system

Abstract

To provide a system.SOLUTION: A system comprises: means for performing a base station design on the basis of geographic information including population and landmark information; means for determining, on the basis of an area coverage ratio, a construction start priority of a base station planned by the base station design; and means for determining, on the basis of road information and field information collected in real time at the time of a disaster, a recovery priority of the base station.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventionally, the design, construction, maintenance, and disaster response of mobile networks (base stations) have been carried out using separate systems and methods, making centralized control and efficient operation difficult. In particular, when carrying out recovery work after a disaster, it is necessary to determine recovery priorities by comprehensively taking into account factors such as area coverage, the latest road information, and local information. [Means for solving the problem]

[0005] This invention provides a system that centrally controls everything from mobile network (base station) design to construction, maintenance, and disaster response via the cloud via GTP. Specifically, it designs base stations based on information such as population and landmarks, and assigns construction start priorities based on factors such as coverage rate, efficiently advancing area construction. It also identifies optimal maintenance methods based on alert types, and in the event of a typhoon, earthquake, or other disaster, prioritizes recovery by combining area coverage rate, the latest road information, and local information, enabling smooth recovery work. This improves the efficiency of mobile network operations and enables rapid recovery work, especially in the event of a disaster. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that centrally controls everything from the design, construction, maintenance, and disaster response of a mobile network (base station) via a cloud via GTP. This system includes a means for designing base station locations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, a means for identifying optimal maintenance methods based on alert types, and a means for prioritizing restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information to ensure smooth restoration work.

[0029] "Example 2"

[0030] Specifically, a geographic information system is used to design base stations, taking into account the topography and distribution of buildings in the area. For example, more base stations are placed in densely populated areas or around landmarks to ensure coverage.

[0031] "Example 3"

[0032] Furthermore, in disaster recovery work, recovery priorities are determined by integrating area coverage rates, the latest road information, local information, etc. For example, if a base station is damaged by a typhoon or earthquake, recovery work will be prioritized from the most accessible base station based on the latest road and local information. This will enable efficient recovery work.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: First, base station locations are designed based on information such as population and landmarks. A geographic information system is used to take into account the local topography and building distribution.

[0036] Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[0037] Step 3: After the base station is installed, determine the optimal maintenance method based on the alert type and perform regular maintenance.

[0038] Step 4: When a disaster such as a typhoon or earthquake occurs, area coverage rates, the latest road information, local information, etc. are combined to prioritize recovery efforts and ensure smooth recovery operations.

[0039] "Example 2"

[0040] Step 1: First, a geographic information system is used to design base stations, taking into account the local topography and the distribution of buildings. For example, more base stations are placed in densely populated areas or around landmarks. Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[0041] "Example 3"

[0042] Step 1: First, area coverage, the latest road information, local information, etc. are combined to determine restoration priorities.

[0043] Step 2: Next, restoration work is carried out by prioritizing accessible base stations based on the latest road and local information, thereby achieving efficient restoration work.

[0044] Example 1

[0045] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0046] There is a need for efficient and effective centralized control of mobile network design, construction, maintenance, and disaster response. In particular, the challenges are optimal base station placement taking into account population density and landmark locations, prioritizing construction based on coverage rates, proposing optimal maintenance methods according to alert types, and rapid recovery operations in the event of a disaster.

[0047] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0048] In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and other factors to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and carrying out smooth restoration work by integrating area coverage rates, the latest road information, and local information in the event of a typhoon, earthquake, or other disaster; means for analyzing population density and landmark locations using a geographic information system to design the optimal base station placement; means for analyzing coverage rates and population density using a data analysis tool to determine construction priorities; means for analyzing past alert data and maintenance history using a machine learning model to propose the optimal maintenance method; and means for collecting the latest road information and local information using a real-time data streaming tool to determine restoration priorities. This enables efficient and effective centralized control of everything from mobile network design to construction, maintenance, and disaster response.

[0049] "Population density" is an indicator that shows the number of people per unit area in a particular area.

[0050] A "landmark" refers to a building, natural object, or important point that serves as a landmark in a particular area.

[0051] "Base station planning" is the process of planning and designing the optimal placement of base stations in a mobile network.

[0052] "Coverage rate" is an indicator that indicates the percentage of the area in which mobile network signals reach in a particular area.

[0053] "Construction start priority" is a criterion for determining the priority when starting construction of a base station.

[0054] "Alert type" is a classification of the types of abnormalities and problems that occur during network operation.

[0055] "Maintenance methods" are repair and maintenance procedures performed to maintain normal network operation.

[0056] "Recovery priority" is a standard for determining the order of priority when carrying out recovery work in the event of a disaster or failure.

[0057] A "geographic information system" is an information system for collecting, analyzing, and displaying geographic data.

[0058] "Data analysis tools" are software and libraries used to analyze collected data and extract meaningful information.

[0059] A "machine learning model" is an algorithm or mathematical model that learns from data and makes predictions or classifications.

[0060] "Real-time data streaming tools" are software or platforms for collecting, processing, and distributing data in real time.

[0061] This invention is a system for collectively controlling everything from the design to the construction, maintenance, and disaster response of a mobile network. A specific embodiment of this system will be described below.

[0062] 1. Mobile Network Design

[0063] The server collects population density data and landmark location data from open data and commercial databases. The server then analyzes this data using geographic information system (GIS) software. Specifically, the server uses the software to visualize population density and landmark locations and design optimal cell tower placement.

[0064] 2. Construction prioritization

[0065] The server collects existing coverage data from the network operations database. Then, using data analysis tools (e.g., the Pandas library in Python), the server analyzes data such as coverage and population density to determine construction priorities. Finally, the server generates a priority list and provides it to the construction team.

[0066] 3. Optimizing maintenance methods

[0067] The server collects alert data from the network using a network monitoring system. Next, the server uses a machine learning model (e.g., TENSORFLOW®) to analyze past alert data and maintenance history and propose the optimal maintenance method. The server then notifies the maintenance team of the maintenance method, including specific steps.

[0068] 4. Disaster response

[0069] The server collects the latest road and local information in real time during a disaster. This is done using a real-time data streaming tool (e.g., Apache Kafka). The server then analyzes the collected real-time data and determines recovery priorities. Finally, the server provides a priority list to the recovery team, helping to ensure smooth recovery operations.

[0070] Examples and prompts

[0071] For example, if a new base station is to be installed in City A, the server collects population density data and landmark location data for City A from open data. Then, the server analyzes this data using geographic information system software to design the optimal base station placement. After that, the server collects existing coverage data from the network operation database and analyzes construction priorities using Python's Pandas library. Finally, the server generates a priority list and provides it to the construction team.

[0072] Prompt Sentence Examples

[0073] "Design the optimal placement of base stations based on population density data and landmark location data for City A. Also, analyze coverage rate data to determine construction priorities, and in the event of a disaster, prioritize restoration efforts based on the latest road and local information."

[0074] In this way, by explaining the system processing from the perspectives of the server, terminal, and user, the specific operations and data flow become clear.

[0075] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0076] Step 1:

[0077] The server collects population density data and landmark location data. As input, it obtains population density data and landmark location data from open data and commercial databases. Specifically, the server downloads these data through APIs and stores them in an internal database. As output, the collected data is stored in the internal database.

[0078] Step 2:

[0079] The server analyzes the collected data using geographic information system (GIS) software. As input, it uses population density data and landmark location data stored in an internal database. Specifically, the server launches the GIS software, imports this data, and visualizes it. As output, it determines the optimal base station placement points.

[0080] Step 3:

[0081] The server collects existing coverage data from the network operation database. As input, it obtains coverage information from the network operation database. As a specific operation, the server executes an SQL query to extract coverage data and saves it in the internal database. As output, the collected coverage data is stored in the internal database.

[0082] Step 4:

[0083] The server uses data analysis tools to analyze coverage and population density and determine construction priorities. It uses coverage and population density data stored in an internal database as input. Specifically, the server uses the Python Pandas library to analyze the data and generate a priority list. The output is a construction priority list.

[0084] Step 5:

[0085] The server collects alert data from the network. As input, it obtains alert information from the network monitoring system. Specifically, the server collects alert data through the monitoring system's API and saves it in an internal database. As output, the collected alert data is stored in the internal database.

[0086] Step 6:

[0087] The server uses a machine learning model to analyze past alert data and maintenance history and propose optimal maintenance methods. As input, it uses alert data and maintenance history data stored in an internal database. Specifically, the server uses TensorFlow to train the machine learning model and predict the optimal maintenance method. As output, it generates specific maintenance procedures that are notified to the maintenance team.

[0088] Step 7:

[0089] The server collects the latest road and local information in real time during a disaster. It uses data from various sensors and traffic information systems as input. Specifically, the server uses Apache Kafka to stream real-time data and saves it in an internal database. As output, the collected real-time data is stored in the internal database.

[0090] Step 8:

[0091] The server analyzes the collected real-time data and determines restoration priorities. As input, it uses road and local information stored in an internal database. Specifically, the server analyzes the data using machine learning models and generates a restoration priority list. As output, it generates a priority list that is provided to the restoration team.

[0092] (Application example 1)

[0093] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] In the past, the design, construction, maintenance, and disaster response of mobile networks were all managed separately, making efficient operation difficult. Furthermore, for autonomous vehicles, it was difficult to obtain real-time network coverage and road information, making it difficult to set optimal routes and quickly respond to disasters.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0096] In this invention, the server includes means for designing base station locations based on information such as population and landmarks, means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, means for determining the optimal maintenance method based on alert types, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, and local information, etc., means for acquiring network coverage rates and road information in real time for autonomous vehicles and calculating the optimal route, means for setting the safest and fastest route based on the latest road information and area coverage rates in the event of a disaster, and means for acquiring network maintenance information in real time and changing the route of autonomous vehicles, thereby enabling efficient operation of mobile networks and safe and fast operation of autonomous vehicles.

[0097] "Population" is the total number of people living in a particular area.

[0098] A "landmark" is an important point such as a building or natural object that serves as a landmark in a particular area.

[0099] "Base station planning" is the planning and design process for installing base stations for mobile networks.

[0100] "Coverage" is the percentage of coverage of communication services provided by a mobile network within a particular area.

[0101] "Construction priority" is a priority for determining the order in which construction projects are started.

[0102] "Alert type" refers to the type of warning or notification issued by the system.

[0103] "Maintenance methods" are the means of maintaining and managing systems and equipment to ensure their normal operation.

[0104] "Area coverage" is the percentage of coverage of communication services provided by a mobile network within a specific geographic area.

[0105] "Latest road information" refers to the latest data including current road conditions and traffic information.

[0106] "Local information" is detailed information about a specific area.

[0107] "Recovery priority" refers to the priority of recovery work in the event of a disaster or failure.

[0108] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensor technology.

[0109] "Real-time" refers to the immediate processing of ongoing events and data.

[0110] An "optimal route" is the most efficient and safest route to reach a destination.

[0111] "Disaster time" refers to a situation when a natural disaster such as a typhoon or earthquake occurs.

[0112] "Maintenance information" is data related to the maintenance and management of systems and equipment.

[0113] A "travel route" is a route set for a vehicle to travel.

[0114] The system for implementing this invention consists of a cloud-based server for centrally controlling the design, construction, maintenance, and disaster response of mobile networks, and an application installed in an autonomous vehicle.

[0115] The server includes the following means:

[0116] 1. A method for designing station locations based on information such as population and landmarks.

[0117] 2. A means of efficiently advancing area construction by assigning construction priority based on coverage rate, etc.

[0118] 3. A means of identifying the optimal maintenance method based on the alert type.

[0119] 4. A means of smoothly carrying out restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, local information, etc., and assigning restoration priorities.

[0120] 5. A means for autonomous vehicles to obtain real-time network coverage and road information and calculate optimal routes.

[0121] 6. A means of planning the safest and quickest route based on the latest road information and area coverage in the event of a disaster.

[0122] 7. A means of obtaining real-time network maintenance information and rerouting autonomous vehicles.

[0123] Hardware and Software Configuration

[0124] The server operates in a cloud computing environment and includes a database, a geographic information system (GIS), and a real-time data processing system.The autonomous vehicle is equipped with a GPS module, a mobile network module, and a vehicle control system.

[0125] Data processing and calculation

[0126] The server obtains population data, landmark data, network coverage rate data, road information data, and disaster information data through the cloud API. Based on this data, it designs base stations, determines construction priority, learns maintenance methods, and determines restoration priority. Autonomous vehicles calculate optimal routes and change their driving routes based on the real-time data provided by the server.

[0127] Specific examples

[0128] For example, if an autonomous vehicle travels from "Location A" to "Location B" in the Tokyo area, the following prompt sentence is input into the generative AI model.

[0129] Calculate the optimal route from Location A to Location B based on the network coverage rate in the Tokyo area and the latest road information.

[0130] By inputting this prompt into the generative AI model, the optimal route is calculated, supporting the operation of autonomous vehicles. The server obtains network coverage and road information in real time and calculates the optimal route. In the event of a disaster, the safest and fastest route is set based on the latest road information and area coverage. Network maintenance information can also be obtained in real time, allowing autonomous vehicle routes to be changed.

[0131] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0132] Step 1:

[0133] The server obtains population data, landmark data, network coverage data, road information data, and disaster information data through the cloud API. This data is collected in real time from various databases. The input is an API request, and the output is various data returned in JSON format.

[0134] Step 2:

[0135] The server uses a geographic information system (GIS) to design base station locations based on the acquired population and landmark data. The inputs are population and landmark data, and the output is the optimal base station installation location. Specifically, the GIS software analyzes the topography and building distribution and calculates the optimal installation location.

[0136] Step 3:

[0137] The server assigns construction start priorities based on the network coverage rate data, and efficiently advances area construction. The input is network coverage rate data, and the output is a construction start priority list. Specifically, the server creates a plan to prioritize the installation of base stations in areas with low coverage rates.

[0138] Step 4:

[0139] The server determines the optimal maintenance method depending on the alert type. The input is alert data, and the output is a list of maintenance methods. Specifically, it selects the appropriate maintenance procedure based on the type of alert.

[0140] Step 5:

[0141] When a disaster such as a typhoon or earthquake occurs, the server determines the restoration priority by integrating area coverage rate, the latest road information, and local information. The inputs are disaster information, area coverage rate data, and road information data, and the output is a restoration priority list. Specifically, the server identifies the areas affected by the disaster and sets the priority of restoration work.

[0142] Step 6:

[0143] An autonomous vehicle obtains network coverage and road information in real time from a server and calculates the optimal route. The inputs are network coverage data and road information data, and the output is the calculation of the optimal route. Specifically, the vehicle's navigation system sets the route based on this data.

[0144] Step 7:

[0145] In the event of a disaster, the autonomous vehicle will set the safest and fastest route based on the latest road information and area coverage rate from the server. The input is the latest road information and area coverage rate data, and the output is the safe route. Specifically, the navigation system will recalculate the route taking into account the disaster information.

[0146] Step 8:

[0147] The autonomous vehicle obtains network maintenance information from the server in real time and changes its route as necessary. The input is maintenance information data, and the output is an updated route. Specifically, the navigation system reconfigures the route based on the maintenance information.

[0148] Example 2

[0149] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0150] In the design, construction, maintenance, and disaster response of conventional mobile networks, station placement design did not adequately take into account the topography and distribution of buildings, making it difficult to improve coverage and communication quality. Furthermore, in disaster recovery work, it was difficult to prioritize tasks efficiently, and a rapid response was required.

[0151] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0152] In this invention, the server includes a means for using a geographic information system to perform base station location design taking into account the topography and building distribution of the area, a means for inputting prompt sentences using a generative AI model to propose optimal base station locations, and a means for dynamically updating recovery priorities taking into account disaster information and fluctuations in communication demand, thereby enabling efficient base station location design and rapid disaster response.

[0153] A "geographic information system" is a system for collecting, managing, analyzing, and displaying geographic information such as topography and building distribution.

[0154] "Base station planning" is the process of planning the placement of base stations in a mobile network and determining the optimal locations.

[0155] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate optimal solutions for specific tasks.

[0156] A "prompt" is an instruction given to a generative AI model that describes the conditions and requirements for performing a specific task.

[0157] A "base station" is a facility for wireless communication in a mobile network, and serves to connect user terminals to the communication network.

[0158] "Coverage rate" is an indicator that indicates the percentage of the area that can be reached by radio waves from a base station in a mobile network.

[0159] "Disaster information" refers to information about natural disasters such as typhoons and earthquakes, including the extent of damage and the extent of the impact.

[0160] "Telecommunications demand" refers to the amount of usage or demand for telecommunications services in a particular area or time period.

[0161] "Restoration priority" is a criterion for determining which areas and facilities should be given priority for restoration in the event of a disaster.

[0162] This invention is a system for efficiently designing, constructing, maintaining, and responding to disasters in a mobile network. Specific embodiments of this system will be described below.

[0163] First, the user accesses a geographic information system (GIS) and uploads the topographical data and building distribution data of the target area to the GIS. These data are required for subsequent analysis.

[0164] The server then analyzes the uploaded terrain and building distribution data. It cross-references these data to identify densely populated areas and landmarks. It then uses a radio wave propagation model that takes into account the effects of terrain and buildings to simulate the radio wave propagation characteristics in the identified areas. For example, it calculates the impact of mountains and tall buildings on radio wave propagation.

[0165] The server calculates the optimal placement of base stations based on the simulation results. A prompt sentence is input using a generative AI model, and the optimal placement of base stations is suggested. An example of a specific prompt sentence is, "We would like to design a new communications network for urban areas. Please use a geographic information system to take into account topographical data and building distribution data, and design it so that many base stations are located in densely populated areas and around landmarks."

[0166] Furthermore, the server dynamically updates recovery priorities, taking into account disaster information and fluctuations in communication demand. For example, when a natural disaster such as a typhoon or earthquake occurs, the server prioritizes recovery by combining area coverage rates, the latest road information, and local information, ensuring smooth recovery work.

[0167] This system enables efficient base station design and rapid disaster response. Users can check the optimal base station placement on the GIS and make adjustments as needed. In the event of a disaster, the server dynamically updates recovery priorities, enabling rapid and efficient recovery work.

[0168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0169] Step 1:

[0170] A user accesses a geographic information system (GIS).

[0171] Specifically, the user accesses the GIS login screen, enters authentication information, and logs in. This allows the user to use the GIS functions.

[0172] Input: User credentials

[0173] Output: GIS access permissions

[0174] Step 2:

[0175] The user inputs the topographical data and building distribution data of the target area.

[0176] Specifically, the user uses the GIS interface to upload topographical data (e.g., elevation data and topographical maps) and building distribution data (e.g., building locations and heights) for the target area.

[0177] Input: Topographical data, building distribution data

[0178] Output: Topographical data and building distribution data stored in GIS

[0179] Step 3:

[0180] The server analyzes the input data and identifies densely populated areas and landmarks.

[0181] Specifically, the server cross-references the topographical data stored in the GIS with building distribution data to identify densely populated areas and landmarks (e.g., train stations and shopping malls).

[0182] Input: Topographical data and building distribution data stored in GIS

[0183] Output: A list of densely populated areas and landmarks

[0184] Step 4:

[0185] The server simulates the propagation characteristics of radio waves.

[0186] Specifically, the server uses a radio wave propagation model that takes into account the effects of terrain and buildings to simulate the radio wave propagation characteristics in the specified area. For example, it calculates the impact of mountains and tall buildings on radio wave propagation.

[0187] Input: List of densely populated areas and landmarks, topographical data, building distribution data

[0188] Output: Simulation results of radio wave propagation characteristics

[0189] Step 5:

[0190] The server calculates the optimal base station placement.

[0191] Specifically, the server calculates the optimal base station placement based on the simulation results, inputs prompts using a generative AI model, and proposes the optimal base station placement.

[0192] Input: Radio wave propagation characteristics simulation results, prompt text

[0193] Output: Optimal base station placement proposal

[0194] Step 6:

[0195] The server provides the calculation results to the user.

[0196] Specifically, the server provides the calculation results to the user through a GIS interface, where the user can check the optimal base station placement and make adjustments as necessary.

[0197] Input: Optimal base station placement proposal

[0198] Output: Displaying placement suggestions to the user

[0199] Step 7:

[0200] The server dynamically updates the recovery priority taking into account disaster information and fluctuations in communication demand.

[0201] Specifically, when a natural disaster such as a typhoon or earthquake occurs, the server will combine area coverage rates, the latest road information, and local information to assign recovery priorities and carry out smooth recovery operations.

[0202] Input: Disaster information, fluctuation data on communication demand

[0203] Output: Dynamically updated recovery priority

[0204] (Application example 2)

[0205] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0206] Conventional mobile network design, construction, maintenance, and disaster response have not fully utilized information such as geographical information, population density, and landmarks, making it difficult to efficiently build areas and carry out smooth restoration work. Furthermore, it has been difficult to provide optimal routes without interruptions to communication when navigating autonomous vehicles.

[0207] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and performing smooth restoration work in the event of a typhoon, earthquake, or the like by integrating area coverage rates, the latest road information, and local information; means for calculating the optimal route for an autonomous vehicle by taking into account the local topography and building distribution using a geographic information system; means for proposing routes that avoid congestion based on information on population density and landmarks; and means for selecting a route that ensures uninterrupted communication by taking into account the location of the autonomous vehicle's base station. This enables efficient area construction, smooth restoration work, and optimal navigation for autonomous vehicles.

[0208] "Population" refers to the total number of people living in a particular area.

[0209] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[0210] "Base station design" refers to the planning and design of base station locations.

[0211] "Coverage rate" refers to the percentage of a particular area to which telecommunications services are provided.

[0212] "Construction priority" refers to the priority of which area of ​​a construction project to start construction on first.

[0213] "Alert type" refers to the type of warning or notification issued by the system.

[0214] "Maintenance methods" refer to the means of maintaining and managing systems and equipment to ensure their normal operation.

[0215] "Geographic information system" refers to an information system for collecting, managing, and analyzing geographic data.

