, the personality index (FACA 9) obtained from the questionnaire is also stored as an explanatory variable. In FIG. 12, the openness (FACA 10) is shown as an example of the personality index.

The correlation between the score (FACA2) and the cohesion degree (FACA7) of the network indicator (FACA5) is 0.47, and 0.01 enclosed in parentheses is the test result. P value is used in this factor coefficient (FAC). The P value is the probability of occurrence of a phenomenon that is less likely to occur than the observed phenomenon. Furthermore, the table is hatched according to the test results, and a dark color if P value <= 0.001, a light color if 0.001 <P value <= 0.01, P value> 0. In the case of 01, it is possible to distinguish such as no color. One-step average (FACB) and two-step variance (FACC) are also the same as the table configuration of the person (FACA).

Correlation analysis (CAE) used correlation to compare variables, but other methods than correlation may be used if useful factors can be found. In addition, it is important to store the factor coefficient (FAC) obtained by correlation analysis (CAE), so if this is satisfied, it may be different from the table configuration used in factor coefficient (FAC). I do not mind.

Factor selection (CAF) is a process of selecting a beneficial factor from the coefficients obtained by correlation analysis (CAE). Among factor coefficients (FAC), select one with high coefficient (correlation) value. Further, it is possible to select not only the correlation value but also the selection result that has a good test result (for example, P value) or one that is covered as an organization activity.

In model drawing (JA), a model is drawn using factor variables (CAF) using an objective variable and an explanatory variable with high coefficient values. An example is shown by scientific management knowledge model (KA). Period: 2009/7 / 1-July 31 (KA10) is the period of the tissue dynamics index used for analysis.

Stress / mental upset risk (KA1) is selected as the objective variable (KA11), and the extroversion (KA2) of the surroundings, the extraversion of the person (KA3), the average working hours of the person (KA4) as the explanatory variables (KA12) , Others (KA5) were selected. Then, a line connects the objective variable (KA11) and the explanatory variable (KA12). As an arrangement, an objective variable (KA11) is arranged on the right side and an explanatory variable (KA12) is arranged on the left side. Furthermore, the factor coefficients of the explanatory variable (KA12) are arranged in descending order from the top. The other (KA5) is a collection of small factor coefficients. The factor coefficients are arranged as (KA6) to (KA9) in the explanatory variable (KA12). Also, if similar explanatory coefficients are selected by factor selection (CAF), they may be integrated.

By modeling analysis, it became clear which tissue activity is a beneficial factor for stress and productivity. Just as each organization has different problems, each organization has different benefit factors and factor coefficients.

Thus, in an organization consisting of a plurality of members serving as an analysis unit, subjective or objective indicators such as stress or productivity of each member are used as objective variables, and they are exhaustive indicators such as body rhythm indicators and face-to-face indicators. Correlation analysis is performed using tissue dynamics indicators as explanatory variables. Thereby, the factor of the subjective or objective index in the organization which is an analysis unit can be specified. Then, this model can specifically identify which organizational behavior should be improved.

Although the tissue dynamics index is determined as the explanatory variable in the present embodiment, the index determined from the questionnaire (G) may be used as the explanatory variable. Furthermore, a tissue dynamics index may be used as the objective variable.

In Example 1, the personality index (FAAE) was determined by the personality index (GA), but learning of the past personality index (GA) and learning of the model, the current organization dynamics index and the personality index from the model Find (FAAE).

Fig. 13 shows personality index extraction (CA1) which learns past personality questionnaire (GA) and obtains personality index coefficient (FAE) which is coefficient of model, and present tissue dynamics index and personality index coefficient (FAE) from present The personality index transformation (CA2) to obtain the personality index (FAF) of

First, personality index extraction (CA1) will be described. Personality index extraction (CA1) is a personality index coefficient using past personality questionnaire (GA) and organization dynamics index (Network index (FAAA), physical rhythm index (FAAB), face-to-face index (FAAC) and activity index (FAAD) The personality index coefficient (FAE) is obtained by extracting (CA1A).

The process until the network index (FAAA), the physical rhythm index (FAAB), the face-to-face index (FAAC), the activity index (FAAD), and the personality index (FAAE) are obtained is the same as that of the first embodiment. The personality index coefficient extraction (CA1A) will be described. In personality index coefficient extraction (CA1A), multiple regression with personality index (FAAE) as objective variable, network index (FAAA), physical rhythm index (FAAB), face-to-face index (FAAC) and activity index (FAAD) as explanatory variables By performing the analysis, coefficient and constant terms in the multiple regression equation are determined.

The personality factor (FAE) of the analysis result table (F) shown in FIGS. 14 and 15 is a table in which the coefficient and constant term in the multiple regression equation in each personality index (FAAE) are summarized. FIG. 14 summarizes the coefficients and constant terms of the multiple regression equation in the tissue, and FIG. 15 summarizes the coefficients and constant terms of the multiple regression equation for each user.

The vertical axis shown in FIG. 14 is the objective variable personality (FAE 1), which is composed of extroversion (FAE 2), harmony (FAE 3), integrity (FAE 4), nervousness (FAE 5) and openness (FAE 6) ing. The horizontal axis is an explanatory variable, in which coefficients of multiple regression equations in indexes such as the network index (FAE 8) and the personality index (FAE 12) are stored. The intercept (FAE14) is a constant term of the multiple regression equation. The same applies to the vertical axis and the horizontal axis shown in FIG. Although multiple regression analysis was used in personality index extraction (CA1), any other method may be used as long as it is used for learning.

Next, personality index conversion (CA2) will be described. Personality index conversion (CA2) uses the current index from among the personality index coefficient (FAE) obtained by personality index extraction (CA1) and the tissue dynamics index to obtain the current personality index (FAG).

In personality index conversion (CA2A), coefficients and constant terms in multiple regression equation stored in personality index coefficient (FAE), network index (FAAA) that is tissue dynamics index, physical rhythm index (FAAB), face-to-face index Use current indicators of (FAAC) and activity indicator (FAAD). Then, by applying them to the multiple regression equation, the personality index is obtained from the tissue dynamics index.

The estimated personality index (FAG) of the analysis result table (F) is a personality index obtained by personality index conversion (CA2A). An example of the table is shown in FIG. The table format is omitted because it is the same as the personality index (FAAE) of the analysis result table (F). Date: August 15, 2009 14:32 (FAG 7) shows the date and time of the current tissue dynamics indicator used in the analysis.

By performing multiple regression analysis using past personality indicators and tissue dynamics indicators, and creating a model, it became possible to obtain personality indicators from current tissue dynamics indicators and models without using a questionnaire. In the case of using a questionnaire, there is a possibility that there will be a restriction on the frequency of implementation, such as once a month, but according to this embodiment such restriction is not received, and the frequency of calculation may be changed appropriately.

In this example, the personality index was sought, but other questionnaires (Leadership / Teamwork Questionnaire (GB), Employee's Challenge / Enrichment Questionnaire (GC), Stress / Mental Disorder Questionnaire (GD), Organization Activation Questionnaire) The same processing may be performed on (GE) and the like.

In the third embodiment, a network diagram capable of simultaneously displaying a set facing a user is generated. Conventional network diagrams can not see facing pairs. The problem is solved by displaying both points of the network diagram (nodes) as a user and a face-to-face set.

FIG. 23 is a diagram showing a processing procedure for simultaneously displaying a set facing the user. A model is configured by the facing group network modeling analysis (CB), and drawing is performed by the facing group network diagram drawing (JB), and the drawn result is a facing group network diagram (KB). This process can be processed by the same framework as that of the first embodiment, and the face-to-face set network modeling analysis (CB) is the control unit (ASCO) of the application server (AS), the face-to-face pair network diagram drawing (JB) Is executed on the display (J) of the client (CL).

The process until the meeting table (FC1A) of the analysis result table (F) is obtained is the same as that of the first embodiment, so the description is omitted. Face-to-face pair by face-to-face time (CBA) is a process for determining face-to-face pairs and their face-to-face times. Since the members facing each other are described in the facing table (FC1A), the set and time will be determined from this. This result is stored in the face-to-face meeting time list (FBA) of the analysis result table (F). The table configuration is composed of a facing set (FBA1) and a facing time (FBA2).

Period: 2009/7 / 1-July 31 (FBA3) is the period used for the facing table (FC1A). Days: 31 days (FBA 4) is the number of days in the period (FBA 3). Actual days: 21 days (FBA5) is the number of business days in the period (FBA3). Meeting determination time: 3 minutes / 1 day (FC1C6) is a threshold value for determining that meeting has occurred. In addition, since it is important to store the facing situation of the face-to-face group, it is important to store the face-to-face meeting time list (FBA), and if this is satisfied, the table configuration used in the face-to-face meeting time list (FBA) It does not matter.

Face-to-face grouping table generation (CBB) is a process of combining face-to-face person facing time lists (FBA) into a face-to-face pair with a user.

Face-to-face hunting (CBB1) is a process for deleting from the face-to-face person face-to-face time list (FBA) those having a small face-to-face time (FBA2). A threshold value may be obtained by multiplying the meeting determination time (FBA6) and the actual number of days (FBA5), and a value larger than the threshold value may be left. In the table generation (CBB2), the facing group table (FBB) is output from the list output by the facing branch hunting (CBB1).

The facing group table (FBB) is composed of a facing group facing time list (FBBA) and a facing group connection matrix (FBBB). An example is shown in FIG. The face-to-face group face-to-face time list (FBBA) indicates the sizes of nodes (points) in the network diagram. It consists of a face-to-face pair (FBBA1) and a face-to-face time (FBBA2). Period: 2009/7 / 1-July 31 (FBBA3) indicates the period used on the facing table (FC1A).

The face-to-face set connection matrix (FBBB) indicates an edge (line) in the network diagram. In the face-to-face pair connection matrix (FBBB), the vertical axis and the horizontal axis are face-to-face pairs with the user. Period: 2009/7 / 1-July 31 (FBBB3) shows the period used on the facing table (FC1A).

