Fixed asset full life cycle management system and method

Abstract

The invention provides a fixed asset full life cycle management system and method. The fixed asset full life cycle management system comprises a perception identification unit, an edge processing unit, a digital twin modeling unit and a decision optimization unit. The sensing identification unit collects position information and operation state data of the assets through the grading identity tags; the edge processing unit realizes multi-protocol analysis, data cleaning and local instant response; the digital twinborn modeling unit constructs an asset virtual entity based on a three-dimensional space model, and generates a dynamically updated digital twinborn body through a real-time synchronization mechanism; and the decision optimization unit analyzes the asset utilization rate, the fault prediction trend and the maintenance cost, generates allocation, maintenance or disposal suggestions, and outputs an emergency disposal instruction when safety events such as border crossing movement and illegal disassembly are detected. The digital twin modeling unit receives the instruction and then issues a control signal to the edge processing unit to trigger physical responses such as label locking or sound-light alarm. The technical response speed and the automatic control level of asset management are remarkably improved.

Description

Technical Field

[0001] This application relates to the technical field of combining digital twins and the Internet of Things, and in particular to a fixed asset lifecycle management system and method. Background Technology

[0002] In large manufacturing parks, hospitals, and universities, fixed assets are numerous, widely distributed, and highly mobile. Traditional management relies on barcode scanning or RFID tags, which suffers from low inventory efficiency and poor positioning accuracy, making it difficult to achieve real-time asset location tracking and status awareness. Existing systems are mostly based on static ledgers, resulting in serious discrepancies between records and actual assets, delayed detection of equipment failures, and reactive maintenance. Although digital twin technology has been applied to some visualization platforms, it generally lacks a millisecond-level synchronization mechanism with physical entities, failing to support dynamic mapping and closed-loop control. Furthermore, asset lifecycle data is fragmented, making it difficult to quantify indicators such as utilization rate and maintenance costs, resulting in a lack of data support for resource allocation and procurement decisions. Summary of the Invention

[0003] The purpose of this application is to provide a fixed asset lifecycle management system and method to alleviate the aforementioned technical problems existing in the prior art.

[0004] In a first aspect, the present invention provides a fixed asset lifecycle management system, comprising a sensing and identification unit, an edge processing unit, a digital twin modeling unit, and a decision optimization unit connected in sequence; The sensing and identification unit is used to assign identity tags to fixed assets and collect multi-source sensor data, including the location information and operating status data of the fixed assets. The edge processing unit is communicatively connected to the sensing and identification unit and is used to perform protocol conversion, anomaly detection, and local response processing on multi-source sensor data. The digital twin modeling unit creates a virtual entity for each fixed asset based on a three-dimensional spatial model, and maps the state changes of the fixed asset to the virtual entity through a real-time data synchronization mechanism to generate a dynamically updated digital twin. The decision optimization unit generates processing information for asset allocation, repair, or disposal based on asset utilization, fault prediction results, and maintenance cost data accumulated in the digital twin, and outputs emergency response instructions when a security incident is detected. The digital twin modeling unit is also used to send corresponding control signals to the edge processing unit after receiving emergency response instructions, so as to trigger a security response action for fixed assets.

[0005] In an optional implementation, the sensing identification unit includes hierarchically configured identity tags and heterogeneous Internet of Things networks; The identification tags are configured according to asset value and management needs: low-value assets are configured with passive RFID tags, medium-value assets are configured with Bluetooth beacon tags, and high-value key equipment is configured with ultra-wideband sensor composite tags. The composite tags integrate positioning, motion detection and temperature and humidity monitoring functions. A heterogeneous Internet of Things (IoT) network consists of IoT gateways that support multiple communication protocols. These gateways are deployed within the management area to receive data from different types of tags and perform initial signal acquisition.

[0006] In an optional implementation, the identification tag is equipped with an anti-tamper alarm structure that triggers a local alarm signal when the tag body is physically separated from the mounting surface. The IoT gateway is equipped with ZigBee, Bluetooth Mesh, and LoRaWAN communication interfaces. It integrates a protocol conversion module to convert raw data from different communication interfaces into a standard message format.

[0007] In an optional implementation, the edge processing unit includes a multimodal data parsing module, an edge computing node, and an instant response module; The multimodal data parsing module is used to perform protocol decoding and timestamp alignment on the received multi-source sensor data; Edge computing nodes are used to perform data filtering operations on multi-source sensor data to remove duplicate tag information and remove invalid location jump and non-steady state data through anomaly cleaning algorithms. When the instant response module detects unauthorized movement or a sudden change in status, it directly drives the on-site audio-visual devices to issue an alert without waiting for instructions from the upper level.

[0008] In an optional implementation, the digital twin modeling unit includes a three-dimensional spatial semantic model construction module, a virtual entity generation module, and a virtual-real synchronization mechanism module; The 3D spatial semantic model construction module reconstructs the 3D structure of the management area based on the building information model or CAD drawings, divides the area into functional zones, and labels each space with corresponding business attributes. The virtual entity generation module calls a pre-set 3D model library based on the asset model and binds the asset's static attributes and dynamic parameter interfaces. The virtual-real synchronization mechanism module triggers real-time data transmission based on key state changes, collects regular indicators at fixed intervals, and ensures real-time synchronization between fixed assets and virtual entities by dynamically adjusting the data update frequency.

[0009] In an optional implementation, the digital twin modeling unit is also equipped with a spatial-asset dynamic alignment protocol, which maps location data in the physical coordinate system to the three-dimensional model coordinate system through semantic matching; Upon receiving a control signal, the virtual entity performs a remote locking operation in the digital space and in turn drives the physical device into a disabled state.

