KR102613227B1 - Electronic device for evaluating water supply reliability in agricultural reservoir based on big data and machine learning and controlling method thereof - Google Patents

Abstract

An electronic device and a method for controlling the electronic device are disclosed. In particular, the electronic device according to the present disclosure receives collection target data from at least one server through a communication unit, a memory, and a communication unit at each preset collection cycle, and determines the water safety level of the agricultural reservoir based on the analysis results of the collection target data. It includes a processor that acquires the evaluation information, and when the collection target data includes press release information, the processor inputs the press release information into a neural network model learned to perform Latent Dirichlet Allocation (LDA), By assigning one topic from among a plurality of predefined topics to each of the plurality of documents included in the press information, and calculating the number of topics related to the water safety of agricultural reservoirs among the topics assigned to each of the plurality of documents, the collection is collected. Among the analysis results of the target data, analysis results of media report information can be obtained.

Description

Electronic devices and control method for agricultural reservoir water safety evaluation based on big data and machine learning

The present disclosure relates to an electronic device and a control method of the electronic device, and specifically to an electronic device and a control method thereof that can evaluate the safety of agricultural reservoir water discharge based on big data and machine learning.

Water safety analysis of agricultural reservoirs serves as an important factor in accurately evaluating and effectively managing the reservoir's cold-resistant capacity. However, domestic agricultural reservoirs were mainly developed in the 1970s, and design standards according to the agricultural environment of the past are applied, and existing water safety is analyzed using these design standards.

On the other hand, compared to the 1970s, environmental changes have occurred due to various factors such as climate, society, culture, and economy, so there is a limitation in that the cold-resistant capacity of current agricultural reservoirs cannot be accurately evaluated through water safety analysis based on past standards. there is.

Meanwhile, in recent years, with the development of Internet technology, information provided through media articles and social media has been flooding in, and the types of data that can be collected, such as satellite image information, are also becoming more diverse. And, among these various data, so-called big data, a vast amount of data related to water safety evaluation of agricultural reservoirs is used to evaluate water water safety of reservoirs that are suitable for the current agricultural environment. can be performed.

However, to date, there is no technology that can accurately evaluate and improve mudwater safety by effectively and efficiently collecting and managing data related to mudwater safety evaluation of agricultural reservoirs, so there is a need for such technology. This is coming to the fore.

The present disclosure is in response to the need as described above, and the purpose of the present disclosure is to provide an electronic device and a control method thereof that can effectively and efficiently evaluate the water safety of agricultural reservoirs and improve the water safety of agricultural reservoirs. .

According to an embodiment of the present disclosure for achieving the above-described object, an electronic device receives data to be collected from at least one server through a communication unit, a memory, and the communication unit at a preset collection period, and collects the data to be collected. Based on the analysis results, it includes a processor that acquires evaluation information indicating the water safety of the agricultural reservoir, and the processor performs Latent Dirichlet Allocation (LDA) when media release information is included in the collection target data. By inputting the press release information into a neural network model learned to perform, one topic among a plurality of predefined topics is assigned to each of a plurality of documents included in the press release information, and assigned to each of the plurality of documents By calculating the number of topics related to water safety of agricultural reservoirs among one topic, an analysis result for the media report information is obtained among the analysis results for the collected data.

Here, the collection target data further includes reservoir information indicating the water storage status of the reservoir, weather information of the area where the reservoir is located, soil moisture information of the area, and satellite image information of the area, and the analysis result is It may include information on changes in the collection target data acquired for each collection cycle.

Here, at least one of the collection period and the amount of the collection target data received for each collection period is individually set for each of the press release information, the reservoir information, the weather information, the soil moisture information, and the satellite image information. It can be.

Here, based on the evaluation information, the processor may add or exclude at least one of the press release information, the reservoir information, the weather information, the soil moisture information, and the satellite image information from the collection target data.

Here, the processor may change the collection cycle for at least one of the press release information, the reservoir information, the weather information, the soil moisture information, and the satellite image information, based on the evaluation information.

Here, based on the evaluation information, the processor may change the collection amount of at least one of the press release information, the reservoir information, the weather information, the soil moisture information, and the satellite image information received at each collection cycle.

Here, the processor may obtain at least one of control information for adjusting the storage amount of the reservoir and design information for designing a new reservoir based on the evaluation information.

Here, it may include a display, and the processor may control the display to display a user interface showing at least one of the analysis results, the evaluation information, the control information, and the design information.

According to an embodiment of the present disclosure for achieving the above-described object, a control method of an electronic device includes media release information including a plurality of documents, reservoir information indicating the water storage status of an agricultural reservoir, at each preset collection cycle, Receiving collection target data including weather information for the area where the reservoir is located, soil moisture information for the area, and satellite image information for the area from at least one server, Latent Dirichlet Allocation (LDA) Inputting press release information included in the collection target data into a neural network model learned to perform, calculating the number of topics related to the water safety of agricultural reservoirs among the topics assigned to each of the plurality of documents, the calculated Based on an analysis result of the collection target data including the number of topics, obtaining evaluation information indicating water safety of an agricultural reservoir, based on the evaluation information, the press release information, the reservoir information, and the weather Changing the collection cycle for at least one of information, the soil moisture information, and the satellite image information, and based on the evaluation information, the press release information, the reservoir information, and the weather information received at each collection cycle; It may include changing the collection amount of at least one of the soil moisture information and the satellite image information.

1 is a block diagram briefly showing the configuration of an electronic device according to an embodiment of the present disclosure;
2 is a block diagram showing a plurality of modules according to an embodiment of the present disclosure;
3 is a block diagram briefly showing the configuration of an electronic device according to an embodiment of the present disclosure;
4 is a diagram illustrating a user interface according to an embodiment of the present disclosure, and
Figure 5 is a flowchart showing a control method of an electronic device according to an embodiment of the present disclosure.

