A home physical-digital double-line twin home number package system

Abstract

The application discloses a kind of based on AI industrial printing and the family physics-digital twin double-line twin home number package system of RFID, comprising: physical space offline management and control link and digital space digital twin management and control link, the two links independently run and are realized real-time data intercommunication by two-way synchronization mechanism;The physical space offline management and control link at least includes an AI industrial printing terminal, for synchronous execution visual label printing, RFID chip data layered writing and AI visual defect checking;A UHF RFID identification carrier;A family acquisition hardware;The digital space digital twin management and control link at least includes, a cloud end home number package digital twin middle station, for storing the digital archives of whole house articles and pipeline, and establish the two-way mapping of physical entity and digital model;A cloud large language model is used to analyze RFID reading data, automatically generate label template, infer cleaning strategy and maintenance plan.

Description

Technical Field

[0001] This invention relates to the field of digital technology, specifically to a smart home and home decoration digital technology. Background Technology

[0002] Currently, the field of smart home and home digital management mainly relies on Simultaneous Localization and Mapping (SLAM) cameras, Ultra Wideband (UWB) positioning, or Bluetooth beacons to achieve object recognition and spatial perception.

[0003] However, such solutions have obvious drawbacks: visual solutions are severely affected by occlusion, changes in lighting, and object displacement, and cannot establish a unique digital identity that is permanently bound to each piece of furniture. Furthermore, the recognition results are easily forged or interfered with, and there is a lack of a trusted root identity at the physical level. Although UWB and Bluetooth solutions can provide location information, they cannot carry structured business data such as item attributes and maintenance records, nor do they have offline read / write capabilities.

[0004] More importantly, existing technologies only process and display data on a purely digital level, lacking an independent offline control link in physical space, and thus cannot achieve bidirectional real-time mapping and closed-loop control between physical entities and digital models.

[0005] In addition, existing radio frequency identification (RFID) applications are mostly single tag reading and writing, and do not integrate artificial intelligence (AI) printing terminals, visual verification, edge-cloud collaboration, and home data collection hardware (such as robot vacuum cleaners) to form an integrated closed loop, making it difficult to meet the comprehensive needs of whole-house asset management, pipeline traceability, and intelligent cleaning collaboration.

[0006] The general AI large model and data annotation industry only processes pure digital text or image data. It lacks physical hardware carriers to solidify digital identity (ID) to physical objects, and thus cannot form a complete integrated software and hardware solution.

[0007] In the field of digitalization and maintenance of home pipelines, existing technologies mainly rely on paper-based Computer-Aided Design (CAD) drawings, electronic files, or Building Information Modeling (BIM) for static storage. These methods can only provide a theoretical pipeline layout at the design time. During on-site construction or maintenance, it is impossible to quickly and accurately locate the actual pipeline positions inside the walls. Repair personnel, relying on experience, are prone to puncturing water pipes or high-voltage cables when drilling, causing serious economic losses and safety hazards.

[0008] For existing houses, second-hand houses, or old houses that have already been renovated, if digital marking of pipelines is required, the existing solutions must break down walls and cut grooves to pre-embed physical markers or re-lay pipelines. The construction costs are extremely high, the construction period is long, and the original decoration surface is severely damaged, so it is not feasible for large-scale promotion.

[0009] Although there are a few Bluetooth or Near Field Communication (NFC) patches on the market for device marking, their read / write distance is short and their storage capacity is small. They cannot record complete business data such as pipe diameter, direction, valve parameters, and maintenance history in layers, and they also lack supporting terminals and software systems for linking and retrieving cloud-based BIM drawings.

[0010] To date, there is no existing, low-cost, and batch-implementable pipeline digitization solution for existing housing that requires no trenching, and no terminal equipment can simultaneously complete the entire process of label creation, data writing, visual verification, and rapid on-site traceability.

[0011] It should be noted that the above description of the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of the present invention and facilitating understanding by those skilled in the art. It should not be assumed that the above technical solutions are known to those skilled in the art simply because they have been described in the background section of this invention. Summary of the Invention

[0012] The purpose of this invention is to overcome the shortcomings of the prior art and provide a home physical-digital dual-line twin data package system based on AI industrial printing and RFID, as well as a method for tracing the pipelines of existing houses.

[0013] This invention discloses a home physical-digital twin home data package system based on AI industrial printing and RFID, comprising: a physical space offline control link and a digital twin control link for the digital space. These two links operate independently and achieve real-time data exchange through a two-way synchronization mechanism. The physical space offline control link includes at least: an AI industrial printing terminal for simultaneously performing visual label printing, RFID chip data layered writing, and AI visual defect verification; an Ultra High Frequency (UHF) RFID tag carrier, whose chip is divided into at least a Reserved area, an Electronic Product Code (EPC) area, a Tag Identifier (TID) area, and a User area for storing the physical root identity, asset code, and business data of physical objects or pipelines; and home data collection hardware, including an external RFID reader / writer module for a robotic vacuum cleaner and / or a Bluetooth handheld reader / writer for automatically collecting RFID tag data throughout the house or in specific areas. The aforementioned digital twin management and control chain for the intelligent space includes at least: a cloud-based digital twin platform for storing digital files of all items and pipelines in the house, and establishing a two-way mapping between physical entities and digital models; and a cloud-based Large Language Model (LLM) for parsing RFID read data, automatically generating tag templates, and inferring cleaning strategies and maintenance plans. The aforementioned two-way synchronization mechanism is as follows: physical object movement or tag data modification in the physical space automatically updates the twin data in the intelligent space; adjustments to business rules or coding templates in the intelligent space are remotely sent to the physical space terminal and modify the RFID carrier data.

[0014] Furthermore, the aforementioned AI industrial printing terminal embeds the following YOLO visual inspection model, used to simultaneously execute the following verification algorithms during label printing: Integrity detection of the printed visual label image and barcode; Readback and comparison of the Electronic Product Code (EPC) and User area data written to the RFID chip; Calculation of the Structural Similarity Index (SSIM) between the printed image and the standard template; if the SSIM is lower than a preset threshold, it is determined to be a printing defect and a reprint instruction is triggered; Based on the grayscale histogram distribution of the label image, the degree of ribbon consumption is identified, and a consumable warning signal is output.

[0015] Furthermore, the aforementioned large-scale cloud-based language model executes the following automatic label format generation algorithm. It receives user natural language input and extracts item category, spatial location, and maintenance cycle parameters through intent recognition and entity extraction. Based on a pre-trained template generation network, it dynamically constructs an RFID User area data structure and QR code image / text layout adapted to the item. The generated template undergoes consistency verification to ensure the User area field length conforms to chip storage specifications and is automatically assigned a unique EPC code.