[0010] In an optional implementation, the decision optimization unit includes a trajectory analysis module, a predictive maintenance module, and an intelligent decision engine; The trajectory analysis module determines whether the current location of the fixed asset exceeds the permitted area or leaves the designated personnel based on the historical movement path and the preset electronic fence rules, and generates corresponding security event records. The predictive maintenance module establishes a health baseline based on the operating status data returned by the edge computing nodes, predicts the remaining service life of the equipment through a time-series neural network model, and generates a maintenance work order when the failure probability exceeds a set threshold. The intelligent decision engine calculates the total lifecycle cost based on asset utilization, energy consumption costs, maintenance frequency, and depreciation cycle, and generates asset allocation strategies or disposal strategies based on the total lifecycle cost.

[0011] In an optional implementation, a main mobile inventory device is also included, which is equipped with a lidar and a multi-antenna RFID reader for autonomously planning the cruise route based on real-time positioning and mapping technology. The main mobile inventory device is activated during non-working hours, collects asset tag information in batches along a predetermined route, and infers the spatial location data of fixed assets based on signal strength and its own positioning coordinates.

[0012] In an optional implementation, the multi-antenna RFID reader of the main mobile inventory device includes a phased array antenna structure for dynamically adjusting the antenna transmission power and scanning direction according to the signal obstruction area pre-stored in the environmental map; During the cruise, based on the real-time constructed spatial topology information, the scanning frequency and signal gain are increased in areas prone to multipath effects or metal shielded areas. The same area is sampled multiple times at different locations during the movement, and the tag data read from multiple points is time-aligned and spatially matched for data integration.

[0013] Secondly, the present invention provides a method for managing the entire life cycle of fixed assets, including: Hierarchical identification tags are configured on fixed assets, and multi-source sensor data is collected through heterogeneous Internet of Things networks. The multi-source sensor data includes the location information and operating status data of the assets. The collected multi-source sensor data is transmitted to the edge processing unit, where communication protocol parsing, data format conversion, abnormal data filtering, and local real-time response processing are performed. A digital spatial scene of the management area is constructed based on building information model or 3D scanning results. In this scene, a corresponding virtual entity is created for each fixed asset, and its static attributes and dynamic parameter interfaces are bound. Through a synchronization mechanism that combines event-driven and timed snapshots, the state changes of physical assets are mapped to virtual entities in real time, generating continuously updated digital twins. The decision optimization unit analyzes the asset utilization rate, fault prediction trend and maintenance cost data accumulated by the digital twin, generates processing suggestions for asset allocation, preventive maintenance or scrapping, and identifies unauthorized movement, illegal dismantling or separation as a safety event and outputs emergency response instructions. When an emergency response command is received, the digital twin modeling unit sends a control signal to the edge processing unit, which then triggers the corresponding asset's identity tag to enter a locked state or activates the on-site audible and visual alarm device to trigger a security response action for the physical asset.

[0014] The fixed asset lifecycle management system and method provided in this application solves the shortcomings of traditional barcodes and RFID in terms of continuous sensing and status lag by uniquely binding asset identities and collecting multi-source data through a sensing and identification unit. The edge processing unit performs protocol fusion and local real-time response, improving the system's reaction speed to abnormal movement or fault signals and avoiding control failures caused by cloud latency. The digital twin modeling unit constructs a three-dimensional virtual entity carrying spatiotemporal semantics and synchronizes physical assets with the digital twin, overcoming the problems of virtual-physical disconnect and inability to reverse control in existing visualization systems. The decision optimization unit generates intelligent suggestions and triggers emergency commands based on various data throughout the lifecycle, shifting from passive recording to proactive intervention. Through a closed-loop command feedback mechanism, security actions such as asset locking can be remotely executed, significantly enhancing the controllability and security of asset management. Overall, by integrating heterogeneous IoT sensing, edge intelligent processing, digital twin modeling, and closed-loop decision optimization, the system achieves high-precision positioning, real-time status mapping, automated inventory, and predictive maintenance throughout the fixed asset lifecycle, significantly improving the technical response speed and automation control level of asset management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1A structural overview diagram of a fixed asset lifecycle management system provided in this application embodiment; Figure 2 A flowchart illustrating a predictive maintenance method based on edge AI, provided for embodiments of this application; Figure 3 A flowchart of a physical asset twinning and synchronization process is provided for an embodiment of this application; Figure 4 A flowchart of a robot inventory process is provided for an embodiment of this application; Figure 5 A structural diagram of a specific fixed asset lifecycle management system provided in this application embodiment; Figure 6 A flowchart illustrating a fixed asset lifecycle management method provided in this application embodiment; Figure 7 An electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] This application provides a fixed asset lifecycle management system, see [link to relevant documentation]. Figure 1 As shown, the system comprises a perception and identification unit, an edge processing unit, a digital twin modeling unit, and a decision optimization unit connected in sequence. This system is designed for complex scenarios such as large manufacturing parks, hospitals, and universities, where assets are numerous, widely distributed, and highly mobile. It aims to solve problems in traditional asset management such as reliance on manual inventory, discrepancies between records and actual assets, lack of status visibility, and passive maintenance.

[0021] The sensing and identification unit is used to assign a unique identification tag to each fixed asset and collect its multi-source sensing data through built-in or external sensors. This data mainly includes location information (such as GPS coordinates and UWB positioning values) and operating status data (such as power on / off status, temperature, vibration, current, etc.), thereby realizing the digital representation and comprehensive perception of physical assets.

[0022] The edge processing unit communicates with the sensing and identification unit and is responsible for protocol conversion, anomaly detection, and localized response processing of the incoming multi-source heterogeneous data. It completes preliminary calculation tasks close to the data source, effectively reducing network transmission load and supporting immediate response to emergencies (such as asset overruns) without cloud intervention.

