system

Abstract

The system according to the embodiment aims to accurately grasp the trends of beneficial and pest insects in farmland and predict their impact on yield and production quality. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit performs simulation prediction based on the analysis results obtained by the analysis unit. The provision unit provides the prediction results obtained by the prediction unit.

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 the problem that it is difficult to accurately grasp the trends of beneficial and pest insects in farmland and predict their impact on yield and production quality.

[0005] The system according to the embodiment aims to accurately grasp the trends of beneficial and pest insects in farmland and predict their impact on yield and production quality. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit performs simulation prediction based on the analysis results obtained by the analysis unit. The provision unit provides the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the trends of beneficial and pest insects in farmland and predict the impact on harvest yield and production quality. [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) An agricultural support system according to an embodiment of the present invention monitors the types, number, time, and frequency of beneficial and pest insects in farmland or cultivated land using fixed-point surveillance cameras and collects the data. The collected data is analyzed by AI to simulate and predict the correlation between the abundance of beneficial and pest insects and the yield and production quality. This can improve the productivity of pesticide-free and organic farming. For example, the agricultural support system installs fixed-point surveillance cameras in farmland or cultivated land and periodically monitors the types, number, time, and frequency of beneficial and pest insects. For example, the cameras capture images at regular intervals and analyze the images to identify the types and numbers of beneficial and pest insects. This data is sent to and stored on a cloud server. Next, the collected data is analyzed by AI. The AI ​​simulates and predicts the correlation between the abundance of beneficial and pest insects and the yield and production quality. For example, it is predicted that an increase in the abundance of beneficial insects will increase the yield, while an increase in the abundance of pest insects will decrease the yield. Based on these simulation results, agricultural professionals can take measures to improve the productivity of pesticide-free and organic farming. This allows agricultural workers to grasp the trends of beneficial and pest insects in real time and take appropriate measures. Furthermore, AI simulation predictions can improve the productivity of pesticide-free and organic farming. For example, harvesting can be maximized by timing it with the period when the abundance of beneficial insects increases. Furthermore, production quality can be maintained by taking pest control measures in accordance with the period when the abundance of pest insects increases. This makes the agricultural support system extremely useful for agricultural workers and a powerful tool for improving the productivity of pesticide-free and organic farming.

[0029] The agricultural support system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, sensor data, image data, and text data. The collection unit periodically monitors the types, number of appearances, appearance times, and frequency of beneficial insects and pests, for example, using fixed-point monitoring cameras. The collection unit can also transmit data to a cloud server. For example, the collection unit transmits image data captured by the fixed-point monitoring cameras to the cloud server and stores the data. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, machine learning or deep learning, but is not limited to, examples. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using a deep learning algorithm. The analysis unit can also improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit improves analysis accuracy by combining machine learning and deep learning. The prediction unit performs simulation prediction based on the analysis results obtained by the analysis unit. The simulation prediction predicts, for example, the correlation between the abundance of beneficial insects and pest insects and the yield or production quality, but is not limited to such an example. For example, the prediction unit predicts that the yield will increase as the abundance of beneficial insects increases. The prediction unit can also predict that the yield will decrease as the abundance of pest insects increases. The prediction unit can also provide multiple prediction results based on different scenarios. For example, the prediction unit provides prediction results based on a scenario that takes weather fluctuations into account. The providing unit provides the prediction results obtained by the prediction unit. The provision is performed, for example, to agricultural stakeholders, but is not limited to such an example. For example, the providing unit provides the prediction results to agricultural stakeholders so that they can take appropriate agricultural measures. The providing unit can also propose measures to improve the productivity of pesticide-free or organic farming. For example, the providing unit proposes measures to harvest in accordance with the period when the abundance of beneficial insects increases. The providing unit can also propose pest control measures in accordance with the period when the abundance of pest insects increases. This allows the agricultural support system according to the embodiment to efficiently collect, analyze, simulate, predict, and provide data.Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may propose countermeasures using an AI model that receives the prediction results obtained by the predicting unit as input and outputs countermeasure proposals to those involved in agriculture.

[0030] The collection unit can periodically observe the types, number of appearances, time of appearance, and frequency of beneficial insects and pest insects using fixed-point monitoring cameras. Fixed-point monitoring cameras include, but are not limited to, high-resolution cameras, infrared cameras, and 360-degree cameras. The collection unit can, for example, observe the types of beneficial insects and pest insects using high-resolution cameras. The collection unit can also observe the movements of beneficial insects and pest insects at night using infrared cameras. The collection unit can also observe the movements of beneficial insects and pest insects over a wide area using 360-degree cameras. This allows the use of fixed-point monitoring cameras to accurately observe the movements of beneficial insects and pest insects. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data captured by the fixed-point monitoring cameras into a generation AI and have the generation AI identify the types and numbers of beneficial insects and pest insects.

[0031] The collection unit can transmit data to a cloud server. Examples of cloud servers include, but are not limited to, AWS (registered trademark), Google (registered trademark), and Microsoft Azure (registered trademark). The collection unit can transmit data using AWS, for example. The collection unit can also transmit data using Google Cloud. The collection unit can also transmit data using Microsoft Azure. By transmitting data to the cloud server, data storage and analysis can be made more efficient. Some or all of the above-described 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 cause the generation AI to select data to be transmitted to the cloud server.

[0032] The analysis unit can analyze the data using machine learning or deep learning. Machine learning algorithms include, but are not limited to, linear regression, decision tree, and random forest. The analysis unit can analyze the data using, for example, linear regression. The analysis unit can also analyze the data using a decision tree. The analysis unit can also analyze the data using a random forest. Deep learning algorithms include, but are not limited to, neural networks, CNNs (convolutional neural networks), and RNNs (recurrent neural networks). The analysis unit can analyze the data using, for example, a neural network. The analysis unit can also analyze the data using CNNs. The analysis unit can also analyze the data using RNNs. This improves the accuracy of data analysis by using machine learning or deep learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data obtained from the collection unit into a generation AI and cause the generation AI to analyze the data.