Since these embodiments can be modified in various ways and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the scope to specific embodiments, and should be understood to include various modifications, equivalents, and/or alternatives to the embodiments of the present disclosure. In connection with the description of the drawings, similar reference numbers may be used for similar components.

In describing the present disclosure, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present disclosure, the detailed description thereof will be omitted.

In addition, the following examples may be modified into various other forms, and the scope of the technical idea of the present disclosure is not limited to the following examples. Rather, these embodiments are provided to make the present disclosure more faithful and complete and to completely convey the technical idea of the present disclosure to those skilled in the art.

The terms used in this disclosure are merely used to describe specific embodiments and are not intended to limit the scope of rights. Singular expressions include plural expressions unless the context clearly dictates otherwise.

In the present disclosure, expressions such as “have,” “may have,” “includes,” or “may include” refer to the presence of the corresponding feature (e.g., component such as numerical value, function, operation, or part). , and does not rule out the existence of additional features.

In the present disclosure, expressions such as “A or B,” “at least one of A or/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, “A or B,” “at least one of A and B,” or “at least one of A or B” includes (1) at least one A, (2) at least one B, or (3) it may refer to all cases including both at least one A and at least one B.

Expressions such as “first,” “second,” “first,” or “second,” used in the present disclosure can modify various components regardless of order and/or importance, and can refer to one component. It is only used to distinguish from other components and does not limit the components.

A component (e.g., a first component) is “(operatively or communicatively) coupled with/to” another component (e.g., a second component). When referred to as being “connected to,” it should be understood that any component may be directly connected to the other component or may be connected through another component (e.g., a third component).

On the other hand, when a component (e.g., a first component) is said to be “directly connected” or “directly connected” to another component (e.g., a second component), It may be understood that no other component (e.g., a third component) exists between other components.

The expression “configured to” used in the present disclosure may mean, for example, “suitable for,” “having the capacity to,” depending on the situation. ," can be used interchangeably with "designed to," "adapted to," "made to," or "capable of." The term “configured (or set to)” may not necessarily mean “specifically designed to” in hardware.

Instead, in some contexts, the expression “a device configured to” may mean that the device is “capable of” working with other devices or components. For example, the phrase "processor configured (or set) to perform A, B, and C" refers to a processor dedicated to performing the operations (e.g., an embedded processor), or by executing one or more software programs stored on a memory device. , may refer to a general-purpose processor (e.g., CPU or application processor) capable of performing the corresponding operations.

In an embodiment, a 'module' or 'unit' performs at least one function or operation, and may be implemented as hardware or software, or as a combination of hardware and software. Additionally, a plurality of 'modules' or a plurality of 'units' may be integrated into at least one module and implemented with at least one processor, except for 'modules' or 'units' that need to be implemented with specific hardware.

Meanwhile, various elements and areas in the drawing are schematically drawn. Accordingly, the technical idea of the present invention is not limited by the relative sizes or spacing drawn in the attached drawings.

Hereinafter, with reference to the attached drawings, embodiments according to the present disclosure will be described in detail so that those skilled in the art can easily implement them.

FIG. 1 is a block diagram briefly illustrating the configuration of an electronic device 100 according to an embodiment of the present disclosure. And, Figure 2 is a block diagram showing a plurality of modules according to an embodiment of the present disclosure.

The electronic device 100 according to the present disclosure refers to a device that can evaluate the safety of mud water from agricultural reservoirs based on the collection of big data and machine learning, and can perform operations to improve water safety based on the evaluation results. There is no particular limitation on the type of electronic device 100 according to the present disclosure.

In the present disclosure, the term water safety of an agricultural reservoir refers to an indicator indicating the degree of safety of water supply that allows the reservoir to meet the water demand for the reservoir, which is the cold resistance of the reservoir. ) may be replaced by the term ability.

As shown in FIG. 1, the electronic device 100 according to an embodiment of the present disclosure may include a communication unit 110, a memory 120, and a processor 130.

The communication unit 110 includes a circuit and can perform communication with an external device. Specifically, the processor 130 can receive various data or information from an external device connected through the communication unit 110, and can also transmit various data or information to the external device.

The communication unit 110 may include at least one of a WiFi module, a Bluetooth module, a wireless communication module, an NFC module, and a UWB module (Ultra Wide Band). Specifically, the WiFi module and the Bluetooth module can each communicate using WiFi and Bluetooth methods. When using a WiFi module or Bluetooth module, various connection information such as SSID is first transmitted and received, and various information can be transmitted and received after establishing a communication connection using this.

Additionally, the wireless communication module can perform communication according to various communication standards such as IEEE, Zigbee, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and 5th Generation (5G). In addition, the NFC module can perform communication using the NFC (Near Field Communication) method using the 13.56MHz band among various RF-ID frequency bands such as 135kHz, 13.56MHz, 433MHz, 860~960MHz, 2.45GHz, etc. In addition, through communication between UWB antennas, the UWB module can accurately measure ToA (Time of Arrival), which is the time for a pulse to reach the target, and AoA (Ange of Arrival), which is the angle of arrival of the pulse at the transmitter, and can thus accurately measure indoor Precise distance and position recognition is possible within an error range of several tens of centimeters.

In particular, in various embodiments according to the present disclosure, the processor 130 may receive collection target data from at least one server through the communication unit 110 at each preset collection cycle. Additionally, the processor 130 may control the communication unit 110 to transmit at least one of the analysis result, evaluation information, control information, and design information according to the present disclosure to an external device such as a user terminal.

