A digital twin management method for sites based on IoT and AI visual recognition technology

Abstract

This application relates to the field of intelligent management technology for logistics sites, and provides a digital twin management method for sites based on IoT combined with AI visual recognition technology. The method collects multi-source heterogeneous data from the logistics site using sensors, RFID devices, GPS devices, and cameras deployed within the site; creates a digital twin model corresponding to the logistics site; performs deep correlation analysis on the multi-source heterogeneous data to obtain potential relationships and pattern information between the data; adjusts the parameters and structure of the digital twin model based on the multi-source heterogeneous data and the corresponding potential relationships and pattern information; and obtains logistics prediction information, intelligent configuration and scheduling information, and optimization suggestions corresponding to the multi-source heterogeneous data, so as to complete the digital twin management of the logistics site within the digital twin model based on the logistics prediction information, intelligent configuration and scheduling information, and optimization suggestions.

Description

Technical Field

[0001] This application relates to the field of intelligent management technology for logistics sites, and in particular to a digital twin management method for sites based on IoT combined with AI visual recognition technology. Background Technology

[0002] With the rapid development of smart logistics, traditional logistics site management faces problems such as isolated multi-source data, insufficient dynamic collaboration capabilities, and low resource scheduling efficiency. Current technologies for logistics site management typically rely on single sensors or information systems (such as Warehouse Management Systems (WMS) and Transportation Management Systems (TMS), which have the following drawbacks:

[0003] 1. Single data collection dimension: It only focuses on local data such as the location of goods or the status of equipment, and lacks collaborative collection of multi-source heterogeneous data such as warehouse environment, personnel behavior, and goods status, making it difficult to fully map the real-time status of the physical site.

[0004] 2. Staticization of digital twin models: Existing digital twin models are mostly based on fixed geometric modeling, lacking in-depth modeling of dynamic processes such as equipment operation, cargo flow, and personnel activities, and cannot adaptively adjust model parameters and structure according to real-time data correlation analysis results, resulting in insufficient model prediction accuracy.

[0005] 3. Weak correlation analysis and decision-making capabilities: Decisions on logistics demand forecasting and resource scheduling optimization rely on empirical rules or simple algorithms, without in-depth mining of potential correlations from multiple data sources, making it difficult to generate intelligent and global scheduling strategies and optimization suggestions.

[0006] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0007] This application provides a site digital twin management method based on IoT combined with AI visual recognition technology. It aims to solve the problem that although some studies have attempted to combine IoT and digital twin technology for logistics management, none of them have formed a complete technical link of "multi-source heterogeneous data collection → deep correlation analysis → dynamic model adjustment → intelligent decision-making closed loop". In particular, it lacks a systematic solution for analyzing personnel behavior and cargo status through AI visual recognition, mining multi-dimensional data correlation relationships using association rules and graph neural networks, and achieving prediction and scheduling optimization based on dynamic model adjustment.

[0008] Firstly, this application provides a site digital twin management method based on IoT combined with AI visual recognition technology, including:

[0009] By deploying sensors, RFID devices, GPS devices and cameras in the logistics site, multi-source heterogeneous data is collected. The multi-source heterogeneous data includes at least two of the following: cargo location, transportation status, vehicle driving trajectory, warehouse environmental parameters, cargo type and quantity, and personnel movement path.

[0010] Create a digital twin model corresponding to the logistics site; conduct in-depth correlation analysis on multi-source heterogeneous data to obtain potential correlations and pattern information between multi-source heterogeneous data; adjust the parameters and structure of the digital twin model according to the multi-source heterogeneous data and the corresponding potential correlations and pattern information;

[0011] The system acquires logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data, so as to complete the digital twin management of logistics sites in the digital twin model based on the logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information. Among them, the logistics forecasting information includes forecasting logistics demand, forecasting transportation time, and forecasting inventory levels; the intelligent configuration and scheduling information includes intelligent scheduling and optimized configuration of logistics resources corresponding to vehicles and warehouse locations; and the optimization suggestion information includes forecasting logistics costs and forecasting business process data, as well as corresponding optimization suggestion strategies.

[0012] In some embodiments, the collection of multi-source heterogeneous data within the logistics site using sensors, RFID devices, GPS devices, and cameras deployed within the logistics site includes: using sensors to collect warehouse environmental parameters in real time, including temperature, humidity, light intensity, and shelf load-bearing data; using RFID devices to read cargo tag information to obtain cargo categories, quantities, and corresponding cargo locations; using GPS devices to track the geographical locations of transport vehicles and mobile devices in real time to generate transport status and vehicle trajectories; and using the cameras to collect image and video data, which are then analyzed by AI visual recognition algorithms into personnel movement paths, cargo stacking status, and abnormal behavior event data.

[0013] In some embodiments, creating a digital twin model corresponding to a logistics site includes: constructing a geometric model of the logistics site based on 3D modeling technology, including warehouse layout, road planning, shelf distribution, and equipment location; establishing a multi-dimensional dynamic data mapping mechanism to bind multi-source heterogeneous data collected by sensors, RFID devices, GPS devices, and cameras to the corresponding entities of the geometric model; and constructing a layered architecture model, including at least a physical layer, a data layer, a model layer, and an application layer, wherein the model layer includes sub-models of equipment operation, cargo flow, and personnel activities.

[0014] In some embodiments, before performing deep correlation analysis on the multi-source heterogeneous data, the method further includes: removing duplicate data and correcting format-incorrect data in the multi-source heterogeneous data using a data cleaning algorithm; converting multi-source heterogeneous data with different protocols into a standardized format based on a unified data interface; synchronizing the multi-source heterogeneous data in time using a timestamp alignment algorithm to generate a time-series aligned dataset; and detecting and repairing outlier data in the multi-source heterogeneous data using statistical methods or machine learning algorithms.

[0015] In some embodiments, creating a digital twin model corresponding to a logistics site includes: using a digital twin modeling engine to train a dynamic model of site operation based on historical data, wherein the dynamic model includes at least a cargo transportation route planning model, a warehouse location allocation model, and an equipment energy consumption prediction model; optimizing the three-dimensional geometric model through lightweight processing technology to ensure the operating efficiency of the dynamic model under real-time data-driven conditions; and establishing a version management mechanism corresponding to the dynamic model to automatically trigger model version iteration based on the data update frequency.

[0016] In some embodiments, the deep correlation analysis of multi-source heterogeneous data to obtain potential correlations and pattern information among the multi-source heterogeneous data includes: using an association rule mining algorithm to analyze the correlation between cargo location data and warehouse environmental parameters, and identifying key environmental factors affecting cargo storage status; constructing a logistics entity relationship graph through a graph neural network model, wherein the nodes corresponding to the logistics entity relationship graph include cargo, vehicles, personnel, equipment, and storage locations, and edge weights represent the interaction frequency between entities, obtaining high-frequency interaction patterns and abnormal interaction behaviors; and using a time-series data analysis algorithm to identify the time-series dependency between vehicle driving trajectory and transportation time and logistics demand, and generating periodic logistics activity patterns.

[0017] In some embodiments, adjusting the parameters and structure of the digital twin model based on multi-source heterogeneous data and corresponding potential relationships and pattern information includes: automatically updating the threshold parameters of the warehouse environment sub-model and triggering equipment control strategy adjustment when abnormal environmental parameters of the cargo storage area are detected; optimizing the road network weight parameters of the cargo transportation route planning model and dynamically adjusting the route planning algorithm based on the high-frequency interaction patterns mined in the entity relationship graph; and increasing the data flow interface between the sub-models corresponding to the digital twin model or correcting the model hierarchy mapping relationship corresponding to the digital twin model through a model structure adaptive algorithm when it is found that the structure of the existing digital twin model cannot fit the newly mined relationships.

[0018] In some embodiments, acquiring logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data includes: using a time series forecasting model to generate logistics forecasting information for future periods based on historical logistics demand, transportation time, and inventory level data; generating intelligent configuration and scheduling information with the goal of maximizing logistics resource utilization through an intelligent optimization algorithm; and identifying inefficient links and generating optimization suggestion information including process refactoring suggestions and resource reallocation strategies based on a cost function model, combined with historical logistics cost data and business process data.

