system

Abstract

The system according to this embodiment aims to efficiently supply ingredients from local farmers and producers. [Solution] The system according to the embodiment comprises a collection unit, a planning unit, and a supply unit. The collection unit collects data from local farmers and producers. The planning unit creates a supply plan based on the data collected by the collection unit. The supply unit supplies local ingredients based on the supply plan created by the planning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the supply of food ingredients from local farmers and producers is not carried out efficiently, and there is room for improvement in optimizing the supply chain.

[0005] The system according to the embodiment aims to efficiently supply food ingredients from local farmers and producers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a planning unit, and a supply unit. The collection unit collects data of local farmers and producers. The planning unit makes a supply plan based on the data collected by the collection unit. The supply unit supplies local food ingredients based on the supply plan made by the planning unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently supply ingredients from local farmers and producers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The carbon-neutral food supply system according to an embodiment of the present invention is a system that realizes a carbon-neutral food supply chain through the construction of a local food supply network, sustainable production methods, optimization of refrigerated and frozen transport, improvement of energy efficiency, reduction of plastics, and consumer education. The carbon-neutral food supply system reduces carbon dioxide emissions by partnering with local farmers and producers and reducing long-distance transport. It improves the efficiency of the food supply chain by supplying local ingredients according to the demand of the local community. Next, as a sustainable production method, it adopts organic farming and sustainable agricultural practices to minimize the use of pesticides and chemical fertilizers. It adopts a circular agricultural system to aim for efficient use of resources and minimization of waste. Furthermore, as an optimization of refrigerated and frozen transport, it reduces energy consumption by using efficient cooling systems and insulation materials in the refrigerated and frozen transport of food. It reduces carbon dioxide emissions by optimizing transport routes to minimize distance and time. In addition, as an improvement of energy efficiency, it introduces renewable energy sources to minimize energy consumption in food production and warehouses. It uses highly efficient lighting, heating and cooling systems, and energy-efficient machinery. Furthermore, to reduce plastic use, we will minimize the use of single-use plastics and adopt biodegradable packaging materials. We will use recyclable packaging materials and streamline the recycling process. Finally, as part of consumer education, we will encourage household and business consumers to be environmentally conscious and provide information to support a carbon-neutral food supply chain. This mechanism will enable a carbon-neutral food supply chain and minimize environmental impact. For example, partnering with local farmers and producers can reduce long-distance transportation and lower carbon emissions. Adopting organic farming and sustainable agricultural practices can minimize the use of pesticides and chemical fertilizers. In addition, using efficient cooling systems and insulation materials can reduce energy consumption. Introducing renewable energy sources can minimize energy consumption in food production and warehousing. Minimizing the use of single-use plastics and adopting biodegradable packaging materials will enable plastic reduction.Consumer education is achieved by encouraging environmental considerations among household and business consumers and providing information to support carbon-neutral food supply chains. This enables carbon-neutral food supply systems to achieve efficient food supply chains while minimizing environmental impact.

[0029] The carbon-neutral food supply system according to this embodiment comprises a collection unit, a planning unit, and a supply unit. The collection unit collects data from local farmers and producers. This data may include, but is not limited to, production volume, harvest time, and crop type. The collection unit may, for example, collect data directly from local farmers and producers. Alternatively, the collection unit may collect data through digital platforms provided by farmers and producers. For example, the collection unit may automatically collect data entered by farmers and producers and store it in a database. The planning unit develops a supply plan based on the data collected by the collection unit. This supply plan may include, for example, supply volume, supply timing, and supply destinations. The planning unit may, for example, analyze the collected data to develop an optimal supply plan. The planning unit may also consider local demand and supply capacity when developing the supply plan. For example, the planning unit may adjust the supply plan based on local demand forecast data. The supply unit supplies local ingredients based on the supply plan developed by the planning unit. The supply unit procures ingredients from local farmers and producers, for example, and supplies them to local consumers. The supply unit can also select efficient transportation routes based on a supply plan and deliver the ingredients. For example, the supply unit can use a transportation route optimization algorithm to deliver ingredients via the shortest distance. This allows the carbon-neutral food supply system according to the embodiment to develop efficient supply plans based on data from local farmers and producers and supply local ingredients.

[0030] The data collection unit collects data from local farmers and producers. This data includes, but is not limited to, production volume, harvest time, and crop type. The data collection unit can, for example, collect data directly from local farmers and producers. It can also collect data through digital platforms provided by farmers and producers. For example, the data collection unit can automatically collect data entered by farmers and producers and store it in a database. Specifically, the data collection unit can acquire data in real time through smartphone apps and web portals used by farmers and producers. This allows the data collection unit to constantly monitor the latest production status and harvest forecasts. The data collection unit can also use IoT sensors to collect environmental data on farmland (e.g., soil moisture, temperature, sunlight). This allows for detailed monitoring of crop growth and improves the accuracy of harvest time predictions. Furthermore, the data collection unit can use drones to acquire aerial images of farmland and analyze the health and growth status of crops. This allows the data collection unit to collect data from local farmers and producers from multiple perspectives and provide highly accurate information. The collected data is stored in a cloud-based database, making it accessible to the planning and supply departments. This allows the data collection department to efficiently and effectively collect data and improve the overall system performance.

[0031] The planning department develops supply plans based on data collected by the data collection department. These plans may include, but are not limited to, supply volume, timing, and destination. For example, the planning department analyzes collected data to formulate optimal supply plans. It can also consider regional demand and supply capacity when developing supply plans. For instance, it adjusts supply plans based on regional demand forecast data. Specifically, it uses AI to analyze collected data and optimize the balance between supply and demand. AI learns from historical data and seasonal trends to predict future demand. For example, it adjusts supply plans considering fluctuations in demand during specific seasons and regional consumption patterns. Furthermore, the planning department can implement measures to minimize environmental impact when developing supply plans. For example, it can prioritize supplying to local consumers to reduce transportation distances. Finally, the planning department monitors the implementation of supply plans and modifies them as needed. For example, it can flexibly adjust supply plans in response to weather changes or unexpected fluctuations in demand. This allows the planning department to develop efficient and sustainable supply plans, improving the reliability and flexibility of the entire system.

[0032] The supply department supplies local ingredients based on the supply plan developed by the planning department. For example, the supply department procures ingredients from local farmers and producers and supplies them to local consumers. The supply department can also select efficient transportation routes based on the supply plan and deliver ingredients accordingly. For example, the supply department uses transportation route optimization algorithms to deliver ingredients via the shortest distance. Specifically, the supply department quickly collects ingredients harvested from local farmers and producers and transports them using refrigerated trucks and electric vehicles. This allows for the preservation of the ingredients' freshness while reducing the environmental impact. The supply department can also accept orders for ingredients from local consumers through an online platform. Consumers can easily order fresh local ingredients using their smartphones or computers. Based on the orders, the supply department can create efficient delivery schedules and deliver ingredients quickly. Furthermore, the supply department can utilize blockchain technology to ensure the traceability of ingredients. This allows consumers to verify the producers and production processes of the ingredients they purchase, enabling a highly reliable supply. Through these initiatives, the supply department can efficiently and sustainably supply local ingredients and promote regional carbon neutrality.

[0033] The provider offers technologies for adopting organic and sustainable agricultural practices. For example, it provides specific technologies and methods for organic farming, such as methods for using organic fertilizers and selecting organic pesticides. It can also provide specific technologies and methods for sustainable agricultural practices, such as soil conservation and water resource management. Furthermore, it can provide technologies and methods for biodiversity protection, such as vegetation management technologies for protecting biodiversity in farmland. In this way, the provider can realize sustainable agriculture by offering technologies for adopting organic and sustainable agricultural practices.

[0034] A circular agricultural system aims for the efficient use of resources and the reduction of waste. For example, a circular agricultural system employs methods of resource reuse. For instance, it composts agricultural waste for reuse. It can also employ waste treatment methods. For example, it recycles and reuses waste. Furthermore, a circular agricultural system can employ methods of efficient resource use. For example, it optimizes the use of water and fertilizer. This enables a circular agricultural system to achieve efficient resource use and minimize waste.

[0035] The cooling system reduces energy consumption by using efficient cooling systems and insulation materials. For example, the cooling system employs efficient cooling systems. For instance, it uses the latest cooling technology to minimize energy consumption. The cooling system can also utilize insulation materials. For example, it uses high-performance insulation materials to improve cooling efficiency. Furthermore, the cooling system can optimize the operation of the cooling system. For instance, it adjusts the operating time of the cooling system to reduce energy consumption. This allows the cooling system to reduce energy consumption.

[0036] The route optimization unit optimizes transportation routes to reduce distance and time. For example, the route optimization unit uses transportation route optimization algorithms. For instance, it calculates the shortest distance transportation route to minimize transportation time. The route optimization unit can also employ methods to reduce transportation costs. For example, it selects a transportation route that minimizes fuel consumption. Furthermore, the route optimization unit can employ methods to improve transportation efficiency. For example, it combines multiple modes of transportation to achieve efficient transport. This allows the route optimization unit to minimize distance and time by optimizing transportation routes.

[0037] The energy integration unit introduces renewable energy sources. For example, the energy integration unit introduces a solar power generation system. For example, the energy integration unit installs solar panels and supplies renewable energy. The energy integration unit can also introduce a wind power generation system. For example, the energy integration unit installs wind turbines and supplies renewable energy. Furthermore, the energy integration unit can also introduce a biomass energy system. For example, the energy integration unit supplies energy using biomass fuel. In this way, the energy integration unit can minimize energy consumption by introducing renewable energy sources.

[0038] The machinery department will use highly efficient lighting, heating and cooling systems, and energy-efficient machinery. For example, the machinery department will use highly efficient lighting, such as LED lighting, to reduce energy consumption. The machinery department may also use heating and cooling systems, such as energy-efficient air conditioners and heat pumps. Furthermore, the machinery department may use energy-efficient machinery, such as energy-saving agricultural machinery and energy-efficient pumps. By using highly efficient lighting, heating and cooling systems, and energy-efficient machinery, the machinery department can minimize energy consumption.

[0039] The packaging department will reduce the use of single-use plastics and adopt biodegradable packaging materials. For example, the packaging department will adopt methods to reduce the use of single-use plastics. For example, the packaging department will reduce disposable packaging materials and use reusable packaging materials. The packaging department may also adopt biodegradable packaging materials. For example, the packaging department will use biodegradable plastics or paper packaging materials. Furthermore, the packaging department may also use recyclable packaging materials. For example, the packaging department will use recyclable plastics or paper packaging materials and streamline the recycling process. This will enable the packaging department to reduce plastic use by minimizing the use of single-use plastics and adopting biodegradable packaging materials.

