KR20230063133A - Method and system for managing advertising of mobile robot device - Google Patents

Abstract

The present invention relates to a method and system for managing advertisements for a mobile robot device, comprising the steps of conducting an auction for advertising products for each mobile robot device; Requesting an advertisement to be displayed on the exterior of the mobile robot device when the advertisement product is successful; Advertising effect is measured based on sensing information including at least one of the number, location, direction, and time spent within a false identification distance of pedestrians located at a distance and direction where the advertisement can be actually identified while the mobile robot device is driving doing; Calculating an advertising cost based on the measured advertising effect; and requesting payment of the calculated advertising cost.

Description

Advertisement management method and system of mobile robot device {METHOD AND SYSTEM FOR MANAGING ADVERTISING OF MOBILE ROBOT DEVICE}

The present invention relates to an advertisement management system, and more particularly, to an advertisement management method and system for a mobile robot device.

Advertising introduces products and services to buyers, and how effectively attracts buyers' attention and keeps them in their memories for a long time is a very important issue.

Many companies advertise their products through newspapers and TV at a high cost, and recently, due to the popularity of the Internet, many advertisements through the Internet have appeared. In addition, outdoor advertisements that install large billboards on ground structures such as buildings or install large screens and broadcast advertisements through them are attracting people's attention.

However, advertisements through newspapers or TV are targeted only to specific people who watch the newspapers or TV, and advertisements through the Internet are effective only to those who visit the relevant sites.

In addition, since the currently used outdoor advertisements are fixedly installed in a certain place, the advertising effect is exerted only on people passing through the area.

As such, current advertising methods are made in the form that is selected by customers, so from the advertiser's point of view, the same product or service is advertised in multiple newspapers, multiple TV broadcasts, multiple Internet sites, and outdoor advertising, including outdoor advertising, in order to create more advertising effects. Advertising is done in several ways. In addition, since an advertisement produced once is advertised for a certain period of time or longer, the contents of the advertisement cannot be easily changed. This is especially true for large outdoor advertisements. This leads to excessive advertising expenditures.

Therefore, in view of the above points, an advertisement management system using a robot that can actively visit people and advertise through a robot rather than a fixed place by transmitting advertisement data to a movable robot has been proposed. However, conventional advertisement management systems have limitations in accurately measuring the exposure effect of advertisements using robots. Therefore, from the standpoint of the advertisement seller, it was difficult to accurately measure the advertisement effect of the robot, and thus, it was difficult to set the price of the advertisement product, and furthermore, it was impossible to gain trust from the advertisement buyer. In addition, advertisement buyers hesitate to purchase advertisement products because there is no evidence to prove the advertising effect of the robot, and even if they purchase advertisement products, it is difficult to determine whether advertisements using their own goods are valid.

Korean Patent Publication No. 10-2010-0135583 Korean Registered Patent Publication No. 10-0846698

The present specification has been devised to solve the above problems, and provides an advertisement management method and system for a mobile robot device capable of presenting objective evidence to prove the advertising effect using a mobile robot device to an advertisement seller and an advertisement buyer. Its purpose is to provide

According to the first embodiment of the present specification for achieving the above object, the advertisement management system of the mobile robot device according to the present specification conducts an auction for advertisement products for each mobile robot device, and then the advertisement product is successful. In the case of requesting placement of an advertisement on the exterior of the mobile robot device, the number, location, direction, and time spent within the fake identification distance of pedestrians located at a distance and direction in which the advertisement obtained during driving of the mobile robot device can be actually identified A management server that measures an advertising effect based on sensing information including at least one, calculates an advertising cost based on the measured advertising effect, and requests payment of the calculated advertising cost; An advertisement buyer who connects to the management server, applies for bidding by selecting the advertisement product, requests publication of an advertisement through the management server as the advertisement product is successful, and pays the advertising cost calculated by the management server. terminal; and after registering the advertisement product in the management server, if the advertisement product is successful in the advertisement purchaser terminal, an advertisement is displayed on the exterior of the mobile robot device according to a request of the advertisement purchaser terminal, and the advertisement purchaser receives the advertisement product from the management server. and an advertisement seller terminal receiving settlement of the advertisement fee paid by the terminal.

Preferably, the advertisement purchaser terminal accesses the management server and selects the advertising product by selecting an external advertising surface of a currently available mobile robot device and an autonomous driving area of the mobile robot device.

Preferably, the management server sets the price of the advertisement product based on the location and shape of the advertisement screen of the mobile robot device, demographic characteristics of the autonomous driving area, and past pedestrian exposure and attention in the autonomous driving area. It is characterized by doing.

Preferably, the management server sets the price of the advertising product by additionally considering the geographical characteristics of the self-driving area.

Preferably, the advertisement buyer terminal is characterized in that it presents a bid price by adding an advertisement cost and additional money according to an advertisement effect to the price of the advertisement product.

Preferably, the advertisement buyer terminal uses any one of a method of deducting an advertisement cost per designated exposure within a period desired by the buyer from a contracted amount and a method of paying in a flat rate regardless of the number of exposures within the period desired by the buyer as an advertisement cost settlement method. It is characterized by selecting one.

Preferably, the management server executes advertisements according to the advertisement cost settlement method selected by the buyer, generates a report according to advertisement publication results based on the time point when the advertising expenses paid by the buyer are exhausted or the contract period ends, It is characterized in that the advertisement is provided to the buyer terminal and the advertisement cost is charged.

Preferably, the management server updates the price of the advertisement product based on the sales history and average bid price of the advertisement product.

Preferably, the management server is characterized in that if the calculated advertisement cost exceeds the successful bid price, re-auction of the advertisement product.

According to the second embodiment of the present specification, the management server according to the present specification includes an auction progressing unit that conducts an auction for advertising products for each mobile robot device; an advertisement requesting unit requesting display of an advertisement on the exterior of the mobile robot device when an advertisement product is a successful bid during the auction process of the auction proceeding unit; Advertising effect is measured based on sensing information including at least one of the number, location, direction, and time spent within a false identification distance of pedestrians located at a distance and direction where the advertisement can be actually identified while the mobile robot device is driving Advertising effect measurement unit to; An advertising cost calculation unit for calculating an advertising cost based on the measured advertising effect; and a payment processing unit requesting payment of the calculated advertising cost.

Preferably, the auction proceeding unit sets the price of the advertisement product based on the position and shape of the advertisement screen of the mobile robot device, demographic characteristics of the autonomous driving area, and past pedestrian exposure and attention in the autonomous driving area. It is characterized by doing.

Preferably, the auction proceeding unit sets the price of the advertising product by additionally considering the geographical characteristics of the autonomous driving area.

Preferably, the auction proceeding unit may update the price of the advertisement product based on a sales history and an average bid price of the advertisement product.

Preferably, the auction proceeding unit may re-auction the advertisement product when the calculated advertisement cost exceeds the successful bid price.

According to the third embodiment of the present specification, an advertisement management method for a mobile robot device according to the present specification includes the steps of conducting an auction for advertising products for each mobile robot device; Requesting an advertisement to be displayed on the exterior of the mobile robot device when the advertisement product is successful; Advertising effect is measured based on sensing information including at least one of the number, location, direction, and time spent within a false identification distance of pedestrians located at a distance and direction where the advertisement can be actually identified while the mobile robot device is driving doing; Calculating an advertising cost based on the measured advertising effect; and requesting payment of the calculated advertising cost.

As described above, according to the present specification, after an auction is conducted for advertisement products for each mobile robot device, when the advertisement product is a successful bid, an advertisement is requested to be posted on the exterior of the mobile robot device, and while the mobile robot device is running The advertisement effect is measured based on sensing information including at least one of the number, location, direction, and time spent within the fake identification distance of pedestrians located in the actual identifiable distance and direction of the obtained advertisement, and the measured advertisement effect By providing an advertisement management method and system for a mobile robot device that calculates advertisement costs based on the basis and requests payment of the calculated advertisement costs, the advertisement seller can reasonably set the price of advertisement products, and the advertisement buyer can purchase advertisement products. In the case of purchase, you can easily recognize how effective the advertisement using your own goods is.