[0023] The digital twin modeling unit constructs a corresponding virtual entity for each asset based on a 3D spatial model, forming a digital twin with geometric shape, attribute information, and dynamic behavior capabilities. By establishing a real-time data synchronization mechanism, state changes in the physical world are mapped to the virtual space in milliseconds, achieving "what you see is what you get" visual management and supporting reverse control of physical devices from the virtual end.

[0024] The decision optimization unit analyzes asset utilization, predicts failure trends, and calculates maintenance costs based on the data accumulated over a long period in the digital twin, generating suggestions for allocation, repair, or disposal. When security incidents such as unauthorized movement are detected, it can output emergency response instructions, realizing a data-driven intelligent decision-making closed loop.

[0025] The aforementioned units work together to build a complete technical chain of "perception-processing-modeling-decision-making", which enables technical control, status visibility and management traceability of fixed assets throughout the entire process from commissioning, use, maintenance to scrapping.

[0026] Based on the above system architecture, the functional composition and implementation mechanism of each module are further detailed below.

[0027] The aforementioned sensing and identification units adopt a hierarchical identity tag strategy, matching different tag types with varying performance based on the value of different assets and management needs: For low-value, mass-deployed assets (such as office desks and chairs, consumable containers), UHF passive RFID tags are configured, which can be read in batches without power supply, suitable for low-cost, high-density identification scenarios; for medium-value assets requiring basic positioning capabilities (such as laptops, projectors), Bluetooth BLE Beacon tags are used, supporting room-level positioning based on signal strength (RSSI) or angle of arrival (AoA), with battery life up to 2-3 years; for high-value, critical equipment (such as mobile CT scanners, AGV carts, precision molds), UWB sensor composite tags are integrated, which not only have centimeter-level (10-30cm) positioning accuracy, but also embed accelerometers and temperature and humidity sensors, enabling real-time transmission of motion status and environmental parameters to meet the needs of refined monitoring.

[0028] The sensing and identification unit comprises a heterogeneous IoT network consisting of IoT gateways supporting multiple communication protocols, deployed at key nodes in the management area. These gateways receive data from different types of tags and perform initial signal acquisition. They are equipped with multiple communication interfaces, including ZigBee, Bluetooth Mesh, and LoRaWAN, ensuring stable access for various wireless signals. Each gateway integrates a protocol conversion module, capable of uniformly parsing industry-specific protocols such as Modbus and OPC UA, as well as raw RFID / BLE / UWB messages, into standard MQTT / HTTP JSON formats. This enables cross-protocol data fusion, eliminating the data silos present in traditional systems.

[0029] In addition, all identification tags are equipped with an anti-tamper alarm structure, meaning there is a physical contact switch between the tag body and the mounting surface. If the tag is forcibly removed or detached from the fixed surface, a local alarm signal is immediately triggered and reported to the system via a wireless link, preventing asset concealment and loss. Simultaneously, the communication link uses TLS encrypted transmission and incorporates a device fingerprint authentication mechanism to prevent counterfeit tags from accessing the system, ensuring system security.

[0030] The aforementioned edge processing unit, serving as the near-end computing core of the system, comprises a multimodal data parsing module, edge computing nodes, and an instant response module. The multimodal data parsing module performs protocol decoding, field extraction, and timestamp alignment on the received multi-source sensor data, ensuring that time-series data from different sensors have a unified time reference, thus providing a foundation for subsequent trajectory reconstruction and state analysis.

[0031] Edge computing nodes deploy lightweight AI inference models (such as MobileNet and 1D-CNN) to perform data filtering and anomaly cleaning locally: removing duplicate RFID read records caused by multipath reflections, smoothing UWB positioning trajectories using Kalman filtering algorithms, and eliminating invalid transition points; simultaneously, based on current waveform characteristics and weak vibration signals, identifying equipment operating modes (idle, standby, full load) and determining whether there are signs of early mechanical wear. This invasive condition monitoring method can intelligently upgrade old, unused equipment without the need for additional complex sensors.

[0032] Figure 2 This paper presents a predictive maintenance method based on edge AI. The specific process includes: First, sensors deployed on the device collect operational data such as vibration and current, which are received and preprocessed by the edge gateway, including noise reduction and filtering. Then, time-frequency domain features are extracted from the preprocessed data and input into an inference model deployed on the edge for real-time status determination. The status determination results include normal operation, standby / idle, and abnormal mode. If it is determined to be normal operation, heartbeat data is uploaded to the cloud periodically. If it is determined to be standby / idle, the idle time of the device is accumulated. If it is determined to be an abnormal mode, the predictive maintenance process is triggered, relevant data is uploaded to the cloud for in-depth analysis, and the remaining service life is predicted using a long short-term memory network model. When the calculated failure probability exceeds a preset threshold, a maintenance work order is automatically generated and maintenance personnel are notified, and the demand for spare parts is predicted simultaneously to prepare inventory. If the failure probability does not exceed the threshold, the device is added to the key observation list for continuous monitoring.

[0033] When the system detects abnormal events such as unauthorized movement, crossing of electronic fences, or sudden high temperatures, the instant response module can bypass the cloud and directly drive the on-site audio-visual devices to issue warnings, and even link with the access control system to block exits, reducing the latency of local response and significantly improving security management efficiency.

[0034] The aforementioned digital twin modeling unit includes a 3D spatial semantic model construction module, a virtual entity generation module, and a virtual-real synchronization mechanism module. The 3D spatial semantic model construction module imports building BIM models or CAD drawings, reconstructs the 3D structure of the physical space, and uses semantic segmentation technology to divide it into functional areas such as "R&D area," "warehousing area," and "maintenance area," assigning corresponding business attributes to each space.

