KR102668771B1 - System for estimating location using beacon based on big data and method thereof - Google Patents

Abstract

A location estimation system and method using big data-based beacons are provided. A location estimation system using a big data-based beacon according to an embodiment of the present invention includes a mask generator that generates a reception strength mask based on pre-measurement data on the distance for each reception strength of the measurement beacon, and a mask generator that receives a signal from the user beacon. A reception strength collection unit that collects the reception strength for the user beacon from a plurality of receivers, and a reception strength mask is applied to the positions of the plurality of receivers based on the reception strength collected from the plurality of receivers, and distribution of the reception strength mask by location is provided. It includes a location calculation unit that calculates the location of the user beacon according to the value.

Description

Location estimation system and method using big data-based beacons {System for estimating location using beacon based on big data and method thereof}

The present invention relates to a location estimation system and method using a big data-based beacon, and in particular, to estimate the location of a beacon based on the reception strength from a plurality of receivers located near the beacon based on pre-measured big data. This relates to a location estimation system and method using big data-based beacons.

Recently, as the demand for a safe living environment for those in need of protection, such as infants, children, and the elderly, has increased, various types of devices utilizing ICT technology are being released. These devices are mainly equipped with functions that can check the user's information and location. Here, the accuracy of the indoor and outdoor location estimation function is very important. To estimate the location of these devices, most Bluetooth-based beacon products are commercially available, and location estimation methods using trilateration are widely used.

This trilateration method uses triangular geometry to find the relative position of an object. It uses two or more reference points and the distance between the object and each reference point to determine the location of the target. At this time, at least three reference points are required to accurately and uniquely determine the relative position.

However, since the location estimation method using trilateration may not form an intersection or may generate a large error rate depending on the received signal strength indicator (RSSI), research to increase accuracy is actively underway. Therefore, there is a need for a location estimation method that replaces the trilateration method while using beacons.

KR 10-2022-0086114 A

In order to solve the problems of the prior art as described above, an embodiment of the present invention can accurately estimate the location of a beacon based on the reception strength from a plurality of receivers located near the beacon based on pre-measured big data. We would like to provide a location estimation system and method using big data-based beacons.

According to one aspect of the present invention for solving the above problem, there is provided a mask generator that generates a reception intensity mask based on pre-measurement data on the distance for each reception intensity of the measuring beacon; a reception strength collection unit that collects reception strength for the user beacon from a plurality of receivers that receive signals from the user beacon; and a location calculation unit that applies the reception strength mask to the positions of the plurality of receivers based on the reception strengths collected from the plurality of receivers and calculates the location of the user beacon according to the distribution value for each location of the reception strength mask. A location estimation system using big data-based beacons including ; is provided.

In one embodiment, the location estimation system using the big data-based beacon includes user beacon information in which information related to the user beacon is stored; Receiver information in which information related to the plurality of receivers is stored; And it may further include a database including reception strength mask information in which the reception strength mask information generated by the mask generator is stored.

In one embodiment, the receiver may be a portable terminal equipped with a BLE scan function.

In one embodiment, the mask generator collects the reception strength measured by distance from the measurement beacon, converts the reception strength measured by distance into a distance by reception strength, and measures the reception strength by distance according to the converted reception strength. The reception strength mask can be calculated by substituting the number of measurements.

In one embodiment, the mask generator may collect the reception intensity within a radius by having the measurement receiver protrude in place for a certain period of time at a designated location spaced a certain distance away from the measurement beacon.

In one embodiment, the mask generator may generate the reception strength mask for a range of -43dbm to -84dbm.

In one embodiment, the mask generator may collect reception strength measured at a distance of 0.5 m to 10 m at 0.5 m intervals from the measurement beacon.

In one embodiment, the location estimation system using the big data-based beacon may further include a beacon identification unit that identifies the user beacon by comparing the information on the user beacon received from the plurality of receivers with the user beacon information in the database. You can.

In one embodiment, the location calculation unit calculates the location of each of the plurality of receivers based on the locations received from the plurality of receivers, applies the reception strength mask to each calculated location of each receiver, and applies the reception strength mask to the calculated location of each receiver. The location where the distribution value of the intensity mask is largest for each location can be calculated as the location of the user beacon.

In one embodiment, the location calculation unit may calculate the location of the user beacon only when the number of the plurality of receivers for the user beacon is greater than or equal to a first threshold.

In one embodiment, the location estimation system using the big data-based beacon may further include an update unit that updates the reception strength mask based on the reception strength collected from the plurality of receivers used to estimate the position of the user beacon. there is.

In one embodiment, when the number of the plurality of receivers is greater than the second threshold value greater than the first threshold value, the update unit receives the information based on information on receivers located within a certain distance from the estimated location of the user beacon. The intensity mask can be updated.