[0019] In some embodiments, the step of completing digital twin management of logistics sites in a digital twin model based on the logistics forecast information, intelligent configuration and scheduling information, and optimization suggestion information includes: inputting the logistics forecast information into the digital twin model for scenario simulation to verify the feasibility of different scheduling schemes; displaying the logistics resource configuration status, equipment operating parameters, and abnormal event warning information in real time through the model visualization interface corresponding to the digital twin model; triggering an early warning mechanism and synchronously updating the scheduling scheme when the deviation between the actual operating data and the model forecast data exceeds a preset threshold; and generating a process optimization work order based on the optimization suggestion information and pushing it to the logistics management system to perform process refactoring operations.

[0020] In some embodiments, the method further includes: constructing a dynamic decision-making model using a reinforcement learning algorithm, with the goal of maximizing the operational efficiency of the logistics site, using real-time operational data output by the digital twin model as state input, and intelligent configuration and scheduling strategies as action output, and generating an adaptive scheduling strategy through interactive training with the digital twin environment; the dynamic decision-making model is periodically updated and synchronized to the logistics management system to achieve dynamic optimization of vehicle scheduling, cargo allocation, and personnel tasks, wherein the efficiency of strategy optimization is improved through experience playback and reward function adjustment mechanisms during the training process.

[0021] This application provides a digital twin management method for logistics sites based on IoT combined with AI visual recognition technology. The method integrates sensors, RFID, GPS, and AI vision cameras to achieve real-time data collection of all elements, including goods, equipment, environment, and personnel, constructing a complete digital mapping of the physical site and solving the problem of data silos in traditional management. Through deep correlation analysis, potential relationships between data are obtained, and model parameters and structure are adjusted in real time, enabling the digital twin model to dynamically adapt to the complex changes in the logistics site and improve the model's fitting accuracy to real-world scenarios. Based on time series forecasting, intelligent optimization algorithms, and cost function models, the method generates logistics demand forecasts, resource scheduling schemes, and process optimization suggestions, forming a closed loop of "data collection-analysis-decision-execution." This significantly improves the utilization rate of logistics resources (such as vehicle scheduling efficiency and warehouse turnover rate), reduces operating costs (such as predicting inventory levels to reduce backlog), and verifies the feasibility of scheduling schemes through scenario simulation, enhancing the scientific and real-time nature of management decisions.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments 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 based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart illustrating the steps of a site digital twin management method based on IoT combined with AI visual recognition technology, provided in one embodiment of this application.

[0025] Figure 2 This is a schematic diagram of the structure of a site digital twin management device based on IoT combined with AI visual recognition technology provided in an embodiment of this application;

[0026] Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0028] 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0030] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0031] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0034] With the rapid development of smart logistics, traditional logistics site management faces problems such as isolated multi-source data, insufficient dynamic collaboration capabilities, and low resource scheduling efficiency. Current technologies for logistics site management typically rely on single sensors or information systems (such as Warehouse Management Systems (WMS) and Transportation Management Systems (TMS), which have the following drawbacks:

[0035] 1. Single data collection dimension: It only focuses on local data such as the location of goods or the status of equipment, and lacks collaborative collection of multi-source heterogeneous data such as warehouse environment (temperature, humidity, shelf load-bearing capacity), personnel behavior, and goods status (stacking method, abnormal events), making it difficult to fully map the real-time status of the physical site.

[0036] 2. Staticization of digital twin models: Existing digital twin models are mostly based on fixed geometric modeling, lacking in-depth modeling of dynamic processes such as equipment operation, cargo flow, and personnel activities, and cannot adaptively adjust model parameters and structure according to real-time data correlation analysis results, resulting in insufficient model prediction accuracy.

[0037] 3. Weak correlation analysis and decision-making capabilities: Decisions on logistics demand forecasting and resource scheduling optimization rely on empirical rules or simple algorithms, without in-depth mining of potential correlations among multi-source data (such as the correlation between cargo storage environment and cargo damage, and the temporal dependence of vehicle trajectory and transportation efficiency), making it difficult to generate intelligent and global scheduling strategies and optimization suggestions.

[0038] While some existing technologies have attempted to combine IoT and digital twin technologies for logistics management, none have established a complete technological chain encompassing "multi-source heterogeneous data collection → deep correlation analysis → dynamic model adjustment → intelligent decision-making closed loop." In particular, they lack a systematic solution for using AI visual recognition to analyze personnel behavior and cargo status, utilizing association rules and graph neural networks to mine multi-dimensional data relationships, and achieving prediction and scheduling optimization based on dynamic model adjustments. Therefore, existing technologies do not provide technical insights for achieving digital twin management of logistics sites through the integration of these multiple technologies. An innovative method that can integrate multi-source data, dynamically optimize models, and provide intelligent decision-making is urgently needed.

[0039] To resolve the above issues, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic flowchart illustrating a site digital twin management method based on IoT combined with AI visual recognition technology, provided in one embodiment of this application. This site digital twin management method based on IoT combined with AI visual recognition technology can be implemented using computer equipment, which can be deployed on a single server or server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc.

[0040] It should be noted that the acquisition of any information involved in the provided methods is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0041] Specifically, such as Figure 1 As shown, the provided site digital twin management method based on IoT combined with AI visual recognition technology includes steps S101 to S103, which are detailed below:

[0042] Step S101. Collect multi-source heterogeneous data in the logistics site by using sensors, RFID devices, GPS devices and cameras deployed in the logistics site. The multi-source heterogeneous data includes at least two of the following: cargo location, transportation status, vehicle driving trajectory, warehouse environmental parameters, cargo type and quantity, and personnel movement path.

[0043] Specifically, by deploying diverse sensing devices, comprehensive data collection of all elements of the logistics site is achieved, covering four dimensions: "people, goods, site, and equipment," thus solving the problem of insufficient data dimensions from traditional single sensors. Data types collected include: Physical environment data: temperature and humidity sensors, shelf pressure sensors, gas concentration sensors, etc., collect warehouse environmental parameters (temperature, humidity, shelf load-bearing capacity, ventilation status); Goods status data: RFID tags track the location, category, and quantity of goods in real time, combined with AI visual recognition technology to analyze the stacking method (whether it exceeds limits, tilting), and abnormal events (packaging damage, liquid leakage); Equipment and transportation data: GPS / BeiDou positioning devices collect vehicle driving trajectories and speeds, onboard sensors acquire transportation status (braking frequency, degree of goods jolting), and AGV / forklift equipment status data (battery level, operating trajectory); Personnel behavior data: through cameras combined with human posture recognition algorithms, analyze personnel operation processes (such as goods handling paths, shelf access behavior), and safety compliance (whether safety helmets are worn, whether restricted areas are entered).

[0044] The deployment of sensing devices involves deploying miniature sensors (temperature, humidity, and pressure sensors) on warehouse shelves and setting up RFID readers every 20cm to achieve precise matching of goods location with environmental parameters; installing wide-angle cameras at warehouse entrances, aisles, and loading / unloading areas, and using AI vision technology (such as YOLO object detection and OpenPose pose estimation) to analyze personnel movements and goods status in real time, supporting spatiotemporal calibration and stitching of multi-camera video streams; and installing onboard GPS and inertial navigation equipment on transport vehicles, which, combined with the OBD interface, acquire vehicle status data, and embedding UWB positioning tags into cargo pallets to achieve centimeter-level positioning.

[0045] Data preprocessing and fusion clean the raw data (removing outliers), synchronize the time (using the NTP protocol to unify timestamps), and convert the format (unifying sensor binary data, video stream feature values, and RFID codes into JSON format) through edge computing nodes; establish a data tagging system, assign a unique ID to each cargo, equipment, and personnel, and link different data sources through the ID (such as the "cargo ID-location-environmental parameters-operator" association chain) to form a multi-dimensional data mapping relationship.

[0046] Step S102. Create a digital twin model corresponding to the logistics site; perform in-depth correlation analysis on multi-source heterogeneous data to obtain potential correlation relationships and pattern information between multi-source heterogeneous data; adjust the parameters and structure of the digital twin model according to the multi-source heterogeneous data and the corresponding potential correlation relationships and pattern information.

[0047] Specifically, a three-in-one digital twin model of "geometric model + dynamic process + data association" is constructed, which solves the problem of static model by driving real-time adaptive adjustment of model parameters and structure through multi-source data.