[0216] "Topography" refers to the shape and characteristics of the Earth's surface.

[0217] "Building distribution" refers to the arrangement and density of buildings in a particular area.

[0218] An "autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[0219] An "optimal route" refers to the most efficient and safe route under specific conditions.

[0220] A "congestion-avoiding route" refers to a route that avoids areas with heavy traffic.

[0221] "Base station placement" refers to determining the location of a base station to provide communication services.

[0222] A "route without interruption of communication" refers to a route where communication can continue without interruption while traveling.

[0223] The system for carrying out the present invention operates in cooperation with three entities: a server, a terminal, and a user. A specific embodiment of the system will be described below.

[0224] Server Processing

[0225] The server uses a geographic information system (GIS) to design base stations, taking into account the local topography and building distribution. Specifically, it collects GIS data and calculates the optimal placement of base stations based on population density and landmark information. It also assigns construction start priorities based on factors such as coverage rate, allowing for efficient area construction. It also determines the optimal maintenance method based on the alert type, and in the event of a disaster such as a typhoon or earthquake, it combines area coverage rate, the latest road information, and local information to assign recovery priorities and ensure smooth recovery work.

[0226] Terminal handling

[0227] The device (e.g., a smartphone) calculates the optimal route for the autonomous vehicle based on data provided by the server. Specifically, it uses a geographic information system to consider the local topography and distribution of buildings, and suggests routes that avoid congestion based on population density and landmark information. It also considers the location of base stations for the autonomous vehicle and selects routes that will ensure communication is not interrupted.

[0228] User operations

[0229] The user can operate the autonomous vehicle using the optimal route information provided through the device. For example, when calculating a route from Shinjuku Station to Shibuya Station in Tokyo, the user can select a route that avoids the densely populated areas of Shinjuku and Shibuya and ensures communication is not interrupted.

[0230] Hardware and software used

[0231] Hardware: GPS-enabled smartphones, self-driving vehicles

[0232] Software: Python, GeoPandas (geographic data processing), Shapely (geographic data manipulation)

[0233] Data processing and calculation

[0234] The server collects geographical information data (buildings, population density, landmarks, base stations) and calculates the optimal base station placement based on this data. The device calculates the optimal route based on the data provided by the server and provides it to the user.

[0235] Specific examples

[0236] For example, when calculating a route from Shinjuku Station to Shibuya Station in Tokyo, the server calculates a route that avoids congestion based on information about the population density and landmarks of Shinjuku and Shibuya.The device then uses this information to select a route that will not cause communication interruptions and provides it to the user.

[0237] Prompt Sentence Examples

[0238] "Calculate the best route from Shinjuku Station to Shibuya Station in Tokyo. Choose a route that avoids densely populated areas and landmarks, and ensures uninterrupted communication."

[0239] This allows for efficient area construction, smooth recovery operations, and optimal navigation for autonomous vehicles.

[0240] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0241] Step 1:

[0242] The server collects geographic information data (buildings, population density, landmarks, base stations). As input, it gets the geographic information data from the GIS database and stores it in its internal database. As output, it gets the collected geographic information data.

[0243] Step 2:

[0244] The server calculates the optimal placement of base stations based on the collected geographic information data. Using the geographic information data collected in step 1 as input, it analyzes population density and landmark information using GIS software (e.g., GeoPandas). The output is coordinate data for the optimal base station placement.

[0245] Step 3:

[0246] The server assigns construction priority based on factors such as coverage rate, and creates a plan for efficient area construction. It uses the coordinate data of the base station locations obtained in step 2 as input and applies an algorithm to calculate the coverage rate. The output is an area construction plan with construction priority assigned.

[0247] Step 4:

[0248] The server determines the optimal maintenance method based on the alert type. As input, it receives alert data from the system and applies an algorithm that determines the maintenance method based on the alert type. As output, it obtains the optimal maintenance method.

[0249] Step 5:

[0250] The server assigns recovery priorities to disasters such as typhoons and earthquakes by integrating area coverage rates, the latest road information, and on-site information. It receives disaster information and fluctuation data on communication demand as input, and applies an algorithm that dynamically updates recovery priorities based on this information. The output is a recovery plan with assigned recovery priorities.

[0251] Step 6:

[0252] The terminal calculates the optimal route for the autonomous vehicle based on the data provided by the server. As input, it receives geographic information data and base station location data sent from the server, and uses a geographic information system (e.g., Shapely) to calculate the route taking into account the local topography and distribution of buildings. The optimal route information is obtained as output.

[0253] Step 7:

[0254] The device then proposes a route that avoids congestion based on population density and landmark information. As input, it uses the optimal route information obtained in step 6 and applies an algorithm that analyzes population density and landmark data. As output, it obtains route information that avoids congestion.

[0255] Step 8:

[0256] The terminal selects a route that will not cause communication interruptions, taking into account the location of base stations for the autonomous vehicle. Using the route information to avoid congestion obtained in step 7 and base station location data as input, it applies an algorithm to calculate a route that will not cause communication interruptions. The output is the optimal route information that will not cause communication interruptions.

[0257] Step 9:

[0258] The user operates the autonomous vehicle using optimal route information provided through the device. As input, the system receives the optimal route information provided by the device and configures the autonomous vehicle's navigation system based on this information. As output, the user is able to reach their destination efficiently and safely.

[0259] Example 3

[0260] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] Mobile network restoration work in the event of a disaster must be carried out quickly and efficiently. However, with conventional systems, it was difficult to determine restoration priorities by comprehensively taking into account area coverage, road information, and local information, which often resulted in delays in restoration work. In addition, it was not possible to dynamically reflect changes in disaster information and communication demand, making it difficult to carry out optimal restoration work.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0263] In this invention, the server includes a means for designing base station locations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, a means for determining the optimal maintenance method based on alert types, a means for collecting and analyzing area coverage rates, the latest road information, and on-site information in the event of a disaster, and a means for calculating restoration priorities based on the collected data and issuing instructions for restoration work, thereby enabling rapid and efficient restoration of mobile networks in the event of a disaster.

[0264] "Population" refers to the total number of people living in a particular area.

[0265] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[0266] "Base station design" refers to the process of planning the location of base stations and determining the optimal placement.

[0267] "Coverage rate" refers to the percentage of the area where a base station can provide communication services.

[0268] "Construction priority" refers to the priority for determining the order in which construction work begins.

[0269] "Alert Type" refers to a category that classifies different types of warnings or notifications.

[0270] "Maintenance methods" refer to the means used to properly maintain systems and equipment and prevent breakdowns.

[0271] "Area coverage rate" refers to the percentage of the area in which communication services are provided in a particular region.

[0272] "Road information" refers to data regarding road traffic conditions and traffic regulations.

[0273] "Local information" refers to data about the current situation or conditions in a particular area.

[0274] "Restoration priority" refers to the order of priority for determining the order of restoration work in the event of a disaster.

[0275] "Recovery work" refers to work to repair systems or equipment damaged by a disaster or failure.

[0276] "Mobile network" refers to a network that provides mobile communication services using wireless communication technology.

[0277] "Cloud" refers to computer resources and services provided over the Internet.

[0278] The present invention provides a system for realizing rapid and efficient restoration of a mobile network in the event of a disaster. A specific embodiment of this system will be described below.

[0279] 1. Generating the system program

[0280] The user creates a system program to streamline disaster recovery work. This program includes an algorithm that determines recovery priorities by combining area coverage, the latest road information, and on-site information.

[0281] 2. Program processing explanation

[0282] The server collects and processes the following data to determine the priority of recovery efforts in the event of a disaster:

[0283] Area Coverage Ratio: The server calculates the area coverage ratio of the base stations, taking into account the coverage area of ​​each base station and the population density within that area.

[0284] Up-to-date road information: The server uses map APIs and open-source map databases to obtain the latest road information, which allows it to determine which roads are passable and which are closed.

[0285] Local information: The server uses SNS APIs and news APIs to collect local damage information from SNS and news sites, thereby assessing the extent and urgency of the damage.

[0286] Based on this data, the server calculates the restoration priority of each base station. Specifically, it designs an algorithm to prioritize restoration work for accessible base stations.

[0287] 3. Examples and prompts

[0288] For example, if multiple base stations are damaged by a typhoon, the server performs the following process.

[0289] 1. The server uses the map API to obtain the latest road information.

[0290] 2. The server uses the SNS API to collect information on the local damage situation.

[0291] 3. The server calculates the area coverage rate of each base station.

[0292] 4. The server combines all this data and determines the priority so that recovery work is carried out first from accessible base stations.

[0293] An example of a prompt sentence to input to the generative AI model is as follows:

[0294] "Multiple base stations have been damaged by the typhoon. Please use the map API to obtain the latest road information and the SNS API to collect local damage information. Please calculate the area coverage rate of each base station and determine priorities so that restoration work is carried out first at accessible base stations."

[0295] In this way, the user can build a system for realizing efficient recovery work. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0296] Step 1:

[0297] Data collection

[0298] The server collects data necessary for disaster recovery operations.

[0299] Input: Base station location information, map API, SNS API

[0300] Specific operation: The server retrieves the base station's latitude and longitude information and coverage area data from the database, and also uses the map API to obtain the latest road information and the SNS API to collect local damage information.

[0301] Output: Base station location and coverage data, latest road information, and local damage status data

[0302] Step 2:

[0303] Data analysis

[0304] The server analyzes the collected data.

[0305] Input: Base station location and coverage data, latest road information, local damage situation data

[0306] Specific operation: The server calculates the population density within the coverage area of ​​each base station and calculates the area coverage rate. It also analyzes the acquired road information to identify passable routes. It also analyzes collected tweets and news articles to quantify the extent of damage.

[0307] Output: Area coverage rate of each base station, passable route information, numerical data on the extent of damage

[0308] Step 3:

[0309] Priority calculation

[0310] The server calculates the recovery priority of each base station based on the analyzed data.

[0311] Input: Area coverage rate of each base station, passable route information, numerical data on the extent of damage

[0312] Specific operation: The server determines the restoration priority of each base station by combining area coverage rate, road information, and local information. Specifically, it designs an algorithm to prioritize restoration work for accessible base stations.

[0313] Output: Recovery priority of each base station

[0314] Step 4:

[0315] Instructions for recovery work

[0316] The server issues instructions for recovery work based on priority.

[0317] Input: Recovery priority of each base station

[0318] Specific operation: The server instructs the terminal to perform the restoration work in order of priority, starting with the base station with the highest priority. Specifically, it sends the restoration work schedule and route information to the terminal.

[0319] Output: Recovery schedule and route information

[0320] In this way, the server builds a system for realizing efficient recovery work.

[0321] (Application example 3)

[0322] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0323] In the design, construction, maintenance, and disaster response of conventional mobile networks, each process is managed separately, making efficient operation difficult. Furthermore, in disaster recovery work, prioritization is not performed taking into account area coverage, the latest road information, and local information, resulting in delays in recovery work. Furthermore, efficient recovery work support using autonomous vehicles is not available, so a rapid response is required in the event of a disaster.

[0324] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or the like by integrating area coverage rates, the latest road information, local information, and the like; and means for supporting efficient restoration work in the event of a disaster using a navigation system installed in an autonomous vehicle. This enables efficient, integrated management of everything from mobile network design to construction, maintenance, and disaster response, enabling rapid and effective restoration work in the event of a disaster.

[0325] "Population" refers to the total number of people living in a particular area.

[0326] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[0327] "Base station design" refers to the planning and design for installing base stations for mobile networks.

[0328] "Coverage rate" refers to the percentage of an area covered by a mobile network.

[0329] "Construction start priority" refers to the priority of which area construction will begin first in a construction project.

[0330] "Alert type" refers to the type of warning or notification issued by the system.

[0331] "Maintenance method" refers to the method of maintaining and managing systems and equipment to ensure their normal operation.

[0332] "Area coverage rate" refers to the percentage of area covered by a mobile network in a particular region.

[0333] "Latest road information" refers to current road conditions and traffic information.

[0334] "Local information" refers to the latest conditions and data for a specific area.

[0335] "Recovery priority" refers to the order of priority for which areas recovery work should be carried out in the event of a disaster.

[0336] An "autonomous vehicle" refers to a vehicle that drives itself using artificial intelligence and sensor technology.

[0337] "Navigation system" refers to a system that provides the optimal route to a destination.

[0338] "Mobile network" refers to a network that provides mobile communications using wireless communication.

[0339] "Cloud" refers to computer resources and services provided over the Internet.

[0340] The system for implementing this invention is composed of the following elements: a server, a terminal, and a user. The specific operation of each element and the hardware and software used will be described below.

[0341] Server Operation

[0342] The server includes the following means:

[0343] 1. Station location design method: Information such as population and landmarks is collected, and station location is designed using a geographic information system (GIS) taking into account the local topography and building distribution.

[0344] 2. Construction start priority assignment method: Based on data such as coverage rate, priority is assigned to efficiently proceed with area construction.

[0345] 3. Maintenance method identification: Identify the optimal maintenance method based on the alert type.

[0346] 4. Recovery prioritization method: In the event of a disaster such as a typhoon or earthquake, recovery priorities are determined by combining area coverage rate, the latest road information, and local information, ensuring smooth recovery work.

[0347] 5. Navigation system means: Using a navigation system installed in an autonomous vehicle, we will support efficient recovery efforts in the event of a disaster.

[0348] Device behavior

[0349] The terminal refers to the navigation system installed in the autonomous vehicle. This navigation system receives the latest road information and restoration priority information provided by the server, calculates the optimal route, and issues instructions to the autonomous vehicle.

[0350] User Actions

[0351] The user refers to a system administrator or recovery worker. The user plans the recovery work based on the information provided by the server and operates the autonomous vehicle as necessary.

[0352] Hardware and software used

[0353] Hardware: Servers, GPS sensors, communication modules, autonomous vehicles

[0354] Software: Geographic Information Systems (GIS), navigation software, data collection APIs, generative AI models

[0355] Specific examples

[0356] For example, if a base station in a certain area is damaged by a typhoon, the server will collect the latest road and local information, calculate the restoration priority, and then use the navigation system to provide the optimal route for autonomous vehicles, enabling rapid restoration work.

[0357] Prompt Sentence Examples

[0358] Below are some example prompts to input to a generative AI model:

[0359] To streamline disaster recovery efforts, design a navigation system that calculates recovery priorities by combining area coverage, the latest road information, and local information, and provides the optimal route. Use the following data:

[0360] Area coverage: https: / / api.example.com / area_coverage

[0361] Latest road information: https: / / api.example.com / road_info

[0362] Local information: https: / / api.example.com / local_info

[0363] In this way, specific modes for carrying out the invention can be provided.

[0364] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0365] Step 1:

[0366] The server collects information such as population and landmarks. This involves using a geographic information system (GIS) to obtain data that takes into account the area's topography and building distribution. The inputs are population data and landmark data, and the output is the geographic information needed for station planning.

[0367] Step 2:

[0368] The server designs base station locations based on the collected geographic information. Specifically, it uses GIS to calculate the optimal locations for base station installation. The input is the geographic information obtained in step 1, and the output is a base station installation plan.

[0369] Step 3:

[0370] The server assigns construction start priorities based on data such as coverage rates. This involves determining which areas to prioritize construction start in, taking into account area coverage rates and communication demand data. The inputs are area coverage rate data and communication demand data, and the output is a construction start priority list.

[0371] Step 4:

[0372] The server determines the optimal maintenance method based on the alert type. Specifically, it analyzes the types of warnings and notifications issued by the system and determines the appropriate maintenance method. The input is alert data, and the output is a list of maintenance methods.

[0373] Step 5:

[0374] In the event of a disaster such as a typhoon or earthquake, the server determines the restoration priority by integrating area coverage rate, the latest road information, and local information. This involves calculating which areas should be prioritized for restoration work based on road information and local information collected in real time. The inputs are area coverage rate data, the latest road information, and local information, and the output is a restoration priority list.

[0375] Step 6:

[0376] The server provides the optimal route to the navigation system installed in the autonomous vehicle based on the recovery priority list. Specifically, it calculates the optimal route using the recovery priority list and the latest road information and issues instructions to the autonomous vehicle. The inputs are the recovery priority list and the latest road information, and the output is the route information sent to the navigation system.

[0377] Step 7:

[0378] The terminal (autonomous vehicle) receives route information provided by the server and moves according to that route. Specifically, the navigation system analyzes the route information and issues instructions to the control system of the autonomous vehicle. The input is the route information sent from the server, and the output is the movement of the autonomous vehicle.

[0379] Step 8:

[0380] The user makes a recovery plan based on the information provided by the server. Specifically, the user decides on a recovery schedule by referring to the recovery priority list and route information from the navigation system. The inputs are the recovery priority list and route information provided by the server, and the output is a recovery plan.

[0381] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0382] "Example 1"

[0383] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and optimizes the communication environment based on this information. Specifically, emotions are recognized from the user's voice, facial expressions, behavioral patterns, etc., and the communication environment is optimized based on this information. For example, if it is recognized that the user is feeling anger or dissatisfaction, measures are taken to improve communication quality. Furthermore, if it is recognized that the user is feeling joy or satisfaction, the current communication environment is maintained.

[0384] "Example 2"

[0385] The emotion engine also adjusts communication quality according to the user's emotional state to optimize the user experience. For example, if it recognizes that the user is feeling stressed, it will take measures to increase communication speed. On the other hand, if it recognizes that the user is relaxed, it will moderately reduce communication speed to conserve communication resources.

[0386] "Example 3"

[0387] Furthermore, the emotion engine grasps the user's emotional state in real time and dynamically adjusts the communication environment according to changes. For example, if the user suddenly starts to feel angry, the communication quality is immediately improved. Similarly, if the user suddenly starts to feel happy, the communication quality is immediately optimized.

[0388] The processing flow of each embodiment will be described below.

[0389] "Example 1"

[0390] Step 1: Recognize emotions from the user's voice, facial expressions, behavioral patterns, etc.

[0391] Step 2: Optimize the communication environment based on the recognized emotional information.

[0392] Step 3: For example, if it is recognized that the user is feeling angry or frustrated, measures are taken to improve the quality of communication.

[0393] Step 4: If it is determined that the user is happy or satisfied, the current communication environment is maintained.

[0394] "Example 2"

[0395] Step 1: Adjust communication quality according to the user's emotional state to optimize the user experience.

[0396] Step 2: For example, if it is recognized that the user is feeling stressed, measures are taken to improve communication speed.

[0397] Step 3: If the system recognizes that the user is relaxed, it moderately reduces the communication speed to conserve communication resources.

[0398] "Example 3"

[0399] Step 1: The emotion engine grasps the user's emotional state in real time and dynamically adjusts the communication environment according to changes.

[0400] Step 2: For example, if a user suddenly starts to feel angry, immediately improve the communication quality.

[0401] Step 3: Also, if the user suddenly starts to feel happy, the communication quality is instantly optimized.

[0402] Example 1

[0403] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] In the past, processes for mobile networks, from design to construction, maintenance, and disaster response, were often managed separately, making efficient operation difficult. Furthermore, the communication environment was not optimized with user feelings in mind, making it difficult to improve user satisfaction. Furthermore, while rapid recovery was required in the event of a disaster, there was insufficient use of the latest information to determine recovery priorities.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0406] In this invention, the server includes: means for designing base station locations based on information such as population and landmarks; means for assigning construction start priorities based on factors such as coverage rates and efficiently promoting area construction; means for determining optimal maintenance methods based on alert types; means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information; means for recognizing emotions from user voices, facial expressions, behavioral patterns, and the like, and optimizing the communication environment based on that information; and means for operating on the cloud and for centrally controlling everything from mobile network design to construction, maintenance, and disaster response. This enables efficient operation of mobile networks, improved user satisfaction, and rapid restoration in the event of a disaster.

[0407] "Population" refers to the total number of people living in a particular area.

[0408] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[0409] "Base station planning" refers to the process of planning and designing the placement of base stations for mobile networks.

[0410] "Coverage rate" refers to the percentage of a geographic area covered by a mobile network.

[0411] "Construction priority" refers to the priority of which area of ​​a construction project should begin construction first.

[0412] "Alert type" refers to the type of warning or notification issued by the system.

[0413] "Maintenance method" refers to the maintenance techniques used to ensure the normal operation of systems and equipment.

[0414] "Area coverage rate" refers to the percentage of mobile network coverage in a particular area.

[0415] "Latest road information" refers to current road conditions and traffic information.

[0416] "Local information" refers to the latest conditions and data for a specific area.

[0417] "Recovery priority" refers to the priority of which areas should be given priority for recovery in the event of a disaster.

[0418] "Emotion recognition" refers to the process of analyzing and recognizing emotions from a user's voice, facial expressions, and behavioral patterns.

[0419] "Communication environment optimization" refers to the process of optimizing communication quality and network settings based on user usage and emotions.

[0420] "Cloud" refers to a collection of computer resources and services provided over the Internet.

[0421] "Mobile network" refers to a network that provides mobile communications using wireless communication.

[0422] "Centralized control" refers to the centralized management and control of multiple processes and functions.

[0423] This invention is a system that provides integrated control over everything from mobile network design to construction, maintenance, and disaster response. It includes a server running on the cloud and a terminal that recognizes user emotions and optimizes the communication environment.

[0424] System configuration

[0425] 1. Server

[0426] The server runs on the cloud and provides centralized control over everything from mobile network design to construction, maintenance, and disaster response.

[0427] The server collects population data and landmark information and uses this information to design base station locations. Specifically, it manages this information using a database (e.g., PostgreSQL) and uses an algorithm (e.g., K-means clustering) to determine the optimal placement of base stations.

[0428] The server analyzes the coverage data and assigns construction start priorities, allowing for efficient area construction. Data analysis tools (e.g., Python's Pandas library) are used for the analysis.

[0429] The server analyzes the alert type and determines the optimal maintenance method. Alert analysis uses a machine learning model (e.g., TensorFlow).

