Brine lotus quality tracing method and system based on augmented reality and cloud digital twinning

Abstract

The invention belongs to the technical field of augmented reality, and relates to a saline lotus quality tracing method and system based on augmented reality and cloud digital twinning. According to the method, a unique identifier is allocated to each salted lotus product, a digital twinborn body is constructed at a cloud end, quality and temperature and humidity data collected by a near infrared spectrum sensor and an environment sensor in a package are received, and data modeling is carried out in combination with production and logistics information; a consumer end identifies a product identifier through an intelligent terminal with an AR function and requests corresponding digital twin data from a cloud platform, and the cloud generates visual content, issues the visual content to the terminal and displays the visual content on a product package real scene picture in an overlapped manner, and supports voice query. Compared with existing two-dimensional code static tracing, dynamic mapping and visual display of the single-grade quality state are achieved, product freshness changes can be reflected, early warning can be provided, and the authenticity, timeliness and user experience of brine lotus quality tracing are improved.

Description

Technical Field

[0001] This invention relates to the field of augmented reality technology, specifically to a method and system for tracing the quality of saltwater lotus based on augmented reality and cloud-based digital twins. Background Technology

[0002] The quality of high-value-added agricultural products such as salted lotus is affected by a combination of factors during production, cold chain transportation, warehousing, and final sales, including raw material freshness, processing technology, temperature and humidity conditions, and logistics time. To improve food safety and consumer trust, the industry is gradually promoting traceability systems based on QR codes, barcodes, and other coding carriers. By printing traceability codes on packaging and using server databases to record production batch information, inspection reports, and logistics milestones, basic query functions for product origin and distribution stages are achieved.

[0003] However, most existing traceability systems manage information on a batch or category basis, collecting data primarily from enterprise input and random inspections. These systems have low update frequencies and struggle to reflect the real-time quality status of individual products. For ready-to-eat products like salted lotus root, key indicators such as internal moisture content and soluble solids content fluctuate dynamically with changes in storage and transportation conditions. Existing systems only conduct laboratory testing at the factory or a few stages, with the results attached as static fields to the traceability record. Consumers at the point of sale still see the product's condition at the time of manufacture, unable to determine if it remains in optimal quality or identify any abnormal exposure or quality deterioration risks during the cold chain.

[0004] On the other hand, the human-computer interaction methods of existing traceability systems are relatively simple. Consumers typically need to open an app or mini-program, scan a QR code on the packaging, and be redirected to a webpage or list page to find the information they are interested in through multi-level menus and large amounts of text. The information presentation lacks hierarchy and intuitiveness. Although some applications based on augmented reality technology have been used for marketing displays on food packaging, overlaying brand images and promotional animations, they often lack deep correlation with actual quality data, transportation environment data, and traceability records, making it difficult to establish an effective mapping between "what is seen" and "what is eaten." At the same time, existing systems rarely incorporate voice and semantic interaction methods, preventing consumers from directly querying specific questions such as "how fresh is it?" or "how long can it be kept?" using natural language. The usage threshold and understanding cost remain relatively high.

[0005] Furthermore, at the data processing level, existing traceability solutions mostly remain at the stage of historical record queries and simple information listing. Environmental data and logistics data collected by sensors, along with limited quality inspection results, are often stored in a scattered manner. There is a lack of a unified model for multi-source data fusion and status characterization of individual products, and even more so, a lack of ability to dynamically assess and predict product freshness levels, quality warning status, and remaining shelf life based on time-series data. Even if some systems introduce IoT sensing or blockchain evidence storage, they are mainly used to enhance link monitoring and data immutability, failing to form a comprehensive solution for consumers and regulators in areas such as "digital modeling of single-item quality," "dynamic visualization," and "interactive decision support."

[0006] In summary, existing technologies for quality traceability of agricultural products such as salted lotus have at least the following problems: First, there is a lack of digital representations of the quality status of individual products that can evolve over time, resulting in a disconnect between traceability information and actual quality. Second, the collected intrinsic quality data and environmental data are not fully integrated, failing to form a model-based understanding of the quality evolution process, let alone a forward-looking assessment of freshness and remaining shelf life. Third, information presentation is mainly based on static pages and lists, lacking intuitive visualization based on AR and natural interaction based on voice, making it difficult for consumers to understand and utilize traceability information in a timely and accurate manner. There is an urgent need for a traceability technology solution that can build single-product-level digital mappings in the cloud, continuously integrate multi-source sensory data, and intuitively display the quality status of salted lotus on the terminal side through augmented reality and voice semantic interaction to overcome the above shortcomings. Summary of the Invention

[0007] This invention provides a method and system for quality traceability of salt water lotus based on augmented reality and digital twins.

