Agricultural product distribution and diet informatization management system

Abstract

The invention relates to the technical field of agricultural product distribution and diet informatization management, in particular to an agricultural product distribution and diet informatization management system which comprises a data management module, a distribution scheduling module, a user information module, a diet planning module and a supply chain monitoring module. The distribution scheduling module is used for optimizing a distribution route and time, the user information module is used for recording individual and meal ordering information of a user, the meal planning module provides a personalized meal scheme according to health requirements of the user, and the supply chain monitoring module is used for tracking the circulation state and quality of agricultural products.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product distribution and dietary information management, and in particular to an agricultural product distribution and dietary information management system. Background Art

[0002] The agricultural product distribution and dietary information management system is a comprehensive system that optimizes the distribution and dietary planning of agricultural products through information technology, intelligent algorithms and big data analysis. The system realizes efficient distribution of agricultural products from suppliers to consumers through multiple modules and process management, and provides personalized dietary plans to ensure the quality, safety and nutritional balance of food. The data acquisition and processing module is responsible for collecting various data such as production, storage and circulation of agricultural products. By connecting with various systems such as agricultural production units, wholesale markets, warehouses and transportation companies, the dynamic information of agricultural products, including origin, suppliers, production batches, inventory, prices, etc., can be obtained in real time. These data will serve as the basis for subsequent decision-making and distribution optimization. The intelligent distribution management module is the core part of agricultural product distribution, which uses big data and intelligent algorithms to optimize distribution routes, distribution time and distribution personnel scheduling. Its main functions include: route optimization: by analyzing traffic conditions, weather, distribution needs and distance, algorithms (such as genetic algorithms, ant colony algorithms, etc.) are used to dynamically optimize distribution routes to reduce distribution costs and improve distribution efficiency. According to different order requirements, intelligent dispatch of distribution personnel and distribution vehicles is carried out to reasonably arrange workload and avoid waste of resources.

[0003] The user information management module is responsible for managing consumers' personal information, including health records, dietary preferences, allergens and other data. By analyzing the user's dietary history, health status, etc., the system can recommend personalized agricultural product delivery and dietary plans for users. This module can also track the user's order status, such as delivery status, receipt feedback, etc. The dietary planning and nutrition management module provides personalized dietary plans based on the user's health needs and dietary habits. By connecting with systems such as nutrition databases and recipe libraries, the dietary planning module can provide users with nutritionally balanced dietary plans that cover different needs such as daily diet, weight loss, and special diets. In addition, the module can also optimize dietary recommendations based on user feedback to ensure both nutrition and taste satisfaction.

[0004] In the process of agricultural product distribution and meal information management, when faced with multiple people delivering food, setting the most convenient transportation route and ensuring the temperature of the meals are of utmost importance. The situation becomes more complicated when the two are combined, and it is difficult for the existing management system to combine them effectively.

[0005] In view of the above situation, in order to overcome the above technical problems, the present invention designs an agricultural product distribution and dietary information management system to solve the above technical problems. Summary of the invention

[0006] The technical purpose to be achieved by the present invention is to design an agricultural product distribution and meal information management system to provide the optimal route and ensure the delivery efficiency and the optimal use temperature of the meal when multiple couriers are simultaneously dealing with the distribution of agricultural products and meals.

[0007] In order to achieve the above technical objectives, the present invention provides the following technical solutions:

[0008] An agricultural product distribution and meal information management system aims to improve the distribution efficiency of agricultural products and the quality of meal management through information technology, and ensure that the entire process of agricultural products from production to dining table is efficient, safe and convenient. The system includes multiple key modules, including data management module, distribution scheduling module, user information module, meal planning module and supply chain monitoring module.

[0009] The data management module is the basic part of the system, which is mainly used to collect, process and store various data of agricultural products. This module connects with data sources such as farms, wholesale markets, and logistics companies to obtain information about agricultural products such as production information, inventory status, transportation conditions, and quality inspection results. These data will be integrated and analyzed to provide a basis for subsequent decision-making in the system, ensuring that the entire flow process from farmland to consumers can be monitored and managed in real time.

[0010] The delivery scheduling module focuses on optimizing the delivery process, and uses intelligent algorithms to dynamically adjust delivery routes and times based on order requirements, delivery addresses, traffic conditions, and other factors. This module can reduce the waste of time and resources during the delivery process, reduce delivery costs, and improve delivery efficiency and accuracy. Using real-time data, the system can quickly respond to emergencies such as traffic jams and weather changes, adjust delivery plans, and ensure on-time delivery.