[0040] The recycling department uses recyclable packaging materials and streamlines the recycling process. For example, the recycling department uses recyclable packaging materials. For example, the recycling department uses recyclable plastic and paper packaging materials. The recycling department can also adopt methods to streamline the recycling process. For example, the recycling department optimizes recycling procedures to improve recycling efficiency. Furthermore, the recycling department can adopt methods for sorting recyclable materials. For example, the recycling department efficiently sorts recyclable materials to streamline the recycling process. This allows the recycling department to reduce plastic waste by using recyclable packaging materials and streamlining the recycling process.

[0041] The Ministry of Education will provide information to encourage environmental awareness among consumers in households and businesses and to support carbon-neutral food supply chains. For example, the Ministry of Education will provide information to encourage environmental awareness among consumers. For example, the Ministry of Education will explain to consumers the importance of carbon-neutral food supply chains. The Ministry of Education can also provide information to encourage concrete actions among consumers. For example, the Ministry of Education will recommend to consumers the use of eco-bags and the practice of recycling. Furthermore, the Ministry of Education can also provide information to encourage environmental awareness among businesses. For example, the Ministry of Education will recommend to businesses sustainable production methods and improved energy efficiency. In this way, the Ministry of Education will achieve consumer education by providing information to encourage environmental awareness among consumers in households and businesses and to support carbon-neutral food supply chains.

[0042] The data collection unit analyzes past production data from farmers and producers to select the optimal data collection method. For example, the data collection unit analyzes past production data. For instance, based on past production data, the data collection unit concentrates data collection during periods of high yield. Furthermore, based on production data, the data collection unit can reduce data collection during periods of low yield, selecting a more efficient collection method. In addition, the data collection unit can analyze past data and collect data according to the harvest time of specific crops. This allows the data collection unit to select the optimal data collection method by analyzing past production data.

[0043] The data collection unit filters data based on the current production status of farmers and producers, as well as the season, during data collection. For example, the data collection unit checks the current production status. For instance, it prioritizes collecting data on crops that have already been harvested. It can also prioritize collecting data on crops that are nearing harvest, depending on the season. Furthermore, the data collection unit can monitor production status in real time and filter and collect data on crops that have already been harvested. This allows the data collection unit to efficiently collect data by filtering it based on the current production status and the season.

[0044] The data collection unit prioritizes collecting highly relevant data by considering the geographical location information of farmers and producers during data collection. For example, the data collection unit collects data based on geographical location information. For example, the data collection unit prioritizes collecting data from nearby farmers. The data collection unit can also prioritize collecting data from producers who are geographically close, enabling efficient data collection. Furthermore, the data collection unit can prioritize collecting data from specific regions by considering geographical location information. As a result, the data collection unit can efficiently collect data by prioritizing the collection of highly relevant data while considering geographical location information.

[0045] The data collection unit analyzes the social media activities of farmers and producers during data collection and gathers relevant data. For example, the data collection unit analyzes social media activity to collect data on harvest time and production status. The data collection unit can also prioritize the collection of data on crops that are nearing harvest based on producers' social media posts. Furthermore, the data collection unit can analyze social media activity to collect data on specific crops. In this way, the data collection unit can efficiently collect relevant data by analyzing social media activity.

[0046] The planning department adjusts the level of detail in its supply plans based on the importance of each ingredient. For example, the planning department evaluates the importance of each ingredient. For instance, it develops detailed supply plans for important ingredients. It can also develop simpler supply plans for less important ingredients. Furthermore, the planning department can adjust the level of detail in its plans according to the importance of each ingredient to create efficient supply plans. This allows the planning department to create efficient supply plans by adjusting the level of detail based on the importance of each ingredient.

[0047] The planning department applies different planning algorithms depending on the category of food ingredients when formulating supply plans. For example, the planning department applies different planning algorithms for each category of food ingredients. For example, the planning department applies different planning algorithms depending on categories such as vegetables, fruits, and meats. The planning department can also select an algorithm to formulate the optimal supply plan for each category of food ingredients. Furthermore, the planning department can formulate an efficient supply plan by applying a planning algorithm according to the category. In this way, the planning department can formulate the optimal supply plan according to the category of food ingredients.

[0048] The planning department prioritizes supply plans based on the harvest times of ingredients when formulating supply plans. For example, the planning department considers the harvest times of ingredients. For instance, the planning department prioritizes incorporating ingredients that are close to their harvest time into the supply plan. The planning department can also prioritize supply plans based on harvest times. Furthermore, the planning department can postpone ingredients that are far from their harvest time and prioritize those that are close to their harvest time. This allows the planning department to create efficient supply plans by prioritizing plans based on the harvest times of ingredients.

[0049] The planning department adjusts the order of ingredients in the supply plan based on their interdependencies. For example, the planning department evaluates the interdependencies of ingredients. For instance, the planning department incorporates highly related ingredients together in the supply plan. The planning department can also adjust the order of ingredients based on their interdependencies. Furthermore, the planning department can prioritize the inclusion of highly related ingredients while postponing less relevant ingredients. This allows the planning department to create efficient supply plans by adjusting the order of ingredients based on their interdependencies.

[0050] The supply department analyzes the past supply history of farmers and producers to select the optimal supply method at the time of supply. For example, the supply department analyzes past supply history. For example, the supply department selects an efficient supply method based on past supply history. The supply department can also select the optimal supply method based on supply history and carry out efficient supply. Furthermore, the supply department can analyze past supply data and optimize the supply method. In this way, the supply department can select the optimal supply method by analyzing past supply history.

[0051] The supply department customizes the means of supply based on the current production status of farmers and producers at the time of supply. For example, the supply department checks the current production status. For example, the supply department selects the optimal means of supply based on the current production status. The supply department can also customize the means of supply based on the production status to ensure efficient supply. Furthermore, the supply department can check the current production status in real time and optimize the means of supply. As a result, the supply department can efficiently supply by customizing the means of supply based on the current production status.

[0052] The supply department selects the optimal supply method at the time of supply, taking into account the geographical location information of farmers and producers. For example, the supply department selects the supply method based on geographical location information. For example, the supply department prioritizes supply from nearby farmers. The supply department can also prioritize supply from geographically close producers to ensure efficient supply. Furthermore, the supply department can prioritize supply from specific regions, taking geographical location information into consideration. In this way, the supply department can select the optimal supply method by taking geographical location information into account, enabling efficient supply.

[0053] The supply department analyzes the social media activities of farmers and producers at the time of supply and proposes supply methods. For example, the supply department analyzes social media activity and proposes supply methods. The supply department can also propose the optimal supply method based on producers' social media posts. Furthermore, the supply department can analyze social media activity and propose efficient supply methods. In this way, the supply department can propose the optimal supply method by analyzing social media activity.

[0054] The provision department analyzes the past technology utilization history of farmers and producers when providing technology to select the optimal provision method. For example, the provision department analyzes past technology utilization history. For example, the provision department selects an efficient technology provision method based on past technology utilization history. The provision department can also select the optimal technology provision method based on technology utilization history and provide technology efficiently. Furthermore, the provision department can analyze past technology data and optimize the technology provision method. In this way, the provision department can select the optimal technology provision method by analyzing past technology utilization history.

[0055] The service provider prioritizes providing highly relevant technologies when offering them, taking into account the geographical location information of farmers and producers. For example, the service provider provides technologies based on geographical location information. For instance, the service provider prioritizes providing highly relevant technologies to nearby farmers. The service provider can also prioritize providing highly relevant technologies to producers who are geographically close, enabling efficient technology provision. Furthermore, the service provider can also prioritize providing highly relevant technologies to farmers in specific regions, taking geographical location information into consideration. This allows the service provider to provide technologies efficiently by prioritizing highly relevant technologies while considering geographical location information.

[0056] A circular agriculture system analyzes the past resource utilization history of farmers and producers to select the optimal utilization method when using resources. For example, a circular agriculture system analyzes past resource utilization history. For example, a circular agriculture system selects an efficient resource utilization method based on past resource utilization history. Furthermore, a circular agriculture system can select the optimal resource utilization method based on resource utilization history and utilize resources efficiently. In addition, a circular agriculture system can analyze past resource data and optimize resource utilization methods. Thus, a circular agriculture system can select the optimal resource utilization method by analyzing past resource utilization history.

[0057] A circular agriculture system selects the optimal resource utilization method when using resources, taking into account the geographical location information of farmers and producers. For example, a circular agriculture system selects resource utilization methods based on geographical location information. For example, a circular agriculture system selects the optimal resource utilization method for nearby farmers. Furthermore, a circular agriculture system can select the optimal resource utilization method for producers who are geographically close, enabling efficient resource utilization. In addition, a circular agriculture system can select the optimal resource utilization method for farmers in a specific region, taking geographical location information into account. As a result, a circular agriculture system enables efficient resource utilization by selecting the optimal resource utilization method while considering geographical location information.

[0058] The cooling unit analyzes the past cooling history of farmers and producers to select the optimal cooling method during the cooling process. For example, the cooling unit analyzes past cooling history. For example, the cooling unit selects an efficient cooling method based on past cooling history. The cooling unit can also select the optimal cooling method based on the cooling history and perform efficient cooling. Furthermore, the cooling unit can analyze past cooling data and optimize the cooling method. In this way, the cooling unit can select the optimal cooling method by analyzing past cooling history.

[0059] The cooling unit selects the optimal cooling method during cooling, taking into account the geographical location information of farmers and producers. For example, the cooling unit selects a cooling method based on geographical location information. For example, the cooling unit selects the optimal cooling method for nearby farmers. The cooling unit can also select the optimal cooling method for producers who are geographically close, enabling efficient cooling. Furthermore, the cooling unit can consider geographical location information and select the optimal cooling method for farmers in a specific region. In this way, the cooling unit can select the optimal cooling method by considering geographical location information, enabling efficient cooling.

[0060] The route optimization unit analyzes the past transportation history of farmers and producers to select the optimal route when optimizing transportation routes. For example, the route optimization unit analyzes past transportation history. For example, the route optimization unit selects an efficient transportation route based on past transportation history. The route optimization unit can also select the optimal transportation route based on transportation history and carry out efficient transportation. Furthermore, the route optimization unit can analyze past transportation data and optimize transportation routes. In this way, the route optimization unit can select the optimal transportation route by analyzing past transportation history.

[0061] The route optimization unit selects the optimal route when optimizing transportation routes, taking into account the geographical location information of farmers and producers. For example, the route optimization unit selects transportation routes based on geographical location information. For example, the route optimization unit selects the optimal transportation route for nearby farmers. The route optimization unit can also select the optimal transportation route for geographically close producers, enabling efficient transportation. Furthermore, the route optimization unit can also select the optimal transportation route for farmers in a specific region, taking geographical location information into consideration. As a result, the route optimization unit enables efficient transportation by selecting the optimal transportation route while considering geographical location information.

