System, device, and method for management of agricultural commodity in warehouse and silos

Abstract

The present disclosure relates to a system (100), a device (104), and a method (200) for managing location of storing goods in warehouses and silos. The system (100) generates a dynamic virtual representation of a physical warehouse or silo and continuously synchronizes representation with real-time data. A data acquisition and communication unit (109) acquire agricultural quality data, micro-environmental data including temperature, humidity, pressure, and moisture, and bioinformatics data relating to microorganisms affecting stored commodities. A correlation unit (110) corelates by processing acquired data to determine microbial risk associated with stored agricultural commodities. Based on correlated data, an analytical unit (111) calculate remaining shelf-life, generate prescriptive treatment recommendations for mitigating microbial growth, and optimize warehouse storage utilization. The system (100) enables real-time prediction of shelf-life, reduction of spoilage, improved storage efficiency, and enhanced decision-making through technical integration of environmental sensing, biological data processing, and digital twin technology within agricultural storage facilities.

Description

SYSTEM, DEVICE, AND METHOD FOR MANAGEMENT OF AGRICULTURAL COMMODITY IN WAREHOUSE AND SILOSTECHNICAL FIELD

[0001] The embodiments of the present disclosure generally relate to the field of digital warehouse management systems. Particularly, a computer-implemented system and method for digitally managing agricultural warehouses and silos using digital twin technology, microenvironment sensing, bioinformatics-based microbial analysis, shelf-life prediction, and warehouse utilization optimization, more specifically, the present disclosure relates to a device, a system, and a method for managing a location of storing goods (terms “goods” and “agricultural commodity” are used interchangeably hereinafter) in a warehouse.BACKGROUND

[0002] Background description includes information that may be useful in understanding the present disclosure . It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0003] The management of goods (for example, cereals, pulses and legumes, spices and condiments, dried fruits and nuts, grains and seeds, processed foods, or any perishable items) in existing warehouse systems faces several challenges. The warehouses which are particularly for cereals processing industries and wholesale buyer companies having more challenges than other warehouses. The handling, storage, and usage planning are crucial for maintaining the quality of cereals and ensuring their optimal utilization.

[0004] Currently the silos and warehouses both are managed physically and only the grains / goods inside are managed inside the warehouse management. In order to create a faster understanding of warehouse management we are adding the digital version of warehouse and silos - and calling it as digital twin.

[0005] Effective management of the agricultural commodity inside the warehouse needs data of micro-environment which will affect the shelf-life and incidence of microorganisms. In warehouse management, the process of tracking and managing goods is important for ensuring efficient storage, retrieval, and inventory management. Traditional methods of managing goods often involve manual scanning and documentation, which can be prone to errors and inefficiencies. Furthermore, optimizing storage locations based on specific parameters likeproduct characteristics or warehouse conditions is challenging without advanced automation and data analysis techniques.

[0006] The farmers need to follow a standard operating procedure for harvesting and postharvest activities, ensure that the goods meet high-quality standards. Such procedures include guidelines on when to harvest, the optimal moisture content at harvest, drying times, and quality assessment methods. The quality, location, and post-harvest details are documented in a quality assessment certificate, which is tagged to the goods bag in the form of barcodes and QR codes.

[0007] When goods are purchased and arrives at the warehouse, the warehouse manager could scan the bags to access detailed quality information. The information helps to determine the appropriate storage location within the warehouse, whether at the front, middle, or back, based on the quality and suggested usage timeline. For example, goods with high moisture content or a high risk of infection should be used immediately, while goods with low moisture and low fungal infection can be stored for longer periods. Hence such information should be analysed to decide the exact storage location of the goods in the warehouse.

[0008] Further, various systems have been developed to improve the management of goods in warehouses. Such systems typically involve barcode scanners and databases to track and store inventory information. However, there remains a need for a system that not only tracks the goods but also provides intelligent recommendations based on inventory data analysis, allowing for more efficient use of storage space and better management of goods in the warehouse. Additionally, with advancements in artificial intelligence (Al) and blockchain technologies, there is an opportunity to improve the accuracy and efficiency of inventory data and related recommendations.

[0009] Thus, there exists a dire need in the art, to provide a device, a system, and a method for managing the location of storing goods in a warehouse.OBJECTS OF DISCLOSURE

[0010] It is an object of the present disclosure to provide a device, a system, and a method for managing a location of storing goods in a warehouse.

[0011] It is another object of the present disclosure to streamline the management of goods in the warehouse, improve quality preservation, and optimize the usage of goods based on detailed quality assessments.

[0012] It is yet another object of the disclosure to provide a device, a system, and a method that uses artificial intelligence and blockchain technologies to enhance the accuracy and security of inventory management and recommendations.

[0013] It is yet another object of the disclosure to provide a system, a device, and a method for automatically mapping barcode data with inventories stored in a server to streamline warehouse management.

[0014] It is yet another object of the present disclosure to provide the system that recommends the storage of the goods within the warehouse and the optimal usage timeline of the stored goods.SUMMARY

[0015] In an aspect of the present disclosure, the present disclosure discloses a system to manage the location of storing the goods. The system may provide comprehensive recommendations and tools for managing cereals from the moment they arrive at the warehouse, through storage, and up to their planned usage. The system optimizes the handling, storage conditions, and inventory management of goods, ensuring quality preservation and efficient utilization. The system includes features for tracking goods batches, monitoring storage conditions, and planning usage schedules to meet processing or distribution needs.

[0016] In an aspect, the system dynamically may correlate real-time micro-environmental sensor data with bioinformatics-based microbial models within a digital twin of an agricultural warehouse to predict shelf-life and generate prescriptive storage and utilization recommendations .

