System

Abstract

An object of a system according to an embodiment is to improve the efficiency of operation of heavy equipment and to eliminate a shortage of manpower.SOLUTION: A system includes a collection unit, a learning unit, an operation unit, and an interface unit. The collection part collects data on the operation of the heavy equipment. The learning unit learns how to operate the construction machine based on the data collected by the collection unit. The operation unit operates the construction machine based on the operation method learned by the learning unit. The interface unit provides an interface for performing remote control from the control room.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] Conventional technology has issues with labor shortages and efficiency in operating heavy machinery, leaving room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of heavy machinery operation and resolve labor shortages. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, an operation unit, and an interface unit. The collection unit collects data related to the operation of the heavy equipment. The learning unit learns how to operate the heavy equipment based on the data collected by the collection unit. The operation unit operates the heavy equipment based on the operation method learned by the learning unit. The interface unit provides an interface for remote operation from a control room. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of heavy machinery operation and resolve labor shortages. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A heavy equipment remote control system according to an embodiment of the present invention uses AI to remotely operate heavy equipment. The system collects data on the operation of heavy equipment, and AI learns how to operate it based on that data. It then remotely controls the equipment from a control room, acting as a proxy for the operation. This system enables 24-hour operation, alleviating labor shortages across the country and improving operational efficiency. For example, the heavy equipment remote control system provides detailed data, such as operating procedures and movement patterns, to the AI, which then learns how to operate the equipment based on that data. Next, an operator issues instructions for the heavy equipment through a remote control interface from the control room, and the AI ​​operates the heavy equipment according to those instructions. This allows the heavy equipment remote control system to operate heavy equipment even when the operator is not on-site. For example, in a store where snow removal needs to be completed before morning business hours, AI can perform snow removal work overnight, ensuring that the snow is removed by morning. Furthermore, in demolition and debris removal sites, AI can operate the work 24 hours a day, improving work efficiency. As a result, the heavy equipment remote control system can alleviate labor shortages and improve work efficiency. For example, the heavy equipment remote control system can operate heavy equipment even when the operator is not on-site, contributing to alleviating labor shortages across the country. It also enables 24-hour operation, which improves work efficiency. For example, in a store that needs to complete snow removal before morning business hours, AI can perform snow removal work at night, so that the snow is completed by morning. In addition, in demolition work and debris removal sites, AI can work 24 hours a day, improving work efficiency.

[0029] A heavy equipment remote operation system according to an embodiment includes a collection unit, a learning unit, an operation unit, and an interface unit. The collection unit collects data related to the operation of the heavy equipment. The data related to the operation of the heavy equipment includes, but is not limited to, operation logs, sensor information, and camera footage. The collection unit collects operation data of the heavy equipment using, for example, sensors and cameras. The collection unit can also collect environmental data (weather, terrain, etc.) in addition to the operation data of the heavy equipment. For example, the collection unit collects weather data and terrain data at the work site and provides data for learning operation methods according to environmental conditions. The learning unit learns how to operate the heavy equipment based on the data collected by the collection unit. Learning is performed using, for example, a machine learning algorithm or deep learning, but is not limited to, examples. For example, the learning unit learns operation procedures and movement patterns of the heavy equipment so that the AI ​​can understand and execute the operation of the heavy equipment. The operation unit operates the heavy equipment based on the operation method learned by the learning unit. Operation is performed by, for example, remote operation, semi-automatic operation, automatic operation, or the like, but is not limited to, examples. For example, the operation unit accurately operates heavy equipment based on data learned by the AI. The interface unit provides an interface for remote operation from a control room. Examples of interfaces include, but are not limited to, a GUI, a voice interface, and a touch panel. For example, the interface unit provides an interface for remotely operating heavy equipment from a control room, allowing an operator to issue operating instructions for the heavy equipment. As a result, the heavy equipment remote operation system according to the embodiment enables heavy equipment to be operated remotely and operates 24 hours a day. For example, the heavy equipment remote operation system allows heavy equipment to be operated even when an operator is not on-site, thereby contributing to alleviating labor shortages across the country. Furthermore, 24-hour operation improves work efficiency. For example, in a store where snow removal needs to be completed before morning business hours, AI can perform snow removal work overnight, so that the snow removal is completed by morning. Furthermore, in demolition work and debris removal sites, AI can operate work 24 hours a day, improving work efficiency.

[0030] The collection unit can collect operation data of the heavy equipment using a sensor or a camera. Examples of sensors include, but are not limited to, an acceleration sensor, a gyro sensor, and a position sensor. Examples of cameras include, but are not limited to, a wide-angle camera, an infrared camera, and a 360-degree camera. The collection unit can collect operation data of the heavy equipment using, for example, a sensor. For example, an acceleration sensor can be used to collect acceleration data of the heavy equipment. The collection unit can also collect operation data of the heavy equipment using a camera. For example, a wide-angle camera can be used to capture a wide range of operation of the heavy equipment and collect operation data. The collection unit can also collect operation data of the heavy equipment using a combination of a sensor and a camera. For example, an acceleration sensor and a wide-angle camera can be combined to collect detailed operation data of the heavy equipment. This allows accurate collection of operation data of the heavy equipment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data acquired by a sensor or camera into AI, which analyzes the data and collects operation data.

[0031] The interface unit can provide an interface for remotely operating the heavy equipment from a control room. Examples of interfaces include, but are not limited to, a GUI, a voice interface, a touch panel, etc. The interface unit can provide an operation interface for the heavy equipment using, for example, a GUI. For example, an operator can issue operation instructions for the heavy equipment through the GUI. The interface unit can also provide an operation interface for the heavy equipment using a voice interface. For example, an operator can issue operation instructions for the heavy equipment by voice. The interface unit can also provide an operation interface for the heavy equipment using a touch panel. For example, an operator can issue operation instructions for the heavy equipment by operating the touch panel. This allows the heavy equipment to be operated remotely from a control room. Some or all of the above-mentioned processing in the interface unit can be performed using, for example, AI, or can be performed without using AI. For example, the interface unit can input operation instructions from an operator to AI, and the AI ​​can operate the heavy equipment based on those instructions.

[0032] The operation unit can operate the heavy equipment based on data learned by the AI. Examples of data learned by the AI ​​include, but are not limited to, operation patterns, environmental data, and anomaly detection data. The operation unit can operate the heavy equipment based on operation patterns learned by the AI. For example, the operation unit can control the operation of the heavy equipment based on the operation patterns learned by the AI. The operation unit can also operate the heavy equipment based on environmental data learned by the AI. For example, the operation of the heavy equipment can be adjusted based on weather data learned by the AI. The operation unit can also operate the heavy equipment based on anomaly detection data learned by the AI. For example, the operation of the heavy equipment can be monitored based on the anomaly detection data learned by the AI, and appropriate action can be taken when an anomaly is detected. This allows the heavy equipment to be accurately operated based on the data learned by the AI. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can control the operation of the heavy equipment based on data learned by the AI.

[0033] The operation unit can operate the heavy equipment on a 24-hour basis. The 24-hour basis can be realized by, for example, a shift system, an automation system, remote monitoring, or the like, but is not limited to these examples. The operation unit can operate the heavy equipment on a 24-hour basis, for example, using a shift system. For example, operators can take turns working in shifts to operate the heavy equipment on a 24-hour basis. The operation unit can also operate the heavy equipment on a 24-hour basis using an automation system. For example, AI can automatically operate the heavy equipment to realize 24-hour operation. The operation unit can also operate the heavy equipment on a 24-hour basis using remote monitoring. For example, the heavy equipment can be remotely monitored from a control room and operated as needed. This allows the heavy equipment to be operated on a 24-hour basis. Some or all of the above-described processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can automatically operate the heavy equipment using AI to realize 24-hour operation.

