Observation method and system for artificial intelligence model-based aerostat

Abstract

The present invention relates to an observation method using an artificial intelligence model-based aerostat, the method being performed by an electronic device. The method comprises the steps of: obtaining sensing data from a sensor module mounted on the aerostat; obtaining observation target data from a control server; and inputting the sensing data and the observation target data to an observation risk analysis artificial intelligence model, which outputs an observation risk index based on the sensing data and the observation target data when the sensing data and the observation target data are input, and thereby obtaining the observation risk index from the observation risk analysis artificial intelligence model.

Description

Artificial Intelligence Model-Based Aerostat Monitoring Method and System

[0001] The present invention relates to a method and system for monitoring aerostats using an artificial intelligence model.

[0002] The patent application for the present invention was submitted as a result of the applicant carrying out the “Gyeongnam Regional Enterprise Growth Ladder Support Project” of the Ministry of SMEs and Startups and Gyeongsangnam-do.

[0003] With the advancement of Artificial Intelligence (AI) technology, various AI models capable of effectively achieving user-desired objectives by utilizing diverse data obtainable from users are being researched, developed, and utilized.

[0004] An aerostat is an aircraft that generates lift by utilizing the buoyancy of a gas lighter than normal air, and various equipment is attached to the aerostat to perform missions for various purposes.

[0005] In particular, as aerostat technology advances to increase dwell time and payload capacity, and as artificial intelligence technology develops to process collected data more effectively, the utilization of aerostats in the field of observation in general or military domains is increasing.

[0006] The present invention aims to provide an optimal surveillance plan using an aerostat by analyzing the risk level according to a surveillance target in the field of observation in general or military domains, and effectively processing data collected from the surveillance target based on an artificial intelligence model in response to the analyzed risk level.

[0007] According to one embodiment of the present disclosure, in order to solve the technical problem described above, a monitoring method using an artificial intelligence model-based aerostat performed by an electronic device is provided. The method comprises: a step of acquiring sensing data from a sensor module mounted on an aerostat; a step of acquiring monitoring target data from a control server; and a step of acquiring a monitoring risk indicator from a monitoring risk analysis artificial intelligence model by inputting the sensing data and the monitoring target data into the monitoring risk analysis artificial intelligence model, which outputs a monitoring risk indicator according to the sensing data and the monitoring target data when the sensing data and the monitoring target data are input.

[0008] According to the present invention, by utilizing a plurality of artificial intelligence models, the risk level of a surveillance target can be analyzed, and furthermore, an optimal surveillance plan for the surveillance target can be provided in response to the analyzed risk level.

[0009] FIG. 1 is a diagram illustrating a process of providing a monitoring method using an aerostat based on a plurality of artificial intelligence models according to one embodiment.

[0010] FIG. 2 is a flowchart illustrating a specific process for providing a monitoring method using an aerostat by utilizing a monitoring risk analysis artificial intelligence model according to one embodiment.

[0011] FIG. 3 is a flowchart illustrating a specific process for providing a monitoring method using an aerostat by utilizing an artificial intelligence model for establishing a monitoring plan according to one embodiment.

[0012] FIG. 4 is a diagram showing a monitoring system using an artificial intelligence model-based aerostat according to one embodiment.

[0013] FIG. 5 is a block diagram of an electronic device according to one embodiment.

[0014] FIG. 6 is a block diagram of an electronic device according to one embodiment.

[0015] FIG. 7 is a block diagram of a server according to one embodiment.

[0016] According to one embodiment of the present disclosure, in order to solve the problem described above, a monitoring method using an artificial intelligence model-based aerostat performed by an electronic device is provided. The method comprises: a step of acquiring sensing data from a sensor module mounted on an aerostat; a step of acquiring monitoring target data from a control server; and a step of acquiring a monitoring risk indicator from a monitoring risk analysis artificial intelligence model by inputting the sensing data and the monitoring target data into the monitoring risk analysis artificial intelligence model, which outputs a monitoring risk indicator according to the sensing data and the monitoring target data when the sensing data and the monitoring target data are input.

[0017] According to the features of the present disclosure, the monitoring risk indicator may be determined by different criteria depending on the monitoring target.

[0018] According to the features of the present disclosure, the monitoring target data may include at least monitoring terrain information, monitoring facility information, and monitoring personnel information.