1 is a diagram showing an example in which a mobile robot device according to an embodiment of the present invention provides a service to a user;
2 is a flowchart illustrating a method for providing an interactive advertisement by a mobile robot device according to a first embodiment of the present invention;
3 is a flow chart showing a method for measuring an advertising effect by a mobile robot device according to a second embodiment of the present invention;
4 is a diagram showing an example of measuring an advertisement effect by interworking with a server by a mobile robot device according to a second embodiment of the present invention;
5 and 6 are block diagrams of a mobile robot device according to an embodiment of the present invention;
7 is a block diagram of a processor according to the present invention;
8 is a block diagram of a data learning unit according to an embodiment of the present invention;
9 is a block diagram of a data recognizing unit according to an embodiment of the present invention;
10 is a configuration diagram of an advertisement management system for a mobile robot device according to a third embodiment of the present invention;
11 is a block diagram showing a schematic configuration of the inside of a management server according to a third embodiment of the present invention, and
12 is a signal flow diagram illustrating an advertisement management method of a mobile robot device according to a third embodiment of the present invention.

It should be noted that technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. In addition, technical terms used in this specification should be interpreted in terms commonly understood by those of ordinary skill in the art to which the present invention belongs, unless specifically defined otherwise in this specification, and are overly inclusive. It should not be interpreted in a positive sense or in an excessively reduced sense. In addition, when the technical terms used in this specification are incorrect technical terms that do not accurately express the spirit of the present invention, they should be replaced with technical terms that those skilled in the art can correctly understand. In addition, general terms used in the present invention should be interpreted as defined in advance or according to context, and should not be interpreted in an excessively reduced sense.

Also, singular expressions used in this specification include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "consisting of" or "comprising" should not be construed as necessarily including all of the various components or steps described in the specification, and some of the components or some of the steps It should be construed that it may not be included, or may further include additional components or steps.

In addition, the suffixes "module" and "unit" for the components used in this specification are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinguished from each other by themselves.

Also, terms including ordinal numbers such as first and second used in this specification may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.

In an embodiment of the present invention, advertisements may be posted by securing a predetermined space on the front, left and right sides, rear and top planes of the mobile robot device.

In addition, the advertisement means 1) an advertisement in the form of a printed matter attached to the robot exterior, or 2) a sculpture made in three dimensions within the range that does not interfere with safe driving and fixedly attached to the robot exterior. It is in the form of a product or symbol, and means a lighting device to increase attention, or 3) an advertisement in the form of a display (including audio output device) attached to the exterior of the robot, and can show still images or videos, It refers to a small video output device that can transmit audio if necessary, and can change and transmit advertisement contents according to the characteristics of the area to which the driving route belongs or the gender and age of the pedestrians encountered, or 4) Pedestrians and vehicles It may mean a banner in the form of a safety flag attached to the robot so that it can be identified in

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings, but the same or similar components are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted.

In addition, in describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description will be omitted. In addition, it should be noted that the accompanying drawings are only for the purpose of easily understanding the spirit of the present invention, and should not be construed as limiting the spirit of the present invention by the accompanying drawings.

1 is a diagram showing an example in which a mobile robot device according to an embodiment of the present invention provides a service to a user.

Referring to FIG. 1 , a mobile robot device 1000 may provide a service to a user by using an arm device. For example, the mobile robot device 1000 may deliver coffee to the user.

In addition, when the mobile robot device 1000 is not performing a separate mission, it may recognize pedestrians it encounters while driving and allow each pedestrian to identify an advertisement. For example, the mobile robot apparatus 1000 may recognize a pedestrian, track the gaze of the pedestrian, change the direction of movement, and adjust the driving speed according to the speed of the pedestrian. Here, the mobile robot device 1000 can identify the locations of several pedestrians at the same time, determines the priority based on the degree of proximity within the visual distance of the advertisement and the direction of gaze, thereby performing the above steps to increase the pedestrian's attention to the advertisement. can be run sequentially.

To this end, the mobile robot apparatus 1000 may acquire a plurality of surrounding images in real time while driving. For example, the mobile robot device 1000 may capture at least one of the front, rear, left, and right sides of the mobile robot device 1000 . The mobile robot device 1000 may identify a pedestrian by obtaining a plurality of images. In addition, the mobile robot apparatus 1000 may store a plurality of images captured while driving in a memory, and delete old image data first when a preset capacity is exceeded.

In addition, the mobile robot device 1000 can increase the degree of attention of advertisements by driving in a driving direction and speed optimized according to the direction and speed of the eyes of a plurality of pedestrians.

In addition, when the type of advertisement is a small image output device, the mobile robot device 1000 estimates the gender of a pedestrian encountered while driving through a deep learning-based object recognition algorithm, and changes and exposes the advertisement according to the result. can For example, the mobile robot device 1000 may expose a razor advertisement to a male pedestrian and change the corresponding advertisement to a cosmetic advertisement when a female pedestrian approaches.

The mobile robot device 1000 measures the number, location, and direction of pedestrians located in the distance and direction where the advertisement can be actually identified through a deep learning-based object recognition algorithm, as well as the time spent within the fake identification distance, and based on this, the advertising effect and the advertising price accordingly. Here, the mobile robot apparatus 1000 extracts location information and direction information of an object included in the advertisement, and when the object included in the advertisement enters the gaze direction of the pedestrian, the entry time point of the object included in the advertisement and, if the object included in the advertisement deviates from the gaze direction of the pedestrian, by determining the departure time of the object included in the advertisement, using the location information and direction information of the extracted object, from the entry point to the departure point The exposure time of advertisements can be calculated.

For example, the mobile robot device 1000 recognizes pedestrians that have entered the false identification distance of advertisements among pedestrians encountered while driving as independent objects, sums up the numbers, and measures the advertising effect based on this. . At this time, the mobile robot device 1000 preferably does not add the number of exposures when the same pedestrian is continuously identified within the temporary identification range on the same driving route.

In addition, the mobile robot device 1000 calculates the time when the gaze direction is toward the advertisement by estimating the face recognition and the direction and speed of the pedestrian entering the temporary identification range, and based on this, the advertisement effect can be measured. . Specifically, the mobile robot device 1000 treats the time when the pedestrian is located within the temporary identification range and the time when the gaze direction is facing the advertisement separately from each other. In the latter case, it can be determined that the advertisement effect is higher.

In addition, the mobile robot device 1000 calculates the distance between the pedestrian and the pedestrian who has entered the temporary identification range, the time spent within the temporary identification range, whether the gaze direction of the pedestrian is toward the advertisement and the time, etc. Can be calculated, and accordingly, the advertising effect can be presented differentially.

Meanwhile, the mobile robot device 1000 may be a delivery robot, a cleaning robot, a household appliance, and other mobile or non-mobile computing devices. However, it is not limited thereto, and the mobile robot apparatus 1000 may include all types of devices that are movable and capable of providing services to users.

In addition, the mobile robot device 1000 may communicate with the mediation server 2000 and other devices (not shown) through a predetermined network in order to increase attention of advertisements or measure advertisement effects. In this case, the network includes a local area network (LAN), a wide area network (WAN), a value added network (VAN), a mobile radio communication network, a satellite communication network, and their mutual It is a comprehensive data communication network that includes a combination and allows each network constituent entity to communicate smoothly with each other, and may include wired Internet, wireless Internet, and mobile wireless communication network. Wireless communication includes, for example, wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy (Bluetooth low energy), Zigbee, WFD (Wi-Fi Direct), UWB (ultra wideband), infrared communication (IrDA, Infrared Data Association) ), Near Field Communication (NFC), etc., but is not limited thereto.

2 is a flowchart illustrating a method of providing an interactive advertisement by a mobile robot device according to a first embodiment of the present invention.

Referring to FIG. 2 , the mobile robot device 1000 identifies a pedestrian using a camera or sensor unit through a deep learning-based object recognition algorithm (S210). At this time, the mobile robot device 1000 may distinguish and record whether or not the advertisement is entered within a visual range capable of identifying the advertisement.

The mobile robot device 1000 estimates the walking direction and the gaze direction of the pedestrian, and then changes the traveling direction according to the pedestrian's gaze direction within a range that does not deviate from the driving path (S220). Therefore, pedestrians can more easily pay attention to advertisements.