In this matrix, 1 is substituted in order to connect the face-to-face set and its constituent members by an edge (line). Furthermore, in the face-to-face pair (FBBB4) having the inclusion relationship, 1 is substituted in order to connect the face-to-face pair having a large number of members including members and the small surface pair with an edge (line) (FBBB5). Then, among members in a large facing pair, members included in a small surface pair are not connected by an edge (line) (FBBB6).

In addition, since it is important to store the facing situation of members and the facing group, the facing group table (FBB) is different from the table configuration used in the facing group table (FBB) if this is satisfied. It does not matter.

The user ID table (IA) of the user / place information table (I) will be described. An example of this table is shown in FIG. The user ID table (IA) is a table for associating user IDs with information such as names and team names. A user ID (IA1), a user name (IA2), a team name (IA3), a job title (IA4), and an organization (IA5).

In the face-to-face combination network diagram drawing (JB), data of member / face-to-face pairs obtained by the face-to-face combination table generation (CBB) is drawn as a face-to-face pair network diagram. The example is shown by the facing group network diagram (KB) of FIG. Period: 2009/7 / 1-July 31 (KX4) has shown the period used by the facing table (FC1A).

The members are indicated by square points (nodes) and the facing set is indicated by circle points (nodes). In addition, the point (node) of the member is placed outside, and the point (node) of the facing set is placed inside. The size of the point (node) is determined by the face-to-face group face-to-face time list (FBBA). In addition, points (nodes) stored with 1 in the facing group connection matrix (FBBB) are connected by lines (edges). Find user name (IA2) from user ID (IA1) using user ID table (IA) as point (node) like Watanabe (KB2), Ito (KB1), Watanabe, Ito (KB3), indicate.

As described above, by generating a network diagram capable of simultaneously displaying a pair facing a user, it becomes possible to know what members are actually active on the network diagram.

In the fourth embodiment, a network diagram capable of simultaneously displaying a user and a place is generated. Conventional network diagrams can not see facing pairs. Resolving this problem by displaying both points of the network diagram (nodes) as both user and location.

FIG. 26 is a diagram showing a processing procedure for simultaneously displaying the user and the place. A model is constructed by field network modeling analysis (CC), and drawing is performed by field network diagram drawing (JC). The result of drawing is a field network diagram (KC).

This process can be processed by the same framework as that of the first embodiment, and field network modeling analysis (CC) is a control unit (ASCO) of an application server (AS), and field network diagram drawing (JC) is a client It is executed by the indication (J) of (CL).

In the place table process (C1D), the meeting situation between members is summarized in chronological order for each given period from infrared data of tissue dynamics data. An infrared terminal emitting infrared light is installed in a location, and the name tag type sensor node (TR) detects the infrared radiation installed in the location to determine that the user is staying in the location.

The place ID table (IB) of the user / place information table (I) will be described. An example of this table is shown in FIG.

The place ID table (IB) is a table for associating a place ID, a place name and an infrared ID. It consists of a place ID (IB1), a place name (IB2), and an infrared ID (IB3). The place name (IB2) is the name of the place, and the infrared ID (IB3) is the ID of the infrared terminal installed in the place ID (IB1). A plurality of infrared terminals may be installed in the place. When the plurality of infrared terminals are installed, the plurality of infrared IDs are described in the infrared ID (IB3).

The extracted result is stored in the place table (FC1D) of the analysis result table (F). One example of the place table (FC1D) is shown in FIG. This is a table in which one day (24 hours) is stored in chronological order with a time resolution of one minute (FC1D3) with one place as a record. In the place table (July 1, 2009) (FC1D3), the vertical axis is a place ID (FC1D1) for determining the place, and the horizontal axis is a resolution time (FC1D2) indicating time by time resolution.

The facing situation in a place at a certain time only needs to read the place having the place ID (FC1D1) and the resolution time (FC1D2). For example, in the case of 2009/7/1 10:02 where the location ID is 00A, there are 2 people registered, and the registered members are 002 and 003. The value included here is either the number of registered persons, or the user ID or NULL if there is a person on the other hand. NULL is stored in the location table (FC1D) when there is no infrared data of tissue dynamics data of the corresponding user at that time.

Since the place table (FC1D) is generated to one day and time resolution, even if the same date, the time resolution is different from one another. For example, although (FC1D4) and (FC1D5) are the same (July 2, 2009), they have different tables because they have different time resolutions.

Also, since it is important to store the place table (FC1D) as the number of registered people and the registered user ID, if this is satisfied, even if it is different from the table configuration used in the place table (FC1D) I do not mind.

Next, the place and user stay time (CCA) will be described. It is a process to find the registered members and groups in the place.

The place table (FC1D) describes the names of members registered on the time series according to the place, so use them to organize the set and time of registration in a table. What is summarized in the table is the face-to-face meeting time list (FCA) of the analysis result table (F). The meeting ID (FCA1) is described as one record, and the meeting time in the member / registered pair (FCA2) is described. Period: 2009/7 / 1-July 31 (FCA3) is the period used for the place table (FC1D). Days: 31 days (FCA 4) is the number of days in the period (FCA 3). Real Days: 21 days (FCA 5) is the number of business days in the period (FCA 3). Meeting determination time: 1 minute / day (FCA 6) is a threshold value for determining that the user is enrolled.

Face-to-face combination table generation (CCB) is a process of combining face-to-face person facing time lists (FCA) into a face-to-face pair with a user.

In place branch hunting (CCB1), it is a process of deleting the one having a small meeting time value from the meeting time list (FCA) for each meeting person. As an example of place hunting, a value obtained by multiplying the face-to-face determination time (FCA 6) and the actual number of days (FCA 5) may be used as a threshold, and a value larger than that value may be left. Since there are more points (nodes) in the network diagram, place branch hunting (CCB1) is performed. If there is another method, it may be used.

In the table generation (CCB2), the user / field table (FCB) is output from the list output by the location branch hunting (CCB1). The user / field table (FCB) is composed of a meeting time list by field (FCBA) and a user / field matrix (FCBB). One example is shown in FIG.

The meeting facing time list (FCBA) shows the sizes of nodes (points) in the network diagram. It consists of a place (FCBA1), a face-to-face pair (FCBA2) and a face-to-face time (FCBA3). Period: July 1-July 31, 2009 (FCBA 4) indicates the period used in the place table (FC 1 D).

The user / field matrix (FCBB) indicates an edge (line) in the network diagram. The user / place matrix (FCBB) has the vertical axis and the horizontal axis with the user. Period: 2009/7 / 1-July 31 (FCBB3) shows the period used in the place table (FC1D). In this matrix, 1 is substituted in order to connect a place and a member registered there by an edge (line). In addition, since it is important to store the meeting situation of the place and the member, the user / place table (FCB) is different from the table configuration used in the user / place table (FCB) if this is satisfied. It does not matter.

In the field network diagram drawing (JC), data of members / fields obtained by the user / field table generation (CCB) is drawn as a field network diagram. The example is shown by the field network diagram (KC) of FIG. Period: 2009/7 / 1-July 31 (KCA4) indicates the period used in the place table (FC1D).

The members are indicated by square points (nodes) and the facing set is indicated by circle points (nodes). In addition, the point (node) of the member is placed outside, and the point (node) of the facing set is placed inside. The size of the point (node) is determined by the meeting time list (FCBA) according to meeting place. In addition, when there are a plurality of places (FCBA1), points (nodes) are displayed as a pie chart. Connect points (nodes) stored with 1 in the user / field matrix (FCBB) with lines (edges). The points (nodes) are displayed like Watanabe (KC2), Ito (KC1), and a conference room (KC3). The points (nodes) of the meeting room (KC3) are displayed as a circle graph, divided by the ratio of the facing time, and the users engaged in it and the facing group (for example, Ito (KC5), Watanabe, Ito (KC4)) are displayed.

As described above, by generating a network diagram capable of simultaneously displaying a user and a place, it becomes possible to identify which place is utilized on the network diagram.

In the fifth embodiment, even when looking at numbers such as team facing time, since the feeling of reality is low, it is difficult for feedback to be given to the user. Therefore, in order to see at a glance what places are actually used, mapping on a sketch is made to improve the sense of reality.

FIG. 29 is a diagram showing a processing procedure for mapping a team to a place.
The model is constructed by the place team modeling analysis (CD), and in the case of display by number of people, drawing is performed by the number of people map drawing (JDA) of the place, and the drawn result is the number of people map of the place (KDA) In the case of inside and outside team display, drawing is performed by the inside and outside team map drawing (JDB) of the place, and the drawn result is the inside and outside team map (KDB) of the place.

This process can be processed by the same framework as that of the first embodiment, and the place team modeling analysis (CD) is the control unit (ASCO) of the application server (AS), and the map drawing by number of people (JDA) The team internal and external map drawing (JDB) of the place is executed by the display (J) of the client (CL).

The process until the location table (FC1D) of the analysis result table (F) is obtained is the same as that of the fourth embodiment, so the description is omitted. Meeting time by place and number of people (CDA) is a process for obtaining meeting time by meeting person in a place. Since the members registered at the place are listed in chronological order in the place table (FC1D), the time for meeting with the number of people is calculated from now. This result is stored in the meeting time (FDA) by number of persons in the field of the analysis result table (F). The table configuration is composed of a place ID (FDA1) and a number of people (FDA2).

Place ID (FDA 1) is one record, and the face-to-face time in the number of people (FDA 2) is described. The number of people (FDA 2) is classified by resolution of 1 person (FDA 6), 2 people (FDA 7), 3-5 people (FDA 8), 6 people or more (FDA 9). Period: 2009/7 / 1-July 31 (FDA3) is the period used for the place table (FC1D). Days: 31 days (FDA 4) is the number of days in the period (FDA 3). Real Days: 21 days (FDA 5) is the number of business days in the period (FDA 3).