[0017] In an aspect, the system may correlate the bioinformatics data of the microorganism with the microenvironment such as temperature, humidity, and moisture inside the warehouse. With the bioinformatics data we could effectively calculate the shelf life of the commodity stored in the warehouse. In order to maintain or extend the shelf-life the recommendations of usage of fungicide, insecticide and other chemicals are also predicted on prescriptive analytics.

[0018] While calculating the shelf-life in order to optimize the warehouse space, focuses on optimal usage of warehouses by providing an option of renting or sub-renting of the warehouses with other warehouse managers in the locality.

[0019] In another aspect, the system may be further configured to generate recommendations using artificial intelligence (Al) and blockchain-based techniques, ensuring data accuracy and security. The recommendations may include, but not limited to, suggestionsfor storage locations, considerations for fungus infection risk, and quality assessments based on parameters such as moisture content, size, and color of the goods.

[0020] In an aspect of the present disclosure, a device to manage the location of storing the goods is disclosed. The device may include a scanner configured to scan a code coupled to a storage bag containing goods. A processor may include a memory storing a set of instructions. The processor, upon execution of the set of instructions, may fetch barcode data from the scanner, map the barcode data with identifiers associated with inventories stored on a server, and retrieve the relevant one or more inventories. The processor then analyzes the retrieved data and generates one or more recommendations for managing the storage location of the goods in the warehouse. The server may provide notifications on which lots to use on specific days, weeks, or months, enhancing the efficiency of goods management and ensuring timely usage.

[0021] In another aspect of the present disclosure, the method for managing the location of storing the goods in the warehouse is disclosed. The method illustrates the step-wise illustration of how the received goods in the warehouse are stored and managed by providing recommendations about the storage location in the warehouse. The method may involve fetching barcode data from the scanner, mapping the barcode data with one or more inventories stored on the server, retrieving the relevant inventory, and analyzing the data to generate recommendations for managing the storage location of goods in the warehouse.

[0022] In an aspect of the disclosure, the system may include a digital twin-based warehouse management that integrates real-time micro-environment sensing, bioinformatics data of microorganisms, predictive shelf-life analytics, prescriptive chemical treatment recommendations, and dynamic warehouse utilization and space-sharing mechanisms.

[0023] In another aspect of the disclosure, the system may create a virtual replica (digital twin) of a physical warehouse or silo and continuously update the virtual replica using sensor data. By correlating micro-environment parameters with microbial bioinformatics data, the system may predict shelf-life and generate actionable recommendations to maintain or extend the quality of stored agricultural goods.

[0024] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.BRIEF DESCRIPTION OF DRAWINGS

[0025] FIG. 1 illustrates an exemplary block diagram of a system to manage a location of storing goods in a warehouse, in accordance with an exemplary embodiment of the present disclosure.

[0026] FIG. 2 illustrates an exemplary flow diagram that describes the step-wise illustration of the method for managing a location of storing goods in a warehouse, in accordance with an embodiment of the present disclosure.

[0027] FIG. 3 illustrates an exemplary representation of a flow chart showing an overall architecture of the system to manage a location of storing goods in a warehouse, in accordance with an embodiment of the present disclosure.

[0028] FIG. 4 illustrates an exemplary representation of a flowchart showing an overall modular architecture of the system and the data layers used therein, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0029] Various example embodiments will now be described more fully with reference to the accompanying drawings in which only some example embodiments are shown. Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. The present disclosure, however, may be embodied in many alternate forms and should not be construed as limited to only the example embodiments set forth herein.

[0030] It may be appreciated by a person skilled in the art, the term “agricultural commodity” refers to any produce, crop, or plant-based or animal-derived agricultural output that is harvested, processed to a limited extent (such as cleaning, drying, grading, or packaging), and stored in storage bags within a warehouse for purposes including preservation, inventory tracking, quality maintenance, distribution, trading, or logistics management, wherein each storage bag is identifiable through a unique code scanned by the system.

[0031] The examples of agricultural commodity can include, but not limited to, cereals and grains, pulses and legumes, oilseeds, spices and condiments, plantation and commercial crops, horticultural and dry produce, or animal-derived agricultural products.

[0032] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings FIG. 1-4.

[0033] The present disclosure provides a system (100) for managing the storage location of the goods (terms “goods” and “agricultural commodity” can be used interchangeablyhereinafter) in the warehouse. In an embodiment, the system (100) can be specially customised for cereals processing industries and wholesale buyer companies. The system (100) can be implemented to optimise the handling, storage, and usage planning of cereals, ensuring quality preservation and efficient utilization.

[0034] FIG. 1 illustrates an exemplary block diagram of the system (100) to manage a location of storing goods in a warehouse, in accordance with an exemplary embodiment of the present disclosure.

[0035] In an embodiment of the present disclosure, the system (100) for managing the location of storing the goods received in the warehouse is disclosed.

[0036] In an exemplary implementation of the embodiment, the system (100) can include several components that work together to track, identify, and recommend optimal storage locations for goods, ensuring that warehouse operations are streamlined and goods are stored in appropriate conditions.

[0037] In the exemplary implementation of the embodiment, the system (100) can include a scanner (101) that is used to scan a code tagged on a storage bag that contains goods. The storage bag can be selected based on quality and quantity of the agricultural commodity, and the same can be sensed by the one or more sensors (106) . The code can serve as a unique identifier for the goods, which can include a barcode, a quick response (QR) code, or a radiofrequency identification (RFID) tag, as indicated by the code's definition in the context of the system (100). The scanner (101) can read the code to initiate the process of identifying the goods and their corresponding one or more inventories.