[0034] The operation unit can perform snow removal or demolition work. Examples of snow removal work include, but are not limited to, road snow removal, parking lot snow removal, and sidewalk snow removal. Examples of demolition work include, but are not limited to, building demolition, bridge demolition, and road demolition. The operation unit can perform, for example, road snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform road snow removal work. The operation unit can also perform parking lot snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform parking lot snow removal work. The operation unit can also perform sidewalk snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform sidewalk snow removal work. The operation unit can also perform building demolition. For example, based on data learned by AI, it can operate heavy machinery to perform building demolition work. The operation unit can also perform bridge demolition. For example, based on data learned by AI, it can operate heavy machinery to perform bridge demolition work. The operation unit can also perform road demolition. For example, it can operate heavy machinery and perform road demolition work based on data learned by AI. This allows snow removal and demolition work to be performed efficiently. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can control the operation of heavy machinery based on data learned by AI to perform snow removal and demolition work.

[0035] The collection unit collects environmental data in addition to the operation data of the heavy equipment, and can learn more accurate operation methods. For example, the collection unit collects weather data at the work site along with the operation data of the heavy equipment, and learns operation methods that suit the weather. The collection unit can also collect terrain data along with the operation data of the heavy equipment, and learn operation methods that suit the terrain. The collection unit can also collect temperature and humidity data of the work environment along with the operation data of the heavy equipment, and learn operation methods that suit the environmental conditions. In this way, by collecting environmental data, more accurate operation methods can be learned. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data and terrain data into AI, and the AI ​​can learn operation methods based on that data.

[0036] The collection unit can be added with a function to collect heavy equipment operation data in real time and detect abnormalities. For example, the collection unit collects heavy equipment operation data in real time and detects abnormal operation patterns. The collection unit can also collect heavy equipment operation data in real time and detect abnormal vibrations or sounds. The collection unit can also collect heavy equipment operation data in real time and detect abnormal temperature increases. This enables rapid response by detecting abnormalities in real time. The definition of real time is, for example, in milliseconds, seconds, minutes, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the operation data collected in real time into AI, which can detect abnormalities.

[0037] When collecting operation data of the heavy equipment, the collection unit can classify the data into different work modes. For example, the collection unit collects operation data during snow removal work and classifies it as a snow removal mode. The collection unit can also collect operation data during demolition work and classify it as a demolition mode. The collection unit can also collect operation data during debris removal work and classify it as a removal mode. In this way, by classifying the data into different work modes, it is possible to learn the optimal operation method for each work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected operation data into AI, and the AI ​​can classify the data into different work modes.

[0038] The collection unit can collect the operator's operation history in addition to the operation data of the heavy equipment and analyze the operation patterns. For example, the collection unit can collect the operator's operation history and analyze frequently used operation patterns. The collection unit can also collect the operator's operation history and analyze efficient operation patterns. The collection unit can also collect the operator's operation history and detect abnormal operation patterns. In this way, efficient operation patterns can be learned by analyzing the operator's operation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's operation history into AI, which can analyze the operation patterns.

[0039] When collecting operation data of a heavy machine, the collection unit can also collect cooperative operation data with other heavy machines. The collection unit, for example, collects operation data when multiple heavy machines work cooperatively. The collection unit can also collect cooperative operation data with other heavy machines and analyze efficient cooperative operations. The collection unit can also collect cooperative operation data with other heavy machines and detect abnormal cooperative operations. In this way, by collecting cooperative operation data with other heavy machines, efficient cooperative operations can be learned. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input cooperative operation data with other heavy machines to AI, which can analyze the cooperative operations.

[0040] When collecting operation data of the heavy equipment, the collection unit can collect voice data from the work site and learn operations based on voice instructions. The collection unit, for example, collects voice data from the work site and learns operation patterns based on voice instructions. The collection unit can also collect voice data from the work site and learn efficient operation methods based on voice instructions. The collection unit can also collect voice data from the work site and detect abnormal operation patterns based on voice instructions. This enables more flexible operations by learning operations based on voice instructions. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input voice data from the work site into AI, which can then learn operations based on voice instructions.

[0041] The learning unit can learn the optimal operation method for each different work mode based on the operation data of the heavy equipment. For example, the learning unit learns the optimal operation method for a snow removal work mode. The learning unit can also learn the optimal operation method for a demolition work mode. The learning unit can also learn the optimal operation method for a rubble removal work mode. In this way, by learning the optimal operation method for each different work mode, optimal operation for each work becomes possible. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input collected operation data into AI, and the AI ​​can learn the optimal operation method for each different work mode.

[0042] During learning, the learning unit can learn an anomaly detection algorithm by referring to past abnormal operation data. For example, the learning unit learns an anomaly detection algorithm by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal vibrations or sounds by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal temperature increases by referring to past abnormal operation data. In this way, by referring to past abnormal operation data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past abnormal operation data into AI, which then learns an anomaly detection algorithm.

[0043] During learning, the learning unit can refer to the maintenance data of the heavy equipment to learn an algorithm for performing maintenance prediction. The learning unit, for example, refers to the maintenance data of the heavy equipment to learn a maintenance prediction algorithm. The learning unit can also refer to the maintenance data of the heavy equipment to learn an algorithm for predicting when to replace parts. The learning unit can also refer to the maintenance data of the heavy equipment to learn an algorithm for detecting signs of failure. By performing maintenance prediction in this way, it is possible to prevent heavy equipment failures before they occur. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the maintenance data of the heavy equipment into AI, which can then learn a maintenance prediction algorithm.

[0044] During learning, the learning unit can integrate data from different heavy equipment to learn a general-purpose operation method. For example, the learning unit can integrate data from heavy equipment from different manufacturers to learn a general-purpose operation method. The learning unit can also integrate data from different types of heavy equipment to learn a general-purpose operation method. The learning unit can also integrate data from heavy equipment in different work environments to learn a general-purpose operation method. In this way, by integrating data from different heavy equipment, a general-purpose operation method can be learned. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different heavy equipment into AI, which can then integrate the data to learn a general-purpose operation method.

[0045] During learning, the learning unit can integrate environmental data in addition to the operation data of the heavy equipment. For example, the learning unit can integrate weather data together with the operation data of the heavy equipment and learn. The learning unit can also integrate terrain data together with the operation data of the heavy equipment and learn. The learning unit can also integrate temperature and humidity data of the work environment together with the operation data of the heavy equipment and learn. In this way, by integrating environmental data, it is possible to learn a more accurate operation method. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input weather data and terrain data into the AI, and the AI ​​can learn an operation method based on that data.

[0046] During learning, the learning unit can refer to the operator's operation history to learn an algorithm that adapts to an individual operation style. For example, the learning unit refers to the operator's operation history to learn an algorithm that adapts to an individual operation style. The learning unit can also refer to the operator's operation history to learn an algorithm that adapts to an efficient operation style. The learning unit can also refer to the operator's operation history to learn an algorithm that detects abnormal operation styles. In this way, it is possible to adapt to an individual operation style by referring to the operator's operation history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the operator's operation history into AI, which can then learn an algorithm that adapts to an individual operation style.