[0430] In the event of a disaster such as a typhoon or earthquake, the server prioritizes recovery by integrating area coverage, the latest road information, and local information. This ensures smooth recovery work. APIs (e.g., Google (registered trademark) Maps API) are used to collect data.

[0431] 2. Terminal

[0432] The device collects the user's voice, facial expressions, and behavioral patterns using hardware such as a microphone and camera.

[0433] The device sends the collected data to the cloud using a mobile network (e.g., 4G, 5G).

[0434] 3. Users

[0435] Users provide voice, facial expressions, and behavioral patterns through their devices, which allows the system to recognize the user's emotions and optimize the communication environment.

[0436] Specific examples

[0437] For example, when installing a new base station in a certain area, the server collects population density data and landmark information for that area to determine the optimal location for the base station. It then analyzes coverage data to determine which area construction should begin in. In the event of a disaster, the server uses the latest road and local information to determine which areas to prioritize for restoration.

[0438] If a user expresses dissatisfaction during a video call, the device captures their facial expressions with a camera and collects audio data with a microphone. These data are then sent to the cloud, where the server analyzes the user's emotions using an emotion recognition algorithm. If the server determines that the user is dissatisfied, it takes measures to improve the quality of the call.

[0439] Prompt Sentence Examples

[0440] "If users are frustrated during video calls, please explain how you would improve the quality of the call."

[0441] "Please explain how you determine restoration priorities based on area coverage and the latest road information in the event of a disaster."

[0442] This will enable efficient operation of mobile networks, improved user satisfaction, and rapid recovery in the event of a disaster.

[0443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0444] A system that comprehensively controls everything from mobile network design to construction, maintenance, and disaster response

[0445] Processing Steps

[0446] Step 1:

[0447] The server collects demographic data and landmark information.

[0448] Input: Population data and landmark information from public government databases and geographic information systems (GIS)

[0449] Data processing: Save to a database (e.g., PostgreSQL) and convert to the required format

[0450] Output: Formatted population data and landmark information

[0451] Specific operation: The server retrieves data through the API and stores it in the database.

[0452] Step 2:

[0453] The server calculates the optimal placement of base stations based on the collected population data and landmark information.

[0454] Input: Formatted population data and landmark information

[0455] Data calculation: Determine the best base station location using K-means clustering algorithm

[0456] Output: Optimal base station placement information

[0457] Specific operation: The server executes the clustering algorithm and plots the calculation results on a map.

[0458] Step 3:

[0459] The server analyzes the coverage data and assigns construction start priorities.

[0460] Input: Coverage data

[0461] Data Calculation: Analyze and prioritize data using Python's Pandas library

[0462] Output: Construction priority list

[0463] Specific operation: The server analyzes the coverage data using a data analysis tool and lists high-priority areas.

[0464] Step 4:

[0465] The server analyzes the alert type and determines the most appropriate maintenance method.

[0466] Input: Alert data

[0467] Data Computing: Using machine learning models (e.g., TensorFlow) to classify alerts and suggest appropriate maintenance actions

[0468] Output: Maintenance method list

[0469] Specific operation: The server analyzes alert data using a machine learning model and determines maintenance methods.

[0470] Step 5:

[0471] In the event of a disaster such as a typhoon or earthquake, the server will determine recovery priorities by combining area coverage rate, the latest road information, and local information.

[0472] Input: Area coverage data, latest road information, local information

[0473] Data calculation: Uses Google Maps API to obtain the latest road information and create restoration plans

[0474] Output: Recovery priority list

[0475] Specific operation: The server obtains the latest road information through the API and creates a restoration plan.

[0476] A system that combines emotion engines

[0477] Processing Steps

[0478] Step 1:

[0479] The device collects the user's voice, facial expressions, and behavioral patterns.

[0480] Input: User's voice data, facial expression data, behavioral pattern data

[0481] Data processing: Capture data using a microphone or camera and temporarily store it in local storage

[0482] Output: Collected emotion data

[0483] Specific operation: The device uses the microphone and camera to collect user data and stores it in local storage.

[0484] Step 2:

[0485] The device sends the collected emotion data to the cloud.

[0486] Input: Collected emotion data

[0487] Data processing: Send data to the cloud using mobile networks (e.g., 4G, 5G)

[0488] Output: Emotion data sent to the cloud

[0489] Specific operation: The device uses the mobile network to send emotion data to the cloud and confirms the success of the transmission.

[0490] Step 3:

[0491] The server analyzes the data received on the cloud.

[0492] Input: Emotion data sent to the cloud

[0493] Data Computing: Analyze user emotions using emotion recognition algorithms (e.g., models combining OpenCV and TensorFlow)

[0494] Output: Parsed emotion data

[0495] Specific operation: The server runs the emotion recognition algorithm and stores the analysis results in a database.

[0496] Step 4:

[0497] The server optimizes the communication environment based on the analysis results.

[0498] Input: Parsed emotion data

[0499] Data calculations: If users are angry or frustrated, take action such as increasing network bandwidth.

[0500] Output: Optimized communication environment

[0501] Specific operation: The server adjusts the network settings based on the analysis results to improve communication quality.

[0502] (Application example 1)

[0503] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0504] In conventional mobile network design, construction, maintenance, and disaster response, each process is managed separately, making efficient operation difficult. Furthermore, when managing robots and machinery in factories, it is difficult to provide optimal communication and working environments that take into account the emotions of workers. This can hinder overall production efficiency and rapid response in the event of a disaster.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0506] In this invention, the server includes: means for designing base station locations based on information such as population and landmarks; means for assigning construction start priorities based on factors such as coverage rates and efficiently promoting area construction; means for determining optimal maintenance methods based on alert types; means for prioritizing restoration efforts and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information; means for recognizing user emotions using an emotion engine and optimizing the communication environment based on that information; means for centrally controlling the design, placement, maintenance, and disaster response of robots and machines in a factory via the cloud; and means for recognizing worker emotions and optimizing the communication and work environments in the factory based on that information. This enables efficient management of mobile networks and robots and machines in a factory, and the provision of optimal communication and work environments that take the emotions of workers into consideration.

[0507] "Population" refers to the total number of people living in a particular area.

[0508] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[0509] "Base station design" refers to the design process for placing mobile network base stations in optimal locations.

[0510] "Coverage rate" refers to the percentage of a geographic area covered by a mobile network.

[0511] "Construction priority" refers to the order of precedence used to determine the order in which construction projects begin.

[0512] "Alert type" refers to the type of warning or notification issued by the system.

[0513] "Maintenance method" refers to the procedures and methods for maintaining the normal operation of a system or machine.

[0514] "Area coverage rate" refers to the percentage of mobile network coverage in a particular area.

[0515] "Latest road information" refers to current road conditions and traffic information.

[0516] "Local information" refers to up-to-date information about a particular area.

[0517] "Recovery priority" refers to the priority order used to determine the order of recovery work when a disaster or failure occurs.

[0518] An "emotion engine" refers to technology that recognizes a user's emotions and optimizes the system based on that information.

[0519] "Communication environment" refers to the physical and logical environment in which data communication takes place.

[0520] "Factory robots" refer to automated machinery that performs work in factories.

[0521] "Machine design" refers to the process of planning the structure and function of a machine and compiling it into drawings and specifications.

[0522] "Placement" refers to the placement of an object or facility in a specific location.

[0523] "Disaster response" refers to the countermeasures and recovery work carried out when natural or man-made disasters occur.

[0524] "Cloud" refers to computer resources and services provided over the Internet.

[0525] "Worker emotions" refers to the emotional state of people working in a factory.

[0526] "Work environment" refers to the physical and psychological environment in which work is performed.

[0527] As an embodiment of the present invention, the following system is constructed. The server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on the coverage rate and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing recovery and carrying out smooth recovery work in the event of a typhoon, earthquake, etc. by integrating the area coverage rate, the latest road information, and local information, etc., means for recognizing user emotions using an emotion engine and optimizing the communication environment based on that information, means for collectively controlling the design, placement, maintenance, and disaster response of robots and machines in a factory via the cloud, and means for recognizing worker emotions and optimizing the communication environment and work environment in the factory based on that information.

[0528] Hardware and software used

[0529] Cloud API: A cloud service for centralized control of factory design, layout, maintenance, and disaster response.

[0530] Emotion engine: Software for recognizing worker emotions.

[0531] Network Optimizer: Software for optimizing the communication environment based on emotional information.

[0532] Data processing and calculation

[0533] 1. Factory layout design: The server inputs population data and landmark data into the cloud API and designs the optimal factory layout.

[0534] 2. Assigning construction priority: The server assigns construction priority via cloud API based on coverage data.

[0535] 3. Obtaining a maintenance plan: The server inputs the alert type into the cloud API and obtains the optimal maintenance method.

[0536] 4. Obtain disaster response plan: The server inputs coverage data, road information, and local information into the cloud API to obtain a plan with recovery priorities.

[0537] 5. Optimization of the communication environment: The server inputs the worker's voice and facial expression data into the emotion engine, and the network optimizer optimizes the communication environment based on the recognized emotion information.

[0538] Specific examples

[0539] Factory layout design: Concentrate machines in densely populated areas to create efficient production lines.

[0540] Construction Priority: Prioritize construction in areas with low coverage to improve overall production efficiency.

[0541] Obtain maintenance plans: When a machine breakdown alert occurs, quickly obtain the optimal maintenance method and minimize downtime.

[0542] Obtain a disaster response plan: In the event of a typhoon or earthquake, quickly develop a recovery plan to quickly resume factory operations.

[0543] Optimizing the communication environment: When workers are stressed, improve communication quality and maintain work efficiency.

[0544] Prompt Sentence Examples

[0545] Design the optimal factory layout based on population and landmark data.

[0546] Assign construction priorities based on coverage data.

[0547] Obtain the optimal maintenance method based on the alert type.

[0548] Get a prioritized recovery plan based on coverage data, road information, and local information.

[0549] Optimize the communication environment based on the voice and facial expression data of workers.

[0550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0551] Step 1:

[0552] The server inputs population data and landmark data into the cloud API, which then designs the optimal factory layout taking into account the local topography and building distribution. The input data is population density and landmark location information, and the output is an optimal layout diagram. Specifically, the server retrieves population data and landmark data from the database and sends it to the cloud API.

[0553] Step 2:

[0554] The server inputs coverage data into the cloud API and assigns construction priorities. The cloud API analyzes the coverage data and determines which areas should be prioritized for construction. The input data is coverage information, and the output is a construction priority list. Specifically, the server collects coverage data and sends it to the cloud API.

[0555] Step 3:

[0556] The server inputs the alert type into the cloud API and obtains the optimal maintenance method. The cloud API analyzes the alert type and provides the appropriate maintenance procedure. The input data is the alert type, and the output is a maintenance plan. Specifically, the server monitors alert information and sends it to the cloud API.

[0557] Step 4:

[0558] The server inputs coverage data, road information, and local information into the cloud API and obtains a recovery plan with priority. The cloud API comprehensively analyzes this data and provides a disaster recovery plan. The input data are coverage, road information, and local information, and the output is a recovery priority list. Specifically, the server collects this data and sends it to the cloud API.

[0559] Step 5:

[0560] The server inputs the worker's voice and facial expression data into the emotion engine, and the network optimizer optimizes the communication environment based on the recognized emotional information. The emotion engine analyzes the voice and facial expression data and recognizes the worker's emotional state. The input data is voice and facial expression data, and the output is emotional information and an optimized communication environment. Specifically, the server collects the worker's voice and facial expression data and sends it to the emotion engine.

[0561] Step 6:

[0562] The server centrally controls the design, placement, maintenance, and disaster response of robots and machines in the factory via the cloud. The cloud API uses this information to provide optimal design, placement, and maintenance plans. The input data is information about the robots and machines, and the output is optimal design drawings, placement drawings, and maintenance plans. Specifically, the server collects information about the robots and machines and sends it to the cloud API.

[0563] Step 7:

[0564] The server recognizes the emotions of workers and optimizes the communication and work environments within the factory based on that information. The emotion engine analyzes the emotional state of the workers, and the network optimizer optimizes the communication environment. The input data is the workers' emotional information, and the output is an optimized communication and work environment. Specifically, the server receives the emotional information from the emotion engine and sends it to the network optimizer.

[0565] Example 2

[0566] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0567] In the past, each process in the design, construction, maintenance, and disaster response of mobile networks was managed separately, making it difficult to efficiently build service areas and quickly respond to disasters. Furthermore, communication quality was not adjusted according to the user's emotional state, and the user experience was not fully optimized. This resulted in problems such as poor communication quality and wasted resources.

[0568] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0569] In this invention, the server includes a means for designing base stations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently proceed with area construction, a means for determining the optimal maintenance method based on alert types, a means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, and local information, and a means for adjusting communication quality according to the user's emotional state. This makes it possible to efficiently manage everything from mobile network design to construction, maintenance, and disaster response in an integrated manner, optimizing the user experience.

[0570] "Population" refers to the total number of people living in a particular area.

[0571] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[0572] "Base station design" refers to a plan for placing mobile network base stations in optimal locations.

[0573] "Coverage rate" refers to the percentage of a particular area to which a mobile network can provide communication services.

[0574] "Construction priority" refers to the priority of which area construction will begin first in an area construction project.

[0575] "Alert type" refers to the type of warning or notification issued by the system.

[0576] "Maintenance Method" refers to the means for maintaining and managing mobile network facilities and systems.

[0577] "Area coverage rate" refers to the percentage of a specific area in which a mobile network can provide communication services.

[0578] "Latest road information" refers to current road conditions and traffic information.

[0579] "Local information" refers to the latest conditions and data for a specific area.

[0580] "Recovery priority" refers to the priority of which areas should undergo recovery work first in the event of a disaster.

[0581] "User's emotional state" refers to the psychological state, such as stress or relaxation, that the user feels.

[0582] "Communication quality" refers to the performance and stability of the communication services provided by mobile networks.

[0583] "Mobile network" refers to a network that provides mobile communication services using wireless communication.

[0584] "Cloud" refers to computer resources and services provided over the Internet.

[0585] This invention is a system that efficiently manages everything from mobile network design to construction, maintenance, and disaster response in an integrated manner, optimizing the user experience. Specific embodiments of this system are described below.

[0586] Embodiment of station placement design

[0587] The server uses a geographic information system (GIS) to analyze the area's topography and the distribution of buildings. Specifically, it uses GIS software such as geographic information system software. This allows it to obtain information on densely populated areas and areas around landmarks, and then plans the optimal placement of base stations. For example, in urban areas with many high-rise buildings, it places base stations taking into account the influence of the buildings to maximize coverage.

[0588] Examples:

[0589] Software used: Geographic Information System software

[0590] Data: Urban terrain data, building distribution data

[0591] Processing content: Optimal placement of base stations in urban areas with many high-rise buildings

[0592] Example prompt sentence:

[0593] "Use geographic information system software to analyze topographical and building distribution data to plan optimal placement of base stations in urban areas with many high-rise buildings."

[0594] Embodiment of communication quality adjustment by emotion engine

[0595] The device is equipped with an emotion engine to analyze the user's emotional state in real time. This emotion engine analyzes the user's facial expressions, voice, input data, etc. to determine whether the user is feeling stressed or relaxed. For example, if the device recognizes that the user is feeling stressed, it will take measures to increase the communication speed. Conversely, if the device recognizes that the user is relaxed, it will moderately reduce the communication speed to conserve communication resources.

[0596] Examples:

[0597] Software used: Sentiment analysis engine

[0598] Data: User facial expression data, voice data, input data

[0599] What it does: Improve communication speed when the user is stressed

[0600] Example prompt sentence:

[0601] "Use an emotion analysis engine to analyze the user's facial expression and voice data so that if it detects that the user is stressed, it will take steps to improve communication speed."

[0602] Disaster response implementation

[0603] The server collects disaster information such as typhoons and earthquakes, and dynamically updates recovery priorities by combining area coverage rates, the latest road information, and local information, enabling fast and efficient recovery work.

[0604] Examples:

[0605] Software used: Disaster Information Analysis System

[0606] Data: disaster information, communication demand data, road information

[0607] Processing content: Dynamic update of recovery priority when a disaster occurs

[0608] Example prompt sentence:

[0609] "When a disaster occurs, analyze disaster information and communication demand data and dynamically update recovery priorities."

[0610] In this way, the server and terminals work together to efficiently manage everything from mobile network design to construction, maintenance, and disaster response, optimizing the user experience.

[0611] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0612] Channel placement design processing

[0613] Step 1: Gather geographic information

[0614] The server uses geographic information system software to collect local topographic and building distribution data, using satellite imagery, map data, and population density data as input, and stores this data in a database.

[0615] Specific behavior:

[0616] The server uses the API of the geographic information system software to download the latest geographic information.

[0617] The server stores the collected data in a database.

[0618] Step 2: Analyze the data

[0619] The server analyzes the collected geographic information to identify the locations of densely populated areas and landmarks, using the stored geographic information data as input and obtaining the analysis results as output.

[0620] Specific behavior:

[0621] The server analyzes the population density data and maps high density areas.

[0622] The server identifies the location of the landmark and analyzes the distribution of buildings around it.

[0623] Step 3: Base station layout planning

[0624] The server then plans the optimal placement of base stations based on the analysis results. It uses the analysis results as input and obtains a list of candidate base station locations as output.

[0625] Specific behavior:

[0626] The server lists candidate locations for the base station.

[0627] The server simulates the coverage rate of each candidate location and determines the optimal placement.

[0628] Adjusting communication quality using an emotion engine

[0629] Step 1: Collecting user emotion data

[0630] The device collects the user's facial expressions, voice, and input data in real time. As input, it uses data from input devices such as a camera, microphone, and keyboard, and as output, it obtains collected emotional data.

[0631] Specific behavior:

[0632] The device uses a camera to capture the user's facial expressions.

[0633] The terminal uses a microphone to record the user's voice.

[0634] The terminal collects input data from a keyboard or touch screen.

[0635] Step 2: Sentiment Analysis

[0636] The terminal sends the collected data to an emotion analysis engine to analyze the user's emotional state. It uses the collected emotion data as input and obtains the emotion analysis result as output.

[0637] Specific behavior:

[0638] The terminal transmits facial expression data and voice data to the emotion analysis engine.

[0639] The sentiment analysis engine determines whether the user is stressed or relaxed.

[0640] Step 3: Adjust communication quality

[0641] The terminal adjusts the communication quality based on the result of the emotion analysis, using the emotion analysis result as input and obtaining the adjusted communication quality as output.

[0642] Specific behavior:

[0643] The terminal issues instructions to the communication module to adjust the communication speed.

[0644] The terminal maximizes communication speed when the user is stressed.

[0645] When the user is relaxed, the terminal reduces the communication speed appropriately.

[0646] Disaster response processing

[0647] Step 1: Collect disaster information

[0648] The server collects disaster information, and synthesizes area coverage, the latest road information, and local information. It uses disaster information data as input and obtains the collected disaster information as output.

[0649] Specific behavior:

[0650] The server acquires the latest disaster information from the disaster information service.

[0651] The server stores the collected disaster information in a database.

[0652] Step 2: Dynamic update of recovery priority

[0653] The server analyzes the collected disaster information and communication demand data and dynamically updates the recovery priority. It uses the disaster information and communication demand data as input and obtains the updated recovery priority as output.

[0654] Specific behavior:

[0655] The server analyzes disaster information and communication demand data.

[0656] The server dynamically updates the recovery priorities and optimizes the recovery plan.

[0657] (Application example 2)

[0658] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0659] Conventional mobile network systems require efficient methods for base station placement design and disaster recovery. Furthermore, optimizing communication quality is important for autonomous vehicles, and it is particularly important to adjust communication quality according to the emotional state of passengers. However, no system exists that meets these requirements, making it difficult to build and operate an efficient and flexible communication network.

[0660] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0661] In this invention, the server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, local information, etc., means for selecting the nearest base station from the current location of the autonomous vehicle, and means for adjusting communication quality according to the emotional state of passengers. This enables efficient placement of base stations and rapid restoration in the event of a disaster, and further enables autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[0662] "Population" is the total number of people living in a particular area.

[0663] A "landmark" is a building or structure that serves as a landmark in a particular area.

[0664] "Base station design" is the process of planning and deciding on the installation locations of communication base stations.

[0665] "Coverage rate" is the percentage of a geographic area that a communications network covers.

[0666] "Construction priority" is a priority for determining the order in which construction projects are started.

[0667] "Alert type" refers to the type of warning or notification issued by the system.

[0668] "Maintenance methods" are the means of maintaining and managing systems and equipment to ensure their normal operation.

[0669] A "typhoon" is a type of tropical cyclone, a natural phenomenon accompanied by strong winds and heavy rain.

[0670] An "earthquake" is a vibration phenomenon caused by sudden movement of the earth's crust.

[0671] "Area coverage rate" refers to the percentage of a specific area that is covered by a communications network.

[0672] "Latest road information" refers to the latest data on current road conditions.

[0673] "Local information" is detailed information about a specific area.

[0674] "Restoration priority" is a priority order for determining the order of restoration work in the event of a disaster or the like.

[0675] An "autonomous vehicle" is a vehicle that drives autonomously without driver intervention.

[0676] A "base station" is a facility for relaying communications in a wireless communication network.

[0677] An "emotional state" is an individual's psychological state or mood.

[0678] "Communication quality" is an index that indicates the performance and reliability of a communication service.

[0679] A system for carrying out this invention is configured as follows: The server includes means for designing base station locations based on information such as population and landmarks, means for assigning construction start priorities based on factors such as coverage rates to efficiently advance area construction, means for determining the optimal maintenance method based on alert types, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information, means for selecting the nearest base station from the current location of an autonomous vehicle, and means for adjusting communication quality according to the emotional state of passengers.

[0680] Program processing explanation

[0681] The server uses a geographic information system (GIS) to design base stations, taking into account the area's topography and the distribution of buildings. Specifically, it analyzes GIS data and allocates more base stations in densely populated areas and around landmarks to ensure coverage. It also determines the priority of construction work for area construction based on coverage rate and communication demand.