[0038] In the exemplary implementation of the embodiment, the scanner (101) is communicably connected to a processor (102), which serves as the central processing unit for the system (100). The processor (102) includes an artificial intelligence (Al) model (102-1) that performs several functions. The processor (102) can include a memory (102-3) storing a set of instructions. The processor (102), upon execution of the set of instructions and upon scanning the code with the scanner (101), can fetch barcode data from a server (103). The barcode data, which can be obtained from the server (103) after the code is scanned, contains essential information about the goods. The server (103) itself is communicably coupled to the device (104), ensuring seamless data retrieval and communication between the server (103) and the device (104).

[0039] In such embodiment, the barcode data can include preharvest plant health data, efficiency of post-harvest processes, physical aspects of the harvested agricultural commodities such as, but not limited to, colour, size, discoloration due to microbial activities, brokenharvested crop, insect activities, moisture, nutritional values, chemical residual values or shelf life.

[0040] Further, Al model (102-1) can be configured to utilize the existing data to formulate generative recommendations. Further, the system (100) can utilize blockchain-based quality data using a blockchain model (102-2) which can be recorded by the code in each bag of the agricultural commodity, which can be scanned by the scanner (101) to generate recommendations about the storing position of the bag.

[0041] In the exemplary implementation of the embodiment, once the barcode data is retrieved, the processor (102) can map the received barcode data with one or more identifiers that are associated with one or more inventories stored at the server ( 103) . The received barcode data can include a unique identifier associated with the storage bag being scanned. The processor (102) then can map the unique identifier against one or more identifiers, which can include, but not limited to, any or a combination of identification number (ID), item number, stock-keeping unit (SKU), product code, product identification number (ID), or batch number. The mapping process can be used for accurately linking the scanned goods with the correct inventory records.

[0042] In the exemplary implementation of the embodiment, after a successful mapping process, the processor (102) can retrieve the mapped one or more inventories from the server (103). The one or more inventories can include a variety of details such as, but not limited to, item name, description, product code, quantity, storage location (which could involve aisle, shelf, or bin number), transaction history, expiration date, and other specific details like foreign materials present in the goods, moisture content, quality parameters, quality assessment results, or pre-harvest and post-harvest details of the goods or agriculture commodities.

[0043] In such embodiment, the system (100) can include an image acquisition unit (105) to capture one or more images associated with the goods and associated surrounding, wherein the image acquisition unit (105) communicably connected to the processor (102). The system (100) can further include one or more sensors (106) to senses one or more parameters associated with goods and the associated surrounding, wherein the one or more sensors (106) is communicably connected to the processor (102).

[0044] In such embodiment, the one or more parameters received from the image acquisition unit (105) as well as the one or more sensors (106) can be used to calculate the perimeter of the warehouse and calculate a height and a width of stock that can be delivered to the warehouse.

[0045] In such embodiment, the image acquisition unit (105) can be selected from any or a combination of a digital camera, a video camera, a thermal imaging camera, or a near-infrared camera configured to capture images of the goods and the associated surrounding.

[0046] In such embodiment, the one or more sensors (106) can include, but not limited to, a thermistor, a capacitive humidity sensor, a dielectric moisture sensor, a load cell, an ultrasonic proximity sensor, a weight sensor, a distance sensor, a position sensor, a load sensor, or a magnetic reed switch. The one or more sensors (106) can be configured to sense at least one of temperature, humidity, moisture content, weight, position, or seal integrity associated with the goods and the associated surrounding.

[0047] In the exemplary implementation of the embodiment, the processor (102) can analyze the retrieved one or more inventories, the one or more images and the one or more parameters to generate one or more recommendations associated with the location of storage the goods within the warehouse. The recommendations can be based on a variety of factors and may include the optimal storage location within the warehouse, such as, but not limited to, whether the goods should be stored at the front, middle, back, top side, bottom side, or comer of a shelf or bin. Further, the recommendations may include considerations related to the risk of fungus infection or other quality parameters such as moisture content, size, color, or the presence of foreign materials in the goods. The recommendations can be generated using advanced technologies such as artificial intelligence or blockchain-based techniques to ensure accuracy and relevance.

[0048] In the exemplary implementation of the embodiment, the device (104) can be any computing hardware capable of performing the required tasks, such as a laptop, desktop, tablet, or smartphone. The flexibility in hardware ensures that the system (100) can be deployed in various operational contexts within the warehouse

[0049] In the exemplary implementation of the embodiment, the quality parameters or the associated parameters of the goods can be gathered from the quality certificate which is obtained from the farmers or buyers of the goods. The quality data or the associated parameters are stored on the server (103).

[0050] In the exemplary implementation of the first embodiment, the code is coupled to the storage bag incorporating the goods. The coupling of the code on the storage bag can be performed by affixing, attaching, printing, tagging, embedding, stitching, sealing, welding, attaching using magnet or removably coupling the code on the storage bag incorporating the goods.

[0051] In the exemplary implementation of the first embodiment, the disclosed system (100) can be implemented to manage the location of storing goods, wherein the goods include but not limited to, cereals, pulses and legumes, spices and condiments, dried fruits and nuts, grains and seeds, animal feed, herbs and medicinal plants, processed foods, bulk agricultural products, or any perishable items.

[0052] In an exemplary implementation of the embodiment, the system (100) can further include a connecting tool (107) communicably connected to the processor (102) to receive and display the generated one or more recommendations.

[0053] The disclosed system (100) can provide a comprehensive solution for managing the location of storing goods in the warehouse, utilizing advanced scanning, processing, and recommendation capabilities to optimize the storage and retrieval processes. The system (100) can enhance efficiency, reduces errors, and helps to maintain the quality and integrity of the goods stored within the warehouse.