[0047] The operation unit can be added with an anomaly detection function when operating heavy machinery, and can be added with a function to automatically stop the operation if an abnormality is detected. For example, the operation unit can detect an abnormal operation pattern when operating heavy machinery and automatically stop the operation. The operation unit can also detect abnormal vibrations or sounds when operating heavy machinery and automatically stop the operation. The operation unit can also detect an abnormal temperature rise when operating heavy machinery and automatically stop the operation. This improves safety by automatically stopping the operation when an abnormality is detected. The anomaly detection function is used to detect, for example, mechanical abnormalities, electrical abnormalities, operation abnormalities, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input operation data of the heavy machinery into AI, which can detect an abnormality and automatically stop the operation if an abnormality is detected.

[0048] The operation unit can apply the optimum operation method for each different work mode when operating the heavy equipment. For example, the operation unit applies the optimum operation method in a snow removal work mode. The operation unit can also apply the optimum operation method in a demolition work mode. The operation unit can also apply the optimum operation method in a debris removal work mode. In this way, by applying the optimum operation method for each different work mode, optimal operation for each work becomes possible. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can apply the optimum operation method for each different work mode based on data learned by AI.

[0049] The operation unit can add a function for cooperative operation with other heavy equipment when operating the heavy equipment. The operation unit applies, for example, an operation method when multiple heavy equipment work in cooperation. The operation unit can also apply an operation method when performing coordinated operation with other heavy equipment. The operation unit can also detect abnormalities when performing cooperative operation with other heavy equipment and take appropriate action. This improves work efficiency by performing cooperative operation with other heavy equipment. Cooperative operation includes, for example, adjustment of work order, adjustment of work scope, adjustment of work timing, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input cooperative operation data with other heavy equipment to AI, which can analyze the cooperative operation and apply the optimal operation method.

[0050] The operation unit can adjust the operation method by taking environmental data into consideration when operating heavy machinery. The operation unit can adjust the operation method by taking weather data into consideration, for example. The operation unit can also adjust the operation method by taking terrain data into consideration. The operation unit can also adjust the operation method by taking temperature and humidity data of the work environment into consideration. This allows for more appropriate operation by taking environmental data into consideration. Environmental data includes, for example, weather data, terrain data, temperature data, etc. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can input environmental data into AI, and the AI ​​can adjust the operation method based on that data.

[0051] The operation unit can be added with a function to perform operations based on voice instructions from the work site when operating heavy machinery. The operation unit, for example, adjusts the operation method based on the voice instructions from the work site. The operation unit can also perform efficient operations based on the voice instructions from the work site. The operation unit can also detect abnormal operations based on the voice instructions from the work site and take appropriate measures. This enables more flexible operations by performing operations based on voice instructions. Voice instructions include, for example, work instructions, warning instructions, confirmation instructions, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input voice data from the work site into AI, and the AI ​​can perform operations based on the voice instructions.

[0052] The operation unit can be added with a function that, when operating heavy equipment, refers to maintenance prediction data and automatically notifies when maintenance is required. The operation unit, for example, refers to the maintenance prediction data and automatically notifies when maintenance is required. The operation unit can also refer to the maintenance prediction data and notify when parts need to be replaced. The operation unit can also refer to the maintenance prediction data and notify of signs of failure. In this way, by automatically notifying when maintenance is required, it is possible to prevent heavy equipment from breaking down. The maintenance prediction data includes, for example, part life data, inspection records, failure history, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the maintenance prediction data into AI, which can then automatically notify when maintenance is required.

[0053] When displaying the interface, the interface unit can select the optimal display method by referring to the operator's past operation history. For example, the interface unit can refer to the operator's past operation history and prioritize displaying frequently used operations. The interface unit can also refer to the operator's past operation history and select a display method that supports efficient operation. The interface unit can also refer to the operator's past operation history, detect an abnormal operation, and select an appropriate display method. In this way, by referring to the operator's past operation history, a display method that supports efficient operation can be provided. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the operator's operation history data into AI, which can select the optimal display method.

[0054] The interface unit can add a function to display the current status of the heavy equipment in real time when displaying the interface. The interface unit, for example, displays the current operating status of the heavy equipment in real time. The interface unit can also display the current location information of the heavy equipment in real time. The interface unit can also display the current environmental data of the heavy equipment (weather, terrain, etc.) in real time. This allows the operator to respond quickly by displaying the current status of the heavy equipment in real time. Real time is defined in units of, for example, milliseconds, seconds, or minutes, but is not limited to such examples. Some or all of the above-mentioned processing in the interface unit may be performed using, or without using, AI. For example, the interface unit can input operating data of the heavy equipment into AI, which then displays the status in real time.

[0055] The interface unit can add a function to automatically display an alert when an abnormality is detected during interface display. The interface unit can automatically display an alert, for example, when an abnormal operation pattern is detected. The interface unit can also automatically display an alert when abnormal vibrations or sounds are detected. The interface unit can also automatically display an alert when an abnormal temperature rise is detected. This enables a rapid response by automatically displaying an alert when an abnormality is detected. Alerts include, for example, mechanical abnormalities, electrical abnormalities, operational abnormalities, etc. Some or all of the above-mentioned processing in the interface unit may be performed using AI, or may be performed without AI. For example, the interface unit can input operation data of heavy equipment into AI, which detects abnormalities and automatically displays an alert when an abnormality is detected.

[0056] When displaying the interface, the interface unit can select the optimal display method by taking into account the operator's device information. For example, if the operator is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the operator is using a tablet, the interface unit can also provide a display method that is optimized for a large screen. Furthermore, if the operator is using a smartwatch, the interface unit can also provide a simple, highly visible display method. This improves operation efficiency by providing the optimal display method according to the operator's device information. The device information includes, for example, the device type, device performance, device settings, etc. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the operator's device information into AI, which then selects the optimal display method.

[0057] The interface unit can make the display content multilingual when displaying the interface according to the operator's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the operator's device. The interface unit can also provide a language switching function when the operator uses multiple languages. The interface unit can also provide the interface in a specific language when the operator selects that language. This improves operation efficiency by making the display content multilingual according to the operator's language setting. Multilingual support includes, for example, English, Japanese, Chinese, etc. Some or all of the above-described processing in the interface unit can be performed using, or without, AI. For example, the interface unit can input the operator's language setting data into AI, which can then provide multilingual display content.

[0058] The interface unit can add a function that allows the user to refer to the operation manual for the heavy equipment when the interface is displayed. The interface unit, for example, provides a function that allows the user to refer to the operation manual for the heavy equipment when the interface is displayed. The interface unit can also provide a manual that explains the operation procedures for the heavy equipment in detail when the interface is displayed. The interface unit can also provide a troubleshooting guide for the heavy equipment when the interface is displayed. This improves operation efficiency by allowing the user to refer to the operation manual for the heavy equipment. The operation manual includes, for example, a manual in PDF format, a video manual, an interactive manual, etc. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input operation manual data into AI, which can then display the operation manual at the appropriate time.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The heavy equipment remote operation system can further include a prediction unit. The prediction unit can predict the operation of the heavy equipment and changes in the environment based on the data obtained from the collection unit. For example, the prediction unit can predict the weather for the next day based on weather data and plan snow removal work. The prediction unit can also predict signs of wear and failure of parts based on operation data of the heavy equipment and suggest the timing of maintenance. Furthermore, the prediction unit can predict changes in the terrain based on topographical data of the work site and take measures to ensure work safety. In this way, the prediction unit can contribute to efficient operation of heavy equipment and improved safety.

[0061] The collection unit can collect voice data from the work site in addition to the operation data of the heavy equipment and learn operations based on voice instructions. For example, the collection unit collects voice data from the work site and learns operation patterns based on voice instructions. The collection unit can also collect voice data from the work site and learn efficient operation methods based on voice instructions. The collection unit can also collect voice data from the work site and detect abnormal operation patterns based on voice instructions. This enables more flexible operation by learning operations based on voice instructions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data from the work site into AI, which can learn operations based on voice instructions.