[0008] The present invention solves the above-mentioned technical problems through the following technical solution:

[0009] A method for quality traceability of saltwater lotus based on augmented reality and cloud-based digital twins, the method comprising:

[0010] S1: Generate a unique identifier for each salt water lotus product and build a corresponding digital twin in the cloud. The digital twin stores the product's lifecycle data.

[0011] S2: Collect near-infrared spectral data, environmental perception data, and logistics status data of the product through the data acquisition terminal, and upload them to the cloud to update the digital twin in real time;

[0012] S3: On the consumer side, use smart terminals with AR recognition capabilities to collect product packaging images and identify unique identifiers from them;

[0013] S4: The smart terminal sends a query request to the cloud, and the cloud calls the corresponding digital twin based on the unique identifier and extracts the current traceability dataset;

[0014] S5: A structured augmented reality visualization content data package is generated from the cloud and sent to the smart terminal;

[0015] S6: The smart terminal overlays this content onto the real-life image of the product packaging using augmented reality technology, achieving an immersive display of quality traceability information.

[0016] In one specific embodiment, the digital twin includes the following data fields:

[0017] a) Static basic information such as production batch information, raw material source, and processing parameters;

[0018] b) Quality indicators such as soluble solids content and moisture content are periodically collected by the near-infrared sensor built into the packaging;

[0019] c) Environmental and location data collected during warehousing and transportation by temperature and humidity sensors or positioning modules;

[0020] d) The product freshness rating, quality status assessment, and remaining shelf life prediction results calculated based on the above data.

[0021] In a specific embodiment, the augmented reality visualization content in S6 includes:

[0022] The product's current quality score, dynamic change curve, logistics evolution path, and evidence summary associated with the product are displayed on the product image in the form of a 3D layer, a floating information window, or a virtual icon.

[0023] In one specific embodiment, the method further includes:

[0024] S7: Receives user voice input and identifies the query intent through the natural language processing module;

[0025] S8: Extract target data from the digital twin based on the stated intent;

[0026] S9: Feedback on the results to the user through voice broadcast and AR interface highlighting, enabling semantic-driven information access.

[0027] In a specific embodiment, the digital twin records the collection and evolution of various types of data along a timeline to construct an evolution model of product quality, and the model can be dynamically updated and trend predicted based on real-time data.

[0028] A saltwater lotus quality traceability system for implementing the method as described in any one of claims 1 to 5, comprising:

[0029] The intelligent terminal unit, cloud service platform unit, and data acquisition terminal unit interact and connect logically using a unique identifier as the index.

[0030] Wherein: a) The intelligent terminal unit includes an AR visual recognition module, an AR rendering module and a voice interaction module, which are used to collect packaging images, recognize product logos, display enhanced content and respond to voice queries;

[0031] b) The cloud service platform unit includes a digital twin database, a quality analysis and modeling module, an AR data generation module, and a semantic query engine, which are used to maintain the digital twin, process query requests, and distribute rendering data;

[0032] c) The data acquisition terminal unit includes a miniature near-infrared spectral sensing module, an environmental sensing module, and a wireless communication module, which are deployed in the product packaging for continuous data acquisition and uploading.

[0033] In a specific embodiment, the cloud service platform has digital twin modeling capabilities. Its analysis module performs feature extraction, index conversion, and lifecycle quality prediction on the collected data, and writes the results into the digital twin for display and query.

[0034] In one specific embodiment, the smart terminal unit builds a user interface based on ARCore or ARKit and supports near-field communication methods such as BLE and NFC to read data cached by the local data acquisition terminal.

[0035] In one specific embodiment, the digital twin manages product status data in a time-series format and supports API interfaces to provide external visualization of content, semantic query responses, and data auditing capabilities.

[0036] The present invention has at least the following beneficial effects:

[0037] This invention maps all data from the production, storage, transportation to consumption of salted lotus into a unified digital space, achieving digitalization of the quality status of individual products throughout the entire process. Compared with existing QR code traceability that only records static information at the batch level, the digital twin of this solution not only contains basic traceability information, but also continuously receives and dynamically updates near-infrared spectral data and environmental data, giving each product a digital mirror that "evolves over time," significantly improving the precision and authenticity of traceability.