[0054] In another embodiment of the present disclosure, a device (104) to manage the location of storing goods within a warehouse is disclosed. The device (104) can be implemented to ensure handling, tracking, and storage of goods, thereby managing the overall operation of warehouse management systems.

[0055] In an exemplary implementation of the embodiment, the device (104) can include the processor (102) that is responsible for several tasks related to the management of goods. The processor (102) can include the memory (102-3) storing a set of instructions. The processor (102) can be configured to interact with the server (103) that stores data about the goods in the warehouse. The processor (102), upon execution of the set of instructions and upon scanning a code, can fetch barcode data from the server (103). The barcode data can be obtained directly after the barcode, or any equivalent code, is scanned. The server (103) is communicably coupled to the processor (102) that can enable the smooth transfer of data between the server (103) and the device (104).

[0056] In the exemplary implementation of the embodiment, after retrieving the barcode data, the processor (102) can be configured to map the received data with one or more identifiers associated with one or more inventories that are present in the one or more inventories stored at the server (103). The barcode data fetched can include a unique identifier that corresponds to the storage bag being scanned. The processor (102) can map the unique identifier against one or more identifiers, ensuring that the goods are accurately linked to their respective inventory records within the server (103). The identifiers used in the mapping process can include, but are not limited to, any or a combination of identification numbers (ID),item numbers, stock-keeping units (SKU), product codes, product identification numbers (ID), or batch numbers, which are all common in warehouse inventory systems.

[0057] In the exemplary implementation of the embodiment, after successfully mapping the unique identifier to the corresponding inventory records, the processor (102) can retrieve the relevant inventory information from the server (103). In an embodiment, the retrieved data can include comprehensive details about the goods, such as item name, description, product code, quantity, storage location (which may include specifics like aisle, shelf, or bin number), transaction history, expiration date, foreign materials present in the goods, moisture content, quality parameters, quality assessment results, or harvest details.

[0058] In such embodiment, the system (100) can include the image acquisition unit (105) to capture one or more images associated with the goods and the associated surroundings, wherein the image acquisition unit (105) is communicably connected to the processor (102). The system (100) can further include one or more sensors (106) to senses one or more parameters associated with goods and the associated surrounding, wherein the one or more sensors (106) is communicably connected to the processor (102).

[0059] In such embodiment, the image acquisition unit (105) can be selected from any or a combination of a digital camera, a video camera, a thermal imaging camera, or a near-infrared camera configured to capture images of the goods and the associated surrounding.

[0060] In such embodiment, the one or more sensors (106) can include, but are not limited to, a thermistor, a capacitive humidity sensor, a dielectric moisture sensor, a load cell, an ultrasonic proximity sensor, a weight sensor, a distance sensor, a position sensor, a load sensor, or a magnetic reed switch. The one or more sensors (106) can be configured to sense at least one of temperature, humidity, moisture content, weight, position, or seal integrity associated with the goods and the associated surroundings.

[0061] In such embodiment, the processor (102) then analyzes the retrieved one or more inventories, the one or more images and the one or more parameters for generating one or more recommendations concerning the storage location of the goods within the warehouse. The recommendations can be formulated to optimize the storage process by considering various factors like the optimal positioning of goods within the warehouse (e.g., front, middle, back, top side, bottom side, or comer of a shelf or bin). Additionally, the recommendations can address issues such as the risk of fungus infection or other quality concerns based on moisture content, size, colour, or the presence of foreign materials in the goods. The processor (102) can use advanced techniques, such as artificial intelligence or blockchain-based methods, to generate these recommendations, ensuring that they are both accurate and relevant.

[0062] In the exemplary implementation of the embodiment, the device (104) can include the scanner (101) that is used to scan the code tagged on the storage bag containing the goods. The code can include various formats such as a barcode, a quick response (QR) code, or a radio-frequency identification (RFID) tag. Each of these codes can serve as a unique identifier for the goods, allowing the device (104) to track and manage the storage locations within the warehouse.

[0063] In the exemplary implementation of the embodiment, the system (100) can further include the connecting tool (107) communicab ly connected to the processor (102) to receive and display the generated one or more recommendations.

[0064] The device (104) can be implemented to provide a comprehensive solution for managing the storage of goods in a warehouse, ensuring that each item can be stored in the most suitable location based on detailed one or more inventories and advanced analysis methods using Al or blockchain-based methods.

[0065] FIG. 2 illustrates an exemplary flow diagram that describes the step-wise illustration of the method (200) for managing a location of storing goods in a warehouse, in accordance with an embodiment of the present disclosure.

[0066] In an embodiment of the disclosure, the method (200) for managing the storage and usage plan of the goods, especially cereals, in the warehouse is disclosed. The method (200) can include various steps illustrated hereinafter.

[0067] At block 201, the method (200) begins with scanning the code, which is coupled to the storage bag incorporating the goods or the agricultural commodity.

[0068] At block 202, the method (200) proceeds with fetching barcode data from the server (103). The barcode data can be initially obtained upon scanning a code that is tagged on a storage bag incorporating goods. The server (103) is communicably coupled to the processor (102), enabling the transfer of the barcode data from the server (103) to the device (104). The code tagged on the storage bag can be in the form of a barcode, a quick response (QR) code, or a radio-frequency identification (RFID) tag, each serving as a unique identifier for the goods.

[0069] At block 203, after the fetching (201) of the barcode data, the method (200) proceeds to the step of mapping (202) the received barcode data with one or more identifiers associated with one or more inventories stored at the server (103). The received barcode data can include a unique identifier corresponding to the storage bag being scanned. The method (200) can map the unique identifier against one or more identifiers associated with the inventories, ensuring accurate identification and tracking of the goods within the warehouse.