[0048] The initialization of the digital twin model includes: geometric modeling: constructing a three-dimensional spatial model of the warehouse based on BIM technology to accurately reproduce the layout of the shelves, aisle width, and equipment parking area; dynamic process modeling: establishing equipment operation models (such as AGV path planning rules), cargo flow models (inbound and outbound process logic), and personnel behavior models (operation time consumption statistics).

[0049] Deep association analysis uses association rule algorithms (such as Apriori and FP-Growth) to mine co-occurrence relationships in the data (such as the association between "high humidity environment + metal goods" and corrosion risk).

[0050] A heterogeneous graph model is constructed based on graph neural networks (GNN). Nodes include goods, equipment, personnel, and environmental parameters, and edges represent interaction relationships (such as "personnel ID-operated goods ID-operation time-environmental parameters" forming a multi-relationship edge). Temporal dependencies (such as the correlation between vehicle historical trajectory and current transportation efficiency) are captured through graph convolution.

[0051] Dynamic model adjustments include: parameter adjustment: dynamically correcting the shelf load-bearing capacity model parameters based on real-time environmental data (such as excessive shelf load capacity), and optimizing the cargo storage density prediction model through machine learning algorithms (such as gradient descent); structural adjustment: automatically updating the geometric model based on real-time location data when new equipment is added or the shelf layout changes, and reconstructing the AGV path planning logic through reinforcement learning algorithms.

[0052] The model building toolchain uses the Unity / UE engine to build a 3D visualization interface and imports BIM models using Python scripts; it uses TensorFlow / PyTorch to build GNN models, with the input data being the associated data chain generated in step S101, and the output being the association weights and potential patterns between nodes (such as "the nonlinear relationship between cargo damage rate and the degree of transportation bumps").

[0053] The real-time iteration mechanism establishes a model version management system, trains the GNN model every 10 minutes based on the latest data, and triggers model parameter recalibration when the confidence of the association rule changes beyond a threshold (e.g., from 90% to 70%). For sudden scenarios (e.g., abnormal temperature and humidity in a warehouse), the emergency model is called in real time through an event-driven mechanism (e.g., temporarily adjusting the storage strategy of goods in the affected area).

[0054] Step S103. Obtain logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data, so as to complete the digital twin management of logistics sites in the digital twin model based on the logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information; wherein, the logistics forecasting information includes forecasting logistics demand, forecasting transportation time, and forecasting inventory levels; the intelligent configuration and scheduling information includes intelligent scheduling and optimized configuration of logistics resources corresponding to vehicles and warehouse locations; the optimization suggestion information includes forecasting logistics costs and forecasting business process data, as well as corresponding optimization suggestion strategies.

[0055] Specifically, based on the dynamic digital twin model, intelligent decisions covering "prediction-scheduling-optimization" are generated, forming a closed loop of "data collection-analysis-decision-feedback".

[0056] Logistics forecasting includes: Demand forecasting: Combining historical order data, seasonal factors, and real-time inventory, the LSTM / Transformer model is used to predict logistics demand (order volume, cargo type distribution) for the next 72 hours; Transportation time forecasting: Based on real-time vehicle trajectories, traffic conditions (external map API), and cargo weight, the XGBoost model is used to optimize transportation time estimates; Inventory level forecasting: Considering inbound and outbound frequency and supplier delivery cycles, a dynamic inventory balance model is constructed to provide early warnings of stockout / overstock risks.

[0057] Resource scheduling and configuration include: Vehicle scheduling: Based on Dijkstra's algorithm and reinforcement learning, combined with real-time cargo distribution and vehicle load, delivery routes are optimized (reducing empty runs and shortening total mileage); Warehouse location allocation: Based on cargo weight, access frequency, and environmental requirements (such as refrigerated goods), warehouse locations are dynamically allocated through genetic algorithms to reduce personnel walking distance and equipment energy consumption.

[0058] The optimization suggestions include: cost prediction: analyzing the correlation between "equipment energy consumption + manpower hours + cargo damage cost" in historical data to identify high-cost links (such as insufficient load-bearing capacity of shelves in a certain area leading to frequent goods replacement); process optimization: mining the correlation between "personnel operation path - task completion time" through GNN to propose process improvement suggestions (such as adjusting the storage location of frequently accessed goods to near the entrance and exit).

[0059] By developing a decision engine module, the predictive model (deployed via TensorFlow Serving) and optimization algorithm (implemented in Python scripts) are encapsulated, supporting API calls. The digital twin interface displays decision results in real time (e.g., color-coding the optimal cargo location and rendering recommended transportation routes on a map). By simulating decision-making schemes in the digital twin model (e.g., rehearsing vehicle scheduling strategies for a certain period), the feasibility of the scheme is evaluated by comparing the simulation results (e.g., estimated energy consumption, completion time) with historical data. The actual execution effect (e.g., the improvement rate of transportation efficiency after the implementation of the scheduling strategy) is fed back to the model, forming a "decision-execution-iteration" closed loop, with the global optimization strategy updated every 24 hours.

[0060] This application integrates sensors, RFID, GPS, and AI vision cameras to achieve real-time data collection of all elements, including goods, equipment, environment, and personnel, constructing a complete digital mapping of the physical site and solving the problem of data silos in traditional management. Through deep correlation analysis, it obtains potential relationships between data and adjusts model parameters and structure in real time, enabling the digital twin model to dynamically adapt to the complex changes in the logistics site and improve the model's fitting accuracy to real-world scenarios. The method, based on time series forecasting, intelligent optimization algorithms, and cost function models, generates logistics demand forecasts, resource scheduling schemes, and process optimization suggestions, forming a closed loop of "data collection-analysis-decision-execution." This significantly improves the utilization rate of logistics resources (such as vehicle scheduling efficiency and warehouse turnover rate) and reduces operating costs (such as predicting inventory levels to reduce backlog). Scenario simulations verify the feasibility of the scheduling scheme, enhancing the scientific and real-time nature of management decisions.

[0061] In some embodiments, the collection of multi-source heterogeneous data within the logistics site using sensors, RFID devices, GPS devices, and cameras deployed within the logistics site includes: using sensors to collect warehouse environmental parameters in real time, including temperature, humidity, light intensity, and shelf load-bearing data; using RFID devices to read cargo tag information to obtain cargo categories, quantities, and corresponding cargo locations; using GPS devices to track the geographical locations of transport vehicles and mobile devices in real time to generate transport status and vehicle trajectories; and using the cameras to collect image and video data, which are then analyzed by AI visual recognition algorithms into personnel movement paths, cargo stacking status, and abnormal behavior event data.

[0062] By clearly defining the specific data collection objects and analysis targets of different sensing devices, a four-dimensional data collection system of "environment-goods-equipment-personnel" is constructed: Sensors: Focus on warehouse environmental parameters, including temperature and humidity, light intensity (affecting goods aging), and shelf load-bearing capacity (monitoring overload risk); RFID devices: Obtain category, quantity, and location through goods tags (combined with reader deployment density to achieve centimeter-level positioning); GPS devices: Track the real-time location of transport vehicles and mobile devices such as AGVs, generating trajectory data and transportation status (speed, start-stop frequency); Camera + AI vision: Analyze visual data into personnel movement paths (optimizing operational efficiency), goods stacking status (compliance), and abnormal events (such as goods tipping over, personnel violations).

[0063] Equipment deployment and data acquisition are achieved by installing triaxial pressure sensors (to monitor load-bearing capacity) and temperature and humidity sensors (every 5m) on each layer of the warehouse shelving. 2 One light sensor is deployed on the top of the warehouse (covering every 10 square meters); UHF RFID tags are embedded in the pallets; fixed readers are deployed at the warehouse entrances and exits (reading distance 3 meters); mobile readers are installed on forklifts (reading shelf tags in real time); transport vehicles are equipped with Beidou / GPS dual-mode positioning modules (positioning accuracy ≤ 5 meters); AGVs integrate UWB positioning tags (accuracy ≤ 10 cm); cameras are 2-megapixel infrared network cameras (supporting night vision), deployed at a density of 2 per channel, using AI algorithms (YOLOv8 to detect goods stacking, DeepSORT to track personnel paths) to output structured data in real time (such as "personnel ID-time-location-operation type").

