KR102317854B1 - User interface for real-time monitoring - Google Patents

Abstract

A computer program stored in a computer-readable storage medium provides a user interface for real-time monitoring when executed by one or more processors of a computing device. The user interface may comprise: a first layer that displays a first image including the geographic information; and a second layer that displays a first graphic including a grid including one or more tiles displayed in a preset size, wherein each of the one or more tiles displays to indicate a rate of change of a corresponding area in the first image.

Description

User interface for real-time monitoring}

The present disclosure relates to a method of providing a user interface for real-time monitoring, and more particularly, to a method of providing a user interface for monitoring an image including geographic information.

With the recent development of deep learning, object identification deep learning models are being widely applied in public, industrial, and military fields. In particular, object identification deep learning models are expected to be highly useful in fields such as CCTV and satellite images. Meanwhile, technology for acquiring high-resolution satellite images from artificial satellites has also become common. As the application of deep learning models for object identification in the field of satellite images increases, the amount and diversity of information acquired from satellite images or information delivered to users from satellite images is also greatly increasing.

Accordingly, in providing information on satellite images, it is necessary to provide user convenience as well. Some service applications create indicators, such as graphics, to display information to be conveyed to users in satellite imagery. In providing information on satellite images, it is required to develop a user interface that can improve the visibility of such various indicators, improve the utilization rate of the screen area, and ultimately deliver a huge amount of information to the user.

Republic of Korea Patent Publication 2020-0087079

The present disclosure is devised in response to the above-described background technology, and relates to a method of providing a user interface for real-time monitoring.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

An object of the present invention is to provide a computer program stored in a computer-readable storage medium for solving the above problems. The computer program, when executed by one or more processors of a computing device, provides a user interface for real-time monitoring, the user interface comprising: a first layer, displaying a first image comprising geographic information; and displaying a first graphic including a grid including one or more tiles displayed in a preset size, wherein each of the one or more tiles indicates a rate of change of a corresponding area in the first image, second layer; may include.

Also, the preset size of the one or more tiles may be fixed regardless of a scale level of geographic information displayed in the first image.

Also, the color of the first tile, which is one of the one or more tiles, may be determined based on at least the change rate of the first area, which is an area in the first image corresponding to the first tile.

In addition, the rate of change includes one or more change rate sections, different color properties are assigned to each of the one or more change rate sections, and the color of the first tile includes the maximum value of the rate of change of the first region. It may be determined based on the color attribute allocated to the one or more change rate intervals.

In addition, the user interface may include: a third layer configured to display a second graphic corresponding to each object detected by inputting the first image into one or more object detection algorithms; further comprising, wherein the second graphic is composed of entities including a first entity, wherein the first entity is at least a part of the object and a first object corresponding to the first entity is detected position can be guided.

Also, the size of the first entity may be determined based on at least the number of the objects included in the first object.

In addition, the color of the first entity may be determined based on at least one of a first object detection algorithm associated with the first object and a first detection time that is a detection time of the first object.

In addition, the color of the first entity has a color series corresponding to the color of the second entity corresponding to the second object detected by inputting the first image into the first object detection algorithm, and the first detection A time point may be visually emphasized if it lags a critical time point.

In addition, the third layer includes the first entity and the third entity when the first entity has a distance less than a threshold from a third entity that is at least some of the entities, and when a preset clustering condition is satisfied. replacing an entity, displaying a clustered entity generated based on the first entity and the third entity, wherein the preset clustering condition comprises: a first object detection algorithm associated with the first object; At least one of whether a third object detection algorithm associated with a third object corresponding to the third entity corresponds to each other, and whether a detection time of at least one of the first object and the third object follows a threshold time. may include.

In addition, the clustered entity may be determined when the first object detection algorithm and the third object detection algorithm correspond to each other, and both the detection times of the first object and the third object precede the threshold time. can be displayed.

In addition, the user interface may include: a fourth layer, which is allowed to display a notification regarding the object or the rate of change when at least one of the object or the rate of change satisfies a condition for generating a notification; further comprising, wherein the notification generation condition includes a condition regarding the detection area of the object, a condition regarding the type of the object, a condition regarding the detection amount of the object, a condition regarding the area in which the change rate is detected, and a condition regarding the change rate. It may include at least one of conditions related to variation.

In addition, the notification is, at a location in the user interface that guides at least one of a location where an object satisfying the notification generation condition is detected or a location where a change rate satisfying the notification generation condition is detected, any of two or more notification levels It may include displaying a third graphic visually representing one level.

In addition, the user interface may include: a fifth layer configured to display a fourth graphic in an area within the first image corresponding to a photographing area of the image received by the computing device; further comprising, wherein the fourth graphic includes, based at least on the scale level of the first image, first image information about a boundary of the photographing area, second image information about a photographing subject of the image, and the image It may be determined to include at least one of the third image information regarding the photographing date and time of .

In addition, the user interface may further include a sixth layer for displaying comprehensive information, and the synthetic information may include information related to at least a portion of the first graphic to the fourth graphic.

A method of displaying a user interface for real-time monitoring in a user terminal, devised in response to the above background art, the method comprising: displaying a first layer, displaying a first image including geographic information; and displaying a first graphic that is a grid including one or more tiles displayed in a preset size, and displaying each of the one or more tiles to indicate a rate of change of a corresponding area in the first image. displaying the layer; may include.

A user terminal devised in response to the above-mentioned background art, the user terminal comprising: a processor including one or more cores; Memory; and an output unit providing a user interface; including, wherein the user interface comprises: a first layer for displaying a first image including geographic information; and displaying a first graphic that is a grid including one or more tiles displayed in a preset size, and displaying each of the one or more tiles to indicate a rate of change of a corresponding area in the first image. Layer; may include.

The technical solutions obtainable in the present disclosure are not limited to the above-mentioned solutions, and other solutions that are not mentioned are clearly to those of ordinary skill in the art to which the present disclosure belongs from the description below. can be understood

According to some embodiments of the present disclosure, a user interface for real-time monitoring may be provided.

Effects obtainable in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present disclosure belongs from the description below. .

Various aspects are now described with reference to the drawings, in which like reference numbers are used to refer to like elements collectively. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It will be evident, however, that such aspect(s) may be practiced without these specific details.
1 is a block diagram illustrating a computing device that provides a user interface for real-time monitoring according to some embodiments of the present disclosure.
2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure;
3 is a block diagram illustrating layers capable of configuring a user interface for real-time monitoring in some embodiments of the present disclosure.
4 is a diagram exemplarily illustrating a user interface according to some embodiments of the present disclosure.
FIG. 5 is an enlarged view of part A of FIG. 4 and exemplarily illustrates a first graphic according to some embodiments of the present disclosure.
6 is a diagram exemplarily illustrating a first graphic according to some embodiments of the present disclosure in comparison with the prior art.
7 is a diagram exemplarily illustrating a method of displaying a second graphic according to some embodiments of the present disclosure.
8 is a flowchart for displaying a second graphic according to some embodiments of the present disclosure.
9 is a diagram exemplarily illustrating a method of displaying a third graphic according to some embodiments of the present disclosure.
10 is a diagram exemplarily illustrating a method of displaying a third graphic according to some embodiments of the present disclosure.
FIG. 11 is an enlarged view of part B of FIG. 4 and exemplarily illustrates a method of displaying a fourth graphic according to some embodiments of the present disclosure.
12 is a flowchart for providing a user interface for real-time monitoring according to some embodiments of the present disclosure.
13 is a simplified, general schematic diagram of an example computing environment in which some embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific descriptions.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components may communicate via a network such as the Internet with another system, for example via a signal having one or more data packets (eg, data and/or signals from one component interacting with another component in a local system, distributed system, etc.) may communicate via local and/or remote processes depending on the data being transmitted).

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the feature and/or element in question is present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements and/or groups thereof. Also, unless otherwise specified or otherwise clear from context to refer to a singular form, the singular in the specification and claims should generally be construed to mean "one or more."

In addition, "more than" and/or "less than" may be interpreted as "more than" and/or "less than", and vice versa.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or combinations of both. It should be recognized that they can be implemented with To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Descriptions of the presented embodiments are provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. This invention is to be construed in its widest scope consistent with the principles and novel features presented herein.

In the present disclosure, a network function, an artificial neural network, and a neural network may be used interchangeably.

In the present disclosure, the term “image including geographic information” may be used as a term meaning a geographic image obtained from an image acquisition device and received by the computing device 100 . The image acquisition device may be one or more satellites, and in this case, the image including geographic information may be a satellite image taken from the satellite. That is, the image including geographic information may be an image obtained by capturing an arbitrary area at an arbitrary time by any satellite. However, the present invention is not limited thereto, and for example, the image including geographic information may be an aerial image including a drone image, an airplane image, and the like.

In the present disclosure, the term "real-time monitoring" refers to, in response to an image being received by the computing device 100 or a server (not shown), the received image is read based on a deep learning algorithm, and the read data is read in real time. It can be used as a term meaning to provide. That is, in the real-time monitoring, the reading may be started based on the deep learning algorithm in real time in response to the reception of the image, rather than the user selecting an image and starting reading through the deep learning algorithm. In addition, real-time monitoring does not provide read data only after the deep learning algorithm reads the received image is completed, but reads related read data in real time when an event such as an object is detected or a change is detected while reading is in progress. may be provided.

In the present disclosure, the term "deep learning algorithm" refers to a series of inference processes and/or inferences that detect objects, detect changes, or otherwise generate data from images including geographic information. may be used as a term to refer to one or more modules that perform For example, the deep learning algorithm in the present disclosure may include a change detection algorithm, or an object detection algorithm.

The change detection algorithm may detect a change pattern from an image of an area photographed by a satellite. For example, the change detection algorithm may calculate a rate of change between any two viewpoints in at least a part of the photographed area.