[0070] At block 204, after the mapping is completed, the method (200) moves to the step of retrieving (203) the mapped one or more inventories from the server (103). The inventories can include detailed information such as, but not limited to, item names, descriptions, product codes, quantities, storage locations (such as aisle, shelf, or bin number), transaction histories, expiration dates, presence of foreign materials, moisture content, and other quality parameters related to the goods.

[0071] At block 205, the final step in the method (200) can be performed by analyzing (204) the retrieved one or more inventories to generate one or more recommendations related to the storage location of the goods in the warehouse. The recommendations are based on the analysis of various factors, including the optimal placement of goods within the warehouse, potential risks like fungus infection, and quality parameters such as moisture content, size, and the presence of foreign materials. The recommendations help in ensuring that the goods are stored in a manner that maintains their quality and facilitates efficient access when needed.

[0072] In an exemplary representation of the embodiment, the method (200) can include the scanner (101) to scan the code tagged on the storage bag. The scanner (101) plays a crucial role in initiating the process by capturing the code, which then allows for the subsequent fetching (201) of barcode data and further processing steps. The method (200) thus provides a systematic approach to managing the storage of goods in a warehouse, ensuring that each item is stored in an optimal location based on detailed one or more inventories and analysis.

[0073] FIG. 3 illustrates an exemplary representation of a flow chart (300) showing an overall architecture of the system (100) to manage the location of storing goods in the warehouse, in accordance with an embodiment of the present disclosure.

[0074] In an embodiment, the flow chart (300) is initiated at a start step (301) upon receipt of goods or agricultural commodities at the warehouse facility. The goods can be delivered in storage bags, each storage bag incorporating a scannable code uniquely associated with the goods.

[0075] At step (302), an arrival and seaming operation can be performed. During the arrival and scanning step (302), the scanner (101) can scan the code coupled to each storage bag. The scanner (101) can generate barcode data containing a unique identifier associated with the respective storage bag. The barcode data can be transmitted to the processor (102), which can be communicably coupled to the scanner (101). The processor (102) can fetch the barcode data and access instructions stored in the memory (102-3) for further processing.

[0076] Following the scanning operation, a retrieve quality data step (303) can be executed. During the retrieve quality data step (303), the processor (102) can map the fetched barcodedata with one or more identifiers associated with one or more inventories stored at the server (103). Upon successful mapping, the processor (102) can retrieve inventory-related data, including quality parameters associated with the goods. The quality parameters can include information related to grade, condition, moisture level, storage duration, and availability status of the goods.

[0077] At step (304), a storage location assignment operation can be performed. The processor (102) can analyze the retrieved inventory data to assign a storage location for the goods within the warehouse. The storage location assignment can be performed based on inventory parameters and warehouse layout information stored at the server (103). The assigned storage location can correspond to predefined zones, racks, or sections within the warehouse.

[0078] Subsequently, a sorting and organization step (305) can be carried out. During the sorting and organization step (305), the processor (102) can generate instructions for arranging the storage bags based on assigned storage locations, type of goods, and inventory priority. The sorting and organization operation can enable structured placement of storage bags to facilitate efficient access and management.

[0079] At step (306), a usage planning operation can be executed. The usage planning step (306) can involve analysis of inventory turnover, expected dispatch schedules, and consumption patterns. The processor (102) can generate planning data related to utilization and movement of goods within the warehouse.

[0080] In parallel, a photo documentation and monitoring step (307) can be performed. During the photo documentation and monitoring step (307), the image acquisition unit (105), communicably coupled to the processor (102), can capture one or more images associated with the goods and the surrounding storage environment. The captured images can include visual information related to storage bag condition, placement, stacking, and surrounding environmental conditions.

[0081] At step (308), an upload photo to the system operation can be executed. The processor (102) can transmit the captured images from the image acquisition unit (105) to the server (103) for storage and further processing. The uploaded images can be linked with corresponding inventory identifiers associated with the scanned storage bags.

[0082] Following the upload operation, an analyze photos step (309) can be performed. The processor (102), using the artificial intelligence (Al) model (102-2), can analyze the uploaded images to identify visual indicators related to storage conditions, dimensionalatributes of goods, and arrangement status. The analysis can include the identification of improper stacking, surface damage, and the spatial distribution of storage bags.

[0083] At step (310), providing notifications on usage operation can be carried out. Based on analysis results from the artificial intelligence (Al) model (102-2), the processor (102) can generate notifications associated with inventory usage, relocation requirements, or handling actions. The notifications can support warehouse personnel in managing storage operations.

[0084] In addition, an environmental recommendations step (311) can be executed. The processor (102) can process data sensed by the one or more sensors (106) associated with goods and the surrounding environment. The sensed parameters can include temperature, humidity, moisture content, weight, position, and seal integrity. Based on sensor data and image analysis, the processor (102) can generate environmental recommendations related to ventilation adjustment, humidity control, temperature regulation, and storage condition optimization. The method (300) can terminate at an end step (312) after generation and delivery of recommendations and notifications.

[0085] In another embodiment of the present disclosure, the system (100) specifically for industries relevant to the agricultural commodities disclosed. The system (100) can include the scanner (101) configured to read barcodes and QR codes tagged on cereal bags, wherein the codes contain information about batch numbers, quality assessment results, harvest details, and specific quality parameters. A storage recommendation module that suggests specific storage locations within the warehouse based on the quality and urgency of the use of the cereals. An organization module that facilitates the sorting and arrangement of cereal bags within the warehouse according to the storage recommendations. A usage planning module that generates a usage plan based on the quality data and storage conditions, providing notifications on which lots to use on specific days, weeks, or months.