At least one instruction regarding the electronic device 100 may be stored in the memory 120 . Additionally, an operating system (O/S) for driving the electronic device 100 may be stored in the memory 120. Additionally, the memory 120 may store various software programs or applications for operating the electronic device 100 according to various embodiments of the present disclosure. Additionally, the memory 120 may include a semiconductor memory such as flash memory or a magnetic storage medium such as a hard disk.

Specifically, the memory 120 may store various software modules for operating the electronic device 100 according to various embodiments of the present disclosure, and the processor 130 executes various software modules stored in the memory 120. Thus, the operation of the electronic device 100 can be controlled. That is, the memory 120 is accessed by the processor 130, and data read/write/modify/delete/update, etc. can be performed by the processor 130.

Meanwhile, in the present disclosure, the term memory 120 refers to memory 120, ROM (not shown), RAM (not shown) in the processor 130, or a memory card (not shown) mounted on the electronic device 100 (e.g. For example, it can be used to mean including micro SD card, memory stick).

In particular, in various embodiments according to the present disclosure, the memory 120 may store collection target data, analysis results, evaluation information, control information, and design information according to the present disclosure. In addition, the memory 120 may store information on the collection cycle according to the present disclosure, the type of data to be collected, and the amount of data to be collected for each cycle. Additionally, data for a plurality of modules and neural network models according to the present disclosure may be stored in the memory 120.

In addition, various information necessary within the scope of achieving the purpose of the present disclosure may be stored in the memory 120, and the information stored in the memory 120 may be updated as it is received from an external device or input by the user. .

The processor 130 controls the overall operation of the electronic device 100. Specifically, the processor 130 is connected to the configuration of the electronic device 100 including the communication unit 110 and the memory 120, and executes at least one instruction stored in the memory 120 as described above, thereby The overall operation of the device 100 can be controlled.

Processor 130 may be implemented in various ways. For example, the processor 130 may include an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, hardware control logic, a hardware finite state machine (FSM), and a digital signal processor. Processor, DSP). Meanwhile, in the present disclosure, the term processor 130 may be used to include a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), and a Micro Processor Unit (MPU).

In particular, in various embodiments according to the present disclosure, the processor 130 may evaluate the water mud safety of an agricultural reservoir and perform operations to improve the mud water safety using a plurality of modules.

As shown in FIG. 2, a plurality of modules according to the present disclosure may include a data collection module 210, a collected data analysis module 220, a completion safety evaluation module 230, and a management module 240. there is. A plurality of modules may be implemented as software modules or hardware modules, and at least two modules among the plurality of modules may be implemented as one integrated module. Additionally, a plurality of modules may be implemented as on-device by the electronic device 100, and at least some of the plurality of modules may be implemented as included in an external device.

The processor 130 may receive data to be collected from at least one server through the communication unit 110 at each preset collection cycle.

The processor 130 may receive data to be collected through the data collection module 210. Specifically, the data collection module 210 controls the communication unit 110 to transmit a signal for requesting transmission of the data to be collected to at least one server that provides the data to be collected at each preset collection cycle, and the communication unit 110 ), when the data to be collected is received from at least one server, the data to be collected can be transmitted to the collected data analysis module 220. Additionally, the data collection module 210 may transmit additional information, such as whether the data to be collected is normally received and the quantity of the data to be collected, to the collected data analysis module 220.

In this disclosure, the term data to be collected refers to media release information including a plurality of documents, reservoir information indicating the water storage status of the reservoir, meteorological information of the area where the reservoir is located, local soil moisture information, and satellite images for the area. It is used as a general term for information, etc.

In addition, the data to be collected may include various information provided by various online platforms such as social media, and social and environmental information such as changes in population and changes in farmland area. Additionally, the types of information included in the data to be collected may include various types such as text, images, videos, and sounds. The data to be collected according to the present disclosure may be constructed from a vast amount of data, referred to as so-called big data, and at least some of the data to be collected may be processed by machine learning and neural network models.

The collection cycle of data to be collected according to the present disclosure may be changed depending on the settings of the developer or user, and in particular, may be set individually for each type of data to be collected. For example, the collection cycle of data to be collected can be set individually for each of press release information, reservoir information, weather information, soil moisture information, and satellite image information. In addition, of course, the amount of data/information collected per collection cycle may also be determined differently depending on the type of data to be collected. For example, the collection cycle for press release information, which generates a large amount of information per day, is set to twice a day, and the collection cycle for satellite image information, which has a large amount of information and requires difficult collection and analysis procedures, is set to once a month. It can be.

The processor 130 may obtain evaluation information indicating the water safety of the agricultural reservoir based on the analysis results of the collected data. Specifically, the processor 130 may obtain analysis results for the collected data, and obtain evaluation information indicating the water safety level of the agricultural reservoir based on the obtained analysis results.

The processor 130 may obtain analysis results for the collected data through the collected data analysis module 220. Specifically, the collected data analysis module 220 may include different detailed modules depending on the type of data to be collected, obtain analysis results in different ways through the detailed modules, and complete the analysis results using the safety evaluation module 230. ) can be transmitted. For example, the collected data analysis module 220 may obtain analysis results by performing Latent Dirichlet Allocation (LDA) on press release information, as described later. In Figure 2, the detailed module that performs LDA analysis is expressed as the LDA analysis module 221.

In addition, the collected data analysis module 220 contains quantified information about reservoir information, weather information, and soil moisture information, so it can be used as analysis results without any special additional process. Additionally, the collected data analysis module 220 may obtain analysis results for satellite image information by performing image processing, image analysis, etc. The image processing process and image analysis process can be performed through a neural network model.

Meanwhile, the analysis result of the collected data may include information about the change trend of the collected data obtained for each collection cycle. That is, the collected data analysis module 220 acquires collection target data at preset intervals and stores the acquired data in the memory 120 in a database format to provide information on the change trend between past data and current data. It can be obtained. For example, the change trend in reservoir information may represent information about how the storage capacity of the reservoir has changed at the time of the current collection cycle compared to the storage capacity of the reservoir at the time of the past collection cycle.