According to another aspect of the present invention, generating a reception strength mask based on pre-measurement data on the distance for each reception strength of the measuring beacon; Collecting reception strength for the user beacon from a plurality of receivers that receive signals from the user beacon; And applying the reception strength mask to the positions of the plurality of receivers based on the reception strengths collected from the plurality of receivers and calculating the position of the user beacon according to the distribution value for each position of the reception strength mask. A location estimation method using big data-based beacons is provided.

In one embodiment, the location estimation method using the big data-based beacon includes user beacon information in which information related to the user beacon is stored; Receiver information in which information related to the plurality of receivers is stored; And it may further include the step of storing reception strength mask information in a database, where the reception strength mask information generated by the mask generator is stored.

In one embodiment, the receiver may be a portable terminal equipped with a BLE scan function.

In one embodiment, the generating step includes collecting reception strength measured by distance from the measurement beacon; Converting the reception intensity measured for each distance into a distance for each reception intensity; And it may include calculating the reception strength mask by substituting the number of measurements for each distance according to the converted reception strength.

In one embodiment, in the step of collecting from the measurement beacon, the measurement receiver may collect the reception strength within the radius by protruding in place for a certain period of time at a designated location spaced a certain distance away from the measurement beacon.

In one embodiment, the generating step may generate the reception strength mask for a range of -43dbm to -84dbm.

In one embodiment, the step of collecting from the measurement beacon may collect reception strength measured at a distance of 0.5 m to 10 m at 0.5 m intervals from the measurement beacon.

In one embodiment, the location estimation method using the big data-based beacon may further include comparing information on the user beacon received from the plurality of receivers with user beacon information in the database to identify the corresponding user beacon. there is.

In one embodiment, the calculating step includes calculating the location of each of the plurality of receivers based on the locations received from the plurality of receivers; applying the reception strength mask to each calculated location of each receiver; And it may include calculating the location where the distribution value for each location of the reception strength mask is greatest as the location of the user beacon.

In one embodiment, the calculating step may calculate the location of the user beacon only when the number of the plurality of receivers for the user beacon is greater than or equal to a first threshold.

In one embodiment, the location estimation method using the big data-based beacon may further include updating the reception strength mask based on reception strength collected from the plurality of receivers used to estimate the position of the user beacon. there is.

In one embodiment, the updating step is performed based on information on receivers located within a certain distance from the estimated location of the user beacon when the number of the plurality of receivers is greater than the second threshold than the first threshold. The reception strength mask can be updated.

According to another aspect of the present invention, a computer-readable recording medium on which a program for performing the above-described method is recorded is provided.

The location estimation system and method using a big data-based beacon according to an embodiment of the present invention estimates the location based on the reception strength from a plurality of receivers located near the beacon based on pre-measured big data. Since the location can be accurately estimated, the reliability of the estimation results can be improved.

In addition, the location estimation system and method using a big data-based beacon according to an embodiment of the present invention estimates the location of the beacon based on information about the beacon signal transmitted from the beacon, thereby Convenience of use can be improved because users who are not good at operating can accurately calculate their location just by carrying a beacon.

In addition, the location estimation system and method using a big data-based beacon according to an embodiment of the present invention estimates the location based on the reception strength from a plurality of receivers that are adjacent to the beacon and know the exact location, Since the error range can be reduced, location accuracy can be improved.

In addition, the location estimation system and method using a big data-based beacon according to an embodiment of the present invention can reduce the amount of calculation for location estimation by estimating the location in a simple manner using a pre-generated reception strength mask. Therefore, system efficiency can be improved.

In addition, the location estimation system and method using a big data-based beacon according to an embodiment of the present invention improves the accuracy of the reception power mask by updating the information collected from a plurality of receivers used for location estimation to the reception power mask. This allows the positioning accuracy to be further improved.

Figure 1 is a configuration diagram of a location estimation system using big data-based beacons according to an embodiment of the present invention.
Figure 2 is a block diagram of a location estimation system using big data-based beacons according to an embodiment of the present invention.
Figure 3 is a diagram illustrating an example of generating a reception strength mask of a location estimation system using a big data-based beacon according to an embodiment of the present invention. (a) is a table showing distribution values according to distance by reception strength, ( b) Coordinates of the reception intensity mask, (c) is a diagram showing the reception intensity mask.
Figure 4 is a schematic diagram of location estimation of a location estimation system using big data-based beacons according to an embodiment of the present invention.
Figure 5 is an example of location estimation of a location estimation system using a big data-based beacon according to an embodiment of the present invention, (a) an example of three receivers, (b) an example of four receivers, (c) 3 Another example of four receivers, (d) is a diagram of another example of four receivers.
Figure 6 is a flowchart of a location estimation method using a big data-based beacon according to an embodiment of the present invention.
Figure 7 is a flowchart of the reception strength mask generation process of the location estimation method using big data-based beacons according to an embodiment of the present invention.
Figure 8 is a flowchart of the location calculation process of the location estimation method using big data-based beacons according to an embodiment of the present invention.

Hereinafter, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. The present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts not related to the description are omitted, and identical or similar components are given the same reference numerals throughout the specification.