The object detection algorithm may detect pre-learned types of objects from an image of an area photographed by a satellite. For example, when a received satellite image is input to the algorithm A for detecting an aircraft object, aircraft objects existing in the input satellite image may be detected.

Meanwhile, one or more algorithms may exist according to an object type for detection, and one or more different algorithms may be used simultaneously in one real-time monitoring project. For example, Algorithm A for detecting aircraft objects and Algorithm B for detecting submarine objects may be used in one real-time monitoring project. In this case, on the user interface, objects detected based on algorithm A (ie, aircraft objects) may be displayed to be visually distinguished from objects detected based on algorithm B (ie, submarine objects). The above-described example is merely an example, and does not limit the present disclosure.

In the present disclosure, the deep learning algorithm may constitute a computer program stored in the memory 130 . That is, the processor 120 may read the stored computer program, input the satellite image to the deep learning algorithm, and obtain read data of the deep learning algorithm. In addition, the user interface 200 may be generated based on the obtained data. For example, in the present disclosure, read data may be displayed through one or more of first to fourth graphics to be described later.

However, the present invention is not limited thereto, and the deep learning algorithm may constitute a computer program stored in a memory of a server (not shown). That is, the processor of the server acquires read data from the satellite image through a deep learning algorithm, and transmits it to the computing device 100 , or transmits the user interface 200 generated based on the obtained read data to the computing device 100 . ) can be transmitted.

In the present disclosure, the term "graphic" may be used as a term referring to a visual element for information transfer, wherein information transfer includes information transfer about an image including the above-described geographic information, or information including geographic information. It may mean delivering information about read data obtained by inputting an image to a deep learning algorithm. The visual element may be, for example, a photograph, graph, diagram, letter, number, symbol, figure, illustration, color, animation, or a set, arrangement, arrangement, or the like thereof.

More specifically, a graphic in the present disclosure includes a first graphic for conveying information regarding a detected change in an image, a second graphic for conveying information regarding a detected object in the image, a detected change in an image and/or an image It may include a third graphic for transmitting notification information about an object detected within, or a fourth graphic for transmitting information regarding photographing of an image. Each of the first to fourth graphics may include any one of the illustrated visual elements, or any other visual elements that can be used for each information transfer.

In the present disclosure, the term “tile” may be used as a term to refer to each of figures of a certain shape that fills an area of an arbitrary size without gaps or overlapping portions. The shape of a figure that can be used as a tile is very diverse, and for example, it may be a polygon such as an equilateral triangle, a square, a regular hexagon, a rhombus, a rectangle, a trapezoid, or an irregular shape of a curved shape.

Regarding the meaning of "graphic" as described above, a set, arrangement, and arrangement of "tile" or "tile" may also be an example of "graphic". In the present disclosure, “tile” is used to describe an example of a first graphic, particularly for conveying information regarding a detected change in an image. That is, it is possible to fill an image, which is a region of an arbitrary size, with figures of a certain shape, and to transmit information about a change detected in a part of the image corresponding to the properties of each figure (ie, tile). The foregoing is merely an example, and does not limit the present disclosure.

1 is a block diagram illustrating a computing device providing a user interface for real-time monitoring according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In an embodiment of the present disclosure, the computing device 100 may include other components for performing the computing environment of the computing device 100 , and only some of the disclosed components may configure the computing device 100 .

The computing device 100 according to an embodiment of the present disclosure may include a network unit 110 , a processor 120 , a memory 130 , an output unit 140 , and an input unit 150 .

The computing device 100 according to embodiments of the present disclosure may include a personal computer (PC), a notebook (note book), a mobile terminal, a smart phone, a tablet PC, and the like. and may include all types of terminals capable of accessing a wired/wireless network.

The network unit 110 according to an embodiment of the present disclosure may use any type of wired or wireless communication systems.

In the present disclosure, the network unit 110 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be a well-known World Wide Web (WWW), and may use a wireless transmission technology used for short-range communication, such as infrared (IrDA) or Bluetooth (Bluetooth).

The techniques described herein may be used in the networks mentioned above, as well as in other networks.

The processor 120 according to an embodiment of the present disclosure may be configured with one or more cores, and may include a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), It may include a processor for data analysis, deep learning, and/or provision of a user interface, such as a tensor processing unit (TPU). The processor 120 may read a computer program stored in the memory 130 to perform data processing for machine learning according to an embodiment of the present disclosure and/or provide a user interface. According to an embodiment of the present disclosure, the processor 120 may perform an operation for learning the neural network. The processor 120 is for learning of a neural network such as processing input data for learning in deep learning (DL), extracting features from input data, calculating an error, and updating the weight of the neural network using backpropagation. calculations can be performed. At least one of a CPU, a GPGPU, and a TPU of the processor 120 may process learning of a network function. For example, the CPU and the GPGPU can process learning of a network function and data classification using the network function together. In addition, in an embodiment of the present disclosure, learning of a network function and data classification using the network function may be processed by using the processors of a plurality of computing devices together. In addition, the computer program executed in the computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

The memory 130 according to an embodiment of the present disclosure may store any type of information generated or determined by the processor 120 and any type of information received by the network unit 110 .

The memory 130 according to an embodiment of the present disclosure may include a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (eg, SD or XD memory, etc.), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read-Only Memory, ROM, Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM) Only memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above-described memory is only an example, and the present disclosure is not limited thereto.

The output unit 140 according to an embodiment of the present disclosure may display a user interface according to an embodiment of the present disclosure. The output unit 140 may display a user interface as shown in FIGS. 3 to 6 and 8 to 10 . The user interfaces shown and described below are examples only, and the present disclosure is not limited thereto.

The output unit 140 according to an embodiment of the present disclosure may output any type of information generated or determined by the processor 120 and any type of information received by the network unit 110 .

The output unit 140 according to an embodiment of the present disclosure includes a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), and an organic light-emitting diode (OLED). , OLED), a flexible display, and a three-dimensional display (3D display) may include at least one. Some of these display modules may be configured as a transparent type or a light transmitting type so that the outside can be viewed through them. This may be referred to as a transparent display module, and a representative example of the transparent display module is a TOLED (Transparent OLED).

A user input may be received through the input unit 150 according to an embodiment of the present disclosure. The input unit 150 according to an embodiment of the present disclosure may include a key and/or buttons for receiving a user input. A computer program for providing a user interface according to embodiments of the present disclosure may be executed according to a user input through the input unit 150 .

The input unit 150 according to embodiments of the present disclosure may receive a signal by sensing a user's button manipulation or a touch input, or may receive a user's voice or motion through a camera or microphone and convert it into an input signal. . For this purpose, speech recognition technology or motion recognition technology may be used.

The input unit 150 according to embodiments of the present disclosure may be implemented as an external input device connected to the computing device 100 . For example, the input device may be at least one of a touch pad, a touch pen, a keyboard, or a mouse for receiving a user input, but this is only an example and the present disclosure is not limited thereto.

The input unit 150 according to an embodiment of the present disclosure may recognize a user touch input. The input unit 150 according to an embodiment of the present disclosure may have the same configuration as the output unit 140 . The input unit 150 may be configured as a touch screen configured to receive a user's selection input. For the touch screen, any one of a contact capacitive method, an infrared light sensing method, a surface ultrasonic wave (SAW) method, a piezoelectric method, and a resistive film method may be used. The detailed description of the above-described touch screen is merely an example according to an embodiment of the present invention, and various touch screen panels may be employed in the computing device 100 . The input unit 150 configured as a touch screen may include a touch sensor. The touch sensor may be configured to convert a change in pressure applied to a specific portion of the input unit 150 or capacitance generated in a specific portion of the input unit 150 into an electrical input signal. The touch sensor may be configured to detect not only the touched position and area, but also the pressure at the time of the touch. When there is a touch input to the touch sensor, a signal(s) corresponding thereto is sent to the touch controller. The touch controller processes the signal(s) and then sends the corresponding data to the processor 120 . Accordingly, the processor 120 can recognize which area of the input unit 150 has been touched, and the like.

In an embodiment of the present disclosure, the server may include other configurations for performing a server environment of the server. The server may include any type of device. The server is a digital device, and may be a digital device equipped with a processor, such as a laptop computer, a notebook computer, a desktop computer, a web pad, and a mobile phone, and having a computing capability with a memory.

A server (not shown) that performs an operation for providing a user interface for real-time monitoring to a user terminal according to an embodiment of the present disclosure may include a network unit, a processor, and a memory.

The server may generate a user interface according to embodiments of the present disclosure. The server may transmit the generated user interface to the user terminal. Alternatively, the server may be a computing system that provides information for generating a user interface according to embodiments of the present disclosure to a user terminal through a network. In this case, the user terminal may be any type of computing device 100 that can access the server. The processor of the server may transmit the user interface to the user terminal through the network unit. Alternatively, the processor of the server may transmit information for generating a user interface to the user terminal through the network unit.

The server according to embodiments of the present disclosure may be, for example, a cloud server. The server may be a web server that processes the service. The above-described types of servers are merely examples, and the present disclosure is not limited thereto.

Each of the network unit, the processor, and the memory included in the server according to the embodiments of the present disclosure performs the same role as the network unit 110 , the processor 120 and the memory 130 included in the above-described computing device 100 . or may be configured in the same way.

In embodiments of the present disclosure, a layer may mean a layer that is displayed on a user interface and is used to overlap and display multiple images. Two or more layers may be overlapped and displayed. When two or more layers are overlapped and displayed, one layer may be obscured by another layer, and thus may not be visible. Alternatively, when two or more layers are overlapped and displayed and an upper layer is partially displayed semi-transparently, at least a portion of a lower layer may be visible. Two or more layers may have the same size. Also, the two or more layers may be of different sizes. A layer may include one or more regions. In addition, a layer may include one or more display objects.

In embodiments of the present disclosure, a layer may be divided into a plurality of regions. The plurality of areas may be one space on the screen that does not overlap. One layer may include only one region or a plurality of regions. One area may include one or more display objects.

