System and methods for maximizing margins to sell food for farming industry
View Patent ↗This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.
1 . A computer system comprising:
one or more processors;
one or more memories;
a first edge-enabled device installed at a farm to locally process sales data and environmental metrics;
a second edge-enabled device deployed within a delivery vehicle of a driver, the second edge-enabled device comprising:
an environmental sensor; and
a global positioning system (GPS) module configured to provide real-time location data to the first edge-enabled device and the second edge-enabled device for route determination between the delivery vehicle to the farm;
wherein the one or more processors executes a queue management system configured to generate and transmit event-driven notification requests for a user;
wherein the one or more processors stores, in one or more relational databases, historical delivery data;
a scheduling algorithm executed by the one or more processors to calculate a delivery time for an order to the user based on:
an availability status of a driver,
a service area constraint, and
the historical delivery data;
wherein the one or more processors execute predictive analytics on the historical delivery data to generate estimates of the delivery time and logistics constraints;
a decentralized data exchange protocol configured to synchronize route updates between the first edge-enabled device and the second edge-enabled device for low-latency communication between the first edge-enabled device and the second edge-enabled device and the one or more processors using event-driven messaging;
wherein the decentralized data exchange protocol comprises message queuing telemetry transport (MQTT) to reduce network bandwidth usage and communication latency;
one or more machine-learning-based predictive models executed at least in part on the first edge-enabled device and the second edge-enabled device to process environmental sensor data from the environmental sensor of the second- edge enabled device and the real-time location data to refine driver-to-delivery matching for delivery of the order;
one or more computer-readable hardware storage devices, the one or more computer-readable hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement a method for providing a farm-to-table delivery platform, the method comprising:
providing a menu of items, by the farm;
creating, using the first edge-enabled device, a quick-response (QR) code encapsulating a product ID, a batch detail, and farm information for one or more products from the farm;
packaging the one or more products from the farm;
affixing the QR code to a packaging of the one or more products;
scanning, using the first edge-enabled device, the QR code of the packaging to add the one or more products to an inventory system;
selecting one or more farms, by the user;
receiving a request for the order including the one or more products from the farm;
extracting, using optical character recognition (OCR), credential data, including: (i) a license number, an expiration date, and registration data, from a driver's license; and (ii) auto insurance policy information, from a driver's insurance documents, and auto-filling the credential data into a driver profile;
validating an authenticity of the credential data of a driver, using a data validation algorithm, to cross-reference the credential data from the driver profile with a government database and an insurance database through an application programming interface (API);
assigning the order to a verified driver, via an application employing a graphical user interface (GUI) on the second edge-enabled device, based on the validating the authenticity of the credential data and executing a geofencing algorithm to locally match the order to the verified driver based on a predefined radius between the second edge-enabled device and the first edge-enabled device using the GPS module provided real-time location data of the first and second edge-enabled devices, the route updates, availability of the verified driver, and vehicle capacity of the delivery vehicle for the order, without server-side recomputation; and
displaying data of the QR code, at the second edge-enabled device, to the verified driver assigned to the order.
2 . The computer system of claim 1 , wherein the QR code comprises encoded information that includes:
a category of the product;
a type of the product; and
a cut type of the product, wherein the cut type is optional based on the category of the product.
3 . The computer system of claim 1 , further comprising:
a subscription-based service to manage inventory using the application, wherein farmers are charged a monthly fee to access features such as inventory management and product visibility.
4 . The computer system of claim 1 , wherein the program code includes:
an algorithm for generating and assigning one or more QR codes to products based on the sales data provided by the farm; and
a barcode scanning feature that allows the farm to scan the one or more QR codes using a camera on the first edge-enabled device.
5 . The computer system of claim 1 , wherein the GUI is configured to allow one or more farmers to update product availability and pricing in real-time.
