JP2010201566A - Mobile body managing system, mobile body managing device and mobile body managing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a mobile body managing system 100 which identifies human beings A and B respectively by using the acceleration of the moving human beings A and B. <P>SOLUTION: The mobile body managing system 100 includes a central control unit 10 and a portable terminal 12. The central control unit 10 having an LRF(18) and the portable terminal 12 having an acceleration sensor (142) establish a short range radio communication. The portable terminal 12 transmits an acceleration outputted from the acceleration sensor (142) to the central control unit 10. The central control unit 10 records a position of the human being by the LRF(18) and calculates the acceleration of the human being from the history of the position. The central control unit 10 calculates a correlation coefficient of the calculated acceleration and the received acceleration to coordinate a human being ID with a sensor ID, records the coordinated IDs in the ID table and identifies the human beings A and B respectively. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a mobile management system, and more particularly to a mobile management system that manages, for example, a plurality of information.

  An example of this type of conventional management apparatus is disclosed in Patent Document 1. The communication robot disclosed in Patent Literature 1 receives information transmitted from an RFID (Radio Frequency Identification) tag possessed by a plurality of users, thereby identifying users existing in the vicinity or in the vicinity. Further, the robot takes a communication action by specifying the nearest user from the radio wave intensity when each RFID tag transmits.

An example of this type of conventional management apparatus is also disclosed in Patent Document 2. The RFID management apparatus disclosed in Patent Document 2 includes a reader that estimates the number of RFID tags existing in the reception area, and manages fluctuations in the number of RFID tags in the reception area. For example, RFID tags are attached to various articles, and their IDs are randomly transmitted after a certain amount of time. The reader receives the transmitted ID and compares it with the ID recorded in the database. As a result, when the number of RFID tags existing in the reception area decreases, the RFID management apparatus notifies the outside by a notification means such as an LED.
JP 2007-320033 A [B25J 13/08, A63H 11/00, A63H 3/33, A63H 3/38] JP 2006-50049 A [H04B 1/59, G06K 17/00, H04B 5/02]

  However, in the background art disclosed in Patent Document 1, the robot cannot acquire information from a user who does not have an RFID tag, and cannot identify a user who does not have an RFID tag. Further, even in the background art disclosed in Patent Document 2, an article to which an RFID tag is not attached cannot be managed.

  Therefore, a main object of the present invention is to provide a novel mobile management device.

  Another object of the present invention is to provide a moving body management apparatus and a management program capable of easily managing a moving person.

  The present invention employs the following configuration in order to solve the above problems. The reference numerals in parentheses, supplementary explanations, and the like indicate the corresponding relationship with the embodiments described in order to help understanding of the present invention, and do not limit the present invention.

  According to a first aspect of the present invention, there is provided an acceleration sensor provided on the first moving body for detecting the first acceleration, a position detecting means for detecting the position of the second moving body, and a history of positions detected by the position detecting means. A first calculating means for calculating two accelerations, a second calculating means for calculating a correlation coefficient between the first acceleration and the second acceleration, and when the correlation coefficient is equal to or greater than a predetermined value, It is a mobile body management system provided with the identification means which identifies as a same mobile body, and the processing means which performs a predetermined | prescribed process according to the identification by an identification means.

  In the first invention, the acceleration sensor (142) is included in the portable terminal (12) possessed by the first moving body such as a human (A, B) and transmitted to the central control device 10 or the like. Further, position detection means (18a, 18b) such as LRF (Laser Range Finder) detects the position where the second moving body such as a human (A, B) is present in the detection area (E). The first calculation means (16, S31) calculates the second acceleration of the second moving body from the walking locus (K) indicating the position history. The second calculation means (16, S61-S73) calculates the Pearson product moment correlation coefficient from the changes in the first acceleration and the second acceleration, using the acceleration value for each predetermined time (one second). To do. The identification means (16, S43) identifies that the first moving body and the second moving body are the same moving body, that is, the same person if the calculated correlation coefficient is equal to or greater than a predetermined value (0.80). To do. And a process means (16, S43, S45) performs various processes according to an identification result. The first moving body and the second moving body can be identified even if there are two or more.

  According to the first aspect of the present invention, each moving person can be managed by identifying the person using the acceleration of the moving person. Furthermore, various processes can be executed by managing a moving person. For example, various processes include a process of recording the identification result as data. If the personal information (public data) of the first moving body is transmitted together with the first acceleration output from the acceleration sensor, the process Also record the data. Further, the personal information can be used to provide a search service for the same mobile body, an information providing service, and the like.

  A second invention is dependent on the first invention, and the predetermined processing means includes a recording means for recording the first ID set in the acceleration sensor and the second ID set in the second moving body in association with each other.

  In the second invention, the first ID (sensor ID) is indicated by an ID number from 001 to M, and the second ID (human ID) is indicated by an ID number from 001 to L. Then, the recording means (16, 22, S43, S45), when identified by the identifying means, records the ID number of the first ID and the ID number of the second ID in association with each other in the ID table or the like.

According to the second aspect, since the moving person can be managed by the ID, the ID table data can be made simple when managing a large number of persons. Then, by simplifying the configuration of the table data, the processing time for the search processing for the table data can be shortened. The third invention is dependent on the first invention or the second invention, Each of the acceleration and the second acceleration is composed of an acceleration in a first direction horizontal to the ground and an acceleration in the second direction perpendicular to the first direction and horizontal to the ground. The first direction calculating means for calculating the correlation coefficient of the acceleration in the first direction and the second direction calculating means for calculating the acceleration in the second direction, and the identifying means includes the first direction calculating means and the second direction calculating means. The first moving body and the second moving body in which the two correlation coefficients calculated by each of the two are equal to or greater than a predetermined value and the sum of the two correlation coefficients is the maximum value are identified as the same moving body.

  In the third invention, the first acceleration and the second acceleration are composed of accelerations in the X-axis direction and the Y-axis direction that are horizontal to the ground. The first direction calculation means (16, S61, S65, S69-S73) calculates the correlation coefficient of the X axis, and the second direction calculation means (16, S63, S67-S73) A correlation coefficient is calculated. Then, the identifying means identifies the first moving body and the second moving body that have two correlation coefficients that are equal to or greater than a predetermined value and that have a maximum sum of the two correlation coefficients as the same moving body.

  According to the third aspect of the invention, it is possible to identify with high reliability by using an acceleration composed of two axes.

  4th invention is a receiving means (16,20) which receives the 1st acceleration which the acceleration sensor (12,142) provided in the 1st moving body (A, B) transmits, and the 2nd moving body (A, B) ) Position detecting means (18a, 18b) for detecting the position, first calculating means (16, S31) for calculating the second acceleration based on the position history detected by the position detecting means, the first acceleration and the second acceleration Second calculating means (16, S61-S73) for calculating a correlation coefficient of acceleration, and an identifying means for identifying the first moving body and the second moving body as the same moving body when the correlation coefficient is equal to or greater than a predetermined value. (16, S43), and a moving body management apparatus including processing means (16, S43, S45) for executing a predetermined process in response to identification by the identification means.

  In the fourth invention as well, as in the first invention, each moving person can be managed by identifying the person using the acceleration of the moving person, and by managing the moving person, Various processes can be executed.

  The fifth invention comprises a receiving means (16, 20) for receiving a first acceleration transmitted from an acceleration sensor (12, 142) provided in the first moving body (A, B), and a second moving body (A, B). The processor (16) of the management device (10) including position detection means (18a, 18b) for detecting the position of B) calculates the second acceleration based on the position history detected by the position detection means. 1 calculating means (S31), second calculating means (S61-S73) for calculating a correlation coefficient between the first acceleration and the second acceleration, and the first moving body and the second movement when the correlation coefficient is equal to or greater than a predetermined value. This is a moving body management program that functions as identification means (S43) for identifying a body as the same moving body, and processing means (S43, S45) for executing predetermined processing in accordance with identification by the identification means.