[0033] The prediction unit can simulate and predict the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. Methods for measuring the correlation include, but are not limited to, correlation coefficients, regression analysis, and factor analysis. The prediction unit can predict the correlation between the abundance of beneficial insects and pest insects and the yield using, for example, a correlation coefficient. The prediction unit can also predict the correlation between the abundance of beneficial insects and pest insects and the production quality using regression analysis. The prediction unit can also predict the correlation between the abundance of beneficial insects and pest insects and the yield or production quality using factor analysis. This improves agricultural productivity by simulating and predicting the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input the analysis results obtained from the analysis unit into a generation AI and cause the generation AI to perform a simulation prediction of the correlation.

[0034] The providing unit can provide the prediction results to agricultural stakeholders. Examples of agricultural stakeholders include, but are not limited to, farmers, agricultural consultants, agricultural researchers, etc. The providing unit, for example, provides the prediction results to farmers. The providing unit can also provide the prediction results to agricultural consultants. The providing unit can also provide the prediction results to agricultural researchers. By providing the prediction results to agricultural stakeholders, appropriate agricultural measures can be taken. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the prediction results obtained from the prediction unit into the generating AI and cause the generating AI to execute a method for providing the results to agricultural stakeholders.

[0035] The providing unit can propose measures to improve the productivity of pesticide-free or organic farming. The definition of "pesticide-free" includes, but is not limited to, not using specific pesticides and using natural pest control methods. The definition of organic farming includes, but is not limited to, meeting the standards for obtaining organic certification and not using chemical fertilizers or synthetic pesticides. For example, the providing unit can propose, as a pesticide-free measure, a measure to harvest at a time when beneficial insects are abundant. The providing unit can also propose, as an organic farming measure, a pest control measure at a time when pest insects are abundant. The providing unit can also propose the timing of fertilization and irrigation to improve the productivity of pesticide-free or organic farming. For example, the providing unit can propose a measure to fertilize at a time when beneficial insects are abundant. The providing unit can also propose a measure to irrigate at a time when pest insects are abundant. By proposing measures to improve the productivity of pesticide-free or organic farming, agricultural efficiency is improved. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the prediction results obtained from the predicting unit into the generating AI and have the generating AI implement measures proposals for pesticide-free or organic farming.

[0036] The collection unit can learn the behavioral patterns of beneficial insects and pest insects and automatically set the optimal observation timing. The collection unit, for example, learns the time periods when beneficial insects are most active and collects data during those time periods. The collection unit can also learn the time periods when pest insects are predicted to appear and collect data during those time periods. The collection unit can also learn the behavioral patterns of beneficial insects and pest insects by season and set the optimal observation timing. In this way, by learning the behavioral patterns of beneficial insects and pest insects, the optimal observation timing can be set. 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 behavioral data of beneficial insects and pest insects into a generation AI and have the generation AI set the optimal observation timing.

[0037] The collection unit can collect data under different weather conditions and analyze the impact of environmental changes. For example, the collection unit collects data during rainy weather and analyzes the behavioral patterns of beneficial insects and pest insects. The collection unit can also collect data during sunny weather and analyze the behavioral patterns of beneficial insects and pest insects. The collection unit can also collect data according to changes in temperature and analyze the behavioral patterns of beneficial insects and pest insects. In this way, by collecting data under different weather conditions, the impact of environmental changes can be analyzed. 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 into the generation AI and cause the generation AI to analyze the impact of environmental changes.

[0038] The collection unit can collect data over a wide range by linking multiple cameras, enabling detailed spatial analysis. The collection unit, for example, installs multiple cameras and collects data over a wide range. The collection unit can also integrate data between the cameras and perform detailed spatial analysis. The collection unit can also adjust the position of the cameras and set an optimal data collection range. This allows multiple cameras to be linked to collect data over a wide range, enabling detailed spatial analysis. 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 data acquired by multiple cameras into a generation AI and have the generation AI perform spatial analysis.

[0039] The collection unit can collect data from different regions based on geographical information and perform analysis taking into account regional characteristics. For example, the collection unit collects data taking into account weather conditions in different regions. The collection unit can also collect data taking into account soil conditions in different regions. The collection unit can also collect data taking into account vegetation conditions in different regions. In this way, by collecting data based on geographical information, analysis taking into account regional characteristics becomes possible. Some or all of the above-described 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 geographical information to the generation AI and cause the generation AI to collect data taking into account regional characteristics.

[0040] The collection unit can collect information from social media to supplement the information on the appearance of beneficial insects and pest insects. For example, the collection unit can analyze the content of posts on social media to collect the information on the appearance of beneficial insects and pest insects. The collection unit can also analyze check-in information on social media to collect the information on the appearance of beneficial insects and pest insects. The collection unit can also analyze image posts on social media to collect the information on the appearance of beneficial insects and pest insects. In this way, the information collected from social media can supplement the information on the appearance of beneficial insects and pest insects. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media post data into the generation AI and cause the generation AI to collect the information on the appearance of beneficial insects and pest insects.

[0041] The collection unit can customize the collection method based on past data and achieve efficient data collection. The collection unit, for example, analyzes past data and sets an optimal data collection method. The collection unit can also adjust the collection frequency based on past data. The collection unit can also adjust the collection range based on past data. This enables efficient data collection by customizing the collection method based on past data. 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 past data into a generation AI and have the generation AI customize the collection method.

[0042] The analysis unit can perform detailed behavioral analysis based on the ecological information of beneficial insects and pest insects. For example, the analysis unit analyzes activity times and behavioral patterns based on the ecological information of beneficial insects. The analysis unit can also analyze appearance times and behavioral patterns based on the ecological information of pest insects. The analysis unit can also analyze interactions based on the ecological information of beneficial insects and pest insects. This improves the accuracy of the analysis by performing detailed behavioral analysis based on the ecological information of beneficial insects and pest insects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ecological data of beneficial insects and pest insects into the generation AI and cause the generation AI to perform behavioral analysis.