[0682] The server then determines the optimal maintenance method based on the alert type. For example, if a communication failure occurs, it selects the optimal maintenance method based on the cause. Furthermore, in the event of a disaster such as a typhoon or earthquake, the system dynamically updates the recovery priority by combining area coverage rate, the latest road information, and local information, allowing for rapid recovery work.

[0683] For autonomous vehicles, the server uses a GPS module to obtain the vehicle's current location and selects the nearest base station. It also analyzes the passenger's emotional state using an emotion engine and adjusts communication quality accordingly. For example, if the passenger is stressed, it increases communication speed, and if the passenger is relaxed, it saves communication resources.

[0684] Specific examples

[0685] As a concrete example, consider a self-driving vehicle in Tokyo. The server obtains the vehicle's current location and selects the nearest base station. Next, if the emotion engine recognizes the passenger's emotional state as "stressed," it sets the communication quality to "high."

[0686] An example of a prompt sentence is as follows:

[0687] Design a system that selects the optimal communication base station based on the terrain and building distribution of the area where the autonomous vehicle will be traveling, and also adjusts communication quality according to the passenger's emotional state. For example, if the vehicle is in Tokyo, it will find the nearest base station and set communication quality higher if the passenger is feeling stressed.

[0688] This will enable efficient deployment of base stations and rapid recovery in the event of a disaster, and will also enable autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[0689] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0690] Step 1:

[0691] The server uses a geographic information system (GIS) to obtain information on the area's topography and building distribution. It uses GIS data as input and obtains information on the area's topography and building distribution as output. Based on this information, it designs base stations to place many in densely populated areas and around landmarks.

[0692] Step 2:

[0693] The server determines the priority of area construction based on the coverage rate and communication demand. It uses coverage rate data and communication demand data as input and obtains construction priority as output. This allows area construction to proceed efficiently.

[0694] Step 3:

[0695] The server determines the optimal maintenance method based on the alert type. It uses alert data as input and obtains the optimal maintenance method as output. For example, if a communication failure occurs, it selects the optimal maintenance method based on the cause of the failure.

[0696] Step 4:

[0697] The server dynamically updates the recovery priority by integrating area coverage rate, the latest road information, and local information during disasters such as typhoons and earthquakes. It uses disaster information, area coverage rate data, the latest road information, and local information as inputs, and obtains the recovery priority as output. This enables rapid recovery work.

[0698] Step 5:

[0699] The server obtains the current location of the autonomous vehicle using a GPS module. It uses GPS data as input and gets the current location of the vehicle as output. Based on this location information, it selects the nearest base station.

[0700] Step 6:

[0701] The server analyzes the passenger's emotional state using an emotion engine. It uses the passenger's biometric and behavioral data as input and obtains the passenger's emotional state as output. For example, if the passenger is feeling stressed, it will recognize the emotion as "stress."

[0702] Step 7:

[0703] The server adjusts the communication quality according to the passenger's emotional state. It uses the emotional state data as input and obtains the communication quality setting as output. For example, it increases the communication speed if the passenger is stressed and saves communication resources if the passenger is relaxed.

[0704] This will enable efficient deployment of base stations and rapid recovery in the event of a disaster, and will also enable autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[0705] Example 3

[0706] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0707] There is a need for rapid and efficient restoration of mobile networks in the event of a disaster, as well as dynamic optimization of the communication environment according to the user's emotional state. Conventional systems do not adequately prioritize restoration work during a disaster, nor do they adjust the communication environment according to the user's emotional state, resulting in problems such as reduced communication quality and delays in restoration work.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0709] In this invention, the server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing restoration and carrying out smooth restoration work in the event of a disaster by integrating area coverage rates, the latest road information, and local information, and means for detecting the user's emotional state in real time and dynamically adjusting the communication environment in response to changes in that state. This enables fast and efficient restoration work in the event of a disaster and optimization of the communication environment in response to the user's emotional state.

[0710] "Population" refers to the total number of people living in a particular area.

[0711] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[0712] "Base station design" refers to a plan for placing mobile network base stations in optimal locations.

[0713] "Coverage rate" refers to the percentage of the area within which communication is possible.

[0714] "Construction priority" refers to the priority for starting construction of an area.

[0715] "Alert type" refers to the type of warning or notification that occurs in the system.

[0716] "Maintenance methods" refer to the means of maintaining and managing systems and equipment to ensure their normal operation.

[0717] "Area coverage rate" refers to the percentage of the area within which communication is possible within a specific area.

[0718] "Latest road information" refers to information about the current road conditions and passability.

[0719] "Local information" refers to up-to-date situational and environmental information about a particular area.

[0720] "Recovery prioritization" refers to determining the order of priority when carrying out recovery work in the event of a disaster.

[0721] "User's emotional state" refers to the type and intensity of the emotion the user is feeling.

[0722] "Communication environment" refers to the quality and settings of the communication services used by the user.

[0723] "Dynamic adjustment" refers to changing settings and parameters in real time depending on the situation.

[0724] "Mobile network" refers to a network that transmits and receives data using wireless communication.

[0725] "Centralized control" refers to the centralized management and operation of multiple functions and processes.

[0726] This invention is a system for realizing rapid and efficient recovery work in the event of a disaster and dynamic optimization of the communication environment according to the emotional state of the user. A specific embodiment of this system is described below.

[0727] Prioritizing disaster recovery efforts

[0728] The server collects the latest road and local information for the area where the disaster occurred. This information is obtained using a map service API. The server calculates the area coverage rate based on the collected information. The area coverage rate indicates the percentage of the area where communication is possible.

[0729] Next, the server combines the area coverage rate, the latest road information, and local information to determine the priority of the restoration work. For example, it may prioritize restoration work starting from accessible base stations. The server then notifies the restoration work team of the results of the prioritization. Notifications are sent via email or a dedicated restoration management system.

[0730] As a concrete example, if base station A and base station B are damaged by a typhoon, base station A is accessible, but the road to base station B is closed. In this case, the server will instruct base station A to take priority in carrying out restoration work.

[0731] Example prompt sentence:

[0732] "A typhoon disaster has occurred. Please determine the restoration priority for base station A and base station B based on the latest road and local information."

[0733] Dynamic adjustment of communication environment using emotion engine

[0734] The device detects the user's emotional state in real time. To detect the emotional state, it uses facial recognition and voice analysis technology using a camera and microphone. Specifically, it uses cloud service Emotion APIs and voice analysis APIs (for example, Microsoft® Azure® Emotion API and Google Cloud's Speech-to-Text API).

[0735] The device transmits the detected emotional state to the server. The server dynamically adjusts the communication environment based on the received emotional state. For example, if the user begins to feel angry, the server increases the bandwidth to improve communication quality. The server then notifies the device of the adjusted communication environment.

[0736] For example, if a user suddenly becomes angry during a video call, the device detects this emotion and sends it to the server, which then increases the bandwidth of the video call to improve communication quality.

[0737] Example prompt sentence:

[0738] "A user has started experiencing anger during a video call. Please make appropriate adjustments to improve communication quality."

[0739] This system makes it possible to realize efficient recovery operations in the event of a disaster and to optimize the communication environment in accordance with the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0740] Prioritizing disaster recovery efforts

[0741] Step 1:

[0742] The server collects the latest road and local information for the area where the disaster occurred.

[0743] Input: Specify the disaster area

[0744] Specific operation: The server calls the map service API to obtain road information and local information for the specified area.

[0745] Output: Dataset of up-to-date road and local information

[0746] Step 2:

[0747] The server calculates the area coverage rate based on the collected information.

[0748] Input: Latest road and local information dataset

[0749] Specific operation: The server analyzes the operating status and communication range of base stations within the area and calculates the area coverage rate.

[0750] Output: Area coverage rate numerical data

[0751] Step 3:

[0752] The server combines area coverage rate, the latest road information, and local information to determine the priority of recovery efforts.

[0753] Input: Area coverage rate data, latest road and local information dataset

[0754] Specific operation: The server executes a prioritization algorithm to prioritize recovery efforts from accessible base stations.

[0755] Output: Priority list of recovery efforts

[0756] Step 4:

[0757] The server notifies the recovery team of the results of the prioritization.

[0758] Input: Priority list of recovery efforts

[0759] Specific operation: The server notifies the recovery team of the results of the prioritization via email or a dedicated recovery management system.

[0760] Output: Message to notify the recovery team

[0761] Dynamic adjustment of communication environment using emotion engine

[0762] Step 1:

[0763] The device detects the user's emotional state in real time.

[0764] Input: User's facial image and voice data

[0765] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice, and sends them to cloud service Emotion APIs or voice analysis APIs (for example, Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API).

[0766] Output: User's emotional state data

[0767] Step 2:

[0768] The device transmits the detected emotional state to the server.

[0769] Input: User's emotional state data

[0770] Specific operation: The terminal uses a communication protocol to transmit emotional state data to the server.

[0771] Output: Send emotional state data to the server

[0772] Step 3:

[0773] The server dynamically adjusts the communication environment based on the received emotional state.

[0774] Input: User's emotional state data

[0775] Specific operation: The server analyzes the emotional state data and changes the network settings to improve communication quality, for example, by increasing the bandwidth.

[0776] Output: Adjusted communication environment settings

[0777] Step 4:

[0778] The server notifies the terminal of the adjusted communication environment.

[0779] Input: Adjusted communication environment settings

[0780] Specific operation: The server generates and sends a message to notify the terminal of changes in the communication environment.

[0781] Output: Notification message to terminal

[0782] In this way, by clearly indicating the specific operations and inputs / outputs at each processing step, it is possible to achieve efficient recovery operations in the event of a disaster and optimize the communication environment according to the user's emotions.

[0783] (Application example 3)

[0784] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0785] The rapid and efficient restoration of communications infrastructure in the event of a disaster is extremely important to society. However, with conventional systems, it was difficult to determine restoration priorities that comprehensively considered area coverage, road information, and local information, which often resulted in delays to restoration work. In addition, the communications environment was not dynamically adjusted according to the emotional state of the workers, which led to problems with reduced work efficiency. Furthermore, the efficiency of disaster restoration work using autonomous vehicles has not been fully realized. A new system to solve these issues is needed.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0787] In this invention, the server includes: means for designing base station placement from information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining optimal maintenance methods from alert types; means for prioritizing restoration and carrying out smooth restoration work by integrating area coverage rates, the latest road information, local information, etc. in the event of a typhoon, earthquake, etc.; means for grasping the emotional state of workers in real time and dynamically adjusting the communication environment in response to changes; and means installed in autonomous vehicles for efficiently carrying out restoration work in the event of a disaster. This enables the rapid and efficient restoration of communication infrastructure in the event of a disaster.

[0788] "Population" is the total number of people living in a particular area.

[0789] A "landmark" is a building or structure that serves as a landmark in a particular area.

[0790] "Base station location design" is the process of planning and designing the installation locations of communication base stations.

[0791] "Coverage rate" is the percentage of the area covered by a communication base station.

[0792] "Construction start priority" refers to the priority given when starting construction work.

[0793] "Alert type" is a classification of different types of alarms or notifications.

[0794] "Maintenance methods" are methods for maintaining and managing communications infrastructure.

[0795] "Area coverage rate" refers to the percentage of a specific area to which communication services are provided.

[0796] "Latest road information" refers to the latest data on current road conditions.

[0797] "Local information" is detailed information about a specific area.

[0798] "Restoration priority" refers to the priority of restoration work in the event of a disaster.

[0799] "Emotional state" refers to an individual's current emotional or mood state.

[0800] The "communication environment" refers to the state of the network and infrastructure when communication takes place.

[0801] "Dynamic adjustment" means changing settings and parameters in real time depending on the situation.

[0802] An "autonomous vehicle" is a vehicle that drives autonomously without driver intervention.

[0803] "Disaster recovery work" refers to work to repair infrastructure and facilities damaged by a disaster.

[0804] The system for carrying out the present invention aims at rapid and efficient restoration of communication infrastructure in the event of a disaster. The system includes the following main means.

[0805] 1. Station location design means

[0806] The server plans and designs the locations of communication base stations based on information such as population and landmarks, and uses a geographic information system (GIS) to take into account the local topography and distribution of buildings to create the optimal base station design.

[0807] 2. Construction start priority assignment method

[0808] The server determines the priority of construction work based on information such as coverage rate, allowing area construction to proceed efficiently.

[0809] 3. Means for learning maintenance methods

[0810] The server determines the optimal maintenance method depending on the alert type, allowing for efficient maintenance and management of the communications infrastructure.

[0811] 4. Recovery Prioritization Methods

[0812] In the event of a disaster such as a typhoon or earthquake, the server determines recovery priorities by combining area coverage rates, the latest road information, and local information, enabling smooth recovery work.

[0813] 5. Means of understanding emotional state

[0814] The server grasps the emotional state of the worker in real time and dynamically adjusts the communication environment according to changes. Using an emotion engine, it improves communication quality when the worker feels angry, and optimizes communication quality when the worker feels happy.

[0815] 6. Disaster Recovery Using Autonomous Vehicles

[0816] The system installed in autonomous vehicles will efficiently carry out recovery operations in the event of a disaster. The vehicle will select the optimal route based on the latest road and local information provided by the server, and will quickly reach the scene.

[0817] Hardware and software used

[0818] Hardware: autonomous vehicles, communication modules, sensors

[0819] Software: Python, API, emotion engine, geographic information system (GIS)

[0820] Data processing and calculation

[0821] The server obtains base station, road, and local information through APIs, and combines this data to determine restoration priorities. It also uses an emotion engine to grasp the emotional state of workers in real time and dynamically adjust the communication environment.

[0822] Specific examples

[0823] For example, if a base station is damaged by a typhoon, the server will prioritize restoration work from accessible base stations based on the latest road and local information. If a worker suddenly becomes angry, the emotion engine will instantly improve communication quality.

[0824] Example prompts for generative AI models

[0825] "Based on the latest base station, road, and local information, prioritize disaster recovery efforts. Also, dynamically adjust the communication environment according to the emotional state of workers."

[0826] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0827] Step 1:

[0828] The server obtains base station information through API. It specifies the API endpoint as input and obtains data including the base station's location and damage level as output. This data is used to determine the subsequent restoration priority.

[0829] Step 2:

[0830] The server obtains the latest road information through the API. It specifies the API endpoint as input and obtains data including road passability and congestion information as output. This data is used to plan routes for restoration work.

[0831] Step 3:

[0832] The server obtains local information through API. It specifies an API endpoint as input and obtains data including damage status and accessibility as output. This data is used to determine recovery priorities.

[0833] Step 4:

[0834] The server determines restoration priorities by combining base station, road, and local information. It uses these data as inputs and generates a list of high-priority base stations as output. This list is used to plan restoration efforts.

[0835] Step 5:

[0836] The server uses an emotion engine to grasp the emotional state of the worker in real time. It uses data from the worker's biosensors as input and obtains the worker's emotional state (e.g., anger, joy) as output. This emotional state is used to adjust the communication environment.

[0837] Step 6:

[0838] The server dynamically adjusts the communication environment according to the worker's emotional state. It uses the worker's emotional state as input and changes the communication quality settings as output. For example, if the worker feels angry, it improves the communication quality.

[0839] Step 7:

[0840] The autonomous vehicle selects the optimal route based on the latest road and local information provided by the server. It uses this information as input and generates the optimal route as output. This route is used to quickly reach the site.

[0841] Step 8:

[0842] The autonomous vehicle follows the generated route to the scene and starts recovery work. It uses the optimal route as input and arrives at the scene and starts recovery work as output, thereby achieving efficient disaster recovery.

[0843] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0844] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0846] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0847] [Second embodiment]

[0848] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0849] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0850] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0851] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0852] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0853] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0854] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0855] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0856] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0857] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0858] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0859] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0860] "Example 1"

[0861] One embodiment of the present invention is a system that centrally controls everything from the design, construction, maintenance, and disaster response of a mobile network (base station) via a cloud via GTP. This system includes a means for designing base station locations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, a means for identifying optimal maintenance methods based on alert types, and a means for prioritizing restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information to ensure smooth restoration work.

[0862] "Example 2"

[0863] Specifically, a geographic information system is used to design base stations, taking into account the topography and distribution of buildings in the area. For example, more base stations are placed in densely populated areas or around landmarks to ensure coverage.

[0864] "Example 3"

[0865] Furthermore, in disaster recovery work, recovery priorities are determined by integrating area coverage rates, the latest road information, local information, etc. For example, if a base station is damaged by a typhoon or earthquake, recovery work will be prioritized from the most accessible base station based on the latest road and local information. This will enable efficient recovery work.

[0866] The processing flow of each embodiment will be described below.

[0867] "Example 1"

[0868] Step 1: First, base station locations are designed based on information such as population and landmarks. A geographic information system is used to take into account the local topography and building distribution.

[0869] Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[0870] Step 3: After the base station is installed, determine the optimal maintenance method based on the alert type and perform regular maintenance.

[0871] Step 4: When a disaster such as a typhoon or earthquake occurs, area coverage rates, the latest road information, local information, etc. are combined to prioritize recovery efforts and ensure smooth recovery operations.

[0872] "Example 2"

[0873] Step 1: First, a geographic information system is used to design base stations, taking into account the local topography and the distribution of buildings. For example, more base stations are placed in densely populated areas or around landmarks. Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[0874] "Example 3"

[0875] Step 1: First, area coverage, the latest road information, local information, etc. are combined to determine restoration priorities.

[0876] Step 2: Next, restoration work is carried out by prioritizing accessible base stations based on the latest road and local information, thereby achieving efficient restoration work.

[0877] Example 1

[0878] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0879] There is a need for efficient and effective centralized control of mobile network design, construction, maintenance, and disaster response. In particular, the challenges are optimal base station placement taking into account population density and landmark locations, prioritizing construction based on coverage rates, proposing optimal maintenance methods according to alert types, and rapid recovery operations in the event of a disaster.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0881] In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and other factors to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and carrying out smooth restoration work by integrating area coverage rates, the latest road information, and local information in the event of a typhoon, earthquake, or other disaster; means for analyzing population density and landmark locations using a geographic information system to design the optimal base station placement; means for analyzing coverage rates and population density using a data analysis tool to determine construction priorities; means for analyzing past alert data and maintenance history using a machine learning model to propose the optimal maintenance method; and means for collecting the latest road information and local information using a real-time data streaming tool to determine restoration priorities. This enables efficient and effective centralized control of everything from mobile network design to construction, maintenance, and disaster response.

[0882] "Population density" is an indicator that shows the number of people per unit area in a particular area.

[0883] A "landmark" refers to a building, natural object, or important point that serves as a landmark in a particular area.

[0884] "Base station planning" is the process of planning and designing the optimal placement of base stations in a mobile network.

[0885] "Coverage rate" is an indicator that indicates the percentage of the area in which mobile network signals reach in a particular area.

[0886] "Construction start priority" is a criterion for determining the priority when starting construction of a base station.

[0887] "Alert type" is a classification of the types of abnormalities and problems that occur during network operation.

[0888] "Maintenance methods" are repair and maintenance procedures performed to maintain normal network operation.

[0889] "Recovery priority" is a standard for determining the order of priority when carrying out recovery work in the event of a disaster or failure.

[0890] A "geographic information system" is an information system for collecting, analyzing, and displaying geographic data.

[0891] "Data analysis tools" are software and libraries used to analyze collected data and extract meaningful information.

[0892] A "machine learning model" is an algorithm or mathematical model that learns from data and makes predictions or classifications.

[0893] "Real-time data streaming tools" are software or platforms for collecting, processing, and distributing data in real time.

[0894] This invention is a system for collectively controlling everything from the design to the construction, maintenance, and disaster response of a mobile network. A specific embodiment of this system will be described below.

[0895] 1. Mobile Network Design

[0896] The server collects population density data and landmark location data from open data and commercial databases. The server then analyzes this data using geographic information system (GIS) software. Specifically, the server uses the software to visualize population density and landmark locations and design optimal cell tower placement.

[0897] 2. Construction prioritization

[0898] The server collects existing coverage data from the network operation database. Then, it uses data analysis tools (e.g., Python's Pandas library) to analyze data such as coverage and population density to determine construction priorities. Finally, the server generates a priority list and provides it to the construction team.

[0899] 3. Optimizing maintenance methods

[0900] The server collects alert data from the network using a network monitoring system. Then, the server uses a machine learning model (e.g., TensorFlow) to analyze past alert data and maintenance history and propose the optimal maintenance method. The server notifies the maintenance team of the maintenance method, including specific steps.

[0901] 4. Disaster response

[0902] The server collects the latest road and local information in real time during a disaster. This is done using a real-time data streaming tool (e.g., Apache Kafka). The server then analyzes the collected real-time data and determines recovery priorities. Finally, the server provides a priority list to the recovery team, helping to ensure smooth recovery operations.

[0903] Examples and prompts

[0904] For example, if a new base station is to be installed in City A, the server collects population density data and landmark location data for City A from open data. Then, the server analyzes this data using geographic information system software to design the optimal base station placement. After that, the server collects existing coverage data from the network operation database and analyzes construction priorities using Python's Pandas library. Finally, the server generates a priority list and provides it to the construction team.

[0905] Prompt Sentence Examples

[0906] "Design the optimal placement of base stations based on population density data and landmark location data for City A. Also, analyze coverage rate data to determine construction priorities, and in the event of a disaster, prioritize restoration efforts based on the latest road and local information."

[0907] In this way, by explaining the system processing from the perspectives of the server, terminal, and user, the specific operations and data flow become clear.

[0908] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0909] Step 1:

[0910] The server collects population density data and landmark location data. As input, it obtains population density data and landmark location data from open data and commercial databases. Specifically, the server downloads these data through APIs and stores them in an internal database. As output, the collected data is stored in the internal database.

[0911] Step 2:

[0912] The server analyzes the collected data using geographic information system (GIS) software. As input, it uses population density data and landmark location data stored in an internal database. Specifically, the server launches the GIS software, imports this data, and visualizes it. As output, it determines the optimal base station placement points.