[0016] Furthermore, the external RFID reader / writer module of the aforementioned robotic vacuum cleaner executes the following spatial fusion positioning and differentiated cleaning scheduling algorithm. During navigation, it simultaneously collects the Received Signal Strength Indicator (RSSI) of the RFID tag and the spatial coordinates output by visual SLAM. A weighted centroid positioning algorithm is used to convert the RSSI value into the tag's relative position, and then Kalman filtering is applied to fuse it with the visual map to calibrate the tag's actual spatial coordinates. Based on the item attributes stored in the tag's User area, the cleaning area is divided into a normal zone, a detour zone, and a low-suction zone, and the cleaning path is dynamically planned. After cleaning is completed, the updated item positions and dirt status are written back to the RFID User area and simultaneously uploaded to the cloud-based data package.

[0017] Furthermore, the aforementioned RFID tag carrier is a digital tag for existing building pipelines that do not require trenching. Its User area stores pipeline type, diameter, direction, pre-buried depth, valve parameters, and maintenance records. The aforementioned dual-line twin data package system also includes the following pipeline drawing mapping algorithm: scanning the tag TID and EPC code to retrieve the corresponding area's BIM pipeline drawings from the cloud; using perspective transformation to correct the drawing orientation and overlaying the relative position of the current reading point in the drawing; automatically highlighting dangerous pipelines (such as high-pressure water pipes and high-voltage cables) in the drawing and generating a risk warning list.

[0018] Furthermore, in the aforementioned two-way synchronization mechanism, the data instructions sent from the digital space to the physical space adopt a differential update strategy: comparing the cloud-based digital archives with the User area data currently read by RFID to generate a difference data block; only the difference fields are rewritten, rather than being fully written; after the rewriting is completed, the visual tag is read again through the AI ​​industrial printing terminal's contact image sensor (CIS) camera to confirm data consistency and generate a synchronization log record.

[0019] This invention also discloses a method for digital traceability of RFID pipelines in existing buildings without the need for wall trenching, comprising the following steps: Locating all exposed pipeline nodes throughout the building, including electrical boxes, switch boxes, water valves, underfloor heating access panels, and central air conditioning interfaces. Generating integrated pipeline RFID tags with QR codes using an AI industrial printing terminal, and writing the pipeline BIM data for each location layered into the RFID chip's EPC and User areas. Affixing the tags to the surface of the wall access panels, eliminating the need for wall trenching. Recording all tag information into the cloud-based digital twin platform to construct a digital twin drawing of the entire building's pipeline system. When reading the tags on-site, retrieving the complete pipeline distribution map within the corresponding wall area using a cloud-based mapping algorithm.

[0020] Furthermore, after the above labels are pasted, the following pipeline drawing mapping and risk warning algorithm is executed. Each label TID is bound to the pipeline node ID in the cloud BIM drawing. When a label is read, the spatial distance between that point and the adjacent node in the drawing is automatically calculated, and it is determined whether there is a deviation between the read position and the position marked on the drawing. If the deviation exceeds a preset error threshold, it is determined that the pipeline on-site construction does not match the drawing, a deviation warning is generated, and drawing correction suggestions are automatically updated.

[0021] Furthermore, the aforementioned RFID tag User area uses a hierarchical coding structure to store pipeline maintenance records and executes the following adaptive prediction algorithm for maintenance cycles. Maintenance records for the same pipeline at different time points are collected to construct time-series data. A lightweight model using exponential smoothing or Long Short-Term Memory (LSTM) networks is employed to predict the next potential failure time. When the predicted time point approaches, the system automatically pushes maintenance reminders and, in conjunction with the data package, generates a list of spare parts and tools.

[0022] Furthermore, when the aforementioned tags are replaced due to aging, the following data migration and consistency verification algorithm is executed. The old tag's TID and EPC data are read to generate the new tag's EPC and User area content. The old tag's TID binding relationship is transferred to the new tag, and the cloud mapping table is updated. A handheld reader is used to perform a dual-tag comparison to confirm that the new tag data is consistent with the old tag, and the replacement time and operator information are recorded to ensure continuous traceability of pipeline records.

[0023] This invention constructs a complete closed-loop architecture that allows for independent operation and real-time synchronization of the physical space offline management link and the digital twin link of the intelligent space. This fundamentally overcomes industry bottlenecks such as the susceptibility to failure of pure visual identification, the lack of a reliable physical root identity, and the inability to achieve a hardware-software integrated closed loop. Simultaneously, it proposes a trenchless, low-cost pipeline digitization solution for existing housing, completely resolving the long-standing pain points of unvisualized wall pipeline distribution and easily damaged pipelines during maintenance. Furthermore, by assigning an unalterable physical root identity to each piece of furniture through RFID read-only TID chips, combined with synchronous coding and visual quality verification by AI industrial printing terminals, it achieves full-process automation and low-threshold operation. Ultimately, the system connects printing terminals, RFID carriers, robotic vacuum cleaners, and a cloud-based digital platform, integrating asset archiving, pipeline traceability, intelligent cleaning, and equipment maintenance across all scenarios, forming a unified "Home Data Package," a digital brain for the entire home.

[0024] The primary core advantage of this invention lies in its pioneering construction of a complete closed-loop architecture that allows for independent operation and real-time synchronization of both the "physical space offline control link" and the "digital twin link of intelligent space." Through the TID zone of the RFID chip, it provides an unalterable physical root identity for each home item and pipeline location, completely eliminating the shortcomings of traditional pure visual identification, which is susceptible to obstruction, displacement, and light interference. Simultaneously, an AI industrial printing terminal synchronously completes label printing, RFID data writing, and visual quality verification. Combined with automatic data collection by a robotic vacuum cleaner and intelligent inference from a cloud-based LLM large model, it achieves fully automated bidirectional integration from physical entities to digital models and from digital instructions to physical execution, forming a complete hardware and software integrated solution that is currently unavailable in the smart home, AI data labeling, and home decoration digitalization industries. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of a home physical-digital dual-line twin data package system based on AI industrial printing and RFID in one embodiment of the present invention.

[0027] Figure 2 This is a flowchart of a method for digital traceability of RFID pipelines in existing buildings without the need for trenching, according to an embodiment of the present invention.