[0035] The aforementioned virtual entity generation module automatically calls upon pre-set 3D model library resources based on the asset model to generate a visual avatar that matches its appearance, and binds its static attributes (purchase date, specifications) and dynamic parameter interfaces (location, temperature, power consumption). Based on this, the operational logic model of the fixed asset is configured; for example, a "projector" has a state machine process of "power on → preheating → operation → heat dissipation → power off," enabling the virtual entity to possess perception and cognitive understanding capabilities, thereby achieving multimodal perception of environmental information, semantic parsing, and contextual reasoning.

[0036] The virtual-real synchronization mechanism module adopts a hybrid strategy of "event-driven + timed snapshots": key state changes (such as power on / off and alarm triggering) are pushed and updated at a millisecond frequency; routine indicators (such as power consumption and ambient temperature and humidity) are collected at a timed interval of minutes, ensuring real-time performance while controlling overall communication overhead. To further improve coordinate mapping accuracy, the system introduces a space-asset dynamic alignment protocol, which automatically maps UWB / GNSS positioning results in the physical world to the three-dimensional coordinate system of the BIM model through semantic matching, achieving high-fidelity virtual-real correspondence without manual calibration.

[0037] Furthermore, the digital twin modeling unit also supports reverse control functionality. That is, when a user clicks on an asset in the digital twin interface and issues a "remote lock" command, the digital twin modeling unit will send a control signal to the edge processing unit, which will be transmitted to the security module on the device via the Internet of Things gateway, causing it to cut off the power or disable the core functions, truly realizing a closed-loop operation from virtual space to the physical world.

[0038] Figure 3 This paper illustrates a physical asset twinning and synchronization process. First, a BIM or 3D scene model is imported into the system to complete spatial semantic division. Then, IoT tags are pasted or installed on the corresponding physical entities, tag IDs are registered, and a unique ThingID is generated. This ThingID is then bound to the digital twin (Avator) in the 3D scene. The system receives physical device status information through a real-time data stream and transmits it to the status update service via the MTQQ message queue. This service processes multiple types of data, including location coordinate updates, operating status updates, and alarm status updates. The updated information drives the digital twin to achieve pose synchronization and triggers changes to the twin's attributes, including color changes, animation demonstrations, and highlighting flashing effects. Finally, all changes are rendered and displayed in real time through a 3D visualization front-end, completing the full-process mapping and dynamic synchronization from physical entities to digital twins.

[0039] The aforementioned decision optimization unit, comprising a trajectory analysis module, a predictive maintenance module, and an intelligent decision engine, serves as the intelligent hub of the entire system. Based on historical movement paths and preset electronic fence rules (including static fences such as "servers must not leave the server room" and dynamic fences such as "tablets must be accompanied by employee ID badges"), the trajectory analysis module determines in real time whether assets have been moved in violation of regulations and automatically generates security event records and pushes alarm notifications.

[0040] The predictive maintenance module establishes a health baseline based on operational status data uploaded from the edge and predicts the remaining useful life (RUL) of the equipment using temporal neural network models such as LSTM or Transformer. When the predicted failure probability exceeds a set threshold (e.g., 80%), the system automatically generates a preventative maintenance work order, reserves the necessary spare parts, and assigns the nearest maintenance engineer. This constructs an intelligent operation and maintenance system based on condition monitoring and failure prediction models, realizing the technological evolution from reactive, responsive maintenance to proactive, predictive maintenance.

[0041] The intelligent decision engine calculates the total cost of ownership (TCO) for each device based on dimensions such as asset utilization heatmaps, energy consumption costs (collected by smart sockets), maintenance frequency, and depreciation cycle. It identifies "rigid assets" or brands with high failure rates that have a long-term interest rate of less than 5%, and then generates suggestions for internal transfer, external leasing, or scrapping and replacement to help enterprises optimize resource allocation and procurement selection.

[0042] To further enhance the level of inventory automation, the system can also be configured with a main mobile inventory device. This device is an autonomous mobile robot (AMR) equipped with LiDAR and a multi-antenna RFID reader. Based on SLAM technology, it builds an environmental map in real time and autonomously plans its navigation path. It is usually set to start during off-peak hours and scans the office area or warehouse along a predetermined route to achieve high-frequency inventory checks without human intervention.

[0043] Its multi-antenna RFID reader adopts a phased array antenna structure, which can dynamically adjust the transmission power and scanning direction according to the pre-stored signal obstruction areas on the environmental map (such as behind metal cabinets or next to liquid storage tanks) to enhance penetration capability. In actual operation, the system actively increases the scanning frequency and signal gain in areas prone to multipath effects or shielded areas based on the real-time constructed spatial topology information. By reading the same area multiple times at multiple locations and integrating data with timing alignment and spatial matching algorithms, it effectively overcomes the signal blind zone problem, improving the inventory accuracy rate to over 99.5%.

[0044] Meanwhile, based on the RFID signal strength (RSSI) and the robot's own positioning coordinates, the system reverse-engineers the specific shelf or workstation where the asset is located, automatically correcting the location information in the ledger to achieve "instant inventory accuracy." For blind spots that the robot cannot access or scenarios requiring manual confirmation, AR glasses can also be used to assist in inventory checks: after the operator wears the AR device, asset card information can be overlaid in their field of vision. If the actual item is found to be inconsistent with the system record, the interface will automatically prompt the difference and support voice or gesture confirmation of the change, improving the efficiency of human-machine collaboration.

[0045] See Figure 4 The diagram illustrates a robotic inventory process. First, the task scheduling system initiates the inventory task at a set time and wakes up the autonomous mobile robot. The robot plans its path based on a pre-set shelf map and begins its patrol movement. During this movement, the RFID array scanner is simultaneously activated to read the EPC codes of asset tags in batches, and the LiDAR is activated for dynamic obstacle avoidance. The read tag information is combined with the robot's real-time positioning coordinates to calculate the actual physical location of each asset, forming an inventory data stream that is uploaded to the backend system. The backend system compares the real-time inventory data with the electronic asset ledger to complete a consistency check: if the data matches, the corresponding asset is marked as inventoried; if the read tag does not exist in the ledger, it is marked as an inventory surplus or location error. Based on the comparison results, the system automatically generates an asset inventory discrepancy report, stores key data and operation logs in the blockchain for evidence storage, and pushes the final report to the administrator for review.