[0011] The user information module provides personalized services for consumers. The system records and manages each user's personal information, health records, dietary preferences and historical order information. Based on this data, the system can not only provide users with accurate delivery services, but also make dietary recommendations and adjustments based on the user's health needs, allergens and other factors to ensure the nutrition and safety of food.

[0012] The dietary planning module provides personalized dietary plans based on the user's health needs. The module combines the user's health data, dietary preferences, weight management needs, etc. to design a reasonable dietary plan and recommend suitable agricultural products to ensure that the user's nutritional intake and eating habits are balanced.

[0013] The supply chain monitoring module is responsible for tracking the entire process of agricultural products from production to distribution to ensure the quality and safety of food. Through the Internet of Things technology, blockchain and other means, this module monitors the circulation of each batch of agricultural products in real time, discovers potential quality problems in a timely manner, and ensures that the products received by consumers always meet safety standards.

[0014] Through efficient data exchange and intelligent decision-making, the entire system can optimize the agricultural product distribution process, improve user satisfaction, ensure food quality and safety, and provide strong support for personalized dietary plans, thereby improving efficiency while ensuring the health and welfare of consumers.

[0015] Preferably, the data management module obtains the production, purchase, inventory and other data of agricultural products in real time by interfacing with agricultural production units, wholesale markets and logistics companies, and performs intelligent analysis to improve procurement and distribution efficiency.

[0016] Preferably, the data management module obtains the production, purchase, inventory and other data of agricultural products in real time by interfacing with agricultural production units, wholesale markets and logistics companies, and performs intelligent analysis to improve procurement and distribution efficiency.

[0017] Preferably, the delivery scheduling module adopts an algorithm based on machine learning to intelligently optimize delivery routes and times according to factors such as order requirements, delivery addresses, real-time traffic conditions and weather conditions, thereby reducing delivery costs and improving delivery efficiency.

[0018] Preferably, the user information module supports multi-user management, can automatically generate personalized recommendations based on the user's historical ordering records, preference settings and health records, and remind the user in real time through push notifications.

[0019] Preferably, the dietary planning module interacts with the nutrition expert system, the recipe database and the user's health data to provide dietary recommendations that are adapted to the user's eating habits and health needs, thereby ensuring daily nutritional balance.

[0020] Preferably, the delivery end can intelligently control the delivery route to minimize the delivery cost during delivery. The specific control steps are as follows:

[0021] Step 1: First, obtain a set of all delivery locations M, M = {1, 2, 3, ..., n}, where 1 represents the departure location and n represents the final destination, and set the number of virtual delivery personnel m, and the virtual delivery personnel form a set L, L = {1, 2, 3, ..., m}, each virtual delivery personnel is randomly distributed to different delivery locations, and prepares for subsequent route planning;

[0022] Step 2: According to the distance, temperature difference, road congestion coefficient and other factors between the delivery locations i and j, the delivery heuristic factor ηi,j is calculated using the following formula: ηi,j = J × (Vi,j × ρ × S × ΔT / di,j) × (Pe / B) × M, where J is the preset delivery insulation coefficient, Vi,j is the average speed of the delivery vehicle from i to j, ρ is the road congestion coefficient, S is the surface area of ​​the car, ΔT is the temperature difference of the car, di,j is the distance from the delivery location i to j, Pe is the gasoline price, B is the gasoline combustion value, and M is the fuel consumption per unit distance; at the same time, the initial delivery information amount ti,j between the delivery location i and the delivery location j is 1;

[0023] Step 3: Randomly distribute m virtual computing couriers to n delivery locations and perform the first route planning. Each virtual computing couriers calculates the probability of selecting the next delivery location j based on the current delivery location i; the probability is calculated by the following formula: P(i→j)=(ti,j×ηi,j)×Σ(ti,u×ηi,u)^(-a)×b, where ti,j is the amount of delivery information, ηi,j is the delivery heuristic factor, and a and b are preset parameters; this step aims to find the best choice from the current delivery location to the next location;

[0024] Step 4: All virtual delivery personnel calculate the probability and select the location with the largest probability value as their next delivery location. This process ensures that each delivery personnel chooses the best next stop to ensure the shortest overall route and the lowest cost.