[0062] The energy introduction unit analyzes the past energy usage history of farmers and producers to select the optimal introduction method when introducing energy. For example, the energy introduction unit analyzes past energy usage history. For example, the energy introduction unit selects an efficient energy introduction method based on past energy usage history. The energy introduction unit can also select the optimal energy introduction method based on energy usage history and perform efficient energy introduction. Furthermore, the energy introduction unit can analyze past energy data and optimize the energy introduction method. In this way, the energy introduction unit can select the optimal energy introduction method by analyzing past energy usage history.

[0063] The energy introduction unit selects the optimal energy introduction method when introducing energy, taking into account the geographical location information of farmers and producers. For example, the energy introduction unit selects the energy introduction method based on geographical location information. For example, the energy introduction unit selects the optimal energy introduction method for nearby farmers. The energy introduction unit can also select the optimal energy introduction method for producers who are geographically close, enabling efficient energy introduction. Furthermore, the energy introduction unit can also select the optimal energy introduction method for farmers in a specific region, taking geographical location information into consideration. In this way, the energy introduction unit can select the optimal energy introduction method by taking geographical location information into account, enabling efficient energy introduction.

[0064] The machinery unit analyzes the past machine usage history of farmers and producers to select the optimal operating method when the machine is in operation. For example, the machinery unit analyzes past machine usage history. For example, the machinery unit selects an efficient machine operating method based on past machine usage history. The machinery unit can also select the optimal machine operating method based on machine usage history and perform efficient machine operation. Furthermore, the machinery unit can analyze past machine data and optimize the machine operating method. In this way, the machinery unit can select the optimal machine operating method by analyzing past machine usage history.

[0065] The machinery unit selects the optimal operating method when the machinery is in operation, taking into account the geographical location information of farmers and producers. For example, the machinery unit selects the operating method based on geographical location information. For example, the machinery unit selects the optimal operating method for nearby farmers. The machinery unit can also select the optimal operating method for producers who are geographically close, enabling efficient machinery operation. Furthermore, the machinery unit can also select the optimal operating method for farmers in a specific region, taking geographical location information into consideration. In this way, the machinery unit can select the optimal operating method by considering geographical location information, enabling efficient machinery operation.

[0066] The packaging department analyzes the past packaging history of farmers and producers to select the optimal packaging method during packaging. For example, the packaging department analyzes past packaging history. For example, the packaging department selects an efficient packaging method based on past packaging history. The packaging department can also select the optimal packaging method based on packaging history and perform efficient packaging. Furthermore, the packaging department can analyze past packaging data and optimize packaging methods. In this way, the packaging department can select the optimal packaging method by analyzing past packaging history.

[0067] The packaging department selects the optimal packaging method during packaging, taking into account the geographical location information of farmers and producers. For example, the packaging department selects a packaging method based on geographical location information. For example, the packaging department selects the optimal packaging method for nearby farmers. The packaging department can also select the optimal packaging method for producers who are geographically close, enabling efficient packaging. Furthermore, the packaging department can consider geographical location information and select the optimal packaging method for farmers in a specific region. In this way, the packaging department can select the optimal packaging method while considering geographical location information, enabling efficient packaging.

[0068] The recycling department analyzes the past recycling history of farmers and producers to select the optimal recycling method. For example, the recycling department analyzes past recycling history. For example, the recycling department selects an efficient recycling method based on past recycling history. The recycling department can also select the optimal recycling method based on recycling history and carry out efficient recycling. Furthermore, the recycling department can analyze past recycling data and optimize recycling methods. In this way, the recycling department can select the optimal recycling method by analyzing past recycling history.

[0069] The recycling department selects the optimal recycling method during recycling, taking into account the geographical location information of farmers and producers. For example, the recycling department selects a recycling method based on geographical location information. For example, the recycling department selects the optimal recycling method for nearby farmers. The recycling department can also select the optimal recycling method for producers who are geographically close, enabling efficient recycling. Furthermore, the recycling department can select the optimal recycling method for farmers in a specific region, taking geographical location information into consideration. In this way, the recycling department can select the optimal recycling method by considering geographical location information, enabling efficient recycling.

[0070] The Ministry of Education analyzes the past educational history of farmers and producers to select the most suitable teaching methods. For example, the Ministry of Education analyzes past educational history. For example, the Ministry of Education selects efficient teaching methods based on past educational history. The Ministry of Education can also select the most suitable teaching methods based on educational history and conduct efficient education. Furthermore, the Ministry of Education can analyze past educational data and optimize teaching methods. In this way, the Ministry of Education can select the most suitable teaching methods by analyzing past educational history.

[0071] The Ministry of Education selects the most suitable teaching method during education, taking into account the geographical location of farmers and producers. For example, the Ministry of Education selects teaching methods based on geographical location information. For instance, the Ministry of Education selects the most suitable teaching method for nearby farmers. Furthermore, the Ministry of Education can select the most suitable teaching method for producers who are geographically close, enabling more efficient education. In addition, the Ministry of Education can select the most suitable teaching method for farmers in a specific region, taking geographical location information into consideration. This allows the Ministry of Education to select the most suitable teaching method while considering geographical location information, thereby enabling more efficient education.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The data collection unit can analyze past production data from farmers and producers and select the optimal data collection method. For example, based on past production data, the data collection unit can concentrate data collection during periods of high yield. It can also reduce data collection during periods of low yield, selecting a more efficient collection method. Furthermore, the data collection unit can analyze past data and collect data according to the harvest time of specific crops. In this way, the data collection unit can select the optimal data collection method by analyzing past production data.

[0074] The planning department can adjust the level of detail in its supply plans based on the importance of each ingredient. For example, it can evaluate the importance of ingredients and create detailed supply plans for important ingredients. It can also create simpler supply plans for less important ingredients. Furthermore, the planning department can adjust the level of detail in its plans according to the importance of each ingredient to create efficient supply plans. This allows the planning department to create efficient supply plans by adjusting the level of detail based on the importance of each ingredient.

[0075] The supply department can analyze the past supply history of farmers and producers to select the optimal supply method at the time of supply. For example, the supply department can select an efficient supply method based on past supply history. Furthermore, the supply department can select the optimal supply method based on supply history and then supply efficiently. In addition, the supply department can analyze past supply data and optimize supply methods. This allows the supply department to select the optimal supply method by analyzing past supply history.

[0076] The energy introduction unit can analyze the past energy usage history of farmers and producers to select the optimal introduction method when introducing energy. For example, the energy introduction unit can select an efficient energy introduction method based on past energy usage history. Furthermore, the energy introduction unit can select the optimal energy introduction method based on energy usage history and implement efficient energy introduction. In addition, the energy introduction unit can analyze past energy data to optimize the energy introduction method. Thus, the energy introduction unit can select the optimal energy introduction method by analyzing past energy usage history.

[0077] The Ministry of Education can analyze the past educational history of farmers and producers to select the most suitable teaching methods during education. For example, the Ministry of Education can select efficient teaching methods based on past educational history. Furthermore, the Ministry of Education can select the most suitable teaching methods based on educational history and implement efficient education. In addition, the Ministry of Education can analyze past educational data to optimize teaching methods. Thus, the Ministry of Education can select the most suitable teaching methods by analyzing past educational history.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The data collection unit collects data from local farmers and producers. This data includes production volume, harvest time, and crop types. The data collection unit collects data either directly from local farmers and producers or through digital platforms provided by them. For example, the data collection unit automatically collects data entered by farmers and producers and stores it in a database. Step 2: The planning department develops a supply plan based on the data collected by the data collection department. The supply plan includes the quantity, timing, and destination of supply. The planning department analyzes the collected data and develops the optimal supply plan. The planning department also considers local demand and supply capacity when developing the supply plan. For example, the planning department adjusts the supply plan based on local demand forecast data. Step 3: The supply department supplies local ingredients based on the supply plan developed by the planning department. The supply department procures ingredients from local farmers and producers and supplies them to local consumers. The supply department selects efficient transportation routes based on the supply plan and delivers the ingredients. For example, the supply department uses a transportation route optimization algorithm to deliver ingredients via the shortest distance.

[0080] (Example of form 2) The carbon-neutral food supply system according to an embodiment of the present invention is a system that realizes a carbon-neutral food supply chain through the construction of a local food supply network, sustainable production methods, optimization of refrigerated and frozen transport, improvement of energy efficiency, reduction of plastics, and consumer education. The carbon-neutral food supply system reduces carbon dioxide emissions by partnering with local farmers and producers and reducing long-distance transport. It improves the efficiency of the food supply chain by supplying local ingredients according to the demand of the local community. Next, as a sustainable production method, it adopts organic farming and sustainable agricultural practices to minimize the use of pesticides and chemical fertilizers. It adopts a circular agricultural system to aim for efficient use of resources and minimization of waste. Furthermore, as an optimization of refrigerated and frozen transport, it reduces energy consumption by using efficient cooling systems and insulation materials in the refrigerated and frozen transport of food. It reduces carbon dioxide emissions by optimizing transport routes to minimize distance and time. In addition, as an improvement of energy efficiency, it introduces renewable energy sources to minimize energy consumption in food production and warehouses. It uses highly efficient lighting, heating and cooling systems, and energy-efficient machinery. Furthermore, to reduce plastic use, we will minimize the use of single-use plastics and adopt biodegradable packaging materials. We will use recyclable packaging materials and streamline the recycling process. Finally, as part of consumer education, we will encourage household and business consumers to be environmentally conscious and provide information to support a carbon-neutral food supply chain. This mechanism will enable a carbon-neutral food supply chain and minimize environmental impact. For example, partnering with local farmers and producers can reduce long-distance transportation and lower carbon emissions. Adopting organic farming and sustainable agricultural practices can minimize the use of pesticides and chemical fertilizers. In addition, using efficient cooling systems and insulation materials can reduce energy consumption. Introducing renewable energy sources can minimize energy consumption in food production and warehousing. Minimizing the use of single-use plastics and adopting biodegradable packaging materials will enable plastic reduction.Consumer education is achieved by encouraging environmental considerations among household and business consumers and providing information to support carbon-neutral food supply chains. This enables carbon-neutral food supply systems to achieve efficient food supply chains while minimizing environmental impact.