On the other hand, in cases where the amount of collected data is vast, such as when the data to be collected is media report information, it is necessary to select only data related to the water safety of agricultural reservoirs and use them as analysis results. Therefore, the collected data analysis module 220, specifically the LDA analysis module 221 included in the collected data analysis module 220, can perform the following operations.

Specifically, when the collection target data includes press release information, the processor 130 inputs the press release information into a neural network model learned to perform latent Dirichlet allocation, and predefines each of the plurality of documents included in the press release information. One topic can be assigned among multiple topics. Here, the neural network model refers to a neural network model that implements the LDA analysis module 221, and there are no special restrictions on the type of neural network included in the neural network model.

Latent Dirichlet allocation is a type of topic modeling, a process of finding topics in a set of documents, and can be performed through a neural network model trained to perform latent Dirichlet allocation. Specifically, the latent Dirichlet allocation process can be performed as follows. When press release information is obtained, the processor 130 may analyze morphemes and keywords for sentences included in each of the plurality of documents through a neural network model. In addition, the processor 130 may obtain a probability distribution indicating the probability that each keyword corresponds to a specific topic among a plurality of predefined topics through a neural network model, and each of the plurality of documents based on the probability distribution for each keyword. A probability distribution representing the probability of corresponding to a specific topic among the plurality of predefined topics can be obtained.

Once a probability distribution indicating the probability that each of the plurality of documents corresponds to a specific topic is obtained, the processor 130 calculates the probability distribution for each keyword included in each of the plurality of documents and the probability for each keyword included in all of the plurality of documents. Based on the distribution, the probability distribution for each of the plurality of documents can be updated. Additionally, the processor 130 may assign one topic to each of the plurality of documents based on the updated probability distribution.

When one topic is assigned to each of a plurality of documents, the processor 130 calculates the number of topics related to the water safety of agricultural reservoirs among the one assigned topic, thereby determining the number of topics related to the water supply safety of agricultural reservoirs, and selecting information about media reports among the analysis results of the data to be collected. Analysis results can be obtained.

In other words, the processor 130 identifies whether the topic assigned to each of the plurality of documents corresponds to a topic predefined as related to the water water safety level of the agricultural reservoir, and according to the identification result, the water water safety level of the agricultural reservoir among the plurality of documents. The number of documents to which topics related to is assigned can be calculated.

For example, the processor 130 predefines topics assigned to each of the plurality of documents as being related to water safety of agricultural reservoirs, such as “reservoir,” “drought,” “rice planting,” “campaign,” and “water supply.” By calculating the number of topics covered, analysis results for media coverage information can be obtained. When the latent Dirichlet allocation process is performed as described above, a topic is assigned to each of the plurality of documents based on the probability distribution for each keyword included in each of the plurality of documents and the probability distribution for each keyword included in all of the plurality of documents. Therefore, it is possible to clearly identify documents with topics related to water safety of agricultural reservoirs among a plurality of documents. For example, when a latent Dirichlet allocation process is performed, a topic related to water safety of an agricultural reservoir called "drought" may not be assigned to a document containing the keyword "bone drought" among a plurality of documents.

Meanwhile, the processor 130 may obtain evaluation information based on the analysis results through the completion safety evaluation module 230. Specifically, when the analysis results for the data to be collected are obtained, the water safety evaluation module 230 applies predefined evaluation rules to the analysis results for each type of data to be collected to obtain a score, thereby improving the water safety of the agricultural reservoir. The evaluation information indicated can be obtained. Evaluation rules for calculating scores for each type of data to be collected can be defined in various ways by developers or users.

Meanwhile, when the analysis result of the collection target data is input, the water flow safety evaluation module 230 may obtain evaluation information using a neural network model learned to output evaluation information indicating the water mud safety of the agricultural reservoir.

The completion safety evaluation module 230 can transmit the acquired evaluation information to the management module 240, along with the analysis results received from the collection data analysis module 220 and the collection target collected from the data collection module 210. Data and additional information related thereto may be transmitted to the management module 240.

For example, if the number of topics called "drought" among the topics related to water safety of agricultural reservoirs increases by 30% compared to the last collection cycle, the probability of precipitation in the area where the reservoir is located remains below the threshold for more than a predetermined period of time. In this case, if the soil moisture in the area where the reservoir is located remains below the critical value for more than a certain period of time, or if global warming is predicted to be accelerating for more than a certain period of time as a result of analysis of satellite image information for the area where the reservoir is located. In this case, the water mud safety evaluation module 230 may evaluate the water mud safety of the agricultural reservoir as low.

In the above example, a related example of evaluating the completion safety level based on the analysis results for each type of data to be collected was given as an example, but the completion safety evaluation module 230 synthesizes the analysis results for each type of data to be collected. Of course, safety can be evaluated. When assessing completion safety by combining analysis results for each type of data to be collected, the completion safety evaluation module 230 quantifies each analysis result for each type of data to be collected to obtain a plurality of scores, and Completion safety can be evaluated by calculating the weighted sum for the course.

Once evaluation information on the collection target data is obtained, the processor 130 may perform various operations to improve the water safety of the agricultural reservoir based on the evaluation information.

The processor 130 may determine an operation to improve water safety of an agricultural reservoir through the management module 240 and control the electronic device 100 or an external device to perform the determined operation. In particular, when the determined operation is an operation performed by the data collection module 210, the management module 240 may transmit a control signal to perform the corresponding operation to the data collection module 210.