[0038] This invention integrates a near-infrared spectroscopy sensing module and an environmental sensing module within the packaging. It collects intrinsic quality indicators such as soluble solids content and moisture content, and through cloud-based analysis and modeling, transforms the "invisible" internal quality into visualized quantitative indicators, which are then written into a digital twin. This combination of "sensors + digital twin" allows traceability information to be directly linked to the current quality status of the actual food product for the first time, rather than remaining at the level of factory labels and inspection reports. This fundamentally enhances the technological sophistication and persuasiveness of traceability information in influencing consumer decision-making.

[0039] This invention utilizes the AR capabilities of smart terminals to encapsulate core data from a digital twin into structured, visualized content, which is then overlaid onto the actual packaging of salted lotus, creating an immersive display method of "physical object + virtual information layer." Users do not need to navigate to other pages to read lengthy text; they can simply point the camera at the packaging to view key information such as quality ratings, dynamic curves, and logistics tracking within the real-world context. They can also ask questions via voice, and the system will retrieve answers from the digital twin as needed, achieving semantic traceability interaction that is "understandable, askable, and responsive." Compared to traditional methods of scanning QR codes to access webpages or displaying information in list formats, this invention offers significant improvements in interactive design and information processing efficiency.

[0040] This invention utilizes a cloud-based quality analysis and modeling module to fuse near-infrared spectral data, environmental time-series data, and logistics information into a digital twin. This generates high-level indicators such as freshness ratings, quality warning status, and remaining shelf-life predictions, which are then presented intuitively through an AR interface. This solution goes beyond passively recording historical data; it constructs a product quality evolution model based on multi-source data, enabling proactive assessment of quality change trends. This endows the traceability system with "prediction" and "early warning" capabilities, representing a substantial expansion of the functional boundaries of traditional traceability systems. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this utility model or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this utility model. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a flowchart of Embodiment 2 of the present invention.

[0043] Figure 2 This is a system framework diagram of Embodiment 1 of the present invention. Detailed Implementation

[0044] The technical solutions of this utility model will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this utility model, and not all embodiments. Based on the embodiments of this utility model, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this utility model.

[0045] Example 1

[0046] In this embodiment, the salt water lotus quality traceability system based on augmented reality (AR) and cloud-based digital twin technology mainly includes the following components:

[0047] Intelligent terminal unit The smart terminal unit includes mobile devices such as smartphones, tablets, or AR glasses that are equipped with AR visual recognition, AR rendering, voice recognition, and processing.

[0048] AR visual recognition module: This module uses the camera of a smart terminal to scan the product packaging, recognize the QR code or specific pattern mark, and extract the product's unique identification information.

[0049] AR rendering module: Through the received digital twin data package, it renders real-time quality information, dynamic change curves, logistics paths and other content of the product, and displays them on the real-world image of the product, forming an immersive AR display effect.

[0050] Voice interaction module: Users can ask questions via voice (such as "How fresh is this salted lotus?") to inquire about detailed product information. The smart terminal uses the voice recognition module to parse the voice command, request data from the cloud, and provide feedback via voice broadcast.

[0051] Cloud service platform unit The cloud service platform unit is the core of the system, containing multiple modules. It is responsible for processing data uploaded from smart terminals and data collection terminals, and providing the necessary traceability data to terminal devices.

[0052] Digital Twin Database: The database stores digital twin data corresponding to each salted lotus product, including static information (such as production batch and raw material source) and dynamic data (such as quality data collected by near-infrared spectroscopy sensor, temperature and humidity sensor data, logistics status, etc.).

[0053] Data Analysis and Modeling Module: Analyzes and models the collected data, constructs an evolution model of product quality, and performs quality status assessment and shelf life prediction.

[0054] AR Content Generation Module: Generates structured augmented reality content data packages based on data from the digital twin, including product information, quality status, logistics routes, real-time environmental data, etc., and sends them to smart terminals for visualization.

[0055] Semantic query engine: Processes voice or text queries issued by users on smart terminals, extracts relevant information based on digital twin data, and returns the answer.

[0056] Data acquisition terminal unit The data acquisition terminal module is deployed inside the packaging of the salt water lotus and is responsible for real-time monitoring of the product's internal quality and environmental data.

[0057] Near-infrared spectral sensor: Collects quality indicators of salt lotus (such as sugar content, moisture content, etc.) and sends the data to the cloud service platform.

[0058] Temperature and humidity sensors: monitor the temperature and humidity inside the packaging to ensure that the storage environment meets requirements.

[0059] Wireless communication module: Through wireless communication technologies such as NB-IoT, LoRa, and Bluetooth, the data collected by the sensors is uploaded to the cloud platform to ensure real-time data transmission and storage.