[0081] The carbon-neutral food supply system according to this embodiment comprises a collection unit, a planning unit, and a supply unit. The collection unit collects data from local farmers and producers. This data may include, but is not limited to, production volume, harvest time, and crop type. The collection unit may, for example, collect data directly from local farmers and producers. Alternatively, the collection unit may collect data through digital platforms provided by farmers and producers. For example, the collection unit may automatically collect data entered by farmers and producers and store it in a database. The planning unit develops a supply plan based on the data collected by the collection unit. This supply plan may include, for example, supply volume, supply timing, and supply destinations. The planning unit may, for example, analyze the collected data to develop an optimal supply plan. The planning unit may also consider local demand and supply capacity when developing the supply plan. For example, the planning unit may adjust the supply plan based on local demand forecast data. The supply unit supplies local ingredients based on the supply plan developed by the planning unit. The supply unit procures ingredients from local farmers and producers, for example, and supplies them to local consumers. The supply unit can also select efficient transportation routes based on a supply plan and deliver the ingredients. For example, the supply unit can use a transportation route optimization algorithm to deliver ingredients via the shortest distance. This allows the carbon-neutral food supply system according to the embodiment to develop efficient supply plans based on data from local farmers and producers and supply local ingredients.

[0082] The data collection unit collects data from local farmers and producers. This data includes, but is not limited to, production volume, harvest time, and crop type. The data collection unit can, for example, collect data directly from local farmers and producers. It can also collect data through digital platforms provided by farmers and producers. For example, the data collection unit can automatically collect data entered by farmers and producers and store it in a database. Specifically, the data collection unit can acquire data in real time through smartphone apps and web portals used by farmers and producers. This allows the data collection unit to constantly monitor the latest production status and harvest forecasts. The data collection unit can also use IoT sensors to collect environmental data on farmland (e.g., soil moisture, temperature, sunlight). This allows for detailed monitoring of crop growth and improves the accuracy of harvest time predictions. Furthermore, the data collection unit can use drones to acquire aerial images of farmland and analyze the health and growth status of crops. This allows the data collection unit to collect data from local farmers and producers from multiple perspectives and provide highly accurate information. The collected data is stored in a cloud-based database, making it accessible to the planning and supply departments. This allows the data collection department to efficiently and effectively collect data and improve the overall system performance.

[0083] The planning department develops supply plans based on data collected by the data collection department. These plans may include, but are not limited to, supply volume, timing, and destination. For example, the planning department analyzes collected data to formulate optimal supply plans. It can also consider regional demand and supply capacity when developing supply plans. For instance, it adjusts supply plans based on regional demand forecast data. Specifically, it uses AI to analyze collected data and optimize the balance between supply and demand. AI learns from historical data and seasonal trends to predict future demand. For example, it adjusts supply plans considering fluctuations in demand during specific seasons and regional consumption patterns. Furthermore, the planning department can implement measures to minimize environmental impact when developing supply plans. For example, it can prioritize supplying to local consumers to reduce transportation distances. Finally, the planning department monitors the implementation of supply plans and modifies them as needed. For example, it can flexibly adjust supply plans in response to weather changes or unexpected fluctuations in demand. This allows the planning department to develop efficient and sustainable supply plans, improving the reliability and flexibility of the entire system.

[0084] The supply department supplies local ingredients based on the supply plan developed by the planning department. For example, the supply department procures ingredients from local farmers and producers and supplies them to local consumers. The supply department can also select efficient transportation routes based on the supply plan and deliver ingredients accordingly. For example, the supply department uses transportation route optimization algorithms to deliver ingredients via the shortest distance. Specifically, the supply department quickly collects ingredients harvested from local farmers and producers and transports them using refrigerated trucks and electric vehicles. This allows for the preservation of the ingredients' freshness while reducing the environmental impact. The supply department can also accept orders for ingredients from local consumers through an online platform. Consumers can easily order fresh local ingredients using their smartphones or computers. Based on the orders, the supply department can create efficient delivery schedules and deliver ingredients quickly. Furthermore, the supply department can utilize blockchain technology to ensure the traceability of ingredients. This allows consumers to verify the producers and production processes of the ingredients they purchase, enabling a highly reliable supply. Through these initiatives, the supply department can efficiently and sustainably supply local ingredients and promote regional carbon neutrality.

[0085] The provider offers technologies for adopting organic and sustainable agricultural practices. For example, it provides specific technologies and methods for organic farming, such as methods for using organic fertilizers and selecting organic pesticides. It can also provide specific technologies and methods for sustainable agricultural practices, such as soil conservation and water resource management. Furthermore, it can provide technologies and methods for biodiversity protection, such as vegetation management technologies for protecting biodiversity in farmland. In this way, the provider can realize sustainable agriculture by offering technologies for adopting organic and sustainable agricultural practices.

[0086] A circular agricultural system aims for the efficient use of resources and the reduction of waste. For example, a circular agricultural system employs methods of resource reuse. For instance, it composts agricultural waste for reuse. It can also employ waste treatment methods. For example, it recycles and reuses waste. Furthermore, a circular agricultural system can employ methods of efficient resource use. For example, it optimizes the use of water and fertilizer. This enables a circular agricultural system to achieve efficient resource use and minimize waste.

[0087] The cooling system reduces energy consumption by using efficient cooling systems and insulation materials. For example, the cooling system employs efficient cooling systems. For instance, it uses the latest cooling technology to minimize energy consumption. The cooling system can also utilize insulation materials. For example, it uses high-performance insulation materials to improve cooling efficiency. Furthermore, the cooling system can optimize the operation of the cooling system. For instance, it adjusts the operating time of the cooling system to reduce energy consumption. This allows the cooling system to reduce energy consumption.

[0088] The route optimization unit optimizes transportation routes to reduce distance and time. For example, the route optimization unit uses transportation route optimization algorithms. For instance, it calculates the shortest distance transportation route to minimize transportation time. The route optimization unit can also employ methods to reduce transportation costs. For example, it selects a transportation route that minimizes fuel consumption. Furthermore, the route optimization unit can employ methods to improve transportation efficiency. For example, it combines multiple modes of transportation to achieve efficient transport. This allows the route optimization unit to minimize distance and time by optimizing transportation routes.

[0089] The energy integration unit introduces renewable energy sources. For example, the energy integration unit introduces a solar power generation system. For example, the energy integration unit installs solar panels and supplies renewable energy. The energy integration unit can also introduce a wind power generation system. For example, the energy integration unit installs wind turbines and supplies renewable energy. Furthermore, the energy integration unit can also introduce a biomass energy system. For example, the energy integration unit supplies energy using biomass fuel. In this way, the energy integration unit can minimize energy consumption by introducing renewable energy sources.

[0090] The machinery department will use highly efficient lighting, heating and cooling systems, and energy-efficient machinery. For example, the machinery department will use highly efficient lighting, such as LED lighting, to reduce energy consumption. The machinery department may also use heating and cooling systems, such as energy-efficient air conditioners and heat pumps. Furthermore, the machinery department may use energy-efficient machinery, such as energy-saving agricultural machinery and energy-efficient pumps. By using highly efficient lighting, heating and cooling systems, and energy-efficient machinery, the machinery department can minimize energy consumption.

[0091] The packaging department will reduce the use of single-use plastics and adopt biodegradable packaging materials. For example, the packaging department will adopt methods to reduce the use of single-use plastics. For example, the packaging department will reduce disposable packaging materials and use reusable packaging materials. The packaging department may also adopt biodegradable packaging materials. For example, the packaging department will use biodegradable plastics or paper packaging materials. Furthermore, the packaging department may also use recyclable packaging materials. For example, the packaging department will use recyclable plastics or paper packaging materials and streamline the recycling process. This will enable the packaging department to reduce plastic use by minimizing the use of single-use plastics and adopting biodegradable packaging materials.

[0092] The recycling department uses recyclable packaging materials and streamlines the recycling process. For example, the recycling department uses recyclable packaging materials. For example, the recycling department uses recyclable plastic and paper packaging materials. The recycling department can also adopt methods to streamline the recycling process. For example, the recycling department optimizes recycling procedures to improve recycling efficiency. Furthermore, the recycling department can adopt methods for sorting recyclable materials. For example, the recycling department efficiently sorts recyclable materials to streamline the recycling process. This allows the recycling department to reduce plastic waste by using recyclable packaging materials and streamlining the recycling process.

[0093] The Ministry of Education will provide information to encourage environmental awareness among consumers in households and businesses and to support carbon-neutral food supply chains. For example, the Ministry of Education will provide information to encourage environmental awareness among consumers. For example, the Ministry of Education will explain to consumers the importance of carbon-neutral food supply chains. The Ministry of Education can also provide information to encourage concrete actions among consumers. For example, the Ministry of Education will recommend to consumers the use of eco-bags and the practice of recycling. Furthermore, the Ministry of Education can also provide information to encourage environmental awareness among businesses. For example, the Ministry of Education will recommend to businesses sustainable production methods and improved energy efficiency. In this way, the Ministry of Education will achieve consumer education by providing information to encourage environmental awareness among consumers in households and businesses and to support carbon-neutral food supply chains.

[0094] The data collection unit estimates the emotions of local farmers and producers and adjusts the timing of data collection based on the estimated emotions. For example, the data collection unit estimates the emotions of local farmers and producers using an emotion engine. For example, the data collection unit estimates emotions using the emotion engine to avoid collecting data when farmers are busy and adjusts the timing to after the harvest. Also, if producers are feeling stressed, the data collection unit can estimate their emotions using the emotion engine and collect data during a relaxed period. Furthermore, the data collection unit can estimate emotions using the emotion engine before farmers enter the harvest season and collect data during a less busy period after the harvest. In this way, the data collection unit can efficiently collect data by adjusting the timing of data collection based on the emotions of local farmers and producers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The data collection unit analyzes past production data from farmers and producers to select the optimal data collection method. For example, the data collection unit analyzes past production data. For instance, based on past production data, the data collection unit concentrates data collection during periods of high yield. Furthermore, based on production data, the data collection unit can reduce data collection during periods of low yield, selecting a more efficient collection method. In addition, the data collection unit can analyze past data and collect data according to the harvest time of specific crops. This allows the data collection unit to select the optimal data collection method by analyzing past production data.

[0096] The data collection unit filters data based on the current production status of farmers and producers, as well as the season, during data collection. For example, the data collection unit checks the current production status. For instance, it prioritizes collecting data on crops that have already been harvested. It can also prioritize collecting data on crops that are nearing harvest, depending on the season. Furthermore, the data collection unit can monitor production status in real time and filter and collect data on crops that have already been harvested. This allows the data collection unit to efficiently collect data by filtering it based on the current production status and the season.