According to one embodiment, the management module 240 may add or exclude at least one of press release information, reservoir information, weather information, soil moisture information, and satellite image information from the collection target data based on the evaluation information. For example, if the evaluation information indicates that the water safety of the reservoir is below the critical level, it can be said that it is necessary to review trends such as climate change in more detail, and the management module 240 provides more information. The types of data to be collected can be expanded to collect.

According to one embodiment, the management module 240 may change the collection cycle for at least one of press release information, reservoir information, weather information, soil moisture information, and satellite image information based on evaluation information. For example, if the evaluation information indicates that the water safety of the reservoir is above a critical level, it may be desirable to reduce the resources and costs consumed in collecting target data rather than reviewing trends such as climate change in more detail. As may be the case, the management module 240 may set the collection cycle to be slower than the current collection cycle. In addition, the management module 240 can be set to further increase or decrease not only the collection period of the data to be collected, but also the amount of data collected for each type of data to be collected at each collection cycle.

According to one embodiment, the management module 240 may obtain at least one of control information for adjusting the storage amount of a reservoir and design information for designing a new reservoir, based on the evaluation information. For example, if the evaluation information indicates that the water discharge safety of the reservoir is below a critical level, it can be said that it is desirable to utilize the effective water storage capacity of the reservoir to the maximum, and the management module 240 provides control information to further increase the water storage rate of the reservoir. The communication unit 110 can be controlled to obtain and transmit control information to an external device that automatically adjusts the storage amount of the reservoir. In addition, the management module 240 may control the communication unit 110 to transmit control information to an external device owned by the manager that controls the water storage amount of the control information reservoir to further increase the storage rate of the reservoir.

According to one embodiment, the management module 240 may provide at least one of analysis results, evaluation information, control information, and design information to the user through the output unit 150 of the electronic device 100 and send it to an external device. The communication unit 110 can be controlled to transmit. A user interface for providing at least one of analysis results, evaluation information, control information, and design information will be described in more detail with reference to FIG. 4.

Meanwhile, the above has described an embodiment in which the management module 240 automatically changes the type, collection cycle, and collection amount of data to be collected, but the type, collection cycle, and amount of data to be collected are determined by the settings of the administrator or user. Of course, things can change. In addition, depending on the embodiment, a user interface is provided by the management module 240 to change the type, collection cycle, and amount of data to be collected, and the type, collection cycle, and amount of data to be collected are provided through the user interface. The type, collection cycle, collection amount, etc. of data to be collected can be changed only when an command from an administrator or user confirming the change is received.

When settings or commands from an administrator or user are received as described above, it goes without saying that the scope of the settings or commands may differ depending on the authority or work field of the administrator or user. To this end, the management module 240 may include detailed modules differentiated by user authority or work field.

Meanwhile, in the above, various embodiments related to evaluating the water safety of one agricultural reservoir were described. However, this is only for convenience of explanation, and of course, various embodiments according to the present disclosure can be used to evaluate the water safety of a plurality of reservoirs. For example, when the present disclosure is used to evaluate the water safety of all reservoirs located throughout the country, the electronic device 100 may collect data for each region in performing the process from receiving data to be collected to obtaining evaluation information. The type of data, collection cycle, collection amount, etc. can be applied differently.

According to the embodiment described above with reference to FIGS. 1 and 2, the electronic device 100 is capable of effectively and efficiently collecting/managing collection target data related to the water safety level of the agricultural reservoir, and accordingly, the It becomes possible to accurately evaluate the safety of the course. Additionally, the electronic device 100 may perform an operation to improve the water safety of the agricultural reservoir based on the evaluation results.

Specifically, the electronic device 100 can build big data by collecting various types of data that can affect the water safety evaluation of the reservoir at each collection cycle, and determines the type, collection cycle, and collection amount of data to be collected. It can also be controlled effectively. In addition, the electronic device 100 can derive analysis results for each type of data to be collected, and in particular, can derive analysis results using information related to the safety of the course, among the overflowing media coverage information.

Furthermore, the electronic device 100 maintains the up-to-dateness of the data to be collected through periodic data collection, and provides an environment in which continuous data management and completion safety evaluation can be performed by monitoring the collection status. Accordingly, it is possible to contribute to effective agricultural reservoir management and policy setting by providing data that can accurately analyze the current agricultural reservoir water safety level.

FIG. 3 is a block diagram briefly illustrating the configuration of an electronic device 100 according to an embodiment of the present disclosure. And, Figure 4 is a diagram showing a user interface according to an embodiment of the present disclosure.

As shown in FIG. 3, the electronic device 100 according to an embodiment of the present disclosure includes not only a communication unit 110, a memory 120, and a processor 130, but also an input unit 140 and an output unit 150. More may be included. However, the configurations shown in FIGS. 1 and 3 are merely exemplary, and in carrying out the present disclosure, new configurations may be added or some configurations may be omitted in addition to the configurations shown in FIGS. 1 and 3. Of course it exists.

The input unit 140 includes a circuit, and the processor 130 can receive user commands for controlling the operation of the electronic device 100 through the input unit 140. Specifically, the input unit 140 may be comprised of a microphone, a camera (not shown), and a remote control signal receiver (not shown). Additionally, the input unit 140 may be implemented as a touch screen included in the display. In particular, the microphone can receive voice signals and convert the received voice signals into electrical signals.

In particular, in various embodiments according to the present disclosure, the processor 130 sets user input, type of collection target data, collection cycle, collection amount, etc. to obtain evaluation information indicating completion safety through the input unit 140. User input to change, user input to set the number of topics allocated during LDA analysis, user input to directly input data to be collected, etc. can be received. The output unit 150 includes a circuit, and the processor 130 can output various functions that the electronic device 100 can perform through the output unit 150. And, the output unit 150 may include at least one of a display, a speaker, and an indicator.