Next, the field list (IC) of the user / place information table (I) will be described with reference to FIG. The field list (IC) is composed of a field map image (ICA) which is a sketch and field coordinates (ICB) in which coordinates of places are described. The venue map image (ICA) is a sketch image. The field coordinates (ICB) are composed of a place ID (ICB1) and coordinate values (ICB2). The location ID (ICB1) is one record, and the X coordinate value (ICB3) and the Y coordinate value (ICB4) in the coordinate value (ICB2) are described.

The map according to the number of people in the place (JDA) is a process of mapping from the meeting time according to the number of people in the place (FDA) to a sketch and drawing. The result of drawing is a map by number of people (KDA) of the place of FIG. Meeting time by number of people meeting time (FDA) by place number of people Meeting time by number of people is plotted on the pie chart at the corresponding place in the sketch. In the pie chart, the central angle of the pie chart changes according to the ratio of the facing time by the number of people. Also, display a legend of the number of people. The size of the pie chart is the sum of the meeting time by number of people. The place name (IB2) is displayed from the place ID table (IB) near the corresponding place in the sketch. Period: 2009/7 / 1-July 31 (KDA1) is the period used for the place table (FC1D).

The team internal and external meeting time (CDB) of the venue is a process for determining the internal and external team meeting time in the venue. Since the members registered at the place are listed in chronological order in the place table (FC1D), it is judged from the user ID table (IA) whether it is an in-team or out-of-team meeting from now on, Find each meeting time. This result is stored in the team internal and external meeting time (FDB) in the field of the analysis result table (F). The table configuration is composed of a place ID (FDB1) and inside and outside (FDB2). Place ID (FDA 1) is described as one record, and the meeting time in the inside and outside (FDB 2) is described. Inside and outside (FDB2) are classified with the resolution of in-team (FDB 6) and out-of-team (FDB 7). Period: 2009/7 / 1-July 31 (FDB3) is the period used for the place table (FC1D). Days: 31 days (FDB 4) is the number of days in the period (FDB 3). Actual days: 21 days (FDB 5) is the number of business days in the period (FDB 3).

The team internal and external map drawing (JDB) of a place is a process which maps and draws from a team internal and external facing time (FDB) of a place to a sketch. The drawn result is a map inside and outside the team (KDB) in the place of FIG. The meeting time by team inside and outside at the place of the team inside and outside meeting time (FDB) of the place is plotted on the pie chart in the corresponding place in the sketch. In the pie chart, the central angle of the pie chart changes according to the ratio of the facing time by the number of people. Also, show legends inside and outside the team. The size of the pie chart is the sum of face-to-face contact times inside and outside the team. The place name (IB2) is displayed from the place ID table (IB) near the corresponding place in the sketch. Period: 2009/7 / 1-July 31 (KDB3) is the period used for the place table (FC1D).

As described above, by mapping team information to the place on the sketch, the feeling of reality can be improved.

Even when looking at numbers such as face-to-face pairs, it is difficult for the user to receive feedback because the feeling of reality is low. Therefore, in the sixth embodiment, in order to see at a glance which place is actually used, mapping is performed on a sketch to improve the actual feeling.

FIG. 32 is a diagram showing a processing procedure for mapping a team to a place. The model is constructed by the place facing set modeling analysis (CE), and in the case of the facing set, drawing is performed by the face set drawing map (JEA) of the place, and the drawing result is the face set map of the place (KEA) In the case of the user, drawing is performed by the user map drawing (JEB) of the place, and it is a user map (KEB) of the place.

This process can be processed by the same framework as that of the first embodiment, and the place facing group modeling analysis (CE) is performed by the control unit (ASCO) of the application server (AS), the face group map drawing of the field (JEA) ) And the user map drawing (JEB) of the place are executed on the display (J) of the client (CL). The process until the location table (FC1D) of the analysis result table (F) is obtained is the same as that of the fourth embodiment, so the description is omitted.

Place-face-to-face group face-to-face time (CEA) is a process for obtaining face-to-face time by face-to-face group in a place. Since the members registered at the place are described in chronological order in the place table (FC1D), the time for which the face group is facing is determined from this. This result is stored in the meeting set facing time (FEA) of the field of the analysis result table (F). The table configuration is composed of a place ID (FEA1) and a user / face-to-face pair (FEA2). The meeting time in the user / face-to-face pair (FEA2) is described with the place ID (FEA1) as one record. The user / face-to-face pair (FEA 2) stores the registered user ID and its face-to-face time. Period: 2009/7 / 1-July 31 (FEA3) is the period used for the place table (FC1D). Days: 31 days (FEA 4) is the number of days in the period (FEA 3). Real Days: 21 days (FEA 5) is the number of business days in the period (FEA 3).

The meeting group map drawing (JEA) of the place is a process of mapping and drawing from the meeting group facing time (FEA) of the place to the sketch. The drawn result is a face-to-face set map (KEA) of the field of FIG. Face-to-face pairing time of meeting place Face-to-face pairing time (FEA) The round-to-face pairing time is plotted at the corresponding place in the floor plan. The diameter of the circle is changed according to the facing time of the facing set. Also, if there are multiple circles in the same place, shift so as not to overlap. Further, the user name (IA2) is selected from the user ID table (IA) and the names of the members of the face-to-face set are displayed. In the vicinity of the corresponding place in the sketch, the place name (IB2) is selected from the place ID table (IB) and displayed. Period: 2009/7 / 1-July 31 (KEA3) is the period used for the place table (FC1D).

Place-user face-to-face time (CDB) is a process for determining the face-to-face time of the user at the place. Since the members registered in the place are described in chronological order in the place table (FC1D), the face-to-face time of each member is determined from the user ID table (IA). This result is stored in the user facing time (FEB) in the field of the analysis result table (F). The table configuration is composed of a place ID (FEB1) and a user ID (FEB2). The meeting time in the user ID (FEB2) is described with the place ID (FEB1) as one record. Period: July 1-July 31, 2009 (FEB3) is the period used for the place table (FC1D). Days: 31 days (FEB 4) is the number of days in the period (FEB 3). Real Days: 21 days (FEB5) is the number of business days in the period (FEB3).

The user map drawing (JEB) of the place is a process of mapping and drawing from the user facing time (FEB) of the place to the sketch. The drawn result is the user map (KEB) of the field of FIG. A circle is plotted at the corresponding place in the floor plan for the user facing time (FEB) location of the place by user. The diameter of the circle changes according to the face-to-face time of the user. Also, if there are multiple circles in the same place, shift so as not to overlap. Also, the user's name (IA2) is selected from the user ID table (IA) and the name of the member is displayed.

In the vicinity of the corresponding place in the sketch, the place name (IB2) is selected from the place ID table (IB) and displayed. Period: 2009/7 / 1-July 31 (KEB1) is the period used for the place table (FC1D). As described above, by mapping the face-to-face set information to the place on the sketch, the feeling of reality can be improved.

Even if you look at numbers such as the use situation of the place, it is hard for feedback to be given to the user because the feeling of reality is low. Therefore, in the seventh embodiment, in order to see at a glance which place is actually used, mapping is performed on a sketch to improve the actual feeling.

FIG. 34 is a diagram showing a processing procedure for mapping the use situation of a place.
A model is constructed by the place use modeling analysis (CF), and in the case of the place use number of people on the time series in the place, the drawing is made by the place attendance graph (JF1) of the place use situation map drawing (JF) In the case of the number of usage hours, drawing is performed by the field usage frequency graph (JF2) of the site usage situation map drawing (JF), and in the case of the temperature change in time series at the place, the field usage situation map drawing (JF) The drawing is performed according to the field temperature flag (JF3), and these results become the field utilization situation map (KFA).

This process can be processed by the same framework as that of the first embodiment, and the place use modeling analysis (CF) is the control unit (ASCO) of the application server (AS) and the field use situation map drawing (JF) It is executed by display (J) of client (CL).

The process until the location table (FC1D) of the analysis result table (F) is obtained is the same as that of the fourth embodiment, so the description is omitted. First, find the number of people in the place in chronological order. The use situation by place (CFA) is a process for obtaining the average use rate and the average number of people in the place. Since the members registered in the place are listed in chronological order in the place table (FC1D), the average use rate and the average number of people in the place are determined from this. This result is stored in the field attendance time (FFA) of the analysis result table (F). The table configuration is composed of a place ID (FFA1) and an index (FFA2).

The usage rate (FFA3) and the average number people (FFA4) are described with the place ID (FFA1) as one record. Period: 2009/7 / 1-July 31 (FFA5) is the period used for the place table (FC1D).

The place attendance graph (JF1) is a process of drawing the use situation of places on a time series from the place table (FC1D) and the place attendance time (FFA). The result of drawing is a field attendance graph (KFA2) of FIG. The number of registered persons is displayed as a line graph on the time series from the place table (FC1D). Then, Usage rate (FFA3) and Average number people (FFA4) obtained from the field attendance time (FFA) are displayed.

Next, the number of usage hours at the place is determined. The face-to-face meeting count (CFB) is a process for determining the number of usage hours at a place.

Since the members registered at the place are described in time series order in the place table (FC1D), the number of times by use time in the place is determined from this. This result is stored in the field use time (FFB) of the analysis result table (F). The table configuration is composed of a place ID (FFB1) and a time (FFB2). A place ID (FFA1) is set as one record, and the number of times of use by time is described. Period: 2009/7 / 1-July 31 (FFA7) has a period using the place table (FC1D).

The site use frequency graph (JF2) is a process of drawing the number of use times at a place from the site use time (FFB). The drawn result is a field utilization time graph (KFA 3) of FIG. The number of times of use is displayed as a bar graph by time from the field use time (FFB). Furthermore, it may be a pie chart.

Finally, the case of temperature change over time in place is determined. In the temperature table process (C1E), temperature data of tissue dynamics data is summarized in time series for each fixed period. The extracted result is stored in the temperature table (FC1E) of the analysis result table (F). One example of the temperature table (FC1E) is shown in FIG. This is a table in which one day (24 hours) is stored in chronological order, with a user as one record and a time resolution of one minute (FC1E3).