[0913] Step 3:

[0914] The server collects existing coverage data from the network operation database. As input, it obtains coverage information from the network operation database. As a specific operation, the server executes an SQL query to extract coverage data and saves it in the internal database. As output, the collected coverage data is stored in the internal database.

[0915] Step 4:

[0916] The server uses data analysis tools to analyze coverage and population density and determine construction priorities. It uses coverage and population density data stored in an internal database as input. Specifically, the server uses the Python Pandas library to analyze the data and generate a priority list. The output is a construction priority list.

[0917] Step 5:

[0918] The server collects alert data from the network. As input, it obtains alert information from the network monitoring system. Specifically, the server collects alert data through the monitoring system's API and saves it in an internal database. As output, the collected alert data is stored in the internal database.

[0919] Step 6:

[0920] The server uses a machine learning model to analyze past alert data and maintenance history and propose optimal maintenance methods. As input, it uses alert data and maintenance history data stored in an internal database. Specifically, the server uses TensorFlow to train the machine learning model and predict the optimal maintenance method. As output, it generates specific maintenance procedures that are notified to the maintenance team.

[0921] Step 7:

[0922] The server collects the latest road and local information in real time during a disaster. It uses data from various sensors and traffic information systems as input. Specifically, the server uses Apache Kafka to stream real-time data and saves it in an internal database. As output, the collected real-time data is stored in the internal database.

[0923] Step 8:

[0924] The server analyzes the collected real-time data and determines restoration priorities. As input, it uses road and local information stored in an internal database. Specifically, the server analyzes the data using machine learning models and generates a restoration priority list. As output, it generates a priority list that is provided to the restoration team.

[0925] (Application example 1)

[0926] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0927] In the past, the design, construction, maintenance, and disaster response of mobile networks were all managed separately, making efficient operation difficult. Furthermore, for autonomous vehicles, it was difficult to obtain real-time network coverage and road information, making it difficult to set optimal routes and quickly respond to disasters.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0929] In this invention, the server includes means for designing base station locations based on information such as population and landmarks, means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, means for determining the optimal maintenance method based on alert types, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, and local information, etc., means for acquiring network coverage rates and road information in real time for autonomous vehicles and calculating the optimal route, means for setting the safest and fastest route based on the latest road information and area coverage rates in the event of a disaster, and means for acquiring network maintenance information in real time and changing the route of autonomous vehicles, thereby enabling efficient operation of mobile networks and safe and fast operation of autonomous vehicles.

[0930] "Population" is the total number of people living in a particular area.

[0931] A "landmark" is an important point such as a building or natural object that serves as a landmark in a particular area.

[0932] "Base station planning" is the planning and design process for installing base stations for mobile networks.

[0933] "Coverage" is the percentage of coverage of communication services provided by a mobile network within a particular area.

[0934] "Construction priority" is a priority for determining the order in which construction projects are started.

[0935] "Alert type" refers to the type of warning or notification issued by the system.

[0936] "Maintenance methods" are the means of maintaining and managing systems and equipment to ensure their normal operation.

[0937] "Area coverage" is the percentage of coverage of communication services provided by a mobile network within a specific geographic area.

[0938] "Latest road information" refers to the latest data including current road conditions and traffic information.

[0939] "Local information" is detailed information about a specific area.

[0940] "Recovery priority" refers to the priority of recovery work in the event of a disaster or failure.

[0941] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensor technology.

[0942] "Real-time" refers to the immediate processing of ongoing events and data.

[0943] An "optimal route" is the most efficient and safest route to reach a destination.

[0944] "Disaster time" refers to a situation when a natural disaster such as a typhoon or earthquake occurs.

[0945] "Maintenance information" is data related to the maintenance and management of systems and equipment.

[0946] A "travel route" is a route set for a vehicle to travel.

[0947] The system for implementing this invention consists of a cloud-based server for centrally controlling the design, construction, maintenance, and disaster response of mobile networks, and an application installed in an autonomous vehicle.

[0948] The server includes the following means:

[0949] 1. A method for designing station locations based on information such as population and landmarks.

[0950] 2. A means of efficiently advancing area construction by assigning construction priority based on coverage rate, etc.

[0951] 3. A means of identifying the optimal maintenance method based on the alert type.

[0952] 4. A means of smoothly carrying out restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, local information, etc., and assigning restoration priorities.

[0953] 5. A means for autonomous vehicles to obtain real-time network coverage and road information and calculate optimal routes.

[0954] 6. A means of planning the safest and quickest route based on the latest road information and area coverage in the event of a disaster.

[0955] 7. A means of obtaining real-time network maintenance information and rerouting autonomous vehicles.

[0956] Hardware and Software Configuration

[0957] The server operates in a cloud computing environment and includes a database, a geographic information system (GIS), and a real-time data processing system.The autonomous vehicle is equipped with a GPS module, a mobile network module, and a vehicle control system.

[0958] Data processing and calculation

[0959] The server obtains population data, landmark data, network coverage rate data, road information data, and disaster information data through the cloud API. Based on this data, it designs base stations, determines construction priority, learns maintenance methods, and determines restoration priority. Autonomous vehicles calculate optimal routes and change their driving routes based on the real-time data provided by the server.

[0960] Specific examples

[0961] For example, if an autonomous vehicle travels from "Location A" to "Location B" in the Tokyo area, the following prompt sentence is input into the generative AI model.

[0962] Calculate the optimal route from Location A to Location B based on the network coverage rate in the Tokyo area and the latest road information.

[0963] By inputting this prompt into the generative AI model, the optimal route is calculated, supporting the operation of autonomous vehicles. The server obtains network coverage and road information in real time and calculates the optimal route. In the event of a disaster, the safest and fastest route is set based on the latest road information and area coverage. Network maintenance information can also be obtained in real time, allowing autonomous vehicle routes to be changed.

[0964] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0965] Step 1:

[0966] The server obtains population data, landmark data, network coverage data, road information data, and disaster information data through the cloud API. This data is collected in real time from various databases. The input is an API request, and the output is various data returned in JSON format.

[0967] Step 2:

[0968] The server uses a geographic information system (GIS) to design base station locations based on the acquired population and landmark data. The inputs are population and landmark data, and the output is the optimal base station installation location. Specifically, the GIS software analyzes the topography and building distribution and calculates the optimal installation location.

[0969] Step 3:

[0970] The server assigns construction start priorities based on the network coverage rate data, and efficiently advances area construction. The input is network coverage rate data, and the output is a construction start priority list. Specifically, the server creates a plan to prioritize the installation of base stations in areas with low coverage rates.

[0971] Step 4:

[0972] The server determines the optimal maintenance method depending on the alert type. The input is alert data, and the output is a list of maintenance methods. Specifically, it selects the appropriate maintenance procedure based on the type of alert.

[0973] Step 5:

[0974] When a disaster such as a typhoon or earthquake occurs, the server determines the restoration priority by integrating area coverage rate, the latest road information, and local information. The inputs are disaster information, area coverage rate data, and road information data, and the output is a restoration priority list. Specifically, the server identifies the areas affected by the disaster and sets the priority of restoration work.

[0975] Step 6:

[0976] An autonomous vehicle obtains network coverage and road information in real time from a server and calculates the optimal route. The inputs are network coverage data and road information data, and the output is the calculation of the optimal route. Specifically, the vehicle's navigation system sets the route based on this data.

[0977] Step 7:

[0978] In the event of a disaster, the autonomous vehicle will set the safest and fastest route based on the latest road information and area coverage rate from the server. The input is the latest road information and area coverage rate data, and the output is the safe route. Specifically, the navigation system will recalculate the route taking into account the disaster information.

[0979] Step 8:

[0980] The autonomous vehicle obtains network maintenance information from the server in real time and changes its route as necessary. The input is maintenance information data, and the output is an updated route. Specifically, the navigation system reconfigures the route based on the maintenance information.

[0981] Example 2

[0982] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0983] In the design, construction, maintenance, and disaster response of conventional mobile networks, station placement design did not adequately take into account the topography and distribution of buildings, making it difficult to improve coverage and communication quality. Furthermore, in disaster recovery work, it was difficult to prioritize tasks efficiently, and a rapid response was required.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0985] In this invention, the server includes a means for using a geographic information system to perform base station location design taking into account the topography and building distribution of the area, a means for inputting prompt sentences using a generative AI model to propose optimal base station locations, and a means for dynamically updating recovery priorities taking into account disaster information and fluctuations in communication demand, thereby enabling efficient base station location design and rapid disaster response.

[0986] A "geographic information system" is a system for collecting, managing, analyzing, and displaying geographic information such as topography and building distribution.

[0987] "Base station planning" is the process of planning the placement of base stations in a mobile network and determining the optimal locations.

[0988] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate optimal solutions for specific tasks.

[0989] A "prompt" is an instruction given to a generative AI model that describes the conditions and requirements for performing a specific task.

[0990] A "base station" is a facility for wireless communication in a mobile network, and serves to connect user terminals to the communication network.

[0991] "Coverage rate" is an indicator that indicates the percentage of the area that can be reached by radio waves from a base station in a mobile network.

[0992] "Disaster information" refers to information about natural disasters such as typhoons and earthquakes, including the extent of damage and the extent of the impact.

[0993] "Telecommunications demand" refers to the amount of usage or demand for telecommunications services in a particular area or time period.

[0994] "Restoration priority" is a criterion for determining which areas and facilities should be given priority for restoration in the event of a disaster.

[0995] This invention is a system for efficiently designing, constructing, maintaining, and responding to disasters in a mobile network. Specific embodiments of this system will be described below.

[0996] First, the user accesses a geographic information system (GIS) and uploads the topographical data and building distribution data of the target area to the GIS. These data are required for subsequent analysis.

[0997] The server then analyzes the uploaded terrain and building distribution data. It cross-references these data to identify densely populated areas and landmarks. It then uses a radio wave propagation model that takes into account the effects of terrain and buildings to simulate the radio wave propagation characteristics in the identified areas. For example, it calculates the impact of mountains and tall buildings on radio wave propagation.

[0998] The server calculates the optimal placement of base stations based on the simulation results. A prompt sentence is input using a generative AI model, and the optimal placement of base stations is suggested. An example of a specific prompt sentence is, "We would like to design a new communications network for urban areas. Please use a geographic information system to take into account topographical data and building distribution data, and design it so that many base stations are located in densely populated areas and around landmarks."

[0999] Furthermore, the server dynamically updates recovery priorities, taking into account disaster information and fluctuations in communication demand. For example, when a natural disaster such as a typhoon or earthquake occurs, the server prioritizes recovery by combining area coverage rates, the latest road information, and local information, ensuring smooth recovery work.

[1000] This system enables efficient base station design and rapid disaster response. Users can check the optimal base station placement on the GIS and make adjustments as needed. In the event of a disaster, the server dynamically updates recovery priorities, enabling rapid and efficient recovery work.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] Step 1:

[1003] A user accesses a geographic information system (GIS).

[1004] Specifically, the user accesses the GIS login screen, enters authentication information, and logs in. This allows the user to use the GIS functions.

[1005] Input: User credentials

[1006] Output: GIS access permissions

[1007] Step 2:

[1008] The user inputs the topographical data and building distribution data of the target area.

[1009] Specifically, the user uses the GIS interface to upload topographical data (e.g., elevation data and topographical maps) and building distribution data (e.g., building locations and heights) for the target area.

[1010] Input: Topographical data, building distribution data

[1011] Output: Topographical data and building distribution data stored in GIS

[1012] Step 3:

[1013] The server analyzes the input data and identifies densely populated areas and landmarks.

[1014] Specifically, the server cross-references the topographical data stored in the GIS with building distribution data to identify densely populated areas and landmarks (e.g., train stations and shopping malls).

[1015] Input: Topographical data and building distribution data stored in GIS

[1016] Output: A list of densely populated areas and landmarks

[1017] Step 4:

[1018] The server simulates the propagation characteristics of radio waves.

[1019] Specifically, the server uses a radio wave propagation model that takes into account the effects of terrain and buildings to simulate the radio wave propagation characteristics in the specified area. For example, it calculates the impact of mountains and tall buildings on radio wave propagation.

[1020] Input: List of densely populated areas and landmarks, topographical data, building distribution data

[1021] Output: Simulation results of radio wave propagation characteristics

[1022] Step 5:

[1023] The server calculates the optimal base station placement.

[1024] Specifically, the server calculates the optimal base station placement based on the simulation results, inputs prompts using a generative AI model, and proposes the optimal base station placement.

[1025] Input: Radio wave propagation characteristics simulation results, prompt text

[1026] Output: Optimal base station placement proposal

[1027] Step 6:

[1028] The server provides the calculation results to the user.

[1029] Specifically, the server provides the calculation results to the user through a GIS interface, where the user can check the optimal base station placement and make adjustments as necessary.

[1030] Input: Optimal base station placement proposal

[1031] Output: Displaying placement suggestions to the user

[1032] Step 7:

[1033] The server dynamically updates the recovery priority taking into account disaster information and fluctuations in communication demand.

[1034] Specifically, when a natural disaster such as a typhoon or earthquake occurs, the server will combine area coverage rates, the latest road information, and local information to assign recovery priorities and carry out smooth recovery operations.

[1035] Input: Disaster information, fluctuation data on communication demand

[1036] Output: Dynamically updated recovery priority

[1037] (Application example 2)

[1038] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1039] Conventional mobile network design, construction, maintenance, and disaster response have not fully utilized information such as geographical information, population density, and landmarks, making it difficult to efficiently build areas and carry out smooth restoration work. Furthermore, it has been difficult to provide optimal routes without interruptions to communication when navigating autonomous vehicles.

[1040] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and performing smooth restoration work in the event of a typhoon, earthquake, or the like by integrating area coverage rates, the latest road information, and local information; means for calculating the optimal route for an autonomous vehicle by taking into account the local topography and building distribution using a geographic information system; means for proposing routes that avoid congestion based on information on population density and landmarks; and means for selecting a route that ensures uninterrupted communication by taking into account the location of the autonomous vehicle's base station. This enables efficient area construction, smooth restoration work, and optimal navigation for autonomous vehicles.

[1041] "Population" refers to the total number of people living in a particular area.

[1042] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[1043] "Base station design" refers to the planning and design of base station locations.

[1044] "Coverage rate" refers to the percentage of a particular area to which telecommunications services are provided.

[1045] "Construction priority" refers to the priority of which area of ​​a construction project to start construction on first.

[1046] "Alert type" refers to the type of warning or notification issued by the system.

[1047] "Maintenance methods" refer to the means of maintaining and managing systems and equipment to ensure their normal operation.

[1048] "Geographic information system" refers to an information system for collecting, managing, and analyzing geographic data.

[1049] "Topography" refers to the shape and characteristics of the Earth's surface.

[1050] "Building distribution" refers to the arrangement and density of buildings in a particular area.

[1051] An "autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[1052] An "optimal route" refers to the most efficient and safe route under specific conditions.

[1053] A "congestion-avoiding route" refers to a route that avoids areas with heavy traffic.

[1054] "Base station placement" refers to determining the location of a base station to provide communication services.

[1055] A "route without interruption of communication" refers to a route where communication can continue without interruption while traveling.

[1056] The system for carrying out the present invention operates in cooperation with three entities: a server, a terminal, and a user. A specific embodiment of the system will be described below.

[1057] Server Processing

[1058] The server uses a geographic information system (GIS) to design base stations, taking into account the local topography and building distribution. Specifically, it collects GIS data and calculates the optimal placement of base stations based on population density and landmark information. It also assigns construction start priorities based on factors such as coverage rate, allowing for efficient area construction. It also determines the optimal maintenance method based on the alert type, and in the event of a disaster such as a typhoon or earthquake, it combines area coverage rate, the latest road information, and local information to assign recovery priorities and ensure smooth recovery work.

[1059] Terminal handling

[1060] The device (e.g., a smartphone) calculates the optimal route for the autonomous vehicle based on data provided by the server. Specifically, it uses a geographic information system to consider the local topography and distribution of buildings, and suggests routes that avoid congestion based on population density and landmark information. It also considers the location of base stations for the autonomous vehicle and selects routes that will ensure communication is not interrupted.

[1061] User operations

[1062] The user can operate the autonomous vehicle using the optimal route information provided through the device. For example, when calculating a route from Shinjuku Station to Shibuya Station in Tokyo, the user can select a route that avoids the densely populated areas of Shinjuku and Shibuya and ensures communication is not interrupted.

[1063] Hardware and software used

[1064] Hardware: GPS-enabled smartphones, self-driving vehicles

[1065] Software: Python, GeoPandas (geographic data processing), Shapely (geographic data manipulation)

[1066] Data processing and calculation

[1067] The server collects geographical information data (buildings, population density, landmarks, base stations) and calculates the optimal base station placement based on this data. The device calculates the optimal route based on the data provided by the server and provides it to the user.

[1068] Specific examples

[1069] For example, when calculating a route from Shinjuku Station to Shibuya Station in Tokyo, the server calculates a route that avoids congestion based on information about the population density and landmarks of Shinjuku and Shibuya.The device then uses this information to select a route that will not cause communication interruptions and provides it to the user.

[1070] Prompt Sentence Examples

[1071] "Calculate the best route from Shinjuku Station to Shibuya Station in Tokyo. Choose a route that avoids densely populated areas and landmarks, and ensures uninterrupted communication."

[1072] This allows for efficient area construction, smooth recovery operations, and optimal navigation for autonomous vehicles.

[1073] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1074] Step 1:

[1075] The server collects geographic information data (buildings, population density, landmarks, base stations). As input, it gets the geographic information data from the GIS database and stores it in its internal database. As output, it gets the collected geographic information data.

[1076] Step 2:

[1077] The server calculates the optimal placement of base stations based on the collected geographic information data. Using the geographic information data collected in step 1 as input, it analyzes population density and landmark information using GIS software (e.g., GeoPandas). The output is coordinate data for the optimal base station placement.

[1078] Step 3:

[1079] The server assigns construction priority based on factors such as coverage rate, and creates a plan for efficient area construction. It uses the coordinate data of the base station locations obtained in step 2 as input and applies an algorithm to calculate the coverage rate. The output is an area construction plan with construction priority assigned.

[1080] Step 4:

[1081] The server determines the optimal maintenance method based on the alert type. As input, it receives alert data from the system and applies an algorithm that determines the maintenance method based on the alert type. As output, it obtains the optimal maintenance method.

[1082] Step 5:

[1083] The server assigns recovery priorities to disasters such as typhoons and earthquakes by integrating area coverage rates, the latest road information, and on-site information. It receives disaster information and fluctuation data on communication demand as input, and applies an algorithm that dynamically updates recovery priorities based on this information. The output is a recovery plan with assigned recovery priorities.

[1084] Step 6:

[1085] The terminal calculates the optimal route for the autonomous vehicle based on the data provided by the server. As input, it receives geographic information data and base station location data sent from the server, and uses a geographic information system (e.g., Shapely) to calculate the route taking into account the local topography and distribution of buildings. The optimal route information is obtained as output.

[1086] Step 7:

[1087] The device then proposes a route that avoids congestion based on population density and landmark information. As input, it uses the optimal route information obtained in step 6 and applies an algorithm that analyzes population density and landmark data. As output, it obtains route information that avoids congestion.

[1088] Step 8:

[1089] The terminal selects a route that will not cause communication interruptions, taking into account the location of base stations for the autonomous vehicle. Using the route information to avoid congestion obtained in step 7 and base station location data as input, it applies an algorithm to calculate a route that will not cause communication interruptions. The output is the optimal route information that will not cause communication interruptions.

[1090] Step 9:

[1091] The user operates the autonomous vehicle using optimal route information provided through the device. As input, the system receives the optimal route information provided by the device and configures the autonomous vehicle's navigation system based on this information. As output, the user is able to reach their destination efficiently and safely.

[1092] Example 3

[1093] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1094] Mobile network restoration work in the event of a disaster must be carried out quickly and efficiently. However, with conventional systems, it was difficult to determine restoration priorities by comprehensively taking into account area coverage, road information, and local information, which often resulted in delays in restoration work. In addition, it was not possible to dynamically reflect changes in disaster information and communication demand, making it difficult to carry out optimal restoration work.

[1095] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1096] In this invention, the server includes a means for designing base station locations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, a means for determining the optimal maintenance method based on alert types, a means for collecting and analyzing area coverage rates, the latest road information, and on-site information in the event of a disaster, and a means for calculating restoration priorities based on the collected data and issuing instructions for restoration work, thereby enabling rapid and efficient restoration of mobile networks in the event of a disaster.

[1097] "Population" refers to the total number of people living in a particular area.

[1098] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[1099] "Base station design" refers to the process of planning the location of base stations and determining the optimal placement.

[1100] "Coverage rate" refers to the percentage of the area where a base station can provide communication services.

[1101] "Construction priority" refers to the priority for determining the order in which construction work begins.

[1102] "Alert Type" refers to a category that classifies different types of warnings or notifications.

[1103] "Maintenance methods" refer to the means used to properly maintain systems and equipment and prevent breakdowns.

[1104] "Area coverage rate" refers to the percentage of the area in which communication services are provided in a particular region.

[1105] "Road information" refers to data regarding road traffic conditions and traffic regulations.

[1106] "Local information" refers to data about the current situation or conditions in a particular area.

[1107] "Restoration priority" refers to the order of priority for determining the order of restoration work in the event of a disaster.

[1108] "Recovery work" refers to work to repair systems or equipment damaged by a disaster or failure.

[1109] "Mobile network" refers to a network that provides mobile communication services using wireless communication technology.

[1110] "Cloud" refers to computer resources and services provided over the Internet.

[1111] The present invention provides a system for realizing rapid and efficient restoration of a mobile network in the event of a disaster. A specific embodiment of this system will be described below.