[0043] The analysis unit can improve the analysis accuracy by combining different analysis algorithms. The analysis unit can improve the analysis accuracy by combining, for example, machine learning and deep learning. The analysis unit can also improve the analysis accuracy by combining different machine learning algorithms. The analysis unit can also improve the analysis accuracy by combining different data analysis methods. In this way, the analysis accuracy is improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to improve the analysis accuracy.

[0044] The analysis unit can learn from past analysis results and continuously improve the analysis model. The analysis unit can, for example, improve the analysis model based on past analysis results. The analysis unit can also learn from past analysis results and improve analysis accuracy. The analysis unit can also introduce new analysis methods based on past analysis results. In this way, the analysis model can be continuously improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI improve the analysis model.

[0045] The analysis unit can integrate different data sources and perform analysis. For example, the analysis unit can integrate weather data and analyze the behavior patterns of beneficial insects and pest insects. The analysis unit can also integrate soil data and analyze the behavior patterns of beneficial insects and pest insects. The analysis unit can also integrate weather data and soil data and analyze the behavior patterns of beneficial insects and pest insects. In this way, by integrating different data sources, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather data and soil data into the generation AI and cause the generation AI to perform an integrated analysis of the data.

[0046] The analysis unit can improve the reliability of the analysis results by referring to related academic papers. The analysis unit can improve the reliability of the analysis results by referring to academic papers on beneficial insects and pests, for example. The analysis unit can also improve the reliability of the analysis results by referring to academic papers on weather data. The analysis unit can also improve the reliability of the analysis results by referring to academic papers on soil data. In this way, by referring to related academic papers, the reliability of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from academic papers into the generation AI and cause the generation AI to improve the reliability of the analysis results.

[0047] The analysis unit can visualize the analysis results to enable the user to intuitively understand them. The analysis unit can visualize the analysis results, for example, in graphs or charts. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. The analysis unit can also visualize the analysis results in animation to enable the user to intuitively understand them. In this way, visualizing the analysis results makes it easier for the user to intuitively understand them. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI perform visualization.

[0048] The prediction unit can perform long-term predictions taking into account seasonal fluctuations of beneficial insects and pest insects. The prediction unit, for example, performs long-term predictions taking into account seasonal fluctuations of beneficial insects. The prediction unit can also perform long-term predictions taking into account seasonal fluctuations of pest insects. The prediction unit can also perform long-term predictions taking into account seasonal fluctuations of beneficial insects and pest insects. This makes it possible to perform long-term predictions by taking into account seasonal fluctuations of beneficial insects and pest insects. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input seasonal fluctuation data into the generation AI and cause the generation AI to perform a long-term prediction.

[0049] The prediction unit can provide multiple prediction results based on different scenarios. For example, the prediction unit can provide prediction results based on a scenario that takes weather fluctuations into account. The prediction unit can also provide prediction results based on a scenario that takes pesticide use into account. The prediction unit can also provide prediction results based on a scenario that takes weather fluctuations and pesticide use into account. This allows for responding to various situations by providing multiple prediction results based on different scenarios. Some or all of the above-mentioned processing in the prediction unit can be performed using AI, for example, or without AI. For example, the prediction unit can input different scenario data into the generation AI and have the generation AI execute multiple prediction results.

[0050] The prediction unit can learn from past prediction results and continuously improve the prediction model. The prediction unit, for example, improves the prediction model based on past prediction results. The prediction unit can also learn from past prediction results and improve prediction accuracy. The prediction unit can also introduce new prediction methods based on past prediction results. In this way, the prediction model can be continuously improved by learning from past prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past prediction results into a generation AI and have the generation AI improve the prediction model.

[0051] The prediction unit can provide prediction results for different regions based on geographical information. The prediction unit can provide prediction results taking into account, for example, weather conditions in different regions. The prediction unit can also provide prediction results taking into account soil conditions in different regions. The prediction unit can also provide prediction results taking into account vegetation conditions in different regions. By providing prediction results based on geographical information, predictions that take into account regional characteristics become possible. Some or all of the above-described processing in the prediction unit can be performed, for example, using AI or without AI. For example, the prediction unit can input geographical information into the generation AI and cause the generation AI to provide prediction results that take into account regional characteristics.

[0052] The prediction unit can evaluate the economic impact of the prediction result by referring to related market data. The prediction unit can evaluate the economic impact of the prediction result based on, for example, market data. The prediction unit can also evaluate the profitability of the prediction result by referring to market data. The prediction unit can also evaluate the cost-effectiveness of the prediction result based on market data. In this way, the economic impact of the prediction result can be evaluated by referring to related market data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input market data into a generation AI and cause the generation AI to evaluate the economic impact.

[0053] The prediction unit can provide the prediction results in different formats to help the user understand. For example, the prediction unit can provide the prediction results in a graph format to make them easier to understand visually. The prediction unit can also provide the prediction results in a text format and add detailed explanations. The prediction unit can also provide the prediction results in an animation format to make them easier to understand dynamically. By providing the prediction results in different formats, the user's understanding can be deepened. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the prediction results to a generation AI and cause the generation AI to provide the prediction results in different formats.

[0054] The providing unit can propose specific agricultural measures based on the prediction results. For example, the providing unit can propose measures to harvest in accordance with the period when the abundance of beneficial insects increases. The providing unit can also propose pest control measures in accordance with the period when the abundance of pest insects increases. The providing unit can also propose optimal timing for fertilization and irrigation based on the prediction results. This improves agricultural efficiency by proposing specific agricultural measures based on the prediction results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prediction results to the generating AI and cause the generating AI to propose specific agricultural measures.

[0055] The providing unit can provide customized advice based on the user's past behavioral history. The providing unit can, for example, suggest the optimal harvest timing based on the user's past harvest data. The providing unit can also suggest optimal pest control measures based on the user's past pest control history. The providing unit can also suggest the optimal fertilization timing based on the user's past fertilization history. In this way, by providing customized advice based on the user's past behavioral history, more appropriate measures can be taken. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past behavioral history into the generating AI and cause the generating AI to provide customized advice.