  In the fifth invention, similarly to the first invention, each moving person can be managed by identifying the person using the acceleration of the moving person, and by managing the moving person, Various processes can be executed.

  According to this invention, since the moving body management system can identify a person using the acceleration of the moving person, it can manage the person.

  The above object, other objects, features, and advantages of the present invention will become more apparent from the following detailed description of embodiments with reference to the drawings.

FIG. 1 is an illustrative view showing one example of a configuration of a management system of the present invention. FIG. 2 is a block diagram showing an electrical configuration of the central controller shown in FIG. FIG. 3 is a block diagram showing an electrical configuration of the mobile terminal shown in FIG. FIG. 4 is an illustrative view showing a measurement region of the LRF shown in FIG. FIG. 5 is an illustrative view showing one example of a human walking trajectory acquired using the LRF shown in FIG. FIG. 6 is a graph showing changes in acceleration calculated from the walking trajectory of the person A shown in FIG. FIG. 7 is a graph showing changes in acceleration calculated from the walking trajectory of the person B shown in FIG. FIG. 8 is a graph showing an example of a change in acceleration output from the acceleration sensor shown in FIG. FIG. 9 is a diagram showing an example of a calculation table and a correlation table stored in the memory of the central controller shown in FIG. FIG. 10 is a diagram showing an example of a sensor table stored in the memory of the central controller shown in FIG. FIG. 11 is a diagram showing an example of an ID table stored in the memory of the central controller shown in FIG. 12 is an illustrative view showing one example of a GUI displayed on the LCD shown in FIG. FIG. 13 is an illustrative view showing another example of a GUI displayed on the LCD shown in FIG. FIG. 14 is an illustrative view showing one example of a memory map of a memory of the central control unit shown in FIG. FIG. 15 is a flowchart showing the position history recording process of the CPU of the central control unit shown in FIG. FIG. 16 is a flowchart showing acceleration sensor history recording processing of the CPU of the central control unit shown in FIG. FIG. 17 is a flowchart showing the management process of the CPU of the central controller shown in FIG. FIG. 18 is a flowchart showing the correlation coefficient calculation process of the CPU of the central control unit shown in FIG. FIG. 19 is a flowchart showing the public data management process of the CPU of the central control unit shown in FIG.

  Referring to FIG. 1, a mobile management system 100 according to this embodiment includes a central control device 10 and a mobile terminal 12. The central control device 10 is wirelessly connected to each of the mobile terminals 12a and 12b possessed by the humans A and B by Bluetooth short-range wireless communication. In addition, the central control device 10 can transmit an instruction for guiding or searching for the humans A and B to the robot 14 via the network 50. This robot 14 is an interaction-oriented robot (communication robot), and includes at least one of body movements such as gestures and voices with communication targets (communication targets) such as humans A and B. It has a function to execute communication behavior. Although not shown in FIG. 1, the central control apparatus 10 acquires the current positions of the person A and the person B within a predetermined range by using a plurality of LRFs.

  When it is not necessary to distinguish between the humans A and B, they are collectively referred to as “human”, and when it is not necessary to distinguish between the mobile terminals 12a and 12b, they are collectively referred to as “mobile terminal 12”.

  FIG. 2 is a block diagram showing an electrical configuration of the central controller 10. Referring to FIG. 2, central control device 10 includes a CPU 16. The CPU 16 is also called a microcomputer or a processor, and is connected to the LRFs 18a and 18b functioning as position detecting means, the short-range wireless device 20 functioning as receiving means, the memory 22, and the communication LAN board 24, respectively. When there is no need to distinguish between the LRFs 18a and 18b, they are collectively referred to as “LRF18”.

  The LRF 18 measures the distance to the object from the time it takes to irradiate the laser and reflect it back to the object (including humans). For example, a laser beam emitted from a transmitter (not shown) is reflected by a rotating mirror (not shown), and the front is scanned in a fan shape by a certain angle (for example, 0.5 degrees). Here, as LRF18, a laser range finder (model LMS200) manufactured by SICK can be used. When this laser range finder is used, a distance of 8 m can be measured with an error of about ± 15 mm. The LRF 18 is installed in various environments such as a company floor, a shopping mall, or an attraction venue, and detects the position of a person.

  The short-range wireless communication device 20 is a device for establishing Bluetooth-type wireless communication. In this embodiment, the short-range wireless communication device 20 establishes wireless communication with the mobile terminal 12 existing in the environment. Although not shown, the memory 22 includes a ROM, an HDD, and a RAM. The ROM and the HDD control device address data necessary for short-range wireless communication and the operation of the central controller 10. These control programs are stored in advance. For example, a program necessary for detecting a human being, which is a mobile body, by the LRF 18, a communication program for transmitting / receiving data and commands for establishing wireless communication with the mobile terminal 12, and the like are recorded. The RAM is used as a work memory or a buffer memory.

  The CPU 16 is connected to the communication LAN board 24. The communication LAN board 24 is configured by a DSP, for example, and provides transmission data given from the CPU 16 to the wireless communication device 26. The wireless communication device 26 sends the transmission data to an external device (the robot 14 and information described later) via the network 50. To the calling device). For example, the transmission data may be a control command signal (command) from the central controller 10 to the robot 14. The communication LAN board 24 receives data via the wireless communication device 26 and provides the received data to the CPU 16.

  FIG. 3 is a block diagram showing an electrical configuration of the mobile terminal 12. With reference to FIG. 3, portable terminal 12 includes a CPU 130. The CPU 130 is also called a microcomputer or a processor, like the CPU 16 of the central control device 10, and is a short-range wireless communication functioning as a speaker 132, a key input device 134, an LCD driver 136, a memory 140, an acceleration sensor 142, and an acceleration transmission means. Connected to each of the devices 146. The speaker 132 is used when outputting music data or audio data recorded in the memory 140.

  The key input device 134 includes direction keys, a determination key, and the like, and accepts a key operation with respect to a GUI (Graphical User Interface) displayed on the LCD 138 controlled by the LCD driver 136. The memory 140 is different from the memory 22 of the central controller 10 and includes a flash memory and a RAM. The flash memory includes device address data necessary for establishing short-distance wireless communication, music data and audio described above. Data and the like are stored, and a control program for controlling the operation of the mobile terminal 12 is stored in advance. The RAM is used as a work memory or a buffer memory.

  The acceleration sensor 142 is a semiconductor type biaxial acceleration sensor, and outputs the acceleration (first acceleration) of each axis to the CPU 130. And CPU130 calculates the inclination of the portable terminal 12, ie, an angle, using an inverse trigonometric function with respect to the acceleration value of each axis. The acceleration sensor 142 may be a semiconductor triaxial acceleration sensor.

  The short-range wireless communication device 146 is used to establish wireless communication with the central control device 10 in the same environment, and the configuration thereof is the same as the short-range wireless communication device 20 included in the central control device 10. Detailed description will be omitted.

  Next, the LRF 18 will be described in detail. Referring to FIG. 4, the measurement range of LRF 18 is indicated by a semicircular shape (fan shape) having a radius R (R≈8 m). That is, the LRF 18 can measure the direction of 90 ° left and right within a predetermined distance (R) when the front direction is the center.

  The laser used is a class 1 laser in the Japanese Industrial Standard JIS C 6802 “Safety Standards for Laser Products”, which is a safe level that does not affect human eyes. In this embodiment, the sampling rate of the LRF 18 is 38.5 Hz. This is because the position of a person who moves by walking or the like is continuously detected.