In embodiments of the present disclosure, an object may be a set of pictures, symbols, or characters corresponding to programs, commands, or data, respectively. According to an embodiment of the present disclosure, an object may be used to receive a user selection input. For example, when receiving a user input for an object, the processor 120 may execute a stored command or data corresponding to the object and display it on the user interface. In embodiments of the present disclosure, an object and a display object may be interpreted as the same meaning.

In embodiments of the present disclosure, “display” may be an operation of displaying data to a user through the output unit 140 . “Display” and “display” may be used interchangeably.

Hereinafter, a method of providing a user interface for real-time monitoring according to embodiments of the present disclosure will be described.

2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure;

Throughout this specification, computational models, neural networks, network functions, and neural networks may be used interchangeably. For example, each of one or more modules constituting the deep learning algorithm in the present disclosure may operate based on a neural network, which will be described in detail below.

A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is configured by including at least one or more nodes. Nodes (or neurons) constituting the neural networks may be interconnected by one or more links.

In the neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node-to-output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the data of the output node may be determined based on data input to the input node. Here, a link interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by the user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and the links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two neural networks having different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may consist of a set of one or more nodes. A subset of nodes constituting the neural network may constitute a layer. Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from the initial input node may constitute n layers. The distance from the initial input node may be defined by the minimum number of links that must be traversed to reach the corresponding node from the initial input node. However, the definition of such a layer is arbitrary for description, and the order of the layer in the neural network may be defined in a different way from the above. For example, a layer of nodes may be defined by a distance from the final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. In addition, the hidden node may mean nodes constituting the neural network other than the first input node and the last output node.

The neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as the number of nodes progresses from the input layer to the hidden layer. have. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the input layer progresses to the hidden layer. can The neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify the latent structures of data. In other words, it can identify the potential structure of photos, texts, videos, voices, and music (for example, what objects are in the photos, what the text and emotions are, what the texts and emotions are, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In an embodiment of the present disclosure, the network function may include an autoencoder. The auto-encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes in each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with reduction from the bottleneck layer to the output layer (symmetrically with the input layer). The auto-encoder can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to a dimension after preprocessing the input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and the decoder) is too small, a sufficient amount of information may not be conveyed, so a certain number or more (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the training of a neural network, iteratively inputs the training data to the neural network, calculates the output of the neural network and the target error for the training data, and calculates the error of the neural network from the output layer of the neural network to the input layer in the direction to reduce the error. It is a process of updating the weight of each node in the neural network by backpropagation in the direction. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (ie, labeled learning data), and in the case of comparative learning, the correct answer may not be labeled in each learning data. That is, for example, the learning data in the case of teacher learning regarding data classification may be data in which categories are labeled in each of the learning data. Labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of comparison learning about data classification, an error may be calculated by comparing the input training data with the neural network output. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. A change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, in the early stage of training of a neural network, a high learning rate can be used to enable the neural network to quickly acquire a certain level of performance, thereby increasing efficiency, and using a low learning rate at the end of learning can increase accuracy.

In the training of neural networks, in general, the training data may be a subset of the actual data (that is, the data to be processed using the trained neural network), so the error on the training data is reduced but the error on the real data is reduced. There may be increasing learning cycles. Overfitting is a phenomenon in which errors on actual data increase by over-learning on training data as described above. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing errors in machine learning algorithms. In order to prevent such overfitting, various optimization methods can be used. In order to prevent overfitting, methods such as increasing the training data, regularization, and dropout that deactivate some of the nodes of the network in the process of learning, and the use of a batch normalization layer are applied. can

3 is a block diagram illustrating layers capable of configuring a user interface for real-time monitoring in some embodiments of the present disclosure.

Referring to FIG. 3 , the user interface 200 according to some embodiments of the present disclosure includes a first layer 310 displaying a first image including geographic information, and one or more tiles displayed in a preset size. A second layer 320 that displays a first graphic that is a grid and displays a rate of change of a corresponding area in the first image in which each of one or more tiles is displayed, and the first image is input to one or more object detection algorithms When at least one of the third layer 330 for displaying a second graphic corresponding to each detected object, the object, or the rate of change satisfies the condition for generating a notification, the third graphic that is a notification regarding the object or rate of change is allowed to be displayed a fourth layer 340 to be a fourth layer 340 , a fifth layer 350 for displaying a fourth graphic in a region within the first image corresponding to a capturing region of an image received by the computing device 100 , and first to fourth graphics It may include at least one of the sixth layer 360 that displays comprehensive information including information related to at least a part of it.

The processor 120 displays a first graphic that is a grid including a first layer 310 that displays a first image including geographic information, and one or more tiles displayed in a preset size, and displays one or more tiles. A second layer 320 for displaying each of the two to indicate a rate of change of a corresponding area in the first image, and a second layer 320 for displaying a second graphic corresponding to each detected object by inputting the first image into one or more object detection algorithms. When at least one of the three layers 330, the object or the rate of change satisfies the condition for generating a notification, the fourth layer 340 that is allowed to display a third graphic that is a notification regarding the object or rate of change is received by the computing device 100 A fifth layer 350 for displaying a fourth graphic in a region within the first image corresponding to the capturing region of the image, and a fifth layer for displaying comprehensive information including information related to at least a portion of the first graphic to the fourth graphic The output unit 140 may be controlled to display at least one of the six layers 360 . However, since the above-described components are not essential in implementing the user interface 200 , the user interface 200 may have more or fewer components than those listed above.

Detailed information on the first to fourth graphics and the comprehensive information will be described later with reference to FIGS. 4 to 11 .

4 is a diagram exemplarily illustrating a user interface according to some embodiments of the present disclosure.

FIG. 5 is an enlarged view of part A of FIG. 4 and exemplarily illustrates a first graphic according to some embodiments of the present disclosure.

6 is a diagram exemplarily illustrating a first graphic according to some embodiments of the present disclosure in comparison with the prior art.

7 is a diagram exemplarily illustrating a method of displaying a second graphic according to some embodiments of the present disclosure.

8 is a flowchart for displaying a second graphic according to some embodiments of the present disclosure.

9 is a diagram exemplarily illustrating a method of displaying a third graphic according to some embodiments of the present disclosure.

10 is a diagram exemplarily illustrating a method of displaying a third graphic according to some embodiments of the present disclosure.

FIG. 11 is an enlarged view of part B of FIG. 4 and exemplarily illustrates a method of displaying a fourth graphic according to some embodiments of the present disclosure.

4 shows an example in which the user interface 200 is displayed to include a first layer 310 and a third layer 330 to a sixth layer 360 .

The first layer 310 may display a first image 210 including geographic information. The processor 120 may control the output unit 140 to display the first image 210 including geographic information, and the network unit 110 to receive the first image 210 for display. can be controlled

The scale level of the first image 210 including geographic information may be changed according to a user input. That is, the first image 210 including geographic information may be zoomed in or zoomed out. In this case, the scale level is an input on a scale level control unit (not shown) (eg, an input for a handle in the slider UI), a scroll input on the first image 210, or a direct input of a scale level value ( For example, it may be changed by inputting a scale level value such as 100% or 150% in the scale level input window) or any other possible input. According to a change in the scale level of the first image 210 including geographic information, the position and/or appearance of at least a portion of first to fourth graphics to be described later may be dynamically changed and displayed.

Meanwhile, FIG. 4 illustrates an example in which the first image 210 including geographic information is displayed on a single area of the first layer 310, but is not limited thereto. For example, the first image 210 including geographic information may be displayed simultaneously in two or more divided regions. For example, when monitoring of two or more different geographic areas in the first image 210 including geographic information is required, the first layer 310 displays images of different geographic areas in two or more divided areas, respectively. can do. Alternatively, when it is necessary to monitor different read data obtained by inputting the first image 210 including geographic information into different deep learning algorithms, the first layer 310 is different from each other in two or more divided areas. Read data can be displayed individually.

Meanwhile, the read data may be displayed through one or more of first to fourth graphics to be described later, and may be displayed after being filtered according to one or more criteria. Specifically, the read data may be filtered and displayed through one or more control units 203 and 204 in the user interface 200 .

For example, according to a user input to the algorithm-related control unit 203 , only read data obtained by inputting the first image 210 into a specific algorithm may be displayed. For example, according to a user input for selecting an algorithm A for detecting an aircraft object in the algorithm-related control unit 203 , the first image 210 is input to the algorithm A and read data obtained, that is, from the first image 210 . Only the second graphic 230 corresponding to the detected aircraft objects such as a large aircraft, a medium aircraft, and a small aircraft may be displayed.

Alternatively, according to a user input to the period-related control unit 204, the first image 210 is input to the algorithm, and from the first time point (indicated as 'start date' in FIG. 4) to the second time point (' Only read data acquired during the first period up to 'end date') can be displayed. For example, only a first graphic 220 relating to a detected rate of change for a first time period and/or a second graphic 230 relating to an object detected during the first time period may be displayed. Meanwhile, when a user input regarding the period is not received, the user interface 200 may display the read data obtained from 0 o'clock today to the present time by default. The above-described example is merely an example, and does not limit the present disclosure.

The second layer 320 may display the first graphic 220 including a grid including one or more tiles 221a, 221b, 221c, 221d, 221e: 221 displayed in a preset size. Alternatively, each of the one or more tiles 221 may be displayed to indicate a change rate of a corresponding region in the first image 210 . The processor 120 displays a first graphic 220 including a grid including one or more tiles 221 displayed at a preset size, each of the one or more tiles 221 corresponding to a corresponding in the first image. The output unit 140 may be controlled to display the second layer that is displayed to indicate the change rate of the area to be used. Meanwhile, the processor 120 may calculate a change rate of a region in the first image 210 to which each of the one or more tiles 221 corresponds. However, the present invention is not limited thereto, and the above-described rate of change may be calculated by the processor of the server (not shown), and the calculated rate of change may be transmitted to the computing device 100 through the network unit 110 .