[0097] The data collection unit estimates the emotions of local farmers and producers and prioritizes the data to be collected based on these estimated emotions. For example, the unit might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the unit might use the emotion engine to estimate farmers' emotions and prioritize collecting important data during periods of low stress. The unit could also prioritize collecting detailed data during periods when producers are relaxed. Furthermore, during busy periods, the unit could collect only basic data, postponing the collection of detailed data. This allows the unit to efficiently collect data by prioritizing data collection based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The data collection unit prioritizes collecting highly relevant data by considering the geographical location information of farmers and producers during data collection. For example, the data collection unit collects data based on geographical location information. For example, the data collection unit prioritizes collecting data from nearby farmers. The data collection unit can also prioritize collecting data from producers who are geographically close, enabling efficient data collection. Furthermore, the data collection unit can prioritize collecting data from specific regions by considering geographical location information. As a result, the data collection unit can efficiently collect data by prioritizing the collection of highly relevant data while considering geographical location information.

[0099] The data collection unit analyzes the social media activities of farmers and producers during data collection and gathers relevant data. For example, the data collection unit analyzes social media activity to collect data on harvest time and production status. The data collection unit can also prioritize the collection of data on crops that are nearing harvest based on producers' social media posts. Furthermore, the data collection unit can analyze social media activity to collect data on specific crops. In this way, the data collection unit can efficiently collect relevant data by analyzing social media activity.

[0100] The planning department estimates the emotions of local farmers and producers and adjusts the presentation of supply plans based on these estimated emotions. For example, the planning department might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the planning department might use the emotion engine to estimate farmers' emotions and provide detailed supply plans during periods of low stress. It could also provide visually easy-to-understand supply plans during periods when producers are relaxed. Furthermore, during busy periods, the planning department could provide concise supply plans, postponing detailed plans. This allows the planning department to create efficient supply plans by adjusting their presentation based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The planning department adjusts the level of detail in its supply plans based on the importance of each ingredient. For example, the planning department evaluates the importance of each ingredient. For instance, it develops detailed supply plans for important ingredients. It can also develop simpler supply plans for less important ingredients. Furthermore, the planning department can adjust the level of detail in its plans according to the importance of each ingredient to create efficient supply plans. This allows the planning department to create efficient supply plans by adjusting the level of detail based on the importance of each ingredient.

[0102] The planning department applies different planning algorithms depending on the category of food ingredients when formulating supply plans. For example, the planning department applies different planning algorithms for each category of food ingredients. For example, the planning department applies different planning algorithms depending on categories such as vegetables, fruits, and meats. The planning department can also select an algorithm to formulate the optimal supply plan for each category of food ingredients. Furthermore, the planning department can formulate an efficient supply plan by applying a planning algorithm according to the category. In this way, the planning department can formulate the optimal supply plan according to the category of food ingredients.

[0103] The planning department estimates the emotions of local farmers and producers and adjusts the length of the supply plan based on the estimated emotions. For example, the planning department uses an emotion engine to estimate the emotions of local farmers and producers. For instance, the planning department uses an emotion engine to estimate farmers' emotions and provides long-term supply plans during periods of low stress. It can also provide detailed long-term plans during periods when producers are relaxed. Furthermore, the planning department can provide short-term supply plans during busy periods, postponing detailed plans. This allows the planning department to create efficient supply plans by adjusting the length of the supply plan based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The planning department prioritizes supply plans based on the harvest times of ingredients when formulating supply plans. For example, the planning department considers the harvest times of ingredients. For instance, the planning department prioritizes incorporating ingredients that are close to their harvest time into the supply plan. The planning department can also prioritize supply plans based on harvest times. Furthermore, the planning department can postpone ingredients that are far from their harvest time and prioritize those that are close to their harvest time. This allows the planning department to create efficient supply plans by prioritizing plans based on the harvest times of ingredients.

[0105] The planning department adjusts the order of ingredients in the supply plan based on their interdependencies. For example, the planning department evaluates the interdependencies of ingredients. For instance, the planning department incorporates highly related ingredients together in the supply plan. The planning department can also adjust the order of ingredients based on their interdependencies. Furthermore, the planning department can prioritize the inclusion of highly related ingredients while postponing less relevant ingredients. This allows the planning department to create efficient supply plans by adjusting the order of ingredients based on their interdependencies.

[0106] The supply unit estimates the emotions of local farmers and producers and adjusts its supply methods based on these estimated emotions. For example, the supply unit might use an emotion engine to estimate the emotions of local farmers and producers. For instance, it might use the emotion engine to estimate farmers' emotions and supply during periods of low stress. It could also select efficient supply methods during periods when producers are relaxed. Furthermore, it could select simpler supply methods during busy periods for farmers, postponing more detailed supply. This allows the supply unit to efficiently supply goods by adjusting its methods based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The supply department analyzes the past supply history of farmers and producers to select the optimal supply method at the time of supply. For example, the supply department analyzes past supply history. For example, the supply department selects an efficient supply method based on past supply history. The supply department can also select the optimal supply method based on supply history and carry out efficient supply. Furthermore, the supply department can analyze past supply data and optimize the supply method. In this way, the supply department can select the optimal supply method by analyzing past supply history.

[0108] The supply department customizes the means of supply based on the current production status of farmers and producers at the time of supply. For example, the supply department checks the current production status. For example, the supply department selects the optimal means of supply based on the current production status. The supply department can also customize the means of supply based on the production status to ensure efficient supply. Furthermore, the supply department can check the current production status in real time and optimize the means of supply. As a result, the supply department can efficiently supply by customizing the means of supply based on the current production status.

[0109] The supply unit estimates the emotions of local farmers and producers and prioritizes supplies based on these estimated emotions. For example, the supply unit might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the supply unit might use an emotion engine to estimate farmers' emotions and prioritize important supplies during periods of low stress. It could also prioritize detailed supplies during periods when producers are relaxed. Furthermore, the supply unit might prioritize basic supplies and postpone detailed supplies during busy periods for farmers. This allows the supply unit to efficiently manage supplies by prioritizing them based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The supply department selects the optimal supply method at the time of supply, taking into account the geographical location information of farmers and producers. For example, the supply department selects the supply method based on geographical location information. For example, the supply department prioritizes supply from nearby farmers. The supply department can also prioritize supply from geographically close producers to ensure efficient supply. Furthermore, the supply department can prioritize supply from specific regions, taking geographical location information into consideration. In this way, the supply department can select the optimal supply method by taking geographical location information into account, enabling efficient supply.

[0111] The supply department analyzes the social media activities of farmers and producers at the time of supply and proposes supply methods. For example, the supply department analyzes social media activity and proposes supply methods. The supply department can also propose the optimal supply method based on producers' social media posts. Furthermore, the supply department can analyze social media activity and propose efficient supply methods. In this way, the supply department can propose the optimal supply method by analyzing social media activity.

[0112] The service provider estimates the emotions of local farmers and producers and adjusts the timing of technical assistance based on these estimated emotions. For example, the service provider estimates the emotions of local farmers and producers using an emotion engine. For example, the service provider estimates the emotions of farmers using an emotion engine and provides technical assistance during periods of low stress. The service provider can also provide efficient technical assistance when producers are relaxed. Furthermore, the service provider can provide concise technical assistance during busy periods for farmers, postponing detailed technical assistance. In this way, the service provider can provide efficient technical assistance by adjusting the timing of technical assistance based on the emotions of local farmers and producers. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The provision department analyzes the past technology utilization history of farmers and producers when providing technology to select the optimal provision method. For example, the provision department analyzes past technology utilization history. For example, the provision department selects an efficient technology provision method based on past technology utilization history. The provision department can also select the optimal technology provision method based on technology utilization history and provide technology efficiently. Furthermore, the provision department can analyze past technology data and optimize the technology provision method. In this way, the provision department can select the optimal technology provision method by analyzing past technology utilization history.

[0114] The service provider estimates the emotions of local farmers and producers and prioritizes the provision of technologies based on these estimated emotions. For example, the service provider might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the service provider might use the emotion engine to estimate farmers' emotions and prioritize providing important technologies during periods of low stress. It could also prioritize providing detailed technologies during periods when producers are relaxed. Furthermore, during busy periods, the service provider might prioritize basic technologies and postpone providing detailed technologies. This allows the service provider to efficiently provide technologies by prioritizing them based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The service provider prioritizes providing highly relevant technologies when offering them, taking into account the geographical location information of farmers and producers. For example, the service provider provides technologies based on geographical location information. For instance, the service provider prioritizes providing highly relevant technologies to nearby farmers. The service provider can also prioritize providing highly relevant technologies to producers who are geographically close, enabling efficient technology provision. Furthermore, the service provider can also prioritize providing highly relevant technologies to farmers in specific regions, taking geographical location information into consideration. This allows the service provider to provide technologies efficiently by prioritizing highly relevant technologies while considering geographical location information.

[0116] A circular agriculture system estimates the emotions of local farmers and producers and adjusts resource utilization based on these estimated emotions. For example, a circular agriculture system might use an emotion engine to estimate the emotions of local farmers and producers. For instance, it might use an emotion engine to estimate farmers' emotions and utilize resources during periods of low stress. It could also select efficient resource utilization methods during periods when producers are relaxed. Furthermore, it could select simplified resource utilization methods during busy periods, postponing more detailed resource utilization. This allows for efficient resource utilization by adjusting resource utilization methods based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] A circular agriculture system analyzes the past resource utilization history of farmers and producers to select the optimal utilization method when using resources. For example, a circular agriculture system analyzes past resource utilization history. For example, a circular agriculture system selects an efficient resource utilization method based on past resource utilization history. Furthermore, a circular agriculture system can select the optimal resource utilization method based on resource utilization history and utilize resources efficiently. In addition, a circular agriculture system can analyze past resource data and optimize resource utilization methods. Thus, a circular agriculture system can select the optimal resource utilization method by analyzing past resource utilization history.

[0118] A circular agriculture system estimates the emotions of local farmers and producers and prioritizes resource use based on these estimated emotions. For example, a circular agriculture system might use an emotion engine to estimate the emotions of local farmers and producers. For instance, it might use an emotion engine to estimate farmers' emotions and prioritize important resource use during periods of low stress. It could also prioritize detailed resource use during periods when producers are relaxed. Furthermore, it could prioritize basic resource use during busy periods, postponing detailed resource use. This allows for efficient resource use by prioritizing resource use based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] A circular agriculture system selects the optimal resource utilization method when using resources, taking into account the geographical location information of farmers and producers. For example, a circular agriculture system selects resource utilization methods based on geographical location information. For example, a circular agriculture system selects the optimal resource utilization method for nearby farmers. Furthermore, a circular agriculture system can select the optimal resource utilization method for producers who are geographically close, enabling efficient resource utilization. In addition, a circular agriculture system can select the optimal resource utilization method for farmers in a specific region, taking geographical location information into account. As a result, a circular agriculture system enables efficient resource utilization by selecting the optimal resource utilization method while considering geographical location information.