[0064] In some embodiments, creating a digital twin model corresponding to a logistics site includes: constructing a geometric model of the logistics site based on 3D modeling technology, including warehouse layout, road planning, shelf distribution, and equipment location; establishing a multi-dimensional dynamic data mapping mechanism to bind multi-source heterogeneous data collected by sensors, RFID devices, GPS devices, and cameras to the corresponding entities of the geometric model; and constructing a layered architecture model, including at least a physical layer, a data layer, a model layer, and an application layer, wherein the model layer includes sub-models of equipment operation, cargo flow, and personnel activities.

[0065] By constructing a digital twin framework that includes geometric models, data mapping, and a layered architecture: the geometric model uses 3D modeling technology to recreate the physical layout of the logistics site (warehouses, roads, shelves, equipment locations); the data mapping mechanism includes binding data collected by sensors / equipment with entities in the geometric model (such as shelves, vehicles, and personnel) to achieve a one-to-one correspondence between "digital entities and physical entities"; the layered architecture is divided into a physical layer (hardware equipment), a data layer (data processing), a model layer (dynamic sub-models), and an application layer (visualization and decision-making), where the model layer includes sub-models of equipment operation, cargo flow, and personnel activities.

[0066] The geometric model is constructed using BIM software (such as Revit) to create a 3D model of the warehouse, which is then imported into a digital twin platform (such as Unity) to accurately mark the coordinates of the shelves (X, Y, Z), aisle widths (accuracy 0.1 meters), and equipment parking areas. The road planning model includes lane directions, speed limit signs, and charging pile locations, and supports dynamic path planning.

[0067] Data mapping and layering are implemented by assigning a unique UUID to each physical entity (e.g., shelf ID: S001, vehicle ID: V007), and binding sensor data (e.g., temperature and humidity of S001) to the corresponding shelf in the model through a data interface; the development of sub-models in the model layer includes: equipment operation model (AGV power consumption curve), goods flow model (inbound and outbound queue simulation), and personnel activity model (operation time probability distribution), and the state transition logic is constructed using Stateflow.

[0068] In some embodiments, before performing deep correlation analysis on the multi-source heterogeneous data, the method further includes: removing duplicate data and correcting format-incorrect data in the multi-source heterogeneous data using a data cleaning algorithm; converting multi-source heterogeneous data with different protocols into a standardized format based on a unified data interface; synchronizing the multi-source heterogeneous data in time using a timestamp alignment algorithm to generate a time-series aligned dataset; and detecting and repairing outlier data in the multi-source heterogeneous data using statistical methods or machine learning algorithms.

[0069] Before correlation analysis, data is cleaned, transformed, synchronized, and anomaly repaired to ensure data quality. This includes: Data cleaning: removing duplicate data (such as high-frequency misreading of the same RFID tag) and correcting format errors (such as unifying the timestamp format to ISO 8601); Protocol conversion: converting data from different protocols such as Modbus and Zigbee to JSON format through a unified data interface (such as MQTT / HTTP); Time synchronization: aligning the timestamps of multi-source data based on the NTP protocol to generate a time-series aligned dataset (with a unified time granularity of 1 second); Outlier repair: using statistical methods (Z-score) or machine learning (Isolation Forest) to detect outliers and repairing them through interpolation (linear interpolation) or model prediction.

[0070] Edge computing node processing utilizes a data preprocessing module deployed on a warehouse edge server to process sensor data streams in real time (e.g., processing 1000+ data points per second); duplicate data detection uses hash value comparison (e.g., RFID tag ID + timestamp combination) to retain the latest valid data; outlier repair uses a threshold set for shelf load-bearing data (e.g., triggering an early warning when exceeding 80% of the design load-bearing capacity), and outliers within the threshold are repaired using a moving average method.

[0071] In some embodiments, creating a digital twin model corresponding to a logistics site includes: using a digital twin modeling engine to train a dynamic model of site operation based on historical data, wherein the dynamic model includes at least a cargo transportation route planning model, a warehouse location allocation model, and an equipment energy consumption prediction model; optimizing the three-dimensional geometric model through lightweight processing technology to ensure the operating efficiency of the dynamic model under real-time data-driven conditions; and establishing a version management mechanism corresponding to the dynamic model to automatically trigger model version iteration based on the data update frequency.

[0072] By constructing a dynamic model based on historical data, the model's operating efficiency is optimized and version management is implemented. The dynamic model includes sub-models such as cargo transportation route planning, warehouse location allocation, and equipment energy consumption prediction, which are trained using historical data (such as transportation trajectory data from the past 3 months). Lightweight processing simplifies the triangular faces of the 3D geometric model (simplification rate ≤30%) to ensure real-time rendering efficiency (frame rate ≥30FPS). Version management automatically triggers model iteration based on data update frequency (such as hourly / daily) and records version change logs (such as v1.0→v1.1, updating the location allocation algorithm).

[0073] Model training and optimization include: Path planning model: trained using Dijkstra's algorithm combined with historical congestion data (from GPS trajectories) to generate a dynamic weighted road network; Cargo location allocation model: based on a genetic algorithm, inputting cargo attributes (weight, access frequency) and shelf status (load capacity, remaining space), and outputting the optimal cargo location coordinates; Energy consumption prediction model: using the XGBoost algorithm, inputting equipment operating data (power, duration), and predicting daily energy consumption (error rate ≤ 5%).

[0074] Lightweighting and version management: Lightweighting of BIM models is achieved by using Blender to remove non-critical details (such as shelf screw holes) and exporting them to GLTF format; Version management system: Built on GitLab, when the optimization target (personnel walking distance) of the location allocation model is improved by more than 5%, a new version is automatically marked and deployed.

[0075] In some embodiments, the deep correlation analysis of multi-source heterogeneous data to obtain potential correlations and pattern information among the multi-source heterogeneous data includes: using an association rule mining algorithm to analyze the correlation between cargo location data and warehouse environmental parameters, and identifying key environmental factors affecting cargo storage status; constructing a logistics entity relationship graph through a graph neural network model, wherein the nodes corresponding to the logistics entity relationship graph include cargo, vehicles, personnel, equipment, and storage locations, and edge weights represent the interaction frequency between entities, obtaining high-frequency interaction patterns and abnormal interaction behaviors; and using a time-series data analysis algorithm to identify the time-series dependency between vehicle driving trajectory and transportation time and logistics demand, and generating periodic logistics activity patterns.

[0076] Multiple algorithms are employed to mine data associations, including environmental factors, entity interactions, and temporal dependencies. These include: Association rule mining: identifying the association between the storage environment of goods (e.g., temperature and humidity) and the condition of goods (e.g., rust, mold) (confidence ≥ 80%); Graph Neural Network (GNN): constructing a heterogeneous graph containing goods, vehicles, personnel, equipment, and storage locations, with edge weights representing interaction frequency (e.g., the number of loading and unloading operations for "vehicle V001 - storage location L05"), and mining high-frequency interaction patterns (e.g., high-frequency use of equipment in a certain area during the morning rush hour); Temporal analysis: identifying the temporal dependency between transportation time and vehicle trajectory (e.g., the lag effect of traffic congestion on a certain road segment leading to subsequent extended transportation time), and generating periodic patterns (e.g., the peak logistics demand every Wednesday afternoon).

[0077] Algorithm applications and graph construction include: Association rules: The FP-Growth algorithm is used to analyze the "environmental parameters + cargo anomaly" dataset, setting a minimum support of 20% and a minimum confidence of 75%, and outputting key factors (e.g., "humidity > 80% + metal cargo → rust risk increased by 60%"); Heterogeneous graph construction: Node attributes include cargo weight, equipment type, and employee type, and edge attributes include interaction time and operation type. The graph is trained using the GraphSAGE algorithm, and the graph embedding vector is updated hourly; Time series analysis: The LSTM network is used to process vehicle trajectory sequence data. Given the trajectory of the past 30 minutes, the transportation time of the next 15 minutes is predicted (MAE ≤ 5 minutes).