The display can output image data under the control of the processor 130. Specifically, the display may output images previously stored in the memory 120 under the control of the processor 130.

In particular, the display according to an embodiment of the present disclosure may display a user interface (User Interface) stored in the memory 120. The display can be implemented as a liquid crystal display panel (LCD), organic light emitting diodes (OLED), etc., and in some cases, the display can also be implemented as a flexible display, transparent display, etc. However, the display according to the present disclosure is not limited to a specific type. The speaker may output audio data under the control of the processor 130, and the indicator may light up under the control of the processor 130.

In particular, in various embodiments according to the present disclosure, the processor 130 may output at least one of analysis results, evaluation information, control information, and design information to the management module 240 through the output unit 150. For example, the processor 130 may control the display to display a user interface showing at least one of analysis results, evaluation information, control information, and design information. Additionally, the processor 130 may control the speaker to output a voice message for guiding at least one of analysis results, evaluation information, control information, and design information.

Referring to the example of FIG. 4, the user interface according to the present disclosure may include reservoir information, weather information, soil moisture information, satellite information, and press release information.

As shown in Figure 4, the reservoir information includes the reservoir water level in the current cycle (36,200 EL.m), the reservoir water level in the previous cycle (35,500 EL.m), a graph showing the increase/decrease trend in reservoir water level from the past cycle to the current cycle, This may include the rate of increase (2.3%) in reservoir water level from the past cycle to the current cycle.

Additionally, meteorological information may include information on current temperature, weather, probability of precipitation, humidity, and wind speed, and soil moisture information may include soil moisture in the current cycle (25.8%), soil moisture in the previous cycle, and from the past cycle to the present. It can include a graph showing the trend of increase or decrease in soil moisture from cycle to cycle, soil moisture increase rate (-0.5%) from the past cycle to the current cycle, etc.

Additionally, the satellite information (phase image information) may include the amount of satellite information collected in the current cycle (35,650) and a graph showing the increase/decrease trend of satellite information from the past cycle to the current cycle.

In addition, the press release information may include information indicating the daily collection amount of press release information, and a UI item indicating the daily collection amount for each topic in the color and size of text.

Meanwhile, as shown in FIG. 4, the user interface according to the present disclosure may include information such as weekly information, reservoir status, etc.

Here, the weekly information may include information about the total amount of data to be collected each week, the amount of data corresponding to previous information among the data to be collected, and the amount of data corresponding to the latest information among the data to be collected.

In addition, the reservoir status may include a quantile map showing the nationwide water storage rate, figures and graphs showing the nationwide water storage rate by region, yesterday's and today's precipitation and information showing the increase/decrease trend in precipitation, etc.

Meanwhile, the user interface of FIG. 4 is only an example, and of course, various information other than the information shown in FIG. 4 may be included in the user interface. For example, the user interface may include information indicating the results of a comprehensive evaluation of the water safety of reservoirs in each region, information indicating a warning message for reservoirs whose water safety is below a critical level, etc. Additionally, the user interface may include UI items for receiving user input for changing the type, collection cycle, and collection amount of data to be collected, UI items for receiving user input for adjusting the storage amount of the reservoir, etc.

FIG. 5 is a flowchart showing a control method of the electronic device 100 according to an embodiment of the present disclosure.

As shown in FIG. 5, the electronic device 100 may receive collection target data from at least one server at each preset collection cycle (S510). The collection cycle of data to be collected according to the present disclosure may be changed depending on the settings of the developer or user, and in particular, may be set individually for each type of data to be collected.

The electronic device 100 can identify whether the collected data includes press release information (S520). When the data to be collected includes press release information (S520-Y), the press release information is input into a neural network model trained to perform Latent Dirichlet Allocation (LDA), and a plurality of documents included in the press release information are entered. One topic from among a plurality of predefined topics can be assigned to each (S530). Latent Dirichlet allocation has been described above with reference to FIGS. 1 and 2.

By calculating the number of topics related to water safety of agricultural reservoirs among one topic assigned to each of a plurality of documents, analysis results for media report information can be obtained among the analysis results for the collected data (S540). Accordingly, the electronic device 100 can identify information related to the water safety of agricultural reservoirs from a vast amount of media report information and use it as an analysis result.

After obtaining the analysis results for the press release information, or if the collection target data does not include the press release information (S520-N), the electronic device 100 generates the analysis results for the other collection target data excluding the press release information. You can obtain (S550). Analysis of collected data other than press release information may be performed in different ways depending on the type of collected data.

The electronic device 100 may obtain evaluation information indicating the water safety of the agricultural reservoir based on the analysis results of the collected data (S560). For example, based on the evaluation information, the electronic device 100 may add or exclude at least one of press release information, reservoir information, weather information, soil moisture information, and satellite image information from the collection target data. Additionally, the electronic device 100 may change at least one of the collection cycle for each type of collection target data and the collection amount for each collection cycle based on the evaluation information.

Meanwhile, the control method of the electronic device 100 according to the above-described embodiment may be implemented as a program and provided to the electronic device 100. In particular, a program including a control method for the electronic device 100 may be stored and provided in a non-transitory computer readable medium.

Specifically, in a non-transitory computer-readable recording medium including a program for executing a control method of the electronic device 100, the control method of the electronic device 100 is performed at least once through the communication unit 110 at each preset collection period. A step of receiving the data to be collected from a server, and obtaining evaluation information indicating the water safety of the agricultural reservoir based on the analysis results of the data to be collected, and the step of acquiring the evaluation information includes the step of receiving the data to be collected. When press release information is included, the press release information is input into a neural network model trained to perform Latent Dirichlet Allocation (LDA), and among a plurality of topics predefined in each of the plurality of documents included in the press release information. By calculating the number of topics related to water safety of agricultural reservoirs among the steps of allocating one topic and one topic assigned to each of a plurality of documents, the analysis results of media report information are obtained among the analysis results of the data to be collected. It may include steps.