6 . A method of order fulfillment comprising:
providing a computer system comprising:
one or more processors;
one or more memories;
a first edge-enabled device installed at a farm to locally process sales data and environmental metrics;
a second edge-enabled device deployed within a delivery vehicle of a driver, the second edge-enabled device comprising:
an environmental sensor; and
a global positioning system (GPS) module configured to provide real-time location data to the first edge-enabled device and the second edge-enabled device for route determination;
wherein the one or more processors executes a queue management system configured to generate and transmit event-driven notification requests;
wherein the one or more processors stores, in one or more relational databases, historical delivery data;
a scheduling algorithm executed by the one or more processors to calculate a delivery time for an order to the user based on:
an availability status of a driver,
a service area constraint, and
the historical delivery data;
wherein the one or more processors execute predictive analytics on the historical delivery data to generate estimates of the delivery time and logistics constraints;
a decentralized data exchange protocol configured to synchronize route updates between the first edge-enabled device and the second edge-enabled device for low-latency communication between the first edge-enabled device and the second edge-enabled device and the one or more processors using event-driven messaging;
wherein the decentralized data exchange protocol comprises message queuing telemetry transport (MQTT) to reduce network bandwidth usage and communication latency;
one or more machine-learning-based predictive models executed at least in part on the first edge-enabled device and the second edge-enabled device to process environmental sensor data from the environmental sensor of the second-edge enabled device and the real- time location data to refine driver-to-delivery matching for delivery of the order; and
one or more computer-readable hardware storage devices, the one or more computer-readable hardware storage devices contain program code executable by the one or more processors via the one or more memories to implement a farm-to-table delivery platform;
providing a menu of items, by the farm;
creating, using the first edge-enabled device, a quick-response (QR) code encapsulating a product ID, a batch detail, and farm information for one or more products from the farm;
packaging the one or more products from the farm;
affixing the QR code to a packaging of the one or more products;
scanning, using the first edge-enabled device, the QR code of the packaging to add the one or more products to an inventory system;
selecting one or more farms, by the user;
receiving a request for an order including the one or more products from the farm;
extracting, using optical character recognition (OCR), credential data, including: (i) a license number, an expiration date, and registration data, from a driver's license; and (ii) auto insurance policy information, from a driver's insurance documents, and auto-filling the credential data into a driver profile;
validating an authenticity of the credential data, using a data validation algorithm, to cross-reference the credential data from the driver profile with a government database and an insurance database through an application programming interface (API);
assigning the order to a verified driver having a predetermined proximity to the farm based on the validating the authenticity of the credential data and executing a geofencing algorithm to locally match the order to the verified driver based on a predefined radius between of the second edge-enabled device and the first edge-enabled device using the GPS module provided real-time location data of the first and second edge-enabled devices, the route updates, availability of the verified driver, and vehicle capacity of the delivery vehicle for the order, without server-side recomputation; and
displaying data of the QR code, at the second edge-enabled device, to the verified driver assigned to the order.
7 . The method of claim 6 , wherein scanning the QR code allows for tracking of product categories, types, and cut types, and the data is stored in the inventory system.
8 . The method of claim 6 , further comprising:
enabling a user associated with the farm to manage an inventory count through an application interface; and
allowing customers to browse and select products from available farms and place orders through the application interface.
9 . The method of claim 6 , further comprising:
displaying the farm information, including certifications, hours of operation, and customer ratings, to assist the customer in making informed purchasing decisions.
10 . The method of claim 6 , wherein the driver receives a notification when the order is ready for pickup, and the notification includes a farm's address, a product detail, and one or more delivery instructions.
11 . The method of claim 6 , wherein the one or more processors:
receiving the real-time location data from the farm and one or more drivers via a communication interface;
updating a status of the driver and proximity of the driver in real-time as new orders are received and availability changes;
optimizing a delivery route for an assigned driver by calculating the delivery route based on real-time traffic conditions, processed by the processors;
providing the assigned driver with data of an order and route information via a user interface; and
enabling the driver to update their availability and vehicle details in real-time through the user interface.
12 . The method of claim 11 , wherein the communication interface further comprises GPS devices located within vehicles and farms for transmitting the real-time location data to the system.
13 . The method of claim 11 , wherein the processor is configured to calculate the proximity using geographic information system (GIS) data and optimize the assignment of orders based on real-time traffic data.
14 . The method of claim 11 , wherein the one or more processors solves a Vehicle Routing Problem (VRP) or Traveling Salesman Problem (TSP) to determine the optimal delivery routes for drivers.