[0056] The providing unit can perform risk assessment based on the prediction results and issue a warning to the user. For example, the providing unit can issue a warning to the user when a prediction is made that the number of pests will increase. The providing unit can also issue a warning to the user when a prediction is made that weather conditions will worsen. The providing unit can also issue a warning to the user when a prediction is made that the harvest yield will decrease. In this way, by performing risk assessment based on the prediction results, it is possible to issue an appropriate warning to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prediction results to the generating AI and cause the generating AI to perform risk assessment and issue a warning.

[0057] The providing unit can provide information compatible with different devices. For example, the providing unit can provide information optimized for smartphones. The providing unit can also provide information optimized for tablets. The providing unit can also provide information optimized for desktops. This allows compatibility with different devices, allowing users to obtain information from various devices. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information for different devices into the generating AI and cause the generating AI to provide information optimized for the device.

[0058] The providing unit can continuously improve the information to be provided based on user feedback. For example, the providing unit can improve the content of the information based on user feedback. The providing unit can also improve the method of providing information based on user feedback. The providing unit can also introduce new information providing methods based on user feedback. This enables more appropriate information to be provided by continuously improving the information based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and have the generating AI improve the information.

[0059] The providing unit can propose a marketing strategy based on the prediction results and support sales promotion. The providing unit, for example, can propose an optimal sales time based on the prediction results. The providing unit can also propose a target market based on the prediction results. The providing unit can also propose an effective promotion strategy based on the prediction results. In this way, by proposing a marketing strategy based on the prediction results, sales promotion becomes more efficient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the prediction results to a generating AI and have the generating AI execute a marketing strategy proposal.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can also monitor the soil components of agricultural land in real time and collect data. For example, sensors can measure the soil's pH value, humidity, and nutrient content, and send the data to a cloud server. The collection unit can also track soil changes over the long term and suggest the timing of soil improvement to agricultural stakeholders. This allows farmers to constantly understand the condition of the soil and take appropriate agricultural measures.

[0062] The analysis unit can estimate the growth stage of crops based on the collected data and propose optimal timing for fertilization and irrigation. For example, it can analyze the color and shape of crop leaves to identify the growth stage. The analysis unit can also combine this with weather data to predict the optimal environmental conditions for crop growth. This allows for optimal crop growth and maximized yields.

[0063] The forecasting unit can use the collected data to predict the risk of pest outbreaks and issue early warnings to agricultural stakeholders. For example, it can combine past data with weather conditions to predict when pests will emerge. The forecasting unit can also suggest appropriate control measures if the risk of pest outbreaks increases. This helps minimize pest damage and maintain crop quality.

[0064] Based on the collected data, the data provision unit can provide advice to agricultural workers on optimizing the harvesting time of crops. For example, it can combine crop growth data and weather data to predict the optimal harvest time. The data provision unit can also make suggestions on optimizing the allocation of labor according to the harvesting time. This can improve the efficiency of harvesting work and maximize the yield.

[0065] The collection unit can collect data under different weather conditions and analyze the impact of environmental changes. For example, data can be collected on rainy days to analyze the behavioral patterns of beneficial and pest insects. Data can also be collected on sunny days to analyze the behavioral patterns of beneficial and pest insects. In this way, by collecting data under different weather conditions, the impact of environmental changes can be analyzed.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data. The data includes, for example, sensor data, image data, text data, etc. The collection unit periodically observes the types, number of appearances, appearance times, and frequency of beneficial and pest insects using fixed-point monitoring cameras. The collection unit can also send data to a cloud server. For example, image data captured by the fixed-point monitoring cameras is sent to the cloud server and stored there. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, machine learning or deep learning. The analysis unit analyzes the data using a machine learning algorithm or a deep learning algorithm, and can also combine different analysis algorithms to improve the accuracy of the analysis. Step 3: The prediction unit performs a simulation prediction based on the analysis results obtained by the analysis unit. The simulation prediction predicts, for example, the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. The prediction unit predicts that an increase in the abundance of beneficial insects will increase the yield, and that an increase in the abundance of pest insects will decrease the yield. The prediction unit can also provide multiple prediction results based on scenarios that take weather fluctuations into account. Step 4: The providing unit provides the prediction results obtained by the prediction unit. This is done, for example, to agricultural professionals. The providing unit provides the prediction results to agricultural professionals so that they can take appropriate agricultural measures. The providing unit can also propose measures to improve the productivity of pesticide-free and organic farming. For example, it proposes measures to harvest in accordance with the period when the abundance of beneficial insects increases, or pest control measures in accordance with the period when the abundance of pest insects increases.

[0068] (Example 2) An agricultural support system according to an embodiment of the present invention monitors the types, number, time, and frequency of beneficial and pest insects in farmland or cultivated land using fixed-point surveillance cameras and collects the data. The collected data is analyzed by AI to simulate and predict the correlation between the abundance of beneficial and pest insects and the yield and production quality. This can improve the productivity of pesticide-free and organic farming. For example, the agricultural support system installs fixed-point surveillance cameras in farmland or cultivated land and periodically monitors the types, number, time, and frequency of beneficial and pest insects. For example, the cameras capture images at regular intervals and analyze the images to identify the types and numbers of beneficial and pest insects. This data is sent to and stored on a cloud server. Next, the collected data is analyzed by AI. The AI ​​simulates and predicts the correlation between the abundance of beneficial and pest insects and the yield and production quality. For example, it is predicted that an increase in the abundance of beneficial insects will increase the yield, while an increase in the abundance of pest insects will decrease the yield. Based on these simulation results, agricultural professionals can take measures to improve the productivity of pesticide-free and organic farming. This allows agricultural workers to grasp the trends of beneficial and pest insects in real time and take appropriate measures. Furthermore, AI simulation predictions can improve the productivity of pesticide-free and organic farming. For example, harvesting can be maximized by timing it with the period when the abundance of beneficial insects increases. Furthermore, production quality can be maintained by taking pest control measures in accordance with the period when the abundance of pest insects increases. This makes the agricultural support system extremely useful for agricultural workers and a powerful tool for improving the productivity of pesticide-free and organic farming.