[0060] System Workflow

[0061] Product Identification and Digital Twin Creation: Each saltwater lotus product is assigned a unique identifier (such as a QR code or RFID tag) during the production process, and a corresponding digital twin is created in the cloud service platform to record static information such as the product's production batch, raw material source, and initial quality data.

[0062] Real-time data acquisition and uploading: The data acquisition terminal periodically collects the physical quality data (such as near-infrared spectral data) and environmental data (such as temperature and humidity) of the saltwater lotus, and uploads this data to the cloud via a wireless communication module. The cloud platform updates the digital twin of the product in real time based on the received data, ensuring the timeliness and accuracy of the data.

[0063] Consumer Inquiry and AR Display: Consumers use smart terminals to scan the QR code or other markings on the salted lotus packaging, obtain the product's unique identifier through the AR visual recognition module, and request the product's traceability data from the cloud service platform.

[0064] Augmented Reality Display: The cloud service platform generates augmented reality content data packages and distributes them through smart terminals. The AR rendering module of the smart terminal presents product traceability information, quality data, and logistics trajectories as 3D layers, which are then overlaid on the image of the actual product packaging.

[0065] Voice-interactive query: Consumers can ask questions to query detailed product information by voice. The smart terminal uses the voice recognition module to convert the question into a query request. The cloud platform extracts relevant information from the digital twin and returns it. The smart terminal displays the answer through voice broadcast or AR interface.

[0066] Example 2

[0067] A Quality Traceability Method for Saltwater Lotus Based on Augmented Reality and Digital Twins

[0068] This embodiment describes in detail a method for quality traceability of salt water lotus based on augmented reality and digital twins. The specific steps are as follows:

[0069] S1: Generate a unique identifier and create a digital twin. During the production of salted lotus, a unique identifier (such as a QR code or RFID tag) is generated for each salted lotus product. The system creates a digital twin in the cloud corresponding to the product's unique identifier. The digital twin stores the product's static information (such as production batch, raw material source, production process, etc.) and dynamic information (such as quality data, environmental data, etc.).

[0070] S2: Data Acquisition and Upload The data acquisition terminal inside the packaging continuously collects near-infrared spectral data of the product, environmental sensing data, and logistics status data. The near-infrared spectral sensor is used to measure the internal quality of the salted lotus (such as sugar content and moisture content); the temperature and humidity sensor monitors the temperature and humidity of the packaging environment. The collected data is uploaded to the cloud platform via a wireless communication module.

[0071] Data Acquisition Frequency and Transmission Method: The data acquisition terminal can be set to collect data periodically (e.g., every hour, every other day) and upload data in real time when network access is available. If there is no network connection, the data will be stored locally in a cache and automatically uploaded once the network is restored.

[0072] S3: Consumer-side identification and query Consumers use smart devices with AR recognition capabilities (such as smartphones or AR glasses) to scan the QR code or RFID tag on the packaging of salted lotus products to extract the product's unique identification information. The smart device sends a query request to the cloud service platform, which extracts digital twin data based on the identifier and returns the query results to the device.

[0073] Data query content: The query data includes static information of the product, real-time quality data (such as sugar content, moisture content, temperature and humidity, etc.) and logistics route information.

[0074] S4: Augmented Reality Visualization The cloud service platform generates augmented reality data packages containing basic product information, quality status, dynamic change curves, and logistics tracking. Smart terminals use AR rendering modules to overlay this information onto an image of the actual product, providing a "what you see is what you get" quality traceability experience. Consumers simply point the camera at the packaging to view the product's current status, historical information, and quality trends.

[0075] S5: Voice Interaction Query and Feedback Consumers can use voice input to query product information (such as "How fresh is this salted lotus?"). The smart terminal uses a voice recognition module to convert the voice into a query request and sends it to the cloud service platform. The cloud platform returns the query results based on the information in the digital twin. The smart terminal uses a voice synthesis module to feed the results back to the consumer, while simultaneously highlighting the relevant information on the AR interface.

[0076] S6: Dynamic Updates and Predictions of Digital Twins As more collected data is uploaded, the digital twin will continuously update, especially data on product quality and environmental conditions. The cloud service platform uses data analysis and modeling to dynamically assess the quality of salted lotus and predict the product's remaining shelf life and freshness. Consumers can see predictions based on real-time data when querying, such as "Current remaining shelf life: 3 days".