[1112] 1. Generating the system program

[1113] The user creates a system program to streamline disaster recovery work. This program includes an algorithm that determines recovery priorities by combining area coverage, the latest road information, and on-site information.

[1114] 2. Program processing explanation

[1115] The server collects and processes the following data to determine the priority of recovery efforts in the event of a disaster:

[1116] Area Coverage Ratio: The server calculates the area coverage ratio of the base stations, taking into account the coverage area of ​​each base station and the population density within that area.

[1117] Up-to-date road information: The server uses map APIs and open-source map databases to obtain the latest road information, which allows it to determine which roads are passable and which are closed.

[1118] Local information: The server uses SNS APIs and news APIs to collect local damage information from SNS and news sites, thereby assessing the extent and urgency of the damage.

[1119] Based on this data, the server calculates the restoration priority of each base station. Specifically, it designs an algorithm to prioritize restoration work for accessible base stations.

[1120] 3. Examples and prompts

[1121] For example, if multiple base stations are damaged by a typhoon, the server performs the following process.

[1122] 1. The server uses the map API to obtain the latest road information.

[1123] 2. The server uses the SNS API to collect information on the local damage situation.

[1124] 3. The server calculates the area coverage rate of each base station.

[1125] 4. The server combines all this data and determines the priority so that recovery work is carried out first from accessible base stations.

[1126] An example of a prompt sentence to input to the generative AI model is as follows:

[1127] "Multiple base stations have been damaged by the typhoon. Please use the map API to obtain the latest road information and the SNS API to collect local damage information. Please calculate the area coverage rate of each base station and determine priorities so that restoration work is carried out first at accessible base stations."

[1128] In this way, the user can build a system for realizing efficient recovery work. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1129] Step 1:

[1130] Data collection

[1131] The server collects data necessary for disaster recovery operations.

[1132] Input: Base station location information, map API, SNS API

[1133] Specific operation: The server retrieves the base station's latitude and longitude information and coverage area data from the database, and also uses the map API to obtain the latest road information and the SNS API to collect local damage information.

[1134] Output: Base station location and coverage data, latest road information, and local damage status data

[1135] Step 2:

[1136] Data analysis

[1137] The server analyzes the collected data.

[1138] Input: Base station location and coverage data, latest road information, local damage situation data

[1139] Specific operation: The server calculates the population density within the coverage area of ​​each base station and calculates the area coverage rate. It also analyzes the acquired road information to identify passable routes. It also analyzes collected tweets and news articles to quantify the extent of damage.

[1140] Output: Area coverage rate of each base station, passable route information, numerical data on the extent of damage

[1141] Step 3:

[1142] Priority calculation

[1143] The server calculates the recovery priority of each base station based on the analyzed data.

[1144] Input: Area coverage rate of each base station, passable route information, numerical data on the extent of damage

[1145] Specific operation: The server determines the restoration priority of each base station by combining area coverage rate, road information, and local information. Specifically, it designs an algorithm to prioritize restoration work for accessible base stations.

[1146] Output: Recovery priority of each base station

[1147] Step 4:

[1148] Instructions for recovery work

[1149] The server issues instructions for recovery work based on priority.

[1150] Input: Recovery priority of each base station

[1151] Specific operation: The server instructs the terminal to perform the restoration work in order of priority, starting with the base station with the highest priority. Specifically, it sends the restoration work schedule and route information to the terminal.

[1152] Output: Recovery schedule and route information

[1153] In this way, the server builds a system for realizing efficient recovery work.

[1154] (Application example 3)

[1155] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1156] In the design, construction, maintenance, and disaster response of conventional mobile networks, each process is managed separately, making efficient operation difficult. Furthermore, in disaster recovery work, prioritization is not performed taking into account area coverage, the latest road information, and local information, resulting in delays in recovery work. Furthermore, efficient recovery work support using autonomous vehicles is not available, so a rapid response is required in the event of a disaster.

[1157] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or the like by integrating area coverage rates, the latest road information, local information, and the like; and means for supporting efficient restoration work in the event of a disaster using a navigation system installed in an autonomous vehicle. This enables efficient, integrated management of everything from mobile network design to construction, maintenance, and disaster response, enabling rapid and effective restoration work in the event of a disaster.

[1158] "Population" refers to the total number of people living in a particular area.

[1159] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[1160] "Base station design" refers to the planning and design for installing base stations for mobile networks.

[1161] "Coverage rate" refers to the percentage of an area covered by a mobile network.

[1162] "Construction start priority" refers to the priority of which area construction will begin first in a construction project.

[1163] "Alert type" refers to the type of warning or notification issued by the system.

[1164] "Maintenance method" refers to the method of maintaining and managing systems and equipment to ensure their normal operation.

[1165] "Area coverage rate" refers to the percentage of area covered by a mobile network in a particular region.

[1166] "Latest road information" refers to current road conditions and traffic information.

[1167] "Local information" refers to the latest conditions and data for a specific area.

[1168] "Recovery priority" refers to the order of priority for which areas recovery work should be carried out in the event of a disaster.

[1169] An "autonomous vehicle" refers to a vehicle that drives itself using artificial intelligence and sensor technology.

[1170] "Navigation system" refers to a system that provides the optimal route to a destination.

[1171] "Mobile network" refers to a network that provides mobile communications using wireless communication.

[1172] "Cloud" refers to computer resources and services provided over the Internet.

[1173] The system for implementing this invention is composed of the following elements: a server, a terminal, and a user. The specific operation of each element and the hardware and software used will be described below.

[1174] Server Operation

[1175] The server includes the following means:

[1176] 1. Station location design method: Information such as population and landmarks is collected, and station location is designed using a geographic information system (GIS) taking into account the local topography and building distribution.

[1177] 2. Construction start priority assignment method: Based on data such as coverage rate, priority is assigned to efficiently proceed with area construction.

[1178] 3. Maintenance method identification: Identify the optimal maintenance method based on the alert type.

[1179] 4. Recovery prioritization method: In the event of a disaster such as a typhoon or earthquake, recovery priorities are determined by combining area coverage rate, the latest road information, and local information, ensuring smooth recovery work.

[1180] 5. Navigation system means: Using a navigation system installed in an autonomous vehicle, we will support efficient recovery efforts in the event of a disaster.

[1181] Device behavior

[1182] The terminal refers to the navigation system installed in the autonomous vehicle. This navigation system receives the latest road information and restoration priority information provided by the server, calculates the optimal route, and issues instructions to the autonomous vehicle.

[1183] User Actions

[1184] The user refers to a system administrator or recovery worker. The user plans the recovery work based on the information provided by the server and operates the autonomous vehicle as necessary.

[1185] Hardware and software used

[1186] Hardware: Servers, GPS sensors, communication modules, autonomous vehicles

[1187] Software: Geographic Information Systems (GIS), navigation software, data collection APIs, generative AI models

[1188] Specific examples

[1189] For example, if a base station in a certain area is damaged by a typhoon, the server will collect the latest road and local information, calculate the restoration priority, and then use the navigation system to provide the optimal route for autonomous vehicles, enabling rapid restoration work.

[1190] Prompt Sentence Examples

[1191] Below are some example prompts to input to a generative AI model:

[1192] To streamline disaster recovery efforts, design a navigation system that calculates recovery priorities by combining area coverage, the latest road information, and local information, and provides the optimal route. Use the following data:

[1193] Area coverage: https: / / api.example.com / area_coverage

[1194] Latest road information: https: / / api.example.com / road_info

[1195] Local information: https: / / api.example.com / local_info

[1196] In this way, specific modes for carrying out the invention can be provided.

[1197] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1198] Step 1:

[1199] The server collects information such as population and landmarks. This involves using a geographic information system (GIS) to obtain data that takes into account the area's topography and building distribution. The inputs are population data and landmark data, and the output is the geographic information needed for station planning.

[1200] Step 2:

[1201] The server designs base station locations based on the collected geographic information. Specifically, it uses GIS to calculate the optimal locations for base station installation. The input is the geographic information obtained in step 1, and the output is a base station installation plan.

[1202] Step 3:

[1203] The server assigns construction start priorities based on data such as coverage rates. This involves determining which areas to prioritize construction start in, taking into account area coverage rates and communication demand data. The inputs are area coverage rate data and communication demand data, and the output is a construction start priority list.

[1204] Step 4:

[1205] The server determines the optimal maintenance method based on the alert type. Specifically, it analyzes the types of warnings and notifications issued by the system and determines the appropriate maintenance method. The input is alert data, and the output is a list of maintenance methods.

[1206] Step 5:

[1207] In the event of a disaster such as a typhoon or earthquake, the server determines the restoration priority by integrating area coverage rate, the latest road information, and local information. This involves calculating which areas should be prioritized for restoration work based on road information and local information collected in real time. The inputs are area coverage rate data, the latest road information, and local information, and the output is a restoration priority list.

[1208] Step 6:

[1209] The server provides the optimal route to the navigation system installed in the autonomous vehicle based on the recovery priority list. Specifically, it calculates the optimal route using the recovery priority list and the latest road information and issues instructions to the autonomous vehicle. The inputs are the recovery priority list and the latest road information, and the output is the route information sent to the navigation system.

[1210] Step 7:

[1211] The terminal (autonomous vehicle) receives route information provided by the server and moves according to that route. Specifically, the navigation system analyzes the route information and issues instructions to the control system of the autonomous vehicle. The input is the route information sent from the server, and the output is the movement of the autonomous vehicle.

[1212] Step 8:

[1213] The user makes a recovery plan based on the information provided by the server. Specifically, the user decides on a recovery schedule by referring to the recovery priority list and route information from the navigation system. The inputs are the recovery priority list and route information provided by the server, and the output is a recovery plan.

[1214] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1215] "Example 1"

[1216] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and optimizes the communication environment based on this information. Specifically, emotions are recognized from the user's voice, facial expressions, behavioral patterns, etc., and the communication environment is optimized based on this information. For example, if it is recognized that the user is feeling anger or dissatisfaction, measures are taken to improve communication quality. Furthermore, if it is recognized that the user is feeling joy or satisfaction, the current communication environment is maintained.

[1217] "Example 2"

[1218] The emotion engine also adjusts communication quality according to the user's emotional state to optimize the user experience. For example, if it recognizes that the user is feeling stressed, it will take measures to increase communication speed. On the other hand, if it recognizes that the user is relaxed, it will moderately reduce communication speed to conserve communication resources.

[1219] "Example 3"

[1220] Furthermore, the emotion engine grasps the user's emotional state in real time and dynamically adjusts the communication environment according to changes. For example, if the user suddenly starts to feel angry, the communication quality is immediately improved. Similarly, if the user suddenly starts to feel happy, the communication quality is immediately optimized.

[1221] The processing flow of each embodiment will be described below.

[1222] "Example 1"

[1223] Step 1: Recognize emotions from the user's voice, facial expressions, behavioral patterns, etc.

[1224] Step 2: Optimize the communication environment based on the recognized emotional information.

[1225] Step 3: For example, if it is recognized that the user is feeling angry or frustrated, measures are taken to improve the quality of communication.

[1226] Step 4: If it is determined that the user is happy or satisfied, the current communication environment is maintained.

[1227] "Example 2"

[1228] Step 1: Adjust communication quality according to the user's emotional state to optimize the user experience.

[1229] Step 2: For example, if it is recognized that the user is feeling stressed, measures are taken to improve communication speed.

[1230] Step 3: If the system recognizes that the user is relaxed, it moderately reduces the communication speed to conserve communication resources.

[1231] "Example 3"

[1232] Step 1: The emotion engine grasps the user's emotional state in real time and dynamically adjusts the communication environment according to changes.

[1233] Step 2: For example, if a user suddenly starts to feel angry, immediately improve the communication quality.

[1234] Step 3: Also, if the user suddenly starts to feel happy, the communication quality is instantly optimized.

[1235] Example 1

[1236] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1237] In the past, processes for mobile networks, from design to construction, maintenance, and disaster response, were often managed separately, making efficient operation difficult. Furthermore, the communication environment was not optimized with user feelings in mind, making it difficult to improve user satisfaction. Furthermore, while rapid recovery was required in the event of a disaster, there was insufficient use of the latest information to determine recovery priorities.

[1238] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1239] In this invention, the server includes: means for designing base station locations based on information such as population and landmarks; means for assigning construction start priorities based on factors such as coverage rates and efficiently promoting area construction; means for determining optimal maintenance methods based on alert types; means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information; means for recognizing emotions from user voices, facial expressions, behavioral patterns, and the like, and optimizing the communication environment based on that information; and means for operating on the cloud and for centrally controlling everything from mobile network design to construction, maintenance, and disaster response. This enables efficient operation of mobile networks, improved user satisfaction, and rapid restoration in the event of a disaster.

[1240] "Population" refers to the total number of people living in a particular area.

[1241] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[1242] "Base station planning" refers to the process of planning and designing the placement of base stations for mobile networks.

[1243] "Coverage rate" refers to the percentage of a geographic area covered by a mobile network.

[1244] "Construction priority" refers to the priority of which area of ​​a construction project should begin construction first.

[1245] "Alert type" refers to the type of warning or notification issued by the system.

[1246] "Maintenance method" refers to the maintenance techniques used to ensure the normal operation of systems and equipment.

[1247] "Area coverage rate" refers to the percentage of mobile network coverage in a particular area.

[1248] "Latest road information" refers to current road conditions and traffic information.

[1249] "Local information" refers to the latest conditions and data for a specific area.

[1250] "Recovery priority" refers to the priority of which areas should be given priority for recovery in the event of a disaster.

[1251] "Emotion recognition" refers to the process of analyzing and recognizing emotions from a user's voice, facial expressions, and behavioral patterns.

[1252] "Communication environment optimization" refers to the process of optimizing communication quality and network settings based on user usage and emotions.

[1253] "Cloud" refers to a collection of computer resources and services provided over the Internet.

[1254] "Mobile network" refers to a network that provides mobile communications using wireless communication.

[1255] "Centralized control" refers to the centralized management and control of multiple processes and functions.

[1256] This invention is a system that provides integrated control over everything from mobile network design to construction, maintenance, and disaster response. It includes a server running on the cloud and a terminal that recognizes user emotions and optimizes the communication environment.

[1257] System configuration

[1258] 1. Server

[1259] The server runs on the cloud and provides centralized control over everything from mobile network design to construction, maintenance, and disaster response.

[1260] The server collects population data and landmark information and uses this information to design base station locations. Specifically, it manages this information using a database (e.g., PostgreSQL) and uses an algorithm (e.g., K-means clustering) to determine the optimal placement of base stations.

[1261] The server analyzes the coverage data and assigns construction start priorities, allowing for efficient area construction. Data analysis tools (e.g., Python's Pandas library) are used for the analysis.

[1262] The server analyzes the alert type and determines the optimal maintenance method. Alert analysis uses a machine learning model (e.g., TensorFlow).

[1263] In the event of a disaster such as a typhoon or earthquake, the server prioritizes recovery by integrating area coverage, the latest road information, and local information. This ensures smooth recovery work. APIs (e.g., Google Maps API) are used to collect data.

[1264] 2. Terminal

[1265] The device collects the user's voice, facial expressions, and behavioral patterns using hardware such as a microphone and camera.

[1266] The device sends the collected data to the cloud using a mobile network (e.g., 4G, 5G).

[1267] 3. Users

[1268] Users provide voice, facial expressions, and behavioral patterns through their devices, which allows the system to recognize the user's emotions and optimize the communication environment.

[1269] Specific examples

[1270] For example, when installing a new base station in a certain area, the server collects population density data and landmark information for that area to determine the optimal location for the base station. It then analyzes coverage data to determine which area construction should begin in. In the event of a disaster, the server uses the latest road and local information to determine which areas to prioritize for restoration.

[1271] If a user expresses dissatisfaction during a video call, the device captures their facial expressions with a camera and collects audio data with a microphone. These data are then sent to the cloud, where the server analyzes the user's emotions using an emotion recognition algorithm. If the server determines that the user is dissatisfied, it takes measures to improve the quality of the call.

[1272] Prompt Sentence Examples

[1273] "If users are frustrated during video calls, please explain how you would improve the quality of the call."

[1274] "Please explain how you determine restoration priorities based on area coverage and the latest road information in the event of a disaster."

[1275] This will enable efficient operation of mobile networks, improved user satisfaction, and rapid recovery in the event of a disaster.

[1276] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1277] A system that comprehensively controls everything from mobile network design to construction, maintenance, and disaster response

[1278] Processing Steps

[1279] Step 1:

[1280] The server collects demographic data and landmark information.

[1281] Input: Population data and landmark information from public government databases and geographic information systems (GIS)

[1282] Data processing: Save to a database (e.g., PostgreSQL) and convert to the required format

[1283] Output: Formatted population data and landmark information

[1284] Specific operation: The server retrieves data through the API and stores it in the database.

[1285] Step 2:

[1286] The server calculates the optimal placement of base stations based on the collected population data and landmark information.

[1287] Input: Formatted population data and landmark information

[1288] Data calculation: Determine the best base station location using K-means clustering algorithm

[1289] Output: Optimal base station placement information

[1290] Specific operation: The server executes the clustering algorithm and plots the calculation results on a map.

[1291] Step 3:

[1292] The server analyzes the coverage data and assigns construction start priorities.

[1293] Input: Coverage data

[1294] Data Calculation: Analyze and prioritize data using Python's Pandas library

[1295] Output: Construction priority list

[1296] Specific operation: The server analyzes the coverage data using a data analysis tool and lists high-priority areas.

[1297] Step 4:

[1298] The server analyzes the alert type and determines the most appropriate maintenance method.

[1299] Input: Alert data

[1300] Data Computing: Using machine learning models (e.g., TensorFlow) to classify alerts and suggest appropriate maintenance actions

[1301] Output: Maintenance method list

[1302] Specific operation: The server analyzes alert data using a machine learning model and determines maintenance methods.

[1303] Step 5:

[1304] In the event of a disaster such as a typhoon or earthquake, the server will determine recovery priorities by combining area coverage rate, the latest road information, and local information.

[1305] Input: Area coverage data, latest road information, local information

[1306] Data calculation: Uses Google Maps API to obtain the latest road information and create restoration plans

[1307] Output: Recovery priority list

[1308] Specific operation: The server obtains the latest road information through the API and creates a restoration plan.

[1309] A system that combines emotion engines

[1310] Processing Steps

[1311] Step 1:

[1312] The device collects the user's voice, facial expressions, and behavioral patterns.

[1313] Input: User's voice data, facial expression data, behavioral pattern data

[1314] Data processing: Capture data using a microphone or camera and temporarily store it in local storage

[1315] Output: Collected emotion data

[1316] Specific operation: The device uses the microphone and camera to collect user data and stores it in local storage.

[1317] Step 2:

[1318] The device sends the collected emotion data to the cloud.

[1319] Input: Collected emotion data

[1320] Data processing: Send data to the cloud using mobile networks (e.g., 4G, 5G)

[1321] Output: Emotion data sent to the cloud

[1322] Specific operation: The device uses the mobile network to send emotion data to the cloud and confirms the success of the transmission.

[1323] Step 3:

[1324] The server analyzes the data received on the cloud.

[1325] Input: Emotion data sent to the cloud

[1326] Data Computing: Analyze user emotions using emotion recognition algorithms (e.g., models combining OpenCV and TensorFlow)

[1327] Output: Parsed emotion data

[1328] Specific operation: The server runs the emotion recognition algorithm and stores the analysis results in a database.

[1329] Step 4:

[1330] The server optimizes the communication environment based on the analysis results.

[1331] Input: Parsed emotion data

[1332] Data calculations: If users are angry or frustrated, take action such as increasing network bandwidth.

[1333] Output: Optimized communication environment

[1334] Specific operation: The server adjusts the network settings based on the analysis results to improve communication quality.

[1335] (Application example 1)

[1336] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1337] In conventional mobile network design, construction, maintenance, and disaster response, each process is managed separately, making efficient operation difficult. Furthermore, when managing robots and machinery in factories, it is difficult to provide optimal communication and working environments that take into account the emotions of workers. This can hinder overall production efficiency and rapid response in the event of a disaster.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1339] In this invention, the server includes: means for designing base station locations based on information such as population and landmarks; means for assigning construction start priorities based on factors such as coverage rates and efficiently promoting area construction; means for determining optimal maintenance methods based on alert types; means for prioritizing restoration efforts and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information; means for recognizing user emotions using an emotion engine and optimizing the communication environment based on that information; means for centrally controlling the design, placement, maintenance, and disaster response of robots and machines in a factory via the cloud; and means for recognizing worker emotions and optimizing the communication and work environments in the factory based on that information. This enables efficient management of mobile networks and robots and machines in a factory, and the provision of optimal communication and work environments that take the emotions of workers into consideration.

[1340] "Population" refers to the total number of people living in a particular area.

[1341] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[1342] "Base station design" refers to the design process for placing mobile network base stations in optimal locations.

[1343] "Coverage rate" refers to the percentage of a geographic area covered by a mobile network.

[1344] "Construction priority" refers to the order of precedence used to determine the order in which construction projects begin.

[1345] "Alert type" refers to the type of warning or notification issued by the system.

[1346] "Maintenance method" refers to the procedures and methods for maintaining the normal operation of a system or machine.

[1347] "Area coverage rate" refers to the percentage of mobile network coverage in a particular area.

[1348] "Latest road information" refers to current road conditions and traffic information.

[1349] "Local information" refers to up-to-date information about a particular area.

[1350] "Recovery priority" refers to the priority order used to determine the order of recovery work when a disaster or failure occurs.

[1351] An "emotion engine" refers to technology that recognizes a user's emotions and optimizes the system based on that information.

[1352] "Communication environment" refers to the physical and logical environment in which data communication takes place.

[1353] "Factory robots" refer to automated machinery that performs work in factories.

[1354] "Machine design" refers to the process of planning the structure and function of a machine and compiling it into drawings and specifications.

[1355] "Placement" refers to the placement of an object or facility in a specific location.

[1356] "Disaster response" refers to the countermeasures and recovery work carried out when natural or man-made disasters occur.