[0120] The cooling unit estimates the emotions of local farmers and producers and adjusts the timing of the cooling system's operation based on these estimated emotions. For example, the cooling unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the cooling unit estimates the farmers' emotions using the emotion engine and operates the cooling system during periods of low stress. It can also operate the cooling system efficiently during periods when producers are relaxed. Furthermore, the cooling unit can operate a simplified cooling system during busy periods, postponing more detailed operation. This allows the cooling unit to achieve efficient cooling by adjusting the timing of the cooling system's operation based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The cooling unit analyzes the past cooling history of farmers and producers to select the optimal cooling method during the cooling process. For example, the cooling unit analyzes past cooling history. For example, the cooling unit selects an efficient cooling method based on past cooling history. The cooling unit can also select the optimal cooling method based on the cooling history and perform efficient cooling. Furthermore, the cooling unit can analyze past cooling data and optimize the cooling method. In this way, the cooling unit can select the optimal cooling method by analyzing past cooling history.

[0122] The cooling unit estimates the emotions of local farmers and producers and prioritizes the cooling system based on these estimated emotions. For example, the cooling unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the cooling unit estimates farmers' emotions using the emotion engine and prioritizes important cooling during periods of low stress. It can also prioritize detailed cooling during periods when producers are relaxed. Furthermore, the cooling unit can prioritize basic cooling during busy periods, postponing detailed cooling. This allows the cooling unit to efficiently cool by prioritizing the cooling system based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The cooling unit selects the optimal cooling method during cooling, taking into account the geographical location information of farmers and producers. For example, the cooling unit selects a cooling method based on geographical location information. For example, the cooling unit selects the optimal cooling method for nearby farmers. The cooling unit can also select the optimal cooling method for producers who are geographically close, enabling efficient cooling. Furthermore, the cooling unit can consider geographical location information and select the optimal cooling method for farmers in a specific region. In this way, the cooling unit can select the optimal cooling method by considering geographical location information, enabling efficient cooling.

[0124] The route optimization unit estimates the emotions of local farmers and producers and optimizes transportation routes based on these estimated emotions. For example, the route optimization unit uses an emotion engine to estimate the emotions of local farmers and producers. For instance, the route optimization unit estimates farmers' emotions using the emotion engine and optimizes transportation routes during periods of low stress. It can also optimize efficient transportation routes during periods when producers are relaxed. Furthermore, the route optimization unit can perform simplified transportation route optimization during busy periods for farmers, postponing detailed optimization. This allows the route optimization unit to optimize transportation routes based on the emotions of local farmers and producers, enabling efficient transportation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The route optimization unit analyzes the past transportation history of farmers and producers to select the optimal route when optimizing transportation routes. For example, the route optimization unit analyzes past transportation history. For example, the route optimization unit selects an efficient transportation route based on past transportation history. The route optimization unit can also select the optimal transportation route based on transportation history and carry out efficient transportation. Furthermore, the route optimization unit can analyze past transportation data and optimize transportation routes. In this way, the route optimization unit can select the optimal transportation route by analyzing past transportation history.

[0126] The route optimization unit estimates the emotions of local farmers and producers and prioritizes transportation routes based on these estimated emotions. For example, the route optimization unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the route optimization unit estimates farmers' emotions using the emotion engine and prioritizes important transportation routes during periods of low stress. It can also prioritize detailed transportation routes during periods when producers are relaxed. Furthermore, the route optimization unit can prioritize basic transportation routes and postpone detailed routes during busy periods for farmers. This enables efficient transportation by prioritizing transportation routes based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The route optimization unit selects the optimal route when optimizing transportation routes, taking into account the geographical location information of farmers and producers. For example, the route optimization unit selects transportation routes based on geographical location information. For example, the route optimization unit selects the optimal transportation route for nearby farmers. The route optimization unit can also select the optimal transportation route for geographically close producers, enabling efficient transportation. Furthermore, the route optimization unit can also select the optimal transportation route for farmers in a specific region, taking geographical location information into consideration. As a result, the route optimization unit enables efficient transportation by selecting the optimal transportation route while considering geographical location information.

[0128] The energy deployment unit estimates the emotions of local farmers and producers and adjusts the timing of renewable energy deployment based on these estimated emotions. For example, the energy deployment unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the energy deployment unit estimates farmers' emotions using the emotion engine and deploys renewable energy during periods of low stress. It can also efficiently deploy renewable energy during periods when producers are relaxed. Furthermore, the energy deployment unit can perform simplified renewable energy deployments during busy periods, postponing more detailed deployments. This allows the energy deployment unit to efficiently deploy energy by adjusting the timing of renewable energy deployment based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The energy introduction unit analyzes the past energy usage history of farmers and producers to select the optimal introduction method when introducing energy. For example, the energy introduction unit analyzes past energy usage history. For example, the energy introduction unit selects an efficient energy introduction method based on past energy usage history. The energy introduction unit can also select the optimal energy introduction method based on energy usage history and perform efficient energy introduction. Furthermore, the energy introduction unit can analyze past energy data and optimize the energy introduction method. In this way, the energy introduction unit can select the optimal energy introduction method by analyzing past energy usage history.

[0130] The energy delivery unit estimates the emotions of local farmers and producers and determines the priority of energy delivery based on the estimated emotions. For example, the energy delivery unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the energy delivery unit estimates farmers' emotions using the emotion engine and prioritizes important energy delivery during periods of low stress. The energy delivery unit can also prioritize detailed energy delivery during periods when producers are relaxed. Furthermore, the energy delivery unit can prioritize basic energy delivery during busy periods, postponing detailed energy delivery. This allows for efficient energy delivery by prioritizing energy delivery based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The energy introduction unit selects the optimal energy introduction method when introducing energy, taking into account the geographical location information of farmers and producers. For example, the energy introduction unit selects the energy introduction method based on geographical location information. For example, the energy introduction unit selects the optimal energy introduction method for nearby farmers. The energy introduction unit can also select the optimal energy introduction method for producers who are geographically close, enabling efficient energy introduction. Furthermore, the energy introduction unit can also select the optimal energy introduction method for farmers in a specific region, taking geographical location information into consideration. In this way, the energy introduction unit can select the optimal energy introduction method by taking geographical location information into account, enabling efficient energy introduction.

[0132] The machine unit estimates the emotions of local farmers and producers and adjusts the timing of machine operation based on the estimated emotions. For example, the machine unit estimates the emotions of local farmers and producers using an emotion engine. For example, the machine unit estimates the emotions of farmers using the emotion engine and operates the machine during periods of low stress. The machine unit can also operate the machine efficiently when producers are relaxed. Furthermore, the machine unit can perform simplified machine operation during busy periods for farmers, postponing more detailed operation. In this way, the machine unit can operate the machine efficiently by adjusting the timing of machine operation based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0133] The machinery unit analyzes the past machine usage history of farmers and producers to select the optimal operating method when the machine is in operation. For example, the machinery unit analyzes past machine usage history. For example, the machinery unit selects an efficient machine operating method based on past machine usage history. The machinery unit can also select the optimal machine operating method based on machine usage history and perform efficient machine operation. Furthermore, the machinery unit can analyze past machine data and optimize the machine operating method. In this way, the machinery unit can select the optimal machine operating method by analyzing past machine usage history.

[0134] The machine unit estimates the emotions of local farmers and producers and prioritizes machine operations based on these estimated emotions. For example, the machine unit estimates the emotions of local farmers and producers using an emotion engine. For instance, the machine unit estimates farmers' emotions using the emotion engine and prioritizes important machine operations during periods of low stress. It can also prioritize detailed machine operations during periods when producers are relaxed. Furthermore, the machine unit can prioritize basic machine operations during busy periods, postponing detailed operations. This allows the machine unit to operate efficiently by prioritizing machines based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The machinery unit selects the optimal operating method when the machinery is in operation, taking into account the geographical location information of farmers and producers. For example, the machinery unit selects the operating method based on geographical location information. For example, the machinery unit selects the optimal operating method for nearby farmers. The machinery unit can also select the optimal operating method for producers who are geographically close, enabling efficient machinery operation. Furthermore, the machinery unit can also select the optimal operating method for farmers in a specific region, taking geographical location information into consideration. In this way, the machinery unit can select the optimal operating method by considering geographical location information, enabling efficient machinery operation.

[0136] The packaging unit estimates the emotions of local farmers and producers and selects packaging materials based on these estimated emotions. For example, the packaging unit might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the packaging unit might use the emotion engine to estimate farmers' emotions and select packaging materials during periods of low stress. It can also efficiently select packaging materials during periods when producers are relaxed. Furthermore, during busy periods, the packaging unit can select simple packaging materials, postponing detailed selection. This allows the packaging unit to efficiently package materials by selecting them based on the emotions of local farmers and producers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The packaging department analyzes the past packaging history of farmers and producers to select the optimal packaging method during packaging. For example, the packaging department analyzes past packaging history. For example, the packaging department selects an efficient packaging method based on past packaging history. The packaging department can also select the optimal packaging method based on packaging history and perform efficient packaging. Furthermore, the packaging department can analyze past packaging data and optimize packaging methods. In this way, the packaging department can select the optimal packaging method by analyzing past packaging history.

[0138] The packaging unit estimates the emotions of local farmers and producers and determines packaging priorities based on these estimated emotions. For example, the packaging unit might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the packaging unit might use the emotion engine to estimate farmers' emotions and prioritize important packaging during periods of low stress. It could also prioritize detailed packaging during periods when producers are relaxed. Furthermore, it could prioritize basic packaging during busy periods, postponing detailed packaging. This allows the packaging unit to achieve efficient packaging by prioritizing packaging based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0139] The packaging department selects the optimal packaging method during packaging, taking into account the geographical location information of farmers and producers. For example, the packaging department selects a packaging method based on geographical location information. For example, the packaging department selects the optimal packaging method for nearby farmers. The packaging department can also select the optimal packaging method for producers who are geographically close, enabling efficient packaging. Furthermore, the packaging department can consider geographical location information and select the optimal packaging method for farmers in a specific region. In this way, the packaging department can select the optimal packaging method while considering geographical location information, enabling efficient packaging.

[0140] The recycling unit estimates the emotions of local farmers and producers and adjusts the timing of the recycling process based on these estimated emotions. For example, the recycling unit uses an emotion engine to estimate the emotions of local farmers and producers. For instance, the recycling unit uses the emotion engine to estimate farmers' emotions and performs the recycling process during periods of low stress. The recycling unit can also perform an efficient recycling process during periods when producers are relaxed. Furthermore, the recycling unit can perform a simplified recycling process during busy periods for farmers, postponing more detailed processes. This allows the recycling unit to achieve efficient recycling by adjusting the timing of the recycling process based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0141] The recycling department analyzes the past recycling history of farmers and producers to select the optimal recycling method. For example, the recycling department analyzes past recycling history. For example, the recycling department selects an efficient recycling method based on past recycling history. The recycling department can also select the optimal recycling method based on recycling history and carry out efficient recycling. Furthermore, the recycling department can analyze past recycling data and optimize recycling methods. In this way, the recycling department can select the optimal recycling method by analyzing past recycling history.