[0069] The agricultural support system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, sensor data, image data, and text data. The collection unit periodically monitors the types, number of appearances, appearance times, and frequency of beneficial insects and pests, for example, using fixed-point monitoring cameras. The collection unit can also transmit data to a cloud server. For example, the collection unit transmits image data captured by the fixed-point monitoring cameras to the cloud server and stores the data. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, machine learning or deep learning, but is not limited to, examples. For example, the analysis unit analyzes the data using a machine learning algorithm. The analysis unit can also analyze the data using a deep learning algorithm. The analysis unit can also improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit improves analysis accuracy by combining machine learning and deep learning. The prediction unit performs simulation prediction based on the analysis results obtained by the analysis unit. The simulation prediction predicts, for example, the correlation between the abundance of beneficial insects and pest insects and the yield or production quality, but is not limited to such an example. For example, the prediction unit predicts that the yield will increase as the abundance of beneficial insects increases. The prediction unit can also predict that the yield will decrease as the abundance of pest insects increases. The prediction unit can also provide multiple prediction results based on different scenarios. For example, the prediction unit provides prediction results based on a scenario that takes weather fluctuations into account. The providing unit provides the prediction results obtained by the prediction unit. The provision is performed, for example, to agricultural stakeholders, but is not limited to such an example. For example, the providing unit provides the prediction results to agricultural stakeholders so that they can take appropriate agricultural measures. The providing unit can also propose measures to improve the productivity of pesticide-free or organic farming. For example, the providing unit proposes measures to harvest in accordance with the period when the abundance of beneficial insects increases. The providing unit can also propose pest control measures in accordance with the period when the abundance of pest insects increases. This allows the agricultural support system according to the embodiment to efficiently collect, analyze, simulate, predict, and provide data.Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may propose countermeasures using an AI model that receives the prediction results obtained by the predicting unit as input and outputs countermeasure proposals to those involved in agriculture.

[0070] The collection unit can periodically observe the types, number of appearances, time of appearance, and frequency of beneficial insects and pest insects using fixed-point monitoring cameras. Fixed-point monitoring cameras include, but are not limited to, high-resolution cameras, infrared cameras, and 360-degree cameras. The collection unit can, for example, observe the types of beneficial insects and pest insects using high-resolution cameras. The collection unit can also observe the movements of beneficial insects and pest insects at night using infrared cameras. The collection unit can also observe the movements of beneficial insects and pest insects over a wide area using 360-degree cameras. This allows the use of fixed-point monitoring cameras to accurately observe the movements of beneficial insects and pest insects. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data captured by the fixed-point monitoring cameras into a generation AI and have the generation AI identify the types and numbers of beneficial insects and pest insects.

[0071] The collection unit can transmit data to a cloud server. Examples of cloud servers include, but are not limited to, AWS, Google Cloud, and Microsoft Azure. The collection unit can transmit data using, for example, AWS. The collection unit can also transmit data using Google Cloud. The collection unit can also transmit data using Microsoft Azure. By transmitting data to a cloud server, data storage and analysis can be made more efficient. Some or all of the above-described 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 cause the generation AI to select data to be transmitted to the cloud server.

[0072] The analysis unit can analyze the data using machine learning or deep learning. Machine learning algorithms include, but are not limited to, linear regression, decision tree, and random forest. The analysis unit can analyze the data using, for example, linear regression. The analysis unit can also analyze the data using a decision tree. The analysis unit can also analyze the data using a random forest. Deep learning algorithms include, but are not limited to, neural networks, CNNs (convolutional neural networks), and RNNs (recurrent neural networks). The analysis unit can analyze the data using, for example, a neural network. The analysis unit can also analyze the data using CNNs. The analysis unit can also analyze the data using RNNs. This improves the accuracy of data analysis by using machine learning or deep learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data obtained from the collection unit into a generation AI and cause the generation AI to analyze the data.

[0073] The prediction unit can simulate and predict the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. Methods for measuring the correlation include, but are not limited to, correlation coefficients, regression analysis, and factor analysis. The prediction unit can predict the correlation between the abundance of beneficial insects and pest insects and the yield using, for example, a correlation coefficient. The prediction unit can also predict the correlation between the abundance of beneficial insects and pest insects and the production quality using regression analysis. The prediction unit can also predict the correlation between the abundance of beneficial insects and pest insects and the yield or production quality using factor analysis. This improves agricultural productivity by simulating and predicting the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input the analysis results obtained from the analysis unit into a generation AI and cause the generation AI to perform a simulation prediction of the correlation.

[0074] The providing unit can provide the prediction results to agricultural stakeholders. Examples of agricultural stakeholders include, but are not limited to, farmers, agricultural consultants, agricultural researchers, etc. The providing unit, for example, provides the prediction results to farmers. The providing unit can also provide the prediction results to agricultural consultants. The providing unit can also provide the prediction results to agricultural researchers. By providing the prediction results to agricultural stakeholders, appropriate agricultural measures can be taken. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the prediction results obtained from the prediction unit into the generating AI and cause the generating AI to execute a method for providing the results to agricultural stakeholders.

[0075] The providing unit can propose measures to improve the productivity of pesticide-free or organic farming. The definition of "pesticide-free" includes, but is not limited to, not using specific pesticides and using natural pest control methods. The definition of organic farming includes, but is not limited to, meeting the standards for obtaining organic certification and not using chemical fertilizers or synthetic pesticides. For example, the providing unit can propose, as a pesticide-free measure, a measure to harvest at a time when beneficial insects are abundant. The providing unit can also propose, as an organic farming measure, a pest control measure at a time when pest insects are abundant. The providing unit can also propose the timing of fertilization and irrigation to improve the productivity of pesticide-free or organic farming. For example, the providing unit can propose a measure to fertilize at a time when beneficial insects are abundant. The providing unit can also propose a measure to irrigate at a time when pest insects are abundant. By proposing measures to improve the productivity of pesticide-free or organic farming, agricultural efficiency is improved. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the prediction results obtained from the predicting unit into the generating AI and have the generating AI implement measures proposals for pesticide-free or organic farming.