[0086] In an exemplary implementation of the embodiment, the barcodes or quick response (QR) codes store comprehensive information about the cereals, including moisture content, size, colour, presence of foreign materials, and fungal infections.

[0087] In the exemplary implementation of the embodiment, a photo documentation module allows warehouse managers to take and upload photographs of the stored cereals, linking the photos to the corresponding barcodes / QR codes. The photo documentation module enables periodic monitoring of the stored cereals and sends the photos to the server (103) for analysis.

[0088] In the exemplary implementation of the embodiment, the connecting tool (107) integrates all the features of the system (100) and provides a user-friendly interface for accessing quality data, storage recommendations, and usage plans.

[0089] In the exemplary implementation of the embodiment, the connecting tool (107) provides recommendations on the optimal environmental conditions for the warehouse, including temperature and humidity levels tailored for different seasons.

[0090] In an exemplary implementation of the present disclosure, the method (200) for managing specifically grains in the warehouse is disclosed. The method (200) can include the steps of- 1. scanning barcodes or QR codes tagged on cereal bags to retrieve quality assessment data, 2. assigning storage locations within the warehouse based on the quality and urgency of use of the grains, 3. organizing cereal bags within the warehouse according to the storage recommendations, 4. generating a usage plan based on the quality data and storage conditions, and providing notifications on which lots to use on specific days, weeks, or months 5. taking and uploading photographs of the stored grains for periodic monitoring and sending the photos to the server (103) for analysis.

[0091] In such embodiment, the software application provides recommendations on the optimal environmental conditions for the warehouse, including temperature and humidity levels. The recommendations are tailored for different seasons to ensure the grains are stored under the best possible conditions throughout the year.

[0092] The present disclosure can be seen as revolutionary within the system wherein storage and usage plan of grains or cereals are great concern. The grains bags are scanned when received in the warehouse using barcodes or QR code scanners. The system (100) retrieves and processes the quality assessment data. The system (100) suggests storage locations within the warehouse based on the quality and urgency of use. Grains with higher moisture content or infection risk are stored at the front for immediate use, while cereals with lower risk is stored at the back for long-term storage, grains bags are organized within the warehouse according to the system's recommendations. Each storage location is clearly marked, and the bags are arranged to facilitate easy access and efficient inventory management. The system (100) generates a usage plan based on the quality data and storage conditions. Warehouse managers receive notifications on which lots to use on specific days, weeks, or months. Initial and periodic photos of the stored grains are taken and uploaded to the system. These photos are analyzed by the processor (104), which provides notifications on which lots to use immediately and which can be stored for longer periods.

[0093] An exemplary application of the disclosed system (100) can be described hereinafter.

[0094] In an exemplary implementation, the system (100) for managing the location of storing goods in a warehouse included the scanner (101), the processor (102), and the server (103). Goods or agricultural commodities were stored in storage bags, each having a unique scannable code. During operation, the scanner (101) scanned the code on a selected storage bag to generate barcode data containing a unique identifier, which was transmitted to the processor (102). The processor (102) fetched the barcode data and mapped the unique identifier with corresponding inventory identifiers stored at the server (103), and upon successful mapping, retrieved the related inventory information.

[0095] The retrieved inventory data included parameters such as quantity, type of goods, storage duration, assigned storage location, and availability status. The processor (102) analyzed this data to generate recommendations for managing the storage location of the goods within the warehouse. In particular, the artificial intelligence (Al) model (102-2) evaluated factors such as storage occupancy, retrieval frequency, and inventory turnover to determine optimal storage strategies, including relocating a storage bag, retaining it at the current location, or prioritizing it for dispatch.

[0096] Additionally, the processor ( 102) incorporated the blockchain-based model ( 102-2) to store the code associated with each storage bag and its corresponding inventory mapping in a distributed and tamper-resistant ledger. This ensured integrity and traceability of the goods throughout storage. The generated recommendations were then used to support efficient and reliable warehouse storage management.

[0097] FIG. 4 illustrates an exemplary representation of a flowchart (400) showing an overall modular architecture of the system (100) and the data layers used therein, in accordance with an embodiment of the present disclosure.

[0098] Referring to FIG. 4, the system (100) can be centered around a digital twin unit (108), which constitutes the primary and controlling module of the system, wherein the digital twin unit (108) can be configured to generate and maintain a dynamic virtual representation of a physical agricultural warehouse or silo, including the structural layout of the warehouse or silo, storage zones, bins and compartments, and the location, quantity and type of agricultural commodities stored therein, the digital twin unit (108)further being configured to continuously synchronize with real-world conditions using data received from subordinate modules to enable real-time visualization, simulation and analytical processing of warehouse operations, andwherein all subsequent modules described herein operate within, or in association with, the digital twin unit (108).

[0099] In such embodiment, the digital twin unit (108) comprises a data acquisition and communication unit (109), configured to collect, transmit, and normalize data from multiple heterogeneous data sources. The data acquisition and communication unit (109) can be architecturally divided into three distinct data layers, as described below.

[0100] In such embodiment, the first layer of the data acquisition and communication unit (109) can include a scanner module (403) configured to capture agricultural quality-related data of commodities entering the warehouse or silo, wherein the scanner module (403) can include, but is not limited to, optical scanners, image capture devices, barcode or QR code readers and quality grading interfaces, and can be further configured to acquire data relating to commodity type and variety, physical quality parameters, visual defects or contamination indicators, and lot or batch identification, and wherein the acquired agricultural quality data is transmitted to the digital twin unit (108) for association with the corresponding stored commodity.