[0019] According to the features of the present disclosure, the method may further include: a step of obtaining monitoring request data from the control server; and a step of obtaining an aerostat monitoring plan from the monitoring plan establishing artificial intelligence model by inputting the monitoring risk indicator and the monitoring request data into the monitoring plan establishing artificial intelligence model, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

[0020] According to the features of the present disclosure, the aerostat monitoring plan may include at least aerostat movement information and aerostat change information.

[0021] According to the features of the present disclosure, the aerostat monitoring plan may be determined by different criteria based on the monitoring risk indicator.

[0022] According to another embodiment of the present disclosure, in order to solve the problem described above, a monitoring system using an artificial intelligence model-based aerostat is provided. The system comprises: an aerostat equipped with a sensor module; and a control server configured to control the aerostat; wherein the control server comprises: a memory for storing one or more instructions; and at least one processor for executing the one or more instructions; wherein the processor, by executing one or more instructions, acquires sensing data from a sensor module equipped with the aerostat; acquires monitoring target data from the control server; and, when the sensing data and monitoring target data are input, acquires a monitoring risk index from the monitoring risk analysis artificial intelligence model by inputting the sensing data and the monitoring target data to the monitoring risk analysis artificial intelligence model, which outputs a monitoring risk index according to the sensing data and the monitoring target data.

[0023] According to the features of the present disclosure, the processor may acquire monitoring request data from the control server by executing one or more instructions; and acquire an aerostat monitoring plan from the monitoring plan establishing artificial intelligence model by inputting the monitoring risk indicator and the monitoring request data into the monitoring plan establishing artificial intelligence model, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

[0024] Specific details of other embodiments are included in the specific details and drawings for carrying out the invention.

[0025] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. In connection with the description of the drawings, similar reference numerals may be used for similar components.

[0026] In this document, expressions such as "have," "can have," "include," or "can include" refer to the existence of the relevant feature (e.g., numerical values, functions, actions, or components, etc.) and do not exclude the existence of additional features.

[0027] In this document, 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 items listed together. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” may refer to cases including (1) at least one A, (2) at least one B, or (3) both at least one A and at least one B.

[0028] Expressions such as “first,” “second,” “first,” or “second” used in this document may modify various components regardless of order and / or importance, and are used only to distinguish one component from another and do not limit said components. For example, the first user device and the second user device may represent different user devices regardless of order or importance. For example, without departing from the scope of rights set forth in this document, the first component may be named the second component, and similarly, the second component may be renamed the first component.

[0029] Where it is stated that a certain component (e.g., a first component) is "(operatively or communicatively) coupled with" or "connected to" another component (e.g., a second component), it should be understood that the said certain component may be directly connected to the said other component or connected through the said other component (e.g., a third component). On the other hand, where it is stated that a certain component (e.g., a first component) is "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 the said certain component and the said other component.

[0030] As used in this document, the expression “configured to” may be replaced, depending on the context, with, for example, “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of.” The term “configured to” does not necessarily mean only that which is “specifically designed to” in hardware. Instead, in some situations, the expression “device configured to” may mean that the device is “capable of” in conjunction with other devices or components. For example, the phrase “processor configured to perform A, B, and C” may mean a dedicated processor for performing those operations (e.g., an embedded processor), or a generic-purpose processor (e.g., a CPU or application processor) capable of performing those operations by executing one or more software programs stored in a memory device.

[0031] The terms used in this document are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this document. Terms used in this document that are defined in general dictionaries may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this document. In some cases, even terms defined in this document may not be interpreted to exclude the embodiments of this document.

[0032] The functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0033] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform a desired characteristic (or objective) are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0034] An artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple nodes and weight values, and performs neural network operations through calculations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, multiple weights can be updated so that the loss value or cost value obtained by the artificial intelligence model during the learning process is reduced or minimized. Additionally, to minimize the loss value or cost value, multiple weights can be updated in a direction that minimizes the gradient associated with the loss value or cost value. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0035] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and as will be fully understood by those skilled in the art, various technical interlocking and operation are possible, and each embodiment may be implemented independently of one another or together in an interlocking relationship.

[0036] Hereinafter, various embodiments of the present invention will be described in detail with reference to the attached drawings.

[0037]

[0038] FIG. 1 is a diagram illustrating a process of providing a monitoring method using an aerostat based on a plurality of artificial intelligence models according to one embodiment.