The mobile robot device 1000 adjusts the driving speed within a safe speed range according to the walking speed of the pedestrian (S230). Therefore, the mobile robot device 1000 can maximize the time for pedestrians to stay within the viewing distance of advertisements.

On the other hand, the mobile robot device 1000 according to the present invention can simultaneously identify the locations of several pedestrians through a deep learning-based object recognition algorithm, and prioritizes advertisements based on the degree of proximity within the visual distance of advertisements and the direction of gaze. Steps S210 to S230 may be sequentially and repeatedly executed in order to increase the pedestrian's attention to water.

3 is a flowchart illustrating a method for measuring an advertising effect by a mobile robot device according to a second embodiment of the present invention.

Referring to FIG. 3 , the mobile robot device 1000 identifies a pedestrian using a camera or sensor unit through a deep learning-based object recognition algorithm (S310). At this time, the mobile robot device 1000 discriminates and records whether or not the advertisement is entered within a visual range capable of identifying it.

The mobile robot device 1000 measures the advertising effect and the corresponding advertising price based on sensing information including the number, location, direction, and time of staying within the fake identification distance of pedestrians located in the actual identifiable distance and direction of the advertisement Do (S320).

For example, the mobile robot device 1000 recognizes pedestrians that have entered the false identification distance of advertisements among pedestrians encountered while driving as independent objects, sums up the numbers, and measures the advertising effect based on this. . At this time, the mobile robot device 1000 preferably does not add the number of exposures when the same pedestrian is continuously identified within the temporary identification range on the same driving route.

In addition, the mobile robot device 1000 calculates the time when the gaze direction is toward the advertisement by estimating the face recognition and the direction and speed of the pedestrian entering the temporary identification range, and based on this, the advertisement effect can be measured. . Specifically, the mobile robot device 1000 treats the time when the pedestrian is located within the temporary identification range and the time when the gaze direction is facing the advertisement separately from each other. In the latter case, it can be determined that the advertisement effect is higher.

In addition, the mobile robot device 1000 calculates the distance between the pedestrian and the pedestrian who has entered the temporary identification range, the time spent within the temporary identification range, whether the gaze direction of the pedestrian is toward the advertisement and the time, etc. Can be calculated, and accordingly, the advertising effect can be presented differentially.

The above method may be implemented through various means. For example, embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.

In the case of hardware implementation, the method according to the embodiments of the present invention includes one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices) , Field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers and microprocessors.

In the case of implementation by firmware or software, the method according to the embodiments of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software codes may be stored in a memory unit and driven by a processor. The memory unit may be located inside or outside the processor and exchange data with the processor by various means known in the art.

4 is a diagram illustrating an example of measuring an advertising effect by interworking with a server by a mobile robot device according to a second embodiment of the present invention.

Referring to FIG. 4 , the mobile robot device 1000 may be connected to the server 2000 through a network, and the advertising effect may be measured according to criteria preset by the server 2000.

In this case, the server 2000, which is a function performed by the mobile robot device 1000 in FIG. 1, a function of determining whether the surroundings of the mobile robot device 1000 is in a dangerous situation, and a type of people around the mobile robot device 1000 At least one of a function of obtaining information about and information about the surrounding environment of the mobile robot device 1000, a function of increasing attention of advertisements, and a function of measuring advertisement effects may be performed.

In this case, the mobile robot device 1000 and the server 2000 may transmit/receive necessary data to each other in order to perform their respective functions. For example, the mobile robot device 1000 may provide the server 2000 with data required for a predetermined function performed by the server 2000, and the mobile robot device 1000 may transfer data from the server 2000 to the server 2000. Result data generated according to the function performed by may be received. In addition, the server 2000 may provide the mobile robot device 1000 with data required for a predetermined function performed by the mobile robot device 1000, and the server 2000 may provide the mobile robot device 1000 with the mobile robot device ( 1000) may receive result data generated according to the function performed.

In addition, the server 2000 provides data necessary for determining whether the surroundings of the mobile robot device 1000 are in a dangerous situation, information on the type of people around the mobile robot device 1000, and the surrounding environment of the mobile robot device 1000. At least one of data necessary to obtain information about advertisements, data necessary to increase attention of advertisements, and data necessary to measure advertisement effects may be managed.

5 and 6 are block diagrams of a mobile robot device according to an embodiment of the present invention.

As shown in FIG. 5 , the mobile robot device 1000 according to the present invention may include a sensing unit 110, a processor 120, an arm device 170, and a moving device 180. However, not all components shown in FIG. 5 are essential components of the mobile robot apparatus 1000. The mobile robot device 1000 may be implemented with more components than those shown in FIG. 5, or the mobile robot device 1000 may be implemented with fewer components than those shown in FIG.

For example, as shown in FIG. 6 , the mobile robot device 1000 according to the present invention includes a sensing unit 110, a processor 120, an arm device 170, and a moving device 180 as well as an output unit. 130, a memory 140, an input unit 150, and a communication unit 160 may be further included.

The sensing unit 110 according to the present invention may acquire sensing information by sensing the surrounding environment of the mobile robot device 1000 while the mobile robot device 1000 is driving. For example, the sensing unit 110 may obtain at least one of an image of the surroundings of the mobile robot device 1000 and temperature information of the surrounding environment. Also, the sensing unit 110 may acquire location information of the mobile robot device 1000 while the mobile robot device 1000 is running.

The sensing unit 110 may include a plurality of sensors for sensing the surrounding environment of the mobile robot device 1000 . For example, the sensing unit 110 may include an image sensor 228 such as a camera to capture an image around the mobile robot device 1000 . In addition, the sensing unit 110 may include a temperature/humidity sensor 232 to obtain temperature information and/or humidity information of the surrounding environment of the mobile robot device 1000 .

Also, the sensing unit 110 may include sensors for obtaining location information of the mobile robot device 1000 . For example, the sensing unit 110 may include a distance sensor such as a RADAR sensor 226 , a LIDAR sensor 227 , and an odometry sensor 230 .

In addition, the sensing unit 110 may include one or more actuators configured to correct the position and/or orientation of a plurality of sensors, and images of the front, rear, and side directions of the mobile robot device 1000, respectively. can be filmed.

The sensing unit 110 may include a plurality of sensors configured to detect information about the surrounding environment in which the mobile robot device 1000 is located, and may include one or more actuators configured to correct the location and/or orientation of the sensors. can include For example, the sensing unit 110 includes a global positioning system (GPS) 224, an inertial measurement unit (IMU) 225, a RADAR sensor 226, a LIDAR sensor 227, an image sensor 228, and an odometery sensor. (230). In addition, the sensing unit 110 may include at least one of a temperature/humidity sensor 232, an infrared sensor 233, an air pressure sensor 235, a proximity sensor 236, and an illuminance sensor (RGB) 237. It may, but is not limited thereto. Since a person skilled in the art can intuitively infer the function of each sensor from its name, a detailed description thereof will be omitted.

In addition, the sensing unit 110 may include a motion sensing unit 238 capable of sensing the motion of the mobile robot device 1000 . The motion sensing unit 238 may include a magnetic sensor 229 , an acceleration sensor 231 , and a gyroscope sensor 234 .

The GPS 224 may be a sensor configured to estimate the geographic location of the mobile robotic device 1000 . That is, the GPS 224 may include a transceiver configured to estimate a position of the mobile robot device 1000 relative to the earth.

The IMU 225 may be a combination of sensors configured to detect position and orientation changes of the mobile robot device 1000 based on inertial acceleration. For example, the combination of sensors may include accelerometers and gyroscopes.

The RADAR sensor 226 may be a sensor configured to detect objects in an environment in which the mobile robot device 1000 is located using radio signals. Additionally, the RADAR sensor 226 may be configured to sense the speed and/or direction of objects.

The LIDAR sensor 227 may be a sensor configured to detect objects in an environment in which the mobile robot device 1000 is located using a laser. More specifically, the LIDAR sensor 227 may include a laser light source and/or laser scanner configured to emit a laser, and a detector configured to detect a reflection of the laser. The LIDAR sensor 227 can be configured to operate in a coherent (eg, using heterodyne detection) or incoherent detection mode.

The image sensor 228 may be a still camera or video camera configured to record the environment outside the mobile robotic device 1000. For example, the image sensor 228 may include multiple cameras, and the multiple cameras may be disposed at multiple locations on the mobile robotic device 1000 .