  Furthermore, as described above, the LRF 18 is arranged in various environments. Specifically, each of the LRFs 18a and 18b is arranged so that the detection regions overlap with each other, and although not illustrated, the LRFs 18a and 18b are fixed to a height of about 90 cm from the floor surface. This height is for enabling detection of the body and arms (both arms) of the subject, and is calculated from, for example, the average height of Japanese adults. Therefore, the height at which the LRF 18 is fixed may be appropriately changed according to the location (region or country) where the central control device 10 is provided and the age or age of the subject (for example, children or adults). In this embodiment, only two LRFs 18 are set, but three or more LRFs 18 may be installed in order to detect a human being in a wider range.

  In the central controller 10 having such a configuration, the CPU 16 estimates a change in the current position of the human using a particle filter based on the output (distance data) from the LRF 18. Then, the estimated change in the current position is recorded as a walking trajectory, and the central controller 10 calculates human acceleration (second acceleration) from the walking trajectory.

  Here, a method for acquiring human acceleration detected in the detection area will be described in detail. Referring to FIG. 5A, LRFs 18a and 18b are installed facing each other, and a range where the measurement ranges of LRFs 18a and 18b overlap is indicated by hatching. A hatched range is a detection area E, and the current positions of the human A and the human B are continuously detected in the detection area E. And the data of the present position detected continuously are shown as a walk locus. For example, the walking trajectories of the human A and the human B are indicated by the walking trajectory Ka and the walking trajectory Kb. In FIG. 5A, the persons A and B are represented by circles. When there is no need to distinguish between the walking tracks Ka and Kb, they are collectively referred to as “walking track K”.

First, a human walking trajectory K acquired within a predetermined time (20 seconds) will be described. Central control unit 10, in the detection region E of FIG. 5 (A), X-axis right direction, the vertical direction as Y axis, the coordinate position of the measurement start time _{T 0} as the origin (0.0,0.0) Record. The central controller 10 records the positions of the X-axis and the Y-axis for 20 seconds every fixed time (for example, 26 milliseconds).

FIGS. 5B and 5C are illustrative views showing the walking trajectory Ka and walking trajectory Kb for human A and human B for 20 seconds, where the horizontal axis is the X axis and the vertical axis is the Y axis. Referring to FIG. 5 (B), human A is 5 seconds after the measurement start time _{T 0} (2.0, 1.0), after 10 seconds (4.5,1.2), after 15 seconds ( 7.0, 1.2) and 20 seconds later, it can be seen that the position has moved to the position (10.0, 0.0). That is, the human A moves 10 meters to the right over 20 seconds and moves 1 mail to the left over 15 seconds, but finally returns to the original position (the zero position on the Y axis).

On the other hand, referring to FIG. 5 (C), the human B is 5 seconds after the measurement start time _{T 0} (0.7, 0.1), after 10 seconds (3.0,0.2), 15 seconds It can be seen later that it has moved to the position of (4.5, 0.1) and 20 seconds later (5.0, 0.0). That is, the human B moves 5 meters in the traveling direction (X-axis direction) over 20 seconds, and moves slightly to the right in the left-right direction (Y-axis direction), but finally the original position (zero on the Y-axis). Return to position.

Next, a method for calculating human acceleration from the walking locus K will be described. From the recorded walking trajectory K, the movement distance at time T _{k + d when} a certain time d has elapsed from a certain time T _k can be obtained. Therefore, the central control device 10 can calculate the acceleration of the human A and the human B by differentiating the moving distance at regular time intervals to calculate the speed, and further differentiating the calculated speed at regular time intervals. .

6A is a graph showing a change in acceleration of the human A in the X-axis direction, and FIG. 6B is a graph showing a change in acceleration of the human A in the Y-axis direction. Referring to FIG. 6 (A), human A in the X-axis direction accelerates rapidly at 0.4 m / s ² after the start of measurement, further accelerates at 0.1 m / s ² after 5 seconds, Move at a constant speed. Then, after further acceleration at 0.1 m / s ² after 14 seconds, it moves again at a constant speed. Referring to FIG. 6B, the human A in the Y-axis direction accelerates at 0.2 m / s ² after the start of measurement, and immediately moves at a constant speed. Further, after 4 seconds, the vehicle decelerates at 0.12 m / s ² , and after 9 seconds, decelerates at 0.05 m / s ² . Then, after 14 seconds, the vehicle decelerates at 0.2 m / s ² and then moves at a constant speed. That is, from the graphs shown in FIGS. 6 (A) and 6 (B), human A is slightly swaying in the left-right direction, but accelerated at a stretch in the right direction, and then moved while increasing the speed. I understand that.

On the other hand, FIG. 7A is a graph showing a change in the acceleration of the human B in the X direction, and FIG.
Is a graph showing a change in acceleration of human B in the Y-axis direction. Referring to FIG. 7A, human B in the X-axis direction accelerates at 0.15 m / s ² after the start of measurement, and further accelerates at 0.3 m / s ² after 5 seconds, and moves at a constant speed. To do. In addition, the vehicle decelerates at 0.15 m / s ² after 9 seconds, and further decelerates at 0.2 m / s ² after 15 seconds to move at a constant speed. Further, with reference to FIG. 7 (B), the acceleration of the human B in the Y-axis direction, after the start of measurement, gently accelerated at 0.05 m / ^{s 2,} after 3 seconds decelerating at 0.05 m / ^{s 2} Then continue to accelerate. Then, after decelerated by gently 0.02 m / ^{s 2} after 10 seconds, the 0.0 m / ^{s 2.} That is, from the graphs shown in FIGS. 7A and 7B, the human B moves to the right while rapidly increasing the speed up to 9 seconds after shaking in the left-right direction, but after 10 seconds have passed. It can be seen that has slowed twice. In this way, the change in acceleration is completely different even for humans A and B moving in the same direction.

  Here, the relationship between the acceleration output from the acceleration sensor 142 and the human acceleration calculated using the LRF 18 will be described.

  First, since the mobile terminal 12 is carried while being stored in a human pocket or bag, the acceleration sensor 142 is stored in the pocket or bag so that the two axes are horizontal to the ground. Is provided. However, depending on the state in which the portable terminal 12 is carried, there may be a case where the X and Y axes in the detection region E of the LRF 18 do not match the two axes of the acceleration sensor 142. Therefore, the central controller 10 detects the two axes of the acceleration sensor 142 by correlating the accelerations in the two directions output from the acceleration sensor 142 with the accelerations in the X and Y axis directions of the detection region E, respectively. The region E is associated with the X axis and the Y axis. In this embodiment, the two axes of the acceleration sensor 142 are referred to as an Xa axis and a Ya axis, respectively.

  Specifically, a correlation coefficient between the X axis and the Y axis in human acceleration is calculated with respect to the acceleration in the Xa axis direction of the acceleration sensor 142, and further, with respect to the acceleration in the Ya axis direction of the acceleration sensor 142, A correlation coefficient between the X axis and the Y axis in human acceleration is calculated. If the correlation coefficient between the Xa axis and the X axis and the correlation coefficient between the Ya axis and the Y axis are larger than the correlation coefficient between the Xa axis and the Y axis and the correlation coefficient between the Ya axis and the X axis, , Xa-axis direction and Ya-axis direction accelerations are X-axis direction and Y-axis direction accelerations. On the other hand, Xa-axis direction and Ya-axis direction accelerations are Y-axis direction and X-axis direction accelerations. . That is, the central controller 10 determines whether the X axis and the Xa axis, and the Y axis and the Ya axis match, and if they match, the Xa axis and the Ya axis are changed to the X axis and the Y axis. If not matched, the Xa axis and the Ya axis are associated with the Y axis and the X axis.

  The central control device 10 calculates a correlation coefficient between the accelerations in the Xa-axis direction and the Ya-axis direction output from the associated acceleration sensor 142 and the calculated accelerations in the X-axis direction and the Y-axis direction. As a result, if the correlation coefficient is equal to or greater than a predetermined value, the person holding the mobile terminal 12 and the person detected by the LRF 18 are identified.