[0046] Figure 5 This paper presents a specific fixed asset lifecycle management system. The system employs a layered architecture, comprising: a physical asset layer equipped with RFID / RL tags, UWB / GPS positioning modules, vibration / current sensors, and an automated inventory robot to collect asset identity, location, status, and environmental information; a perception access layer responsible for receiving and aggregating the aforementioned multi-source perception data; an edge computing layer deployed near the site for protocol parsing and data cleaning, real-time status identification based on edge AI, and local alarm control; a cloud / core platform layer including an IoT device management platform, a digital twin engine synchronized with physical assets, and a big data center to achieve unified management, 3D visualization mapping, and in-depth analysis of asset data; and a business application layer providing full-lifecycle business functions based on the underlying data and models, including global real-time positioning and tracking, intelligent inventory and ledger management, predictive maintenance, and asset performance analysis and decision support; and users accessing the system via PC management screens, mobile terminals, or AR glasses to achieve visualized monitoring and intelligent management of the entire asset lifecycle.

[0047] In summary, this system achieves differentiated and comprehensive asset perception through the sensing and identification unit, completes heterogeneous data fusion and local intelligent response through the edge processing unit, constructs a high-fidelity, interactive virtual mapping relationship based on the digital twin modeling unit, and drives scientific decision-making output based on full lifecycle data through the decision optimization unit. The entire system forms a complete technology chain from physical perception to digital modeling, from data analysis to closed-loop control. It not only achieves precise indoor and outdoor integrated positioning of asset locations, real-time visibility of status, and fully automated inventory execution, but also supports predictive maintenance and intelligent resource allocation, significantly improving the technical response speed, system reliability, and operational intelligence level of asset management.

[0048] Applied to the aforementioned fixed asset lifecycle management system, this application provides a fixed asset lifecycle management method, see [link to relevant documentation]. Figure 6 As shown, the method includes the following steps: The S610 configures hierarchical identification tags on fixed assets and collects multi-source sensor data through a heterogeneous Internet of Things network. The multi-source sensor data includes the asset's location information and operating status data.

[0049] Tiered identification tags are a strategic deployment structure that configures different types of wireless identification devices based on differences in asset value, liquidity, and management needs. Heterogeneous IoT networks integrate multiple low-power wide-area communication protocols and possess multimodal access capabilities for data transmission. Multi-source sensor data includes dynamic status information such as location, motion, environmental, and electrical parameters collected by built-in or external sensors on the tags.

[0050] In this embodiment, a classification management strategy is first implemented for all fixed assets within the park, configuring corresponding smart identity tags according to the L1 to L3 three-level standards. L1 level assets are low-value, high-quantity general-purpose equipment (such as office desks and chairs, consumables), equipped with UHF passive RFID tags, supporting batch reading and requiring no power supply; L2 level assets are medium-value mobile devices requiring area positioning (such as laptops, oscilloscopes), equipped with Bluetooth BLE Beacon tags to achieve room-level positioning, with battery life up to 2-3 years; L3 level assets are high-value or critical production equipment (such as AGV carts, mobile CT scanners, precision molds), which use composite active tags integrating UWB modules with temperature, humidity, and vibration sensors, providing centimeter-level precise positioning and multi-dimensional status perception capabilities.

[0051] The aforementioned tags transmit data back through a heterogeneous IoT communication network deployed within the building. This network is composed of three wireless technologies: LoRaWAN, Wi-Fi 6, and ZigBee. LoRaWAN handles long-distance, low-bandwidth signal coverage in large warehouse areas; Wi-Fi 6 is used for high-volume data uploads in high-density office areas; and ZigBee Mesh is suitable for production line scenarios with dense equipment and the need for self-organizing networks. Through this heterogeneous network architecture, the system can stably collect real-time location coordinates (such as longitude / latitude, floor number, room number), movement trajectory, power on / off status, temperature changes, and abnormal vibrations of each asset, thereby achieving comprehensive status awareness of various data.

[0052] The S620 transmits the collected multi-source sensor data to the edge processing unit, where communication protocol parsing, data format conversion, abnormal data filtering, and local real-time response processing are performed.

[0053] Edge processing units refer to distributed computing nodes located between the perception layer and the cloud, possessing capabilities for protocol adaptation, data preprocessing, and real-time decision response. Communication protocol parsing involves semantic recognition and conversion of the industrial or proprietary communication standards used by the access devices. Local real-time response refers to the ability to complete specific security or control actions without relying on a central server.

[0054] The collected multi-source sensor data is aggregated through the aforementioned heterogeneous IoT network to edge processing units (Edge Gateways) deployed on each floor or in each functional area. These edge processing units integrate multimodal interfaces, enabling them to simultaneously receive data streams from various protocols such as ZigBee, Bluetooth Mesh, and LoRaWAN. They also have a built-in protocol conversion engine that uniformly converts industrial field protocols such as Modbus and OPC UA into standard MQTT or HTTP JSON formats, achieving data normalization from heterogeneous devices.

[0055] During the data processing phase, the edge processing unit performs preliminary data cleaning operations, including removing duplicate RFID tag records, identifying and correcting abnormal location points caused by signal drift (such as an asset instantly moving to a non-connected space), and smoothing and optimizing continuous trajectories using a Kalman filter algorithm. In addition, the system sets several local response rules: when unauthorized out-of-bounds movement of an L3-level asset is detected, the edge processing unit immediately triggers an on-site audible and visual alarm device without waiting for instructions from the cloud; when a tag reports a tamper alarm (physical contact disconnection), the system automatically locks the operation permissions of the asset's digital twin and pushes an event notification to the security system.