The odometery sensor 230 may estimate the position of the mobile robot device 1000 and measure the moving distance. For example, the odometry sensor 230 may measure a position change value of the mobile robot device 1000 by using the number of revolutions of wheels of the mobile robot device 1000 .

The input unit 150 means means for inputting data for controlling the mobile robot device 1000 . For example, the input unit 150 includes a key pad, a dome switch, a touch pad (contact capacitance method, pressure resistive film method, infrared sensing method, surface ultrasonic conduction method, integral tension method) measurement method, piezo effect method, etc.), a jog wheel, a jog switch, and the like, but are not limited thereto. Also, the input unit 150 may include a microphone, and the microphone may be configured to receive audio (eg, a voice command) from a user.

The output unit 130 may output an audio signal or a video signal, and the output unit 130 may include a display 281 and a sound output unit 282 .

The display 281 displays and outputs information processed by the mobile robot device 1000 . For example, the display 281 may display a notification message notifying a person located around the mobile robot device 1000 of a dangerous situation while the mobile robot device 1000 is driving. Also, the display 281 may display a user interface for executing an operation related to the notification. In addition, the display 281 may display advertisements for promoting products or companies and informing the public of various information.

The display 281 may include a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, and a 3D display (3D display). display), and an electrophoretic display. Depending on the implementation form of the output unit 130, the output unit 130 may include two or more displays 281.

The audio output unit 282 outputs audio data received from the communication unit 160 or stored in the memory 140 . For example, the sound output unit 282 may output a notification message notifying a dangerous situation to a person located around the mobile robot device 1000 by sound while the mobile robot device 1000 is driving. Also, the sound output unit 282 may include a speaker, a buzzer, and the like.

The input unit 150 and the output unit 130 may include a network interface and may be implemented as a touch screen.

The communication unit 160 may include at least one antenna for wirelessly communicating with other devices. For example, communication unit 160 may be used to communicate with a cellular network or other wireless protocols and systems wirelessly via Wi-Fi or Bluetooth. The communication unit 160 controlled by the processor 120 may transmit and receive radio signals. For example, the processor 120 may execute a program included in the memory 140 so that the communication unit 160 transmits and receives a wireless signal to and from a cellular network.

The processor 120 controls overall operations of the mobile robot device 1000 in general. For example, the processor 120 executes the programs stored in the memory 140, so that the sensing unit 110, the output unit 130, the input unit 150, the communication unit 160, the arm device 170, the movement Overall control of the device 180 and the like. Also, the processor 120 may perform the functions of the mobile robot device 1000 described in FIGS. 1 to 4 by executing programs stored in the memory 140 .

Specifically, the processor 120 may obtain sensing information obtained by sensing the surrounding environment of the mobile robot device 1000 through the sensing unit 110 . For example, the sensing information may include at least one of an image captured around the mobile robot apparatus 1000 and temperature information of the surrounding environment. For example, the sensing information may include the number, position, and direction of pedestrians located in a distance and direction in which an advertisement can be actually identified, and a time spent within a false identification distance.

The processor 120 according to the present invention may recognize pedestrians who have entered the false identification distance of advertisements among pedestrians encountered while driving as independent objects, sum up the numbers, and measure the advertisement effect based on this.

In addition, the processor 120 may calculate the time when the gaze direction is directed toward the advertisement by estimating the face recognition, traveling direction and speed of the pedestrian who has entered the temporary identification range, and may measure the advertising effect based on this.

In addition, the processor 120 calculates the degree of attention to the advertisement by calculating the distance between the pedestrian and the pedestrian who has entered the temporary identification range, the time spent within the temporary identification range, whether the gaze direction of the pedestrian is toward the advertisement and the time, etc. And, accordingly, the advertising effect can be presented differentially.

The memory 140 may store programs for processing and control of the processor 120, and may store data input to or output from the mobile robot apparatus 1000.

The memory 140 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , an optical disk, and at least one type of storage medium. For example, the memory 140 may store an image around the mobile robot device 1000 captured while the mobile robot device 100 is driving.

Programs stored in the memory 140 may be classified into a plurality of modules according to their functions, and may include, for example, a notification module.

The notification module may output a notification signal in the form of a video signal through the display unit 281, output a notification signal in the form of an audio signal through the sound output unit 282, or output a notification signal in the form of a vibration signal through a vibration motor. A notification signal may be output.

The arm device 170 is an arm of the mobile robot device 1000, and the movement speed, angle, direction, etc. of the arm device 170 may be controlled by the processor 120. In addition, the head device (not shown) is a head part of the mobile robot device 1000, and the movement angle and direction of the head device (not shown) may be controlled by the processor 120.

The traveling device 180 may include a brake unit 221 , a steering unit 222 , and a throttle 223 .

The steering unit 222 may be a combination of mechanisms configured to adjust the direction of the mobile robot device 1000.

The brake unit 221 may be a combination of mechanisms configured to decelerate the mobile robot device 1000 . For example, the brake unit 221 may use friction to reduce the speed of a wheel/wheel of the mobile robot device 1000 .

7 is a block diagram of a processor according to the present invention.

Referring to FIG. 7 , the processor 120 according to the present invention may include a data learning unit 710 and a data recognizing unit 720 .

The data learning unit 710 may learn criteria for identifying pedestrians. The data learner 710 determines which data to use and how to use the data to measure the number, location, and direction of pedestrians located in the actual identifiable distance and direction of the advertisement, as well as the time spent within the fake identification distance. You can learn the criteria for making judgments. The data learning unit 710 may acquire data to be used for learning and learn criteria for identifying a pedestrian by applying the obtained data to a data recognition model to be described later.

The data recognizing unit 720 may determine the number, location, direction, and time of remaining within the fake identification distance of pedestrians located in a distance and direction in which an advertisement can be actually identified based on data. The data recognizing unit 720 may recognize the number, location, direction, and time spent within the temporary identification distance of pedestrians from predetermined data by using the learned data recognition model. The data recognizing unit 720 obtains predetermined data according to a predetermined criterion by learning, and uses the data recognition model by using the obtained data as an input value, thereby determining the number, location, and direction of pedestrians based on the predetermined data. and the time spent within the temporary identification distance can be recognized. In addition, result values output by the data recognition model using the acquired data as input values may be used to update the data recognition model.

At least one of the data learning unit 710 and the data recognizing unit 720 may be manufactured in the form of at least one hardware chip and mounted in an electronic device. For example, at least one of the data learning unit 710 and the data recognizing unit 720 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or an existing general-purpose processor (eg, CPU). Alternatively, it may be manufactured as a part of an application processor) or a graphics-only processor (eg GPU) and mounted in various electronic devices described above.

In this case, the data learning unit 710 and the data recognizing unit 720 may be mounted on one electronic device or may be mounted on separate electronic devices. For example, one of the data learning unit 710 and the data recognizing unit 720 may be included in the mobile robot device 1000, and the other may be included in the server 2000. In addition, the data learning unit 710 and the data recognizing unit 720 may provide model information constructed by the data learning unit 710 to the data recognizing unit 720 through a wired or wireless connection, and the data recognizing unit ( Data input to 720 may be provided to the data learning unit 710 as additional learning data.

Meanwhile, at least one of the data learning unit 710 and the data recognizing unit 720 may be implemented as a software module. When at least one of the data learning unit 710 and the data recognizing unit 720 is implemented as a software module (or a program module including instructions), the software module is computer readable, non-temporary reading It can be stored on a non-transitory computer readable media. Also, in this case, at least one software module may be provided by an Operating System (OS) or a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and the other part may be provided by a predetermined application.

8 is a block diagram of a data learning unit according to an embodiment of the present invention.

Referring to FIG. 8, the data learning unit 710 according to the present invention includes a data acquisition unit 710-1, a pre-processing unit 710-2, a training data selection unit 710-3, and a model learning unit 710-1. 4) and a model evaluation unit 710-5.

The data acquisition unit 710-1 may acquire data necessary for identifying a pedestrian. The data acquisition unit 710-1 may acquire data necessary for learning to identify a pedestrian.