[0101] In such embodiment, the second layer of the data acquisition and communication unit (109) can include a sensor layer (404) configured to capture micro-environmental parameters within the warehouse or silo, wherein the sensor layer (404) can include a plurality of sensors such as, but not limited to, temperature sensors, humidity sensors, pressure sensors, or moisture content sensors. The sensors (404) are being deployed across multiple zones of the warehouse or silo, and configured to continuously or periodically generate micro-environmental data indicative of storage conditions, and wherein the sensor data can be communicated to the digital twin unit (108) via wired or wireless communication protocols, including loT-based networks.

[0102] In such embodiment, the third layer of the data acquisition and communication unit (109) can include a bioinformatics data processing unit (109-1) configured to store and process biological data related to microorganisms affecting agricultural commodities, wherein the bioinformatics data processing layer (405) can include datasets representing growth characteristics of fungi, bacteria, insects and other microorganisms, environmental thresholds influencing microbial proliferation, and commodity-specific susceptibility profiles, the bioinformatics data being derived from experimental studies, historical warehouse records, or validated scientific repositories, and wherein the bioinformatics data processing unit (109-1) can enable incorporation of biological context into the digital twin unit (108).

[0103] In such embodiment, the data acquisition and communication unit (109) can operatively connected to a correlation unit (110), wherein the correlation unit (110) can be configured to computationally correlate agricultural quality data obtained from the Scanner Module Layer, micro-environmental data obtained from the one or more sensors (106), and bioinformatics data obtained from the bioinformatics data processing unit (109-1) to determine relationships between environmental conditions and microbial behaviour, thereby identifying probability of microbial growth, risk of spoilage or infestation, and deviation from optimal storage parameters, and wherein the output of the correlation unit (110) can serve as a foundational input for an analytical unit (111). The analytical unit (111) connected to the correlation unit (110) and embedded with the processor (102) to predict shelf-life of stored goods and generate the one or more recommendations associated with technical storage and utilization within the digital twin unit (108).

[0104] In such embodiment, the correlation unit (110) feeds into a shelf-life calculation unit (111-1) configured to compute the remaining shelf-life of stored agricultural commodities, wherein the shelf-life calculation unit (111-1) can determine remaining usable storage duration, predicted degradation timelines and batch-specific shelf-life values, and wherein the calculations are dynamically updated based on real-time correlated data to enable accurate and commodity-specific shelf-life prediction within the digital twin unit (108).

[0105] Further, the analytical unit (111) can further include a prescriptive analytics unit (111-2) operatively connected to the shelf-life calculation unit (111-1), wherein the prescriptive analytics unit (111-2) can be configured to generate storage recommendations associated with corrective action including recommendation of fungicides, insecticides or other chemical interventions based on correlated biological and environmental data, suggested timing and dosage of application, and preventive measures to mitigate microbial growth. The recommendations being automatically derived based on predicted shelf-life, microbial risk assessment and micro-environmental conditions.

[0106] Furthermore, the analytical unit (111) can further include a warehouse optimization unit (409) configured to optimize physical utilization of warehouse or silo space, wherein the warehouse optimization unit (409) can evaluate current and projected storage occupancy, shelflife timelines of stored commodities and available and under-utilized storage capacity, and wherein based on such evaluation, the warehouse optimization unit (409) can enable dynamic allocation of storage space, optimization of commodity placement, and renting or sub-renting of available warehouse or silo space to external warehouse operators, the optimizationdecisions being driven by technical parameters derived from the digital twin unit (108) so as to avoid purely business-based operation.

[0107] What are described above are merely preferred embodiments of the present disclosure, and are not to limit the present disclosure; any modification, equivalent replacement, and improvement within the principle of the present disclosure should be included in the protection scope of the present disclosure.ADVANTAGES OF THE DISCLOSURE:

[0108] The proposed disclosure provides a system, a device, and a method for managing the location of storing goods in the warehouse.

[0109] The proposed disclosure provides a system that includes a server to provide the recommendations on which lots to use on specific days, weeks, or months, ensuring timely and efficient usage of the goods such as grains or cereals.

[0110] The proposed disclosure provides a system that enhances traceability by maintaining detailed records of the quality and storage conditions of each goods lot, facilitating better inventory management and quality control.[oni] The proposed disclosure introduces enhancement and comfort for goods storage, transforming the traditional warehouse management experience.

[0112] The proposed disclosure provides a system that is integrated with quality assessment data that ensures all quality parameters are recorded and utilized for storage and usage planning.

[0113] The proposed disclosure offers an interface for warehouse managers or end users to access all necessary information and recommendations.

[0114] The proposed disclosure provides centralized digital twin-based control of warehouse operations with seamless integration of biological and environmental data, thereby enabling real-time shelf-life prediction and prescriptive decision-making, which in turn improves storage efficiency and significantly reduces spoilage and post-harvest losses.

Claims

We Claim:

1. A system (100) to manage a location of storing goods in a warehouse, the system (100) comprising:a scanner (101) to scan a code coupled to a storage bag incorporating the goods; and a processor (102) communicably coupled to the scanner (101), wherein the processor (102) having a memory (102-3) storing a set of instructions which, when executed, cause the processor (102) to:fetch barcode data from the scanner (101), wherein the barcode data is obtained upon scanning of a code by the scanner (101);map the fetched barcode data with one or more identifiers associated with one or more inventories stored at a server (103), wherein the fetched barcode data comprises a unique identifier associated with the storage bag being scanned and wherein the unique identifier is mapped against the one or more identifiers;retrieve, upon successfully mapping, the mapped one or more inventories from the server (103); andanalyse the retrieved one or more inventories to generate one or more recommendations associated with managing the location of storage the goods in the warehouse.