For example, FIGS. 8A and 8B show the acceleration in the Xa-axis direction and the acceleration in the Ya-axis direction of the acceleration sensor 142 of a certain mobile terminal 12. Referring to FIG. 8A, the change in acceleration in the Xa-axis direction changes abruptly at 0.4 m / s ² after the start of acquisition of acceleration data, and further increases to 0.4 m / s after 4.5 seconds. s ² changes. After that, it changes to 0.4 m / s ² after 14 seconds and becomes 0.0 m / s ² . Also, referring to FIG. 8B, the change in the acceleration in the Ya-axis direction changes to 0.2 m / s ² after the start of acquisition of acceleration data, and −0.12 m / s after 4.5 seconds. Change to ² . Then, changes in 0.05M / ^{s 2} after 9.5 seconds, the 0.0 m / ^{s 2} from the change in the -0.2 m / ^{s 2} after 14 seconds.

That is, from the graphs shown in FIGS. 8A and 8B, changes in the Xa axis and the Ya axis of the acceleration sensor 142 are similar to changes in the acceleration of the human A, and are also similar to changes in the acceleration of the human B. I understand that it is not. Note that the acceleration data acquisition start time and the measurement start time T ₀ by the LRF 18 are synchronized so as to have the same timing.

  First, a process for making the Xa axis and the Ya axis of the acceleration sensor 142 correspond to either the X axis or the Y axis will be described in detail. In this embodiment, Pearson's product-moment correlation coefficient is used for the process of matching the axes. This Pearson product moment correlation coefficient is obtained by preparing n acceleration values for each axis (n = 20) per second and dividing the covariance of each axis by the product of the standard deviation for each axis. Can do. For example, the correlation coefficient of the acceleration between the Xa axis of the acceleration sensor 142 and the X axis of the human A can be obtained by the equation (1), and the calculation result by the equation (1) is 0.96.

  Further, when the correlation coefficient is calculated for other combinations of axes, the correlation coefficient of the acceleration between the Xa axis and the Y axis is 0.39, the correlation coefficient of the acceleration between the Ya axis and the X axis is 0.34, The correlation coefficient of the acceleration between the Ya axis and the Y axis is 0.88.

  The Pearson product-moment correlation coefficient has a maximum value of 1, a minimum value of -1, and the closer the correlation coefficient is to 1, the higher the positive correlation. On the other hand, the closer the correlation coefficient is to -1, the higher the negative correlation. If the correlation coefficient is 0, it means that there is no correlation.

  Then, the result of calculating the correlation coefficient of each axis combination is recorded in the calculation table shown in FIG. 9A. For example, the ID set in the acceleration sensor 142 included in the mobile terminal 12 is set as the sensor ID (first ID). ): 001, and when the ID set for human A is human ID (second ID): 001, the correlation coefficient between the Xa axis of sensor ID: 001 and the X axis of human ID: 001 is 0.96. Is recorded, 0.39 is recorded as the correlation coefficient between the Xa axis and the Y axis, 0.34 is recorded as the correlation coefficient between the Ya axis and the X axis, and the correlation coefficient between the Ya axis and the Y axis. Is recorded as 0.88.

  As a result, the correlation coefficient (0.96) between the Xa axis and the X axis in the human A and the correlation coefficient (0.88) between the Ya axis and the Y axis are the same as the correlation between the Xa axis and the Y axis in the human A. Since the number of relations (0.39) and the correlation coefficient (0.34) between the Ya axis and the X axis are larger, the central control device 10 uses the Xa axis and Ya axis of the acceleration sensor 142 as the X axis in the detection region E. And correspond to the Y axis. The central controller 10 records the correlation coefficient between the Xa axis and the X axis in the human A and the correlation coefficient between the Ya axis and the Y axis as 0.96 and 0.88.

  Similarly, when the respective correlation coefficients between the Xa axis and the Ya axis of the acceleration sensor 142 and the X axis and the Y axis of the acceleration calculated in the human B are calculated, the Xa axis and the Ya axis are detected regions. Corresponding to the Y-axis direction and the X-axis direction in E. Further, the central control apparatus 10 records the correlation coefficient between the Xa axis and the Y axis in the human B and the correlation coefficient between the Ya axis and the X axis in the human B as 0.50 and 0.13.

  Next, processing for identifying a person whose acceleration is output by the acceleration sensor 142 as either the person A or the person B detected by the LRF 18 will be described in detail. The central control device 10 exceeds a predetermined value (0.80) among the X-axis and Y-axis correlation coefficients corresponding to the humans A and B, and the combination having the largest sum of two correlation values And the acceleration sensor 142 are specified. That is, the correlation coefficient (0.96) between the Xa axis of the acceleration sensor 142 and the X axis in the human A and the correlation coefficient (0.88) between the Ya axis and the Y axis exceed a predetermined value. Since the sum (1.84) of the two correlation coefficients is larger than the sum (0.63) of the two correlation coefficients between the acceleration sensor 142 and the human B, the portable terminal 12 having the acceleration sensor 142 is possessed. Is identified as human A. That is, the person whose acceleration is output by the acceleration sensor 142 is identified as the same as the person A detected by the LRF 18.

Then, by using the correlation coefficient between the X axis and the Y axis, the central control device 10 can perform identification with high reliability. (3)
In this embodiment, instead of the biaxial acceleration sensor 142, a triaxial acceleration sensor may be used. For example, a three-axis acceleration sensor outputs acceleration in a direction perpendicular to the ground (Z-axis direction) in addition to two axes (Xa-axis and Ya-axis) parallel to the ground. At this time, the axis that outputs the gravitational acceleration G (9.8) m / s ² in the three axes is the Z axis, and the remaining two axes are the Xa axis or the Ya axis. Then, the Xa axis and the Ya axis can be associated with either the X axis or the Y axis of the detection area E by the above-described method. In addition, the horizontal direction is set with respect to the detection area E shown in FIG. Although the X axis and the vertical direction are the Y axis, the direction in which the current position change amount is large may be the X axis direction, and the direction perpendicular to the X axis direction may be the Y axis direction. At this time, if it is a three-axis acceleration sensor, the inclination in the three-dimensional space of the mobile terminal 12 can be obtained from the amount of change in each axis. It is possible to estimate a traveling direction that increases and a lateral direction that intersects the traveling direction perpendicularly. Thereby, the correlation coefficient between the acceleration calculated using the LRF 18 and the acceleration acquired using the acceleration sensor 142 may be calculated.

Here, even if the number of humans in the detection area E in the present embodiment is L and the number of acceleration data transmitted from the mobile terminal 12 is M, the central controller 10 Each person having 12 can be identified as each person in the detection region E. That is, since management can be performed using human IDs and sensor IDs, the ID table data can be configured simply when managing a large number of humans. Then, by simplifying the configuration of the ID table data, it is possible to shorten the processing time such as the search processing for the ID table data. (2)
Referring to FIG. 9B, “human ID” is recorded in the short left column in the correlation table, and columns 001 to L indicating the human ID are provided on the right side thereof. On the other hand, “sensor ID” is recorded in the first line in the correlation table, and columns 001 to M indicating the sensor ID are provided in the second line. In each sensor ID column, an X-axis column and a Y-axis column are provided correspondingly.

  As described above, the human ID assigned to the human A is 001, and the sensor ID assigned to the acceleration sensor 142 included in the mobile terminal 12a is 001. Furthermore, the human ID assigned to the human B is set to 003, and the sensor ID assigned to the acceleration sensor 142 included in the mobile terminal 12b is set to 002.

  Then, the two correlation coefficients described above are recorded in the column of the X axis and the Y axis of the sensor ID: 001 corresponding to the human ID: 001. That is, 0.96 is recorded in the column corresponding to the X axis, and 0.88 is recorded in the column corresponding to the Y axis. Further, the above-described two correlation coefficients are also recorded in the X-axis and Y-axis fields of the sensor ID: 001 corresponding to the human ID: 003. That is, 0.50 is recorded in the column corresponding to the X axis, and 0.13 is recorded in the column corresponding to the Y axis.