In the above, the control method of the electronic device 100 and the computer-readable recording medium including the program for executing the control method of the electronic device 100 have been briefly described. However, this is only to omit redundant description, and the electronic device 100 control method has been briefly described. Of course, various embodiments of the device 100 can also be applied to a control method of the electronic device 100 and a computer-readable recording medium including a program that executes the control method of the electronic device 100.

According to various embodiments of the present disclosure as described above, the electronic device 100 can effectively and efficiently collect and manage collection target data related to the water water safety level of an agricultural reservoir, and thereby accurately determine the water water safety level of the agricultural reservoir. can be evaluated. Additionally, the electronic device 100 may perform an operation to improve the water safety of the agricultural reservoir based on the evaluation results.

Functions related to artificial intelligence according to the present disclosure are operated through the processor 130 and memory 120 of the electronic device 100.

The processor 130 may be comprised of one or multiple processors 130. At this time, the one or more processors 130 may include at least one of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a Neural Processing Unit (NPU), but are not limited to the example of the processor 130 described above. No.

The CPU is a general-purpose processor 130 that can perform not only general calculations but also artificial intelligence calculations, and can efficiently execute complex programs through a multi-layer cache structure. CPUs are advantageous for serial processing, which allows organic connection between previous and next calculation results through sequential calculations. The general-purpose processor 130 is not limited to the above-described example, except when specified as the above-described CPU.

GPU is a processor 130 for large-scale operations such as floating-point operations used in graphics processing, and can perform large-scale operations in parallel by integrating a large number of cores. In particular, GPUs may be more advantageous than CPUs in parallel processing methods such as convolution operations. Additionally, the GPU can be used as a co-processor 130 to supplement the functions of the CPU. The processor 130 for mass computation is not limited to the above-described example, except in the case where it is specified as the GPU.

The NPU is a processor 130 specialized in artificial intelligence calculations using an artificial neural network, and each layer constituting the artificial neural network can be implemented with hardware (e.g., silicon). At this time, the NPU is designed specifically according to the company's requirements, so it has a lower degree of freedom than a CPU or GPU, but can efficiently process artificial intelligence calculations requested by the company. Meanwhile, as a processor 130 specialized in artificial intelligence calculations, the NPU can be implemented in various forms such as a TPU (Tensor Processing Unit), IPU (Intelligence Processing Unit), and VPU (Vision processing unit). The artificial intelligence processor 130 is not limited to the above-described example, except for the case specified as the above-described NPU.

Additionally, one or more processors 130 may be implemented as a System on Chip (SoC). At this time, in addition to one or more processors 130, the SoC may further include a memory 120 and a network interface such as a bus for data communication between the processor 130 and the memory 120.

When the SoC (System on Chip) included in the electronic device 100 includes a plurality of processors 130, the electronic device 100 uses some processors 130 among the plurality of processors 130 to provide artificial intelligence and Related operations (for example, operations related to learning or inference of an artificial intelligence model) can be performed. For example, the electronic device 100 performs artificial intelligence and Related operations can be performed. However, this is only an example, and of course, calculations related to artificial intelligence can be processed using the general-purpose processor 130, such as a CPU.

Additionally, the electronic device 100 may perform calculations on functions related to artificial intelligence using multiple cores (eg, dual core, quad core, etc.) included in one processor 130. In particular, the electronic device 100 can perform artificial intelligence operations such as convolution operations, matrix multiplication operations, etc. in parallel using the multi-cores included in the processor 130.

One or more processors 130 controls input data to be processed according to predefined operation rules or artificial intelligence models stored in the memory 120. Predefined operation rules or artificial intelligence models are characterized by being created through learning.

Here, being created through learning means that a predefined operation rule or artificial intelligence model with desired characteristics is created by applying a learning algorithm to a large number of learning data. This learning may be performed on the device itself that performs the artificial intelligence according to the present disclosure, or may be performed through a separate server/system.

An artificial intelligence model may be composed of multiple neural network layers. At least one layer has at least one weight value, and the operation of the layer is performed using the operation result of the previous layer and at least one defined operation. Examples of neural networks include Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), and Deep Neural Network (BRDNN). There are Q-Networks (Deep Q-Networks) and Transformer, and the neural network in this disclosure is not limited to the above-described examples except where specified.

A learning algorithm is a method of training a target device (eg, a robot) using a large number of learning data so that the target device can make decisions or make predictions on its own. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, and the learning algorithm in the present disclosure is specified. Except, it is not limited to the examples described above.

A storage medium that can be read by a device may be provided in the form of a non-transitory storage medium. Here, 'non-transitory storage medium' simply means that it is a tangible device and does not contain signals (e.g. electromagnetic waves). This term refers to cases where data is semi-permanently stored in a storage medium and temporary storage media. It does not distinguish between cases where it is stored as . For example, a 'non-transitory storage medium' may include a buffer where data is temporarily stored.

According to one embodiment, methods according to various embodiments disclosed in this document may be included and provided in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or via an application store (e.g. Play Store ^TM ) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smartphones) or online. In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be stored in machine-readable storage, such as memory 120 of a manufacturer's server, an application store's server, or a relay server. It may be at least temporarily stored in a medium, or may be created temporarily.

Each component (e.g., module or program) according to various embodiments of the present disclosure as described above may be composed of a single or multiple entities, and some of the sub-components described above may be omitted. Alternatively, other sub-components may be further included in various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated into one entity and perform the same or similar functions performed by each corresponding component prior to integration.

According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added. You can.

Meanwhile, the term "unit" or "module" used in the present disclosure includes a unit comprised of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. You can. A “part” or “module” may be an integrated part, a minimum unit that performs one or more functions, or a part thereof. For example, a module may be comprised of an application-specific integrated circuit (ASIC).