In the temperature table (July 1, 2009) (FC1E3), the vertical axis is a user ID (FC1E1) for identifying a member individual, and the horizontal axis is a resolution time (FC1E2) indicating time by time resolution. The temperature table status of the user at a certain time needs only to read the place where the user ID (FC1E1) and the resolution time (FC1E2) are present. For example, the temperature of 2009/7/11 10:02 of the user ID 001 is 23.5. NULL is stored in the temperature table (FC1E) when there is no temperature data of tissue dynamics data of the corresponding user at that time.

Since the temperature table (FC1E) is generated with one day and time resolution, even if the same date, the time resolution is different from the other table. For example, although (FC1E4) and (FC1E5) are the same (July 2, 2009), they have different tables because they have different time resolutions.

Moreover, since it is important to store the temperature table for each user, the temperature table (FC1E) may be different from the table configuration used in the temperature table (FC1E) as long as the temperature table is satisfied.

Field Average Temperature (CFC) is the process of determining the average temperature in a field. Since the members registered in the place are described in chronological order in the place table (FC1D), the average temperature in the place is determined by referring to the temperature table (FC1E).

This result is stored in the field temperature (FFC) of the analysis result table (F). The table configuration is composed of a place ID (FFC1) and an index (FFC2).
The usage rate (FFA3) is described with the place ID (FFA1) as one record. Period: 2009/7 / 1-July 31 (FFA4) is performing the period used for the temperature table (FC1D).

The field temperature flag (JF3) is a process of drawing the use situation of a place on a time series from the temperature table (FC1E) and the field temperature (FFC). The drawn result is a field temperature graph (KFA 4) of FIG. The temperature table (FC1D) is displayed as a line graph of temperature data of the corresponding user on the time series. Then, the usage rate (FFC3) obtained from the field temperature (FFC) is displayed.

In the field map integration (JF4) of the field usage situation map drawing (JF), the field attendance graph (KFB2) which is an image obtained from the field attendance graph (JF1), the field usage frequency graph (JF2) and the field temperature flag (JF3) ), The field utilization time graph (KFB3) and the field temperature graph (KFB4) are processes for mapping and drawing on a sketch.

The result of drawing is the field utilization situation map (KFA) of FIG. As shown in the sketch (KFA1), the icon (for example, (KFA12) or (KFA12)) of the place name is arranged at the corresponding place in the sketch. Then, when the icon of the place name is clicked, the on-site field attendance graph (KFA2), the field usage time graph (KFA3) and the field temperature graph (KFA4) are hopped up. Change the color of the click icon.
As described above, by mapping the use status information to the place on the sketch, the sense of actuality can be improved.

It is important how often the communication is performed. In a normal network diagram, it is only the sum of facing, and it is not possible to know how often it is facing. Therefore, in the eighth embodiment, the communication frequency is visualized by reflecting the meeting period on the network diagram.

FIG. 37 is a diagram showing a processing procedure for reflecting the communication cycle on the network diagram. A meeting time and a meeting period are determined by meeting cycle analysis (CG), and a network diagram is created by a meeting cycle network diagram drawing (JGA), and the result is a meeting cycle network diagram (KGA). Further, in addition to creating a network diagram, a histogram diagram showing a period of time spent on meeting by user according to a meeting period histogram diagram (JGB) is created, and the result is a meeting period histogram diagram (KGB).

This process can be processed by the same framework as in the first embodiment, and the meeting cycle analysis (CG) corresponds to the control unit (ASCO) of the application server (AS), the meeting cycle network diagram drawing (JGA) and the meeting cycle The histogram diagram drawing (JGB) is executed on the display (J) of the client (CL). The process until obtaining the facing matrix (FC1C) of the analysis result table (F) is the same as that of the first embodiment, and hence the description is omitted.

An example of the facing matrix (FC1C) is shown in FIG. In FIG. 5, the number of days used for creation is a plurality of days (period: July 1-July 31, 2009 (FC1C3)), but in Example 8, the meeting of the plurality of days shown in FIG. Find a matrix and a face-to-face matrix with one day.

In the face-to-face binarization (CGA), the face-to-face time stored in the face-to-face matrix (FC1C) for each day is substituted with 1 for a large value and 0 for a small value based on a certain threshold. The threshold is the meeting determination time of the meeting matrix (FC1C): 3 minutes / 1 day (FC1C7). The binarized results are stored in the face-to-face binary matrix (FGA) of the analysis result table (F). Since the format of the file is the same as the facing matrix (FC1C), it is omitted. The difference from the face-to-face matrix (FC1C) is a stored value, and the face-to-face matrix (FC1C) has multiple values, and the face-to-face binary matrix (FGA) has two values.

Meeting cycle extraction (CGB) is a process for obtaining a meeting cycle from the meeting matrix (FC1C) of one day. The meeting cycle is determined from the daily meeting of members. As a method of determining the meeting period, it can be considered that the actual number of days divided by the number of days in which the person has met. The method of determining the meeting period may be another method. The result obtained by the meeting period extraction (CGB) is stored in the meeting period matrix (FGB) of the analysis result table (F).

An example of a facing period matrix (FGB) is shown in FIG. It is a compilation of the meeting cycle results for one month. In the face-to-face period matrix (FGB), the vertical axis is a user ID (FGB1) for identifying a member individual, and the horizontal axis is a user ID (FGB2) indicating a partner who has met. The meeting cycle (number of days) of the user 002 with the user 003 is 1.0, which means that they meet daily. As this value increases, it indicates that the face-to-face period increases. For example, the face-to-face contact with the user 002 in the user 001 is 2.3, but this faces once in about 2 days It means that.

In creating this face-to-face cycle matrix (FGB), it is necessary to describe the original information because a lot of information is aggregated into one matrix. Period: July 1-July 31, 2009 (FGB 3) is the period used for the Meeting Period Matrix (FGB). Days: 31 days (FGB 4) is the number of days in the period (FGB 3). Real number of days: 21 days (FGB 5) is the number of business days in the period (FGB 3).

In addition, since it is important to store the facing situation of the user, the facing cycle matrix (FGB) may be different from the table configuration used in the facing matrix (FC1C) if it is satisfied.

In the face-to-face periodic network diagram drawing (JGA), a network diagram taking account of the face-to-face period of members from the face-to-face period matrix (FGB) for which communication cycles are required and the face-to-face matrix (FC1C) of a plurality of days as communication amounts . The example is shown by a facing periodic network diagram (KGA). Period: 2009/7 / 1-July 31 (KGA1) has shown the period used by the facing period matrix (FGB). Members are indicated by circle points (nodes). Further, lines (edges) connecting members indicate facing time / period. In particular, the thickness of the line indicates the facing time, and the shape of the line (solid line, broken line) indicates the facing period. Use a spring model for placement. The spring model (Hook's law) calculates the force (inward or outward) assuming that there is a spring when two nodes (points) are connected, and further calculates the distance from all nodes not connected with itself. It is a method to make it an optimal arrangement by repeating movement of a position as receiving repulsive force (repulsive force) according to. The points (nodes) are arranged like Takahashi (KGA2), Tanaka (KGA3) or Watanabe (KGA4). The user name (IA2) is obtained from the user ID (IA1) using the user ID table (IA) and displayed. The facing situation of Tanaka (KGA3) and Watanabe (KGA4) is indicated by a line (edge) connecting the two, and (KGA5) indicates that the facing time is short but they meet daily. The case of Tanaka (KGA3) and Takahashi (KGA2) is (KGA6), which indicates that the meeting time is long but the meeting cycle is long (a meeting cycle of several days).

Face-to-face period histogram drawing (JGB) draws a histogram in consideration of the face-to-face period for each member from the face-to-face period matrix (FGB) for which the communication period is determined and the face-to-face matrix (FC1C) of multiple days that is the amount of communication. .

The drawn result is shown by a meeting period histogram chart (KGB). Period: 2009/7 / 1-July 31 (KGB2) has shown the period used by the facing period matrix (FGB). The meeting time according to the meeting period for each member is shown. Takahashi (KGB2), Tanaka (KGB3), and Watanabe (KGB4) are users, and the strip above is the facing time according to the period. The band configuration is the facing time with members of cycle 2 or more (KGB5) and the facing time with members less than cycle 2 (KGB6), and the length of the band indicates the total facing time (KGB7).

In this way, by reflecting on the network diagram how often communication is performed, the quality of communication can be visualized and the actual feeling can be improved.

It is important to know the number of people you meet during communication. In a normal network diagram, communication is displayed in face-to-face communication between two parties, so it is not possible to know how many people were communicating. Therefore, in the ninth embodiment, the communication situation is visualized by reflecting the number of people facing each other at the time of communication on the network diagram.

FIG. 38 is a diagram showing a processing procedure for reflecting the number of people facing each other at the time of communication on the network diagram. The meeting time and the meeting number are obtained by the meeting number analysis (CH), and a network diagram is created by the meeting network diagram drawing (JHA) by the meeting number, and the result is a meeting network view (KHA) by the meeting number. In addition, the network diagram is integrated into one for each meeting number, and a network diagram is created by the maximum meeting time number network diagram (JHB), and the result is the maximum meeting time number network diagram (KHB).

This process can be processed by the same framework as that of the first embodiment, and the meeting number analysis (CH) corresponds to the control unit (ASCO) of the application server (AS), the meeting network diagram drawing by meeting number (JHA), The maximum meeting time number of people network diagram drawing (JHB) is executed on the display (J) of the client (CL). The process until the meeting table (FC1A) of the analysis result table (F) is obtained is the same as that of the first embodiment, so the description is omitted.

Meeting matrix generation (CHA) according to the number of people in a meeting is a process of generating a matrix for each number of people in a meeting. The basic matrix generation method is the same as in face-to-face matrix generation (C1C). However, it differs by only one point, which means that the meeting matrix to be stored differs depending on the number of people facing each other at the resolution time (FC1A2) of the meeting table (FC1A). Specifically, when the number of people meeting at resolution time (FC1A2) is 2, the number of people meeting at resolution time (FC1A2) is 3 in substitution matrix for two-person meeting matrix (FHAA) of meeting matrix according to number of people facing each other (FHA) From 5 to 5, it is substituted in the 3-party-five-person meeting matrix (FHAB) of the meeting person-by-person meeting matrix (FHA), and when the number of meeting people at resolution time (FC1A2) is 6 or more FHA) will be assigned to substitution matrix for face-to-face matrix (FHAC) over six parties. The meeting matrix generation (CHA) according to the number of meeting persons is a process of generating a matrix according to the predetermined number of meeting persons, and the range of the number of meeting persons in the meeting matrix can be arbitrarily determined.