[0039] Referring to FIG. 1, the electronic device (1000) may use a plurality of artificial intelligence models, namely a surveillance risk analysis artificial intelligence model (2000) and a surveillance plan establishment artificial intelligence model (3000), to provide a surveillance method using an aerostat.

[0040] In the present disclosure, if a surveillance risk analysis AI model (2000) or a surveillance plan establishment AI model (3000) is described for convenience as the subject, i.e., the agent of the operation, rather than an electronic device (1000), it can be understood that the electronic device (1000) performed the operation using the surveillance risk analysis AI model (2000) or the surveillance plan establishment AI model (3000).

[0041] An electronic device (1000) according to one embodiment may be implemented in various forms. For example, the electronic device (1000) described herein may include a mobile terminal, a smartphone, a laptop computer, a tablet PC, an e-book terminal, a digital broadcasting terminal, a PDA (Personal Digital Assistant), but is not limited thereto.

[0042] Meanwhile, the electronic device (1000) can perform operations to model, learn, modify, or update the surveillance risk analysis artificial intelligence model (2000) and the surveillance plan establishment artificial intelligence model (3000).

[0043] According to one embodiment, a plurality of artificial intelligence models used by the electronic device (1000), such as a surveillance risk analysis artificial intelligence model (2000) and a surveillance plan establishment artificial intelligence model (3000), are artificial neural network (ANN) models that refer to a computing system based on biological neural networks. Examples of artificial neural network models include a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), and deep Q-networks. In the following, unless otherwise stated, the surveillance risk analysis artificial intelligence model (2000) and the surveillance plan establishment artificial intelligence model (3000) according to the present disclosure will be described as examples of deep neural network (DNN) models among artificial neural network models.

[0044]

[0045] FIG. 2 is a flowchart illustrating a specific process for providing a monitoring method using an aerostat by utilizing a monitoring risk analysis artificial intelligence model according to one embodiment.

[0046] In step S210, the electronic device (1000) can obtain sensing data from a sensor module mounted on an aerostat.

[0047] In step S220, the electronic device (1000) can obtain monitoring target data from the control server.

[0048] In step S230, the electronic device (1000) can obtain a surveillance risk indicator from a surveillance risk analysis artificial intelligence model by inputting the sensing data and the surveillance target data into the surveillance risk analysis artificial intelligence model, which outputs a surveillance risk indicator based on the sensing data and the surveillance target data when the sensing data and the surveillance target data are input.

[0049] According to one embodiment, the monitoring target data may include at least monitoring terrain information, monitoring facility information, and monitoring personnel information.

[0050]

[0051] FIG. 3 is a flowchart illustrating a specific process for providing a monitoring method using an aerostat by utilizing an artificial intelligence model for establishing a monitoring plan according to one embodiment.

[0052] In step S310, the electronic device (1000) can obtain monitoring request data from the control server.

[0053] In step S320, the electronic device (1000) can obtain an aerostat monitoring plan from the monitoring plan establishing artificial intelligence model by inputting the monitoring risk indicator and the monitoring request data into the monitoring plan establishing artificial intelligence model, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

[0054] According to one embodiment, the aerostat monitoring plan may include at least aerostat movement information and aerostat change information.

[0055] According to one embodiment, the aerostat monitoring plan may be determined by different criteria based on the monitoring risk indicator.

[0056]

[0057] Meanwhile, the surveillance risk analysis artificial intelligence model (2000) can be trained based on sensing learning data, surveillance target learning data, and surveillance risk indicator learning data. More specifically, the electronic device (1000) can match the sensing learning data, surveillance target learning data, and surveillance risk indicator learning data, and train the surveillance risk analysis artificial intelligence model (2000) to output a surveillance risk analysis indicator based on the matched sensing learning data, surveillance target learning data, and surveillance risk indicator learning data.

[0058] The artificial intelligence model (3000) for establishing a surveillance plan can be trained based on surveillance risk indicator training data, surveillance request training data, and aerostat surveillance plan training data. More specifically, the electronic device (1000) can match the surveillance risk indicator and surveillance request data and the aerostat surveillance plan training data, and train the artificial intelligence model (3000) for establishing a surveillance plan to output an aerostat surveillance plan based on the matched surveillance risk indicator and surveillance request data and the aerostat surveillance plan training data.

[0059]

[0060] FIG. 4 is a diagram showing a monitoring system using an artificial intelligence model-based aerostat according to one embodiment.