[0076] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to quickly acquire data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The collection unit can learn the behavioral patterns of beneficial insects and pest insects and automatically set the optimal observation timing. The collection unit, for example, learns the time periods when beneficial insects are most active and collects data during those time periods. The collection unit can also learn the time periods when pest insects are predicted to appear and collect data during those time periods. The collection unit can also learn the behavioral patterns of beneficial insects and pest insects by season and set the optimal observation timing. In this way, by learning the behavioral patterns of beneficial insects and pest insects, the optimal observation timing can be set. 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 behavioral data of beneficial insects and pest insects into a generation AI and have the generation AI set the optimal observation timing.

[0078] The collection unit can collect data under different weather conditions and analyze the impact of environmental changes. For example, the collection unit collects data during rainy weather and analyzes the behavioral patterns of beneficial insects and pest insects. The collection unit can also collect data during sunny weather and analyze the behavioral patterns of beneficial insects and pest insects. The collection unit can also collect data according to changes in temperature and analyze the behavioral patterns of beneficial insects and pest insects. In this way, by collecting data under different weather conditions, the impact of environmental changes can be analyzed. 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 into the generation AI and cause the generation AI to analyze the impact of environmental changes.

[0079] The collection unit can collect data over a wide range by linking multiple cameras, enabling detailed spatial analysis. The collection unit, for example, installs multiple cameras and collects data over a wide range. The collection unit can also integrate data between the cameras and perform detailed spatial analysis. The collection unit can also adjust the position of the cameras and set an optimal data collection range. This allows multiple cameras to be linked to collect data over a wide range, enabling detailed spatial analysis. 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 data acquired by multiple cameras into a generation AI and have the generation AI perform spatial analysis.

[0080] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The collection unit can collect data from different regions based on geographical information and perform analysis taking into account regional characteristics. For example, the collection unit collects data taking into account weather conditions in different regions. The collection unit can also collect data taking into account soil conditions in different regions. The collection unit can also collect data taking into account vegetation conditions in different regions. In this way, by collecting data based on geographical information, analysis taking into account regional characteristics becomes possible. Some or all of the above-described 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 geographical information to the generation AI and cause the generation AI to collect data taking into account regional characteristics.

[0082] The collection unit can collect information from social media to supplement the information on the appearance of beneficial insects and pest insects. For example, the collection unit can analyze the content of posts on social media to collect the information on the appearance of beneficial insects and pest insects. The collection unit can also analyze check-in information on social media to collect the information on the appearance of beneficial insects and pest insects. The collection unit can also analyze image posts on social media to collect the information on the appearance of beneficial insects and pest insects. In this way, the information collected from social media can supplement the information on the appearance of beneficial insects and pest insects. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media post data into the generation AI and cause the generation AI to collect the information on the appearance of beneficial insects and pest insects.

[0083] The collection unit can customize the collection method based on past data and achieve efficient data collection. The collection unit, for example, analyzes past data and sets an optimal data collection method. The collection unit can also adjust the collection frequency based on past data. The collection unit can also adjust the collection range based on past data. This enables efficient data collection by customizing the collection method based on past data. 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 past data into a generation AI and have the generation AI customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis results are presented according to the user's emotions, the user's understanding can be deepened. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The analysis unit can perform detailed behavioral analysis based on the ecological information of beneficial insects and pest insects. For example, the analysis unit analyzes activity times and behavioral patterns based on the ecological information of beneficial insects. The analysis unit can also analyze appearance times and behavioral patterns based on the ecological information of pest insects. The analysis unit can also analyze interactions based on the ecological information of beneficial insects and pest insects. This improves the accuracy of the analysis by performing detailed behavioral analysis based on the ecological information of beneficial insects and pest insects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ecological data of beneficial insects and pest insects into the generation AI and cause the generation AI to perform behavioral analysis.

[0086] The analysis unit can improve the analysis accuracy by combining different analysis algorithms. The analysis unit can improve the analysis accuracy by combining, for example, machine learning and deep learning. The analysis unit can also improve the analysis accuracy by combining different machine learning algorithms. The analysis unit can also improve the analysis accuracy by combining different data analysis methods. In this way, the analysis accuracy is improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to improve the analysis accuracy.

[0087] The analysis unit can learn from past analysis results and continuously improve the analysis model. The analysis unit can, for example, improve the analysis model based on past analysis results. The analysis unit can also learn from past analysis results and improve analysis accuracy. The analysis unit can also introduce new analysis methods based on past analysis results. In this way, the analysis model can be continuously improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI improve the analysis model.

[0088] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the level of detail of the analysis results according to the user's emotions, the user's understanding can be deepened. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The analysis unit can integrate different data sources and perform analysis. For example, the analysis unit can integrate weather data and analyze the behavior patterns of beneficial insects and pest insects. The analysis unit can also integrate soil data and analyze the behavior patterns of beneficial insects and pest insects. The analysis unit can also integrate weather data and soil data and analyze the behavior patterns of beneficial insects and pest insects. In this way, by integrating different data sources, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input weather data and soil data into the generation AI and cause the generation AI to perform an integrated analysis of the data.

[0090] The analysis unit can improve the reliability of the analysis results by referring to related academic papers. The analysis unit can improve the reliability of the analysis results by referring to academic papers on beneficial insects and pests, for example. The analysis unit can also improve the reliability of the analysis results by referring to academic papers on weather data. The analysis unit can also improve the reliability of the analysis results by referring to academic papers on soil data. In this way, by referring to related academic papers, the reliability of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from academic papers into the generation AI and cause the generation AI to improve the reliability of the analysis results.

[0091] The analysis unit can visualize the analysis results to enable the user to intuitively understand them. The analysis unit can visualize the analysis results, for example, in graphs or charts. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. The analysis unit can also visualize the analysis results in animation to enable the user to intuitively understand them. In this way, visualizing the analysis results makes it easier for the user to intuitively understand them. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI and have the generation AI perform visualization.