Meeting matrix generation (CHA) by meeting number of people removed time series information from the meeting table (FC1A) arranged in time series, and put together how many people are meeting for each user in a two-dimensional matrix by meeting number of people It is a thing. The extracted result is stored in a meeting matrix by face number (FHA) of the analysis result table (F). An example of a face-to-face meeting matrix (FHA) by face-to-face number is shown in FIG. It is a compilation of one month's meeting results.

The Face-to-face Meeting Matrix (FHA) is composed of a plurality of face-to-face matrixes, and the two-to-two face matrix (FHAA), the three-to-five face matrix (FHAB) or the six-to-more face matrix ( FHAC). In the two-party face-to-face matrix (FHAA), the vertical axis is a user ID (FHAA1) for identifying a member individual, and the horizontal axis is a user ID (FHAA2) indicating the other party who has met. For example, the meeting time of the user 003 with the user 004 is 543 minutes. The views of the three-to-five face-to-face matrix (FHAB) and the face-to-face matrix of six or more faces (FHAC) are the same.

In creating this face-to-face meeting matrix (FHA), it is necessary to describe the original information because a lot of information is integrated into one matrix. Period: 2009/7 / 1-July 31 (FHA1) The period used for the meeting matrix (FHA) according to the number of persons meeting. Days: 31 days (FHA2) is the number of days in the period (FHA1). Real Days: 21 days (FHA3) is the number of business days in the period (FHA1). Temporal resolution: 1 minute (FHA 4) is the temporal resolution in the facing table (FC 1A). Meeting determination time: 3 minutes / 1 day (FHA 5) is a threshold value for determining that meeting has occurred. Even in the case of passing each other, if the infrared rays react, it is determined that they meet, so a few reactions are likely to be noise, so this threshold is introduced.

Also, since it is important to store the user's face-to-face situation, the face-to-face face-to-face matrix (FHA) differs from the table configuration used in the face-to-face face-to-face matrix (FHA) if this is satisfied. I don't care.

Meeting network diagram by meeting number (JHA) is a process of drawing a network diagram by meeting number from the meeting matrix (FHA) by meeting number showing the meeting situation by meeting number.

An example is shown by a meeting network diagram (KHA) according to the number of people facing each other. Period: 2009/7 / 1-July 31 (KHA1) indicates the period used in the Meeting by Face-to-face Meeting Matrix (FHA). The meeting network diagram (KHA) according to the number of people in the meeting is made up of three network diagrams, and the network diagram between two parties (KHAA) in the case of two parties, and three parties-five parties in the case of three to five. It is a network diagram (FHAB), a network diagram between six or more parties (FHAC) in the case of six or more parties.

The two-party network diagram (KHAA) will be described as an example. Members are indicated by circle points (nodes). Further, lines (edges) connecting members indicate facing time. In particular, the thickness of the line indicates the facing time. Use a spring model for placement. The spring model (Hook's law) calculates the force (inward or outward) assuming that there is a spring when two nodes (points) are connected, and further calculates the distance from all nodes not connected with itself. It is a method to make it an optimal arrangement by repeating movement of a position as receiving repulsive force (repulsive force) according to.

The points (nodes) are arranged like Ito (KHAA1), Watanabe (KHAA2) and Yamamoto (KHAA3). The user name (IA2) is obtained from the user ID (IA1) using the user ID table (IA) and displayed.

Since Ito (KHAA1) and Watanabe (KHAA2) face each other, they are connected by a line (edge) (KHAA4). Also, the case of Ito (KHAA1) and Yamamoto (KHAA3) is (KHAA5). When connecting points (nodes) between points (nodes), it is not necessary to connect as a noise if the meeting person-by-person meeting matrix (FHA) has a small value. As the threshold value, a value obtained by multiplying the meeting determination time of the meeting matrix (FHA) for each meeting number of people: 3 minutes / 1 day (FHA 5) and the real number of days: 21 days (FHA 3) may be used. Further, similarly to the two-party network diagram (KHAA), a three-party five-party network diagram (FHAB) and a six-or-more network diagram (FHAC) are also obtained.

Maximum Face-to-face Time Matrix Generation (CHB) is a process of selecting the maximum face-to-face time according to the number of face-to-face persons and storing it in the matrix. Specifically, the largest value is selected from each matrix of the face-to-face meeting matrix (FHA) and stored in the maximum facing time matrix (FHB). The maximum facing time matrix (FHB) will be described with reference to FIG. In the maximum face-to-face time matrix (FHB), the vertical axis is a user ID (FHB6) for identifying a member individual, and the horizontal axis is a user ID (FHB7) indicating a partner who has met. For example, the meeting time of the user 003 with the user 004 is 543 minutes. The method of obtaining is the user 004 in the user 003 of the two-party facing matrix (FHAA) of the facing matrix (FHA) according to the number of facing persons (FHAA), the three-five person facing matrix (FHAB) and the six or more facing matrix (FHAC) The face-to-face times of 543, 93, 0 are compared, and 543, which is the maximum value, is selected.

In creating this maximum facing time matrix (FHB), it is necessary to describe the original information because a lot of information is aggregated into one matrix. Period: July 1-July 31, 2009 (FHB1) is the period used for the maximum facing time matrix (FHB). Days: 31 days (FHB2) is the number of days in the period (FHB1). Real Days: 21 days (FHB3) is the number of business days in the period (FHB1). Temporal resolution: 1 minute (FHB4) is the temporal resolution on the facing table (FC1A). Meeting determination time: 3 minutes / 1 day (FHB 5) is a threshold value for determining that meeting has occurred. Even in the case of passing each other, if the infrared rays react, it is determined that they meet, so a few reactions are likely to be noise, so this threshold is introduced. Also, since it is important to store the user's face-to-face situation, the maximum face-to-face matrix (FHB) may differ from the table configuration used in the maximum face-to-face matrix (FHB) if this is satisfied. Absent.

The maximum meeting time number of people matrix generation (CHC) is a process of storing the number of people in the meeting when the maximum meeting time in each meeting number of people is selected in the matrix. Specifically, by storing the number of people in charge of the matrix in which the maximum value is stored from the matrix according to the number of people in the person-to-person matrix (FHA) by storing in the maximum meeting time number of people matrix (FHC) is there.

The maximum meeting time people matrix generation (CHC) will be described. In the maximum meeting time number of people matrix (FHC), the vertical axis is a user ID (FHC6) for identifying a member individual, and the horizontal axis is a user ID (FHC7) indicating a partner who has met. For example, the user 004 in the user 003 is 1. The stored values (FHC 8) indicate that 1 is between two parties, 2 is between three and five, and 3 is between six and more. This is the same as the meeting number range of the meeting person by face meeting matrix (FHA). The method of obtaining is the user 004 in the user 003 of the two-party facing matrix (FHAA) of the facing matrix (FHA) according to the number of facing persons (FHAA), the three-five person facing matrix (FHAB) and the six or more facing matrix (FHAC) The face-to-face times of 543, 93, 0 are compared, and 543, which is the maximum value, is selected. And, 543 is from the two-party face-to-face matrix (FHAA) and substitute 1 which means that.

In creating this maximum facing time number of people matrix (FHC), it is necessary to describe the original information because a lot of information is integrated into one matrix. Period: 2009/7 / 1-July 31 (FHC1) is the period used for the Maximum Face-to-Face Hour People Matrix (FHC). Days: 31 days (FHC2) is the number of days in the period (FHC1). Real Days: 21 days (FHC3) is the number of business days in the period (FHC1). Temporal resolution: 1 minute (FHC4) is the temporal resolution in the facing table (FC1A). Meeting determination time: 3 minutes / 1 day (FHC5) is a threshold value for determining that meeting has occurred. Even in the case of passing each other, if the infrared rays react, it is determined that they meet, so a few reactions are likely to be noise, so this threshold is introduced.

In addition, since it is important to store the user's face-to-face situation, the maximum face-to-face time person matrix (FHC) differs from the table configuration used in the maximum face-time time face person matrix (FHC) if this is satisfied. I don't care.

The maximum meeting time number of people network diagram drawing (JHB) is a process of drawing the number of people meeting at the time of maximum meeting time from the maximum meeting time matrix (FHB) and the maximum meeting time number matrix (FHC) in the network diagram.

An example is shown by the maximum meeting time number of people network diagram 41 (KHB). Period: 2009/7 / 1-July 31 (KHB1) shows the period used in the maximum facing time matrix (FHB) and the maximum facing time people matrix (FHC).

Members are indicated by circle points (nodes). Further, lines (edges) connecting members indicate facing time. In particular, the thickness of the line indicates the facing time. Use a spring model for placement. The spring model (Hook's law) calculates the force (inward or outward) assuming that there is a spring when two nodes (points) are connected, and further calculates the distance from all nodes not connected with itself. It is a method to make it an optimal arrangement by repeating movement of a position as receiving repulsive force (repulsive force) according to. The points (nodes) are arranged like Ito (KHB2), Tanaka (KHB3) and Watanabe (KHB4). The user name (IA2) is obtained from the user ID (IA1) using the user ID table (IA) and displayed.