[0142] The recycling department estimates the emotions of local farmers and producers and determines recycling priorities based on these estimated emotions. For example, the recycling department might use an emotion engine to estimate the emotions of local farmers and producers. For instance, the recycling department might use the emotion engine to estimate farmers' emotions and prioritize important recycling during periods of low stress. It could also prioritize detailed recycling during periods when producers are relaxed. Furthermore, the recycling department might prioritize basic recycling during busy periods, postponing detailed recycling. This allows the recycling department to achieve efficient recycling by prioritizing recycling based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0143] The recycling department selects the optimal recycling method during recycling, taking into account the geographical location information of farmers and producers. For example, the recycling department selects a recycling method based on geographical location information. For example, the recycling department selects the optimal recycling method for nearby farmers. The recycling department can also select the optimal recycling method for producers who are geographically close, enabling efficient recycling. Furthermore, the recycling department can select the optimal recycling method for farmers in a specific region, taking geographical location information into consideration. In this way, the recycling department can select the optimal recycling method by considering geographical location information, enabling efficient recycling.

[0144] The Ministry of Education estimates the emotions of local farmers and producers and adjusts educational content based on these estimates. For example, the Ministry of Education uses an emotion engine to estimate the emotions of local farmers and producers. For instance, the Ministry of Education estimates farmers' emotions using the emotion engine and provides educational content during periods of low stress. The Ministry of Education can also provide efficient educational content during periods when producers are relaxed. Furthermore, the Ministry of Education can provide concise educational content during busy periods, postponing detailed instruction. This allows the Ministry of Education to provide efficient education by adjusting educational content based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0145] The Ministry of Education analyzes the past educational history of farmers and producers to select the most suitable teaching methods. For example, the Ministry of Education analyzes past educational history. For example, the Ministry of Education selects efficient teaching methods based on past educational history. The Ministry of Education can also select the most suitable teaching methods based on educational history and conduct efficient education. Furthermore, the Ministry of Education can analyze past educational data and optimize teaching methods. In this way, the Ministry of Education can select the most suitable teaching methods by analyzing past educational history.

[0146] The Ministry of Education estimates the emotions of local farmers and producers and determines educational priorities based on these estimated emotions. For example, the Ministry of Education uses an emotion engine to estimate the emotions of local farmers and producers. For instance, the Ministry of Education estimates farmers' emotions using the emotion engine and prioritizes important education during periods of low stress. The Ministry of Education can also prioritize detailed education during periods when producers are relaxed. Furthermore, the Ministry of Education can prioritize basic education and postpone detailed education during busy periods for farmers. This allows the Ministry of Education to conduct efficient education by determining educational priorities based on the emotions of local farmers and producers. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0147] The Ministry of Education selects the most suitable teaching method during education, taking into account the geographical location of farmers and producers. For example, the Ministry of Education selects teaching methods based on geographical location information. For instance, the Ministry of Education selects the most suitable teaching method for nearby farmers. Furthermore, the Ministry of Education can select the most suitable teaching method for producers who are geographically close, enabling more efficient education. In addition, the Ministry of Education can select the most suitable teaching method for farmers in a specific region, taking geographical location information into consideration. This allows the Ministry of Education to select the most suitable teaching method while considering geographical location information, thereby enabling more efficient education.

[0148] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0149] The data collection unit can estimate the emotions of local farmers and producers and adjust the timing of data collection based on those estimated emotions. For example, the unit can use an emotion engine to estimate farmers' emotions and avoid collecting data during busy periods. It can also estimate producers' emotions using the emotion engine and collect data during relaxed periods. Furthermore, it can estimate farmers' emotions using the emotion engine before the harvest season and collect data during the post-harvest period when they have more free time. This allows the data collection unit to efficiently collect data by adjusting the timing of collection based on the emotions of local farmers and producers.

[0150] The service provider can estimate the emotions of local farmers and producers and adjust the timing of technical assistance based on those estimates. For example, the service provider can use an emotion engine to estimate farmers' emotions and avoid providing technical assistance when farmers are stressed. It can also provide efficient technical assistance when producers are relaxed. Furthermore, it can provide concise technical assistance when farmers are busy, postponing detailed assistance. In this way, the service provider can provide efficient technical assistance by adjusting the timing of assistance based on the emotions of local farmers and producers.

[0151] A circular agriculture system can estimate the emotions of local farmers and producers and adjust resource utilization based on those estimated emotions. For example, a circular agriculture system can use an emotion engine to estimate farmers' emotions and avoid resource utilization during periods when farmers are stressed. It can also select efficient resource utilization methods during periods when producers are relaxed. Furthermore, during busy periods, a circular agriculture system can select simplified resource utilization methods, postponing more detailed resource utilization. In this way, a circular agriculture system enables efficient resource utilization by adjusting resource utilization methods based on the emotions of local farmers and producers.

[0152] The cooling unit can estimate the emotions of local farmers and producers and adjust the timing of the cooling system's operation based on those estimated emotions. For example, the cooling unit can use an emotion engine to estimate farmers' emotions and avoid operating the cooling system when farmers are stressed. It can also operate the cooling system efficiently when producers are relaxed. Furthermore, the cooling unit can operate a simplified cooling system when farmers are busy, postponing more detailed operation. In this way, the cooling unit can achieve efficient cooling by adjusting the timing of the cooling system's operation based on the emotions of local farmers and producers.

[0153] The route optimization unit can estimate the emotions of local farmers and producers and optimize transportation routes based on those estimated emotions. For example, the route optimization unit can use an emotion engine to estimate farmers' emotions and avoid optimizing transportation routes when farmers are stressed. It can also optimize efficient transportation routes when producers are relaxed. Furthermore, the route optimization unit can perform simplified transportation route optimization during busy periods for farmers, postponing detailed optimization. In this way, the route optimization unit enables efficient transportation by optimizing transportation routes based on the emotions of local farmers and producers.

[0154] The data collection unit can analyze past production data from farmers and producers and select the optimal data collection method. For example, based on past production data, the data collection unit can concentrate data collection during periods of high yield. It can also reduce data collection during periods of low yield, selecting a more efficient collection method. Furthermore, the data collection unit can analyze past data and collect data according to the harvest time of specific crops. In this way, the data collection unit can select the optimal data collection method by analyzing past production data.

[0155] The planning department can adjust the level of detail in its supply plans based on the importance of each ingredient. For example, it can evaluate the importance of ingredients and create detailed supply plans for important ingredients. It can also create simpler supply plans for less important ingredients. Furthermore, the planning department can adjust the level of detail in its plans according to the importance of each ingredient to create efficient supply plans. This allows the planning department to create efficient supply plans by adjusting the level of detail based on the importance of each ingredient.

[0156] The supply department can analyze the past supply history of farmers and producers to select the optimal supply method at the time of supply. For example, the supply department can select an efficient supply method based on past supply history. Furthermore, the supply department can select the optimal supply method based on supply history and then supply efficiently. In addition, the supply department can analyze past supply data and optimize supply methods. This allows the supply department to select the optimal supply method by analyzing past supply history.

[0157] The energy introduction unit can analyze the past energy usage history of farmers and producers to select the optimal introduction method when introducing energy. For example, the energy introduction unit can select an efficient energy introduction method based on past energy usage history. Furthermore, the energy introduction unit can select the optimal energy introduction method based on energy usage history and implement efficient energy introduction. In addition, the energy introduction unit can analyze past energy data to optimize the energy introduction method. Thus, the energy introduction unit can select the optimal energy introduction method by analyzing past energy usage history.

[0158] The Ministry of Education can analyze the past educational history of farmers and producers to select the most suitable teaching methods during education. For example, the Ministry of Education can select efficient teaching methods based on past educational history. Furthermore, the Ministry of Education can select the most suitable teaching methods based on educational history and implement efficient education. In addition, the Ministry of Education can analyze past educational data to optimize teaching methods. Thus, the Ministry of Education can select the most suitable teaching methods by analyzing past educational history.

[0159] The following briefly describes the processing flow for example form 2.

[0160] Step 1: The data collection unit collects data from local farmers and producers. This data includes production volume, harvest time, and crop types. The data collection unit collects data either directly from local farmers and producers or through digital platforms provided by them. For example, the data collection unit automatically collects data entered by farmers and producers and stores it in a database. Step 2: The planning department develops a supply plan based on the data collected by the data collection department. The supply plan includes the quantity, timing, and destination of supply. The planning department analyzes the collected data and develops the optimal supply plan. The planning department also considers local demand and supply capacity when developing the supply plan. For example, the planning department adjusts the supply plan based on local demand forecast data. Step 3: The supply department supplies local ingredients based on the supply plan developed by the planning department. The supply department procures ingredients from local farmers and producers and supplies them to local consumers. The supply department selects efficient transportation routes based on the supply plan and delivers the ingredients. For example, the supply department uses a transportation route optimization algorithm to deliver ingredients via the shortest distance.

[0161] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0162] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0163] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0164] Each of the multiple elements described above, including the collection unit, planning unit, and supply unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data from local farmers and producers using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing unit 12 via the control unit 46A. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and formulates a supply plan. The supply unit is implemented by, for example, the control unit 46A of the smart device 14, which supplies local ingredients based on the supply plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0166] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the collection unit, planning unit, and supply unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data from local farmers and producers using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A transmits the data to the data processing unit 12. The planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and formulates a supply plan. The supply unit is implemented, for example, by the control unit 46A of the smart glasses 214, which supplies local ingredients based on the supply plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0181] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0182] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0183] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0184] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0185] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0187] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0188] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0189] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0190] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0191] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0192] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0193] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0195] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0196] Each of the multiple elements described above, including the collection unit, planning unit, and supply unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data from local farmers and producers using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A transmits the data to the data processing unit 12. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and formulates a supply plan. The supply unit is implemented by, for example, the control unit 46A of the headset terminal 314, which supplies local ingredients based on the supply plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0197] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0198] As shown in Figure 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.

[0199] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0200] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0201] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0203] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0204] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0205] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0207] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0208] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0209] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0210] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0211] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0212] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0213] Each of the multiple elements described above, including the collection unit, planning unit, and supply unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data from local farmers and producers using the camera 42 and microphone 238 of the robot 414, and the control unit 46A transmits the data to the data processing unit 12. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and formulates a supply plan. The supply unit is implemented by, for example, the control unit 46A of the robot 414, which supplies local ingredients based on the supply plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0214] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0215] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0216] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0217] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0218] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0219] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0220] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0221] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0222] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0223] 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.