  Further, although human acceleration corresponding to the human ID: 002 is not described in detail, 0.65 is recorded in the X-axis column and 0.44 is recorded in the Y-axis column. The human acceleration corresponding to the human ID: L is not described in detail, but −0.33 is recorded in the X-axis column and 0.51 is recorded in the Y-axis column. Further, although the acceleration sensor 142 corresponding to the sensor ID: 002 and the sensor ID: M is not described in detail, the correlation coefficient with each human ID is calculated and recorded in the same manner.

  Then, for each sensor ID, central controller 10 specifies a human ID for which the correlation coefficient between the X axis and the Y axis is equal to or greater than a predetermined value and the sum of the two correlation coefficients is the maximum value. That is, human ID: 001 is specified for sensor ID: 001, human ID: 003 is specified for sensor ID: 002, and human ID: 002 is specified for sensor ID: M, that is, identified. The identified result is recorded in an ID table which will be described later and stored in the memory 22 of the central controller 10.

  Thus, if each of the plurality of persons in the detection area E has the portable terminal 12, it can be identified.

  Here, the mobile terminal 12 is not only the acceleration data output from the acceleration sensor 142 but also the device address data necessary for the short-range wireless communication, the data recorded in the mobile terminal 12 that can be disclosed, and the data can be disclosed. Together with data (restricted data) indicating the public data disclosure range. Then, the central control device 10 records each data transmitted from each mobile terminal 12 in the sensor table data.

  Referring to FIG. 10, the sensor table data includes a “sensor ID” column, a “device address” column, a “public range” column, and a “public data” column. In the “sensor ID” column, all sensor IDs assigned to the respective mobile terminals 12 that output acceleration data are recorded. For example, 001 to M are recorded in the same manner as the sensor ID row in the correlation table. In the “device address” column, a device address indicated by an alphanumeric string of 12 digits is recorded corresponding to the sensor ID to be recorded. For example, the device address is expressed in hexadecimal and is represented by “00: 00: 00: 00: 01” or the like.

  Further, in the column “publication range”, data indicating a range in which data recorded in the mobile terminal 12 is disclosed is recorded. For example, “family”, “address designation”, “all”, and “non- Public ". “Family” is group information estimated from position history information of each person. When the group information is set for the person in the detection area E from the position history information and “family” is set as the disclosure range, the mobile terminal 12 held by the person who has the same “family” is assigned. On the other hand, it means that the device address, public data, etc. are disclosed. Note that “friend” or the like may be set as the group information, or “same” or the like indicating that the same group may be used regardless of the group name.

  “Address designation” means that a device address set in another mobile terminal 12 is arbitrarily designated. The address to be designated is recorded together in the field where the address designation is recorded. “All” means that the public data is disclosed to other mobile terminals 12 or information transmission devices without limitation. “Non-public” means the opposite of “all”, and means that no public data is disclosed to other mobile terminals 12, robots 14, information transmission devices, or the like.

  In the “public data” column, “device address”, “name: CCC”, “hobby”, and the like are recorded. “Device address” means a device address set in itself. “Name: CCC” means the name of the person who owns the mobile terminal 12, and is not limited to CCC, but may be AAA, BBB, and the like. “Hobby” means a hobby of a person who possesses the mobile terminal 12 and includes, for example, home appliances, reading and listening to music. Specific contents such as “hobby” are recorded in advance on the mobile terminal 12 by a human, and the file name “hobby” is set and recorded in the mobile terminal 12.

  Next, an ID table for associating a human ID with a sensor ID and recording a disclosure range and disclosure data corresponding to the sensor ID will be described. Referring to FIG. 11, the ID table includes a “human ID” column, a “sensor ID” column, a “public range” column, and a “public data” column.

  As described above, human IDs from 001 to L are recorded in the column of “human ID”. In the “sensor ID” column, sensor IDs from 001 to M are recorded, and device addresses are recorded together. For example, “00: 00: 00: 00: 01” is recorded together with the sensor ID: 001.

  Note that the “sensor ID” column is not recorded so that the sensor IDs are permutated, unlike the “human ID” column. Further, since a person in the detection area E does not necessarily have the portable terminal 12, the sensor ID, the disclosure range, and the disclosure data may not be stored in association with each other.

  That is, the central control device 10 records the person and the mobile terminal 12 present in the detection area E in association with each other, and if personal information such as the name and hobby of the person possessed by the mobile terminal 12 is recorded, Information is also recorded. Thereby, the mobile management system 100 can also identify an individual by a method different from the method using the RFID tag.

  Next, the GUI for setting the disclosure range and the disclosure data by the mobile terminal 12 will be described in detail, and further, a service providing method using the information of the disclosure range and the disclosure data will be described.

  First, the GUI for setting the disclosure range and the disclosure data will be described. Referring to FIG. 12, a disclosure range setting screen is displayed on LCD 138 of portable terminal 12. This public range setting screen is provided with check boxes 200a-200d for determining the public range and public data displays 202a-202c for displaying public data corresponding to the respective check boxes 200a-200c. The contents displayed on the public data display 202a-202c may be directly input as a character string in response to a key operation on the key input device 134, or data stored in the memory 140 by menu selection. May be specified.

  The check box 200a corresponds to the group information described above, and corresponds to the group information of “family” in FIG. In addition, in the public data display 202a, data to be disclosed to the mobile terminal 12 possessed by a person set as “family” is displayed. For example, “device address” is displayed here. In addition, “device address” is displayed in the public data display 202a, and the device address set in the mobile terminal 12 that displays this GUI (for example, “00: 00: 00: 00: 00: 01”). Means that it is set as public data. If the check box 200a is checked, the contents displayed in the public data display 202a are released. In other words, the device address is disclosed to the portable terminal 12 possessed by a person set to the same “family”.

  The check box 200b corresponds to “address specification” in the “public range” column illustrated in FIG. If the device address to be specified is set, “00: 00: 00: 00: 00: 03” is added and displayed under the address specification as shown in FIG. Furthermore, since “name: AAA” is displayed in the public data display 202b, the name of the person who holds the portable terminal 12 that displays this GUI is “AAA”, and the name is set as public data. Means that If the check box 200b is checked, the contents displayed in the public data display 202b are released. That is, the name “AAA” is disclosed to the mobile terminal 12 in which the device address “00: 00: 00: 00: 03” is set.

  The check box 202c corresponds to “all”, and as described above, the public data is disclosed to other mobile terminals 12, the robot 14, and the information transmission device without limitation. In the public data display 202c, “hobby” is displayed. If the check box 202c is checked, the contents displayed in the public data display 202c are released. That is, the hobby of the person who possesses this portable terminal 12 is disclosed to external devices (other portable terminals 12, information transmission devices, robots 14, etc.) without limitation.

  The check box 202d corresponds to “unpublished” and means that data is not disclosed to the external device as described above. That is, if the check box 202d is checked, the mobile terminal 12 does not disclose data.

  Note that, at the time of factory shipment, the check box 202d is checked in the initial setting. Moreover, the number of public data is not limited to one, and may be a plurality of data.

  Next, a method for providing a service using information on the disclosure range and the disclosure data will be described. The service provided to the user (human) is a search service or an information providing service. That is, the central control device 10 can provide various services to the user (human) using the disclosure range and the disclosure data recorded in the ID table. First, in the search service, when a request to search for another person in the detection area E is made, the central control device 10 provides information indicating the current position of the person requested to the requesting mobile terminal 12. Send.