[0025] Step 5: After selecting the next delivery location, update the delivery information volume ti,j using the formula, i.e., ti,j = ti,j + w × zk × Lmk / Pli,j, where w is the time loss coefficient, zk is the information volume adjustment coefficient, Lmk is the total delivery distance of the deliveryman m, and Pli,j is the set of deliverymen from location i to j;

[0026] Step 6: Update the route planning of all delivery personnel and recalculate the delivery heuristic factor based on the updated delivery information. Repeat steps S4 and S5 until n deliveries are made and the route optimization is completed. After each delivery, calculate the amount of information of each virtual delivery person in the delivery and summarize it as the adjusted information amount zk for the next route planning;

[0027] Step 7: Based on the updated path information, a new route planning is restarted; the starting point of each route planning is the location where the last delivery ended, and all parameters remain unchanged; the virtual delivery personnel continue to plan the delivery route according to the same rules until the route planning of all delivery personnel is consistent, and then output;

[0028] Step S8: When the route plans of all virtual delivery personnel are consistent, this route map is used as the final delivery route, and the delivery location numbered 1 is used as the starting point to start formal delivery.

[0029] Preferably, the order receiving step includes a process in which the user logs into the platform, selects a product and submits the order. The system automatically obtains the user's location information, provides multiple payment methods, and generates a corresponding order number for subsequent delivery tracking.

[0030] Preferably, the delivery route optimization step is performed based on analysis of historical data to evaluate traffic flow at different time periods and routes, thereby minimizing delivery time and adjusting delivery routes in real time after an order is generated.

[0031] Preferably, the step of dispatching delivery personnel adopts an algorithm based on artificial intelligence to perform intelligent dispatch according to the delivery personnel's work performance, historical delivery data, etc., so as to reasonably allocate tasks, maximize delivery efficiency, and reduce personnel idle time.

[0032] The beneficial effects of the present invention are as follows:

[0033] (1) The present invention takes agricultural product transportation, user information and meal planning into consideration by setting up a data management module, a distribution scheduling module, a user information module, a meal planning module and a supply chain monitoring module, and designs the best route plan based on factors such as distance, temperature difference and road congestion coefficient. By comprehensively considering multiple factors such as distance, temperature difference and road congestion coefficient, the system can accurately design the optimal distribution route. This means that the delivery personnel will be able to avoid congested roads and avoid unnecessary detours, thereby reducing the time required for distribution. By optimizing the route, the system can also reduce vehicle fuel consumption, reduce energy waste, and thus save distribution costs. In addition, the intelligent algorithm can dynamically adjust the route based on real-time data to ensure that efficient distribution can be maintained even in the event of changing traffic conditions or sudden weather events.

[0034] (2) For agricultural product distribution, temperature and transportation conditions are crucial to product quality. Temperature difference is an important factor affecting the freshness and shelf life of agricultural products. Through the data management module and the distribution scheduling module combined with temperature monitoring, the system can adjust the distribution route and transportation conditions in real time. For example, during transportation, the system can consider the difference between the current ambient temperature and the temperature inside the carriage to avoid the deterioration or loss of agricultural product quality due to excessively high or low temperatures. In addition, the system can also design the most suitable distribution routes and environmental conditions for different agricultural products (such as fruits, vegetables, meat, etc.) according to their characteristics, ensuring that the products reach consumers fresh and safe. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] The above and other aspects of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0037] Figure 1 It is a schematic diagram of the overall system structure of the present invention;

[0038] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0040] like Figure 1-2 As shown in the figure, an agricultural product distribution and meal information management system aims to improve the distribution efficiency of agricultural products and the quality of meal management through information technology, and ensure that the whole process of agricultural products from production to dining table is efficient, safe and convenient. The system includes multiple key modules, including data management module, distribution scheduling module, user information module, meal planning module and supply chain monitoring module.

[0041] The data management module is the basic part of the system, which is mainly used to collect, process and store various data of agricultural products. This module connects with data sources such as farms, wholesale markets, and logistics companies to obtain information about agricultural products such as production information, inventory status, transportation conditions, and quality inspection results. These data will be integrated and analyzed to provide a basis for subsequent decision-making in the system, ensuring that the entire flow process from farmland to consumers can be monitored and managed in real time.