With respect to the first graphic 220 , reference may be made to FIGS. 5 and 6 . Specifically, FIG. 5 shows the user interface 200 of part A of FIG. 4 , and shows an example in which the second layer 320 including the first graphic 220 is additionally displayed.

Control on whether the second layer 320 is displayed may be performed according to a user input to the controller 222 , for example. 4 and 5 show an example in which the control unit 222 includes a toggle button capable of receiving a user input for controlling whether the second layer 320 is displayed. However, the present invention is not limited thereto, and for example, the second layer 320 may be displayed at all times regardless of user input, or a preset condition (eg, when a change is detected through deep learning algorithm reading, or a region of interest) It can be automatically displayed when the rate of change of ' exceeds a threshold, etc.).

The one or more tiles 221 may be displayed in a preset size, and the preset size may be fixed regardless of the scale level of the geographic information displayed in the first image 210 . That is, even when the first image 210 is zoomed in or zoomed out due to a change in the scale level according to a user input, the sizes of the one or more tiles 221 may be fixedly displayed to a preset size.

For example, the size of the one or more tiles 221 may be preset to a square of 50px*50px. In this case, the first graphic 220 that is a grid including one or more tiles 221 displayed in a size of 50px*50px may be displayed. Also, the size of the one or more tiles 221 is 50px*50px regardless of the scale level of the first image 210 , ie, when the scale level of the first image 210 is 100% and 150%. can be expressed in the size of

However, the present invention is not limited thereto, and the size of the one or more tiles 221 may be preset to an arbitrary size. In addition, the value of the size of the one or more tiles 221 may be set as a default value in a computer program, and then may be set by being changed to a value different from the default value by a user input. For example, a default value of the size of the one or more tiles 221 may be set to 50px*50px and then changed to 80px*80px by a user input. In this case, the size of the one or more tiles 221 displayed in the size of 80px*80px is independent of the scale level of the first image 210 , that is, for example, when the scale level of the first image 210 is 100%. and 150%, it can be displayed in a size of 80px*80px.

In the above description, a case in which one or more tiles 221 have a square shape is exemplified, but the present invention is not limited thereto. For example, the shape of the one or more tiles 221 may be set to an arbitrary shape, such as a hexagon or a star shape. Alternatively, the shape of the one or more tiles 221 may be set as a default value in a computer program, and then changed and set to a value different from the default value by a user input. For example, a default value of the shape of the one or more tiles 221 may be set to a square and then changed to a hexagon according to a user's input.

Meanwhile, each of the one or more tiles 221 may be displayed to indicate a change rate of a corresponding area in the first image 210 . In detail, the color of the first tile, which is one of the one or more tiles 221 , may be determined based on a change rate of the first area, which is an area in the first image 210 corresponding to the first tile.

The rate of change of the first area may be a value obtained by quantitatively calculating a detected change during a period between the first time point and the second time point in the geographic area corresponding to the first area. For example, the rate of change of the first region may be a rate of change of pixels included in the first region. For example, the rate of change of the pixel between the first time point and the second time point may be calculated based on the properties (color, brightness, etc.) of each of the pixels included in the first area. However, the present invention is not limited thereto, and the rate of change of the first region may be, for example, a ratio of an area occupied by a pixel in which a change is detected among the areas of the first region, or a value calculated by synthesizing the above-described values.

The rate of change may include one or more change rate intervals, and different color attributes may be assigned to each of the one or more rate of change intervals. The control unit 222 of FIG. 4 shows an example in which the rate of change includes five change rate sections. For example, the change rate section is divided based on the change rate values 0.2, 0.4, 0.6, 0.8, and 1.0, and the change rate section of 0.0 or more and less than 0.2 is colorless, the change rate section of 0.2 or more and less than 0.4 is light yellow, and 0.4 or more and less than 0.6. The color attribute of forsythia may be assigned to the change rate section, orange to the change rate section 0.6 or more and less than 0.8, and red to the change rate section 0.8 or more and less than 1.0.

In this case, each of the one or more tiles 221 is a colorless 221a, a light yellow 221b, a forsythia 221c, an orange 221d, and a red color based on a change rate of a region in the corresponding first image. Any one of colors 221e may be displayed. For example, when the change rate of the first area is 0.5, the first tile corresponding to the first area may be determined to display the forsythia color 221c.

However, the present invention is not limited thereto. For example, the rate of change may include a number of change rate sections other than 5 (eg, 2 or 10 change rate sections, etc.), or may not divide the rate of change sections. For example, a color attribute that is continuously changed according to an increase or decrease in a change rate may be assigned. For example, a color property of colorless is assigned to a rate of change of 0.0, a color property of red is assigned to a rate of change of 1.0, and a continuous gradient color property between colorless and red for a rate of change between 0.0 and 1.0. can be assigned.

Meanwhile, the color of the first tile may be determined based on a color allocated to one or more change rate sections including the maximum change rate of the first region to which the first tile corresponds. For example, when the rate of change of the first region is the rate of change of pixels included in the first region, the color of the first tile includes any one change rate interval including the maximum value among the rates of change of pixels included in the first region, and the corresponding It may be determined based on the color attribute assigned to the change rate interval. For example, when the maximum value among the rate of change of pixels included in the first region is 0.5, a rate of change section including a rate of change of 0.5 (for example, a section with a rate of change of 0.4 or more and less than 0.6) may be identified and assigned to the corresponding rate of change section. A color according to a color attribute (eg, forsythia color) may be determined as the color of the first tile.

In the above description, different color properties are assigned to each change rate section and the change rate of the area is indicated through the colors of the tiles, but the present invention is not limited thereto. For example, the tiles may be displayed to indicate the change rate of the corresponding area through any visually identifiable property other than the color. For example, pattern properties such as hatches and dots having different intervals may be assigned to each of the one or more rate-of-change sections, and in this case, each of the tiles may be filled with patterns having different intervals according to the rate of change of the corresponding area. Alternatively, different properties of the appearance (color, thickness, transparency, etc.) of the border are assigned to each of one or more rate of change sections, or the same color property is assigned to each of one or more rate of change sections, but the color saturation and/or brightness properties It may be assigned differently. In this case, each of the tiles, for example, may be displayed as a red color with low saturation and/or brightness for a low change rate, and may be displayed as a red color with high saturation and/or brightness for a high change rate. The above-described example is merely an example, and does not limit the present disclosure.

Meanwhile, the transparency of the first graphic 220 may be changed according to a user input. For example, according to a user input to the transparency controller 223 , the transparency of the first graphic 220 may be changed. 4 and 5 show, as the transparency control unit 223 includes a slider and a handle for changing the transparency, while one or more tiles 221 have an arbitrary transparency value set in the transparency control unit 223, An example in which regions in the first image corresponding to each of the tiles 221 are visually recognizable is illustrated. However, the present invention is not limited thereto, and for example, the second layer 320 may be fixed to have a preset transparency value irrespective of a user input, or may not have a transparency value.

As described above, by representing the rate of change of the image including geographic information through one or more tiles 221 having a fixed size regardless of the scale level, it is more intuitively wider than the prior art of expressing the rate of change in a pixel unit method. You can check the rate of change of the region, and you can easily search for an area with a high rate of change. In this regard, reference may be made to FIG. 6 .

FIG. 6( a ) shows an example of expressing a change rate detected from an image including geographic information in a pixel-by-pixel manner according to the prior art. In contrast, FIG. 6B illustrates an example of expressing a rate of change detected from an image including geographic information in a tile-by-tile manner, according to some embodiments of the present disclosure. Meanwhile, although the illustration of the first image is omitted in FIG. 6(b) for ease of comparison with FIG. 6(a), it is shown in FIG. It will be apparent from the foregoing description with reference.

The prior art displays each of the pixels in which a change is detected to be visually distinguished according to the detected rate of change. For example, pixels having a high rate of change are shaded to the right or blue color 229a, pixels having a medium change rate are hatched to the left or green color 228a, and pixels having a low rate of change are dot or red color. (227a).

Expressing the rate of change in units of pixels may be advantageous in identifying the specific location where the change was detected. However, in the case of monitoring a large area, it may be difficult to identify the area where the change is detected, and it may not be easy to identify the trend of the detected change.

For example, referring to FIG. 6(a) , pixels 227a having a low rate of change and pixels 228a having a rate of change of an intermediate value are displayed very adjacent to each other in the central left portion, while at the lower center portion Pixels 228a having an intermediate value of change are displayed where the pixels 229a having a high value of change are surrounded. In this case, as the scale level decreases, that is, as the first image is zoomed out, it becomes difficult to visually identify the different rates of change represented by the pixels. Furthermore, it is not easy to grasp the tendency of change, such as that the rate of change increases as it approaches the lower center of the first image.

In contrast, according to some embodiments of the present disclosure, a change rate detected in a region in the first image is displayed through a color of each of tiles corresponding to the region. For example, referring to FIG. 6B , a tile 227b corresponding to an area mainly including pixels having a low change rate is a tile 229b corresponding to an area mainly including pixels having a high change rate. It may be displayed in a color that distinguishes it from In this case, since the approximate distribution of the change detected in the first image can be seen at a glance, the change rate of a wide area can be intuitively confirmed, and it is possible to easily search for an area with a high change rate.

Furthermore, since the size of one or more tiles according to some embodiments of the present disclosure is fixed regardless of the scale level of the first image, it has the advantage of confirming the specific location where the change is detected, like the conventional method of displaying the change rate in units of pixels. can That is, even when the first image 210 is zoomed in, the sizes of one or more tiles are fixed to a preset size without being enlarged at a corresponding ratio, so that the color of each of the tiles is dynamically changed to indicate the change rate of the corresponding area. may be changed, and accordingly, a specific location where the change is detected may be confirmed, as well as a detailed rate of change in a narrow area may be confirmed.