[0224] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0225] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0226] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0227] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0228] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0229] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0230] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0231] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0232] (Note 1) A system characterized by comprising: a data collection unit that collects data on local farmers and producers; a planning unit that creates a supply plan based on the data collected by the data collection unit; and a supply unit that supplies local ingredients based on the supply plan created by the planning unit. (Note 2) The system according to Appendix 1, characterized by comprising a provisioning unit that provides technology for adopting organic farming and sustainable agricultural practices. (Note 3) The system described in Appendix 1 is characterized by employing a circular agricultural system and incorporating a system that aims for the efficient use of resources and the reduction of waste. (Note 4) The system according to Appendix 1, characterized by having a cooling unit that reduces energy consumption by using an efficient cooling system and insulation material. (Note 5) The system according to Appendix 1, characterized by comprising a route optimization unit for optimizing transportation routes to reduce distance and time. (Note 6) The system according to Appendix 1, characterized by comprising an energy introduction unit for introducing renewable energy sources. (Note 7) The system according to Appendix 1, characterized by comprising a mechanical section for using efficient lighting, heating and cooling systems, and energy-efficient machinery. (Note 8) The system according to Appendix 1, characterized by having a packaging section for reducing the use of single-use plastics and employing biodegradable packaging materials. (Note 9) The system according to Appendix 1, characterized by using recyclable packaging materials and having a recycling unit for streamlining the recycling process. (Note 10) The system as described in Appendix 1, characterized by having an education department that provides information to encourage environmental considerations among household and corporate consumers and to support carbon-neutral food supply chains. (Note 11) The aforementioned collection unit is We estimate the sentiments of local farmers and producers, and adjust the timing of data collection based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze past production data from farmers and producers to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, filtering is performed based on the current production status and season of farmers and producers. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We estimate the sentiments of local farmers and producers, and prioritize the data to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting data, the geographical location information of farmers and producers is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, we analyze the social media activities of farmers and producers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning department, We estimate the sentiments of local farmers and producers, and adjust the way supply plans are presented based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning department, When developing a supply plan, adjust the level of detail in the plan based on the importance of each ingredient. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, When developing a supply plan, different planning algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, Estimate the sentiments of local farmers and producers, and adjust the length of the supply plan based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When developing a supply plan, prioritize the plan based on the harvesting season of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, When creating a supply plan, adjust the order of the plan based on the relationships between ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is We estimate the sentiments of local farmers and producers and adjust supply methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is At the time of supply, the optimal supply method is selected by analyzing the past supply history of farmers and producers. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is At the time of supply, the means of supply will be customized based on the current production status of farmers and producers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is The system estimates the sentiments of local farmers and producers and determines supply priorities based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is When supplying goods, the optimal supply method is selected by considering the geographical location information of farmers and producers. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is During supply, we analyze the social media activities of farmers and producers and propose supply methods. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We estimate the sentiments of local farmers and producers and adjust the timing of technology provision based on those estimated sentiments. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing technology, we analyze the past technology usage history of farmers and producers to select the most suitable method of provision. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the sentiments of local farmers and producers and prioritize the technologies we offer based on those estimated sentiments. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing technology, we prioritize providing highly relevant technologies by considering the geographical location information of farmers and producers. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned circular agricultural system is We estimate the sentiments of local farmers and producers, and adjust resource utilization methods based on those estimated sentiments. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned circular agricultural system is When utilizing resources, the optimal utilization method is selected by analyzing the past resource utilization history of farmers and producers. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned circular agricultural system is The system estimates the sentiments of local farmers and producers, and determines priority for resource use based on those estimated sentiments. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned circular agricultural system is When utilizing resources, the optimal resource utilization method will be selected by considering the geographical location information of farmers and producers. The system described in Appendix 3, characterized by the features described herein. (Note 37) The cooling unit is The system estimates the sentiments of local farmers and producers and adjusts the timing of the cooling system's operation based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 38) The cooling unit is During the cooling process, the optimal cooling method is selected by analyzing the past cooling history of farmers and producers. The system described in Appendix 4, characterized by the features described herein. (Note 39) The cooling unit is The system estimates the sentiments of local farmers and producers, and prioritizes cooling systems based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 40) The cooling unit is During the cooling process, the optimal cooling method is selected considering the geographical location information of farmers and producers. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned route optimization unit, The system estimates the sentiments of local farmers and producers, and optimizes transportation routes based on these estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 42) The aforementioned route optimization unit, When optimizing transportation routes, the optimal route is selected by analyzing the past transportation history of farmers and producers. The system described in Appendix 5, characterized by the features described herein. (Appendix 43) The route optimization unit estimates the feelings of local farmers and producers and determines the priority of the transportation route based on the estimated feelings. The system according to Appendix 5, characterized in that. (Appendix 44) The route optimization unit selects an optimal route considering the geographical location information of farmers and producers during transportation route optimization. The system according to Appendix 5, characterized in that. (Appendix 45) The energy introduction unit estimates the feelings of local farmers and producers and adjusts the timing of renewable energy introduction based on the estimated feelings. The system according to Appendix 6, characterized in that. (Appendix 46) The energy introduction unit selects an optimal introduction method by analyzing the past energy usage history of farmers and producers during energy introduction. The system according to Appendix 6, characterized in that. (Appendix 47) The energy introduction unit estimates the feelings of local farmers and producers and determines the priority of energy introduction based on the estimated feelings. The system according to Appendix 6, characterized in that. (Appendix 48) The energy introduction unit selects an optimal energy introduction method considering the geographical location information of farmers and producers during energy introduction. The system according to Appendix 6, characterized in that. (Appendix 49) The machine unit estimates the feelings of local farmers and producers and adjusts the operation timing of the machine based on the estimated feelings. The system according to Appendix 7, characterized in that. (Appendix 50) The machine unit When operating machinery, the system analyzes the past usage history of farmers and producers to select the optimal operating method. The system described in Appendix 7, characterized by the features described herein. (Note 51) The aforementioned mechanical part is The system estimates the sentiments of local farmers and producers and prioritizes the use of machinery based on those estimated sentiments. The system described in Appendix 7, characterized by the features described herein. (Note 52) The aforementioned mechanical part is When operating machinery, the optimal operating method is selected by considering the geographical location information of farmers and producers. The system described in Appendix 7, characterized by the features described herein. (Note 53) The aforementioned packaging part is The system estimates the sentiments of local farmers and producers, and selects packaging materials based on those estimated sentiments. The system described in Appendix 8, characterized by the features described herein. (Note 54) The aforementioned packaging part is During packaging, the optimal packaging method is selected by analyzing the past packaging history of farmers and producers. The system described in Appendix 8, characterized by the features described herein. (Note 55) The aforementioned packaging part is We estimate the sentiments of local farmers and producers and determine packaging priorities based on those estimated sentiments. The system described in Appendix 8, characterized by the features described herein. (Note 56) The aforementioned packaging part is When packaging, the optimal packaging method is selected considering the geographical location information of farmers and producers. The system described in Appendix 8, characterized by the features described herein. (Note 57) The aforementioned recycling unit is We estimate the sentiments of local farmers and producers and adjust the timing of the recycling process based on those estimated sentiments. The system described in Appendix 9, characterized by the features described herein. (Note 58) The aforementioned recycling unit is When recycling, analyze the past recycling history of farmers and producers to select the optimal recycling method The system according to Appendix 9, characterized by this (Appendix 59) The recycling unit is Estimate the feelings of local farmers and producers, and determine the priority of recycling based on the estimated feelings The system according to Appendix 9, characterized by this (Appendix 60) The recycling unit is When recycling, consider the geographical location information of farmers and producers to select the optimal recycling method The system according to Appendix 9, characterized by this (Appendix 61) The Ministry of Education is Estimate the feelings of local farmers and producers, and adjust the educational content based on the estimated feelings The system according to Appendix 10, characterized by this (Appendix 62) The Ministry of Education is When educating, analyze the past educational history of farmers and producers to select the optimal educational method The system according to Appendix 10, characterized by this (Appendix 63) The Ministry of Education is Estimate the feelings of local farmers and producers, and determine the priority of education based on the estimated feelings The system according to Appendix ed 10, characterized by this (Appendix 64) The Ministry of Education is When educating, consider the geographical location information of farmers and producers to select the optimal educational method The system according to Appendix 10, characterized by this

Explanation of Signs

[0233] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: a data collection unit that collects data on local farmers and producers; a planning unit that creates a supply plan based on the data collected by the data collection unit; and a supply unit that supplies local ingredients based on the supply plan created by the planning unit.

2. The system according to claim 1, characterized by comprising a provisioning unit that provides technology for adopting organic farming and sustainable agricultural practices.

3. The system according to claim 1, characterized in that it employs a circular agricultural system and includes a system that aims for the efficient use of resources and the reduction of waste.

4. The system according to claim 1, characterized by comprising a cooling unit that reduces energy consumption by using an efficient cooling system and insulation material.

5. The system according to claim 1, characterized by comprising a route optimization unit for optimizing transportation routes to reduce distance and time.

6. The system according to claim 1, characterized in that it includes an energy introduction unit for introducing renewable energy sources.

7. The system according to claim 1, characterized by comprising a mechanical section for using efficient lighting, heating and cooling systems, and energy-efficient machinery.

8. The system according to claim 1, characterized in that it includes a packaging section for reducing the use of single-use plastics and employing biodegradable packaging materials.

9. The system according to claim 1, characterized in that it uses recyclable packaging materials and includes a recycling unit for streamlining the recycling process.

10. The system according to claim 1, characterized by having an education department that provides information to encourage household and corporate consumers to be environmentally conscious and to support carbon-neutral food supply chains.

11. The aforementioned collection unit is We estimate the sentiments of local farmers and producers, and adjust the timing of data collection based on those estimated sentiments. The system according to feature 1.

12. The aforementioned collection unit is Analyze past production data from farmers and producers to select the optimal data collection method. The system according to feature 1.

13. The aforementioned collection unit is When collecting data, filtering is performed based on the current production status and season of farmers and producers. The system according to feature 1.

14. The aforementioned collection unit is We estimate the sentiments of local farmers and producers, and prioritize the data to collect based on those estimated sentiments. The system according to feature 1.

15. The aforementioned collection unit is When collecting data, the geographical location information of farmers and producers is taken into consideration to prioritize the collection of highly relevant data. The system according to feature 1.

16. The aforementioned collection unit is During data collection, we analyze the social media activities of farmers and producers and collect relevant data. The system according to feature 1.

17. The aforementioned planning department, We estimate the sentiments of local farmers and producers, and adjust the way supply plans are presented based on those estimated sentiments. The system according to feature 1.

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