[0061] A surveillance system using an artificial intelligence model-based aerostat includes an aerostat (410) and a control server (420) configured to control the aerostat (410). The surveillance system using an artificial intelligence model-based aerostat can perform military demarcation line surveillance or coastline surveillance.

[0062] According to one embodiment, the control server includes a memory for storing one or more instructions; and at least one processor for executing the one or more instructions. The processor may be characterized by acquiring sensing data from a sensor module mounted on an aerostat by executing one or more instructions; acquiring monitoring target data from the control server; and acquiring a monitoring risk indicator from the monitoring risk analysis artificial intelligence model by inputting the sensing data and the monitoring target data into the monitoring risk analysis artificial intelligence model, which outputs a monitoring risk indicator based on the sensing data and the monitoring target data when the sensing data and the monitoring target data are input.

[0063] According to one embodiment, the processor may acquire monitoring request data from the control server by executing one or more instructions; and acquire an aerostat monitoring plan from the monitoring plan establishing artificial intelligence model by inputting the monitoring risk indicator and the monitoring request data into the monitoring plan establishing artificial intelligence model, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

[0064]

[0065] FIG. 5 is a block diagram of an electronic device according to one embodiment.

[0066] Referring to FIG. 5, an electronic device (1000) according to one embodiment may include a processor (510) and a memory (520). However, not all illustrated components are essential. The electronic device (1000) may be implemented with more components than illustrated, or with fewer components. For example, as shown in FIG. 6, an electronic device (1000) according to one embodiment may further include a user input unit (610), a communication unit (620), and a display (630).

[0067] The processor (510) controls the overall operation of the electronic device (1000) by executing one or more instructions in memory (520). For example, the processor (510) can control the user input unit (610), communication unit (620), display (630), etc., by executing one or more instructions stored in memory (520). Additionally, the processor (510) can perform the operation and function of the electronic device (1000) described in relation to FIGS. 1 to 3 by executing one or more instructions stored in memory (520).

[0068] The processor (510) may be composed of one or more processors, and the one or more processors may be general-purpose processors such as CPUs, APs, DSPs (Digital Signal Processors), graphics-only processors such as GPUs, VPUs (Vision Processing Units), or artificial intelligence (AI)-only processors such as NPUs. According to one embodiment, when the processor (510) is implemented as a plurality of processors or graphics-only processors or artificial intelligence-only processors such as NPUs, at least some of the plurality of processors or graphics-only processors or artificial intelligence-only processors such as NPUs may be installed in an electronic device (1000) and another electronic device or server (4000) connected to the electronic device (1000).

[0069] According to one embodiment, the processor (510) acquires sensing data from a sensor module mounted on an aerostat; acquires monitoring target data from a control server; and acquires a monitoring risk indicator from the monitoring risk analysis artificial intelligence model by inputting the sensing data and the monitoring target data into the monitoring risk analysis artificial intelligence model, which outputs a monitoring risk indicator based on the sensing data and the monitoring target data when the sensing data and the monitoring target data are input.

[0070] According to one embodiment, the processor (510) obtains monitoring request data from the control server; and can obtain an aerostat monitoring plan from the monitoring plan establishing artificial intelligence model by inputting the monitoring risk indicator and the monitoring request data into the monitoring plan establishing artificial intelligence model, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

[0071] The memory (520) may include one or more instructions for controlling the operation of the electronic device (1000). The memory (520) may include artificial intelligence models used by the electronic device (1000), for example, a surveillance risk analysis artificial intelligence model (2000) and a surveillance plan establishment artificial intelligence model (3000).

[0072] According to one embodiment, the memory (520) may include at least one type of storage medium among, for example, a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk, but is not limited thereto.

[0073] The user input unit (610) can receive user input for controlling the operation of the electronic device (1000). For example, the user input unit (610) may include a key pad, a dome switch, a touch pad (contact capacitive method, pressure resistive method, infrared sensing method, surface ultrasonic conduction method, integral tension measurement method, piezo effect method, etc.), a jog wheel, a jog switch, etc., but is not limited thereto.

[0074] The communication unit (620) may include one or more communication modules for communication with the server (4000). For example, the communication unit (620) may include at least one of a short-range communication unit or a mobile communication unit.

[0075] The short-range wireless communication unit may include, but is not limited to, a Bluetooth communication unit, a BLE (Bluetooth Low Energy) communication unit, a Near Field Communication unit, a WLAN (Wi-Fi) communication unit, a Zigbee communication unit, an infrared (IrDA, infrared Data Association) communication unit, a WFD (Wi-Fi Direct) communication unit, an UWB (ultra wideband) communication unit, an Ant+ communication unit, etc.