Various embodiments of the present disclosure may be implemented as software including instructions stored in a machine-readable storage media that can be read by a machine (e.g., a computer). The device calls the stored instructions from the storage medium. And, the device capable of operating according to the called command may include the electronic device 100 (eg, the electronic device 100) according to the disclosed embodiments.

When the instruction is executed by a processor, the processor may perform the function corresponding to the instruction directly or using other components under the control of the processor. Instructions may contain code generated or executed by a compiler or interpreter.

In the above, preferred embodiments of the present disclosure have been shown and described, but the present disclosure is not limited to the specific embodiments described above, and may be used in the technical field to which the disclosure pertains without departing from the gist of the present disclosure as claimed in the claims. Of course, various modifications can be made by those skilled in the art, and these modifications should not be understood individually from the technical ideas or perspectives of the present disclosure.

100: Electronic device 110: Communication department
120: memory 130: processor

Claims (9)

In electronic devices,
display;
Ministry of Communications;
Memory; and
At each preset collection cycle, from at least one server through the communication unit, press release information, reservoir information indicating the water storage status of the reservoir, weather information of the area where the reservoir is located, soil moisture information of the area, and satellite for the area. Receive data to be collected, including video information,
The collection obtained for each of the collection cycles for each of the press release information, reservoir information indicating the water storage status of the reservoir, weather information for the area where the reservoir is located, soil moisture information for the area, and satellite image information for the area. Obtaining multiple analysis results containing information on changes in target data,
Based on each of the plurality of analysis results, a plurality of scores indicating the water safety of the agricultural reservoir are obtained and corresponding to each of the plurality of analysis results,
Based on the weighted sum of the plurality of scores, evaluation information indicating the water safety of the agricultural reservoir is obtained,
If the evaluation information indicates that the water safety of the reservoir is below a critical level, control information to increase the collection cycle, increase the amount of the collection target data received for each increased collection cycle, and increase the storage rate of the reservoir. and obtaining design information for the design of new reservoirs;
If the evaluation information indicates that the water safety of the reservoir is above a critical level, the collection period is reduced, the amount of the collection target data received per the reduced collection period is reduced, and the satellite image is received from the at least one server. Controls not to receive information,
a processor controlling the display to display a user interface representing the plurality of analysis results; Including,
The processor,
By inputting the press release information into a first neural network model trained to perform Latent Dirichlet Allocation (LDA), one topic among a plurality of topics predefined in each of a plurality of documents included in the press release information assigns ,
Obtaining analysis results for the press release information among the plurality of analysis results by calculating the number of topics related to water safety of agricultural reservoirs among one topic assigned to each of the plurality of documents,
Inputting the satellite image information into a second neural network model learned to perform image analysis on a phase image, to obtain an analysis result included in the satellite image information among the plurality of analysis results,
The user interface includes a first UI item indicating information about the current weather, a second UI item indicating current soil moisture, a third UI item indicating the amount of the satellite image information received in the current cycle, and water flow of the agricultural reservoir. A fourth UI item indicating the number of the topics related to safety in color and size of text, a fifth UI item for receiving a user input for adjusting the water storage amount of the reservoir, and the type of data to be collected, the collection cycle, and the An electronic device including a sixth UI item for receiving user input for changing the amount of data to be collected.
delete delete delete delete delete delete delete In a method of controlling an electronic device,
From at least one server at each preset collection cycle, it includes press release information, reservoir information indicating the water storage status of the reservoir, weather information of the area where the reservoir is located, soil moisture information of the area, and satellite image information of the area. Receiving data to be collected;
The collection obtained for each of the collection cycles for each of the press release information, reservoir information indicating the water storage status of the reservoir, weather information for the area where the reservoir is located, soil moisture information for the area, and satellite image information for the area. Obtaining a plurality of analysis results including information on changes in target data;
Based on each of the plurality of analysis results, indicating a water safety degree of an agricultural reservoir and obtaining a plurality of scores corresponding to each of the plurality of analysis results;
Obtaining evaluation information indicating water safety of an agricultural reservoir based on a weighted sum of the plurality of scores;
If the evaluation information indicates that the water safety of the reservoir is below a critical level, control information to increase the collection cycle, increase the amount of the collection target data received for each increased collection cycle, and increase the storage rate of the reservoir. and obtaining design information for the design of a new reservoir;
If the evaluation information indicates that the water safety of the reservoir is above a critical level, the collection period is reduced, the amount of the collection target data received per the reduced collection period is reduced, and the satellite image is received from the at least one server. Controlling not to receive information; and
displaying a user interface representing the plurality of analysis results; Including,
The step of obtaining the plurality of analysis results is,
By inputting the press release information into a first neural network model trained to perform Latent Dirichlet Allocation (LDA), one topic among a plurality of topics predefined in each of a plurality of documents included in the press release information allocating;
Obtaining an analysis result of the press release information among the plurality of analysis results by calculating the number of topics related to water safety of agricultural reservoirs among one topic assigned to each of the plurality of documents; and
Inputting the satellite image information into a second neural network model trained to perform image analysis on a phase image, and obtaining an analysis result included in the satellite image information among the plurality of analysis results; Includes,
The user interface includes a first UI item indicating information about the current weather, a second UI item indicating current soil moisture, a third UI item indicating the amount of the satellite image information received in the current cycle, and water flow of the agricultural reservoir. A fourth UI item indicating the number of the topics related to safety in the color and size of text, a fifth UI item for receiving a user input for adjusting the water storage amount of the reservoir, and the type of data to be collected, the collection cycle, and the A control method of an electronic device including a sixth UI item for receiving a user input that changes the amount of data to be collected.

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