Hereinafter, a location estimation system using a big data-based beacon according to an embodiment of the present invention will be described in more detail with reference to the drawings. Figure 1 is a configuration diagram of a location estimation system using big data-based beacons according to an embodiment of the present invention.

Referring to FIG. 1, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention may be configured to interwork with the receivers 12 to 25 and the guardian terminal 30.

The location estimation system 100 using big data-based beacons is a system for estimating the location of a beacon by introducing a new location estimation algorithm to improve the problems of the trilateration method. The location estimation formula is approximated through a countless number of samples. It is a system that utilizes the concept of the Monte Carlo method to calculate . In other words, the location estimation system 100 using big data-based beacons distributes the beacons for which the location is to be estimated using data on the reception strength measured at existing distances in locations corresponding to the reception strength measured at multiple locations. By recording, the most frequent location can be estimated as the location of the beacon. At this time, the location estimation system 100 using a big data-based beacon can receive a signal transmitted from the user beacon 10 through the receivers 21 to 25.

The user beacon 10 is an object whose location is to be estimated in the location estimation system 100 using a big data-based beacon, and can transmit its identification ID on a regular time basis. This user beacon 10 may be a device carried by the person whose location has been requested by the guardian terminal 30. For example, the user beacon 10 may be a wearable type device that can be worn by a person requiring protective care, such as an infant or an elderly person. At this time, the user beacon 10 may be in the form of a necklace or with a hook attached for easy portability.

The receivers 21 to 25 can receive signals transmitted from the user beacon 10. Here, these receivers 21 to 25 may be electronic devices installed with a BLE (Bluetooth Low Energy) scanner application. That is, the receivers 21 to 25 may be portable terminals equipped with a BLE scan function. For example, the receivers 21 to 25 may be portable electromagnetic devices such as smartphones, notepads, or laptops. In the drawing, the number of receivers 21 to 25 is exemplary, but the number is not particularly limited, and the number is not limited as long as the BLE scanner application is installed on the electronic device.

The guardian terminal 30 is a guardian terminal of a person wearing the user beacon 10, and can request location estimation of the user beacon 10 from the location estimation system 100 using a big data-based beacon. For example, the guardian terminal 30 may be a portable electronic device such as a smartphone, note pad, or laptop.

In this way, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention estimates the location based on the reception strength from a plurality of receivers located near the beacon based on pre-measured big data. By doing so, the location of the beacon can be accurately estimated, thereby improving the reliability of the estimation result.

Figure 2 is a block diagram of a location estimation system using big data-based beacons according to an embodiment of the present invention.

Referring to FIG. 2, the location estimation system 100 using a big data-based beacon may include a communication unit 110, a database 120, and a control unit 130.

The communication unit 110 can perform long-distance communication with a plurality of receivers 12 to 25 and the guardian terminal 30 through a wired or wireless communication network. For example, the communication unit 110 may communicate with a plurality of receivers 12 to 25 and the guardian terminal 30 through 4G communication or 5G communication.

The database 120 can store overall information necessary for the operation of the location estimation system 100 using a big data-based beacon. This database 120 may include user beacon information 122, receiver information 124, reception strength mask information 126, and guardian information 128.

The user beacon information 122 may store information related to the user beacon 10. For example, the user beacon information 122 may include the identification ID and user information of the user beacon 10. Here, the user information may include personal information and guardian information of the person registered to possess the user beacon 10. At this time, the user beacon information 122 may be generated when the user beacon 10 is issued and a location estimation service is requested.

The receiver information 124 may store information related to a plurality of receivers 21 to 25 on which the BLE scanner application is installed. As an example, the receiver information 124 may include the owner name and phone number of the receivers 21 to 25. At this time, the receiver information 124 may be generated by receiving from the receivers 21 to 25 when the BLE scanner application is downloaded and installed.

The reception strength mask information 126 may store reception strength mask information previously generated by the mask generator 131. For example, the reception strength mask information 126 may be generated for each reception strength. Here, the reception strength mask information 126 may include reception distribution according to distance.

The guardian information 128 may store information about the guardian who requested the location estimation service of the user beacon 10. For example, the guardian information 128 may include the name and phone number of the owner of the guardian terminal 30.

The control unit 130 is communicatively connected to the communication unit 110 and the database 120 and can control the overall operation of the location estimation system 100 using a big data-based beacon. This control unit 130 may include a mask generator 131, a reception intensity collection unit 132, a beacon identification unit 133, a location calculation unit 134, and an update unit 135.

The mask generator 131 may generate a reception strength mask based on prior measurement data on the distance for each reception strength of a measurement beacon (not shown). At this time, the mask generator 131 collects data by collecting reception intensity samples at intervals of 0.5 m while the measuring beacon (not shown) is fixed, and counts the number of data by distance for each reception strength to create a reception strength mask. can be created.