[0028] The reference numerals in the above figures are as follows:

[0029] 100. Cloud Layer; 110. Large-Scale Language Model; 120. OTA Management Center; 130. Digital Twin Platform for Data Packages; 140. General Capability Module; 200. Client Layer; 210. Local Touchscreen; 220. Mobile App; 230. MES / ERP Integration SDK; 300. Service Layer; 310. Print Management Module; 320. YOLO Visual Inspection Model; 330. ASR / TTS Voice Module; 340. Intelligent Early Warning Model for Ribbon Loss; 350. RFID Data Parsing Module; 400. Basic Service Layer; 410. Android Operating System; 420. Printer Underlying Firmware; 430. Hardware Driver / RF Read / Write / Image Acquisition The system includes: 500, Hardware Layer; 510, AI Industrial Printing Terminal; 511, Hexa-core Processor; 512, Independent NPU; 513, IPS Touch Screen; 514, CIS Vision Camera; 520, UHF RFID Identifier; 521, Pre-embedded Waterproof Pipeline RFID; 522, Wall-mounted RFID; 523, Anti-metal RFID for Home Appliances; 530, Home Data Acquisition Hardware; 531, RFID Reader / Write Module for Robotic Vacuum Cleaners; 532, Bluetooth Handheld Reader / Write Device; 533, Microphone / Speaker; 600, Physical Space Offline Control Link; 700, Digital Twin Control Link for Intelligent Space; 800, Two-way Synchronization Mechanism; and steps S1 to S5. Detailed Implementation

[0030] To better understand this invention, the following embodiments are provided in conjunction with the accompanying drawings. It should be understood that the embodiments of this invention are for illustrative purposes only and not for limiting the invention; the scope of protection of this invention is defined solely by the claims. The embodiments provided are merely preferred embodiments and are not intended to limit the invention in any way. Those skilled in the art can make changes, equivalent substitutions, or modifications based on the content of this invention, resulting in different implementation methods. However, any changes and modifications, or equivalent substitutions, made to the method of this invention without departing from the inventive concept are within the scope of protection of this invention.

[0031] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0033] First, please refer to Figure 1 . Figure 1 This is a schematic diagram of a home physical-digital dual-line data package system based on AI industrial printing and RFID.

[0034] like Figure 1 As shown, this invention's home physical-digital dual-line data package system based on AI industrial printing and RFID simultaneously constructs two independent and bidirectionally synchronized technical systems: a physical space offline control link 600 and a digital twin control link 700. The physical space offline control link 600 includes an AI industrial printing terminal 510, equipped with a six-core processor 511 and an independent Neural Network Processing Unit (NPU) 512, used to simultaneously perform visual label printing, RFID chip data layered writing, and AI defect verification by the CIS vision camera 514. The UHF RFID tag carrier 520's chip is divided into a Reserved area for setting read / write passwords, an EPC area for storing material codes, a TID area for storing the chip's unique factory serial number as an unalterable physical root identity, and a User area for storing business data such as space location, pipeline parameters, and maintenance records. The home data collection hardware 530 includes an external RFID reader / writer module 531 for robotic vacuum cleaners and a Bluetooth handheld reader / writer 532, used for automatic whole-house navigation or manual fixed-point tag data reading. The digital twin management link 700 for the intelligent space includes: a cloud-based home data package digital twin platform 130 for storing digital files of all items and pipelines in the house and establishing a two-way mapping between physical entities and digital models; and a cloud-based large-scale language model 110 for parsing RFID read data, automatically generating tag templates, and inferring cleaning strategies and maintenance plans. A two-way synchronization mechanism 800 enables real-time data exchange between the two links. When physical objects are moved or tag data is modified in the physical space, the system automatically uploads the changes to the home data package digital twin platform 130 to update the digital twin model. When business rules or coding templates are adjusted in the intelligent space, the system remotely sends instructions to the AI ​​industrial printing terminal 510 to modify the RFID carrier data and selectively reprint tags, achieving a complete closed loop of physical changes synchronizing with digital data and digital instructions being implemented in the physical environment.

[0035] For example, when a user moves the living room sofa to the study, the robot vacuum cleaner's external RFID reader / writer module 531 automatically reads the new location of the sofa tag and uploads it, while the digital twin platform 130 updates the digital twin map in real time. Conversely, when a user modifies the sofa's warranty period via the mobile app 220, the system remotely sends a command to the AI ​​industrial printing terminal 510 to rewrite the RFID's User area data and reprint the tag, ensuring complete consistency between the physical carrier and the digital file.

[0036] It should be further explained that, in addition to writing structured business data such as asset codes, spatial locations, pipe diameter parameters, valve parameters, and maintenance records of items or pipelines into the EPC area and User area of ​​the RFID chip of the present invention, other unique and authentic digital identification information can also be written according to the needs of different application scenarios.

[0037] Specifically, the data types that can be written to the aforementioned EPC area and User area include, but are not limited to: Internet Protocol version 6 (IPv6) addresses, Internet Protocol version 9 (IPv9) addresses, Media Access Control (MAC) addresses, International Mobile Equipment Identity (IMEI), Universally Unique Identifier (UUID), Global Location Number (GLN), Digital Object Unique Identifier (DOI), International Standard Book Number (ISBN), International Standard Serial Number (ISSN), Unified Social Credit Code, ID card number, passport number, Vehicle Identification Number (VIN), and other unique identifiers issued by authoritative institutions or conforming to international / national standards.

[0038] In addition, the User area of ​​the aforementioned RFID chip can also be written with the product serial number, production batch number, inspection report number, quality certification mark number of the item, as well as graphic or image data such as product photos, QR code images, and product logo patterns of the equipment (stored in a compressed encoding format). This allows the RFID tag to not only carry structured text information, but also serve as a digital ID card for the physical object, supporting unified identification and authentication across systems and platforms.

[0039] By leveraging the aforementioned ability to write diverse and unique identifiers, the RFID tags of this invention can be seamlessly integrated with existing Internet infrastructure, IoT identification systems, and existing coding systems in various industries, greatly expanding the applicability and interoperability of this invention in different physical spaces (homes, offices, industrial plants, municipal pipelines, etc.) and different application fields (asset management, logistics tracking, identity authentication, smart city infrastructure, etc.).

[0040] It is worth noting that in this invention, the Bluetooth handheld reader 532 integrates a Bluetooth chip (such as a system-on-chip (SoC) supporting the Bluetooth Low Energy (BLE) protocol) and a UHF RFID reader module. The two interact and schedule commands through the same core board or microcontroller unit (MCU), forming an integrated handheld data acquisition terminal. As an important data bridge between physical space and digital space, it plays a key role in ensuring smooth data flow between the physical space offline control link 600 and the digital twin control link 700 in digital space.