[0056] This method can effectively reduce network bandwidth pressure, improve response timeliness, and ensure that critical security events can be responded to within milliseconds.

[0057] S630 constructs a digital spatial scene of the management area based on building information model or 3D scanning results. In this scene, a corresponding virtual entity is created for each fixed asset, and its static attributes and dynamic parameter interfaces are bound.

[0058] Digital spatial scenes are three-dimensional geometric and semantic spatial structures reconstructed from BIM models or laser-scanned point clouds. Virtual entities are visual objects that map physical assets in digital space, including geometric models, behavioral logic, and data binding relationships.

[0059] By importing existing BIM (Building Information Modeling) model files from the enterprise's existing building design phase, or by acquiring spatial point cloud data through 3D laser scanning of the actual building environment, the system performs semantic segmentation of the building structure, dividing it into basic units such as floors, rooms, workstations, and warehouse shelves, and assigning them business attribute labels (such as "R&D laboratory", "IT server room", "maintenance and inspection area"), generating a digital management base map with spatial semantics.

[0060] Based on this, a virtual entity (Avatar) is created for each registered fixed asset. This virtual entity consists of three parts: first, a geometric model, which uses a standard 3D model (such as a brand projector or a specific model server rack) from a pre-set asset library to render and display the physical asset; second, a physical attribute model, which records static metadata such as purchase date, specifications, weight, dimensions, and user department; and third, a dynamic parameter interface, which configures a set of updatable fields, including current location coordinates, temperature value, power level, and operating status code, for subsequent integration with real sensor data.

[0061] All virtual entities are bound to the identity tags of physical assets through a unique Thing ID, ensuring that the correspondence between the virtual and the real is clear and cannot be confused, laying the foundation for subsequent real-time synchronization and interactive control.

[0062] The S640 uses a synchronization mechanism that combines event-driven and timed snapshots to map the state changes of physical assets to virtual entities in real time, generating continuously updated digital twins.

[0063] Event-driven synchronization refers to triggering data synchronization only when significant state changes occur, while timed snapshots periodically push current state snapshots to maintain data consistency. A digital twin is a dynamic virtual image with real-time, bidirectional, and closed-loop feedback capabilities.

[0064] To balance communication resource consumption and system response speed, a hybrid synchronization strategy combining event-driven and timed snapshots is adopted to achieve state mapping between physical assets and their virtual entities. For critical state change events, such as asset power-on / off, entering an alarm state, crossing electric fence boundaries, or tag removal, the edge processing unit immediately pushes real-time notifications to the digital twin modeling engine via MQTT message queues. This ensures that the virtual entities in the digital space synchronously change color, flash, or play warning animations within milliseconds, achieving millisecond-level state synchronization and high-fidelity visual mapping between physical entities and digital twins. This ensures that the geometric representation, attribute parameters, and behavioral states in the virtual space maintain spatiotemporal consistency and data continuity with the actual assets.

[0065] For routine status indicators, such as asset battery level, ambient temperature and humidity, and equipment vibration amplitude, a timed snapshot update mode of once per minute is set to avoid network congestion caused by high-frequency data uploads. The system also supports reverse control paths: when an administrator remotely selects an asset and issues a "lock" command in the digital twin interface, the command can also be transmitted back to the edge processing unit through the same channel, and then act on the physical tag to remotely disable it or issue a buzzer alert.

[0066] The resulting digital twin not only reflects the current state of the asset, but can also overlay analytical information such as historical trajectory heatmaps and utilization curves. As a multifunctional data hub connecting physical assets and upper-level systems, it integrates sensor data access, edge computing inference, and bidirectional communication interfaces to support the collaborative operation of monitoring and early warning, intelligent analysis, and remote control.

[0067] The S650 analyzes the asset utilization rate, fault prediction trend and maintenance cost data accumulated by the digital twin in the decision optimization unit, generates processing suggestions for asset transfer, preventive maintenance or scrapping, and identifies out-of-bounds movement, illegal dismantling or separation as a safety event and outputs emergency response instructions.

[0068] The decision optimization unit refers to an intelligent decision support module built based on historical data analysis and machine learning models. Processing suggestions are automatically generated, executable solutions oriented towards the asset management process. Security events refer to a set of behaviors that violate preset asset security management rules.

[0069] The decision optimization unit periodically extracts historical operational data of assets from the digital twin database and focuses on analyzing three core indicators: first, asset utilization rate, which calculates the percentage of time that equipment is in operation per unit of time and evaluates its output efficiency in conjunction with the OEE (Overall Equipment Effectiveness) model; second, fault prediction trend, which uses the LSTM time series model to extrapolate the decline trend of early signs such as current fluctuations and vibration spectrum shifts and predicts the remaining useful life (RUL); and third, maintenance cost accumulation, which integrates past maintenance work orders, replacement parts costs, and energy consumption expenditures to calculate the total life cycle cost (TCO).

[0070] Based on the above analysis results, the system automatically generates several management suggestions: if the utilization rate of a certain piece of equipment is less than 5% for three consecutive months, it is marked as an idle asset, and it is recommended to initiate an internal transfer process or start an external leasing procedure; if the average failure rate of similar brand equipment is significantly higher than the industry benchmark, it is added to the procurement blacklist to assist in the next stage of procurement selection; when the predicted failure probability exceeds the 80% threshold, a preventive maintenance work order is automatically generated and the nearest available technician is scheduled.