More specifically, the mask generator 131 may collect reception strength measured by distance from a measurement beacon (not shown). At this time, the mask generator 131 can collect the reception intensity within the radius by having the measurement receiver (not shown) move in place for a certain period of time at a designated location that is a certain distance away from the measurement beacon (not shown). For example, the mask generator 131 may collect the reception strength measured at a distance of 0.5 m to 10 m at 0.5 m intervals from a measurement beacon (not shown) through a measurement receiver (not shown).

The mask generator 131 may convert the reception intensity measured for each distance into a distance for each reception intensity. For example, the mask generator 131 may calculate a reception strength mask by substituting the number of measurements for each distance according to the converted reception strength. At this time, the mask generator 131 may generate a reception strength mask for a reception strength range of -43 dbm to -84 dbm. That is, the mask generator 131 can delete data outside the reception strength range of -43 dbm to -84 dbm from the collected reception strength data.

Figure 3 is a diagram illustrating an example of generating a reception strength mask of a location estimation system using a big data-based beacon according to an embodiment of the present invention. (a) is a table showing distribution values according to distance by reception strength, ( b) Coordinates of the reception intensity mask, (c) is a diagram showing the reception intensity mask.

Referring to Figure 3, as shown in (a), in order to build a beacon database, the reception strength measured by distance was converted to a distance by reception strength, and a distribution value according to the distance by reception strength was calculated. Here, the measurement beacon (not shown) is a sample manufactured identically to the user beacon 10, and the measurement receiver (not shown) used the Galaxy S21+ model with the BLE scanner application installed, and the total reception intensity at each distance was used. Samples were collected. At this time, the measurement receiver (not shown) measured the reception strength using a BLE scanner application.

Here, a measuring beacon (not shown) was fixed at the origin (0,0) and measurements were made up to 10 m at intervals of 0.5 m along the X-axis. For data measurement, the reception strength within the radius was collected by linking the BLE scanner application of the measurement beacon (not shown) and the measurement receiver (not shown) at a designated location and then rotating in place for 5 minutes. 151 pieces of data were collected for each distance. did.

After organizing the collected specific reception strengths into the number of measurements for each distance, the distance value for each coordinate for the measurement location was calculated to create a mask in the form of coordinates for the specific reception strength. By measuring the reception strength at distances of 0.5m to 10m, the reception strength was measured in the range of -43dBm to -84dBm. Excluding the overlapping reception strengths in the entire section, the number of reception strengths for each distance as shown in (a) of Figure 3 was calculated.

As in (b), the distance value for each coordinate can be expressed at 0.5m intervals from 0.5m to 10m centered on the origin. Although not shown in the drawing, each circle may be subdivided into a grid of unit sizes.

As in (c), the signal distribution of a specific reception strength is shown by substituting the number of measurements for each distance for a specific reception strength into the distance value for each coordinate. (c) shows the distribution for reception intensity -65 dmb in (a). Here, the number written in the distance value box for each coordinate refers to the corresponding distance for a specific reception intensity, so substituting the number of measurements belonging to the distance creates a reception intensity mask for the specific reception intensity. At this time, as described above, each circle is subdivided into grids, so when multiple reception intensity masks are overlapped, the numbers can be added up for each unit grid.

Referring again to FIG. 2, the reception strength collection unit 132 may collect the reception strength for the user beacon 10. At this time, the reception strength collection unit 132 may collect reception strength from a plurality of receivers 21 to 25 that receive signals from the user beacon 10. Here, when a plurality of receivers 21 to 25 detect a nearby user beacon 10 by the installed BLE scanner application, the signal received from the user beacon 10 is used to determine a location estimation system using a big data-based beacon. 100). At this time, the plurality of receivers 21 to 25 may transmit the identification ID and reception strength of the user beacon 10 to the location estimation system 100 using a big data-based beacon.

The beacon identification unit 133 may identify the user beacon 10 through information on the user beacon 10 received from the plurality of receivers 21 to 25. At this time, the beacon identification unit 133 may identify the corresponding user beacon 10 by comparing the information on the received user beacon 10 with the user beacon information 122 in the database 120. That is, the beacon identification unit 133 can identify personal information and guardian information of the corresponding user beacon 10.

The location calculation unit 134 may apply a reception strength mask to the positions of the plurality of receivers 21 to 25 based on the reception strength collected from the plurality of receivers 21 to 25. At this time, the location calculation unit 134 may calculate the location of the user beacon 10 according to the location distribution value of the reception strength mask.

More specifically, the location calculation unit 134 may calculate the location of each of the plurality of receivers 21 to 25 based on the positions received from the plurality of receivers 21 to 25. Here, the plurality of receivers 21 to 25 may transmit their own locations together when transmitting information on the user beacon 10 to the location estimation system 100 using a big data-based beacon. At this time, the plurality of receivers 21 to 25 may transmit location information according to the built-in GPS module.