According to an embodiment of the present invention, the data acquisition unit 710-1 may obtain sensing information by sensing the surrounding environment while the mobile robot device 1000 is driving. For example, the data acquisition unit 710 - 1 may receive an image captured in real time while the mobile robot device 1000 is driving. In addition, the data acquisition unit 710-1 may receive data through an input device (eg, a microphone, a camera, or a sensor) of the mobile robot device 1000. Alternatively, the data acquisition unit 710-1 may obtain data through an external device communicating with the mobile robot device 1000.

According to an embodiment of the present invention, a plurality of surrounding images may be captured, and may be a video including a plurality of images. For example, the data acquisition unit 710-1 may communicate with the camera of the mobile robot device 1000 including the data learning unit 710 or the mobile robot device 1000 including the data learning unit 710 and communicate with an external device. Video may be received through a camera (eg, CCTV, etc.) of .

A camera may include one or more image sensors (eg, a front sensor or a rear sensor), a lens, an image signal processor (ISP), or a flash (eg, an LED or xenon lamp).

The pre-processing unit 710-2 may pre-process the acquired data so that the acquired data can be used for learning for pedestrian identification. The pre-processing unit 710-2 may process the acquired data into a preset format so that the model learning unit 710-4, which will be described later, can use the acquired data for learning for pedestrian identification. For example, the pre-processor 710-2 overlaps at least a portion of a plurality of images based on a common region included in each of a plurality of images (or frames) constituting at least a portion of the input video. Thus, a single composite image can be created. In this case, a plurality of composite images may be generated from one video. The common area may be an area including the same or similar common objects (eg, objects, animals, plants, or people) in each of the plurality of images. Alternatively, the common area may be an area having the same or similar color, shade, RGB value, or CMYK value in each of the plurality of images.

The learning data selector 710-3 may select data necessary for learning from preprocessed data. The selected data may be provided to the model learning unit 710-4. The learning data selector 710-3 may select data necessary for learning from among pre-processed data according to a predetermined criterion for pedestrian identification. In addition, the learning data selection unit 710-3 may select data according to a predetermined criterion through learning by the model learning unit 710-4, which will be described later.

For example, data regarding the number, location, direction, and time spent within a temporary identification distance of pedestrians located at a distance and direction in which advertisements can be actually identified around the mobile robot device 1000 may be selected.

The model learning unit 710-4 may learn criteria for pedestrian identification based on the learning data. Also, the model learning unit 710 - 4 may learn a criterion for which learning data to use for pedestrian identification.

According to an embodiment of the present invention, the model learning unit 710-4 based on the sensing information, based on the number, position, direction, and time of the pedestrians located within the distance and direction where the advertisement can be actually identified, and the time they stayed within the fake identification distance standards can be learned.

Also, the model learning unit 710 - 4 may learn a data recognition model used for pedestrian identification using learning data. In this case, the data recognition model may be a pre-built model. For example, the data recognition model may be a model built in advance by receiving basic training data (eg, a sample image, etc.).

The data recognition model may be constructed in consideration of the field of application of the recognition model, the purpose of learning, or the computer performance of the device. The data recognition model may be, for example, a model based on a neural network. For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), or a bidirectional recurrent deep neural network (BRDNN) may be used as a data recognition model, but is not limited thereto.

According to various embodiments, when a plurality of pre-built data recognition models exist, the model learning unit 710-4 selects a data recognition model having a high correlation between the input training data and the basic training data as a data recognition model to be learned. can decide In this case, the basic training data may be classified for each type of data, and the data recognition model may be pre-built for each type of data. For example, the basic training data is classified according to various criteria such as the region where the training data was created, the time the training data was created, the size of the training data, the genre of the training data, the creator of the training data, and the type of object in the training data. may have been

In addition, the model learning unit 710-4 learns a data recognition model using a learning algorithm including, for example, error back-propagation or gradient descent. can make it

In addition, the model learning unit 710 - 4 may learn a data recognition model through supervised learning using learning data as an input value, for example. In addition, the model learning unit 710-4, for example, through unsupervised learning that discovers a criterion for pedestrian identification by self-learning the type of data necessary for pedestrian identification without any guidance, You can train a data recognition model. In addition, the model learning unit 710 - 4 may train the data recognition model through reinforcement learning using, for example, feedback about whether a result of pedestrian identification according to learning is correct.

Also, when the data recognition model is learned, the model learning unit 710 - 4 may store the learned data recognition model. In this case, the model learning unit 710 - 4 may store the learned data recognition model in the memory of the mobile robot device 1000 including the data recognition unit 720 . Alternatively, the model learning unit 710 - 4 may store the learned data recognition model in the memory of the mobile robot device 1000 including the data recognition unit 720 to be described later. Alternatively, the model learning unit 710 - 4 may store the learned data recognition model in the memory of the server 2000 connected to the mobile robot device 1000 through a wired or wireless network.

In this case, the memory in which the learned data recognition model is stored may also store commands or data related to at least one other component of the mobile robot apparatus 1000, for example. Also, the memory may store software and/or programs. Programs may include, for example, kernels, middleware, application programming interfaces (APIs), and/or application programs (or "applications").

The model evaluation unit 710-5 may input evaluation data to the data recognition model, and may cause the model learning unit 710-4 to learn again when a recognition result output from the evaluation data does not satisfy a predetermined criterion. there is. In this case, the evaluation data may be preset data for evaluating the data recognition model.

For example, the model evaluation unit 710-5 determines a predetermined criterion when the number or ratio of evaluation data for which the recognition result is not accurate among the recognition results of the learned data recognition model for the evaluation data exceeds a preset threshold. It can be evaluated as unsatisfactory. For example, when the predetermined criterion is defined as a ratio of 2% and the learned data recognition model outputs an incorrect recognition result for evaluation data exceeding 20 out of a total of 1000 evaluation data, the model evaluation unit 710-5 can evaluate that the trained data recognition model is not suitable.

On the other hand, when there are a plurality of learned data recognition models, the model evaluation unit 710-5 evaluates whether each learned data recognition model satisfies a predetermined criterion, and recognizes the final data as a model that satisfies the predetermined criterion. can be determined as a model. In this case, when there are a plurality of models that satisfy a predetermined criterion, the model evaluation unit 710 - 5 may determine one or a predetermined number of models preset in order of high evaluation scores as the final data recognition model.

Meanwhile, a data acquisition unit 710-1, a pre-processing unit 710-2, a training data selection unit 710-3, a model learning unit 710-4, and a model evaluation unit ( At least one of 710-5) may be manufactured in the form of at least one hardware chip and mounted on the mobile robot device 1000. For example, among the data acquisition unit 710-1, the pre-processing unit 710-2, the training data selection unit 710-3, the model learning unit 710-4, and the model evaluation unit 710-5. At least one may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or manufactured as part of an existing general-purpose processor (eg CPU or application processor) or graphics-only processor (eg GPU) to It may be mounted on various mobile robot devices 1000.

In addition, the data acquisition unit 710-1, the pre-processing unit 710-2, the training data selection unit 710-3, the model learning unit 710-4, and the model evaluation unit 710-5 are one It may be mounted on an electronic device or may be mounted on separate electronic devices. For example, among the data acquisition unit 710-1, the pre-processing unit 710-2, the training data selection unit 710-3, the model learning unit 710-4, and the model evaluation unit 710-5. Some may be included in the mobile robot device 1000, and some may be included in the server 2000.

In addition, at least one of the data acquisition unit 710-1, the pre-processing unit 710-2, the training data selection unit 710-3, the model learning unit 710-4, and the model evaluation unit 710-5. may be implemented as a software module. At least one of the data acquisition unit 710-1, the pre-processing unit 710-2, the training data selection unit 710-3, the model learning unit 710-4, and the model evaluation unit 710-5 is software. When implemented as a module (or a program module including instructions), the software module may be stored in a computer-readable, non-transitory computer readable recording medium. Also, in this case, at least one software module may be provided by an Operating System (OS) or a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and the other part may be provided by a predetermined application.

9 is a block diagram of a data recognizing unit according to an embodiment of the present invention.

Referring to FIG. 9 , the data recognition unit 720 according to the present invention includes a data acquisition unit 720-1, a pre-processing unit 720-2, a recognition data selection unit 720-3, and a recognition result providing unit 720. -4), and a model updating unit 720-5.