[0078] In some embodiments, adjusting the parameters and structure of the digital twin model based on multi-source heterogeneous data and corresponding potential relationships and pattern information includes: automatically updating the threshold parameters of the warehouse environment sub-model and triggering equipment control strategy adjustment when abnormal environmental parameters of the cargo storage area are detected; optimizing the road network weight parameters of the cargo transportation route planning model and dynamically adjusting the route planning algorithm based on the high-frequency interaction patterns mined in the entity relationship graph; and increasing the data flow interface between the sub-models corresponding to the digital twin model or correcting the model hierarchy mapping relationship corresponding to the digital twin model through a model structure adaptive algorithm when it is found that the structure of the existing digital twin model cannot fit the newly mined relationships.

[0079] The model parameters and structure are dynamically adjusted based on the correlation analysis results, including threshold updates, path optimization, and adaptive model structure. Specifically: Parameter adjustment: When environmental parameters are abnormal (e.g., temperature > 30℃), the warning threshold of the warehouse environment sub-model is updated, and automatic control of the air conditioning equipment is triggered; Path optimization: Based on the high-frequency interaction patterns mined by GNN, the road network weights of the path planning model are adjusted (e.g., increasing the priority of high-frequency traffic segments); Structure adjustment: When new data correlations are added (e.g., a correlation between "personnel operation process - cargo damage rate" is discovered), data flow interfaces for sub-models (personnel activity model and cargo flow model) in the model layer are added.

[0080] Threshold and parameter updates include: Environmental sub-model preset thresholds: temperature 20-25℃, humidity 40-60%. When the humidity in a certain area exceeds 70% for 10 consecutive minutes, the storage risk level of goods in that area is automatically increased, and ventilation equipment is triggered. Path planning model: Every hour, based on the high-frequency path output by the GNN (e.g., the number of passages for "Warehouse A door → Shelf area 3 → Outlet 2" increases by 20%), the weight of that path is reduced by 15% (to avoid congestion). When correlation analysis reveals a correlation between "forklift operator fatigue (judged through behavior recognition) and cargo collision rate," a sub-module of "fatigue-operational error probability" is added to the personnel activity model, and a data interaction interface is established with the equipment operation model.

[0081] In some embodiments, acquiring logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data includes: using a time series forecasting model to generate logistics forecasting information for future periods based on historical logistics demand, transportation time, and inventory level data; generating intelligent configuration and scheduling information with the goal of maximizing logistics resource utilization through an intelligent optimization algorithm; and identifying inefficient links and generating optimization suggestion information including process refactoring suggestions and resource reallocation strategies based on a cost function model, combined with historical logistics cost data and business process data.

[0082] Three types of decision information are generated through predictive models, optimization algorithms, and cost analysis, including:

[0083] Logistics forecasting: Based on time series models, forecast demand, transportation time, and inventory levels (for the next hour / day / week); Intelligent scheduling: With resource utilization (vehicle loading rate ≥90%, shelf space utilization rate ≥85%) as the target, generate vehicle scheduling and cargo location allocation schemes; Optimization suggestions: Based on the cost function (total cost = equipment energy consumption + personnel cost + cargo damage cost), identify inefficient links (such as cargo damage rate in a certain area being higher than the average of 30%), and propose process reengineering suggestions (such as adjusting the cargo storage method in that area).

[0084] The prediction and optimization algorithms include: Demand forecasting: using the Transformer model, inputting historical orders, holiday calendars, and weather data, outputting hourly demand forecasts for each category of goods (MAPE≤10%); Storage location allocation: using the simulated annealing algorithm, with the objective function being "personnel walking distance + goods storage and retrieval efficiency", and re-optimizing storage locations after every 50 inbound and outbound operations; Cost analysis: establishing an ABC cost model, linking goods damage costs with storage environment and operational processes, and generating a cost heatmap (red areas represent high-cost areas).

[0085] In some embodiments, the step of completing digital twin management of logistics sites in a digital twin model based on the logistics forecast information, intelligent configuration and scheduling information, and optimization suggestion information includes: inputting the logistics forecast information into the digital twin model for scenario simulation to verify the feasibility of different scheduling schemes; displaying the logistics resource configuration status, equipment operating parameters, and abnormal event warning information in real time through the model visualization interface corresponding to the digital twin model; triggering an early warning mechanism and synchronously updating the scheduling scheme when the deviation between the actual operating data and the model forecast data exceeds a preset threshold; and generating a process optimization work order based on the optimization suggestion information and pushing it to the logistics management system to perform process refactoring operations.

[0086] Digital twin models enable scenario simulation, visual monitoring, anomaly alerts, and process optimization, including: Scenario simulation: Inputting predictive information to pre-simulate scheduling plans (e.g., simulating vehicle scheduling for the next 2 hours and assessing total mileage); Visual monitoring: Real-time display of resource configuration (vehicle location, shelf status), equipment parameters (AGV battery level, forklift speed), and abnormal events (flashing red markers); Anomaly handling: When the deviation between actual data and model predictions exceeds 15% (e.g., transportation time exceeds expectations), an alert is triggered and alternative solutions are automatically generated; Process optimization: Based on optimization suggestions, work orders are generated (e.g., "adjust the cargo classification of storage locations L08-L12") and pushed to the WMS system for execution.

[0087] Visualization and closed-loop control include: Digital twin interface: developed using WebGL technology, supporting 3D perspective switching, real-time refresh rate of 1 second / time, and abnormal event pop-up prompts (with handling suggestions); Deviation detection: comparing actual transportation time with predicted value every 5 minutes, and automatically retrieving the best solution for similar historical scenarios if the deviation exceeds 20%; Work order system: optimization suggestions are automatically generated into PDF work orders, including problem description (e.g., "load capacity of storage location L05 exceeds limit"), solution (e.g., "transfer overweight goods to reinforced racks"), and responsible department (warehousing department).

[0088] In some embodiments, the method further includes: constructing a dynamic decision-making model using a reinforcement learning algorithm, with the goal of maximizing the operational efficiency of the logistics site, using real-time operational data output by the digital twin model as state input, and intelligent configuration and scheduling strategies as action output, and generating an adaptive scheduling strategy through interactive training with the digital twin environment; the dynamic decision-making model is periodically updated and synchronized to the logistics management system to achieve dynamic optimization of vehicle scheduling, cargo allocation, and personnel tasks, wherein the efficiency of strategy optimization is improved through experience playback and reward function adjustment mechanisms during the training process.

[0089] A reinforcement learning (RL) framework is constructed with the goal of improving site operation efficiency (order processing volume / energy consumption ratio). Adaptive strategies are trained through interaction with a digital twin environment, including: a state space containing real-time data (inventory level, equipment status, personnel location) and model prediction data (peak demand time); an action space containing intelligent scheduling strategies (vehicle dispatch, storage location adjustment, personnel task allocation); and a reward function designed as "order processing efficiency × 0.6 - energy cost × 0.3 - damage rate × 0.1", with training efficiency optimized through experience replay buffer.

[0090] The training and deployment of the RL model includes: Algorithm selection: using the PPO (Proximal Policy Optimization) algorithm, with a 100-dimensional feature vector as the state input (including cargo location, equipment status, etc.) and discrete policies as the action output (such as 10 scheduling schemes); Training process: simulating different scenarios (peak / off-peak hours) in a digital twin environment, updating the policy once every 1000 interactions, with an experience replay pool capacity of 100,000 entries; Policy deployment: synchronizing the latest policy to the logistics management system every hour, dynamically adjusting vehicle scheduling (such as increasing the number of spare vehicles by 30% during peak hours) and cargo location allocation (centralizing the storage of high-frequency goods).

[0091] In some embodiments, to address the data privacy protection needs of logistics companies across multiple warehouses / parks, a federated learning (FL) framework is constructed to achieve collaborative training of "model moving while data remains stationary" across different sites. This solves the data silo and privacy leakage problems of traditional centralized modeling, including: Federated mechanism design: Each logistics site preprocesses data locally (such as anonymized equipment energy consumption and order volume), and aggregates model gradients through an encrypted parameter server to avoid uploading raw data; Differentiated modeling: A personalized layer (such as special parameters of the shelf layout in site A) is introduced during the federated learning process to balance the generalization of the global model with the adaptability of the local scenario; Enhanced privacy protection: Homomorphic encryption (HE) and differential privacy (DP) technologies are used to ensure the security of parameters during gradient aggregation.