[0041] Specifically, the aforementioned Bluetooth handheld reader / writer 532 establishes a wireless connection with the mobile app 220 via its Bluetooth chip, receives tag reading and writing commands from the app 220 or the cloud-based digital twin platform 130, and transmits the commands to the RFID reader / writer module to execute tag operations. After the RFID reader / writer module reads the tag's TID, EPC, and User area data, it transmits the data back to the mobile app 220 in real time via the Bluetooth chip. The mobile app 220 then performs data preprocessing and forwards the data to the cloud-based digital twin platform 130, realizing the rapid convergence of data collected in the physical space to the digital space.

[0042] In addition, the Bluetooth chip, in conjunction with the mobile APP 220, can also achieve the following auxiliary functions: (1) Receive tag update instructions or drawing retrieval requests issued by the cloud-based digital twin platform 130 via the mobile APP 220, and drive the Bluetooth handheld reader 532 to perform fixed-point rewriting or data verification operations on the specified RFID tags. (2) In the event of network interruption or cloud unavailability, the Bluetooth chip, in conjunction with the local caching function of the mobile APP 220, can temporarily store the read tag data, and automatically upload it to the cloud-based digital twin platform 130 after the network is restored, ensuring that physical space data is not lost and that the digital space files are ultimately consistent. (3) Support the mobile APP 220 to upgrade the firmware of the Bluetooth handheld reader 532 via Over-The-Air (OTA) download, ensuring that the functions of the data acquisition terminal can be continuously iterated and optimized.

[0043] By combining RFID and Bluetooth chips, the aforementioned Bluetooth handheld reader 532 possesses both the long-range batch identification capability of RFID (capable of reading multiple tags in batches within a range of 0 to 15 meters) and the low-power wireless data transmission capability of Bluetooth, forming a collaborative architecture of "long-range sensing + short-range control". This enables the handheld data acquisition terminal to truly become a mobile data gateway connecting the dual-line twin architecture, filling the data acquisition blind spots where fixed data acquisition devices (such as the external RFID reading module 531 of a robot vacuum cleaner) cannot effectively read tags in confined spaces, elevated areas, or environments with signal obstruction. This significantly improves the data acquisition integrity and operational flexibility of the system in scenarios such as whole-house and other physical spaces (such as offices, commercial venues, industrial plants, etc.), further perfecting the overall technical closed loop of the physical-digital dual-line twin of this invention.

[0044] It is worth noting that, in one embodiment of the present invention, the YOLO visual inspection model 320 embedded in the AI ​​industrial printing terminal 510 executes a synchronous verification algorithm during the label printing process to ensure that the visualized text and RFID chip data are completely matched and of qualified quality. After the label printing is completed, the CIS visual camera 514 captures the label surface image in real time. The YOLO visual inspection model 320 first performs target detection and integrity checks on the Quick Response Matrix Code (QR Code) or barcode area, text area, and border in the image to confirm that there are no defects, broken lines, blurring, or offsets. At the same time, the RFID data parsing module 350 reads back the EPC area and User area data of the chip and compares them byte by byte with the expected data in the printing template to confirm that the two are completely consistent.

[0045] Building upon this, the YOLO visual inspection model 320 further calculates the structural similarity index (SSIM) between the printed image and the standard template. The formula for calculating SSIM is as follows:

[0046] (Equation 1)

[0047] In the formula, α, β, and γ are parameters for adjusting the weights of each item (default values ​​such as α=β=γ=1).

[0048] In the formula, This is a brightness comparison function. (Equation 2)

[0049] Where, μ x With μ y C1 is the mean of the graph, and C1 is a constant to avoid the denominator being zero.

[0050] c(x,y) is the contrast comparison function. (Equation 3)

[0051] Where, σ x With σ y σ is the standard deviation. xy C2 is the covariance, and C2 is a constant to avoid the denominator being zero;

[0052] s(x,y) is a structural comparison function. (Equation 4)

[0053] Where, σ x With σ y σ is the standard deviation. xy C3 is the covariance, and C3 is a constant to avoid the denominator being zero.

[0054] If the SSIM value is lower than the system default threshold (e.g., 0.85), it is judged as a printing defect, and the system automatically triggers a reprint command and marks the label as abnormal.

[0055] Simultaneously, the model analyzes the gray-level histogram distribution of the printed image and calculates the average gray-level value of the image. (Equation 5)

[0056] and grayscale standard deviation (Equation 6)

[0057] When the average grayscale value continuously decreases with increasing print volume and the grayscale standard deviation shrinks, it indicates that ribbon consumption is causing the output to become lighter. When Ī is below the set threshold or σ I When the loss is less than the allowable range, the system outputs a ribbon loss warning signal to the ribbon loss intelligent warning model 340 to remind the user to replace the consumables, ensuring that the image quality and RFID data writing of each tag meet the standards.

[0058] It is worth noting that, in one embodiment of the present invention, the cloud-based large language model 110 executes an automatic tag format generation algorithm to achieve an intelligent tag generation process that does not require manual setting.

[0059] The cloud-based large-scale language model 110 receives user commands in natural language, such as "Help me create a label for the living room sofa that includes the purchase date and warranty period." Through its built-in intent recognition and entity extraction modules, it uses a Transformer-based named entity recognition model to extract key parameters from the input text, including item category (sofa), spatial location (living room), maintenance cycle, and warranty information. The objective function for entity extraction using conditional random field labeling is... (Equation 7)

[0060] In the formula, x is the input word sequence, y is the corresponding label sequence, and f k Let λ be the characteristic function. k Z(x) represents the learning weights, and Z(x) represents the normalization factor.

[0061] Then, based on the pre-trained template generation network, the model dynamically constructs a suitable RFID User area data structure definition for the item according to the extracted parameters. This includes the domain name, data type, bit group length limit, and corresponding QR Code graphic layout configuration, ensuring that the generated template conforms to the chip storage capacity specification. The User area data structure is defined as follows:

[0062] (Equation 8)

[0063] Each field

[0064] (Equation 9)

[0065] And must satisfy (Equation 10)

[0066] In the formula, L max This represents the maximum number of available bytes in the chip's User area.

[0067] Finally, the model performs a consistency check on the generated template to confirm that the total length of each field does not exceed the available storage space of the chip, and automatically assigns a globally unique EPC code, with the encoding rule as follows:

[0068] EPC = Manufacturer Prefix + Item Category Code + Serial Number + Check Code (Formula 11)

[0069] In this formula, the check code is calculated using the CRC-16 algorithm. After the calculation is completed, the template is sent to the print management module 310 to perform the printing and writing job.