[0071] Meanwhile, the system continuously monitors the security and compliance status of assets: once it is found that an asset leaves the set electronic fence range, the tag shows an illegal removal signal, or the distance between the L2 level BLE tag and the designated employee's name tag exceeds 5 meters (triggering the "asset accompaniment" rule), it is immediately identified as a security incident. The decision optimization unit then outputs emergency response instructions, including sending SMS alarms to the responsible person, freezing asset borrowing permissions, recording event logs, and preparing audit evidence packages.

[0072] When the S660 receives an emergency response command, the digital twin modeling unit sends a control signal to the edge processing unit. The edge processing unit then triggers the corresponding asset's identity tag to enter a locked state or activates the on-site audible and visual alarm device to trigger a security response action for the physical asset.

[0073] Control signals are instruction data packets with execution intent sent from the upper-layer digital system to the lower-layer edge devices. A locked state refers to a security mode that causes the tag to cease normal communication responses or restrict its functional output; security response actions are warnings or restrictions implemented in the physical world.

[0074] When the decision optimization unit outputs an emergency response instruction, the instruction is first transmitted to the digital twin modeling unit. The latter uses the unique Thing ID of the target asset to find the address of the edge processing unit to which it currently belongs, and encapsulates an encrypted control signal, which is then transmitted to the corresponding edge node via a secure communication link (TLS encrypted channel).

[0075] Upon receiving the signal, the edge processing unit immediately executes a preset security response sequence: for critical assets equipped with composite UWB tags, it controls them to enter a locked mode, which manifests as disabling the wireless broadcast function, cutting off the remote wake-up interface, and storing alarm flags locally to prevent unauthorized activation; for assets deployed in public areas, it simultaneously activates nearby audible and visual alarm devices (such as buzzers and warning lights) to attract the attention of on-site personnel.

[0076] Furthermore, the edge processing unit also feeds back the status of this response action to the digital twin system, updates the asset security status icon (such as displaying a red "locked" indicator), and activates the trajectory tracking enhancement mode (increasing the location sampling frequency) to facilitate subsequent tracking of the handling progress. The entire process forms a closed-loop control chain of "perception—judgment—decision—execution—feedback," truly achieving deterministic, low-latency two-way control from virtual space to terminal devices based on real-time data-driven physical asset status monitoring, intelligent diagnosis, and verifiable remote intervention.

[0077] The fixed asset lifecycle management method provided by this invention achieves comprehensive and real-time collection of asset location and operating status by constructing a heterogeneous Internet of Things sensing network and a hierarchical identity tagging system. Combined with the protocol parsing and local response mechanism of the edge processing unit, it improves data processing efficiency and security event response speed. Based on a digital spatial scene constructed using BIM or 3D scanning, it enables precise mapping between physical assets and their virtual entities, and ensures high timeliness and low bandwidth overhead for the digital twin through a synchronization strategy combining event-driven and timed snapshots. At the decision optimization level, it integrates multi-dimensional data such as asset utilization, fault prediction trends, and maintenance costs to generate intelligent suggestions for allocation, repair, and scrapping, significantly improving the scientific nature of management. For security events such as unauthorized movement or dismantling, the system can trigger remote locking and audible and visual alarms, forming a closed-loop control system. The overall solution effectively solves problems such as low inventory efficiency, discrepancies between records and actual assets, lack of visibility of status, and passive maintenance in traditional asset management. It significantly improves asset utilization and operational security, reduces operation and maintenance costs, and achieves a closed-loop upgrade of asset management from static ledger maintenance to dynamic usage status monitoring, and then to data-driven resource optimization and allocation.

[0078] The fixed asset lifecycle management method provided in this application has the same implementation principle and technical effects as the aforementioned system embodiments. For the sake of brevity, any parts not mentioned in the embodiments of the fixed asset lifecycle management method can be referred to the corresponding content in the aforementioned fixed asset lifecycle management system embodiments.

[0079] This application also provides an electronic device, such as... Figure 7The diagram shows the structure of the electronic device 100, which includes a processor 71 and a memory 70. The memory 70 stores computer-executable instructions that can be executed by the processor 71. The processor 71 executes the computer-executable instructions to implement any of the above-mentioned fixed asset lifecycle management methods.

[0080] exist Figure 7 In the illustrated embodiment, the electronic device further includes a bus 72 and a communication interface 73, wherein the processor 71, the communication interface 73, and the memory 70 are connected via the bus 72.

[0081] The memory 70 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 73 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 72 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 72 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0082] The processor 71 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 71 or by instructions in software form. The processor 71 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory. The processor 71 reads the information in the memory and, in conjunction with its hardware, completes the steps of the fixed asset lifecycle management method described in the foregoing embodiment.

[0083] This application also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described fixed asset lifecycle management method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0084] The computer program product of the fixed asset lifecycle management system and method provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0085] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0086] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A fixed asset lifecycle management system, characterized in that, It includes a perception and identification unit, an edge processing unit, a digital twin modeling unit, and a decision optimization unit connected in sequence; The sensing and identification unit is used to configure identity tags for fixed assets and collect multi-source sensor data, including the location information and operating status data of the fixed assets. The edge processing unit is communicatively connected to the sensing and identification unit and is used to perform protocol conversion, anomaly detection, and local response processing on the multi-source sensing data. The digital twin modeling unit establishes a virtual entity for each fixed asset based on a three-dimensional spatial model, and maps the state changes of the fixed assets to the virtual entity through a real-time data synchronization mechanism to generate a dynamically updated digital twin. The decision optimization unit generates processing information for asset allocation, repair, or disposal based on the asset utilization rate, fault prediction results, and maintenance cost data accumulated in the digital twin, and outputs emergency response instructions when a security incident is detected. The digital twin modeling unit is also used to send a corresponding control signal to the edge processing unit after receiving an emergency response instruction, so as to trigger a security response action on the fixed asset.