15 . The method of claim 11 , wherein the assignment of orders to drivers is dynamically updated based on changes in driver availability, workload, and proximity, and a reassignment is handled by the processors in real-time.
16 . The method of claim 11 , wherein the user interface further comprises an application that allows drivers to update availability and vehicle data, and receive notifications about newly assigned orders.
17 . The method of claim 11 , wherein the processor dynamically adjusts a number of available drivers based on the predictive analytics of order demand and forecasts future delivery requirements based on historical data.
18 . A computer system for implementing an order fulfillment process via a farm-to-table delivery platform, comprising:
one or more processors;
one or more memories;
a first edge-enabled device installed at a farm to locally process sales data and environmental metrics;
a second edge-enabled device deployed within a delivery vehicle, the second edge-enabled device comprising:
an environmental sensor; and
a global positioning system (GPS) module configured to provide real-time location data to the first edge-enabled device and the second edge-enabled device for route determination between the delivery vehicle to the farm;
wherein the one or more processors executes a queue management system configured to generate and transmit event-driven notification requests;
wherein the one or more processors stores, in one or more relational databases, historical delivery data;
a scheduling algorithm executed by the one or more processors to calculate a delivery time for an order based on:
an availability status of a driver,
a service area constraint, and
the historical delivery data;
wherein the one or more processors execute predictive analytics on the historical delivery data to generate estimates of the delivery time and logistics constraints;
a decentralized data exchange protocol configured to synchronize route updates between the first edge-enabled device and the second edge-enabled device for low-latency communication between the first edge-enabled device and the second edge-enabled device and the one or more processors using event-driven messaging;
wherein the decentralized data exchange protocol comprises message queuing telemetry transport (MQTT) to reduce network bandwidth usage and communication latency;
one or more machine-learning-based predictive models executed at least in part on the first edge-enabled device and the second edge-enabled device to process environmental sensor data from the environmental sensor of the second-edge enabled device and the real-time location data to refine driver-to-delivery matching;
one or more computer-readable hardware storage devices storing program code executable by the processors to facilitate farm-to-table delivery operations, the operations comprising:
providing a menu of products from the farm at a graphic user interface (GUI) of an electronic device;
creating, using the first edge-enabled device, a Quick Response (QR) code encapsulating a product ID, a batch detail, and farm information for one or more products from the farm;
packaging the products;
affixing the QR code to the packaging;
scanning, using the first edge-enabled device, the QR code to add the products to an inventory system;
enabling the user to select one or more farms and place the order for one or more products;
extracting, using optical character recognition (OCR), credential data, including: (i) a license number, an expiration date, and registration data, from a driver's license; and (ii) auto insurance policy information, from a driver's insurance documents, and auto-filling the credential data into a driver profile;
validating an authenticity of the credential data of a driver, using a data validation algorithm, to cross-reference the credential data from the driver profile with a government database and an insurance database through an application programming interface (API);
assigning the order to a verified driver located within a predetermined proximity to the farm based on the validating the authenticity of the credential data and executing a geofencing algorithm to locally match the order to the verified driver based on a predefined radius between the second edge-enabled device and the first edge-enabled device using the GPS module provided real-time location data of the first and second edge-enabled devices, the route updates, availability of the verified driver, and vehicle capacity of the delivery vehicle for the order, without server-side recomputation;
displaying data of the QR code, at the second edge-enabled device, to the driver;
completing the order fulfillment process via coordination between the farm, the driver, and a customer.
19 . The system of claim 18 , wherein the QR code comprises encoded information that includes:
a category of the product;
a type of the product; and
a cut type of the product, wherein the cut type is optional based on the category of the product.
20 . The system of claim 18 , further comprising:
a subscription-based service for one or more farmers to manage their inventory using the platform, wherein the one or more farmers are charged a monthly fee to access inventory management and product visibility; and
wherein the program code includes:
an algorithm for generating and assigning QR codes to products based on sales data provided by the farmer; and
a barcode scanning feature that allows the farmer to scan QR codes using a built-in camera on a smartphone or tablet.