The third layer 330 may display the second graphic 230 corresponding to each object detected by inputting the first image 210 into one or more object detection algorithms. The processor 120 may control the output unit 140 to display the second graphic 230 corresponding to each object detected by inputting the first image 210 into one or more object detection algorithms. Meanwhile, the processor 120 may acquire the detected object by inputting the first image 2210 into the object detection algorithm in response to the reception of the first image 210 . However, the present invention is not limited thereto, and the above-described object detection may be performed by a processor of a server (not shown), and data regarding the detected object may be transmitted to the computing device 100 through the network unit 110 .

The second graphic 230 displayed on the third layer 330 may include entities including the first entity. Entities 231a, 231b, 231c, 231d: 231, 232a, 232b, 232c: 232 may represent information regarding detection of objects corresponding to each of the entities. In addition, the position and/or appearance of the entities 231 and 232 may be dynamically changed and displayed.

With respect to the second graphic 230 , reference may be made to FIGS. 7 and 8 . Specifically, in FIG. 7 , each of the entities 231 and 232 is displayed based on at least the detected position of the corresponding object and/or the number of the corresponding object, and the displayed entities 231 and 232 are displayed in the first image. An example of dynamically changing based on at least the scale level of , and/or the detection time of the corresponding object is shown. 8 shows an exemplary flow chart in which the entities constituting the second graphic are dynamically changed based at least on the scale level of the first image and/or the detection time of the corresponding object.

Meanwhile, with respect to the shapes of the entities 231 and 232 , FIG. 7 exemplifies a case in which the entities are circular figures, but is not limited thereto. For example, the entities 231 and 232 may be figures of any shape such as a hexagon or a star shape, or an icon indicating information about a corresponding object (eg, an icon that visualizes the type of the corresponding object, etc.) or text (eg, the number of corresponding objects or the name of an algorithm associated with the corresponding object, etc.). The shape of the entities 231 and 232 may be set as a default value in a computer program, and then may be changed and set to a value different from the default value by a user input. For example, a default value of the shape of the entities 231 and 232 may be set to a circular shape, and then changed to a hexagonal shape according to a user's input. The above-described example is merely an example, and does not limit the present disclosure.

Entities 231 and 232 may be displayed such that each of the entities indicates information regarding detection of a corresponding object. Specifically, the first entity may guide a location where the first object corresponding to the first entity is detected. The size of the first entity may be determined based on at least the number of objects included in the first object. The color of the first entity may be determined based on at least one of a first object detection algorithm associated with the first object and a first detection time point that is a detection time point of the first object. For example, all entities corresponding to objects detected through the same object detection algorithm (eg, the first object detection algorithm) may have colors belonging to the same color series. And/or, the color of the entity corresponding to the object having the detection time following the threshold time may be visually emphasized compared to the color of the entity corresponding to the object having the detection time preceding the threshold time. Determination of the position, size, and color of the entity will be described in more detail below.

First, the first entity may guide the location where the first object corresponding to the first entity is detected. 7 illustrates an example in which an object corresponding to each of the entities 231 and 232 is displayed on a detected position. For example, when the first object corresponding to the first entity is detected at a position having a coordinate value of (41.13331, 17.21532), the first object is located on the position of the first image 210 corresponding to the coordinate of (41.13331, 17.21532). Entities can be displayed. Alternatively, when the first object corresponding to the first entity includes two or more objects, that is, for example, when the first entity is clustered entities 231a and 232c to be described later, the first entity is the coordinate value of each of the two or more objects. can be displayed at the midpoint of For example, the first entity is displayed on the position of the first image 210 corresponding to the coordinate value that is the average of the coordinate values of each of the two or more objects, or calculated by weighting the object type of each of the two or more objects, The first entity may be displayed on the position of the first image 210 corresponding to the coordinate value that is the weighted average of the respective coordinate values.

However, the present invention is not limited thereto, and for example, the first entity may indicate a location where the first object corresponding to the first entity is detected. That is, in addition to being displayed on the position where the first object is detected, an indicator (eg, a graphic such as an arrow, or text such as coordinate values), or an indicator indicating a position where the first object is detected in a separate area may be displayed.

Next, the size of the first entity may be determined based on at least the number of objects included in the first object. That is, as the number of objects included in the first object corresponding to the first entity increases, the first entity may be displayed in a larger size. 7 illustrates an example in which the entities 231a and 232c corresponding to three objects are displayed in a larger size than the entities 232a, 232b, 231b, 231c, and 231d corresponding to one object.

The correlation between the size of the first entity and the number of objects included in the first object may be based on a preset logic. For example, the preset logic may be a direct proportional function, an exponential function, a log function, etc., or may be a table such as:

entity size number of corresponding objects 100% One 120% 2 - 10 140% 10 - 50 160% 50 - 100 180% 100 - 150 200% 150 - 200 220% 200 - 300 240% 300 - 400 +20% +100

In the above description, the case in which the number of corresponding objects is expressed through the size of the entity is exemplified, but the present invention is not limited thereto. For example, the number of objects corresponding to each entity may be expressed according to not only the size of the entity, but also the color of the entity, the shape of the border of the entity, a tag that can be rendered adjacent to the entity, or text displayed on the entity. may be For example, as the number of objects corresponding to an entity increases, a color inside the entity or a border of the entity is displayed to be visually emphasized (eg, a color close to the primary color or a bold border, etc.), or a user input for selecting an entity In response (eg, click, hover, etc.), a tag including information on the number of objects corresponding to an entity may be rendered and displayed.

Alternatively, information on the number of objects corresponding to the corresponding entity may be displayed together on the entity. 7 shows, in the case of entities 231a and 232c corresponding to two or more objects, information on the number of objects (ie, "3") is displayed on the entities, and entities 232a, 232b, 231b corresponding to one object are displayed. 231c and 231d) show an example in which information on the number of objects is not displayed. In this case, by preventing excessive number information from being displayed and improving the visual recognition of an entity including two or more objects, the user can quickly search for an area requiring analysis, and ultimately the detected object information can be efficiently communicated to users.

Next, the color of the first entity may be determined based on at least one of a first object detection algorithm associated with the first object and a first detection time that is a detection time of the first object. For example, all entities corresponding to objects detected through the same object detection algorithm (eg, the first object detection algorithm) may have colors belonging to the same color series.

In this regard, FIG. 7(a) shows that the entity 231a associated with the algorithm 1 has a black color (shown as a dot pattern in FIG. 7), and the entities 232a and 232b associated with the algorithm 2 are An example having a purple-based color (shown as a pattern upward in FIG. 7 ) is shown. However, the present invention is not limited thereto, and entities related to algorithms corresponding to each other may be displayed in the same type as the shape of the border or the shape of the entity in addition to the color series. For example, entities associated with algorithm 1 may be displayed in a circular shape, and entities associated with algorithm 2 may be displayed in a star shape.

And/or, among entities corresponding to each of the objects detected through the same object detection algorithm, a color of an entity corresponding to an object having a detection time that follows a threshold time corresponds to an object whose detection time precedes the threshold time It can be visually emphasized compared to the color of the entity. That is, among entities having the same color series, an entity corresponding to the latest detection object (that is, an object whose detection time is later than the threshold time) corresponds to an accumulated object (ie, an object whose detection time precedes the threshold time) It can be visually emphasized compared to the entity being

Here, the threshold time may be set to various values, for example, the threshold time may be within one hour in the past from the current time. In this case, when the time of the current time is 14:00, the threshold time is 13:00, for example, the entity corresponding to the object detected at 13:10 (that is, the object whose detection time follows the threshold time) is 12 The object detected at :50 (ie, an object whose detection time point precedes the threshold time point) may be visually emphasized.

In relation to the method of visually highlighting according to the detection point, FIG. 7A shows an example in which the entity 232a corresponding to the latest detection object is displayed in a darker color than the entity 232b corresponding to the accumulated object. (In FIG. 7, it is shown that the narrower the spacing between the hatches, the darker the color). However, the present invention is not limited thereto. For example, in the case of the latest detection object, it can be visually distinguished from the accumulated object by displaying it blinking, displaying it as an entity of a different color, or making the shape of the entity or the shape of the edge of the entity different. have.

Meanwhile, two or more entities may be replaced with a clustered entity and displayed when they have an interval less than a threshold value and when a preset clustering condition is satisfied. In this regard, reference may be made to FIG. 8 .

First, it may be determined whether the first entity has an interval less than a threshold value from a third entity that is at least some of the entities ( S110 ). For example, when detected objects are located adjacent to each other, entities corresponding to each object may be placed close enough to have an interval less than a threshold value. Alternatively, even in the case of two or more entities having a spacing greater than or equal to a threshold, the display may be dynamically changed and displayed to have a spacing less than the threshold according to a change in the scale level of the first image (ie, when the first image is zoomed out). can

In this regard, (a) and (b) of FIG. 7 shows that the clustered entity 231a is three independent entities 231b that do not overlap each other as the scale level of the first image is changed (ie, zoomed in). , 231c, 231d) shows an example of being changed and displayed. Meanwhile, as described above with respect to the correlation between the size of the entity and the number of objects, as the clustered entity 231a corresponding to three objects zooms in, independent entities 231b and 231c corresponding to one object, respectively. , 231d), and it can be seen that the size of each entity is also reduced.

Meanwhile, the interval less than the threshold may be calculated based on the center point of the first entity and the third entity, or the boundary line between the first entity and the third entity. Alternatively, when the first entity and the third entity overlap, it may be determined that the first entity and the third entity have a distance less than a threshold.

When it is determined that the first entity and the third entity have a distance greater than or equal to the threshold (S110, No), the first entity and the third entity do not create separate clustered entities, and the first entity and the third entity remain as they are. can be displayed.

On the other hand, if it is determined that the first entity and the third entity have an interval greater than or equal to the threshold (S110, Yes), the first entity and the third entity are replaced, and the clustered clustered entity generated based on the first entity and the third entity is replaced. In order to determine whether to display the entity, it may proceed to a determination as to whether the preset logic ( S120 , S130 ) is satisfied.