The position calculation unit 134 may apply a reception strength mask to each calculated position of each receiver 21 to 25. Here, the location calculation unit 134 may apply a reception strength mask corresponding to the reception strength received by each receiver (21 to 25) to the positions of the corresponding receivers (21 to 25). At this time, the location calculation unit 134 may calculate the location where the distribution value of the reception strength mask for each location is largest as the location of the user beacon 10.

In this way, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention can reduce the amount of calculation for location estimation by estimating the location in a simple manner using a pre-generated reception strength mask. Therefore, system efficiency can be improved.

Figure 4 is a schematic diagram of location estimation of a location estimation system using big data-based beacons according to an embodiment of the present invention.

Referring to FIG. 4, the location calculation unit 134 may generate a mask map by overlapping reception strength masks corresponding to reception strengths measured at four locations for each location. In the drawing, the reception strength masks corresponding to each reception strength are indicated by color, and the distribution number of positions where the reception strength masks overlap is expressed by summing the distribution numbers of the measurement data for each reception strength and separation distance for each reception strength mask. In addition, visibility was increased by linking the color density according to the distribution number.

The receiver closest to the beacon (yellow) has a reception strength of -58dbm, the next closest receiver (blue) has a reception strength of -64dbm, and the receiver in the next position (red) has a reception strength of -77dbm, the highest. The receiver located far away (gray) has a reception strength of -84dbm. If a reception strength mask corresponding to the reception strength at each location is arranged, it can be seen that the distribution number due to the overlap of the reception strength masks is largest near the actual beacon location. At this time, the reception strength of each receiver means the reception strength received at the same time in the location estimation system 100 using a big data-based beacon.

As such, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention estimates the location based on the reception strength from a plurality of receivers that are adjacent to the beacon and know the exact location, Since the position error range can be reduced, position accuracy can be improved.

Figure 5 is an example of location estimation of a location estimation system using a big data-based beacon according to an embodiment of the present invention, (a) an example of three receivers, (b) an example of four receivers, (c) 3 Another example of four receivers, (d) is a diagram of another example of four receivers.

Referring to FIG. 5, (a) and (c) show examples of positions calculated through three receivers, and (b) and (d) show examples of positions calculated through four receivers. Here, (a) and (b) are results for the same location. At this time, the actual location of the beacon (green) and the estimated beacon location (red) were found to be more accurate in the case of four receivers. (c) and (d) are results for another same location. Similar to what was described above, the actual location of the beacon (green) and the estimated beacon location (red) were found to be more accurate in the case of four receivers.

At this time, the location calculation unit 134 can calculate the location of the user beacon 10 only when the number of the plurality of receivers 21 to 25 with respect to the user beacon 10 is greater than or equal to the first threshold. Here, when the number of receivers 21 to 25 is too small, location estimation based on the reception strength from the receivers 21 to 25 may be inaccurate, so the location calculation unit 134 is configured to provide a sufficient number of receivers 21 to 25. The location can be calculated only when the reception strength from 25) is received. For example, the location calculation unit 134 can calculate the location of the user beacon 10 only when the locations of the plurality of receivers 21 to 25 are five or more.

In addition, the location calculation unit 134 can calculate the location at regular time intervals. That is, the location calculation unit 134 can calculate the location using the reception strength received from the plurality of receivers 21 to 25 at the same time.

In this way, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention estimates the location of the beacon based on information about the beacon signal transmitted from the beacon, thereby Convenience of use can be improved because users who are not good at operating a beacon can accurately calculate their location just by carrying a beacon.

The update unit 135 may update the reception strength mask based on the reception strength collected from the plurality of receivers 21 to 25 used to estimate the location of the user beacon 10. At this time, the update unit 135 may update the reception strength mask when the number of the plurality of receivers 21 to 25 is greater than or equal to the second threshold. Here, the update unit 135 may update the reception strength mask based on information on the receivers 21 to 25 located within a certain distance from the estimated location of the user beacon 10. Here, the second threshold may be greater than the first threshold. For example, the update unit 135 may update the reception strength mask when the number of receivers 21 to 25 is 10 or more.

In this way, the location estimation system 100 using a big data-based beacon according to an embodiment of the present invention improves the accuracy of the reception strength mask by updating the information collected from the plurality of receivers used for position estimation to the reception strength mask. As this can be improved, the positioning accuracy can be further improved.

Hereinafter, a location estimation method using a big data-based beacon according to an embodiment of the present invention will be described with reference to FIGS. 6 to 8. Figure 6 is a flowchart of a location estimation method using a big data-based beacon according to an embodiment of the present invention.

Referring to FIG. 6, the location estimation method 200 using a big data-based beacon includes a step of generating a reception strength mask (S210), a step of collecting the reception strength of the user beacon 10 (S220), and a reception strength mask. It may include a step of calculating the location using the method (S230), and a step of updating the reception strength mask (S240).

In more detail, as shown in FIG. 6, first, the location estimation system 100 using a big data-based beacon determines the reception strength based on prior measurement data on the distance by reception strength of the measurement beacon (not shown). Create a mask (step S210). At this time, the location estimation system 100 using a big data-based beacon collects data by collecting reception intensity samples at 0.5m intervals while the measurement beacon (not shown) is fixed, and calculates the number of data by distance for each reception strength. By counting, you can create a reception strength mask.