2. The system (100) as claimed in claim 1, comprises:a digital twin unit (108) to generate a virtual representation of the warehouse and to determine storage capacity of the warehouse, wherein the digital twin unit (108) comprising:a data acquisition and communication unit (109) operatively coupled with the digital twin unit (108) to acquire agricultural quality data, micro-environmental data, and bioinformatics data;a correlation unit (110) to correlate the acquired agricultural quality data, the micro-environmental data, and the bioinformatics data to determine microbial risk to obtain correlated data; andan analytical unit (111) operatively coupled to the correlation unit (110) and embedded with the processor (102) to predict shelf-life of stored goods and generate the one or more recommendations associated with technical storage and utilization within the digital twin unit (108).

3. The system (100) as claimed in claim 2, wherein the analytical unit (111) comprises a shelf-life calculation unit (111-1) to compute remaining shelf-life of stored goods based on the correlated data, and wherein the computed remaining shelf-life is updated based on real-time changes in the micro-environmental data.

4. The system (100) as claimed in claim 2, wherein the analytical unit (111) comprises a prescriptive analytics unit (111-2) to generate one or more recommendations associated with corrective action based on the determined microbial risk.

5. The system (100) as claimed in claim 2, wherein the analytical unit (111) comprises the warehouse optimization unit (111-3) to optimize storage utilization based on the predicted shelf-life and the storage capacity determined by the digital twin unit (108).

6. The system (100) as claimed in claim 2, wherein the data acquisition and communication unit (109) comprise a bioinformatics data processing unit (109-1) configured to process biological data associated with microorganisms affecting the stored goods.

7. The system (100) as claimed in claim 1, comprises:an image acquisition unit (105) to capture one or more images associated with the goods and associated surrounding, wherein the image acquisition unit (105) communicably coupled to the processor (102); andone or more sensors (106) to senses one or more parameters associated with goods and the associated surrounding, wherein the one or more sensors (106) is communicably coupled to the processor (102).

8. The system (100) as claimed in claim 1, wherein the processor (102) comprises an artificial intelligence (Al) model (102-2) to analyse the retrieved one or more inventories, the one or more images and the one or more parameters for generating the one or more recommendations, and a blockchain-based model (102-2) to store the code on the storage bag incorporating the goods.

9. The system (100) as claimed in claim 1, comprises a connecting tool (107) communicably coupled to the processor (102) to receive and display the generated one or more recommendations.

10. A device (104) to manage a location of goods in a warehouse, the device (104) comprising:a processor (102) having a memory (102-3) storing a set of instructions which, when executed, cause the processor (102) to:fetch barcode data from a scanner (101);map the fetched barcode data with one or more identifiers associated with one or more inventories stored at a server (103), wherein the fetched barcode data comprises a unique identifier associated with the storage bag being scanned and wherein the unique identifier is mapped against the one or more identifiers;retrieve, upon successfully mapping, the mapped one or more inventories from the server (103); andanalyse the retrieved one or more inventories to generate one or more recommendations associated with managing the location of storage the goods in the warehouse.

11. The device (104) as claimed in claim 10, wherein the scanner (101) is communicably coupled to the processor (102), the scanner (101) is configured to scan a code tagged on a storage bag incorporating goods, wherein the code comprises any or a combination of a barcode, a quick response (QR) code, or a radio-frequency identification (RFID) tag.

12. The device (104) as claimed in claim 10, comprises:an image acquisition unit (105) to capture one or more images associated with the goods and associated surrounding, wherein the image acquisition unit (105) communicably coupled to the processor (102); andone or more sensors (106) to senses one or more parameters associated with goods and the associated surrounding, wherein the one or more sensors (106) is communicably coupled to the processor (102),wherein the processor (102) comprises an artificial intelligence (Al) model (102-2) to analyse the retrieved one or more inventories, the one or more images, andthe one or more parameters for generating the one or more recommendations, and a blockchain-based model (102-2) to store the code on the storage bag incorporating the goods.

13. A method (200) for managing a location of goods in a warehouse, the method (200) comprising:scanning (201), by a scanner (101) a code coupled to a storage bag incorporating the goods;fetching (202), by a processor (102), barcode data from the scanner (101), wherein the barcode data is obtained upon scanning of a code by the scanner (101), wherein the processor (102) is communicably coupled to the scanner (101), and having a memory (102-3) storing a set of instructions for execution;mapping (203), by the processor (102), the fetched barcode data with one or more identifiers associated with one or more inventories stored at a server (103), wherein the fetched barcode data comprises a unique identifier associated with the storage bag being scanned and wherein the unique identifier is mapped against the one or more identifiers;retrieving (204), by the processor (102), the mapped one or more inventories from the server (103) upon successfully mapping; andanalysing (205), by the processor (102), the retrieved one or more inventories to generate one or more recommendations for managing the location of storage the goods in the warehouse.

14. The method (200) as claimed in claim 13, wherein the method (200) comprises:an image acquisition unit ( 105) to capture one or more images associated with the goods and associated surrounding, wherein the image acquisition unit (105) communicably coupled to the processor (102); andone or more sensors (106) to senses one or more parameters associated with goods and the associated surrounding, wherein the one or more sensors (106) is communicably coupled to the processor (102),wherein the processor (102) comprises an artificial intelligence (Al) model (102-2) to analyse the retrieved one or more inventories, the one or more images and the one or more parameters for generating the one or more recommendations, and a blockchain-based model (102-2) to store the code on the storage bag incorporating the goods.

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