The preset clustering condition is determined whether the first object detection algorithm associated with the first object corresponding to the first entity and the third object detection algorithm associated with the third object corresponding to the third entity correspond to each other. It can be done (S120). Here, the object detection algorithms corresponding to each other may refer to, for example, the same algorithm. That is, the preset clustering condition may determine whether the first object detection algorithm and the third object detection algorithm are identical to each other. However, the present invention is not limited thereto, and when both the first object detection algorithm and the third object detection algorithm are algorithms for detecting objects of a similar or substantially the same type, or the first object detection algorithm and the third object detection algorithm Even in the case of an algorithm generated to perform a project for the same purpose (for example, when both the first detection algorithm and the third object detection algorithm are algorithms generated to perform a project for the purpose of corona prevention, etc.), the first object detection algorithm and the It may be determined that the third object detection algorithms correspond to each other.

When it is determined that the first object detection algorithm and the third object detection algorithm do not correspond to each other ( S120 , NO), the first entity and the third entity do not create separate clustered entities, but the first entity and the third entity Entities can be displayed as they are. That is, even when the first entity and the third entity have an interval less than the threshold, the first entity and the third entity having algorithms that do not correspond to each other may not be clustered even when the interval is less than the threshold. In this regard, FIG. 7B illustrates an example in which the entity 231c associated with the algorithm 1 and the entity 232b associated with the algorithm 2 are displayed independently of each other without being clustered even though they overlap each other.

In this way, in the case where two or more entities are not clustered in association with a different algorithm despite having an interval less than the threshold, an entity having a more recent detection time of a corresponding object among the two or more entities is higher than other entities can be displayed on Alternatively, an entity selected by a user (eg, click or hover, etc.) among the two or more entities may be displayed above other entities. Alternatively, an entity associated with a particular algorithm may be preset to be displayed above entities associated with another algorithm. The above-described example is merely an example, and does not limit the present disclosure.

Meanwhile, when it is determined that the first object detection algorithm and the third object detection algorithm correspond to each other ( S120 , YES), the first entity associated with the first object detection algorithm and the third entity associated with the third object detection algorithm A clustered entity is generated based on , and the clustered entity may be displayed by replacing the first entity and the third entity. In this regard, FIG. 7(b) shows an example in which two or more entities 232a and 232b are associated with Algorithm 2, which is an object detection algorithm corresponding to each other, and FIG. 7(c) shows the two or more entities ( An example in which a clustered entity 232c generated based on 232a and 232b is displayed is shown.

Alternatively, as for the preset clustering condition, when it is determined that the first object detection algorithm and the third object detection algorithm correspond to each other (S120, YES), the detection time of at least one of the first object and the third object is critical It may be further determined whether or not it follows the starting point (S130).

When it is determined that the detection time of at least one of the first object and the third object follows the threshold time (S130, NO), the first entity and the third entity do not create a separate clustered entity, and the first entity and the third entity may be displayed as it is. That is, the first entity and the third entity may not be clustered even when the first entity and the third entity have an interval less than a threshold and have the same algorithm as each other.

On the other hand, when it is determined that both the detection times of the first object and the third object precede the threshold time (S130, YES), based on the first entity corresponding to the first object and the third entity corresponding to the third object A clustered entity is created, and the clustered entity may be displayed replacing the first entity and the third entity.

In this regard, in (b) and (c) of Figure 7, the detection time of any one of the two or more entities 232a and 232b follows the threshold time and is displayed independently without being clustered with each other ( In (b) of FIG. 7 ), when time has elapsed and the detection time point of the two or more entities all comes before the critical time point, the clustered entity generated based on the two or more entities 232a and 232b ( An example in which 232c is displayed replacing two or more entities 232a and 232b is shown.

Here, the threshold time may be set to various values, for example, the threshold time may be within one hour in the past from the current time. In this case, if the time of the current time is 14:00, for example, if the detection time of an object corresponding to any one of two entities related to the same algorithm and overlapping each other is 13:10 (ie, the detection time is later than the threshold time) object), the two entities overlapping each other are not clustered and can be displayed independently regardless of the correspondence of the algorithm. Conversely, if the detection times of the objects corresponding to each of the two overlapping entities are all before 13:00 (eg, 12:50), a clustered object is created based on the two overlapping entities, and the clustered objects are mutually It can be displayed by replacing two overlapping entities.

In this way, by inputting the first image into the object detection algorithm and expressing information about the detected objects using various elements of the location and/or appearance of each entity, multidimensional information about the detected object is provided to the user. It can be provided in the form of a simple graphic. In addition, by changing the scale level of the first image and/or dynamically changing and displaying each entity according to the passage of time, the user interface is prevented from becoming too complicated, and the user can easily search for information more necessary for the user. can be provided intuitively.

Meanwhile, FIG. 8 illustrates an example of first determining whether the object detection algorithms correspond to each other (S120) and subsequently determining whether the detection time is later than a critical time (S130), but is not limited thereto. That is, on the contrary, it may be determined first whether the detection time point follows the critical time point, and it may be subsequently determined whether the object detection algorithms correspond to each other.

The fourth layer 340 may display a third graphic 240 that is a notification about the object or the change rate when the object or the detected change rate through deep learning algorithm reading satisfies the notification generation condition. . When the detected object or the detected change rate based on the first image 210 satisfies a condition for generating a notification, the processor 120 outputs to display the third graphic 240 that is a notification regarding the corresponding object or the corresponding change rate. The unit 140 may be controlled. Meanwhile, the memory 130 may store a notification generation condition preset by the user or set as a default value, and the processor 120 reads the notification generation condition stored in the memory 130 and detects the object. Alternatively, it may be determined whether to generate a notification regarding the detected change. In this case, determining whether to generate a notification is determining whether to display the third graphic 240 and/or determining how to display the third graphic 240 (eg, when the third graphic 240 is displayed) location and/or appearance). However, the present invention is not limited thereto, and the determination of whether the above-described notification is generated is performed by the processor of the server (not shown), and when the notification is determined, data for displaying the third graphic 240 is transmitted to the network unit 110 . may be transmitted to the computing device 100 through

The notification generating condition may include at least one of a condition regarding a detection area of an object, a condition regarding the type of object, and a condition regarding a detection amount of an object, a condition regarding an area in which a rate of change is detected, and a condition regarding a change in the rate of change. can

The condition regarding the detection region of the object may be to set the region of interest in relation to the object detection. For example, 'Atlanta Intl. Airport (Atlanta International Airport) as the region of interest, and Atlanta Intl. It can be set to generate a notification when an object is detected in the Airport area. Or, in combination with other notification generating conditions, for example, Atlanta Intl. When a specific object is detected in the Airport area and/or Atlanta Intl. A notification may be set to occur when the detection amount of objects in the airport area changes at a specific rate and/or number.

The condition regarding the type of object may be to set the object of interest. For example, a medium-sized aircraft 'B330' may be set as an object of interest, and a notification may be generated when the B330 object is detected. Alternatively, in combination with other notification generation conditions, for example, it can be set to generate a notification when the B330 object is detected in a specific area and/or when the detection amount of the B330 object is changed at a specific ratio and/or number.

The condition regarding the detection amount of the object may be set to generate a notification when the detection amount of the object is changed by a specific ratio and/or number. For example, when detecting an object, when the detection amount is increased or decreased by an arbitrary ratio and/or number compared to the previous image, when the average detection amount is increased or decreased by an arbitrary ratio and/or number compared to yesterday, or when the daily deviation from yesterday is increased or decreased by an arbitrary ratio and/or It can be set to generate a notification when the number increases or decreases. Alternatively, in combination with other notification generating conditions, for example, a notification may be set to occur when the detection amount of a specific object is changed at a specific ratio and/or number and the corresponding variation is detected in a specific area.

The condition regarding the region in which the rate of change is detected may be to set the region of interest in relation to the detection of the rate of change. For example, 'Atlanta Intl. Airport' as the region of interest, and Atlanta Intl. An alert can be set to occur when a change is detected in the Airport area. Or, in combination with other notification generating conditions, for example, Atlanta Intl. An alert can be set to occur when a change rate greater than a threshold is detected in the airport area.

The condition regarding the change of the rate of change may be set to generate a notification when the detected rate of change is changed by a specific rate and/or value. For example, a notification may be set to occur when the detected rate of change exceeds or falls below a threshold, or when the detected rate of change increases or decreases by a specific rate and/or value compared to yesterday. Alternatively, in combination with other notification generating conditions, for example, a notification may be set to occur when a change rate is changed at a specific rate and/or value and the corresponding variation is detected in a specific area.

When the above-described notification generation condition is satisfied, a third graphic 240 for guiding at least one of a location where an object satisfying the notification generation condition is detected or a location where a change rate satisfying the notification generation condition is detected is displayed on the user interface 200 ) can be displayed. For example, the third graphic may be displayed on a position at which an object satisfying the notification generating condition is detected or a position at which a change rate satisfying the notification generating condition is detected. Alternatively, the third graphic may indicate a position at which an object satisfying a notification generating condition is detected or a position at which a change rate satisfying the notification generating condition is detected. For example, the object is displayed including an indicator (eg, a graphic such as an arrow, or text such as a coordinate value) in a position adjacent to the position where the object is detected or the position where the rate of change is detected, or a banner or the like. The indicator may be displayed in the same separate area.

Meanwhile, the third graphic 240 may visually represent any one of two or more notification levels. The notification level may be divided into two or more different levels according to the degree of interest of the user to the content of the generated notification. The third graphics representing different notification levels may be displayed in different ways (eg, different locations and/or appearances).

The notification level may be set together with a condition for generating a notification. That is, by matching the notification generation condition with the notification level, it is possible to preset which notification level will be generated when a notification is generated according to the notification generation condition.