[0062] The operation unit can be added with an anomaly detection function when operating heavy machinery, and can be added with a function to automatically stop the operation if an abnormality is detected. For example, the operation unit can detect an abnormal operation pattern when operating heavy machinery and automatically stop the operation. The operation unit can also detect abnormal vibrations or sounds when operating heavy machinery and automatically stop the operation. The operation unit can also detect an abnormal temperature rise when operating heavy machinery and automatically stop the operation. This improves safety by automatically stopping the operation when an abnormality is detected. The anomaly detection function is used to detect, for example, mechanical abnormalities, electrical abnormalities, operation abnormalities, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input operation data of the heavy machinery into AI, which can detect an abnormality and automatically stop the operation if an abnormality is detected.

[0063] The collection unit can collect the operator's operation history in addition to the operation data of the heavy equipment and analyze the operation patterns. For example, the collection unit can collect the operator's operation history and analyze frequently used operation patterns. The collection unit can also collect the operator's operation history and analyze efficient operation patterns. The collection unit can also collect the operator's operation history and detect abnormal operation patterns. In this way, efficient operation patterns can be learned by analyzing the operator's operation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's operation history into AI, which can analyze the operation patterns.

[0064] During learning, the learning unit can learn an anomaly detection algorithm by referring to past abnormal operation data. For example, the learning unit learns an anomaly detection algorithm by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal vibrations or sounds by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal temperature increases by referring to past abnormal operation data. In this way, by referring to past abnormal operation data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past abnormal operation data into AI, which then learns an anomaly detection algorithm.

[0065] The operation unit can add a function for cooperative operation with other heavy equipment when operating the heavy equipment. The operation unit applies, for example, an operation method when multiple heavy equipment work in cooperation. The operation unit can also apply an operation method when performing coordinated operation with other heavy equipment. The operation unit can also detect abnormalities when performing cooperative operation with other heavy equipment and take appropriate action. This improves work efficiency by performing cooperative operation with other heavy equipment. Cooperative operation includes, for example, adjustment of work order, adjustment of work scope, adjustment of work timing, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input cooperative operation data with other heavy equipment to AI, which can analyze the cooperative operation and apply the optimal operation method.

[0066] The interface unit can make the display content multilingual when displaying the interface according to the operator's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the operator's device. The interface unit can also provide a language switching function when the operator uses multiple languages. The interface unit can also provide the interface in a specific language when the operator selects that language. This improves operation efficiency by making the display content multilingual according to the operator's language setting. Multilingual support includes, for example, English, Japanese, Chinese, etc. Some or all of the above-described processing in the interface unit can be performed using, or without, AI. For example, the interface unit can input the operator's language setting data into AI, which can then provide multilingual display content.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects data related to the operation of the heavy equipment. Data related to the operation of the heavy equipment includes operation logs, sensor information, camera footage, etc. The collection unit uses sensors and cameras to collect operation data of the heavy equipment, and can also collect environmental data (weather, terrain, etc.). Step 2: The learning unit learns how to operate the heavy equipment based on the data collected by the collection unit. Learning is carried out using machine learning algorithms and deep learning to learn the operating procedures and movement patterns of the heavy equipment. Step 3: The control unit operates the heavy equipment based on the control method learned by the learning unit. The operation can be performed by remote control, semi-automatic operation, automatic operation, or other methods. Step 4: The interface unit provides an interface for remote operation from the control room. The interface can include a GUI, a voice interface, a touch panel, etc.

[0069] (Example 2) A heavy equipment remote control system according to an embodiment of the present invention uses AI to remotely operate heavy equipment. The system collects data on the operation of heavy equipment, and AI learns how to operate it based on that data. It then remotely controls the equipment from a control room, acting as a proxy for the operation. This system enables 24-hour operation, alleviating labor shortages across the country and improving operational efficiency. For example, the heavy equipment remote control system provides detailed data, such as operating procedures and movement patterns, to the AI, which then learns how to operate the equipment based on that data. Next, an operator issues instructions for the heavy equipment through a remote control interface from the control room, and the AI ​​operates the heavy equipment according to those instructions. This allows the heavy equipment remote control system to operate heavy equipment even when the operator is not on-site. For example, in a store where snow removal needs to be completed before morning business hours, AI can perform snow removal work overnight, ensuring that the snow is removed by morning. Furthermore, in demolition and debris removal sites, AI can operate the work 24 hours a day, improving work efficiency. As a result, the heavy equipment remote control system can alleviate labor shortages and improve work efficiency. For example, the heavy equipment remote control system can operate heavy equipment even when the operator is not on-site, contributing to alleviating labor shortages across the country. It also enables 24-hour operation, which improves work efficiency. For example, in a store that needs to complete snow removal before morning business hours, AI can perform snow removal work at night, so that the snow is completed by morning. In addition, in demolition work and debris removal sites, AI can work 24 hours a day, improving work efficiency.

[0070] A heavy equipment remote operation system according to an embodiment includes a collection unit, a learning unit, an operation unit, and an interface unit. The collection unit collects data related to the operation of the heavy equipment. The data related to the operation of the heavy equipment includes, but is not limited to, operation logs, sensor information, and camera footage. The collection unit collects operation data of the heavy equipment using, for example, sensors and cameras. The collection unit can also collect environmental data (weather, terrain, etc.) in addition to the operation data of the heavy equipment. For example, the collection unit collects weather data and terrain data at the work site and provides data for learning operation methods according to environmental conditions. The learning unit learns how to operate the heavy equipment based on the data collected by the collection unit. Learning is performed using, for example, a machine learning algorithm or deep learning, but is not limited to, examples. For example, the learning unit learns operation procedures and movement patterns of the heavy equipment so that the AI ​​can understand and execute the operation of the heavy equipment. The operation unit operates the heavy equipment based on the operation method learned by the learning unit. Operation is performed by, for example, remote operation, semi-automatic operation, automatic operation, or the like, but is not limited to, examples. For example, the operation unit accurately operates heavy equipment based on data learned by the AI. The interface unit provides an interface for remote operation from a control room. Examples of interfaces include, but are not limited to, a GUI, a voice interface, and a touch panel. For example, the interface unit provides an interface for remotely operating heavy equipment from a control room, allowing an operator to issue operating instructions for the heavy equipment. As a result, the heavy equipment remote operation system according to the embodiment enables heavy equipment to be operated remotely and operates 24 hours a day. For example, the heavy equipment remote operation system allows heavy equipment to be operated even when an operator is not on-site, thereby contributing to alleviating labor shortages across the country. Furthermore, 24-hour operation improves work efficiency. For example, in a store where snow removal needs to be completed before morning business hours, AI can perform snow removal work overnight, so that the snow removal is completed by morning. Furthermore, in demolition work and debris removal sites, AI can operate work 24 hours a day, improving work efficiency.

[0071] The collection unit can collect operation data of the heavy equipment using a sensor or a camera. Examples of sensors include, but are not limited to, an acceleration sensor, a gyro sensor, and a position sensor. Examples of cameras include, but are not limited to, a wide-angle camera, an infrared camera, and a 360-degree camera. The collection unit can collect operation data of the heavy equipment using, for example, a sensor. For example, an acceleration sensor can be used to collect acceleration data of the heavy equipment. The collection unit can also collect operation data of the heavy equipment using a camera. For example, a wide-angle camera can be used to capture a wide range of operation of the heavy equipment and collect operation data. The collection unit can also collect operation data of the heavy equipment using a combination of a sensor and a camera. For example, an acceleration sensor and a wide-angle camera can be combined to collect detailed operation data of the heavy equipment. This allows for accurate collection of operation data of the heavy equipment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data acquired by a sensor or camera into AI, which analyzes the data and collects operation data.