[0042] The delivery scheduling module focuses on optimizing the delivery process, and uses intelligent algorithms to dynamically adjust delivery routes and times based on order requirements, delivery addresses, traffic conditions, and other factors. This module can reduce the waste of time and resources during the delivery process, reduce delivery costs, and improve delivery efficiency and accuracy. Using real-time data, the system can quickly respond to emergencies such as traffic jams and weather changes, adjust delivery plans, and ensure on-time delivery.

[0043] The user information module provides personalized services for consumers. The system records and manages each user's personal information, health records, dietary preferences and historical order information. Based on this data, the system can not only provide users with accurate delivery services, but also make dietary recommendations and adjustments based on the user's health needs, allergens and other factors to ensure the nutrition and safety of food.

[0044] The dietary planning module provides personalized dietary plans based on the user's health needs. The module combines the user's health data, dietary preferences, weight management needs, etc. to design a reasonable dietary plan and recommend suitable agricultural products to ensure that the user's nutritional intake and eating habits are balanced.

[0045] The supply chain monitoring module is responsible for tracking the entire process of agricultural products from production to distribution to ensure the quality and safety of food. Through the Internet of Things technology, blockchain and other means, this module monitors the circulation of each batch of agricultural products in real time, discovers potential quality problems in a timely manner, and ensures that the products received by consumers always meet safety standards.

[0046] Through efficient data exchange and intelligent decision-making, the entire system can optimize the agricultural product distribution process, improve user satisfaction, ensure food quality and safety, and provide strong support for personalized dietary plans, thereby improving efficiency while ensuring the health and welfare of consumers.

[0047] The data management module achieves seamless connection with the systems of all parties through interfaces with multiple partners such as agricultural production units, wholesale markets and logistics companies, and obtains key information such as production, purchase, inventory, circulation, etc. of agricultural products in real time. These data cover the production site, production batch, storage conditions, market demand, inventory quantity and logistics distribution progress of agricultural products. Through intelligent analysis technology, the system can deeply mine and analyze these real-time data, identify potential problems in the supply chain, such as insufficient inventory, imbalance between supply and demand, etc., and respond in advance. By optimizing procurement plans, adjusting inventory strategies and distribution routes, the system can greatly improve procurement and distribution efficiency, reduce resource waste, avoid excess or shortage, and ensure that agricultural products are delivered to consumers on time and efficiently. At the same time, data analysis also helps predict future demand and provides strong support for accurate procurement and production scheduling.

[0048] The data management module establishes interfaces with agricultural production units, wholesale markets, logistics companies and other parties to obtain key information about the production, purchase, inventory and other key information of agricultural products in real time. Specifically, the module can obtain data such as the origin, production batch, quality inspection and other data of agricultural products from agricultural production units, obtain the current market supply and demand conditions and purchase data from wholesale markets, and obtain information such as distribution routes, transportation time, and vehicle conditions from logistics companies. After these data are integrated, the system processes them through intelligent analysis technology to identify potential supply chain bottlenecks and risk points. For example, the system can predict future demand fluctuations, analyze whether inventory is sufficient, and adjust procurement and inventory strategies. By optimizing procurement plans, adjusting inventory distribution and distribution routes, the system can effectively improve distribution efficiency, reduce inventory costs, and reduce resource waste, ensuring that agricultural products can reach consumers on time, efficiently, and at low cost.

[0049] The distribution scheduling module adopts an optimization algorithm based on machine learning, which can intelligently adjust the distribution route and delivery time according to a variety of real-time data factors. These factors include the specific needs of the order, the delivery address, real-time traffic conditions, weather conditions, etc. By continuously analyzing and learning historical distribution data, the system can identify the optimal distribution path and the best delivery time, avoiding the common waste of resources and inefficiency in traditional manual scheduling. In the face of emergencies such as traffic congestion and bad weather, the system will adjust the distribution plan in real time and automatically select the best alternative route to ensure the punctuality and stability of distribution. At the same time, by optimizing the route, the system can reduce the fuel consumption and driving distance of the vehicle, thereby reducing the distribution cost. The algorithm based on machine learning can also continuously iterate and optimize as the distribution task proceeds, improve the intelligence level of the system, and enable each distribution to achieve the best efficiency and cost control.