At this time, the location estimation system 100 using a big data-based beacon can store user beacon information, receiver information, reception strength mask information, and guardian information in the database 120. Here, the user beacon information 122 may store information related to the user beacon 10. The receiver information 124 may store information related to a plurality of receivers 21 to 25 on which the BLE scanner application is installed. The reception strength mask information 126 may store reception strength mask information previously generated by the mask generator 131. The guardian information 128 may store information about the guardian who requested the location estimation service of the user beacon 10.

Next, the location estimation system 100 using a big data-based beacon collects the reception strength for the user beacon 10 (step S220). At this time, the location estimation system 100 using a big data-based beacon can collect reception strength from a plurality of receivers 21 to 25 that receive signals from the user beacon 10. Here, the receivers 21 to 25 may be portable terminals equipped with a BLE scan function.

At this time, when a nearby user beacon 10 is detected by the installed beacon scanner application, the plurality of receivers 21 to 25 use the signal received from the user beacon 10 to a location estimation system using a big data-based beacon ( 100). Here, the plurality of receivers 21 to 25 may transmit the identification ID and reception strength of the user beacon 10 to the location estimation system 100 using a big data-based beacon.

Next, the location estimation system 100 using a big data-based beacon calculates the location of the user beacon 10 using a reception strength mask based on the reception strength received from the plurality of receivers 21 to 25 (step S230). At this time, the location estimation system 100 using a big data-based beacon receives by applying a reception strength mask to the positions of the plurality of receivers (21 to 25) based on the reception strength collected from the plurality of receivers (21 to 25). The location of the user beacon 10 can be calculated according to the distribution value for each location of the intensity mask.

Next, the location estimation system 100 using a big data-based beacon updates the reception strength mask based on the reception strength collected from the plurality of receivers 21 to 25 used to estimate the position of the user beacon 10 ( Step S240). At this time, the location estimation system 100 using a big data-based beacon can update the reception strength mask when the number of the plurality of receivers 21 to 25 is greater than or equal to the second threshold. Here, the location estimation system 100 using a big data-based beacon can update the reception strength mask based on information on the receivers 21 to 25 located within a certain distance from the estimated location of the user beacon 10. Here, the second threshold may be greater than the first threshold. For example, the location estimation system 100 using a big data-based beacon can update the reception strength mask when the number of receivers 21 to 25 is 10 or more.

Figure 7 is a flowchart of the reception strength mask generation process of the location estimation method using big data-based beacons according to an embodiment of the present invention.

Referring to FIG. 7, the reception strength mask generation process 210 includes the steps of collecting reception strength for each distance (S211), converting the reception strength into distance for each reception strength (S212), and calculating the reception strength mask (S213). may include.

In more detail, as shown in FIG. 7, first, the location estimation system 100 using a big data-based beacon collects the reception strength measured by distance from a measurement beacon (not shown) (step S211). At this time, the location estimation system 100 using a big data-based beacon is a measurement receiver (not shown) at a designated location spaced a certain distance away from the measurement beacon (not shown) by rotating in place for a certain period of time to increase the reception strength within the radius. It can be collected. For example, the location estimation system 100 using a big data-based beacon collects the reception strength measured at a distance of 0.5m to 10m at 0.5m intervals from a measurement beacon (not shown) through a measurement receiver (not shown). You can.

Next, the location estimation system 100 using a big data-based beacon converts the reception strength measured by distance into a distance by reception strength (step S212). At this time, the location estimation system 100 using a big data-based beacon can convert the reception strength measured by distance into a distance by reception strength and calculate a distribution value according to the distance by reception strength.

Next, the location estimation system 100 using a big data-based beacon calculates a reception strength mask by substituting the number of measurements for each distance according to the converted reception strength (step S213). At this time, the location estimation system 100 using a big data-based beacon can generate a reception strength mask for the reception strength range of -43dbm to -84dbm. In other words, the location estimation system 100 using a big data-based beacon can delete data other than the reception strength range of -43dbm to -84dbm from the collected reception strength data.

Figure 8 is a flow chart of the location calculation process of the location estimation method using big data-based beacons according to an embodiment of the present invention.

Referring to FIG. 8, the location calculation process 230 includes a step of identifying the user beacon 10 (231), a step of calculating the positions of the receivers 21 to 25 (S232), and a step of applying a reception strength mask for each location. (S233), and calculating the location of the user beacon 10 (S234).

In more detail, as shown in FIG. 8, first, the location estimation system 100 using a big data-based beacon determines the user beacon through information on the user beacon 10 received from a plurality of receivers 21 to 25. (10) is identified (step S231). At this time, the location estimation system 100 using a big data-based beacon can identify the corresponding user beacon 10 by comparing the information on the received user beacon 10 with the user beacon information 122 in the database 120. . In other words, the location estimation system 100 using a big data-based beacon can identify the personal information and guardian information of the corresponding user beacon 10.