[0092] The prediction unit can estimate the user's emotions and adjust the display method of the prediction result based on the estimated user's emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible prediction result. Furthermore, if the user is relaxed, the prediction unit can provide a detailed prediction result. Furthermore, if the user is in a hurry, the prediction unit can provide a prediction result that focuses on the main points. By adjusting the display method of the prediction result according to the user's emotions, the user's understanding can be deepened. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the prediction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The prediction unit can perform long-term predictions taking into account seasonal fluctuations of beneficial insects and pest insects. The prediction unit, for example, performs long-term predictions taking into account seasonal fluctuations of beneficial insects. The prediction unit can also perform long-term predictions taking into account seasonal fluctuations of pest insects. The prediction unit can also perform long-term predictions taking into account seasonal fluctuations of beneficial insects and pest insects. This makes it possible to perform long-term predictions by taking into account seasonal fluctuations of beneficial insects and pest insects. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input seasonal fluctuation data into the generation AI and cause the generation AI to perform a long-term prediction.

[0094] The prediction unit can provide multiple prediction results based on different scenarios. For example, the prediction unit can provide prediction results based on a scenario that takes weather fluctuations into account. The prediction unit can also provide prediction results based on a scenario that takes pesticide use into account. The prediction unit can also provide prediction results based on a scenario that takes weather fluctuations and pesticide use into account. This allows for responding to various situations by providing multiple prediction results based on different scenarios. Some or all of the above-mentioned processing in the prediction unit can be performed using AI, for example, or without AI. For example, the prediction unit can input different scenario data into the generation AI and have the generation AI execute multiple prediction results.

[0095] The prediction unit can learn from past prediction results and continuously improve the prediction model. The prediction unit, for example, improves the prediction model based on past prediction results. The prediction unit can also learn from past prediction results and improve prediction accuracy. The prediction unit can also introduce new prediction methods based on past prediction results. In this way, the prediction model can be continuously improved by learning from past prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past prediction results into a generation AI and have the generation AI improve the prediction model.

[0096] The prediction unit can estimate the user's emotions and prioritize the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can prioritize providing important prediction results. Furthermore, if the user is relaxed, the prediction unit can prioritize providing detailed prediction results. Furthermore, if the user is in a hurry, the prediction unit can prioritize providing prediction results that can be provided quickly. Thus, by prioritizing the prediction results according to the user's emotions, important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or without an AI. For example, the prediction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The prediction unit can provide prediction results for different regions based on geographical information. The prediction unit can provide prediction results taking into account, for example, weather conditions in different regions. The prediction unit can also provide prediction results taking into account soil conditions in different regions. The prediction unit can also provide prediction results taking into account vegetation conditions in different regions. By providing prediction results based on geographical information, predictions that take into account regional characteristics become possible. Some or all of the above-described processing in the prediction unit can be performed, for example, using AI or without AI. For example, the prediction unit can input geographical information into the generation AI and cause the generation AI to provide prediction results that take into account regional characteristics.

[0098] The prediction unit can evaluate the economic impact of the prediction result by referring to related market data. The prediction unit can evaluate the economic impact of the prediction result based on, for example, market data. The prediction unit can also evaluate the profitability of the prediction result by referring to market data. The prediction unit can also evaluate the cost-effectiveness of the prediction result based on market data. In this way, the economic impact of the prediction result can be evaluated by referring to related market data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input market data into a generation AI and cause the generation AI to evaluate the economic impact.

[0099] The prediction unit can provide the prediction results in different formats to help the user understand. For example, the prediction unit can provide the prediction results in a graph format to make them easier to understand visually. The prediction unit can also provide the prediction results in a text format and add detailed explanations. The prediction unit can also provide the prediction results in an animation format to make them easier to understand dynamically. By providing the prediction results in different formats, the user's understanding can be deepened. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the prediction results to a generation AI and cause the generation AI to provide the prediction results in different formats.

[0100] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. By adjusting the way the information is presented according to the user's emotions, the user's understanding can be deepened. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] The providing unit can propose specific agricultural measures based on the prediction results. For example, the providing unit can propose measures to harvest in accordance with the period when the abundance of beneficial insects increases. The providing unit can also propose pest control measures in accordance with the period when the abundance of pest insects increases. The providing unit can also propose optimal timing for fertilization and irrigation based on the prediction results. This improves agricultural efficiency by proposing specific agricultural measures based on the prediction results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prediction results to the generating AI and cause the generating AI to propose specific agricultural measures.

[0102] The providing unit can provide customized advice based on the user's past behavioral history. The providing unit can, for example, suggest the optimal harvest timing based on the user's past harvest data. The providing unit can also suggest optimal pest control measures based on the user's past pest control history. The providing unit can also suggest the optimal fertilization timing based on the user's past fertilization history. In this way, by providing customized advice based on the user's past behavioral history, more appropriate measures can be taken. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past behavioral history into the generating AI and cause the generating AI to provide customized advice.

[0103] The providing unit can perform risk assessment based on the prediction results and issue a warning to the user. For example, the providing unit can issue a warning to the user when a prediction is made that the number of pests will increase. The providing unit can also issue a warning to the user when a prediction is made that weather conditions will worsen. The providing unit can also issue a warning to the user when a prediction is made that the harvest yield will decrease. In this way, by performing risk assessment based on the prediction results, it is possible to issue an appropriate warning to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prediction results to the generating AI and cause the generating AI to perform risk assessment and issue a warning.

[0104] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. In this way, by determining the priority of information according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0105] The providing unit can provide information compatible with different devices. For example, the providing unit can provide information optimized for smartphones. The providing unit can also provide information optimized for tablets. The providing unit can also provide information optimized for desktops. This allows compatibility with different devices, allowing users to obtain information from various devices. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information for different devices into the generating AI and cause the generating AI to provide information optimized for the device.

[0106] The providing unit can continuously improve the information to be provided based on user feedback. For example, the providing unit can improve the content of the information based on user feedback. The providing unit can also improve the method of providing information based on user feedback. The providing unit can also introduce new information providing methods based on user feedback. This enables more appropriate information to be provided by continuously improving the information based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and have the generating AI improve the information.