The data acquisition unit 720-1 may acquire data necessary for pedestrian identification, and the pre-processor 720-2 may pre-process the acquired data so that the acquired data may be used for pedestrian identification. The pre-processing unit 720-2 may process the acquired data into a preset format so that the recognition result providing unit 720-4, which will be described later, can use the acquired data for pedestrian identification.

The recognition data selector 720-3 may select data necessary for pedestrian identification from among preprocessed data. The selected data may be provided to the recognition result provider 720-4. The recognition data selection unit 720-3 may select some or all of the preprocessed data according to a predetermined criterion for pedestrian identification. In addition, the recognition data selection unit 720-3 may select data according to a predetermined criterion by learning by a model learning unit 710-4 to be described later.

The recognition result provider 720-4 applies the selected data to the data recognition model to recognize the number, location, and direction of pedestrians located in the distance and direction where the advertisement can be actually identified, as well as the time spent within the fake identification distance. . The recognition result provider 720-4 may provide recognition results according to data recognition purposes. The recognition result provider 720-4 may apply the selected data to the data recognition model by using the data selected by the recognition data selector 720-3 as an input value. Also, the recognition result may be determined by a data recognition model.

The model updating unit 720-5 may update the data recognition model based on the evaluation of the recognition result provided by the recognition result providing unit 720-4. For example, the model updater 720-5 provides the recognition result provided by the recognition result provider 720-4 to the model learner 710-4 so that the model learner 710-4 can You can update the data recognition model.

Meanwhile, a data acquisition unit 720-1, a pre-processing unit 720-2, a recognition data selection unit 720-3, a recognition result providing unit 720-4, and a model updating unit in the data recognizing unit 720 At least one of (720-5) may be manufactured in the form of at least one hardware chip and mounted on the mobile robot device 1000. For example, the data acquisition unit 720-1, the pre-processing unit 720-2, the recognition data selection unit 720-3, the recognition result providing unit 720-4, and the model update unit 720-5 At least one of them may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or manufactured as part of an existing general-purpose processor (eg CPU or application processor) or graphics-only processor (eg GPU) It may be mounted on various mobile robot devices 1000 described above.

In addition, the data acquisition unit 720-1, the pre-processing unit 720-2, the recognition data selection unit 720-3, the recognition result providing unit 720-4, and the model update unit 720-5 are one. It may be mounted on an electronic device, or may be mounted on separate electronic devices. For example, the data acquisition unit 720-1, the pre-processing unit 720-2, the recognition data selection unit 720-3, the recognition result providing unit 720-4, and the model update unit 720-5 Some of them may be included in the mobile robot device 1000, and some may be included in the server 2000.

In addition, at least one of the data acquisition unit 720-1, the pre-processing unit 720-2, the recognition data selection unit 720-3, the recognition result providing unit 720-4, and the model update unit 720-5. One can be implemented as a software module. At least one of the data acquisition unit 720-1, the pre-processing unit 720-2, the recognition data selection unit 720-3, the recognition result providing unit 720-4, and the model update unit 720-5 When implemented as a software module (or program module including instructions), the software module may be stored in a computer-readable, non-transitory computer readable recording medium (non-transitory computer readable media). Also, in this case, at least one software module may be provided by an Operating System (OS) or a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and the other part may be provided by a predetermined application.

10 is a configuration diagram of an advertisement management system for a mobile robot device according to a third embodiment of the present invention.

Referring to FIG. 10 , the advertisement management system according to the present invention may include an advertisement seller terminal 10 , an advertisement buyer terminal 20 and a management server 30 . Here, the management server 30 may be the mediation server 2000 of FIG. 4 . Meanwhile, in FIG. 10, for convenience of description, one advertisement seller terminal 10 and one advertisement buyer terminal 20 are shown, but the present invention is not limited thereto, and the advertisement seller terminal 10 and the advertisement buyer terminal 20 may be provided in a plurality of can

The advertisement seller terminal 10 is a terminal capable of accessing the management server 30 through a communication network, and generally refers to a desktop computer, a notebook computer, a personal information terminal (PDA), an ultra-mobile PC (UMPC), a palmtop computer, etc. The invention is not limited thereto.

The advertisement seller terminal 10 may request an advertisement service by accessing the management server 30 to directly attract advertisements and registering advertisement products displaying advertisements on the exterior of the mobile robot device. The advertisement seller terminal 10 posts an advertisement on the exterior of the mobile robot device according to the request of the advertisement buyer terminal 20 when the advertisement product is won by the advertisement buyer terminal 20, and receives the advertisement buyer terminal from the management server 30. (20) can get the settlement of the paid advertisement fee.

The advertisement buyer terminal 20 is a terminal that bids on advertisement products of the management server 30 so as to display advertisements on the exterior of the mobile robot device, and is generally a desktop computer, a laptop computer, a personal digital assistant (PDA), an ultra-mobile PC ( UMPC), palm top computer, etc., but the present invention is not limited thereto.

Specifically, the advertisement buyer terminal 20 may apply for a bid by selecting an advertisement product, and may request publication of an advertisement through the management server 30 as the advertisement product is a successful bid. Here, the advertisement purchaser terminal 20 may select an advertisement product by selecting an advertisement surface of the exterior of a currently available mobile robot device and an autonomous driving area of the mobile robot device. In addition, it is preferable that the advertisement buyer terminal 20 presents a bid price by adding the advertisement cost and additional money according to the advertisement effect to the price of the advertisement product.

In addition, the advertisement purchaser terminal 20 may check information on advertising costs through cost calculation details corresponding to reports provided by the management server 30 and pay the advertising costs calculated by the management server 30 . Here, the advertisement buyer terminal 20 is an advertising cost settlement method, which is a method of deducting an advertising cost per designated exposure within a period desired by the buyer from an agreed amount or a method of paying in a flat rate regardless of the number of exposures within the period desired by the buyer. You can choose one.

The management server 30 interworks with the application programs installed and executed in the advertisement seller terminal 10 and the advertisement buyer terminal 20 through a communication network or responds to the setting of the website provided by the management server 30 to the web site. After conducting an auction for advertisement products for each mobile robot device according to the site address (URL: Uniform Resource Locator), if the advertisement product is a successful bid, the advertisement is requested to be displayed on the exterior of the mobile robot device, and while the mobile robot device is driving The advertisement effect is measured based on sensing information including at least one of the number, location, direction, and time spent within the fake identification distance of pedestrians located in the actual identifiable distance and direction of the obtained advertisement, and the measured advertisement effect Based on this, advertising costs are calculated. In addition, the management server 30 stores advertisement information according to the calculated advertisement cost, generates cost calculation details for the corresponding advertisement cost, and provides it to the advertisement buyer terminal 20, thereby allowing the advertisement buyer terminal 20 to pay the advertisement cost. let it do

The management server 30 conducts an auction for each mobile robot device, sets the successful bid price in order of highest bid price as the bid price for the mobile robot device increases according to the recognition of the mobile robot device, and for the successful advertisement product, the advertisement buyer terminal ( According to the request of 20), the advertisement seller terminal 10 of the corresponding advertisement product may be requested to publish an advertisement.

Here, the management server 30 may set the price of the advertisement product based on the location and shape of the advertising surface of the mobile robot device, the demographic characteristics of the autonomous driving area, and the past pedestrian exposure and attention in the autonomous driving area. . In addition, the management server 30 additionally considers the geographical characteristics of the autonomous driving area along with the position and shape of the advertisement screen of the mobile robot device, the demographic characteristics of the autonomous driving area, and the past pedestrian exposure and attention in the autonomous driving area. In this way, the price of the advertising product can be set.

In addition, the management server 30 may update the price of the advertisement product based on the sales history and average bid price of the advertisement product.

In addition, the management server 30 may re-auction the advertisement product when the calculated advertisement cost exceeds the successful bid price.

The management server 30 is based on sensing information including at least one of the number, location, direction, and time spent within the temporary identification distance of pedestrians located in the actual identifiable distance and direction of the advertisement obtained while the mobile robot device is driving to measure the advertising effect, and calculate the advertising cost based on the measured advertising effect.

In addition, the management server 30 generates cost calculation details according to the calculated advertising costs, and when payment is made from the advertisement buyer terminal 20 that has inquired about the generated cost calculation details, it generates and stores corresponding payment information.