[0070] It is worth noting that, in one embodiment of the present invention, the external RFID reader / writer module 531 of the aforementioned robotic vacuum cleaner executes a spatial fusion positioning and differentiated cleaning scheduling algorithm to achieve precise spatial positioning of tags throughout the house and intelligent cleaning path planning. The robotic vacuum cleaner simultaneously performs two tasks during its whole-house patrol, with the visual SLAM module outputting real-time spatial coordinates (x, y, z). SLAM , y SLAM Meanwhile, the robot vacuum cleaner's external RFID reader / writer module 531 scans all the tags in the house and records the RSSI signal strength value P of each tag. r(Unit: decibel-milliwatt (dBm)) The system uses a logarithmic distance path loss model to convert RSSI into an estimated distance between the tag and the robot. (Equation 12)

[0071] In the formula, d0 is the reference distance (usually taken as 1 meter), P t PL(d0) represents the tag's transmission power, PL(d0) represents the path loss at the reference distance, and n represents the environmental path loss index (usually 2 to 4 for indoor environments).

[0072] The system reads the same label at multiple sampling points and collects N sets of distance estimates (d). i , θ i )(θ i (where the robot's orientation angle is used) and a weighted centroid localization algorithm is employed to calculate the estimated tag position. (Equation 13)

[0073] In the formula, the weights (Equation 14)

[0074] The closer the distance, the greater the weight.

[0075] Then, the RFID-estimated location and the visual SLAM output coordinates are fused using Kalman filtering. The prediction step is as follows: (Equation 15)

[0076] In the formula, superscript — This indicates the prior, which is the predicted value that has not yet been corrected for the current observation value; Let A be the prior state estimate at time k; A is the motion model, and is the state transition matrix. It represents the final position at the previous moment; Buk is the control command, which is the external control input applied to the system at time k (e.g., the acceleration command for a robotic vacuum cleaner); using the final position at the previous moment... With the addition of the control command Buk, the prior position at the current moment is predicted;

[0077] (Equation 16)

[0078] In the formula, P is the covariance of the error. P is the covariance of the prior error at the current moment. k-1 A is the covariance of the posterior error at the previous time step. TLet A be the transpose of A, and Q be the process noise covariance matrix.

[0079] The update step is Kalman Gain. (Equation 17)

[0080] In the formula, H is the observation matrix. This reflects the degree of coupling between prediction uncertainty and the observation model. The physical meaning is to project (map) the prediction uncertainty of the state space onto the observation space, where R is the observation noise covariance matrix, representing the observation noise;

[0081] (Equation 18)

[0082] To determine the posterior optimal position, the final estimate equals the initial guess plus the Kalman gain multiplied by the new information from the observations.

[0083] (Equation 19)

[0084] After fusion, the final precise spatial coordinates of each tag are output.

[0085] After completing the positioning, the system reads the item attributes stored in the User area of ​​each tag, and divides the cleaning area into a normal area, a detour area (avoiding fragile or valuable items), and a low-suction area (reducing suction power to prevent damage) based on the attributes. It then plans the optimal cleaning path using a traveling salesman problem algorithm, with the objective function being...

[0086] Dynamically adjust the suction parameters of each zone

[0087] (Equation 20)

[0088] In the formula, the valuable items area α=0 (complete detour), the fragile items area α=0.3, and the ordinary area α=1.0. After cleaning, the latest location and dirt status of the items are updated and written back to the RFID tag User area, and simultaneously uploaded to the cloud-based digital twin platform 130.

[0089] It is worth noting that, in one embodiment of the present invention, the RFID tag carrier 520 used for existing buildings is a trenchless pipeline digital tag. The User area of ​​this trenchless pipeline digital tag stores complete pipeline business data such as pipeline type, diameter, direction, pre-buried depth, valve parameters, and maintenance records. When reading the tag on-site, the system executes a pipeline drawing mapping algorithm. After the Bluetooth handheld reader 532 or the external RFID reading module 531 of the robot vacuum cleaner reads the tag's TID and EPC code, the code is uploaded to the cloud-based digital twin platform 130. The cloud-based digital twin platform 130 indexes the corresponding BIM pipeline drawing according to the code, and stores the drawing as a set of pipeline vector data. (Equation 21)

[0090] Each location includes three-dimensional coordinates (x, y, z), pipeline type, pipe diameter, and pre-buried depth.

[0091] The dual-line twin data package system then performs perspective transformation correction on the drawing and calculates the homography matrix.

[0092] (Equation 22)

[0093] satisfy (Equation 23) (Equation 24)

[0094] Align the drawing orientation with the actual wall orientation on site, and display the relative position of the current reading point in the drawing as a highlighted icon on the local touch screen 210 or mobile APP 220.

[0095] At the same time, the system automatically identifies dangerous pipelines (high-pressure water pipes or high-voltage cables) within a radius R (e.g., R=30 cm) of the area on the drawing, marks them in red, and generates a risk warning list. The recommended safe drilling locations must be at least a safe distance D from any dangerous pipeline. safe (e.g., D) safe =5 cm), that is (Equation 25)

[0096] It also displays the distance between adjacent pipelines for maintenance personnel to refer to on-site, effectively avoiding the risk of pipeline damage during construction.

[0097] It is worth noting that in one embodiment of the present invention, when the digital twin control link 700 of the intelligent space sends data instructions to the offline control link 600 of the physical space in the bidirectional synchronization mechanism 800, a differential update strategy is adopted to improve synchronization efficiency and extend the service life of the RFID chip (the number of times an RFID chip can be erased and rewritten is usually limited, about 100,000 times).

[0098] The specific implementation method is as follows. The system first compares the latest digital file content of the tag in the cloud-based digital twin platform 130.

[0099] (Equation 26)

[0100] Compared with the most recent RFID user area data read from the physical space

[0101] (Equation 27)

[0102] Generate differential data blocks

[0103] (Equation 28)

[0104] This difference block only records the fields that differ between the two and their new values;

[0105] Then, the dual-line twin data package system, through the RFID writing module of the AI ​​industrial printing terminal 510, performs a partial rewrite operation only on the fields recorded in the difference block ΔD, instead of erasing all data in the User area. The instruction format for the partial rewrite is as follows:

[0106]

[0107] Among them, offset i The length is the starting byte offset of this field in the User area. i For field length, new_value i This is the new value.

[0108] After the rewriting is completed, the CIS vision camera 514 of the AI ​​industrial printing terminal 510 reads the QR Code or barcode on the visual label again. After decoding, it is compared with the EPC and User area data that was just written. After confirming that the two are consistent, the system generates a synchronization log (including synchronization time, operator, list of difference fields and verification results) and stores it in the general capability module 140 for future auditing and tracking.