Then, Ito (KHB2) and Watanabe (KHB4) read 543 minutes from the maximum meeting time matrix (FHB) and 1 from the maximum meeting time people matrix (FHC) and read that the two parties are performing 543 minutes. And the line (edge) becomes like (KHB5). Also, Ito (KHB2) and Tanaka (KHB3) are 215 minutes from the Maximum Meeting Time Matrix (FHB) and 2 from the Maximum Meeting Time People Matrix (FHC). Read it. And a line (edge) becomes like (KHB7). In particular, the thickness of the line indicates the facing time, and the shape of the line (solid line, broken line) indicates the number of facing people. When connecting lines (edges) between points (nodes), it is not necessary to connect as noise if the maximum facing time matrix (FHB) has a small value. As the threshold value, a value obtained by multiplying the face-to-face determination time of the maximum face-to-face time matrix (FHB): 3 minutes / 1 day (FHB5) and the actual number of days: 21 days (FHB3) may be used.

In this way, by reflecting on the network diagram how many members of the communication are facing each other, it is possible to visualize the quality of communication and improve the feeling of reality.

Organizations often have different ways of working and different moods, and usually they can tell the culture of the organization when they are involved. Also, I would like to know the difference between tissues, but it is often subjective and can not be quantified. Therefore, in the tenth embodiment, the comparison between the tissues is visually understood at a glance without regard to the organization.

FIG. 42 is a diagram showing a processing procedure for visualizing the culture for each tissue. Frequency principal component extraction (CIA) obtains principal components from behavior index and personality index, and tissue frequency calculation (CIB) combines them into time series trends for each tissue, and tissue frequency graph drawing (JI), time series for each tissue A graph is generated, the result of which is the tissue frequency (KI).

This process can be processed by the same framework as that of the first embodiment, tissue frequency analysis (CI) is a control unit (ASCO) of an application server (AS), tissue frequency graph drawing (JI) is a client (CL) ) Is executed (J). The process until the explanatory variable (FFA) of the analysis result table (F) is obtained is the same as that of the first embodiment, so the description is omitted.

Frequency principal component extraction (CIA) is a process for obtaining a feature in the activity of a tissue from an explanatory variable (FFA) of an analysis result table (F) for each individual. Specifically, the characteristic in the activity is clarified by performing principal component analysis on the explanatory variable (FFA) of the analysis result table (F). This result is stored in the frequency main component (FIA) of the analysis result table (F). An example is shown in FIG.

The frequency main component (FIA) will be described. The Frequency Principal Component (FIA) is a table that stores features of tissue activity. Period: July 1, 2009 (FIA1) is the period / date used for analysis. Tissue (FIA2) is a tissue to be analyzed. The explanatory variable (FIA3) is an element at the time of analysis, and the item is the same as the explanatory variable (FFA) of the analysis result table (F). The first principal component (FIA 4) is the value of the first principal component. The second principal component (FIA5) is the value of the second principal component. Although principal component analysis is used as this example, other methods may be used if characteristics in tissue activity are clarified. Further, although the second main component is stored in the frequency main component (FIA), the main components after that (third and subsequent ones) may be stored.

Tissue frequency calculation (CIB) is a process of collecting the indices obtained in frequency main component extraction (CIA) into one for each time series. Specifically, the first principal component (FIA 4) and the second principal component (FIA 5) for each tissue are two-dimensionally mapped from the frequency principal component (FIA) every two days to determine the center of gravity. And let the distance from the origin be the tissue frequency value.

The tissue frequency (FIB) will be described. Tissue frequency (FIB) is a table that stores features of tissue activity for each time series. Tissue (FIB1) is a tissue to be analyzed. The date (FIB2) is the date of analysis. The view point of this table is that the organizational frequency of Organization A on July 2, 2009 is 1.5.

In this example, the first principal component and the second principal component of the explanatory variable are two-dimensionally mapped for each tissue to obtain the center of gravity, but other methods may be obtained if the characteristics in tissue activity are known. .

Tissue frequency graph drawing (JI) is a process of drawing tissue frequencies in a time series for each tissue from a tissue frequency (FIB) as a line graph. The example is shown by the tissue frequency graph (KI) of FIG. The horizontal axis is date (KI1), and the vertical axis is tissue frequency (KI2). The values from tissue frequency (FIB) are plotted in a line graph for each tissue. In this way, by plotting work methods and atmospheres as tissue frequencies on a time series basis for each tissue, it becomes possible to understand at a glance the comparisons between tissues, regardless of the tissue.

There are various measures from the analysis results, but it is important to improve them unconsciously by using them regularly. The eleventh embodiment will describe seat repositioning as a measure for enhancing activation and reducing stress.

An example of using the results of face-to-face network analysis of members in an organization and personality indicators to determine the seating arrangement in an actual organization will be described using FIG.

This process can be processed by the same framework as in the first embodiment, and the seat change analysis (CJ) is a control unit (ASCO) of the application server (AS), and the seat arrangement drawing (JJ) in the place is a client It is executed by the indication (J) of (CL). The process until finding the facing matrix (FC1C) and the personality index (FAAE) of the analysis result table (F) is the same as that of the first embodiment, and therefore the description thereof is omitted.

In general, seating arrangements for members within an organization are implemented with different goals in each organization. For example, reducing the stress of each member in an organization or activating communication in an organization is an example of the purpose. Here, using the face-to-face network analysis results analyzed from the data acquired by the sensors and the personality index, it is possible to reduce stress in the organization and to activate communication by using members of the organization. An example of determining the seat arrangement will be described.

The flow of processing is shown in FIG. From the face-to-face matrix (FC1C) obtained by the sensor, a network diagram is created, and using the coordinate values, the reach distance on the network diagram between persons, ie, the face-to-face distance is calculated (CJA). A facing distance network diagram (CJC) is drawn from the facing distance matrix (CJB). On the other hand, adaptability (GFB) to be an index related to stress is calculated (CJD) from the tissue personality index (FAAE) obtained by the personality questionnaire. The present inventors conducted research into the relationship between stress on members and personality indicators (extroversion, harmony, integrity, nervousness, openness) indicating the degree of fitness to society. In order to find that there is a strong relationship, in the present embodiment, a stress related index is calculated based on the personality index.

The seat arrangement constraint (CJF) is information for the user to constrain the seats arranged by the present embodiment. The constraint is, for example, a function of designating that a person is forced to be placed in a specific seat, or conversely, designating that it is not placed in a specific seat. This information is given by the user from the client (CL) using a keyboard or the like.

Perform seat layout optimization (CJE) using face-to-face distance network diagram (CJC) and adaptability (GFB), and finally fit it with the field list (IC) showing the seat position in the organization. Get a seating arrangement for a place and draw a seating chart (JJ). Also, seat placement constraints (CIJ) can be used to place constraints on the seats being placed.

The network diagram (ZB) in FIG. 46 is an example of a network diagram in an organization. This network diagram (ZB) is drawn from the facing matrix (FC1C) obtained by the sensor, and (ZB1) to (ZB7) represent nodes representing persons, and (ZB8) to (ZB15) represent members facing each other. It consists of the line (edge) which connected. Similar to FIG. 7, the facing members are arranged on the network diagram using, for example, a spring model. As a result, members frequently facing each other in the organization are arranged close on the network diagram, and members not facing each other are arranged far.

As described above, the network diagram (FAAA), which is an index for evaluating the activity of communication in the organization from the network diagram, the order (FAAA2), the cohesion (FAAA2), and the two-step attainment (FAAA3) and so on. The order (FAAA2) is the number of edges connected to a node, the cohesion (FAAA2) is the density of nodes around itself, and the two-step reach (FAAA3) is within a total of two steps or less It is the proportion of nodes.

It is obvious that the degree (FAAA2), the cohesion degree (FAAA2), and the two-step attainment degree (FAAA3) have larger values if the persons placed far away on the network communicate directly. In other words, in order to activate communication within the organization, communication between persons located far away on this network diagram may be encouraged.

Here, it is possible to easily communicate by placing the seats of the members placed far apart on the network diagram close to each other and reducing the physical distance between the members, as a result, between the members Consider activating communication.

FIG. 47 shows the number of steps for all members (person (CJA 1A) to person (CJA 1B)) on the network diagram of FIG. 46 to reach on the network. The smaller the number of steps, the tighter the communication, and the larger the number of steps, the more distant the communication. It is the facing distance matrix (CJB) of FIG. It is a facing distance network diagram (CJC) shown in FIG. 49 that this facing distance matrix (CJB) is created as a facing distance network diagram, similarly to creating a facing network diagram from the facing matrix.

In FIG. 49, the edge of the facing step number 1 is omitted for simplification, and the edge of the facing step number 2 is represented by a dashed line. The thick solid line (CJC8) shows the edge of the step number 4, and the thin solid line shows (CJC9), (CJC10), (CJC10), (CJC11), (CJC12), and (CJC13) the edge of the step number 3.

In the face-to-face network, in order to increase the attainment of the index 2 step (FAAA3), an edge with three or more steps indicated by a solid line in the face-to-face distance network diagram shown in FIG. 49 ((CJC8) to (CJC13)) You can arrange communication between people who have the same seating position in the actual organization.

Now, five personality indexes of extroversion, harmony, integrity, nervousness and openness are calculated as personality indexes (FAAE) from the personality questionnaire (GA). These have values of 0 to 1, respectively. The sum of all five personality indexes is called adaptability (GFB). Adaptability (GFB) has values from 0 to 5.

Here, in the in-house face-to-face network, if there are many people whose adaptability (GFB) is higher than that of the person's immediate surroundings (people whose edges are directly connected on the network), the person's stress becomes high. Suppose there is a tendency.

In this case, if a person with high adaptability (GFB) is not concentrated directly on the face-to-face network of the person, stress of the person can be reduced. Specifically, stress of a person is reduced by preventing concentration of a person with high adaptability (GFB) around the seat of the person.

Generally, there are innumerable ways of arranging a seat in which a person is not surrounded by a person with higher adaptability (GFB) than the person. This arrangement may be determined by computer simulation or calculation. Here, as an example, an example realized by combining two persons with high adaptability (GFB) or two persons with low adaptability (GFB) and alternately arranging them two sets each as shown in FIG. Shown in. A person with high adaptability (GFB) represented by (CJE1) is shown by a shaded circle, and a person with low adaptability (GFB) represented by (CJE3) is shown by a white circle. People who have high adaptability (GFB) are paired (CJE2), and those who have low adaptability (GFB) are paired (CJE4). In this arrangement, if attention is paid to a person with low adaptability (GFB), even if one side is in contact with a person with high adaptability (GFB), different sides always contact with a person with low adaptability (GFB). It is assumed that stress will not be high.