[0092] The system architecture includes: Edge layer: Local FL nodes are deployed in each warehouse, including a data preprocessing module (cleaning, de-identification, retaining 90% of features but replacing specific warehouse location IDs) and a sub-model training module (such as LSTM predicting local inventory); Central server: The FedAvg algorithm is used to aggregate model parameters from each site. Laplacian noise is added to the gradient before each aggregation round (privacy budget ε=0.5), and the aggregation cycle is once per hour; Personalized adaptation: Based on the global model (such as an order demand prediction model), a fully connected layer is added to each site as a local adaptation layer. When parameters are updated, the local layer weight update rate is set to 0.8 (higher than the global layer's 0.2). Application scenario: Cross-regional warehouse collaborative training of an "inventory turnover rate prediction model". When a site is out of stock, the global model can be called in combination with local historical data to generate replenishment suggestions. The prediction accuracy is improved by 12% compared to independent modeling, and the risk of data leakage is reduced by 95%.

[0093] In some embodiments, a knowledge graph (KG) in the logistics domain is constructed to integrate multi-dimensional entity relationships such as cargo attributes, equipment status, personnel skills, and environmental parameters, enabling intelligent reasoning and decision support. This includes: multi-source knowledge fusion: structuring RFID cargo tag data, equipment operation and maintenance manuals, and historical abnormal event reports and importing them into the graph to construct a "cargo-storage conditions-equipment-personnel" association network; inference engine development: implementing path reasoning based on graph neural networks (GNNs) (e.g., "cargo A (lithium battery) → prohibited storage environment (high temperature) → associated equipment (explosion-proof air conditioner) → operating procedures (regular inspection)"), supporting real-time anomaly tracing and compliance checks; dynamic knowledge updates: automatically parsing new equipment manuals through natural language processing (NLP) and incrementally updating the entity attributes of the graph (e.g., adding charging power parameters for new AGV models).

[0094] The knowledge graph construction process includes: Entity extraction: Using the BERT-NER model to extract entities (goods category, equipment model, and operation specification keywords) from logistics documents and equipment manuals, with an accuracy of 92%; Relationship modeling: Defining 50+ relationship types such as "stored in," "requires maintenance," and "related operations," and storing them using the Neo4j graph database, with an estimated node scale of 100,000+ (30,000+ goods, 20,000+ equipment, and 10,000+ personnel); Inference application: When a camera detects personnel using non-explosion-proof tools in the lithium battery storage area, graph inference triggers an automatic response of "violation of regulations → high fire risk → linkage with access control to lock the area + push safety procedures"; Visual decision support: Developing a graph query interface that supports natural language queries (such as "query all goods that require constant temperature storage and have a recent loss rate >5% and their associated storage equipment"), with a response time of ≤2 seconds.

[0095] In some embodiments, to address the modeling challenge of lacking historical data for newly built logistics sites, transfer learning (TL) technology is used to transfer the parameters of digital twin models from mature sites to new sites, achieving "zero-sample rapid initialization + local data fine-tuning." This includes: cross-domain feature alignment: aligning environmental features (such as similarity in shelf layout and differences in equipment type) between old and new sites using adversarial transfer networks (ATN) to reduce modeling errors caused by distribution differences; hierarchical transfer strategy: directly reusing the bottom-level geometric model (warehouse structure), transferring the backbone network and fine-tuning the end parameters of the mid-level dynamic model (such as path planning), and completely reconstructing the top-level application model (anomaly detection); and small-sample incremental learning: after deployment at the new site, model personalization can be completed with only 72 hours of real-time data (reducing the modeling cycle by 80% compared to traditional methods).

[0096] The transfer learning architecture design includes: Source domain model: Selecting a digital twin model of a mature warehouse of the same type (such as an automated three-dimensional warehouse model), containing 3 million+ training parameters; Feature alignment layer: Adding domain-adversarial training to the model input layer, and using a gradient reversal layer to confuse the distribution of data from the old and new sites, achieving an alignment accuracy of ≥85%; Fine-tuning strategy: In the initial stage of deployment at the new site, only 1000+ pieces of equipment operation data are collected in the first 24 hours to fine-tune the road network weight parameters of the path planning model (learning rate set to 0.001), while freezing the geometric model parameters.

[0097] In some embodiments, to address the complexity of collaborative scheduling of multiple devices (forklifts, AGVs, sorting robots), a closed-loop system of "digital twin simulation environment + reinforcement learning (RL) decision-making" is constructed to achieve millisecond-level dynamic optimization. This includes: Twin environment modeling: accurately simulating the kinematic characteristics of the devices (such as the turning radius of AGVs and the load acceleration of forklifts) in the digital twin, and constructing a high-fidelity simulation space containing 200+ state variables; Layered RL architecture: the top-level policy network (PPO algorithm) outputs a global scheduling scheme (such as task allocation priority), and the bottom-level control network (PID algorithm) processes the details of device motion, reducing the dimension of the action space; Virtual-real interaction optimization: generating virtual experience data (with an average daily increase of 100,000+ new data entries) by rehearsing RL strategies in the digital twin (such as simulating device scheduling under heavy rain), solving the problem of insufficient data in real scenarios.

[0098] The joint training process includes: Twin environment construction: using the Gazebo physics engine to simulate equipment movement with centimeter-level accuracy (e.g., AGV positioning error ≤ 2cm), supporting multi-device collision detection; RL network design: state input includes equipment position (XY coordinates), task queue (priority, distance), and energy consumption status (battery percentage), totaling 150 dimensions; action output is equipment scheduling instructions (e.g., "AGV3 goes to location A2 to pick up goods"), totaling 80 discrete actions; experience utilization: using Experience ReplayBuffer to store virtual and real experience, with virtual data accounting for 70%, and balancing data distribution through importance sampling (IS), improving training efficiency by 3 times. Real-time scheduling application: during peak hours (order volume ≥ 200 orders / hour), the system updates the scheduling strategy every 10 seconds. Compared to traditional rule-based scheduling, equipment idle time is reduced by 25%, and task completion time is shortened by 18%.

[0099] In some embodiments, a spatiotemporal graph convolutional network (ST-GCN) model is designed to address the spatiotemporal correlation of abnormal logistics events (such as cargo backlog and equipment failure). This model integrates time series and spatial location features to achieve advanced prediction, including: spatiotemporal feature modeling: constructing a logistics entity location map in the spatial dimension (nodes are shelves and equipment, and edges are physical distances), and using LSTM to capture sequence dependencies in the temporal dimension to achieve joint modeling of "spatial proximity + temporal periodicity"; abnormal pattern recognition: distinguishing between normal and abnormal spatiotemporal trajectory patterns through contrastive learning (e.g., a forklift remaining stationary in a non-operational area for a long time is considered abnormal), supporting multi-scale early warning (minute-level / hour-level); and interpretability enhancement: using Grad-CAM to visualize key spatiotemporal regions (e.g., when the load on a certain shelf area increases abnormally for 30 consecutive minutes, highlighting the area and its surrounding equipment).

[0100] Model architecture and training include: Spatiotemporal graph construction: node attributes include shelf load capacity, equipment status, and goods inventory, with edge weights based on Euclidean distance (edges within a 5-meter threshold), constructing an undirected graph with 500+ nodes; ST-GCN model: containing two spatiotemporal convolutional layers (spatial kernel size 3, temporal window length 12), outputting anomaly probability values ​​(threshold ≥ 0.8 triggers an alert), training data using a year's worth of anomaly event logs (3000+ positive samples, 100,000+ negative samples); Contrastive learning strategy: randomly adding noise to normal trajectories to generate negative samples, maximizing the feature differences between positive and negative samples using the InfoNCE loss function, improving anomaly recognition accuracy to 94%. Alert application scenario: when ST-GCN detects that "the load capacity of three adjacent shelves in shelf area B increases continuously within 15 minutes, and the forklift dwell time in the corresponding area exceeds twice the normal average," it issues a 30-minute advance warning of goods backlog risk and automatically assigns a sorting robot to handle the situation.