  For example, when an operation for searching for the current position of a person (search destination) whose name is “CCC” is performed by a person (requester) who possesses a certain mobile terminal 12, a person whose name is “CCC” is searched. A search request is transmitted from a mobile terminal 12 to the central control device 10. Then, the central control apparatus 10 searches for the character string “name: CCC” from the “public data” column in the ID table, and acquires information on the public range according to the search result. Next, since the acquired disclosure range is the address designation of the device address “00: 00: 00: 00: 00: 01”, the device address of a certain portable terminal 12 that has transmitted the search request is confirmed. At this time, if the device address of a certain mobile terminal 12 is “00: 00: 00: 00: 00: 01”, the central controller 10 sends a human name “CCC” to a certain mobile terminal 12. Data of a map of the place where the person is and an icon indicating the current position of the person are transmitted. Referring to FIG. 13A, an LCD 138 of a certain mobile terminal 12 displays a map of an environment where a person whose name is “CCC” is present, and an icon indicating the current position where the person is present. .

  In this way, the user can search for another person at an arbitrary place using the mobile terminal 12. In addition, the user restricts the disclosure range of the public data, so that an unrelated person does not know his / her whereabouts.

  If the device address of a certain portable terminal 12 is not “00: 00: 00: 00: 00: 01”, the central control device 10 stores image or character string data indicating that the information cannot be disclosed, or To the portable terminal 12.

  In the search service, it is also possible to guide a person to search to the person who requested the search. For example, when the central control device 10 receives a search request for assisting a search for a person whose name is “CCC” by the robot 14 from a certain mobile terminal 12, the central control apparatus 10 and the current position of the person whose name is “CCC” The current position of the person holding the mobile terminal 12 is transmitted to the robot 14. Furthermore, the central control apparatus 10 also transmits a guidance instruction for guiding a person whose name is “CCC” to a person who has a certain mobile terminal 12. When the robot 14 receives the information indicating the current positions of the two people and the guidance instruction, the robot 14 guides the person according to the guidance instruction. That is, a person whose name is “CCC” is guided by the robot 14 to a person who has a certain mobile terminal 12. That is, when searching for a lost child or the like, the user can bring the lost child to his / her own by using the robot 14.

  Thus, when a person who has the mobile terminal 12 searches for his / her family (lost child), he / she can find it using the mobile terminal 12 owned by the family.

  Next, an information providing service using the information transmission device will be described. A store in the detection area E is provided with an information transmitting device, and this information transmitting device can transmit information (for example, special sale information) related to the provided store.

  For example, the information transmission device transmits a public data acquisition request to the central control device 10. Then, the central control device 10 transmits (provides) the public data and the sensor ID whose public range is set to “all” in the ID table to the information transmission device in response to the received public data acquisition request. The information transmitting device searches the received public data for the name of the customer list recorded in itself and the character string related to the installed store, and based on the sensor ID corresponding to the search result, the sale information etc. It transmits to the portable terminal 12.

  Then, with reference to FIG. 13B, the special sale information is displayed on the LCD 138 of the portable terminal 12 to which the special sale information is transmitted. For example, home appliance sale information is transmitted from an information transmission device installed in an electrical store. In other words, the user can obtain necessary information by visiting a place of interest. Furthermore, the information transmission device can specify the mobile terminal 12 that can transmit information about the provided store by transmitting a public data acquisition request (publication request) to the central control device 10. As a result, the store clerk in which the information transmission device is provided can efficiently provide special sales information and the like.

  FIG. 14 is an illustrative view showing one example of a memory map 300 of the memory 22 of the central control apparatus 10 shown in FIG. As shown in FIG. 14, the memory 22 includes a program storage area 302 and a data storage area 304. The program storage area 302 stores a position history recording program 312, an acceleration sensor history recording program 314, a management program 316, a public data management program 318, and the like. However, the management program 316 includes a correlation coefficient calculation program 316a.

  The position history recording program 312 is a program that records the position information of each person in the detection area E at regular intervals. Further, the acceleration sensor history recording program 314 is a program that records each time acceleration data transmitted from each mobile terminal 12 is received.

  The management program 316 is a program for specifying and recording the portable terminal 12 possessed by each person in the detection area E. The correlation coefficient calculation program 316a, which is a subroutine of the management program 316, is a program for calculating a correlation coefficient between acceleration calculated from human position history information and acceleration output from the acceleration sensor 142. The public data management program 318 transmits each data recorded in the “public data” column constituting the ID table in response to a search request or an acquisition request with reference to the corresponding “public range” column. It is a program for.

  Although illustration is omitted, the program for operating the central controller 10 includes a program for establishing short-range wireless communication, a program for performing data communication via the network 50, and the like.

  In the data storage area 304, position history data 330, acceleration sensor history data 332, calculation table data 334, correlation table data 336, sensor table data 338, ID table data 340, and map data 342 are recorded.

  The position history data 330 is composed of data in which the position of each person existing in the detection region E by the LRF 18 is recorded at a certain period, and is used to calculate the acceleration of the person. The position history data 330 is updated by the position history recording program 312. The acceleration sensor history data 332 includes data in which acceleration data transmitted from the plurality of acceleration sensors 142 is recorded at regular intervals. The acceleration sensor history data 332 is updated by the acceleration sensor history recording program 314. Each data constituting the position history data 330 and the acceleration sensor history data 332 is associated with the time recorded in the memory 22 or the time acquired by the central control device 10.

  The calculation table data 334 is data of the calculation table shown in FIG. The correlation table data 336 is data of the correlation table shown in FIG. The calculation table data 334 and the correlation table data 336 are used when the correlation coefficient calculation program 316a is executed. The sensor table data 338 is data of the sensor table shown in FIG.

  The ID table data 340 is data of the ID table shown in FIG. 11, and is used when the contents recorded by executing the management program 316 are updated and the public data management program 318 is executed. The map data 342 is data indicating a map in the detection area E.

  Although not shown, the data storage area 304 is provided with a buffer for temporarily storing the results of various calculations and other counters and flags necessary for the operation of the central controller 10. .

  Specifically, the CPU 16 of the central control apparatus 10 executes a plurality of processes in parallel including the processes shown in FIGS.

  As shown in FIG. 15, when the CUP 16 of the central controller 10 executes the position history recording process, the detected time is recorded in step S1. That is, the time when the person in the detection area E is detected by the LRF 18 is recorded. Subsequently, in step S3, the coordinates of each detected person are calculated. That is, the X-axis coordinates and Y-axis coordinates of each person detected in the detection area E are calculated. Subsequently, in step S5, the calculated coordinates are recorded. That is, the X-axis coordinates and Y-axis coordinates calculated for each person are recorded. When the process in step S5 is completed, the time recorded in step S1 and the coordinates recorded in step S5 are stored in the memory 22 as history data constituting the position history data 330. Note that the processing time of steps S1-S5 is a fixed time (about 26 milliseconds), and the position history recording process is repeatedly executed in synchronization with the detection period (38.5 Hz) of the LRF 18.

  FIG. 16 is a flowchart of the acceleration sensor history recording process. As shown in FIG. 16, the CPU 16 of the central control apparatus 10 determines whether or not acceleration has been received in step S11. That is, it is determined whether or not the acceleration output from the acceleration sensor 142 is received from the mobile terminal 12. If “NO” in the step S11, that is, if no acceleration is received, the process of the step S11 is repeatedly executed. On the other hand, if “YES” in the step S11, that is, if an acceleration is received, the time received in the step S13 is recorded. That is, the time when the acceleration data transmitted by the mobile terminal 12 is received is recorded. In step S15, the received acceleration data is recorded. That is, the acceleration data transmitted by each mobile terminal 12 is recorded. When the processing in step S15 is completed, the time recorded in step S13 and the data of each acceleration recorded in step S15 are stored in the memory 22 as history data constituting the acceleration sensor history data 332.