[0050] The user information module supports multi-user management functions and can efficiently handle the personalized needs of a large number of users. By collecting and analyzing each user's historical ordering records, dietary preferences, health records and other data, the system can accurately judge the user's preferences and needs, and automatically generate personalized recommendations. For example, if a user prefers low-sugar, high-protein foods, the system will recommend corresponding agricultural products or dietary plans based on this preference. At the same time, the user's health records (such as weight, allergens, etc.) will also be taken into consideration to ensure the safety and suitability of the recommended content. In addition, the system can also continuously optimize based on the user's behavioral data to ensure that the recommended content always meets the user's taste and health needs. In order to improve the user experience, the system will push notifications to remind users of relevant delivery progress, dietary recommendations and promotional information in real time to ensure that users get the information they need in the first place and improve user satisfaction and participation.

[0051] The dietary planning module can provide accurate dietary recommendations based on the user's personal health status, dietary preferences and nutritional needs through deep interaction with the nutrition expert system, recipe database and user health data. The module first analyzes the user's health data, such as weight, exercise, allergens and chronic disease information, and then designs a dietary plan that meets health needs in combination with the nutritional matching model provided by the nutrition expert system. For example, if the user has high blood sugar, the system will recommend low-sugar, high-fiber foods and provide corresponding recipes to help users control blood sugar levels. By docking with the recipe database, the system can not only recommend scientifically matched foods, but also provide suggestions for dishes that are simple to make and taste good. The system also makes personalized adjustments based on the user's eating habits (such as vegetarian, low-fat, etc.) to ensure that daily meals meet the standards of nutritional balance, thereby helping users maintain good health and improve their quality of life.

[0052] The delivery end can intelligently control the delivery route to minimize the delivery cost during delivery. The specific control steps are as follows:

[0053] Step 1: First, obtain a set of all delivery locations M, M = {1, 2, 3, ..., n}, where 1 represents the departure location and n represents the final destination, and set the number of virtual delivery personnel m, and the virtual delivery personnel form a set L, L = {1, 2, 3, ..., m}, each virtual delivery personnel is randomly distributed to different delivery locations, and prepares for subsequent route planning;

[0054] Step 2: According to the distance, temperature difference, road congestion coefficient and other factors between the delivery locations i and j, the delivery heuristic factor ηi,j is calculated using the following formula: ηi,j = J × (Vi,j × ρ × S × ΔT / di,j) × (Pe / B) × M, where J is the preset delivery insulation coefficient, Vi,j is the average speed of the delivery vehicle from i to j, ρ is the road congestion coefficient, S is the surface area of ​​the car, ΔT is the temperature difference of the car, di,j is the distance from the delivery location i to j, Pe is the gasoline price, B is the gasoline combustion value, and M is the fuel consumption per unit distance; at the same time, the initial delivery information amount ti,j between the delivery location i and the delivery location j is 1;

[0055] Step 3: Randomly distribute m virtual computing couriers to n delivery locations and perform the first route planning. Each virtual computing couriers calculates the probability of selecting the next delivery location j based on the current delivery location i; the probability is calculated by the following formula: P(i→j)=(ti,j×ηi,j)×Σ(ti,u×ηi,u)^(-a)×b, where ti,j is the amount of delivery information, ηi,j is the delivery heuristic factor, and a and b are preset parameters; this step aims to find the best choice from the current delivery location to the next location;

[0056] Step 4: All virtual delivery personnel calculate the probability and select the location with the largest probability value as their next delivery location. This process ensures that each delivery personnel chooses the best next stop to ensure the shortest overall route and the lowest cost.

[0057] Step 5: After selecting the next delivery location, update the delivery information volume ti,j using the formula, i.e., ti,j = ti,j + w × zk × Lmk / Pli,j, where w is the time loss coefficient, zk is the information volume adjustment coefficient, Lmk is the total delivery distance of the deliveryman m, and Pli,j is the set of deliverymen from location i to j;

[0058] Step 6: Update the route planning of all delivery personnel and recalculate the delivery heuristic factor based on the updated delivery information. Repeat steps S4 and S5 until n deliveries are made and the route optimization is completed. After each delivery, calculate the amount of information of each virtual delivery person in the delivery and summarize it as the adjusted information amount zk for the next route planning;

[0059] Step 7: Based on the updated path information, a new route planning is restarted; the starting point of each route planning is the location where the last delivery ended, and all parameters remain unchanged; the virtual delivery personnel continue to plan the delivery route according to the same rules until the route planning of all delivery personnel is consistent, and then output;

[0060] Step S8: When the route plans of all virtual delivery personnel are consistent, this route map is used as the final delivery route, and the delivery location numbered 1 is used as the starting point to start formal delivery.