[0077] Detailed implementation steps

[0078] Initial data upload: On the salt water lotus production line, the system scans raw materials and production processes through automated equipment to generate a digital twin containing information such as batch, raw materials, and processing technology, and associates it with the product identifier.

[0079] Quality data acquisition and real-time upload: The data acquisition terminal automatically activates the near-infrared spectral sensor according to a set cycle to acquire and upload real-time quality data. When the transportation and storage environment changes, the environmental sensor will update the temperature and humidity data to ensure that the quality status is always under monitoring.

[0080] Consumer inquiry experience: After consumers scan the unique identifier on the packaging with their smartphones or AR glasses, the system displays various real-time indicators of the product through AR, helping consumers quickly understand the current quality status of the product.

[0081] Using the methods and systems described above, consumers can more intuitively and conveniently check the quality status of salted lotus seeds, ensuring that the quality of the purchased products meets their expectations. This invention not only improves the accuracy of food safety traceability but also significantly enhances the user experience, overcoming the limitations of existing systems in information presentation and interaction methods.

[0082] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0083] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quality traceability of saltwater lotus based on augmented reality and cloud-based digital twins, characterized in that, The method includes: S1: Generate a unique identifier for each salt water lotus product and build a corresponding digital twin in the cloud. The digital twin stores the product's lifecycle data. S2: Collect near-infrared spectral data, environmental perception data, and logistics status data of the product through the data acquisition terminal, and upload them to the cloud to update the digital twin in real time; S3: On the consumer side, use smart terminals with AR recognition capabilities to collect product packaging images and identify unique identifiers from them; S4: The smart terminal sends a query request to the cloud, and the cloud calls the corresponding digital twin based on the unique identifier and extracts the current traceability dataset; S5: A structured augmented reality visualization content data package is generated from the cloud and sent to the smart terminal; S6: The smart terminal overlays this content onto the real-life image of the product packaging using augmented reality technology, achieving an immersive display of quality traceability information.

2. The method according to claim 1, characterized in that, The digital twin includes the following data fields: a) Static basic information such as production batch information, raw material source, and processing parameters; b) Quality indicators such as soluble solids content and moisture content are periodically collected by the near-infrared sensor built into the packaging; c) Environmental and location data collected during warehousing and transportation by temperature and humidity sensors or positioning modules; d) The product freshness rating, quality status assessment, and remaining shelf life prediction results calculated based on the above data.

3. The method according to claim 1, characterized in that, The augmented reality visualization content in S6 includes: The product's current quality score, dynamic change curve, logistics evolution path, and evidence summary associated with the product are displayed on the product image in the form of a 3D layer, a floating information window, or a virtual icon.

4. The method according to claim 1, characterized in that, The method further includes: S7: Receives user voice input and identifies the query intent through the natural language processing module; S8: Extract target data from the digital twin based on the stated intent; S9: Feedback on the results to the user through voice broadcast and AR interface highlighting, enabling semantic-driven information access.

5. The method according to claim 1, characterized in that, The digital twin records the collection and evolution of various data along a timeline, constructs an evolution model of product quality, and the model can be dynamically updated and trend predicted based on real-time data.

6. A saltwater lotus quality traceability system for implementing the method as described in any one of claims 1 to 5, characterized in that, include: The intelligent terminal unit, cloud service platform unit, and data acquisition terminal unit interact and connect logically using a unique identifier as the index. Wherein: a) The intelligent terminal unit includes an AR visual recognition module, an AR rendering module and a voice interaction module, which are used to collect packaging images, recognize product logos, display enhanced content and respond to voice queries; b) The cloud service platform unit includes a digital twin database, a quality analysis and modeling module, an AR data generation module, and a semantic query engine, which are used to maintain the digital twin, process query requests, and distribute rendering data; c) The data acquisition terminal unit includes a miniature near-infrared spectral sensing module, an environmental sensing module, and a wireless communication module, which are deployed in the product packaging for continuous data acquisition and uploading.

7. The system according to claim 6, characterized in that, The cloud service platform has digital twin modeling capabilities. Its analysis module performs feature extraction, index conversion, and lifecycle quality prediction on the collected data, and writes the results into the digital twin for display and query.

8. The system according to claim 6, characterized in that, The intelligent terminal unit builds a user interface based on ARCore or ARKit and supports near-field communication methods such as BLE and NFC to read data cached by the local data acquisition terminal.

9. The system according to claim 6, characterized in that, The digital twin manages product status data in a time-series format and supports API interfaces to provide visualized content encapsulation, semantic query responses, and data auditing capabilities.

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