[0072] The interface unit can provide an interface for remotely operating the heavy equipment from a control room. Examples of interfaces include, but are not limited to, a GUI, a voice interface, a touch panel, etc. The interface unit can provide an operation interface for the heavy equipment using, for example, a GUI. For example, an operator can issue operation instructions for the heavy equipment through the GUI. The interface unit can also provide an operation interface for the heavy equipment using a voice interface. For example, an operator can issue operation instructions for the heavy equipment by voice. The interface unit can also provide an operation interface for the heavy equipment using a touch panel. For example, an operator can issue operation instructions for the heavy equipment by operating the touch panel. This allows the heavy equipment to be operated remotely from a control room. Some or all of the above-mentioned processing in the interface unit can be performed using, for example, AI, or can be performed without using AI. For example, the interface unit can input operation instructions from an operator to AI, and the AI ​​can operate the heavy equipment based on those instructions.

[0073] The operation unit can operate the heavy equipment based on data learned by the AI. Examples of data learned by the AI ​​include, but are not limited to, operation patterns, environmental data, and anomaly detection data. The operation unit can operate the heavy equipment based on operation patterns learned by the AI. For example, the operation unit can control the operation of the heavy equipment based on the operation patterns learned by the AI. The operation unit can also operate the heavy equipment based on environmental data learned by the AI. For example, the operation of the heavy equipment can be adjusted based on weather data learned by the AI. The operation unit can also operate the heavy equipment based on anomaly detection data learned by the AI. For example, the operation of the heavy equipment can be monitored based on the anomaly detection data learned by the AI, and appropriate action can be taken when an anomaly is detected. This allows the heavy equipment to be accurately operated based on the data learned by the AI. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can control the operation of the heavy equipment based on data learned by the AI.

[0074] The operation unit can operate the heavy equipment on a 24-hour basis. The 24-hour basis can be realized by, for example, a shift system, an automation system, remote monitoring, or the like, but is not limited to these examples. The operation unit can operate the heavy equipment on a 24-hour basis, for example, using a shift system. For example, operators can take turns working in shifts to operate the heavy equipment on a 24-hour basis. The operation unit can also operate the heavy equipment on a 24-hour basis using an automation system. For example, AI can automatically operate the heavy equipment to realize 24-hour operation. The operation unit can also operate the heavy equipment on a 24-hour basis using remote monitoring. For example, the heavy equipment can be remotely monitored from a control room and operated as needed. This allows the heavy equipment to be operated on a 24-hour basis. Some or all of the above-described processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can automatically operate the heavy equipment using AI to realize 24-hour operation.

[0075] The operation unit can perform snow removal or demolition work. Examples of snow removal work include, but are not limited to, road snow removal, parking lot snow removal, and sidewalk snow removal. Examples of demolition work include, but are not limited to, building demolition, bridge demolition, and road demolition. The operation unit can perform, for example, road snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform road snow removal work. The operation unit can also perform parking lot snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform parking lot snow removal work. The operation unit can also perform sidewalk snow removal. For example, based on data learned by AI, it can operate heavy machinery to perform sidewalk snow removal work. The operation unit can also perform building demolition. For example, based on data learned by AI, it can operate heavy machinery to perform building demolition work. The operation unit can also perform bridge demolition. For example, based on data learned by AI, it can operate heavy machinery to perform bridge demolition work. The operation unit can also perform road demolition. For example, it can operate heavy machinery and perform road demolition work based on data learned by AI. This allows snow removal and demolition work to be performed efficiently. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can control the operation of heavy machinery based on data learned by AI to perform snow removal and demolition work.

[0076] The collection unit can estimate the operator's emotions and adjust the timing of data collection based on the estimated operator's emotions. For example, if the operator is stressed, the collection unit can reduce the frequency of data collection to reduce the operator's burden. Furthermore, if the operator is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the operator is tired, the collection unit can adjust the timing of data collection to ensure the operator's rest time. This reduces the operator's burden by adjusting the timing of data collection according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the operator's facial expression data into AI, which can estimate the operator's emotions and adjust the timing of data collection based on the results.

[0077] The collection unit collects environmental data in addition to the operation data of the heavy equipment, and can learn more accurate operation methods. For example, the collection unit collects weather data at the work site along with the operation data of the heavy equipment, and learns operation methods that suit the weather. The collection unit can also collect terrain data along with the operation data of the heavy equipment, and learn operation methods that suit the terrain. The collection unit can also collect temperature and humidity data of the work environment along with the operation data of the heavy equipment, and learn operation methods that suit the environmental conditions. In this way, by collecting environmental data, more accurate operation methods can be learned. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data and terrain data into AI, and the AI ​​can learn operation methods based on that data.

[0078] The collection unit can be added with a function to collect heavy equipment operation data in real time and detect abnormalities. For example, the collection unit collects heavy equipment operation data in real time and detects abnormal operation patterns. The collection unit can also collect heavy equipment operation data in real time and detect abnormal vibrations or sounds. The collection unit can also collect heavy equipment operation data in real time and detect abnormal temperature increases. This enables rapid response by detecting abnormalities in real time. The definition of real time is, for example, in milliseconds, seconds, minutes, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the operation data collected in real time into AI, which can detect abnormalities.

[0079] When collecting operation data of the heavy equipment, the collection unit can classify the data into different work modes. For example, the collection unit collects operation data during snow removal work and classifies it as a snow removal mode. The collection unit can also collect operation data during demolition work and classify it as a demolition mode. The collection unit can also collect operation data during debris removal work and classify it as a removal mode. In this way, by classifying the data into different work modes, it is possible to learn the optimal operation method for each work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected operation data into AI, and the AI ​​can classify the data into different work modes.

[0080] The collection unit can estimate the operator's emotions and determine the priority of data to be collected based on the estimated operator's emotions. For example, if the operator is stressed, the collection unit can prioritize collecting only important data. Furthermore, if the operator is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the operator is tired, the collection unit can reduce the amount of data to be collected, thereby reducing the operator's burden. Thus, by determining the priority of data according to the operator's emotions, the burden on the operator can be reduced. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the operator's facial expression data into AI, which can estimate the operator's emotions and determine the priority of data to be collected based on the results.

[0081] The collection unit can collect the operator's operation history in addition to the operation data of the heavy equipment and analyze the operation patterns. For example, the collection unit can collect the operator's operation history and analyze frequently used operation patterns. The collection unit can also collect the operator's operation history and analyze efficient operation patterns. The collection unit can also collect the operator's operation history and detect abnormal operation patterns. In this way, efficient operation patterns can be learned by analyzing the operator's operation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's operation history into AI, which can analyze the operation patterns.

[0082] When collecting operation data of a heavy machine, the collection unit can also collect cooperative operation data with other heavy machines. The collection unit, for example, collects operation data when multiple heavy machines work cooperatively. The collection unit can also collect cooperative operation data with other heavy machines and analyze efficient cooperative operations. The collection unit can also collect cooperative operation data with other heavy machines and detect abnormal cooperative operations. In this way, by collecting cooperative operation data with other heavy machines, efficient cooperative operations can be learned. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input cooperative operation data with other heavy machines to AI, which can analyze the cooperative operations.