[0107] The providing unit can propose a marketing strategy based on the prediction results and support sales promotion. The providing unit, for example, can propose an optimal sales time based on the prediction results. The providing unit can also propose a target market based on the prediction results. The providing unit can also propose an effective promotion strategy based on the prediction results. In this way, by proposing a marketing strategy based on the prediction results, sales promotion becomes more efficient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the prediction results to a generating AI and have the generating AI execute a marketing strategy proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the camera 42 of the smart device 14 to observe the type, number, time, and frequency of beneficial and pest insects and transmits the observed data to the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, performs simulation prediction based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the smart device 14, provides the prediction results to agricultural professionals. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision 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 uses the camera 42 of the smart glasses 214 to observe the type, number of appearances, appearance time, and frequency of beneficial and pest insects and transmits the information to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs simulation prediction based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the prediction results to agricultural professionals. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to observe the type, number of appearances, appearance time, and frequency of beneficial and pest insects and transmits the observed data to the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, performs simulation prediction based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the headset terminal 314, provides the prediction results to agricultural professionals. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 of the robot 414 to observe the type, number, time, and frequency of beneficial and pest insects and transmits the information to the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, performs simulation prediction based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the robot 414, provides the prediction results to agricultural professionals. The collection unit can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing device 12.

[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 collection unit can also monitor the soil components of agricultural land in real time and collect data. For example, sensors can measure the soil's pH value, humidity, and nutrient content, and send the data to a cloud server. The collection unit can also track soil changes over the long term and suggest the timing of soil improvement to agricultural stakeholders. This allows farmers to constantly understand the condition of the soil and take appropriate agricultural measures.

[0110] The analysis unit can estimate the growth stage of crops based on the collected data and propose optimal timing for fertilization and irrigation. For example, it can analyze the color and shape of crop leaves to identify the growth stage. The analysis unit can also combine this with weather data to predict the optimal environmental conditions for crop growth. This allows for optimal crop growth and maximized yields.

[0111] The forecasting unit can use the collected data to predict the risk of pest outbreaks and issue early warnings to agricultural stakeholders. For example, it can combine past data with weather conditions to predict when pests will emerge. The forecasting unit can also suggest appropriate control measures if the risk of pest outbreaks increases. This helps minimize pest damage and maintain crop quality.

[0112] Based on the collected data, the data provision unit can provide advice to agricultural workers on optimizing the harvesting time of crops. For example, it can combine crop growth data and weather data to predict the optimal harvest time. The data provision unit can also make suggestions on optimizing the allocation of labor according to the harvesting time. This can improve the efficiency of harvesting work and maximize the yield.

[0113] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. On the other hand, if the user is relaxed, it can provide detailed analysis results. This allows the user to deepen their understanding by adjusting the way the analysis results are presented according to the user's emotions.

[0115] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible prediction result can be provided. On the other hand, if the user is relaxed, a detailed prediction result can be provided. This allows the user to deepen their understanding by adjusting the display method of the prediction results according to the user's emotions.

[0116] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible information can be provided. On the other hand, if the user is relaxed, detailed information can be provided. In this way, adjusting the way information is presented according to the user's emotions deepens the user's understanding.

[0117] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is nervous, important information can be provided with priority. Also, if the user is relaxed, detailed information can be provided with priority. In this way, by determining the priority of information according to the user's emotions, important information can be provided with priority.

[0118] The collection unit can collect data under different weather conditions and analyze the impact of environmental changes. For example, data can be collected on rainy days to analyze the behavioral patterns of beneficial and pest insects. Data can also be collected on sunny days to analyze the behavioral patterns of beneficial and pest insects. In this way, by collecting data under different weather conditions, the impact of environmental changes can be analyzed.

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

[0120] Step 1: The collection unit collects data. The data includes, for example, sensor data, image data, text data, etc. The collection unit periodically observes the types, number of appearances, appearance times, and frequency of beneficial and pest insects using fixed-point monitoring cameras. The collection unit can also send data to a cloud server. For example, image data captured by the fixed-point monitoring cameras is sent to the cloud server and stored there. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, machine learning or deep learning. The analysis unit analyzes the data using a machine learning algorithm or a deep learning algorithm, and can also combine different analysis algorithms to improve the accuracy of the analysis. Step 3: The prediction unit performs a simulation prediction based on the analysis results obtained by the analysis unit. The simulation prediction predicts, for example, the correlation between the abundance of beneficial insects and pest insects and the yield or production quality. The prediction unit predicts that an increase in the abundance of beneficial insects will increase the yield, and that an increase in the abundance of pest insects will decrease the yield. The prediction unit can also provide multiple prediction results based on scenarios that take weather fluctuations into account. Step 4: The providing unit provides the prediction results obtained by the prediction unit. This is done, for example, to agricultural professionals. The providing unit provides the prediction results to agricultural professionals so that they can take appropriate agricultural measures. The providing unit can also propose measures to improve the productivity of pesticide-free and organic farming. For example, it proposes measures to harvest in accordance with the period when the abundance of beneficial insects increases, or pest control measures in accordance with the period when the abundance of pest insects increases.

[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 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.

[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 type 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 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.

[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, the 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 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.

[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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," 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; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that performs simulation prediction based on the analysis results obtained by the analysis unit; a providing unit that provides the prediction result obtained by the prediction unit; Equipped with A system characterized by:

2. The collecting unit Regularly observe the types, numbers, times, and frequency of beneficial and harmful insects using fixed-point surveillance cameras.

2. The system of claim 1.

3. The collecting unit Sending data to a cloud server 2. The system of claim 1.

4. The analysis unit Analyze data using machine learning or deep learning 2. The system of claim 1.

5. The prediction unit Simulate and predict the correlation between beneficial and pest insect abundance and yield or production quality 2. The system of claim 1.

6. The providing unit Providing prediction results to agricultural stakeholders 2. The system of claim 1.

7. The providing unit Propose measures to improve productivity of pesticide-free or organic farming 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

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