[0076] A mobile communication unit transmits and receives wireless signals with at least one of a base station, an external terminal, and a server on a mobile communication network. Here, the wireless signal may include various forms of data resulting from voice call signals, video call call signals, or the transmission and reception of text / multimedia messages.

[0077] The display (630) can display information processed by the electronic device (1000). For example, the display (630) can display an interface for controlling the electronic device (1000), an interface for displaying the status of the electronic device (1000), etc.

[0078]

[0079] FIG. 7 is a block diagram of a server according to another embodiment.

[0080] According to one embodiment, a monitoring method using an aerostat based on an artificial intelligence model performed by an electronic device (1000) can be performed on a server (4000) connected to and capable of communicating with the electronic device (1000).

[0081] The server (4000) may include a communication interface (710), a database (720), and a processor (730). For example, the communication interface (710) of the server (4000) according to the present disclosure may correspond to the communication unit (620) of the electronic device (1000), the database (720) of the server (4000) may correspond to the memory (510) of the electronic device (1000), and the processor (730) of the server (4000) may correspond to the processor (510) of the electronic device (1000). Additionally, the processor (4300) of the server (4000) may perform a monitoring method using an aerostat based on an artificial intelligence model described in relation to FIGS. 1 to 3.

[0082]

[0083] Although embodiments of the present invention have been described in more detail with reference to the attached drawings, the present invention is not necessarily limited to these embodiments and may be modified in various ways within the scope of the technical spirit of the present invention. Accordingly, the embodiments disclosed in the present invention are intended to explain, not limit, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. Various modifications and improvements by those skilled in the art using the basic concept of the present disclosure as defined in the following claims also fall within the scope of the rights of the present disclosure.

Claims

1. A monitoring method using an artificial intelligence model-based aerostat performed by an electronic device, A step of acquiring sensing data from a sensor module mounted on an aerostat; A step of obtaining monitored target data from a control server; and A method comprising the step of obtaining a surveillance risk indicator from a surveillance risk analysis artificial intelligence model by inputting the sensing data and the surveillance target data into the artificial intelligence model, which outputs a surveillance risk indicator based on the sensing data and the surveillance target data when the sensing data and the surveillance target data are input.

2. In Paragraph 1, The above-mentioned monitoring risk indicator is determined by different criteria depending on the monitoring target, in a method.

3. In Paragraph 1, A method in which the above-mentioned monitoring target data includes at least monitoring terrain information, monitoring facility information, and monitoring personnel information.

4. In Paragraph 1, A step of obtaining monitoring request data from the above-mentioned control server; and A method further comprising the step of obtaining an aerostat monitoring plan from an artificial intelligence model for establishing a monitoring plan, by inputting the monitoring risk indicator and the monitoring request data into the artificial intelligence model for establishing a monitoring plan, which outputs an aerostat monitoring plan according to the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

5. In Paragraph 4, The above-described aerostat monitoring plan includes at least aerostat movement information and aerostat replacement information.

6. In Paragraph 4, The above aerostat monitoring plan is a method determined by different criteria according to the above monitoring risk indicator.

7. In a surveillance system using an artificial intelligence model-based aerostat, Aerostat equipped with a sensor module; and A control server configured to control the above aerostat; including, The above control server is, Memory for storing one or more instructions; and It includes at least one processor that executes one or more of the above instructions, The above processor executes one or more instructions, Acquire sensing data from a sensor module mounted on an aerostat; Acquire monitoring target data from the control server; and A system characterized by obtaining a surveillance risk indicator from a surveillance risk analysis artificial intelligence model by inputting the sensing data and the surveillance target data into the artificial intelligence model, which outputs a surveillance risk indicator based on the sensing data and the surveillance target data when the sensing data and the surveillance target data are input.

8. In Paragraph 7, The above processor executes one or more instructions, Obtain monitoring request data from the above-mentioned control server; and A system characterized by obtaining an aerostat monitoring plan from an artificial intelligence model for establishing a monitoring plan, by inputting the monitoring risk indicator and the monitoring request data into the artificial intelligence model for establishing a monitoring plan, which outputs an aerostat monitoring plan based on the monitoring risk indicator and the monitoring request data when the monitoring risk indicator and the monitoring request data are input.

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