[0083] When collecting operation data of the heavy equipment, the collection unit can collect voice data from the work site and learn operations based on voice instructions. The collection unit, for example, collects voice data from the work site and learns operation patterns based on voice instructions. The collection unit can also collect voice data from the work site and learn efficient operation methods based on voice instructions. The collection unit can also collect voice data from the work site and detect abnormal operation patterns based on voice instructions. This enables more flexible operations by learning operations based on voice instructions. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input voice data from the work site into AI, which can then learn operations based on voice instructions.

[0084] The learning unit can estimate the operator's emotions and adjust the learning algorithm based on the estimated operator's emotions. For example, if the operator is stressed, the learning unit adjusts the parameters of the learning algorithm to reduce stress. Furthermore, if the operator is relaxed, the learning unit can adjust the parameters of the learning algorithm to learn more detailed data. Furthermore, if the operator is tired, the learning unit can adjust the parameters of the learning algorithm to reduce the learning burden. This improves learning accuracy by adjusting the learning algorithm according to the operator's emotions. Emotions are estimated using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and adjusts the learning algorithm based on the results.

[0085] The learning unit can learn the optimal operation method for each different work mode based on the operation data of the heavy equipment. For example, the learning unit learns the optimal operation method for a snow removal work mode. The learning unit can also learn the optimal operation method for a demolition work mode. The learning unit can also learn the optimal operation method for a rubble removal work mode. In this way, by learning the optimal operation method for each different work mode, optimal operation for each work becomes possible. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input collected operation data into AI, and the AI ​​can learn the optimal operation method for each different work mode.

[0086] During learning, the learning unit can learn an anomaly detection algorithm by referring to past abnormal operation data. For example, the learning unit learns an anomaly detection algorithm by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal vibrations or sounds by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal temperature increases by referring to past abnormal operation data. In this way, by referring to past abnormal operation data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past abnormal operation data into AI, which then learns an anomaly detection algorithm.

[0087] During learning, the learning unit can refer to the maintenance data of the heavy equipment to learn an algorithm for performing maintenance prediction. The learning unit, for example, refers to the maintenance data of the heavy equipment to learn a maintenance prediction algorithm. The learning unit can also refer to the maintenance data of the heavy equipment to learn an algorithm for predicting when to replace parts. The learning unit can also refer to the maintenance data of the heavy equipment to learn an algorithm for detecting signs of failure. By performing maintenance prediction in this way, it is possible to prevent heavy equipment failures before they occur. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the maintenance data of the heavy equipment into AI, which can then learn a maintenance prediction algorithm.

[0088] The learning unit can estimate the operator's emotions and select learning data based on the estimated operator's emotions. For example, if the operator is feeling stressed, the learning unit can prioritize learning data to reduce stress. Furthermore, if the operator is relaxed, the learning unit can prioritize learning detailed data. Furthermore, if the operator is tired, the learning unit can prioritize learning data to reduce the learning burden. This improves learning efficiency by selecting learning data according to the operator's emotions. Emotions are estimated using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and selects learning data based on the results.

[0089] During learning, the learning unit can integrate data from different heavy equipment to learn a general-purpose operation method. For example, the learning unit can integrate data from heavy equipment from different manufacturers to learn a general-purpose operation method. The learning unit can also integrate data from different types of heavy equipment to learn a general-purpose operation method. The learning unit can also integrate data from heavy equipment in different work environments to learn a general-purpose operation method. In this way, by integrating data from different heavy equipment, a general-purpose operation method can be learned. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different heavy equipment into AI, which can then integrate the data to learn a general-purpose operation method.

[0090] During learning, the learning unit can integrate environmental data in addition to the operation data of the heavy equipment. For example, the learning unit can integrate weather data together with the operation data of the heavy equipment and learn. The learning unit can also integrate terrain data together with the operation data of the heavy equipment and learn. The learning unit can also integrate temperature and humidity data of the work environment together with the operation data of the heavy equipment and learn. In this way, by integrating environmental data, it is possible to learn a more accurate operation method. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input weather data and terrain data into the AI, and the AI ​​can learn an operation method based on that data.

[0091] During learning, the learning unit can refer to the operator's operation history to learn an algorithm that adapts to an individual operation style. For example, the learning unit refers to the operator's operation history to learn an algorithm that adapts to an individual operation style. The learning unit can also refer to the operator's operation history to learn an algorithm that adapts to an efficient operation style. The learning unit can also refer to the operator's operation history to learn an algorithm that detects abnormal operation styles. In this way, it is possible to adapt to an individual operation style by referring to the operator's operation history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the operator's operation history into AI, which can then learn an algorithm that adapts to an individual operation style.

[0092] The operation unit can estimate the operator's emotions and adjust the operation method based on the estimated operator's emotions. For example, if the operator is feeling stressed, the operation unit can simplify the operation method to reduce stress. Furthermore, if the operator is relaxed, the operation unit can provide detailed operation methods to support efficient operation. Furthermore, if the operator is tired, the operation unit can adjust the operation method to reduce the operator's burden. This improves operation efficiency by adjusting the operation method according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or without AI. For example, the operation unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and adjusts the operation method based on the results.

[0093] The operation unit can be added with an anomaly detection function when operating heavy machinery, and can be added with a function to automatically stop the operation if an abnormality is detected. For example, the operation unit can detect an abnormal operation pattern when operating heavy machinery and automatically stop the operation. The operation unit can also detect abnormal vibrations or sounds when operating heavy machinery and automatically stop the operation. The operation unit can also detect an abnormal temperature rise when operating heavy machinery and automatically stop the operation. This improves safety by automatically stopping the operation when an abnormality is detected. The anomaly detection function is used to detect, for example, mechanical abnormalities, electrical abnormalities, operation abnormalities, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input operation data of the heavy machinery into AI, which can detect an abnormality and automatically stop the operation if an abnormality is detected.

[0094] The operation unit can apply the optimum operation method for each different work mode when operating the heavy equipment. For example, the operation unit applies the optimum operation method in a snow removal work mode. The operation unit can also apply the optimum operation method in a demolition work mode. The operation unit can also apply the optimum operation method in a debris removal work mode. In this way, by applying the optimum operation method for each different work mode, optimal operation for each work becomes possible. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can apply the optimum operation method for each different work mode based on data learned by AI.

[0095] The operation unit can add a function for cooperative operation with other heavy equipment when operating the heavy equipment. The operation unit applies, for example, an operation method when multiple heavy equipment work in cooperation. The operation unit can also apply an operation method when performing coordinated operation with other heavy equipment. The operation unit can also detect abnormalities when performing cooperative operation with other heavy equipment and take appropriate action. This improves work efficiency by performing cooperative operation with other heavy equipment. Cooperative operation includes, for example, adjustment of work order, adjustment of work scope, adjustment of work timing, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input cooperative operation data with other heavy equipment to AI, which can analyze the cooperative operation and apply the optimal operation method.

[0096] The operation unit can estimate the operator's emotions and determine the priority of operations based on the estimated operator's emotions. For example, when the operator is stressed, the operation unit can prioritize important operations. Furthermore, when the operator is relaxed, the operation unit can prioritize detailed operations. Furthermore, when the operator is tired, the operation unit can prioritize operations that reduce the burden of operation. This improves operation efficiency by determining the priority of operations according to the operator's emotions. Emotions are estimated using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and determines the priority of operations based on the results.

[0097] The operation unit can adjust the operation method by taking environmental data into consideration when operating heavy machinery. The operation unit can adjust the operation method by taking weather data into consideration, for example. The operation unit can also adjust the operation method by taking terrain data into consideration. The operation unit can also adjust the operation method by taking temperature and humidity data of the work environment into consideration. This allows for more appropriate operation by taking environmental data into consideration. Environmental data includes, for example, weather data, terrain data, temperature data, etc. Some or all of the above-mentioned processing in the operation unit may be performed using AI, for example, or may be performed without using AI. For example, the operation unit can input environmental data into AI, and the AI ​​can adjust the operation method based on that data.