In addition, the management server 30 receives membership registration according to the access of the advertisement buyer terminal 20, stores member information, and when an advertisement service is requested through a website provided by the advertisement buyer terminal 20, a service procedure is performed. After determining the type of advertisement, it is possible to set which mobile robot device to post the advertisement to be posted. Here, the type of advertisement is a type in which the advertisement is serviced, and may be a text (text) advertisement, an image advertisement, or a video advertisement.

In addition, advertisement information (type of advertisement, contract period, advertisement cost, etc.) set while the management server 30 proceeds with a procedure for requesting an advertisement service can be integrated and managed through a database built in the management server 30.

11 is a block diagram showing a schematic configuration of the inside of a management server according to a third embodiment of the present invention.

Referring to FIG. 11, the management server 30 according to the present invention includes an auction progressing unit 1110, an advertisement requesting unit 1120, an advertising effect measuring unit 1130, an advertising cost calculating unit 1140, and a payment processing unit 1150. , It may include an advertisement information database 1160, a member information database 1170, and a payment information database 1180.

The auction proceeding unit 1110 registers an advertisement product from the advertisement seller terminal 10 and receives a bid for the advertisement product from the advertisement purchaser terminal 20 while conducting an auction on the advertisement product for each mobile robot device. When conducting an auction for each mobile robot device, the auction advancing unit 1110 may determine successful bid prices in ascending order of bidding prices by increasing the bid price for the mobile robot device according to the recognition of the mobile robot device.

Here, the auction proceeding unit 1110 may set the price of the advertisement product based on the location and shape of the advertisement screen of the mobile robot device, the demographic characteristics of the autonomous driving area, and the past pedestrian exposure and attention in the autonomous driving area. there is. In addition, the auction proceeding unit 1110 additionally determines the location and shape of the advertising screen of the mobile robot device, the demographic characteristics of the autonomous driving area, and the geographical characteristics of the autonomous driving area along with past pedestrian exposure and attention in the autonomous driving area. It can be taken into consideration to set the price of the advertisement product.

In addition, the auction progressing unit 1110 may update the price of the advertisement product based on the sales history and average bid price of the advertisement product.

In addition, the auction proceeding unit 1110 may re-auction the advertisement product when the calculated advertisement cost exceeds the successful bid price.

The advertisement requesting unit 1120 sends a mobile robot to the advertising seller terminal 10 according to the request of the advertisement buyer terminal 20 when an advertisement product is a successful bid during the auction progressing by the auction proceeding unit 1110 for the successful advertisement product. Request the placement of advertisements on the exterior of the device.

The advertisement effect measuring unit 1130 is sensing information including at least one of the number, location, direction, and time spent within the fake identification distance of pedestrians located at a distance and direction in which an advertisement obtained while the mobile robot device is driving can be actually identified. Based on the advertising effectiveness is measured.

Here, the advertising effect measurer 1130 extracts location information and direction information of an object included in the advertisement, and when the object included in the advertisement enters the gaze direction of the pedestrian, the entry point of the object included in the advertisement and, if the object included in the advertisement deviates from the gaze direction of the pedestrian, by determining the departure time of the object included in the advertisement, using the location information and direction information of the extracted object, from the entry point to the departure point The exposure time of the advertisement can be calculated.

For example, the advertisement effect measurer 1130 recognizes pedestrians encountered while driving and enters the false identification distance of an advertisement as independent objects, sums up the number of pedestrians, and measures the advertisement effect based on this. there is. At this time, when the same pedestrian is continuously identified within the temporary identification range on the same driving route, the advertising effect measurer 1130 preferably does not add the number of exposures.

In addition, the advertisement effect measurement unit 1130 calculates the time when the gaze direction is directed toward the advertisement by estimating the face recognition and the direction and speed of the pedestrian entering the fake identification range, and based on this, the advertisement effect can be measured there is. Specifically, the advertisement effect measurer 1130 treats the time when the pedestrian is located within the temporary identification range and the time when the gaze direction is facing the advertisement separately from each other. In the latter case, it can be determined that the advertisement effect is higher. .

In addition, the advertisement effect measurement unit 1130 calculates the distance between the pedestrian and the pedestrian who entered the temporary identification range, the time spent within the temporary identification range, whether the pedestrian's gaze direction was toward the advertisement and the time, etc. The degree of attention may be calculated, and accordingly, advertisement effects may be presented differently.

The advertising cost calculation unit 1140 calculates the advertising cost based on the advertising effect measured by the advertising effect measurer 1130 . The advertising cost calculation unit 1140 may generate cost calculation details according to the calculated advertising costs and store the generated cost calculation details in the advertisement information database 1160 .

The payment processing unit 1150 may request the advertisement purchaser terminal 20 to pay the advertising cost for the advertising cost calculated by the advertising cost calculation unit 1140 . In addition, the payment processing unit 1150 does not directly request the advertisement buyer terminal 20 to pay the advertisement fee, but the advertisement buyer terminal 20 searches for cost calculation details stored in the advertisement information database 1160 and pays the advertisement fee. You may. In addition, the advertisement buyer terminal 20 is an advertising cost settlement method, which is a method of deducting an advertising cost per designated exposure within a period desired by the buyer from an agreed amount or a method of paying in a flat rate regardless of the number of exposures within the period desired by the buyer. Of course you can choose one.

The advertisement information database 1160 may collectively store data generated by the auction advancing unit 1110 , the advertising effect measuring unit 1130 , and the advertising cost calculating unit 1140 as advertisement information. Here, the advertisement information may include the type of advertisement, contract period of advertisement, price of advertisement product, bid price, successful bid price, advertisement effect, advertisement cost, and the like.

The member information database 1170 serves to store member information generated by the auction proceeding unit 1110 . Here, the member information may include a member ID, a member password, and a unique identification code.

The payment information database 1180 receives payment information generated as the advertisement purchaser terminal 20 pays advertisement costs from the payment processing unit 1150 and stores them collectively. Here, the payment information may basically include a payment subject (company name or personal name and resident registration number), payment amount, number of payments, payment account number, account holder, and the like.

12 is a signal flow diagram illustrating an advertisement management method of a mobile robot device according to a third embodiment of the present invention.

Referring to FIG. 12 , the advertisement seller terminal 10 accesses the management server 30 to directly attract advertisements and registers advertisement products displaying advertisements on the exterior of the mobile robot device (S1210).

The management server 30 conducts an auction for advertising products for each mobile robot device (S1220). Here, in conducting the auction for each mobile robot device, the management server 30 may set the successful bid price in ascending order of bid prices for the mobile robot device according to the awareness of the mobile robot device. In addition, the management server 30 may set the price of an advertisement product based on the location and shape of the advertising screen of the mobile robot device, the demographic characteristics of the autonomous driving area, and the past pedestrian exposure and attention in the autonomous driving area. . In addition, the management server 30 additionally considers the geographical characteristics of the autonomous driving area along with the position and shape of the advertisement screen of the mobile robot device, the demographic characteristics of the autonomous driving area, and the past pedestrian exposure and attention in the autonomous driving area. In this way, the price of the advertising product can be set. In addition, the management server 30 may update the price of the advertisement product based on the sales history and average bid price of the advertisement product. In addition, the management server 30 may re-auction the advertisement product when the calculated advertisement cost exceeds the successful bid price.

The advertisement buyer terminal 20 accesses the management server 30 and applies for a bid by selecting an advertisement product (S1230). Here, the advertisement purchaser terminal 20 may select an advertisement product by selecting an advertisement surface of the exterior of the currently available mobile robot device and an autonomous driving area of the mobile robot device. In addition, the advertisement buyer terminal 20 may present a bid price by adding an advertisement cost according to an advertisement effect and additional money to the price of an advertisement product.

The management server 30 determines whether the advertisement product has been successful (S1240). At this time, the management server 30 assumes that the advertisement purchaser terminal 20 that presented the highest bid price has won the advertisement product.

The advertisement buyer terminal 20 requests the advertisement seller terminal 10 to publish an advertisement through the management server 30 as the advertisement product is successful (S1250).

The advertisement seller terminal 10 posts an advertisement on the exterior of the mobile robot device according to the request of the advertisement buyer terminal 20 (S1260).