[1357] "Cloud" refers to computer resources and services provided over the Internet.

[1358] "Worker emotions" refers to the emotional state of people working in a factory.

[1359] "Work environment" refers to the physical and psychological environment in which work is performed.

[1360] As an embodiment of the present invention, the following system is constructed. The server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on the coverage rate and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing recovery and carrying out smooth recovery work in the event of a typhoon, earthquake, etc. by integrating the area coverage rate, the latest road information, and local information, etc., means for recognizing user emotions using an emotion engine and optimizing the communication environment based on that information, means for collectively controlling the design, placement, maintenance, and disaster response of robots and machines in a factory via the cloud, and means for recognizing worker emotions and optimizing the communication environment and work environment in the factory based on that information.

[1361] Hardware and software used

[1362] Cloud API: A cloud service for centralized control of factory design, layout, maintenance, and disaster response.

[1363] Emotion engine: Software for recognizing worker emotions.

[1364] Network Optimizer: Software for optimizing the communication environment based on emotional information.

[1365] Data processing and calculation

[1366] 1. Factory layout design: The server inputs population data and landmark data into the cloud API and designs the optimal factory layout.

[1367] 2. Assigning construction priority: The server assigns construction priority via cloud API based on coverage data.

[1368] 3. Obtaining a maintenance plan: The server inputs the alert type into the cloud API and obtains the optimal maintenance method.

[1369] 4. Obtain disaster response plan: The server inputs coverage data, road information, and local information into the cloud API to obtain a plan with recovery priorities.

[1370] 5. Optimization of the communication environment: The server inputs the worker's voice and facial expression data into the emotion engine, and the network optimizer optimizes the communication environment based on the recognized emotion information.

[1371] Specific examples

[1372] Factory layout design: Concentrate machines in densely populated areas to create efficient production lines.

[1373] Construction Priority: Prioritize construction in areas with low coverage to improve overall production efficiency.

[1374] Obtain maintenance plans: When a machine breakdown alert occurs, quickly obtain the optimal maintenance method and minimize downtime.

[1375] Obtain a disaster response plan: In the event of a typhoon or earthquake, quickly develop a recovery plan to quickly resume factory operations.

[1376] Optimizing the communication environment: When workers are stressed, improve communication quality and maintain work efficiency.

[1377] Prompt Sentence Examples

[1378] Design the optimal factory layout based on population and landmark data.

[1379] Assign construction priorities based on coverage data.

[1380] Obtain the optimal maintenance method based on the alert type.

[1381] Get a prioritized recovery plan based on coverage data, road information, and local information.

[1382] Optimize the communication environment based on the voice and facial expression data of workers.

[1383] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1384] Step 1:

[1385] The server inputs population data and landmark data into the cloud API, which then designs the optimal factory layout taking into account the local topography and building distribution. The input data is population density and landmark location information, and the output is an optimal layout diagram. Specifically, the server retrieves population data and landmark data from the database and sends it to the cloud API.

[1386] Step 2:

[1387] The server inputs coverage data into the cloud API and assigns construction priorities. The cloud API analyzes the coverage data and determines which areas should be prioritized for construction. The input data is coverage information, and the output is a construction priority list. Specifically, the server collects coverage data and sends it to the cloud API.

[1388] Step 3:

[1389] The server inputs the alert type into the cloud API and obtains the optimal maintenance method. The cloud API analyzes the alert type and provides the appropriate maintenance procedure. The input data is the alert type, and the output is a maintenance plan. Specifically, the server monitors alert information and sends it to the cloud API.

[1390] Step 4:

[1391] The server inputs coverage data, road information, and local information into the cloud API and obtains a recovery plan with priority. The cloud API comprehensively analyzes this data and provides a disaster recovery plan. The input data are coverage, road information, and local information, and the output is a recovery priority list. Specifically, the server collects this data and sends it to the cloud API.

[1392] Step 5:

[1393] The server inputs the worker's voice and facial expression data into the emotion engine, and the network optimizer optimizes the communication environment based on the recognized emotional information. The emotion engine analyzes the voice and facial expression data and recognizes the worker's emotional state. The input data is voice and facial expression data, and the output is emotional information and an optimized communication environment. Specifically, the server collects the worker's voice and facial expression data and sends it to the emotion engine.

[1394] Step 6:

[1395] The server centrally controls the design, placement, maintenance, and disaster response of robots and machines in the factory via the cloud. The cloud API uses this information to provide optimal design, placement, and maintenance plans. The input data is information about the robots and machines, and the output is optimal design drawings, placement drawings, and maintenance plans. Specifically, the server collects information about the robots and machines and sends it to the cloud API.

[1396] Step 7:

[1397] The server recognizes the emotions of workers and optimizes the communication and work environments within the factory based on that information. The emotion engine analyzes the emotional state of the workers, and the network optimizer optimizes the communication environment. The input data is the workers' emotional information, and the output is an optimized communication and work environment. Specifically, the server receives the emotional information from the emotion engine and sends it to the network optimizer.

[1398] Example 2

[1399] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1400] In the past, each process in the design, construction, maintenance, and disaster response of mobile networks was managed separately, making it difficult to efficiently build service areas and quickly respond to disasters. Furthermore, communication quality was not adjusted according to the user's emotional state, and the user experience was not fully optimized. This resulted in problems such as poor communication quality and wasted resources.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1402] In this invention, the server includes a means for designing base stations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently proceed with area construction, a means for determining the optimal maintenance method based on alert types, a means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, and local information, and a means for adjusting communication quality according to the user's emotional state. This makes it possible to efficiently manage everything from mobile network design to construction, maintenance, and disaster response in an integrated manner, optimizing the user experience.

[1403] "Population" refers to the total number of people living in a particular area.

[1404] A "landmark" refers to a building or structure that serves as a landmark in a particular area.

[1405] "Base station design" refers to a plan for placing mobile network base stations in optimal locations.

[1406] "Coverage rate" refers to the percentage of a particular area to which a mobile network can provide communication services.

[1407] "Construction priority" refers to the priority of which area construction will begin first in an area construction project.

[1408] "Alert type" refers to the type of warning or notification issued by the system.

[1409] "Maintenance Method" refers to the means for maintaining and managing mobile network facilities and systems.

[1410] "Area coverage rate" refers to the percentage of a specific area in which a mobile network can provide communication services.

[1411] "Latest road information" refers to current road conditions and traffic information.

[1412] "Local information" refers to the latest conditions and data for a specific area.

[1413] "Recovery priority" refers to the priority of which areas should undergo recovery work first in the event of a disaster.

[1414] "User's emotional state" refers to the psychological state, such as stress or relaxation, that the user feels.

[1415] "Communication quality" refers to the performance and stability of the communication services provided by mobile networks.

[1416] "Mobile network" refers to a network that provides mobile communication services using wireless communication.

[1417] "Cloud" refers to computer resources and services provided over the Internet.

[1418] This invention is a system that efficiently manages everything from mobile network design to construction, maintenance, and disaster response in an integrated manner, optimizing the user experience. Specific embodiments of this system are described below.

[1419] Embodiment of station placement design

[1420] The server uses a geographic information system (GIS) to analyze the area's topography and the distribution of buildings. Specifically, it uses GIS software such as geographic information system software. This allows it to obtain information on densely populated areas and areas around landmarks, and then plans the optimal placement of base stations. For example, in urban areas with many high-rise buildings, it places base stations taking into account the influence of the buildings to maximize coverage.

[1421] Examples:

[1422] Software used: Geographic Information System software

[1423] Data: Urban terrain data, building distribution data

[1424] Processing content: Optimal placement of base stations in urban areas with many high-rise buildings

[1425] Example prompt sentence:

[1426] "Use geographic information system software to analyze topographical and building distribution data to plan optimal placement of base stations in urban areas with many high-rise buildings."

[1427] Embodiment of communication quality adjustment by emotion engine

[1428] The device is equipped with an emotion engine to analyze the user's emotional state in real time. This emotion engine analyzes the user's facial expressions, voice, input data, etc. to determine whether the user is feeling stressed or relaxed. For example, if the device recognizes that the user is feeling stressed, it will take measures to increase the communication speed. Conversely, if the device recognizes that the user is relaxed, it will moderately reduce the communication speed to conserve communication resources.

[1429] Examples:

[1430] Software used: Sentiment analysis engine

[1431] Data: User facial expression data, voice data, input data

[1432] What it does: Improve communication speed when the user is stressed

[1433] Example prompt sentence:

[1434] "Use an emotion analysis engine to analyze the user's facial expression and voice data so that if it detects that the user is stressed, it will take steps to improve communication speed."

[1435] Disaster response implementation

[1436] The server collects disaster information such as typhoons and earthquakes, and dynamically updates recovery priorities by combining area coverage rates, the latest road information, and local information, enabling fast and efficient recovery work.

[1437] Examples:

[1438] Software used: Disaster Information Analysis System

[1439] Data: disaster information, communication demand data, road information

[1440] Processing content: Dynamic update of recovery priority when a disaster occurs

[1441] Example prompt sentence:

[1442] "When a disaster occurs, analyze disaster information and communication demand data and dynamically update recovery priorities."

[1443] In this way, the server and terminals work together to efficiently manage everything from mobile network design to construction, maintenance, and disaster response, optimizing the user experience.

[1444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1445] Channel placement design processing

[1446] Step 1: Gather geographic information

[1447] The server uses geographic information system software to collect local topographic and building distribution data, using satellite imagery, map data, and population density data as input, and stores this data in a database.

[1448] Specific behavior:

[1449] The server uses the API of the geographic information system software to download the latest geographic information.

[1450] The server stores the collected data in a database.

[1451] Step 2: Analyze the data

[1452] The server analyzes the collected geographic information to identify the locations of densely populated areas and landmarks, using the stored geographic information data as input and obtaining the analysis results as output.

[1453] Specific behavior:

[1454] The server analyzes the population density data and maps high density areas.

[1455] The server identifies the location of the landmark and analyzes the distribution of buildings around it.

[1456] Step 3: Base station layout planning

[1457] The server then plans the optimal placement of base stations based on the analysis results. It uses the analysis results as input and obtains a list of candidate base station locations as output.

[1458] Specific behavior:

[1459] The server lists candidate locations for the base station.

[1460] The server simulates the coverage rate of each candidate location and determines the optimal placement.

[1461] Adjusting communication quality using an emotion engine

[1462] Step 1: Collecting user emotion data

[1463] The device collects the user's facial expressions, voice, and input data in real time. As input, it uses data from input devices such as a camera, microphone, and keyboard, and as output, it obtains collected emotional data.

[1464] Specific behavior:

[1465] The device uses a camera to capture the user's facial expressions.

[1466] The terminal uses a microphone to record the user's voice.

[1467] The terminal collects input data from a keyboard or touch screen.

[1468] Step 2: Sentiment Analysis

[1469] The terminal sends the collected data to an emotion analysis engine to analyze the user's emotional state. It uses the collected emotion data as input and obtains the emotion analysis result as output.

[1470] Specific behavior:

[1471] The terminal transmits facial expression data and voice data to the emotion analysis engine.

[1472] The sentiment analysis engine determines whether the user is stressed or relaxed.

[1473] Step 3: Adjust communication quality

[1474] The terminal adjusts the communication quality based on the result of the emotion analysis, using the emotion analysis result as input and obtaining the adjusted communication quality as output.

[1475] Specific behavior:

[1476] The terminal issues instructions to the communication module to adjust the communication speed.

[1477] The terminal maximizes communication speed when the user is stressed.

[1478] When the user is relaxed, the terminal reduces the communication speed appropriately.

[1479] Disaster response processing

[1480] Step 1: Collect disaster information

[1481] The server collects disaster information, and synthesizes area coverage, the latest road information, and local information. It uses disaster information data as input and obtains the collected disaster information as output.

[1482] Specific behavior:

[1483] The server acquires the latest disaster information from the disaster information service.

[1484] The server stores the collected disaster information in a database.

[1485] Step 2: Dynamic update of recovery priority

[1486] The server analyzes the collected disaster information and communication demand data and dynamically updates the recovery priority. It uses the disaster information and communication demand data as input and obtains the updated recovery priority as output.

[1487] Specific behavior:

[1488] The server analyzes disaster information and communication demand data.

[1489] The server dynamically updates the recovery priorities and optimizes the recovery plan.

[1490] (Application example 2)

[1491] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1492] Conventional mobile network systems require efficient methods for base station placement design and disaster recovery. Furthermore, optimizing communication quality is important for autonomous vehicles, and it is particularly important to adjust communication quality according to the emotional state of passengers. However, no system exists that meets these requirements, making it difficult to build and operate an efficient and flexible communication network.

[1493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1494] In this invention, the server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, local information, etc., means for selecting the nearest base station from the current location of the autonomous vehicle, and means for adjusting communication quality according to the emotional state of passengers. This enables efficient placement of base stations and rapid restoration in the event of a disaster, and further enables autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[1495] "Population" is the total number of people living in a particular area.

[1496] A "landmark" is a building or structure that serves as a landmark in a particular area.

[1497] "Base station design" is the process of planning and deciding on the installation locations of communication base stations.

[1498] "Coverage rate" is the percentage of a geographic area that a communications network covers.

[1499] "Construction priority" is a priority for determining the order in which construction projects are started.

[1500] "Alert type" refers to the type of warning or notification issued by the system.

[1501] "Maintenance methods" are the means of maintaining and managing systems and equipment to ensure their normal operation.

[1502] A "typhoon" is a type of tropical cyclone, a natural phenomenon accompanied by strong winds and heavy rain.

[1503] An "earthquake" is a vibration phenomenon caused by sudden movement of the earth's crust.

[1504] "Area coverage rate" refers to the percentage of a specific area that is covered by a communications network.

[1505] "Latest road information" refers to the latest data on current road conditions.

[1506] "Local information" is detailed information about a specific area.

[1507] "Restoration priority" is a priority order for determining the order of restoration work in the event of a disaster or the like.

[1508] An "autonomous vehicle" is a vehicle that drives autonomously without driver intervention.

[1509] A "base station" is a facility for relaying communications in a wireless communication network.

[1510] An "emotional state" is an individual's psychological state or mood.

[1511] "Communication quality" is an index that indicates the performance and reliability of a communication service.

[1512] A system for carrying out this invention is configured as follows: The server includes means for designing base station locations based on information such as population and landmarks, means for assigning construction start priorities based on factors such as coverage rates to efficiently advance area construction, means for determining the optimal maintenance method based on alert types, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information, means for selecting the nearest base station from the current location of an autonomous vehicle, and means for adjusting communication quality according to the emotional state of passengers.

[1513] Program processing explanation

[1514] The server uses a geographic information system (GIS) to design base stations, taking into account the area's topography and the distribution of buildings. Specifically, it analyzes GIS data and allocates more base stations in densely populated areas and around landmarks to ensure coverage. It also determines the priority of construction work for area construction based on coverage rate and communication demand.

[1515] The server then determines the optimal maintenance method based on the alert type. For example, if a communication failure occurs, it selects the optimal maintenance method based on the cause. Furthermore, in the event of a disaster such as a typhoon or earthquake, the system dynamically updates the recovery priority by combining area coverage rate, the latest road information, and local information, allowing for rapid recovery work.

[1516] For autonomous vehicles, the server uses a GPS module to obtain the vehicle's current location and selects the nearest base station. It also analyzes the passenger's emotional state using an emotion engine and adjusts communication quality accordingly. For example, if the passenger is stressed, it increases communication speed, and if the passenger is relaxed, it saves communication resources.

[1517] Specific examples

[1518] As a concrete example, consider a self-driving vehicle in Tokyo. The server obtains the vehicle's current location and selects the nearest base station. Next, if the emotion engine recognizes the passenger's emotional state as "stressed," it sets the communication quality to "high."

[1519] An example of a prompt sentence is as follows:

[1520] Design a system that selects the optimal communication base station based on the terrain and building distribution of the area where the autonomous vehicle will be traveling, and also adjusts communication quality according to the passenger's emotional state. For example, if the vehicle is in Tokyo, it will find the nearest base station and set communication quality higher if the passenger is feeling stressed.

[1521] This will enable efficient deployment of base stations and rapid recovery in the event of a disaster, and will also enable autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[1522] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1523] Step 1:

[1524] The server uses a geographic information system (GIS) to obtain information on the area's topography and building distribution. It uses GIS data as input and obtains information on the area's topography and building distribution as output. Based on this information, it designs base stations to place many in densely populated areas and around landmarks.

[1525] Step 2:

[1526] The server determines the priority of area construction based on the coverage rate and communication demand. It uses coverage rate data and communication demand data as input and obtains construction priority as output. This allows area construction to proceed efficiently.

[1527] Step 3:

[1528] The server determines the optimal maintenance method based on the alert type. It uses alert data as input and obtains the optimal maintenance method as output. For example, if a communication failure occurs, it selects the optimal maintenance method based on the cause of the failure.

[1529] Step 4:

[1530] The server dynamically updates the recovery priority by integrating area coverage rate, the latest road information, and local information during disasters such as typhoons and earthquakes. It uses disaster information, area coverage rate data, the latest road information, and local information as inputs, and obtains the recovery priority as output. This enables rapid recovery work.

[1531] Step 5:

[1532] The server obtains the current location of the autonomous vehicle using a GPS module. It uses GPS data as input and gets the current location of the vehicle as output. Based on this location information, it selects the nearest base station.

[1533] Step 6:

[1534] The server analyzes the passenger's emotional state using an emotion engine. It uses the passenger's biometric and behavioral data as input and obtains the passenger's emotional state as output. For example, if the passenger is feeling stressed, it will recognize the emotion as "stress."

[1535] Step 7:

[1536] The server adjusts the communication quality according to the passenger's emotional state. It uses the emotional state data as input and obtains the communication quality setting as output. For example, it increases the communication speed if the passenger is stressed and saves communication resources if the passenger is relaxed.

[1537] This will enable efficient deployment of base stations and rapid recovery in the event of a disaster, and will also enable autonomous vehicles to provide optimal communication quality according to the emotional state of passengers.

[1538] Example 3

[1539] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1540] There is a need for rapid and efficient restoration of mobile networks in the event of a disaster, as well as dynamic optimization of the communication environment according to the user's emotional state. Conventional systems do not adequately prioritize restoration work during a disaster, nor do they adjust the communication environment according to the user's emotional state, resulting in problems such as reduced communication quality and delays in restoration work.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1542] In this invention, the server includes means for designing base station placement from information such as population and landmarks, means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction, means for determining the optimal maintenance method from the alert type, means for prioritizing restoration and carrying out smooth restoration work in the event of a disaster by integrating area coverage rates, the latest road information, and local information, and means for detecting the user's emotional state in real time and dynamically adjusting the communication environment in response to changes in that state. This enables fast and efficient restoration work in the event of a disaster and optimization of the communication environment in response to the user's emotional state.

[1543] "Population" refers to the total number of people living in a particular area.

[1544] A "landmark" refers to a building or natural object that serves as a landmark in a particular area.

[1545] "Base station design" refers to a plan for placing mobile network base stations in optimal locations.

[1546] "Coverage rate" refers to the percentage of the area within which communication is possible.

[1547] "Construction priority" refers to the priority for starting construction of an area.

[1548] "Alert type" refers to the type of warning or notification that occurs in the system.

[1549] "Maintenance methods" refer to the means of maintaining and managing systems and equipment to ensure their normal operation.

[1550] "Area coverage rate" refers to the percentage of the area within which communication is possible within a specific area.

[1551] "Latest road information" refers to information about the current road conditions and passability.

[1552] "Local information" refers to up-to-date situational and environmental information about a particular area.

[1553] "Recovery prioritization" refers to determining the order of priority when carrying out recovery work in the event of a disaster.

[1554] "User's emotional state" refers to the type and intensity of the emotion the user is feeling.

[1555] "Communication environment" refers to the quality and settings of the communication services used by the user.

[1556] "Dynamic adjustment" refers to changing settings and parameters in real time depending on the situation.

[1557] "Mobile network" refers to a network that transmits and receives data using wireless communication.

[1558] "Centralized control" refers to the centralized management and operation of multiple functions and processes.

[1559] This invention is a system for realizing rapid and efficient recovery work in the event of a disaster and dynamic optimization of the communication environment according to the emotional state of the user. A specific embodiment of this system is described below.

[1560] Prioritizing disaster recovery efforts

[1561] The server collects the latest road and local information for the area where the disaster occurred. This information is obtained using a map service API. The server calculates the area coverage rate based on the collected information. The area coverage rate indicates the percentage of the area where communication is possible.

[1562] Next, the server combines the area coverage rate, the latest road information, and local information to determine the priority of the restoration work. For example, it may prioritize restoration work starting from accessible base stations. The server then notifies the restoration work team of the results of the prioritization. Notifications are sent via email or a dedicated restoration management system.

[1563] As a concrete example, if base station A and base station B are damaged by a typhoon, base station A is accessible, but the road to base station B is closed. In this case, the server will instruct base station A to take priority in carrying out restoration work.

[1564] Example prompt sentence:

[1565] "A typhoon disaster has occurred. Please determine the restoration priority for base station A and base station B based on the latest road and local information."

[1566] Dynamic adjustment of communication environment using emotion engine

[1567] The device detects the user's emotional state in real time using facial recognition and voice analysis technology using a camera and microphone. Specifically, it uses cloud service Emotion APIs and voice analysis APIs (for example, Microsoft Azure's Emotion API and Google Cloud's Speech-to-Text API).

[1568] The device transmits the detected emotional state to the server. The server dynamically adjusts the communication environment based on the received emotional state. For example, if the user begins to feel angry, the server increases the bandwidth to improve communication quality. The server then notifies the device of the adjusted communication environment.

[1569] For example, if a user suddenly becomes angry during a video call, the device detects this emotion and sends it to the server, which then increases the bandwidth of the video call to improve communication quality.

[1570] Example prompt sentence:

[1571] "A user has started experiencing anger during a video call. Please make appropriate adjustments to improve communication quality."