[0061] The order receiving step includes the complete process of the user logging in to the platform, selecting products and submitting orders. First, the user needs to log in to the system platform, enter personal information and verify identity to ensure account security. After successful login, the user can browse various agricultural products on the platform, select the desired products according to personal needs, and add them to the shopping cart. After confirming the shopping list, the user submits the order, and the system automatically obtains the user's location information, including the delivery address and contact information, to ensure accurate delivery arrangements. In the payment link, the system provides a variety of payment methods, such as online payment, bank card payment, e-wallet, etc., so that users can choose the appropriate method to complete the payment according to their own needs. Once the payment is completed, the system will generate a unique order number as an identifier of the order, which is convenient for users to track in subsequent inquiries. The order number will also be connected to the distribution system to ensure that each order can accurately track the delivery status, and push relevant progress information to the user in a timely manner to ensure that the order can be delivered smoothly.

[0062] The delivery route optimization step analyzes historical data, comprehensively evaluates traffic flow in different time periods and routes, and intelligently selects the optimal delivery route. The system will establish an accurate traffic flow model based on factors such as previous delivery records, peak traffic hours, and road congestion. After the order is generated, the system analyzes the current traffic conditions in real time and evaluates the congestion level of different routes to select the most suitable delivery route to ensure that the delivery is completed in the shortest time. At the same time, the system can dynamically adjust according to real-time traffic information. If there is a sudden traffic congestion, accident or weather change, the system will quickly recalculate and adjust the route to avoid delays. Through this optimization step, the system can effectively reduce delivery time, improve delivery efficiency, and reduce transportation costs. For each order, the delivery person will receive an updated optimal route prompt to ensure that the agricultural products are delivered to the user in a timely and efficient manner, improving user experience and satisfaction.

[0063] The step of dispatching delivery personnel adopts an algorithm based on artificial intelligence to realize intelligent dispatching by analyzing factors such as the delivery personnel's work performance, historical delivery data, delivery speed, and delivery area. The system will evaluate the efficiency, vehicle condition, delivery experience, and current workload of each delivery personnel, and automatically optimize the task allocation. Through deep learning of historical delivery data, the system can predict the performance of delivery personnel in specific areas and time periods, so as to reasonably arrange delivery tasks, ensure the balance of workload for each delivery personnel, and avoid over-concentration or over-distribution. The intelligent dispatching system will also take into account factors such as traffic conditions, weather changes, and the urgency of orders to maximize delivery efficiency. Through this dispatching method, the system can reduce the idle time of delivery personnel, improve the overall delivery capacity, and ensure that all tasks can be completed efficiently and in a timely manner. In addition, the system can also dynamically adjust the task allocation strategy to respond to changes in the delivery process in real time to ensure the best work arrangement and user experience.

[0064] Various modifications to the present disclosure will be apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but should be given the widest scope consistent with the principles and novel features disclosed herein. Although one or more exemplary embodiments of the present disclosure have been described with reference to the accompanying drawings, it will be understood by those of ordinary skill in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure as defined in the appended claims.

Claims

1. An agricultural product distribution and meal information management system, characterized in that: It includes a data management module, a distribution scheduling module, a user information module, a meal planning module and a supply chain monitoring module. The data management module is used to collect and process agricultural product information, the distribution scheduling module is used to optimize the distribution route and time, the user information module is used to record the user's personal and meal ordering information, the meal planning module provides personalized meal plans according to the user's health needs, and the supply chain monitoring module is used to track the circulation status and quality of agricultural products.

2. The agricultural product distribution and dietary information management system according to claim 1, characterized in that: The data management module obtains the production, purchase, inventory and other data of agricultural products in real time by interfacing with agricultural production units, wholesale markets and logistics companies, and performs intelligent analysis to improve procurement and distribution efficiency.

3. The agricultural product distribution and meal information management system according to claim 1, characterized in that: The data management module obtains the production, purchase, inventory and other data of agricultural products in real time by interfacing with agricultural production units, wholesale markets and logistics companies, and performs intelligent analysis to improve procurement and distribution efficiency.

4. The agricultural product distribution and dietary information management system according to claim 1, characterized in that: The delivery scheduling module adopts an algorithm based on machine learning to intelligently optimize delivery routes and times according to factors such as order requirements, delivery addresses, real-time traffic conditions and weather conditions, thereby reducing delivery costs and improving delivery efficiency.