Next, the location estimation system 100 using a big data-based beacon calculates the location of each of the plurality of receivers 21 to 25 based on the positions received from the plurality of receivers 21 to 25 (step S232). Here, the plurality of receivers 21 to 25 may transmit their own locations together when transmitting information on the user beacon 10 to the location estimation system 100 using a big data-based beacon. At this time, the plurality of receivers 21 to 25 may transmit location information according to the built-in GPS module.

Next, the location estimation system 100 using a big data-based beacon applies a reception strength mask to each calculated location of each receiver (21 to 25) (step S233). At this time, the location estimation system 100 using a big data-based beacon can apply a reception strength mask corresponding to the reception strength received for each receiver (21 to 25) to the location of the corresponding receiver (21 to 25).

Next, the location estimation system 100 using a big data-based beacon calculates the location where the distribution value of the reception strength mask is greatest for each location as the location of the user beacon 10 (step S234). At this time, the location estimation system 100 using a big data-based beacon can calculate the location of the user beacon 10 only when the number of the plurality of receivers 21 to 25 for the user beacon 10 is greater than or equal to the first threshold. You can. Here, when the number of receivers 21 to 25 is too small, location estimation based on the reception strength from the receivers 21 to 25 may be inaccurate, so the location estimation system 100 using big data-based beacons is sufficient. The location can be calculated only when the reception strength from the number of receivers (21 to 25) is received. For example, the location estimation system 100 using a big data-based beacon can calculate the location of the user beacon 10 only when the locations of the plurality of receivers 21 to 25 are five or more.

Here, the location estimation system 100 using a big data-based beacon can calculate the location at regular time intervals. That is, the location estimation system 100 using a big data-based beacon can calculate the location using the reception strength received at the same time from a plurality of receivers 21 to 25.

The above methods may be implemented by the location estimation system 100 using a big data-based beacon as shown in FIG. 1, and in particular, may be implemented as a software program that performs these steps. In this case, Programs may be stored in a computer-readable recording medium or transmitted by a computer data signal coupled with a carrier wave in a transmission medium or communication network.

At this time, the computer-readable recording medium includes all types of recording devices that store data readable by a computer system, such as ROM, RAM, CD-ROM, DVD-ROM, DVD-RAM, magnetic tape, It may be a floppy disk, hard disk, optical data storage device, etc.

Although an embodiment of the present invention has been described above, the spirit of the present invention is not limited to the embodiment presented in the present specification, and those skilled in the art who understand the spirit of the present invention can add components within the scope of the same spirit. , other embodiments can be easily proposed by change, deletion, addition, etc., but this will also be said to be within the scope of the present invention.

100: Location estimation system using big data-based beacons
110: Communication Department 120: Database
122: User beacon information 124: Receiver information
126: Reception strength mask information 128: User information
130: Control unit 131: Mask creation unit
132: reception intensity collection unit 133: beacon identification unit
134: location calculation unit 135: update unit
10: User beacon 21~25: Receiver
30: Guardian terminal

Claims (25)