[0109] If changes to the digital space involve adjustments to the label layout or encoding rules (such as changes to the QR code size or EPC encoding structure), the system will simultaneously trigger the print management module 310 to re-output the physical label. Otherwise, only RFID data rewriting will be performed, without reprinting, in order to save consumables and improve work efficiency, ensure that the data of the physical carrier and the digital file are completely consistent, and maximize the lifespan of the RFID chip.

[0110] Please refer to Figure 2 . Figure 2 This is a flowchart illustrating a method for digital traceability of RFID pipelines in existing buildings without the need for trenching, according to an embodiment of the present invention. Figure 2 As shown, the above-mentioned dual-line twin home data package system provides a method for digital traceability of existing housing pipelines using RFID without the need for wall trenching, comprising the following steps: S1. Locate all exposed pipeline nodes in the house, including electrical boxes, switch boxes, water valves, underfloor heating access ports, and central air conditioning interfaces. S2. Use an AI industrial printing terminal to generate integrated pipeline RFID tags with QR codes, and write the pipeline BIM data of that location into the RFID chip EPC and User areas in layers. S3. Affix the tags to the surface of the wall access ports without wall trenching. S4. Input all tag information into the cloud-based home data package digital twin platform to construct a digital twin drawing of the entire house's pipelines. S5. When reading the tags on-site, retrieve the complete pipeline distribution map within the corresponding area using a cloud mapping algorithm.

[0111] For example, in step S1, the exposed pipe nodes throughout the house are located, including electrical boxes and switch boxes, water pipe valves, underfloor heating access ports and central air conditioning interfaces. The maintenance personnel use a handheld Bluetooth mini reader 532 to perform preliminary detection and coordinate marking on each node to confirm that the location is a key position where the pipe turns or branches or valves are installed.

[0112] In step S2, the printing management module 310 of the AI ​​industrial printing terminal 510 generates an integrated pipeline RFID tag 522 with a QR code. During printing, the pipeline BIM data of the point is written to the chip in layers through the RFID writing module. The TID area stores the chip's unique serial number as the unalterable physical root identity of the pipeline point. The EPC area stores the unit number and the unique code of the point. The User area stores the pipeline type, diameter, direction, pre-buried depth, valve parameters, and maintenance records. The Reserved area is set with a read / write password to prevent the data from being maliciously tampered with.

[0113] In step S3, the printed label 522 is directly pasted onto the surface of the wall access panel, switch panel, or exposed valve interface. There is no need to groove or drill holes in the wall. The construction process causes zero damage to the existing decoration surface, and the single label pasting operation takes no more than 30 seconds.

[0114] In step S4, the TID and EPC codes of all point labels and their corresponding pipeline BIM data are uploaded to the cloud-based digital twin platform 130. The digital twin platform 130 integrates the data of all points in the house and establishes the connectivity between each node using graph theory algorithms to generate a complete digital twin drawing of the whole house pipeline. Each label TID is mapped one-to-one with the corresponding pipeline node ID in the drawing, forming a digital file that binds the three elements of "label - point - drawing".

[0115] During subsequent on-site tracing, in step S5, maintenance personnel use a Bluetooth handheld reader 532 or an external RFID reader / writer module 531 on a robot vacuum cleaner to read the wall tag 522. They can then retrieve the complete pipeline distribution map of the corresponding area within the wall in real time via a mobile app 220 or a local touch screen 210, and overlay the dangerous pipeline markings and safe construction areas.

[0116] For example, in a kitchen that has already been renovated, maintenance personnel only need to affix a pipe label 522 to the exposed interface of the water pipe valve under the sink, and write the corresponding hot water pipe, cold water pipe and drain pipe path data inside the wall into the User area of ​​the label and upload it to the central platform 130. When drilling holes in the wall to install brackets in the future, the label can be read to display the direction of the pipes inside the wall in real time on the mobile APP 220, accurately avoid the location of the pipes, and completely solve the long-standing pain point of invisible pipes in existing houses.

[0117] It is worth noting that, in one embodiment of the present invention, after the pipeline label 522 is pasted, the dual-line twin data package system executes the pipeline drawing mapping and risk warning algorithm, and the specific steps are as follows.

[0118] First, the dual-line twin data package system binds the TID of each tag 522 to the pipeline node ID in the cloud BIM drawing, establishing a mapping relationship.

[0119] (Equation 29)

[0120] In the formula, i is the tag index, and j is the drawing node index; when maintenance personnel read a tag 522 using a Bluetooth handheld reader 532, the dual-line twin data package system retrieves the corresponding NodeID from the cloud-based digital twin platform 130 based on the TID, and then retrieves it from the drawing vector database. (Equation 21)

[0121] Extract the three-dimensional coordinates of the node and its neighboring nodes;

[0122] Then, the dual-line twin data package system automatically calculates the spatial deviation between the reading point and the node in the drawing, and defines the deviation vector. (Formula 30)

[0123] And calculate the Euclidean distance. (Equation 31)

[0124] If d deviation Greater than the system default error tolerance threshold T deviation (e.g., T) deviation If the deviation is less than 5 cm, the system determines that the actual construction location of the pipeline on site does not match the original BIM drawing. The system immediately generates a deviation warning and displays a message on the mobile APP 220 or local touch screen 210: "The deviation between the on-site location and the drawing annotation exceeds the allowable value. Please confirm the actual pipeline route." At the same time, the system automatically corrects the local drawing of the area based on the deviation vector, updates the position of the node in the drawing by reading the coordinates on site, and records the correction time and operator information to form a dynamic drawing update log, which is stored in the general capability module 140 for subsequent maintenance reference.

[0125] For example, if the actual pre-embedded position of a wall socket box is 6 centimeters to the left of the original design drawing, the system will trigger a deviation warning when reading the label 522 at that location, and will correct the coordinates of the corresponding pipeline node of the socket to the left by 6 centimeters. This ensures that all users who read the label will obtain the correct pipeline location information, effectively avoiding construction errors caused by inconsistencies between the drawings and the actual site.

[0126] It is worth noting that, in one embodiment of the present invention, the User area of ​​the RFID tag 522 uses a hierarchical coding structure to store pipeline maintenance records. The data structure of the User area is defined as follows:

[0127] (Equation 31)

[0128] In the formula, the Header includes basic pipeline data (pipeline type, diameter, direction, burial depth, and valve parameters), and the Body is a variable-length array storing all maintenance records. (Equation 32)

[0129] The footer stores the installation date, design life, and total number of maintenance required for the pipeline.