[0098] The operation unit can be added with a function to perform operations based on voice instructions from the work site when operating heavy machinery. The operation unit, for example, adjusts the operation method based on the voice instructions from the work site. The operation unit can also perform efficient operations based on the voice instructions from the work site. The operation unit can also detect abnormal operations based on the voice instructions from the work site and take appropriate measures. This enables more flexible operations by performing operations based on voice instructions. Voice instructions include, for example, work instructions, warning instructions, confirmation instructions, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input voice data from the work site into AI, and the AI ​​can perform operations based on the voice instructions.

[0099] The operation unit can be added with a function that, when operating heavy equipment, refers to maintenance prediction data and automatically notifies when maintenance is required. The operation unit, for example, refers to the maintenance prediction data and automatically notifies when maintenance is required. The operation unit can also refer to the maintenance prediction data and notify when parts need to be replaced. The operation unit can also refer to the maintenance prediction data and notify of signs of failure. In this way, by automatically notifying when maintenance is required, it is possible to prevent heavy equipment from breaking down. The maintenance prediction data includes, for example, part life data, inspection records, failure history, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the maintenance prediction data into AI, which can then automatically notify when maintenance is required.

[0100] The interface unit can estimate the operator's emotions and adjust the interface display method based on the estimated operator's emotions. For example, if the operator is nervous, the interface unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the operator is having fun, the interface unit can provide an interface with bright colors to make the operation more enjoyable. Furthermore, if the operator is tired, the interface unit can provide a simple, highly visible interface to make the operation easier. This improves operation efficiency by adjusting the interface display method according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or without AI. For example, the interface unit can input the operator's facial expression data into AI, which can estimate the operator's emotions and adjust the interface display method based on the results.

[0101] When displaying the interface, the interface unit can select the optimal display method by referring to the operator's past operation history. For example, the interface unit can refer to the operator's past operation history and prioritize displaying frequently used operations. The interface unit can also refer to the operator's past operation history and select a display method that supports efficient operation. The interface unit can also refer to the operator's past operation history, detect an abnormal operation, and select an appropriate display method. In this way, by referring to the operator's past operation history, a display method that supports efficient operation can be provided. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the operator's operation history data into AI, which can select the optimal display method.

[0102] The interface unit can add a function to display the current status of the heavy equipment in real time when displaying the interface. The interface unit, for example, displays the current operating status of the heavy equipment in real time. The interface unit can also display the current location information of the heavy equipment in real time. The interface unit can also display the current environmental data of the heavy equipment (weather, terrain, etc.) in real time. This allows the operator to respond quickly by displaying the current status of the heavy equipment in real time. Real time is defined in units of, for example, milliseconds, seconds, or minutes, but is not limited to such examples. Some or all of the above-mentioned processing in the interface unit may be performed using, or without using, AI. For example, the interface unit can input operating data of the heavy equipment into AI, which then displays the status in real time.

[0103] The interface unit can add a function to automatically display an alert when an abnormality is detected during interface display. The interface unit can automatically display an alert, for example, when an abnormal operation pattern is detected. The interface unit can also automatically display an alert when abnormal vibrations or sounds are detected. The interface unit can also automatically display an alert when an abnormal temperature rise is detected. This enables a rapid response by automatically displaying an alert when an abnormality is detected. Alerts include, for example, mechanical abnormalities, electrical abnormalities, operational abnormalities, etc. Some or all of the above-mentioned processing in the interface unit may be performed using AI, or may be performed without AI. For example, the interface unit can input operation data of heavy equipment into AI, which detects abnormalities and automatically displays an alert when an abnormality is detected.

[0104] The interface unit can estimate the operator's emotions and adjust the interface operation procedures based on the estimated operator's emotions. For example, if the operator is nervous, the interface unit can provide simple, highly visible operation procedures. Furthermore, if the operator is relaxed, the interface unit can also provide detailed operation procedures. Furthermore, if the operator is tired, the interface unit can simplify the operation procedures to reduce the operator's burden. This improves operation efficiency by adjusting the operation procedures according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or without AI. For example, the interface unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and adjusts the operation procedures based on the results.

[0105] When displaying the interface, the interface unit can select the optimal display method by taking into account the operator's device information. For example, if the operator is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the operator is using a tablet, the interface unit can also provide a display method that is optimized for a large screen. Furthermore, if the operator is using a smartwatch, the interface unit can also provide a simple, highly visible display method. This improves operation efficiency by providing the optimal display method according to the operator's device information. The device information includes, for example, the device type, device performance, device settings, etc. Some or all of the above-mentioned processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the operator's device information into AI, which then selects the optimal display method.

[0106] The interface unit can make the display content multilingual when displaying the interface according to the operator's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the operator's device. The interface unit can also provide a language switching function when the operator uses multiple languages. The interface unit can also provide the interface in a specific language when the operator selects that language. This improves operation efficiency by making the display content multilingual according to the operator's language setting. Multilingual support includes, for example, English, Japanese, Chinese, etc. Some or all of the above-described processing in the interface unit can be performed using, or without, AI. For example, the interface unit can input the operator's language setting data into AI, which can then provide multilingual display content.

[0107] The interface unit can add a function that allows the user to refer to the operation manual for the heavy equipment when the interface is displayed. The interface unit, for example, provides a function that allows the user to refer to the operation manual for the heavy equipment when the interface is displayed. The interface unit can also provide a manual that explains the operation procedures for the heavy equipment in detail when the interface is displayed. The interface unit can also provide a troubleshooting guide for the heavy equipment when the interface is displayed. This improves operation efficiency by allowing the user to refer to the operation manual for the heavy equipment. The operation manual includes, for example, a manual in PDF format, a video manual, an interactive manual, etc. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit can input operation manual data into AI, which can then display the operation manual at the appropriate time. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, operation unit, and interface unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect operation data of the heavy equipment using the camera 42 and sensors of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect operation logs and environmental data of the heavy equipment. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns how to operate the heavy equipment based on the collected data. The operation unit, realized, for example, by the control unit 46A of the smart device 14, operates the heavy equipment based on the learned operation method. The interface unit provides an interface for remote operation from a control room using, for example, the touch panel 38A or voice interface of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, operation unit, and interface unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect operation data of the heavy equipment using the camera 42 and sensors of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect operation logs and environmental data of the heavy equipment. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns how to operate the heavy equipment based on the collected data. The operation unit, realized, for example, by the control unit 46A of the smart glasses 214, operates the heavy equipment based on the learned operation method. The interface unit provides an interface for remote operation from a control room, for example, using the voice interface or touch panel of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, learning unit, operation unit, and interface unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect operation data of the heavy equipment using the camera 42 or sensors of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect operation logs and environmental data of the heavy equipment. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns how to operate the heavy equipment based on the collected data. The operation unit, realized, for example, by the control unit 46A of the headset terminal 314, operates the heavy equipment based on the learned operation method. The interface unit provides an interface for remote operation from a control room, for example, using the voice interface or touch panel of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, learning unit, operation unit, and interface unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect operation data of the heavy equipment using the camera 42 and sensors of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect operation logs and environmental data of the heavy equipment. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns how to operate the heavy equipment based on the collected data. The operation unit, realized, for example, by the control unit 46A of the robot 414, operates the heavy equipment based on the learned operation method. The interface unit provides an interface for remote operation from a control room, for example, using the robot 414's voice interface or touch panel.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The heavy equipment remote operation system can further include a prediction unit. The prediction unit can predict the operation of the heavy equipment and changes in the environment based on the data obtained from the collection unit. For example, the prediction unit can predict the weather for the next day based on weather data and plan snow removal work. The prediction unit can also predict signs of wear and failure of parts based on operation data of the heavy equipment and suggest the timing of maintenance. Furthermore, the prediction unit can predict changes in the terrain based on topographical data of the work site and take measures to ensure work safety. In this way, the prediction unit can contribute to efficient operation of heavy equipment and improved safety.