delete A mask generator 131 that generates a reception strength mask based on pre-measurement data on the distance for each reception strength of the measurement beacon;
a reception strength collection unit 132 that collects reception strength for the user beacon from a plurality of receivers that receive signals from the user beacon;
A location calculation unit that applies the reception strength mask based on reception strengths collected from the plurality of receivers to the coordinate positions of the plurality of receivers and calculates the location of the user beacon according to the distribution value for each location of the reception strength mask. (134); and
A database 120 provided for use by the control unit 130 including the mask generator 131, the reception intensity collection unit 132, and the location calculation unit 134,
User beacon information 122 in which information related to the user beacon is stored;
Receiver information 124 in which information related to the plurality of receivers is stored; and
In a location estimation system using a big data-based beacon, which includes a database 120 containing reception strength mask information 126 in which the reception strength mask generated by the mask generator 131 is stored,
The mask generator 131,
Collect the reception strength measured by the distance at which the coordinate positions of the plurality of receivers are separated from the measurement beacon, and the measurement receiver rotates in place for a certain period of time at a designated location spaced a certain distance away from the measurement beacon. By collecting the reception strength within the radius, the reception strength measured by the separation distance is collected,
Converts the reception intensity measured for each separation distance into a separation distance for each reception intensity,
Accumulating and collecting the converted data on the separation distance for each reception strength, securing data on the number of measurements for each separation distance for each reception strength, and substituting this to calculate the reception strength mask, and
The location calculation unit 134,
Calculate the location of each of the plurality of receivers based on coordinate information received from the plurality of receivers,
A mask map is generated by overlapping the calculated reception strength masks corresponding to the reception strengths measured for each location of each receiver for each location, summing and cumulative mapping the measurement count data for each separation distance for each reception strength, and
A location estimation system using a big data-based beacon, characterized in that it is configured to calculate as the location of the user beacon the location with the largest distribution value of the number of measurements for each separation distance by reception strength summed and accumulated in the mask map. According to paragraph 2,
The receiver is a location estimation system using a big data-based beacon, a portable terminal equipped with a BLE scan function. delete delete According to paragraph 2,
The mask generator is a location estimation system using a big data-based beacon that generates the reception strength mask for the range of -43dbm to -84dbm. According to paragraph 2,
The mask generator is a location estimation system using a big data-based beacon that collects reception strength measured at a distance of 0.5m to 10m at 0.5m intervals from the measuring beacon. According to paragraph 2,
A location estimation system using a big data-based beacon, further comprising a beacon identification unit that compares the user beacon information received from the plurality of receivers with user beacon information in the database to identify the corresponding user beacon. delete According to paragraph 2,
The location calculation unit is a location estimation system using a big data-based beacon that calculates the location of the user beacon only when the number of the plurality of receivers for the user beacon is greater than or equal to a first threshold. According to clause 10,
A location estimation system using a big data-based beacon, further comprising an update unit that updates the reception strength mask based on reception strength collected from the plurality of receivers used to estimate the position of the user beacon. According to clause 11,
The update unit updates the reception strength mask based on information on receivers located within a certain distance from the estimated location of the user beacon when the number of the plurality of receivers is greater than the second threshold value than the first threshold value. Location estimation system using big data-based beacons. delete Generating a reception intensity mask based on pre-measurement data on the distance for each reception intensity of the measurement beacon (S210);
Collecting reception strength for the user beacon from a plurality of receivers that receive signals from the user beacon (S220); and
Applying the reception strength mask to the positions of the plurality of receivers based on the reception strengths collected from the plurality of receivers and calculating the position of the user beacon according to the distribution value for each position of the reception strength mask (S230) A location estimation method using big data-based beacons, including:
User beacon information where information related to the user beacon is stored;
Receiver information in which information related to the plurality of receivers is stored; and
In the location estimation method using a big data-based beacon, further comprising the step of storing the reception power mask information in the database 120, where the reception power mask information generated in the step of generating the reception power mask (S210) is stored. ,
In the step of generating the reception strength mask (S210),
Collecting the reception strength measured by the distance at which the coordinate positions of the plurality of receivers are separated from the measurement beacon (S211);
Converting the reception intensity measured for each separation distance into a separation distance for each reception intensity (S212); and
A step (S213) of accumulating and collecting the converted data on the separation distance by reception strength to secure measurement number data by separation distance by reception strength, and calculating a reception strength mask by substituting this data,
The step (S211) of collecting the reception strength measured by the separation distance from the measurement beacon is,
The measurement receiver is configured to collect the reception intensity measured by the separation distance by collecting the reception intensity within the radius by protruding in place for a certain period of time at a designated location spaced apart from the measurement beacon by a certain distance, and
The step of calculating the location of the user beacon (S230),
Calculating the location of each of the plurality of receivers based on coordinate information received from the plurality of receivers (S232);
The calculated reception power masks corresponding to the measured reception strengths for each location of each receiver are overlapped for each location, and the measured number data for each reception strength and separation distance are added and accumulated to create a mask map, thereby generating the calculated positions of each receiver. Applying the reception strength mask separately (S233); and
Location estimation using a big data-based beacon, characterized in that it includes the step (S234) of calculating as the location of the user beacon the location with the largest distribution value of the number of measurements for each separation distance by reception intensity accumulated in the mask map. method. According to clause 14,
The receiver is a location estimation method using a big data-based beacon, which is a portable terminal equipped with a BLE scan function. delete delete According to clause 14,
The step of generating the reception strength mask (S210) is a location estimation method using a big data-based beacon that generates the reception strength mask for the range -43dbm to -84dbm. According to clause 14,
The step of collecting from the measurement beacon is a location estimation method using a big data-based beacon that collects the reception strength measured at a distance of 0.5m to 10m at 0.5m intervals from the measurement beacon. According to clause 14,
A location estimation method using a big data-based beacon, further comprising comparing the user beacon information received from the plurality of receivers with user beacon information in the database to identify the corresponding user beacon. delete According to clause 14,
The calculating step is a location estimation method using a big data-based beacon in which the location of the user beacon is calculated only when the number of the plurality of receivers for the user beacon is greater than or equal to a first threshold. According to clause 22,
A location estimation method using a big data-based beacon, further comprising updating the reception strength mask based on reception strength collected from the plurality of receivers used to estimate the position of the user beacon. According to clause 23,
The updating step includes, when the number of the plurality of receivers is greater than the second threshold value greater than the first threshold value, the reception strength mask is set based on information on receivers located within a certain distance from the estimated location of the user beacon. Location estimation method using updating big data-based beacons. A computer-readable recording medium recording a program for performing the method of any one of claims 14, 15, 18, 19, 20, and 22 to 24.

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