For example, if the user has the highest level of interest in the detection of the B330 aircraft, the user may set the alert of the highest alert level (eg, alert level) to occur when the B330 aircraft is detected. The alert level notification may mean a notification that can be delivered to the user prior to all situations. When a B330 aircraft is detected and an alert level alert is generated, the third graphic according to the alert is a red icon displayed to blink on the location where the B330 aircraft is detected, and a popup rendered to include information about the detection of the B330 aircraft It may include windows.

Alternatively, the user may register for Atlanta Intl. If you have a high level of interest (less than the highest level described above) for rate-of-change detection in the Airport area, you can use the Atlanta Intl. When a change rate is detected in the airport area, it may be set to generate a notification of a high notification level (eg, attention level). Atlanta Intl. When a caution level alert is generated as a rate of change is detected in the Airport area, the third graphic according to the alert is an Atlanta Intl. It may include a yellow icon that is displayed to blink on a specific location in the Airport area, and a pop-up banner that is rendered at the top of the screen to include information about change rate detection.

Alternatively, when the user has a moderate level of interest in the detection of a submarine, the user may set a notification of a normal level (eg, a normal level) to be generated when a submarine is detected. When a submarine is detected and a general level notification is generated, the third graphic according to the notification may include a blue icon displayed so as not to blink on the detected position of the submarine.

The above-described examples in relation to the third graphic visually indicating the notification level are merely exemplary and do not limit the present disclosure. For example, the third graphics corresponding to each of the different notification levels may be visually distinguished by having different borders, different shapes, or different sizes.

Meanwhile, reference may be made to FIG. 9 in relation to the above-described content of the third graphic. Specifically, FIG. 9 shows that the third graphic 241a indicating the general level, the third graphic 241b indicating the attention level, and the third graphic 241c indicating the alert level, respectively, satisfy each notification occurrence level. An example is shown in which an object is displayed on a detected position.

Also, reference may be made to FIG. 10 in relation to the setting of the notification generating condition and the setting of the notification level. Specifically, FIG. 10 shows an example of a user interface for setting a condition for generating a notification according to object detection. The user interface shown in FIG. 10 is, for example, a region constituting the user interface 200 or a region that can be rendered according to a user's selection on the user interface 200 (eg, the user interface 200 ). ) may be an area within a separate layer rendered according to a user's input, such as a sliding menu displayed in response to a click of a left/right top hamburger menu (not shown) or a pop-up window. However, the present invention is not limited thereto.

The left diagram of FIG. 10 shows an example of a method of selecting an object of interest and/or a condition related to a detection region of the object of interest. For example, the setting of the object of interest may be performed by first selecting an algorithm from the drop-down menu 242 and subsequently selecting at least some of the detailed object types included in the corresponding algorithm from the subsequent drop-down menu. The condition regarding the detection region may also be performed by selecting at least one region of interest from the drop-down menu 243 . When the selection of conditions regarding the object of interest and/or the detection region of the object of interest is completed, a box 244a including information on the object of interest may be rendered, and the 'create notification' button 245 may be selected to provide more detailed information. You can set the notification trigger condition.

The central diagram of FIG. 10 shows an example of a method of setting a more detailed notification generation condition and notification level with respect to the added object of interest 244a. For example, according to the selection of the 'make notification' button 245, a context menu may be rendered. The context menu may include a drop-down menu 246 for setting a notification generating condition and/or a drop-down menu 247 for setting a notification level. In particular, in the case of the drop-down menu 246 for setting a notification generation condition, an input window for setting detailed conditions may be further included. For example, when a notification generation condition such as 'increase in detection amount compared to the previous image' is set, an input window capable of setting a value of increase in detection amount in units of a ratio or number may be activated. However, the present invention is not limited thereto, and when a notification generation condition such as 'when an object is detected' is simply set, the input window for setting the detailed condition may be inactivated or not displayed. Since examples of possible notification generating conditions and/or notification levels have been described above, a detailed description thereof will be omitted herein.

The right diagram of FIG. 10 shows an example of a state in which a notification generation condition and a notification level have been set. Specifically, an example of a notification 244b set to occur at the attention level when the detection amount of the added object of interest increases by more than N% compared to the previous image in either of the two regions (AOI 1, AOI 2) is shown. .

In the above description regarding notification generation conditions and notification level settings, examples of drop-down menus and input windows have been described, but the present invention is not limited thereto. The selection of each value of the object of interest, detection region, notification generating condition, and/or notification level may be performed, for example, by selecting any one of the values provided by two or more auto-recommended tags, or by using a sliding button to select any one of the two or more values. It may be performed in any possible manner, such as selecting one or selecting one of the search results through input of a search term in the search box.

In this way, by immediately displaying a notification in response to detection of an object that satisfies the notification generation condition and/or detection of a change, the user can check in real time that the event of interest is detected even while reading the received image. have.

Meanwhile, the above description of the notification of the present disclosure has been mainly described with respect to displaying the third graphic 240 , but is not limited thereto. For example, the notification may be an auditory element (eg, generation of a notification sound with a volume, a pattern, etc. distinguished according to the notification level, etc.) or a visual element (eg, the above-described third graphic 240 or text, etc.). possible visual elements) and auditory elements. That is, the notification according to the present disclosure may be configured with any method and/or element for recognizing the detection of the object of interest to the user.

The fifth layer 350 may display the fourth graphic 250 in an area within the first image 210 corresponding to the capturing area of the received image. The processor 120 may control the output unit 140 to display the fourth graphic 250 in an area within the first image 210 corresponding to the capturing area of the image received through the network unit 110 . .

The fourth graphic 250 may include, based on at least the scale level of the first image 210 , first image information about the boundary of the photographing area, second image information about the subject of the image, and the capture date and time of the image. It may be determined to include at least one of the related third image information.

Regarding the fourth graphic 250 , reference may be made to FIG. 11 . 11A shows an example of the fourth graphic displayed at a low scale level, and FIG. 11B shows an example of the fourth graphic displayed at a relatively high scale level. More specifically, the fourth graphic 250 at a low scale level (FIG. 11(a)) may display only the first image information 251 about the boundary of the shooting area, that is, a rectangular shape that is a boundary line indicating the shooting area. can In contrast, the fourth graphic 250 at a high scale level (FIG. 11(b)) shows the identification of the subject of the image, ie, the satellite in this case, as well as the first image information 251 about the boundary of the imaging area. The second image information 252b related to the information and the third image information 252a related to the capture date and time of the image may also be displayed. However, the present invention is not limited thereto.

In other words, for example, when the scale level is lower than the threshold level, the fourth graphic 250 may be displayed to include less information, conversely, when the scale level is higher than the threshold level, the fourth graphic 250 is more informational. can be displayed to include Accordingly, it is possible to prevent the user interface from becoming too complicated and ultimately provide a user interface through which the user can easily check information more necessary for the user.

Meanwhile, the fifth layer 350 including the fourth graphic 250 may not be displayed according to a user input. Referring back to FIG. 4 in this regard, control on whether the fifth layer 350 is displayed may be performed according to, for example, a user input to the controller 253 . 4 shows an example in which the control unit 253 includes 'hide footprint' and 'hide foot traffic' buttons. For example, when a selection input for a 'hide footprint' button is received, the fifth layer 350 may not be displayed. Alternatively, when a deselect input for the 'hide footprint' button is received, the fifth layer 350 may be displayed. Alternatively, when a deselect input for the 'hide footprint' button is received and a selection input for the 'hide foot traffic' button is received, regardless of the scale level of the first image 210, the fourth graphic is captured It may be displayed to include only the first image information 251 about the boundary of the region. The above-described examples are merely exemplary and do not limit the present disclosure.

The sixth layer 360 may display the comprehensive information 260 including information related to at least a portion of the first to fourth graphics described above. The processor 120 may control the output unit 140 to display the comprehensive information 260 including information related to at least a portion of the first graphic to the fourth graphic. Regarding the aggregate information 260 , reference may be made again to FIG. 4 .

4 is an example in which the comprehensive information 260 is displayed to include information related to the detection amount 261 and the detection amount change trend 262 of the object detected from the first image, that is, information related to the second graphic 230 . shows Specifically, the comprehensive information 260 of FIG. 4 includes information on the amount of detection of all objects during the first period from the first time point to the second time point (indicated as 'total detection objects' in FIG. 4), the first period Information on the detection amount of each object type for each object type during the period (261, each object type is denoted as 'lowest level' in FIG. 4), and the detection amount for the second period preceding the first period compared with the detection amount for the first period Information 262 on whether or not the detection amount is increased or decreased during the period is included.

In particular, the information 262 on whether the detection amount is increased or decreased may be displayed by being composed of letters, numbers, symbols, or figures that can transmit whether the detection amount is increased or decreased to a user. For example, if the detection amount for the second period for an arbitrary object is 10 and the detection amount for the first period is 17, the information 262 about the comparison of the first detection amount and the second detection amount is 17 (+7), It can be displayed as 17(10), 17(+), 10 > 17, or +7.

However, it is not limited thereto, and the comprehensive information 260 includes, in addition to the second graphic 230 , the first graphic 220 about the rate of change, the third graphic 240 about the notification, and/or the fourth graphic ( 250) and related information.

For example, when the aggregate information 260 includes information associated with the first graphic 220 , for example, information about an average rate of change or a maximum rate of change during the first period (ie, a value of an average rate of change, or a maximum rate of change) information about this detected area, etc.), or whether the average rate of change or the maximum rate of change during the first period has increased or decreased compared to the average rate of change or the maximum rate of change for a second period preceding the first period. have.

Alternatively, when the comprehensive information 260 includes information related to the third graphic 240 , for example, information about the total number of notifications generated during the first period, the number of notifications generated during the first period per notification level information, information on the object or change rate corresponding to the alert generated during the first period (ie, the type of object that satisfies the alert generating condition, the detection area, the detection amount, or the detection area of the change rate satisfying the alert generating condition) information, etc.) may be included.