The management server 30 is based on sensing information including at least one of the number, location, direction, and time spent within the temporary identification distance of pedestrians located in the actual identifiable distance and direction of the advertisement obtained while the mobile robot device is driving To measure the advertising effect (S1270).

For example, when the number of pedestrians is 3, the advertising effect may be 3, and when the number of pedestrians is 5, the advertising effect may be 5.

In addition, the management server 30 may measure the advertising effect by scoring points according to the location of the mobile robot device. For example, when the mobile robot device is located in a place with a large flow (eg, a place with a flow of 50 people), the advertising effect may be 5, and the mobile robot device is located in a place with a small flow (eg, a place with a flow of 10 people). ), the advertising effect may be 1.

Also, when the mobile robot device has its back to the pedestrian, the advertising effect may be zero.

In addition, when the time the pedestrian stays within the temporary identification distance is 5 minutes, the advertisement effect may be 5, and when the time the pedestrian stays within the temporary identification distance is 1 minute, the advertisement effect may be 1.

As such, the management server 30 may measure the advertisement effect by integrating the advertisement effect for each of the number, location, direction, and time spent within the fake identification distance of the pedestrian located in the actual identifiable distance and direction of the advertisement. Meanwhile, in the embodiment of the present invention, for convenience of description, the number of pedestrians and the time spent within the temporary identification distance have been described as specific numbers, but are not limited thereto and may also be within a specific range.

Then, the management server 30 calculates the advertising cost based on the measured advertising effect (S1280). For example, if the measured advertising effect is 100, the advertising cost may be 100,000 won, and if the measured advertising effect is 500, the advertising cost may be 500,000 won.

Finally, the management server 30 requests payment of the calculated advertising cost (S1290). The management server 30 may not directly request the advertisement purchaser terminal 20 to pay the advertisement fee, and the advertisement purchaser terminal 20 may pay the advertisement fee after inquiring the cost calculation details stored in the advertisement information database 1160. there is. In addition, the advertisement buyer terminal 20 is an advertising cost settlement method, which is a method of deducting an advertising cost per designated exposure within a period desired by the buyer from an agreed amount or a method of paying in a flat rate regardless of the number of exposures within the period desired by the buyer. Of course you can choose one.

The above method may be implemented through various means. For example, embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.

In the case of hardware implementation, the method according to the embodiments of the present invention includes one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices) , Field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers and microprocessors.

In the case of implementation by firmware or software, the method according to the embodiments of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software codes may be stored in a memory unit and driven by a processor. The memory unit may be located inside or outside the processor and exchange data with the processor by various means known in the art.

In the above, the embodiments disclosed in this specification have been described with reference to the accompanying drawings. As such, the embodiments shown in each drawing should not be construed as being limited, and may be combined with each other by those skilled in the art who have been familiar with the contents of this specification, and when combined, it may be interpreted that some components may be omitted.

Here, terms or words used in this specification and claims should not be construed as being limited to ordinary or dictionary meanings, but should be interpreted as meanings and concepts consistent with the technical spirit disclosed in this specification.

Therefore, the embodiments described in this specification and the configurations shown in the drawings are only the embodiments disclosed in this specification, and do not represent all of the technical ideas disclosed in this specification, and various equivalents that can replace them at the time of this application It should be understood that there may be waters and variations.

1110: Auction progress unit 1120: Advertisement request unit
1130: Advertising effect measurement unit 1140: Advertising cost calculation unit
1150: Payment processing unit 1160: Advertisement information database
1170: Member information database 1180: Payment information database

Claims (15)

After conducting an auction for advertising products for each mobile robot device, if the advertising product is a successful bid, an advertisement is requested to be posted on the exterior of the mobile robot device, and the actual identifiable distance of the advertisement obtained while the mobile robot device is running The advertising effect is measured based on the sensing information including at least one of the number of pedestrians located in the direction, the location, the direction, and the time spent within the fake identification distance, the advertising cost is calculated based on the measured advertising effect, and the calculated advertising cost Management server requesting payment;
An advertisement buyer who connects to the management server, applies for bidding by selecting the advertisement product, requests publication of an advertisement through the management server as the advertisement product is successful, and pays the advertising cost calculated by the management server. terminal; and
After registering an advertisement product in the management server, if the advertisement product is a successful bid for the advertisement purchaser terminal, an advertisement is displayed on the exterior of the mobile robot device according to a request of the advertisement purchaser terminal, and the advertisement purchaser terminal receives the advertisement product from the management server. an advertisement seller terminal receiving settlement of the paid advertisement fee;
Advertising management system of a mobile robot device comprising a.
According to claim 1,
The advertisement purchaser terminal connects to the management server and selects the advertisement product by selecting an advertisement surface of the exterior of the currently available mobile robot device and an autonomous driving area of the mobile robot device. Advertisement management of the mobile robot device, characterized in that system.
According to claim 1,
The management server sets the price of the advertisement product based on the location and shape of the advertisement screen of the mobile robot device, demographic characteristics of the autonomous driving area, and past pedestrian exposure and attention in the autonomous driving area. Advertising management system of a mobile robot device.
According to claim 3,
The advertisement management system of the mobile robot device, characterized in that the management server sets the price of the advertisement product by additionally considering the geographical characteristics of the autonomous driving area.
According to claim 3,
The advertising management system of the mobile robot device, characterized in that the advertisement buyer terminal presents a bid price by adding the advertising cost and additional money according to the advertising effect to the price of the advertising product.
According to claim 1,
The advertisement buyer terminal selects one of the method of deducting the advertisement cost per designated exposure within the period desired by the buyer from the contracted amount and the method of paying in a fixed amount regardless of the number of exposures within the period desired by the buyer as an advertising cost settlement method Advertising management system of a mobile robot device, characterized in that.
According to claim 1,
The management server executes advertisements according to the advertisement cost settlement method selected by the buyer, generates a report according to the result of advertisement execution based on the time point when the advertisement fee paid by the buyer is exhausted or the contracted period ends, and transmits the report to the advertisement buyer terminal. An advertisement management system for a mobile robot device, characterized in that it provides and charges an advertisement fee.
According to claim 1,
Wherein the management server updates the price of the advertisement product based on the sales history and average bid price of the advertisement product.
According to claim 1,
Wherein the management server proceeds with a re-auction of the advertisement product when the calculated advertisement cost exceeds the successful bid price.
an auction advancing unit that conducts an auction for advertisement products for each mobile robot device;
an advertisement requesting unit requesting display of an advertisement on the exterior of the mobile robot device when an advertisement product is a successful bid during the auction process of the auction proceeding unit;
Advertising effect is measured based on sensing information including at least one of the number, location, direction, and time spent within a false identification distance of pedestrians located at a distance and direction where the advertisement can be actually identified while the mobile robot device is driving Advertising effect measurement unit to;
An advertising cost calculation unit for calculating an advertising cost based on the measured advertising effect; and
a payment processing unit requesting payment of the calculated advertising cost;
A management server that includes a.
According to claim 10,
The auction proceeding unit sets the price of the advertisement product based on the location and shape of the advertising screen of the mobile robot device, demographic characteristics of the autonomous driving area, and past pedestrian exposure and attention in the autonomous driving area. management server.
According to claim 10,
The management server according to claim 1 , wherein the auction proceeding unit determines the price of the advertising product by additionally considering the geographical characteristics of the autonomous driving area.
According to claim 10,
The management server according to claim 1 , wherein the auction proceeding unit updates the price of the advertisement product based on a sales history and an average bid price of the advertisement product.
According to claim 10,
The management server according to claim 1 , wherein the auction proceeding unit proceeds with a re-auction of the advertisement product when the calculated advertisement cost exceeds the successful bid price.
In the advertisement management method of the mobile robot device of the management server,
Conducting an auction for advertising products for each mobile robot device;
Requesting an advertisement to be displayed on the exterior of the mobile robot device when the advertisement product is successful;
Advertising effect is measured based on sensing information including at least one of the number, location, direction, and time spent within a false identification distance of pedestrians located at a distance and direction where the advertisement can be actually identified while the mobile robot device is driving doing;
Calculating an advertising cost based on the measured advertising effect; and
requesting payment of the calculated advertising cost;
Advertisement management method of a mobile robot device comprising a.

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