[0101] In some embodiments, a series of advanced technologies are employed for real-time big data processing to ensure the efficient collection, storage, and analysis of massive amounts of logistics data. In the data collection phase, technologies such as sensors, RFID (Radio Frequency Identification), and GPS (Global Positioning System) are comprehensively utilized to achieve full and real-time collection of various data, including cargo location, transportation status, vehicle trajectory, and warehouse environmental parameters, during the logistics process. For example, installing GPS sensors and onboard monitoring equipment on transport vehicles enables real-time acquisition of information such as vehicle location, speed, and route; deploying temperature and humidity sensors, smoke sensors, etc., inside the warehouse allows for real-time monitoring of the warehouse environment, ensuring the safety of stored goods.

[0102] For data storage, a combination of distributed file systems (such as the Hadoop Distributed File System, HDFS) and NoSQL databases (such as Cassandra and MongoDB) is used. HDFS offers high fault tolerance and scalability, enabling the distributed storage of massive amounts of data across multiple nodes, ensuring data reliability and security. NoSQL databases, with their flexible data models and efficient read / write performance, meet the diverse and real-time storage needs of logistics data. For example, logistics order data can be stored in MongoDB, utilizing its document-oriented data structure for convenient and rapid querying and updating of order information. Large amounts of time-series data collected by sensors can be stored in Cassandra, leveraging its distributed architecture and efficient read / write performance for rapid data storage and retrieval.

[0103] In the data processing and analysis phase, stream computing frameworks (such as Apache Flink) and batch processing frameworks (such as Apache Hadoop MapReduce) are employed. Apache Flink can process real-time data streams, enabling real-time data analysis and early warning. For example, by analyzing the driving data of transport vehicles in real time, Flink can issue timely warnings when abnormal vehicle speeds or deviations from planned routes are detected, allowing managers to take appropriate measures. Apache Hadoop MapReduce is used for batch processing and in-depth analysis of historical data to uncover its potential value. For instance, by analyzing logistics order data from the past year, MapReduce can identify patterns in logistics demand across different regions and time periods, providing a basis for the rational allocation of logistics resources. Furthermore, data mining algorithms (such as association rule mining and cluster analysis) are used to deeply mine logistics data, discovering relationships and potential patterns between data points, and providing more targeted optimization suggestions for logistics operations.

[0104] In the digital age, digital twin technology has brought new changes and development opportunities to various industries. Among them, the self-service creation technology of digital twin sites, with its unique advantages, has become a powerful tool for improving efficiency and optimizing management in many fields.

[0105] This technology innovatively integrates a self-service site drawing engine with auxiliary tools. The self-service site drawing engine employs advanced vector graphics algorithms to accurately process and display various graphic elements, ensuring smooth lines and accurate graphics. Simultaneously, it utilizes an event-driven interaction design concept to quickly respond to user mouse clicks, drags, and other operation commands. The auxiliary tools leverage machine learning technology to achieve intelligent annotation, automatically identifying and annotating various common site elements, such as building outlines, road layouts, and facility distributions, through learning from large amounts of site data. Furthermore, it utilizes a template library to provide a rich selection of preset templates covering various scenarios and industries, lowering the barrier to entry for drawing, allowing even those without professional drawing skills to quickly get started. When the site changes, users can quickly edit and modify maps using this tool combination, improving their ability to respond to dynamic site changes and significantly increasing work efficiency.

[0106] Real-time preview technology utilizes the WebSocket real-time data transmission protocol to achieve instant data transmission. Combined with a dynamic front-end update mechanism, it generates a visual preview effect simultaneously during the user's map drawing or editing process. This allows users to see the results in real time, adjust their drawing strategies and details promptly, and improve drawing accuracy and efficiency. Rendering acceleration technology employs ray tracing algorithms to simulate light propagation paths, achieving more realistic lighting effects. Simultaneously, it utilizes parallel computing technology, leveraging multi-core processors to process rendering tasks in parallel, significantly reducing rendering time while maintaining high-quality rendering effects. Even complex, large-scale site maps can be rendered with high quality in a short time, providing users with a clear and realistic digital twin scene display, facilitating intuitive understanding and analysis of site information.

[0107] Version management is based on a relational database management system, such as MySQL or Oracle, to store map data and version information. It employs timestamp and operation log recording technology, automatically recording detailed information such as the time, content of the modification, and the operator each time the map is modified. Utilizing version control algorithms, such as the distributed version control algorithm used by Git, it achieves effective management of different versions. Users can view historical versions at any time, compare differences, and understand the entire process of site changes. This feature ensures orderly management of different versions, providing strong support for retrospective analysis and meeting the needs of various scenarios such as compliance review and project debriefing, ensuring data integrity and traceability.

[0108] This method fully leverages artificial intelligence and machine learning algorithms to achieve intelligent prediction, intelligent scheduling, and intelligent decision-making, thereby improving the intelligence level and efficiency of logistics operations. In terms of intelligent prediction, it utilizes machine learning algorithms such as time series analysis and neural networks to conduct in-depth analysis and mining of logistics data, predicting key indicators such as future logistics demand, transportation time, and inventory levels. For example, by analyzing historical order data using time series analysis algorithms, it predicts the order volume of different regions and categories of goods over a future period, enabling advance inventory preparation and logistics resource allocation. Using neural network algorithms, combined with vehicle driving data, traffic information, and weather data, it predicts the transportation time of goods, providing customers with more accurate logistics timeliness commitments.

[0109] Intelligent scheduling is one of the key applications of this method. By employing optimization algorithms such as genetic algorithms and simulated annealing, combined with real-time logistics data and business rules, intelligent scheduling and optimized allocation of logistics resources can be achieved. For example, in vehicle scheduling, based on information such as delivery needs, vehicle location and load capacity, and traffic conditions, genetic algorithms are used to solve for the optimal vehicle scheduling scheme, ensuring that vehicles meet delivery requirements while minimizing total transportation costs and maximizing transportation efficiency. In warehouse location scheduling, based on factors such as the frequency of goods entering and leaving the warehouse, volume, and weight, simulated annealing algorithms are used to optimize the layout of storage locations, improving warehouse space utilization and the efficiency of goods entering and leaving the warehouse.

[0110] In terms of intelligent decision-making, the method utilizes machine learning algorithms to construct decision models, providing scientific decision support for logistics operations. For example, by building a risk assessment model and combining logistics data and market information, it can conduct real-time assessment and early warning of risks in the logistics transportation process, helping companies to formulate risk response strategies in advance. Machine learning algorithms are used to analyze logistics cost data and business process data to identify high-cost and low-efficiency links, providing optimization suggestions for companies and achieving effective control of logistics costs and improved operational efficiency. Furthermore, natural language processing technology is used to automatically analyze and understand logistics text data (such as customer feedback and logistics reports), extracting valuable information to provide reference for corporate decision-making.

[0111] Please see Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a site digital twin management device 200 based on IoT combined with AI visual recognition technology, provided in an embodiment of this application. This site digital twin management device 200 is used to execute the steps of the site digital twin management method based on IoT combined with AI visual recognition technology shown in the above embodiments. The site digital twin management device 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0112] like Figure 2 As shown, the site digital twin management device 200 based on IoT combined with AI visual recognition technology includes:

[0113] The data acquisition unit 201 is used to collect multi-source heterogeneous data in the logistics site through sensors, RFID devices, GPS devices and cameras deployed in the logistics site. The multi-source heterogeneous data includes at least two of the following: cargo location, transportation status, vehicle driving trajectory, warehouse environmental parameters, cargo category and quantity and personnel movement path.

[0114] Model creation unit 202 is used to create a digital twin model corresponding to the logistics site; to perform deep correlation analysis on multi-source heterogeneous data to obtain potential correlation relationships and pattern information between multi-source heterogeneous data; and to adjust the parameters and structure of the digital twin model according to the multi-source heterogeneous data and the corresponding potential correlation relationships and pattern information.

[0115] The management completion unit 203 is used to acquire logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data, so as to complete the digital twin management of logistics sites in the digital twin model based on the logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information; wherein, the logistics forecasting information includes forecasting logistics demand, forecasting transportation time, and forecasting inventory levels; the intelligent configuration and scheduling information includes intelligent scheduling and optimized configuration of logistics resources corresponding to vehicles and warehouse locations; and the optimization suggestion information includes forecasting logistics costs and forecasting business process data, as well as corresponding optimization suggestion strategies.

[0116] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the site digital twin management device and its modules based on IoT combined with AI visual recognition technology described above can be referred to the corresponding processes in the site digital twin management method embodiments based on IoT combined with AI visual recognition technology described above, and will not be repeated here.