For example, FIG. 51 is a table describing the adaptability (GFB) of a person on the facing network diagram (ZB). The average value of all the adaptability (GFB) was 2.5, and those over this were indicated by shading. In order to realize the seat arrangement which reduces stress, for example, the seat may be determined in accordance with the arrangement rule shown in FIG.

FIG. 52 shows an example determined by applying the above policy to optimize the seating arrangement (CJE), and fitting it to the organization place list (IC) by drawing the seat arrangement on the place (JJ).

This seating arrangement places people with little face-to-face communication close to each other to activate communication, and a person with high adaptability (GFB) does not surround a person with low adaptability (GFB), stress throughout the organization It is a seating arrangement that simultaneously realizes the reduction of

In this embodiment, it is possible to automatically optimize the seat arrangement of an organization using a face-to-face network analysis result analyzed from data acquired by a sensor and a personality index. If there is a desire to control the placement of the seat, seat placement constraints (CJE) can constrain the automatically placed seats. This allows the user to make comparisons and studies while making changes to the deployed results.

By arranging seating in the actual organization using both face-to-face network analysis results of members in the organization and personality index, measures to enhance activation and reduce stress are realized in everyday use situations be able to.

In the twelfth embodiment, a network diagram capable of simultaneously displaying a face-to-face communication and a hierarchy of job positions is generated. In the conventional network diagram, the relationship with the hierarchy of the current position can not be seen. The problem is solved by considering the hierarchy (nodes) of the job position when deciding the layout of the network diagram.

FIG. 53 is a diagram showing a processing procedure for simultaneously displaying the face-to-face communication and the job position hierarchy. The model is constructed by the job position hierarchy network analysis (CK), the drawing is carried out by the job position hierarchy network diagram drawing (JK), and the drawn result is a job position hierarchy network diagram (KK).

This process can be processed by the same framework as that of the first embodiment, and the job position hierarchy network modeling analysis (CK) is the control unit (ASCO) of the application server (AS), job position hierarchy network diagram drawing (JK) Is executed on the display (J) of the client (CL).

The process until obtaining the facing matrix (FC1C) of the analysis result table (F) is the same as that of the first embodiment, and hence the description is omitted.

Explain intra- and intra-hierarchical tissue frequency analysis (CKA). In the intra-hierarchical and intra-hierarchical tissue frequency analysis (CKA), an organizational frequency index within and outside the job position hierarchy is obtained. The processing flow is shown in FIG.

The intra-hierarchical and intra-hierarchical tissue frequency analysis (CKA) can be processed by the same framework as in Example 10, and is the same as Example 10 until the explanatory variable (FFA) of the analysis result table (F) is determined. Therefore, the explanation is omitted.

In intra- and intra-layer frequency processing (CKA1), the explanatory variable (FFA) of the analysis result table (F), which is an index obtained for each user, and the user ID table (I) of the user / place information table (I) showing the affiliation of members. Let IA) be an input. The intra-hierarchical frequency is selected from data of members with the same job position (IA4) from the explanatory variables (FFA), and the values such as average or variance of facing time (FAAC2) or cohesion degree (FAAA3) as feature amount It is a thing.

Also, with the out-of-hierarchy frequency, select the data (for example, the person in charge and the section manager) of a member belonging to two positions (IA4) from among the explanatory variables (FFA), and meet time (FAAC2) as a feature value. And the cohesion degree (FAAA3) as a value such as average or variance. The calculation formula for calculating the intra-layer frequency and the extra-layer frequency may use other calculation methods in addition to the average and the variance. Further, it is possible to obtain an index of intra-layer frequency or extra-layer frequency for each team name (IA3) or organization (IA5).

Next, position hierarchy network diagram coordinate identification (CKB) will be described. In the job position hierarchical network diagram coordinate specification (CKB), coordinate values for generating a network diagram capable of simultaneously displaying the face-to-face communication and the job position hierarchy are obtained. The processing flow is shown in FIG.

FIG. 55 shows steps for obtaining coordinate values of each member in the processing flow from Step 1 (CKBA) to Step 4 (CKBD). Each step will be described.

Step 1 (CKBA) is an initial arrangement. A placement area for each job position is determined in advance on the screen, and members are placed in accordance with the job position (IA4) of the user ID table (IA). Thus, each member is given a coordinate value. Furthermore, the facing time between the two parties is shown from the facing matrix (FC1C). When there is a face-to-face time longer than a certain time, a line is connected to the arranged members. At that time, the thickness of the line may be changed in proportion to the facing time. As an example, Takahashi (CKBA1) and Tanaka (CKBA2) have a meeting time of a certain level or more, and thus, two lines are connected like a line (CKBA3).

Optimal placement processing is performed in Step 2 (CKBB) and Step 3 (CKBC). Step 2 (CKBB) and Step 3 (CKBC) are repeated, and the process is not completed until a predetermined number of times and the threshold value are reached.

Step 2 (CKBB) is distance calculation. In Step 1 (CKBA), arrangement is performed and coordinate values are given. For those connected by a line, determine the line length, and calculate the total sum value of the lines in the position hierarchy network diagram. As an example, the distance between the line connecting Takahashi (CKBB1) and Tanaka (CKBB2) is 8 (CKBB3), and the total value of the lines (CKBB4) is 141.

Step 3 (CKBC) is an exchange of one same hierarchy member. In order to optimize placement in the layer, select 2 people in the layer and exchange coordinate values. As an example, Kobayashi (CKBC1) and Yamamoto (CKBC2) are exchanging (CKBC3).

Then, the process returns to Step 2 (CKBB), performs distance calculation, compares the total values of the lines before exchange, and if the value is smaller, it is regarded as success and the coordinate values of the respective members are updated. If the value does not decrease, the coordinate value is returned to the base. These are repeated, and it does not finish until it becomes less than a threshold for a predetermined number of times.

Step 4 (CKBD) is the calculation of the assigned centroid. Identify where members of the same team name (IA3) or members of the same organization (IA5) are distributed. The coordinate value of the member is determined from the team name (IA4) of the user ID table (IA) and the organization (IA5), and the average value of the coordinate of the member is obtained. In the example, the coordinate values of the sales center of gravity (CKBD1) and the development center of gravity are determined. The calculation formula may use other calculation method besides the average value. Furthermore, you may consider the position.

The position hierarchy network diagram coordinate specification (CKB) may be any other process as long as it is possible to obtain an optimal coordinate value.

The position hierarchy network diagram coordinate list (FK) stores the coordinate values of the position hierarchy network diagram coordinate specification (CKB). An example of the job title hierarchical network diagram coordinate list (FK) of the analysis result table (F) is shown in FIG.

The period (FK1) of the job position hierarchical network diagram coordinate list (FK) indicates the period included in the data, which is the same as the period (FC1C3) of the facing matrix (FC1C). The number of days (FK2) indicates the number of days included in the data, which is the same as the number of days (FC1C4). The actual number of days (FK3) represents the number of business days of the period (FK1), which is the same as the number of business days (FC1C5). The time resolution (FK4) is the time resolution in the facing table (FC1A), which is the same as the time resolution (FC1C6). The face-to-face determination time (FK5) is a threshold value for determining that the face-to-face is met, and is the same as the face-to-face determination time (FC1C7).

The user ID (FK6) indicates the ID of the user and corresponds to the user ID table (IA). As coordinate values (FK7), coordinate values of members obtained by position hierarchy network diagram coordinate specification (CKB) are stored.

The team name (FK8) indicates the name of the team and corresponds to the user ID table (IA). The coordinate value (FK9) stores the coordinate value of the center of gravity of the team obtained by the position hierarchy network diagram coordinate specification (CKB).

The job position hierarchy network diagram drawing (JK) uses the job position hierarchy network diagram coordinate list (FK) generated by job position hierarchy network diagram coordinate specification (CKB) and the user ID table (IA) of the user / place information table (I). , Draw a network diagram that can simultaneously display face-to-face communication and the position hierarchy, as shown in FIG. 53.

Period: July 1-July 31, 2009 (KK1) shown by the job position hierarchy network diagram (KK) shows the period (FK1) used in the job position hierarchy network diagram coordinate list (FK).

In the network diagram (KKA), first, the placement area by job position is determined in advance on the screen, and the section manager (KKA1) and the section manager (KKA2) described in the example according to the job position (IA4) of the user ID table (IA). Draw a job area, like the one in charge (KKA3)).

A figure is plotted based on the user ID (FK6) and its coordinate value (FK7) stored in the job position hierarchical network diagram coordinate list (FK). In addition, the user name (IA2) of the user ID table (IA) is described around the plotted figure. The shape of the figure may be changed depending on the team name (IA3), the job title (IA4) and the organization (IA5) described in the user ID table (IA).

Moreover, the facing time between two parties is shown from the facing matrix (FC1C) of the analysis result table (F). When there is a face-to-face time longer than a certain time, a line is connected to the arranged members. At that time, the thickness of the line may be changed in proportion to the facing time.

Further, based on the team name (FK8) and the coordinate value (FK9) stored in the job position hierarchical network diagram coordinate list (FK), a plot of an area graphic centering on the barycentric coordinate value of the team is performed. Draw the area of the team name, such as sales (KKA5) and development (KKA6) described in the example.

Next, in the intra- and intra-hierarchy index (KKB), intra-hierarchical and inter-hierarchical tissue frequency indicators determined by intra-hierarchical and intra-hierarchical tissue frequency analysis (CKA) are described. Although the indicator inside and outside the hierarchy (KKB) in FIG. 53 is a display in tabular form, it may be displayed as a graph (a polygonal line, a bar, a circle, a band, a scatter chart, a radar chart).