Alternatively, when the comprehensive information 260 includes information related to the fourth graphic 250 , for example, information about a photographing area of a received image (name, coordinates, etc.), or identification of a satellite that is a photographing subject of the image It may include information about information (names of satellites, etc.). The above-described example is merely an example, and does not limit the present disclosure.

Meanwhile, the first period may be a period from 0 o'clock today to the present time. That is, the read data acquired during the period from today's 0 o'clock to the current time point may be displayed through one or more of the first to fourth graphics, and in conjunction with this, the comprehensive information 260 is displayed from today's 0 o'clock to the current time point. may include information associated with at least a portion of the first to fourth graphics that are read data displayed during the period of .

However, the present invention is not limited thereto, and the first period may be set to an arbitrary period according to a user input. For example, according to a user input to the aforementioned period-related control unit 204, a first period from a first time point (indicated as 'start date' in FIG. 4) to a second time point (indicated as 'end date' in FIG. 4) The read data acquired during the period may be displayed through one or more of the first graphic to the fourth graphic, and in conjunction therewith, the comprehensive information 260 is at least one of the first graphic to the fourth graphic which is the read data displayed during the first period. It may include information associated with some.

Meanwhile, the second period may be a period preceding the above-described first period. For example, if the first period is a period from 00:00 today to the present time (eg, 14:00), the second period is a period from 00:00 to 24:00 of the previous day, or a period from 00:00 to 14:00 of the previous day, It may be a period from the start of real-time monitoring to 24 hours the day before, or any other period designated by the user. In this case, the comprehensive information 260 may display, for example, information 252 on whether the detection amount from 0 o'clock to 14:00 on the day increased or decreased compared with the detection amount of the object from 0 o'clock to 14:00 of the previous day.

12 is a flowchart for providing a user interface for real-time monitoring according to an embodiment of the present disclosure.

The computing device 100 may display a first layer, which displays a first image including geographic information (step S110).

The computing device 100 displays a first graphic that is a grid including one or more tiles displayed in a preset size, and displays a second graphic such that each of the one or more tiles indicates a rate of change of a corresponding area in the first image. A layer may be displayed (step S120).

The user interface for real-time monitoring according to an embodiment of the present disclosure may be implemented by logic providing real-time monitoring.

The logic for providing the real-time monitoring described above may be implemented by a module, circuit or means corresponding thereto.

For detailed descriptions of the computing device 100 , the first layer 310 , and the second layer 320 for providing a user interface for real-time monitoring according to an embodiment of the present disclosure, refer to FIGS. 1 to 12 . Therefore, it can be replaced with the above-mentioned contents, and the method for providing a user interface for real-time monitoring based on the above-described contents can be added, modified, or deleted step by step.

13 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally as being capable of being implemented by a computing device, those skilled in the art will appreciate that the present disclosure is a combination of hardware and software and/or in combination with computer-executable instructions and/or other program modules that may be executed on one or more computers. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like. It will be appreciated that each of these may be implemented in other computer system configurations, including operable in conjunction with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer can be a computer-readable medium, and such computer-readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. including removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data, etc. in a modulated data signal such as a carrier wave or other transport mechanism, and Includes any information delivery medium. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 implementing various aspects of the disclosure is shown including a computer 1102 , the computer 1102 including a processing unit 1104 , a system memory 1106 , and a system bus 1108 . do. The system bus 1108 couples system components, including but not limited to system memory 1106 , to the processing device 1104 . The processing device 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 1104 .

The system bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, EEPROM, etc., which is the basic input/output system (BIOS) that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also be configured with an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes-, magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and optical disk drive 1120 (eg, CD-ROM) for reading from, or writing to, disk 1122, or other high capacity optical media such as DVD. The hard disk drive 1114 , the magnetic disk drive 1116 , and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124 , the magnetic disk drive interface 1126 , and the optical drive interface 1128 , respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible computer-readable media such as etc. may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112 , including an operating system 1130 , one or more application programs 1132 , other program modules 1134 , and program data 1136 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 1112 . It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 via one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146 . In addition to the monitor 1144, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be workstations, computing device computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and are typically connected to computer 1102 . Although it includes many or all of the components described for it, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, eg, a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156 . Adapter 1156 may facilitate wired or wireless communication to LAN 1152 , which also includes a wireless access point installed therein for communicating with wireless adapter 1156 . When used in a WAN networking environment, the computer 1102 may include a modem 1158, be connected to a communication computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. have other means. A modem 1158 , which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via a serial port interface 1142 . In a networked environment, program modules described for computer 1102 , or portions thereof, may be stored in remote memory/storage device 1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computer 1102 may be associated with any wireless device or object that is deployed and operates in wireless communication, for example, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communications satellites, wireless detectable tags. It operates to communicate with any device or place, and phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks may operate in unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). .

One of ordinary skill in the art of this disclosure will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields particles or particles, or any combination thereof.

Those of ordinary skill in the art of the present disclosure will recognize that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein include electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art of the present disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash drives. memory devices (eg, EEPROMs, cards, sticks, key drives, etc.). Also, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (16)

A computer program stored on a computer-readable storage medium, wherein the computer program provides a user interface for real-time monitoring when executed by one or more processors of a computing device, the user interface comprising:
a first layer displaying a first image including geographic information;
displaying a first graphic including a grid including one or more tiles displayed in a preset size, each of the one or more tiles indicating a rate of change of a corresponding area in the first image, 2 layers; and
a third layer for displaying a second graphic corresponding to each detected object by inputting the first image into one or more object detection algorithms;
including,
The second graphic is
It is composed of entities including a first entity, and the color of the first entity is at least a part of the object, and a first detection time, which is a detection time of a first object corresponding to the first entity, is a critical time. Visually emphasized in the following cases,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
the preset size of the one or more tiles,
fixed irrespective of the scale level of geographic information displayed in the first image;
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The color of the first tile, which is any one of the one or more tiles,
determined based at least on the rate of change of a first area that is an area in the first image corresponding to the first tile;
A computer program stored on a computer-readable storage medium.
4. The method of claim 3,
The rate of change is
one or more rate-of-change intervals, wherein different color properties are assigned to each of the one or more rate-of-change intervals;
The color of the first tile is
determined based on the color attribute assigned to the one or more rate-of-change sections including the maximum value of the rate of change of the first region;
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The first entity guides the location where the first object is detected,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The size of the first entity is,
is determined based on at least the number of the objects included in the first object,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The color of the first entity is,
determined further based on a first object detection algorithm associated with the first object;
A computer program stored on a computer-readable storage medium.
8. The method of claim 7,
The color of the first entity is,
having a color series corresponding to a color of a second entity corresponding to a second object detected by inputting the first image into the first object detection algorithm;
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The third layer is
If the first entity has a distance less than a threshold from a third entity that is at least some of the entities, and if a preset clustering condition is satisfied, replace the first entity and the third entity, the first entity Display an entity and a clustered entity generated based on the third entity,
The preset clustering condition is
Whether a first object detection algorithm associated with the first object and a third object detection algorithm associated with a third object corresponding to the third entity correspond to each other, and at least one of the first object and the third object at least one of whether one detection time point lags a threshold time point;
A computer program stored on a computer-readable storage medium.
10. The method of claim 9,
The clustered entity is
Displayed when the first object detection algorithm and the third object detection algorithm correspond to each other, and both the detection times of the first object and the third object precede the threshold time,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The user interface is
a fourth layer that is allowed to display a notification regarding the object or the rate of change when at least one of the object or the rate of change satisfies a condition for generating a notification;
further comprising,
The conditions for generating the notification are:
At least one of a condition related to the detection area of the object, a condition related to the type of the object, a condition related to the detection amount of the object, a condition related to an area in which the change rate is detected, and a condition related to a change of the change rate,
A computer program stored on a computer-readable storage medium.
12. The method of claim 11,
The notification is
Any one of two or more notification levels is visually displayed at a location in the user interface that guides at least one of a location where an object satisfying the notification generation condition is detected or a location where a change rate satisfying the notification generation condition is detected. displaying a third graphic representing
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The user interface is
a fifth layer for displaying a fourth graphic in an area within the first image corresponding to a capturing area of the image received by the computing device;
further comprising,
The fourth graphic is
Based on at least the scale level of the first image, at least one of first image information about the boundary of the photographing area, second image information about the subject of the image, and third image information about the capture date and time of the image determined to include one,
A computer program stored on a computer-readable storage medium.
14. The method of claim 13,
The user interface is
Further comprising a sixth layer for displaying the aggregate information,
The general information is
comprising information associated with at least a portion of the first graphic to the fourth graphic,
A computer program stored on a computer-readable storage medium.
A method of displaying a user interface for real-time monitoring in a user terminal, comprising:
displaying a first layer, displaying a first image including geographic information;
A second layer that displays a first graphic that is a grid including one or more tiles displayed in a preset size, and displays each of the one or more tiles to indicate a rate of change of a corresponding area in the first image. displaying; and
displaying a third layer by inputting the first image into one or more object detection algorithms to display a second graphic corresponding to each detected object;
including,
The second graphic is
It is composed of entities including a first entity, and the color of the first entity is at least a part of the object, and a first detection time, which is a detection time of a first object corresponding to the first entity, is a critical time. Visually emphasized in the following cases,
A method of displaying a user interface for real-time monitoring in a user terminal.
As a user terminal,
a processor including one or more cores;
Memory; and
an output unit providing a user interface;
including,
The user interface is
a first layer displaying a first image including geographic information;
A second layer that displays a first graphic that is a grid including one or more tiles displayed in a preset size, and displays each of the one or more tiles to indicate a rate of change of a corresponding area in the first image. ; and
a third layer for displaying a second graphic corresponding to each detected object by inputting the first image into one or more object detection algorithms;
including,
The second graphic is
It is composed of entities including a first entity, and the color of the first entity is at least a part of the object, and a first detection time, which is a detection time of a first object corresponding to the first entity, is a critical time. Visually emphasized in the following cases,
user terminal.

Images