[0117] The aforementioned site digital twin management method based on IoT combined with AI visual recognition technology can be implemented as a computer program, which can be used in, for example... Figure 2 It runs on the device shown.

[0118] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0119] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any site digital twin management method based on IoT combined with AI visual recognition technology.

[0120] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0121] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any site digital twin management method based on IoT combined with AI visual recognition technology.

[0122] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0124] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0125] By deploying sensors, RFID devices, GPS devices and cameras in the logistics site, multi-source heterogeneous data is collected. The multi-source heterogeneous data includes at least two of the following: cargo location, transportation status, vehicle driving trajectory, warehouse environmental parameters, cargo type and quantity, and personnel movement path.

[0126] Create a digital twin model corresponding to the logistics site; conduct in-depth correlation analysis on multi-source heterogeneous data to obtain potential correlations and pattern information between multi-source heterogeneous data; adjust the parameters and structure of the digital twin model according to the multi-source heterogeneous data and the corresponding potential correlations and pattern information;

[0127] The system acquires logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information corresponding to multi-source heterogeneous data, so as to complete the digital twin management of logistics sites in the digital twin model based on the logistics forecasting information, intelligent configuration and scheduling information, and optimization suggestion information. Among them, the logistics forecasting information includes forecasting logistics demand, forecasting transportation time, and forecasting inventory levels; the intelligent configuration and scheduling information includes intelligent scheduling and optimized configuration of logistics resources corresponding to vehicles and warehouse locations; and the optimization suggestion information includes forecasting logistics costs and forecasting business process data, as well as corresponding optimization suggestion strategies.

[0128] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the site digital twin management method based on IoT combined with AI visual recognition technology provided in the above embodiments of this application.

[0129] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A site digital twin management method based on IOT combined with AI visual recognition technology, characterized in that, The method comprises the following steps: Collecting multi-source heterogeneous data in the logistics site by deploying sensors, RFID devices, GPS devices and cameras in the logistics site, the multi-source heterogeneous data including at least two of the following: cargo location, transportation status, vehicle driving trajectory, warehouse environment parameters, cargo category quantity and personnel action path; Creating a digital twin model corresponding to the logistics site, including: training a dynamics model of site operation based on historical data by using a digital twin modeling engine, the dynamics model including at least a cargo transportation path planning model, a warehouse storage location allocation model and a device energy consumption prediction model; optimizing a three-dimensional geometric model by using a lightweight processing technology to ensure the running efficiency of the dynamics model under real-time data driving; establishing a version management mechanism corresponding to the dynamics model, and automatically triggering model version iteration based on data update frequency; performing deep correlation analysis on the multi-source heterogeneous data to obtain potential correlation relationships and pattern information among the multi-source heterogeneous data, including: using an association rule mining algorithm to analyze the correlation between cargo location data and warehouse environment parameters, and identifying key environmental factors affecting cargo storage status; constructing a logistics entity relationship graph by using a graph neural network model, the nodes of the logistics entity relationship graph including cargo, vehicle, personnel, device and storage location, and the edge weight representing the interaction frequency between entities to obtain high-frequency interaction patterns and abnormal interaction behaviors; using a time series data analysis algorithm to identify the time sequence dependence relationship between the vehicle driving trajectory, transportation time and logistics demand, and generating a periodic logistics activity pattern; adjusting the parameters and structure of the digital twin model according to the multi-source heterogeneous data and the corresponding potential correlation relationships and pattern information, including: when detecting that the environmental parameters of the cargo storage area are abnormal, automatically updating the threshold parameters of the warehouse environment sub-model and triggering device control strategy adjustment; optimizing the road network weight parameters of the cargo transportation path planning model based on the high-frequency interaction patterns mined in the entity relationship graph, and dynamically adjusting the path planning algorithm; when it is found that the structure of the existing digital twin model cannot fit the newly mined correlation relationship, adding a data flow interface between the sub-models corresponding to the digital twin model or modifying the model level mapping relationship corresponding to the digital twin model by using a model structure self-adaptive algorithm; Obtaining logistics prediction information, intelligent configuration and scheduling information and optimization suggestion information corresponding to the multi-source heterogeneous data to complete digital twin management of the logistics site in the digital twin model according to the logistics prediction information, intelligent configuration and scheduling information and optimization suggestion information; wherein the logistics prediction information includes predicted logistics demand, predicted transportation time and predicted inventory level; the intelligent configuration and scheduling information includes intelligent scheduling and optimized configuration of logistics resources corresponding to vehicles and warehouse storage locations; the optimization suggestion information includes predicted logistics cost, predicted business process data and corresponding optimization suggestion strategy.

2. The method of claim 1, wherein, The method for collecting multi-source heterogeneous data in the logistics site by deploying sensors, RFID devices, GPS devices and cameras in the logistics site comprises the following steps: Collecting warehouse environment parameters in real time by using sensors, the warehouse environment parameters including temperature, humidity, light intensity and shelf load data; Reading the cargo tag information through the RFID device to obtain the cargo category quantity and the corresponding cargo location; Real-time tracking the geographical position of the transport vehicle and the mobile device based on the GPS device to generate the transport status and the vehicle driving track; Collecting image and video data through the camera and analyzing them into personnel action path, cargo stacking status and abnormal behavior event data through the AI visual recognition algorithm.

3. The method of claim 1, wherein, The creation of the digital twin model corresponding to the logistics site includes: Constructing a geometric model of the logistics site based on three-dimensional modeling technology, including warehouse layout, road planning, shelf distribution and equipment location; Establishing a multi-dimensional dynamic data mapping mechanism to bind the multi-source heterogeneous data collected by sensors, RFID devices, GPS devices and cameras to the corresponding entities of the geometric model; Constructing a hierarchical architecture model, including at least a physical layer, a data layer, a model layer and an application layer, wherein the model layer includes sub-models of device operation, cargo flow and personnel activity.

4. The method of claim 1, wherein, Before the deep correlation analysis of the multi-source heterogeneous data, it also includes: Eliminating duplicate data in the multi-source heterogeneous data and correcting format error data through a data cleaning algorithm; Converting multi-source heterogeneous data of different protocols into a standardized format based on a unified data interface; Synchronizing the multi-source heterogeneous data in time through a timestamp alignment algorithm to generate a time series aligned data set; Detecting and repairing abnormal value data in the multi-source heterogeneous data using statistical methods or machine learning algorithms.

5. The method of claim 1, wherein, The acquisition of logistics prediction information, intelligent configuration and scheduling information and optimization suggestion information corresponding to the multi-source heterogeneous data includes: Using a time series prediction model to generate logistics prediction information for future periods based on historical logistics demand, transport time and inventory level data; Through an intelligent optimization algorithm, generate intelligent configuration and scheduling information with the goal of maximizing logistics resource utilization; Based on the cost function model, combining historical logistics cost data and business process data, identify inefficient links and generate optimization suggestion information including process reconstruction suggestions and resource reallocation strategies.

6. The method of claim 1, wherein, The digital twin management of the logistics site in the digital twin model according to the logistics prediction information, intelligent configuration and scheduling information and optimization suggestion information includes: Input the logistics prediction information into the digital twin model for scenario simulation to verify the feasibility of different scheduling schemes; Real-time display of logistics resource configuration status, device operation parameters and abnormal event warning information through the model visualization interface corresponding to the digital twin model; When the deviation between actual running data and model prediction data exceeds the preset threshold, trigger the warning mechanism and update the scheduling scheme synchronously; Generate process optimization work orders based on optimization suggestion information and push them to the logistics management system to perform process reconstruction operations.

7. The method of claim 1, wherein, The method further includes: Constructing a dynamic decision-making model using reinforcement learning algorithm, with the goal of maximizing the running efficiency of the logistics site, taking the real-time running data output by the digital twin model as the state input and the intelligent configuration and scheduling strategy as the action output, through the interaction with the digital twin environment, generate adaptive scheduling strategy; The dynamic decision model is periodically updated and synchronized to the logistics management system, realizing dynamic optimization of vehicle scheduling, cargo location allocation and personnel tasks, wherein the strategy optimization efficiency is improved through experience replay and reward function adjustment mechanism in the training process.

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