Furthermore, in the network diagram (KKA) of the job position hierarchical network diagram (KK) in FIG. 53, an example is shown in which the organization (IA5) in the user ID table (IA) displays financial members. Is a network diagram (KKC) of FIG. Similarly, when the network diagram (KKA) is created, the corresponding member is extracted, and the figure is plotted with reference to the job position hierarchical network diagram coordinate list (FK) described by the coordinate values of the member. In the example of the network diagram (KKC), only the sales members in the team name (IA3) are selected and displayed. The meeting time between two parties is shown from the meeting matrix (FC1C) of the analysis result table (F). When there is a face-to-face time longer than a certain time, a line is connected to the arranged members. At that time, the thickness of the line may be changed in proportion to the facing time.

Further, the network diagram (KKD) of FIG. 57 shows the connection between members of the organization and external members. Similarly, when creating a network diagram (KKA) or a network diagram (KKC), the corresponding members are extracted, and the position hierarchy network diagram coordinate list (FK) described by the coordinate values of the members is plotted, and the figure is plotted. Do. In the example of the network diagram (KKD), members of sales in team name (IA3) and members connected with members of sales (external members) are selected and displayed. The meeting time between two parties is shown from the meeting matrix (FC1C) of the analysis result table (F). When there is a face-to-face time longer than a certain time, a line is connected to the arranged members. At that time, the thickness of the line may be changed in proportion to the facing time. Moreover, it is not necessary to connect a line between members about the facing of the external members.

Furthermore, according to the network diagram (KKC) and the network diagram (KKD), it is possible to display the intra-tier index (KKB).

According to this embodiment, by generating a network diagram capable of simultaneously displaying the face-to-face communication and the hierarchy of the job position, it becomes possible to know what kind of team configuration is actually performed on the network diagram.

As mentioned above, although the Example of this invention was described, this invention is not limited to the said Example, A various deformation | transformation implementation is possible, It is possible to combine each Example mentioned above suitably. It will be understood by the trader.

TR name tag type sensor node GW base station SS sensor net server AS application server CL client NW network ASME storage unit ASCO control unit CA modeling analysis ASCC communication control ASSR transmission / reception unit

Claims (14)

1. An organization behavior analysis apparatus for analyzing an organization composed of a plurality of persons, comprising:
   A receiving unit that receives sensor data acquired by the infrared transmitting / receiving unit and the acceleration sensor of the terminal attached to each of the plurality of persons, and data indicating a subjective evaluation or an objective evaluation of each of the plurality of persons;
   A control unit that analyzes the sensor data and data indicating the subjective evaluation or the objective evaluation;
   A recording unit that records analysis conditions for the control unit to analyze and a result of analysis performed by the control unit;
   The control unit
   For each of the plurality of persons, an index indicating an inter-person relationship in the organization and an action in the organization is calculated from the sensor data based on the analysis condition and recorded in the recording unit.
   The subjective evaluation of each of the plurality of persons or the data indicating the objective evaluation is correlated with the relationship between the persons in the organization and the index indicating the behavior in the organization, An organization behavior analysis device which specifies a factor of data indicating an evaluation or the above-mentioned objective evaluation.
2. In the organization behavior analysis device according to claim 1,
   The control unit
   For each of the plurality of persons, an index indicating characteristics of behavior and thinking is calculated from data indicating the subjective evaluation based on the analysis condition and recorded in the recording unit.
   The data showing the subjective evaluation or the objective evaluation in the organization by correlating the data indicating the subjective evaluation or the objective evaluation of each of the plurality of persons with the index indicating the characteristics of the behavior and thought Organizational behavior analysis device that identifies factors.
3. In the organization behavior analysis device according to claim 1,
   The control unit
   A meeting table creation unit that creates a meeting table indicating the meeting situations of each of the plurality of persons from the data acquired by the infrared transmission / reception unit;
   A body rhythm table creation unit for creating a body rhythm table indicating the movement of each of the plurality of persons from the data acquired by the acceleration sensor;
   A network index extraction unit for extracting a network index indicating a connection between each of the plurality of persons with another person based on the network diagram created from the face-to-face table;
   A body rhythm index extracting unit for extracting a body rhythm index including the frequency of appearance of the frequency indicating the movement of each of the plurality of persons and the continuity of the frequency from the body rhythm table;
   A face-to-face index extraction unit that extracts a face-to-face index indicating the face-to-face time and the opposite area polarity of each of the plurality of persons based on the face-to-face table and the body rhythm table;
   An organization activity index extracting unit that calculates an organization activity index indicating an activity time of each of the plurality of persons based on the facing table and the body rhythm table;
   A tissue behavior analysis apparatus using the network index, the body rhythm index, the face-to-face index, and the organization activity index as an index indicating a relationship between persons in the organization and an action in the organization.
4. In the organization behavior analysis device according to claim 1,
   The data indicating the objective evaluation is a data indicating the productivity of each of the plurality of persons and / or at least one of accident defects.
5. In the organization behavior analysis device according to claim 1,
   The data indicating the subjective evaluation is at least one of a leadership / teamwork index for each of the plurality of persons, a challenge / enhancement index, and a stress / mental disorder index.
6. In the organization behavior analysis device according to claim 1,
   The control unit is data indicating a subjective evaluation or an objective evaluation of each of the plurality of persons, and an index indicating a relationship between persons in the organization among the other persons in the organization and an action in the organization. A tissue behavior analysis device which correlates with the feature value calculated from.
7. An infrared transmitting / receiving unit which is attached to each of a plurality of persons constituting a tissue and acquires data indicating meeting, an acceleration sensor acquiring acceleration data, a transmitting unit transmitting data indicating the meeting and the acceleration data as sensor data And a terminal having
   A receiving unit that receives the sensor data and receives data indicating a subjective evaluation or an objective evaluation of each of the plurality of persons, and analyzes the sensor data and data indicating the subjective evaluation or the objective evaluation A tissue behavior analysis device including a control unit, a recording unit that records analysis conditions for the control unit to analyze, and a result of analysis performed by the control unit;
   The control unit
   For each of the plurality of persons, an index indicating an inter-person relationship in the organization and an action in the organization is calculated from the sensor data based on the analysis condition and recorded in the recording unit.
   The subjective evaluation of each of the plurality of persons or the data indicating the objective evaluation is correlated with the relationship between the persons in the organization and the index indicating the behavior in the organization, An organizational behavior analysis system for identifying factors of data indicating evaluation or the above-mentioned objective evaluation.
8. In the organization behavior analysis system according to claim 7,
   The control unit
   For each of the plurality of persons, an index indicating characteristics of behavior and thinking is calculated from data indicating the subjective evaluation based on the analysis condition and recorded in the recording unit.
   The data showing the subjective evaluation or the objective evaluation in the organization by correlating the data indicating the subjective evaluation or the objective evaluation of each of the plurality of persons with the index indicating the characteristics of the behavior and thought Organizational behavior analysis system to identify factors.
9. In the organization behavior analysis system according to claim 7,
   The control unit
   A meeting table creation unit that creates a meeting table indicating the meeting situations of each of the plurality of persons from the data acquired by the infrared transmission / reception unit;
   A body rhythm table creation unit for creating a body rhythm table indicating the movement of each of the plurality of persons from the data acquired by the acceleration sensor;
   A network index extraction unit for extracting a network index indicating a connection between each of the plurality of persons with another person based on the network diagram created from the face-to-face table;
   A body rhythm index extracting unit for extracting a body rhythm index including the frequency of appearance of the frequency indicating the movement of each of the plurality of persons and the continuity of the frequency from the body rhythm table;
   A face-to-face index extraction unit that extracts a face-to-face index indicating the face-to-face time and the opposite area polarity of each of the plurality of persons based on the face-to-face table and the body rhythm table;
   An organization activity index extracting unit that calculates an organization activity index indicating an activity time of each of the plurality of persons based on the facing table and the body rhythm table;
   An organization behavior analysis system using the network indicator, the body rhythm indicator, the face-to-face indicator, and the organization activity indicator as an indicator indicating a relationship between persons in the organization and an action in the organization.
10. In the organization behavior analysis system according to claim 7,
    The data which shows said objective evaluation are data which show at least any one of productivity of each of the said several persons, and an accident failure, and the organization behavior analysis system.
11. In the organization behavior analysis system according to claim 7,
    The data indicating the above-mentioned subjective evaluation is at least one of a leadership / teamwork index, a challenging / enhancement index, and a stress / mental disorder index for each of the plurality of persons.
12. In the organization behavior analysis system according to claim 7,
    The control unit is data indicating a subjective evaluation or an objective evaluation of each of the plurality of persons, and an index indicating a relationship between persons in the organization among the other persons in the organization and an action in the organization. A tissue behavior analysis system that correlates with feature amounts calculated from.
13. An organization behavior analysis apparatus for analyzing an organization composed of a plurality of persons, comprising:
    A receiving unit that receives data indicating the subjective evaluation of each of the plurality of persons;
    A control unit that analyzes data indicating the subjective evaluation;
    The recording unit configured to record data indicating the position of the seat in the tissue, an analysis condition for the control unit to analyze, and a result of the analysis performed by the control unit;
    The control unit
    An index calculation unit that calculates, for each of the plurality of persons, an index related to stress from data indicating the subjective evaluation based on the analysis condition;
    And a seat arrangement determination unit configured to determine a seat arrangement of each of the plurality of persons in the organization based on the data indicating the seat position and the index related to the stress.
14. In the organization behavior analysis device according to claim 13,
    The receiving unit receives sensor data acquired by an infrared transmitting and receiving unit of a terminal attached to each of the plurality of persons;
    The control unit generates a meeting table indicating a meeting situation of each of the plurality of persons from the data acquired by the infrared transmitting and receiving unit based on the analysis condition, and a network generated from the meeting table And a face-to-face distance calculation unit that calculates the face-to-face distance between the plurality of persons based on the diagram using the analysis condition,
    The tissue behavior analysis device according to claim 1, wherein the seat arrangement determination unit determines the seat arrangement in the organization of each of the plurality of persons based on the data indicating the seat position and the facing distance.

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