  As shown in FIG. 17, when the CPU 16 of the central control device 10 detects a person in the detection area E and receives acceleration data from the acceleration sensor 142, the CPU 16 executes a management process. Determine whether. For example, it is determined whether or not an operation for ending the management process has been performed by the administrator. If “YES” in the step S 21, the management process is ended, and if “NO”, the data of the portable terminal 12 is recorded in a step S 23, and IDs are set for all of the human and the acceleration sensor 142. That is, the device address, the disclosure range, and the disclosure data transmitted by each mobile terminal 12 are recorded in the sensor table data 338. Then, a human ID is set for each person in the detection area E, and a sensor ID is set according to the received device address, public range, and number of public data. That is, by executing the processing of step S23, the number of columns and the number of rows in the correlation table shown in FIG. 9B are determined. Subsequently, in step S25, the variable A of the human ID is initialized. That is, the value of the variable A for designating each of the human IDs 001 to L is set to 0.

  Subsequently, in step S27, it is determined whether or not the variable A is the maximum value. That is, it is determined whether or not the value of the variable A matches L, which is the maximum value of the human ID. If “NO” in the step S27, that is, if the variable A is not the maximum value, a position history of the human ID: A within a predetermined time is acquired in a step S29. That is, the human history information corresponding to the sensor ID specified by the variable A is read from the position history data for 20 seconds. Subsequently, in step S31, the X-axis and Y-axis accelerations are calculated from the position history information. That is, the movement distance per second is calculated from the read history information, and the acceleration in the X-axis direction and the Y-axis direction is calculated by differentiating the calculated movement distance twice with respect to time. In addition, the CPU 16 that executes the process of step S31 functions as a first calculation unit.

  In step S33, the sensor ID variable B is initialized. That is, the value of the variable B for designating each of the sensor IDs: 001 to M is set to 0. Subsequently, in step S35, it is determined whether or not the variable B is the maximum value. That is, it is determined whether or not the variable B matches M, which is the maximum value of the sensor ID. If “NO” in the step S35, that is, if the variable B is not the maximum value, a correlation coefficient calculating process is executed in a step S37. Since the process of step S37 will be described later, a detailed description thereof is omitted here. Subsequently, in step S39, the variable B is incremented (B = B + 1), and the process returns to step S35. That is, in step S39, the variable B is incremented so that the variable B designates the next sensor ID.

  If “YES” in the step S35, that is, if the variable B is the maximum value, the variable A is incremented (A = A + 1) in a step S41, and the process returns to the step S27. That is, in step S41, the variable A is incremented so that the variable A designates the next human ID.

  If “YES” in the step S27, that is, if the variable A is the maximum value, the human ID and the sensor ID are associated with each other and recorded in the ID table based on the correlation coefficient in a step S43. That is, a combination of a human ID and a sensor ID, each of which has two correlation coefficients of 0.80 or more and the sum of the two correlation coefficients is the maximum value, is identified (identified) from the correlation table, and the ID table To record. For example, from the correlation table shown in FIG. 9A, a combination of human ID: 001 and sensor ID: 001, a combination of human ID: 002 and sensor ID: M, and a human ID: 003 and sensor ID: 002. A combination or the like is specified (identified). The identified result is recorded in the ID table shown in FIG.

  Subsequently, in step S45, the received disclosure range and disclosure data are recorded in the ID table data, and the process returns to step S21. For example, referring to the sensor table shown in FIG. 10, the disclosure range and the disclosure data corresponding to the sensor ID: 001 are read out, and the read disclosure range is read in the column corresponding to the sensor ID: 001 in the ID table shown in FIG. And public data.

  In addition, CPU16 which performs the process of step S43 functions as an identification means. Further, the CPU 16 that executes the processes of steps S43 and S45 functions as a processing means. Further, the CPU 16 that executes the processes of steps S43 and S45 and the memory 22 function as recording means.

  FIG. 18 is a flowchart of the correlation coefficient calculation process in step S37 shown in FIG. As shown in FIG. 18, when the CPU 16 of the central controller 10 starts the correlation coefficient calculation process, in step S61, the acceleration in the Xa-axis direction at the sensor ID: B and the acceleration in the X-axis direction at the human ID: A The correlation coefficient BxAx is calculated. For example, if the variable A and the variable B are “1”, the human ID is 001 and the sensor ID is 001. At this time, the CPU 16 reads the value of the acceleration sensor 142 corresponding to the sensor ID: 001 from the acceleration sensor history data 332. The correlation coefficient (0.96) calculated from the acceleration in the Xa-axis direction output from the acceleration sensor 142 and the acceleration in the X-axis direction of the human A calculated by the upper routine is the correlation coefficient BxAx. The correlation coefficient BxAx is recorded in the column of the X axis corresponding to the Xa axis in the calculation table shown in FIG.

  Subsequently, in step S63, a correlation coefficient ByAx between the acceleration in the Ya-axis direction at the sensor ID: B and the acceleration in the X-axis direction at the human ID: A is calculated. That is, as in step S61, a correlation coefficient (0.34) calculated from the acceleration in the Ya axis direction output from the acceleration sensor 142 and the acceleration in the X axis direction of the human A is the correlation coefficient ByAx. The correlation coefficient ByAx is recorded in the X-axis column corresponding to the Ya-axis in the calculation table.

  Subsequently, in step S65, a correlation coefficient BxAy between the acceleration in the Xa axis direction at the sensor ID: B and the acceleration in the Y axis direction at the human ID: A is calculated. That is, as in step S61, the correlation coefficient (0.39) calculated from the acceleration in the Xa-axis direction output from the acceleration sensor 142 and the acceleration in the X-axis direction of the human A is the correlation coefficient BxAy. The correlation coefficient BxAy is recorded in the column of the Y axis with respect to the Xa axis in the calculation table.

  Subsequently, in step S67, a correlation coefficient ByAy between the acceleration in the Ya-axis direction at the sensor ID: B and the acceleration in the Y-axis direction at the human ID: A is calculated. That is, as in step S61, a correlation coefficient (0.88) calculated from the acceleration in the Ya axis direction output from the acceleration sensor 142 and the acceleration in the Y axis direction of the human A is the correlation coefficient ByAy. The correlation coefficient ByAy is recorded in the column of the Y axis with respect to the Ya axis in the calculation table.

  Subsequently, in step S69, it is determined whether BxAx> ByAx and ByAy> BxAy. That is, it is determined whether or not the directions of the Xa axis and the Ya axis output from the acceleration sensor 142 coincide with the directions of the X axis and the Y axis in the detection region E. If “YES” in the step S69, that is, if the axial directions coincide with each other, the correlation coefficient (X, Y) of the human ID: A with respect to the sensor ID: B is set to (BxAx, ByAy) in a step S71. After the relation number calculation process is completed, the process returns to the management process. For example, in step S71, the correlation coefficient between the acceleration sensor 142 on the X axis and the human A is 0.96 (BxAx), and the correlation coefficient between the acceleration sensor 142 on the Y axis and the human A is the correlation coefficient 0. 88 (ByAy). That is, as in the correlation table shown in FIG. 9B, 0.96 is recorded in the X-axis column corresponding to human ID: 001 and sensor ID 001, and 0.88 is recorded in the Y-axis column. The

  If “NO” in the step S69, that is, if the axial directions do not match, the correlation coefficient (X, Y) of the human ID: A with respect to the sensor ID: B is set to (BxAy, ByAx) in a step S73. Then, the correlation coefficient calculation process ends. That is, in step S73, the correlation coefficient between the acceleration sensor 142 on the X axis and the human is BxAy, and the correlation coefficient between the acceleration sensor 142 on the Y axis and the human is ByAx. That is, the correlation coefficient is recorded in the X-axis column and Y-axis column corresponding to the human ID: 001 and sensor ID 001 in the correlation table.