[0130] The dual-line twin data package system executes an adaptive prediction algorithm for maintenance cycles, with the following steps.

[0131] Step 1: Extract maintenance and repair records for the same pipeline at different times from the User area Body to construct time series data.

[0132] (Equation 33)

[0133] In the formula, t k This is the timestamp for the k-th repair.

[0134] And calculate the interval between each maintenance.

[0135] (Equation 34)

[0136] Step two: Calculate the predicted time to the next potential failure using the exponential smoothing method. The recursive formula is as follows:

[0137] (Equation 35)

[0138] In the formula, X t S represents the current maintenance interval for observation. t Let be the smoothed predicted value for period t, and α be the smoothing constant (0 < α < 1, system default α = 0.3).

[0139] Alternatively, a lightweight recurrent neural network, LSTM, can be used for time series prediction. Its core unit operation is...

[0140] (Equation 36)

[0141] (Equation 37)

[0142] (Equation 38)

[0143] (Equation 39)

[0144] (Formula 40)

[0145] (Equation 41)

[0146] In the formula, f t For the Gate of Oblivion, i t For the input gate, o t For output gate, C t In cellular state, h t In the hidden state, the interval Δt between maintenance operations is used. k As the input sequence, predict the next maintenance interval Δt. n+1 .

[0147] Step 3: Calculate and predict the next failure time point.

[0148] (Equation 42)

[0149] When the current time t current satisfy

[0150] t current ≥t n+1 -T warning (Equation 43)

[0151] (T) warning This is the system's default number of days to issue an advance warning, for example, T. warning =7 days) When the system automatically pushes maintenance reminders to the mobile APP 220 or local touch screen 210, it will also link with the digital twin platform 130 to generate a list of spare parts and tools. The list content is automatically generated based on the most frequently replaced parts in the historical maintenance records, realizing proactive and intelligent pipeline maintenance management.

[0152] For example, the hot water pipes of a certain residence experienced leaks and required repairs in the 8th, 16th, and 25th months of the past three years, respectively. The system uses the repair interval [8, 9, 9] months as input, and the LSTM model predicts that the next repair interval will be approximately 9.5 months. Therefore, when it is about 9 months since the last repair, the system will send an early reminder that "the hot water pipes are about to reach the predicted failure period, please arrange for maintenance" and list commonly used spare parts such as gaskets and connectors to facilitate users' preparation in advance.

[0153] It is worth noting that, in one embodiment of the present invention, when the label 522 needs to be replaced due to aging, damage, or detachment, the system executes a data migration and consistency verification algorithm, the specific steps of which are as follows.

[0154] In step S1, the maintenance personnel use a Bluetooth handheld reader 532 to read the complete data of the old tag 522_old, including all fields of TID_old, EPC_old and User_old. The system temporarily stores the old tag data in the local buffer or the mobile APP 220.

[0155] In step S2, the AI ​​industrial printing terminal 510 generates a new label 522_new based on the old label data. The TID_new of the new label is the new unique serial number of the chip at the factory (the old TID cannot be copied because the TID is read-only). EPC_new copies the old EPC_old, and User_new copies all maintenance records and pipeline parameters of the old User_old field by field to ensure that all business data is transferred completely.

[0156] Step S3: The system updates the mapping relationship in the cloud-based digital twin platform 130, establishing an association between the old tag TID_old and the new tag TID_new, and updating the mapping table.

[0157] M(TID_new) = NodeID (Equation 44)

[0158] It also records migration logs, including the last read time of the old tag, the generation time of the new tag, the reason for the change, and the operator.

[0159] Step S4: Perform dual-tag reading and comparison verification. The maintenance personnel simultaneously use a Bluetooth handheld reader 532 to read the old tag 522_old and the new tag 522_new. The system automatically compares the EPC and User area data of the two tags and calculates the consistency pointer.

[0160] (Equation 45)

[0161] If Consistency=1 (all fields are consistent), the data migration is considered successful; if Consistency<1, the system lists the difference fields and automatically rewrites them into the new label based on the old label data, repeating the comparison until they are completely consistent.

[0162] Step S5: After confirming data consistency, the system records the replacement time and operator information, and marks the old label status as "retired" in the cloud-based digital twin platform 130, while marking the new label as "in operation," to ensure that pipeline files are continuously traceable and uninterrupted.

[0163] For example, the QR code on the pipe label under the kitchen sink became blurred and unscannable due to long-term moisture. The maintenance personnel used a Bluetooth handheld reader 532 to read the old label and obtain complete data. Then, they used an AI industrial printing terminal 510 to generate a new label and copy all the data. After confirming that the two labels matched, the new label was pasted in the original position. The system simultaneously updated the label TID mapping relationship of the cloud-based digital twin platform 130. Subsequent readings of the pipe location will be based on the new label. All historical maintenance records of the old label are completely preserved in the User area of ​​the new label, realizing seamless transfer and long-term traceability of pipe data.

[0164] It is worth noting that although the system and method of this invention are described using a typical residential scenario, their application scope is not limited to this. This invention is fully applicable to the digital archiving, asset management, and traceability of concealed pipelines in various physical spaces such as offices, commercial venues, industrial plants, and public buildings. Its physical-digital twin architecture is spatially independent and can be migrated to any physical environment where there is a need for both physical identification and digital twins.

[0165] Furthermore, when RFID tags and printing consumables are packaged with corrosion-resistant, high-temperature-resistant, and chemical-resistant anti-corrosion processes, this invention can be further extended to the identification, control, and digital operation and maintenance of special pipeline systems such as petrochemical pipelines, gas transmission pipelines, municipal underground pipe networks, and marine engineering facilities, enabling long-term reliable traceability and full life-cycle management of pipeline locations under harsh working conditions.

[0166] All higher-level concept applications, scenario migrations, or adaptive modifications derived from the core architecture of this invention are within the scope of protection of this invention, and the substantive technical contributions of this invention should not be limited due to changes in application scenarios.

[0167] The embodiments of the present invention described above can be implemented in various hardware, software codes, or combinations thereof. For example, embodiments of the present invention can also be program code executing the above methods in a Digital Signal Processor (DSP). The present invention can also relate to various functions executed by a computer processor, digital signal processor, microprocessor, or Field Programmable Gate Array (FPGA). The processor described above can be configured to perform specific tasks according to the present invention, which are accomplished by executing machine-readable software code or firmware code defining the specific methods disclosed in the present invention. The software code or firmware code can be developed into different programming languages ​​and different formats or forms. The software code can also be compiled for different target platforms. However, the different code styles, types, and languages ​​of the software code performing tasks according to the present invention and other types of configuration code do not depart from the spirit and scope of the present invention.