[0110] The collection unit can collect voice data from the work site in addition to the operation data of the heavy equipment and learn operations based on voice instructions. For example, the collection unit collects voice data from the work site and learns operation patterns based on voice instructions. The collection unit can also collect voice data from the work site and learn efficient operation methods based on voice instructions. The collection unit can also collect voice data from the work site and detect abnormal operation patterns based on voice instructions. This enables more flexible operation by learning operations based on voice instructions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data from the work site into AI, which can learn operations based on voice instructions.

[0111] The interface unit can estimate the operator's emotions and adjust the interface display method based on the estimated operator's emotions. For example, if the operator is nervous, the interface unit can provide a subdued interface color to reduce visual stress. If the operator is having fun, the interface unit can provide a bright interface color to make the operation more enjoyable. If the operator is tired, the interface unit can provide a simple, highly visible interface to make the operation easier. This improves operation efficiency by adjusting the interface display method according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or without AI. For example, the interface unit can input the operator's facial expression data into AI, which can estimate the operator's emotions and adjust the interface display method based on the results.

[0112] The operation unit can be added with an anomaly detection function when operating heavy machinery, and can be added with a function to automatically stop the operation if an abnormality is detected. For example, the operation unit can detect an abnormal operation pattern when operating heavy machinery and automatically stop the operation. The operation unit can also detect abnormal vibrations or sounds when operating heavy machinery and automatically stop the operation. The operation unit can also detect an abnormal temperature rise when operating heavy machinery and automatically stop the operation. This improves safety by automatically stopping the operation when an abnormality is detected. The anomaly detection function is used to detect, for example, mechanical abnormalities, electrical abnormalities, operation abnormalities, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input operation data of the heavy machinery into AI, which can detect an abnormality and automatically stop the operation if an abnormality is detected.

[0113] The operation unit can estimate the operator's emotions and adjust the operation method based on the estimated operator's emotions. For example, if the operator is feeling stressed, the operation unit can simplify the operation method to reduce stress. Furthermore, if the operator is relaxed, the operation unit can provide detailed operation methods to support efficient operation. Furthermore, if the operator is tired, the operation unit can adjust the operation method to reduce the operator's burden. This improves operation efficiency by adjusting the operation method according to the operator's emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or without AI. For example, the operation unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and adjusts the operation method based on the results.

[0114] The collection unit can collect the operator's operation history in addition to the operation data of the heavy equipment and analyze the operation patterns. For example, the collection unit can collect the operator's operation history and analyze frequently used operation patterns. The collection unit can also collect the operator's operation history and analyze efficient operation patterns. The collection unit can also collect the operator's operation history and detect abnormal operation patterns. In this way, efficient operation patterns can be learned by analyzing the operator's operation history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's operation history into AI, which can analyze the operation patterns.

[0115] The learning unit can estimate the operator's emotions and adjust the learning algorithm based on the estimated operator's emotions. For example, if the operator is stressed, the learning unit adjusts the parameters of the learning algorithm to reduce stress. Furthermore, if the operator is relaxed, the learning unit can adjust the parameters of the learning algorithm to learn more detailed data. Furthermore, if the operator is tired, the learning unit can adjust the parameters of the learning algorithm to reduce the learning burden. This improves learning accuracy by adjusting the learning algorithm according to the operator's emotions. Emotions are estimated using, for example, facial expression recognition, voice analysis, biometric sensors, etc. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input the operator's facial expression data into AI, which then estimates the operator's emotions and adjusts the learning algorithm based on the results.

[0116] During learning, the learning unit can learn an anomaly detection algorithm by referring to past abnormal operation data. For example, the learning unit learns an anomaly detection algorithm by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal vibrations or sounds by referring to past abnormal operation data. The learning unit can also learn an algorithm for detecting abnormal temperature increases by referring to past abnormal operation data. In this way, by referring to past abnormal operation data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past abnormal operation data into AI, which then learns an anomaly detection algorithm.

[0117] The operation unit can add a function for cooperative operation with other heavy equipment when operating the heavy equipment. The operation unit applies, for example, an operation method when multiple heavy equipment work in cooperation. The operation unit can also apply an operation method when performing coordinated operation with other heavy equipment. The operation unit can also detect abnormalities when performing cooperative operation with other heavy equipment and take appropriate action. This improves work efficiency by performing cooperative operation with other heavy equipment. Cooperative operation includes, for example, adjustment of work order, adjustment of work scope, adjustment of work timing, etc. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input cooperative operation data with other heavy equipment to AI, which can analyze the cooperative operation and apply the optimal operation method.

[0118] The interface unit can make the display content multilingual when displaying the interface according to the operator's language setting. The interface unit can automatically set the interface language based on, for example, the language setting of the operator's device. The interface unit can also provide a language switching function when the operator uses multiple languages. The interface unit can also provide the interface in a specific language when the operator selects that language. This improves operation efficiency by making the display content multilingual according to the operator's language setting. Multilingual support includes, for example, English, Japanese, Chinese, etc. Some or all of the above-described processing in the interface unit can be performed using, or without, AI. For example, the interface unit can input the operator's language setting data into AI, which can then provide multilingual display content.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data related to the operation of the heavy equipment. Data related to the operation of the heavy equipment includes operation logs, sensor information, camera footage, etc. The collection unit uses sensors and cameras to collect operation data of the heavy equipment, and can also collect environmental data (weather, terrain, etc.). Step 2: The learning unit learns how to operate the heavy equipment based on the data collected by the collection unit. Learning is carried out using machine learning algorithms and deep learning to learn the operating procedures and movement patterns of the heavy equipment. Step 3: The control unit operates the heavy equipment based on the control method learned by the learning unit. The operation can be performed by remote control, semi-automatic operation, automatic operation, or other methods. Step 4: The interface unit provides an interface for remote operation from the control room. The interface can include a GUI, a voice interface, a touch panel, etc.

[0121] 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.

[0122] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] 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.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[0166] 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.

[0167] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data related to the operation of the heavy equipment; a learning unit that learns how to operate the heavy equipment based on the data collected by the collection unit; an operation unit that operates the heavy machinery based on the operation method learned by the learning unit; and an interface unit for remotely operating the device from a control room. A system characterized by:

2. The collecting unit Collecting operational data of heavy machinery using sensors or cameras 2. The system of claim 1.

3. The interface unit Provides an interface for remotely operating heavy machinery from a control room 2. The system of claim 1.

4. The operation unit includes: Operate heavy machinery based on data learned by AI 2. The system of claim 1.

5. The operation unit includes: Operate heavy machinery 24 hours a day 2. The system of claim 1.

6. The operation unit includes: Perform snow removal or demolition work 2. The system of claim 1.

7. The collecting unit Estimate the operator's emotions and adjust the timing of data collection based on the estimated operator emotions.

2. The system of claim 1.

8. The collecting unit Collecting environmental data in addition to operational data on heavy machinery to learn more precise control methods 2. The system of claim 1.

Images