[1572] This system makes it possible to realize efficient recovery operations in the event of a disaster and to optimize the communication environment in accordance with the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1573] Prioritizing disaster recovery efforts

[1574] Step 1:

[1575] The server collects the latest road and local information for the area where the disaster occurred.

[1576] Input: Specify the disaster area

[1577] Specific operation: The server calls the map service API to obtain road information and local information for the specified area.

[1578] Output: Dataset of up-to-date road and local information

[1579] Step 2:

[1580] The server calculates the area coverage rate based on the collected information.

[1581] Input: Latest road and local information dataset

[1582] Specific operation: The server analyzes the operating status and communication range of base stations within the area and calculates the area coverage rate.

[1583] Output: Area coverage rate numerical data

[1584] Step 3:

[1585] The server combines area coverage rate, the latest road information, and local information to determine the priority of recovery efforts.

[1586] Input: Area coverage rate data, latest road and local information dataset

[1587] Specific operation: The server executes a prioritization algorithm to prioritize recovery efforts from accessible base stations.

[1588] Output: Priority list of recovery efforts

[1589] Step 4:

[1590] The server notifies the recovery team of the results of the prioritization.

[1591] Input: Priority list of recovery efforts

[1592] Specific operation: The server notifies the recovery team of the results of the prioritization via email or a dedicated recovery management system.

[1593] Output: Message to notify the recovery team

[1594] Dynamic adjustment of communication environment using emotion engine

[1595] Step 1:

[1596] The device detects the user's emotional state in real time.

[1597] Input: User's facial image and voice data

[1598] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice, and sends them to cloud service Emotion APIs or voice analysis APIs (for example, Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API).

[1599] Output: User's emotional state data

[1600] Step 2:

[1601] The device transmits the detected emotional state to the server.

[1602] Input: User's emotional state data

[1603] Specific operation: The terminal uses a communication protocol to transmit emotional state data to the server.

[1604] Output: Send emotional state data to the server

[1605] Step 3:

[1606] The server dynamically adjusts the communication environment based on the received emotional state.

[1607] Input: User's emotional state data

[1608] Specific operation: The server analyzes the emotional state data and changes the network settings to improve communication quality, for example, by increasing the bandwidth.

[1609] Output: Adjusted communication environment settings

[1610] Step 4:

[1611] The server notifies the terminal of the adjusted communication environment.

[1612] Input: Adjusted communication environment settings

[1613] Specific operation: The server generates and sends a message to notify the terminal of changes in the communication environment.

[1614] Output: Notification message to terminal

[1615] In this way, by clearly indicating the specific operations and inputs / outputs at each processing step, it is possible to achieve efficient recovery operations in the event of a disaster and optimize the communication environment according to the user's emotions.

[1616] (Application example 3)

[1617] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1618] The rapid and efficient restoration of communications infrastructure in the event of a disaster is extremely important to society. However, with conventional systems, it was difficult to determine restoration priorities that comprehensively considered area coverage, road information, and local information, which often resulted in delays to restoration work. In addition, the communications environment was not dynamically adjusted according to the emotional state of the workers, which led to problems with reduced work efficiency. Furthermore, the efficiency of disaster restoration work using autonomous vehicles has not been fully realized. A new system to solve these issues is needed.

[1619] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1620] In this invention, the server includes: means for designing base station placement from information such as population and landmarks; means for assigning construction start priorities based on coverage rates and the like to efficiently proceed with area construction; means for determining optimal maintenance methods from alert types; means for prioritizing restoration and carrying out smooth restoration work by integrating area coverage rates, the latest road information, local information, etc. in the event of a typhoon, earthquake, etc.; means for grasping the emotional state of workers in real time and dynamically adjusting the communication environment in response to changes; and means installed in autonomous vehicles for efficiently carrying out restoration work in the event of a disaster. This enables the rapid and efficient restoration of communication infrastructure in the event of a disaster.

[1621] "Population" is the total number of people living in a particular area.

[1622] A "landmark" is a building or structure that serves as a landmark in a particular area.

[1623] "Base station location design" is the process of planning and designing the installation locations of communication base stations.

[1624] "Coverage rate" is the percentage of the area covered by a communication base station.

[1625] "Construction start priority" refers to the priority given when starting construction work.

[1626] "Alert type" is a classification of different types of alarms or notifications.

[1627] "Maintenance methods" are methods for maintaining and managing communications infrastructure.

[1628] "Area coverage rate" refers to the percentage of a specific area to which communication services are provided.

[1629] "Latest road information" refers to the latest data on current road conditions.

[1630] "Local information" is detailed information about a specific area.

[1631] "Restoration priority" refers to the priority of restoration work in the event of a disaster.

[1632] "Emotional state" refers to an individual's current emotional or mood state.

[1633] The "communication environment" refers to the state of the network and infrastructure when communication takes place.

[1634] "Dynamic adjustment" means changing settings and parameters in real time depending on the situation.

[1635] An "autonomous vehicle" is a vehicle that drives autonomously without driver intervention.

[1636] "Disaster recovery work" refers to work to repair infrastructure and facilities damaged by a disaster.

[1637] The system for carrying out the present invention aims at rapid and efficient restoration of communication infrastructure in the event of a disaster. The system includes the following main means.

[1638] 1. Station location design means

[1639] The server plans and designs the locations of communication base stations based on information such as population and landmarks, and uses a geographic information system (GIS) to take into account the local topography and distribution of buildings to create the optimal base station design.

[1640] 2. Construction start priority assignment method

[1641] The server determines the priority of construction work based on information such as coverage rate, allowing area construction to proceed efficiently.

[1642] 3. Means for learning maintenance methods

[1643] The server determines the optimal maintenance method depending on the alert type, allowing for efficient maintenance and management of the communications infrastructure.

[1644] 4. Recovery Prioritization Methods

[1645] In the event of a disaster such as a typhoon or earthquake, the server determines recovery priorities by combining area coverage rates, the latest road information, and local information, enabling smooth recovery work.

[1646] 5. Means of understanding emotional state

[1647] The server grasps the emotional state of the worker in real time and dynamically adjusts the communication environment according to changes. Using an emotion engine, it improves communication quality when the worker feels angry, and optimizes communication quality when the worker feels happy.

[1648] 6. Disaster Recovery Using Autonomous Vehicles

[1649] The system installed in autonomous vehicles will efficiently carry out recovery operations in the event of a disaster. The vehicle will select the optimal route based on the latest road and local information provided by the server, and will quickly reach the scene.

[1650] Hardware and software used

[1651] Hardware: autonomous vehicles, communication modules, sensors

[1652] Software: Python, API, emotion engine, geographic information system (GIS)

[1653] Data processing and calculation

[1654] The server obtains base station, road, and local information through APIs, and combines this data to determine restoration priorities. It also uses an emotion engine to grasp the emotional state of workers in real time and dynamically adjust the communication environment.

[1655] Specific examples

[1656] For example, if a base station is damaged by a typhoon, the server will prioritize restoration work from accessible base stations based on the latest road and local information. If a worker suddenly becomes angry, the emotion engine will instantly improve communication quality.

[1657] Example prompts for generative AI models

[1658] "Based on the latest base station, road, and local information, prioritize disaster recovery efforts. Also, dynamically adjust the communication environment according to the emotional state of workers."

[1659] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1660] Step 1:

[1661] The server obtains base station information through API. It specifies the API endpoint as input and obtains data including the base station's location and damage level as output. This data is used to determine the subsequent restoration priority.

[1662] Step 2:

[1663] The server obtains the latest road information through the API. It specifies the API endpoint as input and obtains data including road passability and congestion information as output. This data is used to plan routes for restoration work.

[1664] Step 3:

[1665] The server obtains local information through API. It specifies an API endpoint as input and obtains data including damage status and accessibility as output. This data is used to determine recovery priorities.

[1666] Step 4:

[1667] The server determines restoration priorities by combining base station, road, and local information. It uses these data as inputs and generates a list of high-priority base stations as output. This list is used to plan restoration efforts.

[1668] Step 5:

[1669] The server uses an emotion engine to grasp the emotional state of the worker in real time. It uses data from the worker's biosensors as input and obtains the worker's emotional state (e.g., anger, joy) as output. This emotional state is used to adjust the communication environment.

[1670] Step 6:

[1671] The server dynamically adjusts the communication environment according to the worker's emotional state. It uses the worker's emotional state as input and changes the communication quality settings as output. For example, if the worker feels angry, it improves the communication quality.

[1672] Step 7:

[1673] The autonomous vehicle selects the optimal route based on the latest road and local information provided by the server. It uses this information as input and generates the optimal route as output. This route is used to quickly reach the site.

[1674] Step 8:

[1675] The autonomous vehicle follows the generated route to the scene and starts recovery work. It uses the optimal route as input and arrives at the scene and starts recovery work as output, thereby achieving efficient disaster recovery.

[1676] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1677] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1678] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1679] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1680] [Third embodiment]

[1681] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1682] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1683] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1684] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1685] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1686] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1687] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1688] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1689] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1690] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1691] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1692] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1693] "Example 1"

[1694] One embodiment of the present invention is a system that centrally controls everything from the design, construction, maintenance, and disaster response of a mobile network (base station) via a cloud via GTP. This system includes a means for designing base station locations based on information such as population and landmarks, a means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, a means for identifying optimal maintenance methods based on alert types, and a means for prioritizing restoration work in the event of a typhoon, earthquake, or other disaster by integrating area coverage rates, the latest road information, and local information to ensure smooth restoration work.

[1695] "Example 2"

[1696] Specifically, a geographic information system is used to design base stations, taking into account the topography and distribution of buildings in the area. For example, more base stations are placed in densely populated areas or around landmarks to ensure coverage.

[1697] "Example 3"

[1698] Furthermore, in disaster recovery work, recovery priorities are determined by integrating area coverage rates, the latest road information, local information, etc. For example, if a base station is damaged by a typhoon or earthquake, recovery work will be prioritized from the most accessible base station based on the latest road and local information. This will enable efficient recovery work.

[1699] The processing flow of each embodiment will be described below.

[1700] "Example 1"

[1701] Step 1: First, base station locations are designed based on information such as population and landmarks. A geographic information system is used to take into account the local topography and building distribution.

[1702] Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[1703] Step 3: After the base station is installed, determine the optimal maintenance method based on the alert type and perform regular maintenance.

[1704] Step 4: When a disaster such as a typhoon or earthquake occurs, area coverage rates, the latest road information, local information, etc. are combined to prioritize recovery efforts and ensure smooth recovery operations.

[1705] "Example 2"

[1706] Step 1: First, a geographic information system is used to design base stations, taking into account the local topography and the distribution of buildings. For example, more base stations are placed in densely populated areas or around landmarks. Step 2: Next, construction priority is assigned based on the coverage rate of the designed base stations, and area construction is carried out efficiently.

[1707] "Example 3"

[1708] Step 1: First, area coverage, the latest road information, local information, etc. are combined to determine restoration priorities.

[1709] Step 2: Next, restoration work is carried out by prioritizing accessible base stations based on the latest road and local information, thereby achieving efficient restoration work.

[1710] Example 1

[1711] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1712] There is a need for efficient and effective centralized control of mobile network design, construction, maintenance, and disaster response. In particular, the challenges are optimal base station placement taking into account population density and landmark locations, prioritizing construction based on coverage rates, proposing optimal maintenance methods according to alert types, and rapid recovery operations in the event of a disaster.

[1713] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1714] In this invention, the server includes: means for designing base station placement based on information such as population and landmarks; means for assigning construction start priorities based on coverage rates and other factors to efficiently proceed with area construction; means for determining the optimal maintenance method based on alert types; means for prioritizing restoration and carrying out smooth restoration work by integrating area coverage rates, the latest road information, and local information in the event of a typhoon, earthquake, or other disaster; means for analyzing population density and landmark locations using a geographic information system to design the optimal base station placement; means for analyzing coverage rates and population density using a data analysis tool to determine construction priorities; means for analyzing past alert data and maintenance history using a machine learning model to propose the optimal maintenance method; and means for collecting the latest road information and local information using a real-time data streaming tool to determine restoration priorities. This enables efficient and effective centralized control of everything from mobile network design to construction, maintenance, and disaster response.

[1715] "Population density" is an indicator that shows the number of people per unit area in a particular area.

[1716] A "landmark" refers to a building, natural object, or important point that serves as a landmark in a particular area.

[1717] "Base station planning" is the process of planning and designing the optimal placement of base stations in a mobile network.

[1718] "Coverage rate" is an indicator that indicates the percentage of the area in which mobile network signals reach in a particular area.

[1719] "Construction start priority" is a criterion for determining the priority when starting construction of a base station.

[1720] "Alert type" is a classification of the types of abnormalities and problems that occur during network operation.

[1721] "Maintenance methods" are repair and maintenance procedures performed to maintain normal network operation.

[1722] "Recovery priority" is a standard for determining the order of priority when carrying out recovery work in the event of a disaster or failure.

[1723] A "geographic information system" is an information system for collecting, analyzing, and displaying geographic data.

[1724] "Data analysis tools" are software and libraries used to analyze collected data and extract meaningful information.

[1725] A "machine learning model" is an algorithm or mathematical model that learns from data and makes predictions or classifications.

[1726] "Real-time data streaming tools" are software or platforms for collecting, processing, and distributing data in real time.

[1727] This invention is a system for collectively controlling everything from the design to the construction, maintenance, and disaster response of a mobile network. A specific embodiment of this system will be described below.

[1728] 1. Mobile Network Design

[1729] The server collects population density data and landmark location data from open data and commercial databases. The server then analyzes this data using geographic information system (GIS) software. Specifically, the server uses the software to visualize population density and landmark locations and design optimal cell tower placement.

[1730] 2. Construction prioritization

[1731] The server collects existing coverage data from the network operation database. Then, it uses data analysis tools (e.g., Python's Pandas library) to analyze data such as coverage and population density to determine construction priorities. Finally, the server generates a priority list and provides it to the construction team.

[1732] 3. Optimizing maintenance methods

[1733] The server collects alert data from the network using a network monitoring system. Then, the server uses a machine learning model (e.g., TensorFlow) to analyze past alert data and maintenance history and propose the optimal maintenance method. The server notifies the maintenance team of the maintenance method, including specific steps.

[1734] 4. Disaster response

[1735] The server collects the latest road and local information in real time during a disaster. This is done using a real-time data streaming tool (e.g., Apache Kafka). The server then analyzes the collected real-time data and determines recovery priorities. Finally, the server provides a priority list to the recovery team, helping to ensure smooth recovery operations.

[1736] Examples and prompts

[1737] For example, if a new base station is to be installed in City A, the server collects population density data and landmark location data for City A from open data. Then, the server analyzes this data using geographic information system software to design the optimal base station placement. After that, the server collects existing coverage data from the network operation database and analyzes construction priorities using Python's Pandas library. Finally, the server generates a priority list and provides it to the construction team.

[1738] Prompt Sentence Examples

[1739] "Design the optimal placement of base stations based on population density data and landmark location data for City A. Also, analyze coverage rate data to determine construction priorities, and in the event of a disaster, prioritize restoration efforts based on the latest road and local information."

[1740] In this way, by explaining the system processing from the perspectives of the server, terminal, and user, the specific operations and data flow become clear.

[1741] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1742] Step 1:

[1743] The server collects population density data and landmark location data. As input, it obtains population density data and landmark location data from open data and commercial databases. Specifically, the server downloads these data through APIs and stores them in an internal database. As output, the collected data is stored in the internal database.

[1744] Step 2:

[1745] The server analyzes the collected data using geographic information system (GIS) software. As input, it uses population density data and landmark location data stored in an internal database. Specifically, the server launches the GIS software, imports this data, and visualizes it. As output, it determines the optimal base station placement points.

[1746] Step 3:

[1747] The server collects existing coverage data from the network operation database. As input, it obtains coverage information from the network operation database. As a specific operation, the server executes an SQL query to extract coverage data and saves it in the internal database. As output, the collected coverage data is stored in the internal database.

[1748] Step 4:

[1749] The server uses data analysis tools to analyze coverage and population density and determine construction priorities. It uses coverage and population density data stored in an internal database as input. Specifically, the server uses the Python Pandas library to analyze the data and generate a priority list. The output is a construction priority list.

[1750] Step 5:

[1751] The server collects alert data from the network. As input, it obtains alert information from the network monitoring system. Specifically, the server collects alert data through the monitoring system's API and saves it in an internal database. As output, the collected alert data is stored in the internal database.

[1752] Step 6:

[1753] The server uses a machine learning model to analyze past alert data and maintenance history and propose optimal maintenance methods. As input, it uses alert data and maintenance history data stored in an internal database. Specifically, the server uses TensorFlow to train the machine learning model and predict the optimal maintenance method. As output, it generates specific maintenance procedures that are notified to the maintenance team.

[1754] Step 7:

[1755] The server collects the latest road and local information in real time during a disaster. It uses data from various sensors and traffic information systems as input. Specifically, the server uses Apache Kafka to stream real-time data and saves it in an internal database. As output, the collected real-time data is stored in the internal database.

[1756] Step 8:

[1757] The server analyzes the collected real-time data and determines restoration priorities. As input, it uses road and local information stored in an internal database. Specifically, the server analyzes the data using machine learning models and generates a restoration priority list. As output, it generates a priority list that is provided to the restoration team.

[1758] (Application example 1)

[1759] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1760] In the past, the design, construction, maintenance, and disaster response of mobile networks were all managed separately, making efficient operation difficult. Furthermore, for autonomous vehicles, it was difficult to obtain real-time network coverage and road information, making it difficult to set optimal routes and quickly respond to disasters.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1762] In this invention, the server includes means for designing base station locations based on information such as population and landmarks, means for assigning construction start priorities based on coverage rates and other factors to efficiently advance area construction, means for determining the optimal maintenance method based on alert types, means for prioritizing restoration and carrying out smooth restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, and local information, etc., means for acquiring network coverage rates and road information in real time for autonomous vehicles and calculating the optimal route, means for setting the safest and fastest route based on the latest road information and area coverage rates in the event of a disaster, and means for acquiring network maintenance information in real time and changing the route of autonomous vehicles, thereby enabling efficient operation of mobile networks and safe and fast operation of autonomous vehicles.

[1763] "Population" is the total number of people living in a particular area.

[1764] A "landmark" is an important point such as a building or natural object that serves as a landmark in a particular area.

[1765] "Base station planning" is the planning and design process for installing base stations for mobile networks.

[1766] "Coverage" is the percentage of coverage of communication services provided by a mobile network within a particular area.

[1767] "Construction priority" is a priority for determining the order in which construction projects are started.

[1768] "Alert type" refers to the type of warning or notification issued by the system.

[1769] "Maintenance methods" are the means of maintaining and managing systems and equipment to ensure their normal operation.

[1770] "Area coverage" is the percentage of coverage of communication services provided by a mobile network within a specific geographic area.

[1771] "Latest road information" refers to the latest data including current road conditions and traffic information.

[1772] "Local information" is detailed information about a specific area.

[1773] "Recovery priority" refers to the priority of recovery work in the event of a disaster or failure.

[1774] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensor technology.

[1775] "Real-time" refers to the immediate processing of ongoing events and data.

[1776] An "optimal route" is the most efficient and safest route to reach a destination.

[1777] "Disaster time" refers to a situation when a natural disaster such as a typhoon or earthquake occurs.

[1778] "Maintenance information" is data related to the maintenance and management of systems and equipment.

[1779] A "travel route" is a route set for a vehicle to travel.

[1780] The system for implementing this invention consists of a cloud-based server for centrally controlling the design, construction, maintenance, and disaster response of mobile networks, and an application installed in an autonomous vehicle.

[1781] The server includes the following means:

[1782] 1. A method for designing station locations based on information such as population and landmarks.

[1783] 2. A means of efficiently advancing area construction by assigning construction priority based on coverage rate, etc.

[1784] 3. A means of identifying the optimal maintenance method based on the alert type.

[1785] 4. A means of smoothly carrying out restoration work in the event of a typhoon, earthquake, etc. by integrating area coverage rates, the latest road information, local information, etc., and assigning restoration priorities.

[1786] 5. A means for autonomous vehicles to obtain real-time network coverage and road information and calculate optimal routes.

[1787] 6. A means of planning the safest and quickest route based on the latest road information and area coverage in the event of a disaster.

[1788] 7. A means of obtaining real-time network maintenance information and rerouting autonomous vehicles.

[1789] Hardware and Software Configuration

[1790] The server operates in a cloud computing environment and includes a database, a geographic information system (GIS), and a real-time data processing system.The autonomous vehicle is equipped with a GPS module, a mobile network module, and a vehicle control system.

[1791] Data processing and calculation

[1792] The server obtains population data, landmark data, network coverage rate data, road information data, and disaster information data through the cloud API. Based on this data, it designs base stations, determines construction priority, learns main...

Claims

1. A server computer uses geographic information system software to analyze geographic information including population and landmark information, and calculates the placement of base stations taking into account the topography and building distribution information of the area, thereby performing station placement design; a means for determining the construction start priority of the base stations planned by the station placement design by the server computer analyzing the area coverage rate and population density using a data analysis tool and determining the construction start priority of the base stations based on the analysis results; a means for the server computer to collect road information, local information, and area coverage rate in real time during a disaster, and input a prompt to a generative AI model to instruct the server computer to determine the restoration priority of the base station so that base stations accessible by vehicles are preferentially restored based on the collected road information, local information, and area coverage rate; and A system including:

2. A means for detecting the emotional state of a user of a terminal in real time by analyzing emotional data including the voice, facial expression, or behavioral patterns of the user using an emotion recognition engine, and dynamically adjusting the communication environment in response to changes in the emotional state so as to adjust the communication quality of the terminal based on the analysis results, including means for improving the communication speed of the terminal when the emotional state indicates stress or dissatisfaction, and adjusting to conserve the communication resources of the terminal when the emotional state indicates relaxation. The system of claim 1 .

Images