Claims

1. A home physical-digital dual-line data package system based on AI industrial printing and RFID, comprising: The physical space offline control link and the digital twin control link of the intelligent space operate independently and achieve real-time data communication through a two-way synchronization mechanism; The physical space offline control link includes at least the following: An AI industrial printing terminal is used to simultaneously perform visual label printing, RFID chip data layer writing, and AI visual defect verification. A UHF RFID tag carrier, whose chip is divided into at least a Reserved area, an EPC area, a TID area and a User area, is used to store the physical root identity, asset code and business data of the physical object or pipeline; A home data collection hardware, including an external RFID reader / writer module for a robot vacuum cleaner and / or a Bluetooth handheld reader / writer, is used to automatically collect RFID tag data throughout the house or in specific areas. The digital twin control link of the digital intelligence space includes at least the following: A cloud-based digital twin platform is used to store digital files of all items and pipelines in the house, and to establish a two-way mapping between physical entities and digital models. A large-scale language model in the cloud is used to parse RFID read data, automatically generate tag templates, and infer cleaning strategies and maintenance plans. The two-way synchronization mechanism is as follows: when physical objects are moved or tag data is modified in the physical space, the twin data in the digital space is automatically updated synchronously; when business rules or coding templates in the digital space are adjusted, they are remotely sent to the physical space terminal and the RFID carrier data is modified.

2. The system according to claim 1, characterized in that, The AI ​​industrial printing terminal is embedded with a YOLO vision inspection model, which is used to synchronously execute the following verification algorithm during the label printing process: Perform integrity checks on the printed visual label images and barcodes; The EPC and User area data written to the RFID chip are read back and compared. Calculate the structural similarity index (SSIM) between the printed image and the standard template. If the SSIM is lower than a preset threshold, it is determined to be a printing defect and a reprint instruction is triggered. By combining the grayscale histogram distribution of the label image, the degree of carbon ribbon consumption can be identified, and a consumable warning signal can be output.

3. The system according to claim 1, characterized in that, The cloud-based large-scale language model executes an automatic tag format generation algorithm: It receives natural language input from users and extracts product category, spatial location, and maintenance cycle parameters through intent recognition and entity extraction. Based on a pre-trained template generation network, dynamically construct the RFID User area data structure and QR code image and text layout adapted to the item; Perform consistency checks on the generated template to ensure that the length of the User area field conforms to the chip storage specification, and automatically assign a unique EPC code.

4. The system according to claim 1, characterized in that, The external RFID reader / writer module of the robotic vacuum cleaner executes a spatial fusion positioning and differentiated cleaning scheduling algorithm: During the cruise, the RSSI signal strength of the RFID tag and the spatial coordinates output by the visual SLAM are collected simultaneously. A weighted centroid localization algorithm is used to convert RSSI values ​​into relative label positions and fuse them with the visual map using Kalman filtering to calibrate the actual spatial coordinates of the labels. Based on the item attributes stored in the User tag area, the cleaning area is divided into a normal area, a detour area, and a low suction area, and the cleaning path is dynamically planned. After cleaning is completed, the updated item locations and dirt status are written back to the RFID User area and simultaneously uploaded to the cloud-based data package.

5. The system according to claim 1, characterized in that, The RFID tag carrier is a digital tag for existing building trenchless pipelines. Its User area stores pipeline type, diameter, direction, pre-buried depth, valve parameters, and maintenance records. The dual-line twin data package system also includes a pipeline drawing mapping algorithm. Scan the TID and EPC codes to retrieve the corresponding area's BIM pipeline drawings from the cloud; Use perspective transformation to correct the drawing orientation and overlay the relative position of the current reading point in the drawing; Hazardous pipelines in drawings are automatically highlighted and a risk warning list is generated.

6. The system according to claim 1, characterized in that, In the aforementioned two-way synchronization mechanism, data instructions sent from the digital space to the physical space adopt a differential update strategy: Compare the cloud-based digital archives with the User area data currently read by RFID to generate a difference data block; Only rewrite the difference fields, instead of writing the entire data; After the rewriting is completed, the visual label is read again by the CIS camera of the AI ​​industrial printing terminal to confirm the data consistency and generate a synchronization log record.

7. A method for digital traceability of RFID pipelines in existing buildings without the need for trenching, applied to the system described in any one of claims 1 to 6, comprising the following steps: Locate all exposed pipe and wiring nodes throughout the house, including electrical boxes for both high and low voltage, switch boxes, water pipes and valves, underfloor heating access panels, and central air conditioning interfaces; The AI ​​industrial printing terminal is used to generate an integrated pipeline RFID tag with a QR code, and the pipeline BIM data of that location is written into the RFID chip EPC and User areas in layers. Simply stick the label to the surface of the wall access panel; no wall grooves are required. Enter all location tag information into the cloud-based digital twin platform of the Home Data Package to construct a digital twin drawing of the whole house's pipelines; When reading tags on-site, a complete pipeline distribution map of the corresponding area inside the wall is retrieved through a cloud mapping algorithm.

8. The method according to claim 7, characterized in that, After the label is pasted, the pipeline drawing mapping and risk warning algorithm is executed: Bind each tag TID to the pipeline node ID in the cloud BIM drawing; When a label is read, the system automatically calculates the spatial distance between that point and the adjacent node in the drawing, and determines whether there is a deviation between the read position and the marked position on the drawing. If the deviation exceeds the preset error threshold, it is determined that the pipeline construction on site does not match the drawings, a deviation warning is generated, and the drawings are automatically updated with correction suggestions.

9. The method according to claim 7, characterized in that, The RFID tag's User area uses a hierarchical coding structure to store pipeline maintenance records and executes an adaptive prediction algorithm for maintenance cycles. Collect maintenance and repair records of the same pipeline at different points in time to construct time series data; Lightweight models such as exponential smoothing or LSTM are used to predict the next potential failure time point; As the predicted time approaches, the system automatically sends maintenance reminders and generates a list of spare parts and tools in conjunction with the product package.

10. The method according to claim 7, characterized in that, When the tag ages and is replaced, a data migration and consistency verification algorithm is executed: Read the old tag TID and EPC data, and generate the new tag's EPC and User area content; Transfer the old tag TID binding relationship to the new tag and update the cloud mapping table; By using a handheld reader to compare and read the two tags, the data of the new tag is confirmed to be consistent with that of the old tag. The replacement time and operator information are recorded to ensure that the pipeline records are continuously traceable.

Images