  In addition, CPU16 which performs the process of step S61-S73 functions as a 2nd calculation means. Further, the CPU 16 that executes the processes of steps S61, S65, and S69-S73 also functions as a first direction calculating unit, and the CPU 16 that executes the processes of steps S63 and S67-S73 also functions as a second direction calculating unit.

  FIG. 19 is a flowchart of the public data management process. As shown in FIG. 19, the CPU 16 of the central controller 10 determines whether or not there is a search request in step S101. That is, it is determined whether a search request transmitted by the mobile terminal 12 has been received. The CPU 16 that executes the process of step S101 functions as a search request receiving unit. If “NO” in the step S101, that is, if there is no search request, the process proceeds to a step S111. On the other hand, if “YES” in the step S101, that is, if there is a search request, it is determined whether or not the requesting source is restricted in a step S103. That is, in step S103, the searchable portable terminal 12 is searched from the ID table, and further, the data in the column “publication range” is read based on the search result. Then, it is determined whether or not the device address or group information of the requesting mobile terminal 12 is included in the disclosure range.

  If “YES” in the step S103, that is, if the request source is restricted from being disclosed, the process proceeds to a step S111. On the other hand, if “NO” in the step S103, that is, if the request source is not restricted from being disclosed, it is determined whether or not there is an assistance request from the robot 14 in a step S105. That is, it is determined whether an assistance request from the robot 14 is received together with the search request. If “YES” in the step S105, that is, if there is an assistance request from the robot 14, a search instruction is transmitted to the robot 14 in a step S107, and the process returns to the step S101. That is, a search instruction for guiding the search target person to the requesting person is transmitted to the robot 14.

  On the other hand, if “NO” in the step S105, that is, if the assistance request by the robot 14 is not made, the search destination current position and map data are transmitted to the requesting source in a step S109, and the process returns to the step S101. That is, the current position of the search destination is read from the position history data 330, and a map corresponding to the current position is read from the map data 342 and transmitted to the requesting mobile terminal 12. The CPU 16 that executes the process of step S109 functions as a position transmission unit.

  In step S111, it is determined whether there is a public data acquisition request. That is, it is determined whether or not there is a request for obtaining public data recorded in the ID table. Further, the CPU 16 that executes the process of step S111 functions as an acquisition request receiving unit. If “NO” in the step S111, that is, if there is no acquisition request, the process returns to the step S101. On the other hand, if “YES” in the step S111, that is, if there is an acquisition request, it is determined whether or not the request source is restricted from being released in a step S113. Note that the processing in step S113 is the same as that in step S103, and thus detailed description thereof is omitted.

  If “YES” in the step S113, that is, if restricted, the process returns to the step S101. On the other hand, if “NO” in the step S113, that is, if not restricted, the public data is transmitted in a step S115, and the process returns to the step S101. For example, when an acquisition request transmitted by the information transmission device is received, public data corresponding to a column in which “all” or the like is recorded in the “public range” column of the ID table is transmitted to the information transmission device. The CPU 16 that executes the process of step S115 functions as a public data transmission unit.

  According to this embodiment, the mobile management system 100 includes a central control device 10 and a mobile terminal 12. The central controller 10 has an LRF 18, and the mobile terminal 12 has an acceleration sensor 142. The central control device 10 and the mobile terminal 12 can establish Bluetooth short-range wireless communication, and the mobile terminal 12 transmits the acceleration output from the acceleration sensor 142 to the central control device 10. Further, the central controller 10 continuously acquires the positions of the persons A and B in the detection area E by the LRF 18 and stores them as position history data 330. From the position history data 330, the accelerations of the humans A and B can be calculated. The central control device 10 calculates a correlation coefficient with the acceleration output from the acceleration sensor 142. The central control device 10 The moving person is identified by recording the ID and the sensor ID in association with each other.

  As a result, each person in the detection area E is identified and managed using acceleration. Then, by recording the public data and the public range data transmitted from the mobile terminal 12 in the ID table, a search service, an information providing service, and the like can be provided.

  In the present embodiment, the LRF 18 is used, but the human position history information may be acquired by using an ultrasonic distance sensor, a millimeter wave radar, or the like instead of the LRF 18. Further, the short-range wireless communication format is not limited to the Bluetooth format, but may be other wireless LAN standards such as the ZigBee (registered trademark) format and the Wi-Fi (registered trademark) format. The mobile terminal 12 may be a small mobile terminal provided with a ZigBee module with an acceleration sensor, a mobile phone provided with an acceleration sensor 142, a mobile music player, or the like, or a mobile game machine to which the acceleration sensor 142 can be retrofitted. Also good. Further, since the LRF 18 can detect not only the position of a human but also the position of a moving body such as the robot 14, the robot 14 may carry the mobile terminal 12.

  When the number of humans detected in the detection area E or the number of received acceleration data changes, only the difference in change may be recalculated in the management process. That is, specifically, in the second and subsequent processes in the management process shown in FIG. 17, a process for monitoring changes in the number of persons or the number of data of the acceleration sensor may be added before step S21. In the processing after step S23, only the difference is recalculated.

DESCRIPTION OF SYMBOLS 10 ... Central control apparatus 12 ... Portable terminal 14 ... Robot 16 ... CPU
18a, 18b ... LRF
DESCRIPTION OF SYMBOLS 20 ... Near field communication apparatus 22 ... Memory 24 ... Communication LAN board 26 ... Wireless communication apparatus 50 ... Network 100 ... Management system 130 ... CPU
138 ... LCD
142 ... acceleration sensor 146 ... short-range wireless communication device

Claims (5)

An acceleration sensor provided on the first moving body for detecting the first acceleration;
Position detecting means for detecting the position of the second moving body;
First calculation means for calculating a second acceleration based on a history of positions detected by the position detection means;
Second calculating means for calculating a correlation coefficient between the first acceleration and the second acceleration;
When the correlation coefficient is equal to or greater than a predetermined value, an identification unit that identifies the first moving body and the second moving body as the same moving body, and a process that executes a predetermined process in accordance with the identification by the identification unit A mobile management system comprising means.   The mobile object management system according to claim 1, wherein the predetermined processing means includes a recording means for recording the first ID set in the acceleration sensor and the second ID set in the second mobile object in association with each other. Each of the first acceleration and the second acceleration is composed of acceleration in a first direction horizontal to the ground and acceleration in a second direction perpendicular to the first direction and horizontal to the ground,
The second calculation means includes first direction calculation means for calculating a correlation coefficient of acceleration in the first direction and second direction calculation means for calculating acceleration in the second direction,
The identification means has two correlation coefficients calculated by the first direction calculation means and the second direction calculation means that are not less than a predetermined value, and the sum of the two correlation coefficients is a maximum value. The mobile body management system according to claim 1 or 2, wherein the first mobile body and the second mobile body are identified as the same mobile body. Receiving means for receiving a first acceleration transmitted by an acceleration sensor provided in the first moving body;
Position detecting means for detecting the position of the second moving body;
First calculation means for calculating a second acceleration based on a history of positions detected by the position detection means;
Second calculating means for calculating a correlation coefficient between the first acceleration and the second acceleration;
When the correlation coefficient is equal to or greater than a predetermined value, an identification unit that identifies the first moving body and the second moving body as the same moving body, and a process that executes a predetermined process in accordance with the identification by the identification unit A mobile unit management device comprising means. A processor of a management device comprising: receiving means for receiving a first acceleration transmitted by an acceleration sensor provided in the first moving body; and position detecting means for detecting the position of the second moving body,
First calculation means for calculating a second acceleration based on a history of positions detected by the position detection means;
Second calculating means for calculating a correlation coefficient between the first acceleration and the second acceleration;
When the correlation coefficient is equal to or greater than a predetermined value, an identification unit that identifies the first moving body and the second moving body as the same moving body, and a process that executes a predetermined process in accordance with the identification by the identification unit A mobile management program that functions as a means.

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