2. The fixed asset lifecycle management system according to claim 1, characterized in that, The sensing and identification unit includes hierarchically configured identity tags and a heterogeneous Internet of Things network; The identity tags are configured according to asset value and management needs: low-value assets are configured with passive RFID tags, medium-value assets are configured with Bluetooth beacon tags, and high-value key equipment is configured with ultra-wideband sensor composite tags. The composite tags integrate positioning, motion detection and temperature and humidity monitoring functions. The heterogeneous Internet of Things (IoT) network consists of IoT gateways that support multiple communication protocols. These IoT gateways are deployed within the management area to receive data from different types of tags and perform preliminary signal acquisition.

3. The fixed asset lifecycle management system according to claim 2, characterized in that, The identification tag is equipped with an anti-tamper alarm structure, which triggers a local alarm signal when the tag body is physically separated from the mounting surface. The IoT gateway is equipped with ZigBee, Bluetooth Mesh, and LoRaWAN communication interfaces, and integrates a protocol conversion module to convert raw data from different communication interfaces into a standard message format.

4. The fixed asset lifecycle management system according to claim 1, characterized in that, The edge processing unit includes a multimodal data parsing module, an edge computing node, and an instant response module; The multimodal data parsing module is used to perform protocol decoding and timestamp alignment on the received multi-source sensor data; The edge computing node is used to perform data filtering operations on the multi-source sensing data to remove duplicate tag information and remove invalid location jump and non-steady state data through an anomaly cleaning algorithm. When the instant response module detects unauthorized movement or a sudden change in status, it directly drives the on-site audio-visual devices to issue an alert without waiting for instructions from the upper level.

5. The fixed asset lifecycle management system according to claim 1, characterized in that, The digital twin modeling unit includes a three-dimensional spatial semantic model construction module, a virtual entity generation module, and a virtual-real synchronization mechanism module; The three-dimensional spatial semantic model construction module reconstructs the three-dimensional structure of the management area based on the building information model or CAD drawings, divides the area into functional zones, and labels each space with corresponding business attributes. The virtual entity generation module calls a pre-set 3D model library according to the asset model and binds the static attributes and dynamic parameter interfaces of the asset. The virtual-real synchronization mechanism module triggers real-time data transmission based on key state changes, collects regular indicators at fixed intervals, and ensures real-time synchronization between fixed assets and virtual entities by dynamically adjusting the data update frequency.

6. The fixed asset lifecycle management system according to claim 5, characterized in that, The digital twin modeling unit is also equipped with a space-asset dynamic alignment protocol, which maps the location data in the physical coordinate system to the three-dimensional model coordinate system through semantic matching; Upon receiving a control signal, the virtual entity performs a remote locking operation in the digital space and in turn drives the physical device into a disabled state.

7. The fixed asset lifecycle management system according to claim 1, characterized in that, The decision optimization unit includes a trajectory analysis module, a predictive maintenance module, and an intelligent decision engine; The trajectory analysis module determines whether the current location of the fixed asset exceeds the permitted area or leaves the designated personnel based on the historical movement path and the preset electronic fence rules, and generates corresponding security event records. The predictive maintenance module establishes a health baseline based on the operating status data returned by the edge computing node, predicts the remaining service life of the equipment through a time-series neural network model, and generates a maintenance work order when the failure probability exceeds a set threshold. The intelligent decision engine calculates the total lifecycle cost based on asset utilization, energy consumption cost, maintenance frequency, and depreciation cycle, and generates asset allocation strategies or disposal strategies based on the total lifecycle cost.

8. The fixed asset lifecycle management system according to claim 1, characterized in that, It also includes a main mobile inventory device, which is equipped with a lidar and a multi-antenna radio frequency identification reader, for autonomously planning the cruise route based on real-time positioning and map building technology; The main mobile inventory device is activated during non-working hours, collects asset tag information in batches along a predetermined route, and infers the spatial location data of the fixed assets based on signal strength and its own positioning coordinates.

9. The fixed asset lifecycle management system according to claim 8, characterized in that, The multi-antenna RFID reader of the main mobile inventory device includes a phased array antenna structure, which is used to dynamically adjust the antenna transmission power and scanning direction according to the signal obstruction area pre-stored in the environmental map. During the cruise, based on the real-time constructed spatial topology information, the scanning frequency and signal gain are increased in areas prone to multipath effects or metal shielded areas. The same area is sampled multiple times at different locations during the movement, and the tag data read from multiple points is time-aligned and spatially matched for data integration.

10. A method for managing the entire life cycle of fixed assets, characterized in that, include: Hierarchical identification tags are configured on fixed assets, and multi-source sensor data is collected through a heterogeneous Internet of Things network. The multi-source sensor data includes the location information and operating status data of the assets. The collected multi-source sensor data is transmitted to the edge processing unit, where communication protocol parsing, data format conversion, abnormal data filtering, and local real-time response processing are performed. A digital spatial scene of the management area is constructed based on building information model or 3D scanning results. In this scene, a corresponding virtual entity is created for each fixed asset, and its static attributes and dynamic parameter interfaces are bound. Through a synchronization mechanism that combines event-driven and timed snapshots, the state changes of physical assets are mapped to the virtual entity in real time, generating a continuously updated digital twin. The decision optimization unit analyzes the asset utilization rate, fault prediction trend and maintenance cost data accumulated by the digital twin, generates processing suggestions for asset allocation, preventive maintenance or scrapping, and identifies unauthorized movement, illegal dismantling or separation as a safety event and outputs emergency response instructions. When an emergency response command is received, the digital twin modeling unit sends a control signal to the edge processing unit, which then triggers the corresponding asset's identity tag to enter a locked state or activates the on-site audible and visual alarm device to trigger a security response action for the physical asset.

Images