5. The agricultural product distribution and meal information management system according to claim 1, characterized in that: The user information module supports multi-user management, and can automatically generate personalized recommendations based on the user's historical ordering records, preference settings, and health records, and remind the user in real time through push notifications.

6. The agricultural product distribution and meal information management system according to claim 1, characterized in that: The dietary planning module interacts with the nutrition expert system, recipe database and user health data to provide dietary suggestions that adapt to the user's eating habits and health needs, ensuring daily nutritional balance.

7. The agricultural product distribution and meal information management system according to claim 1, characterized in that: The distribution scheduling module uses intelligent control of distribution routes to minimize distribution costs. The specific steps are as follows: Step 1: First, obtain a set of all delivery locations M, M = {1, 2, 3, ..., n}, where 1 represents the departure location and n represents the final destination, and set the number of virtual delivery personnel m, and the virtual delivery personnel form a set L, L = {1, 2, 3, ..., m}, each virtual delivery personnel is randomly distributed to different delivery locations, and prepares for subsequent route planning; Step 2: According to the distance, temperature difference, road congestion coefficient and other factors between the delivery locations i and j, the delivery heuristic factor ηi,j is calculated using the following formula: ηi,j = J × (Vi,j × ρ × S × ΔT / di,j) × (Pe / B) × M, where J is the preset delivery insulation coefficient, Vi,j is the average speed of the delivery vehicle from i to j, ρ is the road congestion coefficient, S is the surface area of ​​the car, ΔT is the temperature difference of the car, di,j is the distance from the delivery location i to j, Pe is the gasoline price, B is the gasoline combustion value, and M is the fuel consumption per unit distance; at the same time, the initial delivery information amount ti,j between the delivery location i and the delivery location j is 1; Step 3: Randomly distribute m virtual computing delivery personnel to n delivery locations and perform the first route planning. Each virtual computing delivery personnel calculates the probability of selecting the next delivery location j based on the current delivery location i. The probability is calculated by the following formula: P(i→j) = (ti,j×ηi,j)×Σ(ti,u×ηi,u)^(-a)×b, where ti,j is the amount of delivery information, ηi,j is the delivery heuristic factor, and a and b are preset parameters; This step aims to find the best choice from the current delivery location to the next location; Step 4: All virtual delivery personnel calculate the probability and select the location with the largest probability value as their next delivery location, ensuring that each delivery personnel's next stop is optimal, so as to ensure the shortest overall route and the lowest cost; Step 5: After selecting the next delivery location, update the delivery information volume ti,j using the formula, i.e., ti,j = ti,j + w × zk × Lmk / Pli,j, where w is the time loss coefficient, zk is the information volume adjustment coefficient, Lmk is the total delivery distance of the deliveryman m, and Pli,j is the set of deliverymen from location i to j; Step 6: Update the route planning of all delivery personnel, and recalculate the delivery heuristic factor based on the updated delivery information. Repeat steps 4 and 5 until n deliveries are made and the route optimization is completed. After each delivery, calculate the amount of information of each virtual delivery personnel in this delivery and summarize it as the adjustment information zk for the next route planning. Step 7: Based on the updated path information, a new route planning is restarted; the starting point of each route planning is the location where the last delivery ended, and all parameters remain unchanged; the virtual delivery personnel continue to plan the delivery route according to the same rules until the route planning of all delivery personnel is consistent, and then output; Step 8: When the route plans of all virtual delivery personnel are consistent, this route map will be used as the final delivery route, and the delivery location marked as 1 will be used as the starting point to start the formal delivery.

8. The agricultural product distribution and meal information management system according to claim 1, characterized in that: The delivery scheduling module also includes the functions of order reception, route optimization, and dispatching delivery personnel. Order reception includes the process of users logging into the platform, selecting products and submitting orders. The system automatically obtains the user's location information, provides multiple payment methods, and generates a corresponding order number for subsequent delivery tracking.

9. The agricultural product distribution and meal information management system according to claim 8, characterized in that: The delivery route optimization is performed based on analysis of historical data, evaluating traffic flow at different time periods and routes, so as to minimize delivery time and adjust the delivery route in real time after an order is generated.

10. The agricultural product distribution and meal information management system according to claim 8, characterized in that: The dispatcher uses an artificial intelligence-based algorithm to perform intelligent dispatch according to the delivery personnel's work performance, historical delivery data, etc., reasonably allocate tasks, maximize delivery efficiency, and reduce personnel idle time.

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