Airport baggage flow swarm intelligence ai-driven digital twin low-carbon scheduling system

Abstract

The application relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligence AI-driven digital twin low-carbon scheduling system, which comprises a physical perception layer, a digital twin engine layer, a group intelligence decision layer, a blockchain storage layer and a dynamic scheduling execution layer, forms a closed loop of "perception-modeling-decision-storage-execution", collects equipment and luggage state data through the physical perception layer, constructs a full-element twin body through the digital twin engine layer, realizes physical system synchronous mapping and carbon accounting, adopts an improved carbon-sensitive ant colony algorithm through the group intelligence decision layer, generates a multi-objective optimization strategy of total energy consumption, retention time and load balancing, guarantees carbon data credibility and traceability through the blockchain storage layer, and converts the decision into a control signal through the dynamic scheduling execution layer and corrects simulation and actual deviation. The application realizes luggage scheduling low carbonization, high efficiency and reliability, and is suitable for green operation of a smart airport.

Description

Technical Field

[0001] This invention relates to the field of intelligent airport logistics technology, specifically to a digital twin low-carbon scheduling system driven by intelligent AI for airport baggage flow. Background Technology

[0002] Current airport baggage handling systems largely rely on manual experience or simple automated scheduling, which has significant limitations:

[0003] Low operational efficiency: Traditional scheduling strategies are mostly based on fixed path planning, which makes it difficult to respond in real time to dynamic scenarios such as baggage flow fluctuations and sudden equipment failures. This can easily lead to problems such as chute blockage and empty conveyor belts, increasing baggage dwell time and affecting flight connection efficiency.

[0004] Lack of carbon emission control: The energy consumption and carbon emissions of equipment operation (such as conveyor belts and robots) are not included in the core objectives of scheduling decisions. The "full load operation" mode is often adopted, resulting in ineffective energy consumption (such as idling and overload). Furthermore, there is a lack of accurate carbon footprint accounting mechanism, which makes it difficult to support the airport's carbon neutrality target.

[0005] Insufficient equipment coordination: Each processing unit (sorting area, transfer area, robot operation area) operates independently, lacking global collaborative optimization, resulting in uneven equipment load. Some equipment has a shortened service life due to long-term overload, and maintenance costs remain high.

[0006] Furthermore, the limitations of existing technologies in terms of intelligence and decarbonization are as follows:

[0007] The application of digital twin technology is insufficient: Although some airports have introduced digital twin technology, it is mostly limited to the level of equipment status monitoring and has not achieved deep integration with scheduling decisions. It is impossible to predict the energy consumption and carbon emission impact of scheduling strategies through virtual simulation, resulting in "disconnect between simulation and execution".

[0008] Insufficient adaptability of swarm intelligence algorithms: Existing swarm intelligence algorithms (such as traditional ant colony and genetic algorithms) mostly prioritize "efficiency" in baggage scheduling and do not embed carbon-sensitive decision-making mechanisms, making it difficult to balance the contradiction between "efficiency" and "low carbon".

[0009] Carbon data has poor credibility and traceability: carbon emission data mostly rely on manual statistics or single device recording, which poses a risk of data tampering and lacks a full-process evidence storage and auditing mechanism, failing to meet the needs of carbon quota management, third-party compliance audits, etc.

[0010] Weak dynamic response and fault tolerance: Traditional systems lack real-time correction mechanisms to address “simulation-actual deviations” caused by sensor errors, equipment aging, etc.; when a fault occurs, redundancy switching relies on manual intervention, which can easily cause interruptions in the processing flow.

[0011] Therefore, a digital twin low-carbon scheduling system driven by AI for airport baggage flow is proposed to address the above issues. Summary of the Invention

[0012] The purpose of this invention is to provide a digital twin low-carbon scheduling system driven by AI for airport baggage flow to solve the problems mentioned in the background art.

[0013] To achieve the above objectives, the present invention provides the following technical solution:

[0014] An AI-driven digital twin low-carbon scheduling system for airport baggage flow includes:

[0015] The physical sensing layer consists of RFID scanners, current sensors, and laser rangefinders deployed in the baggage handling area, which collects real-time data such as conveyor belt speed, robot joint angles, chute blockage rate, and baggage three-dimensional coordinates.

[0016] The digital twin engine layer constructs a full-element twin including a physical model of the equipment, a baggage flow network model, and a carbon accounting model, and achieves synchronous mapping of the physical system through real-time data-driven processes.

[0017] The swarm intelligence decision-making layer consists of intelligent agent modules distributed across various processing units, and uses an improved carbon-sensitive ant colony algorithm to generate scheduling strategies.

[0018] The blockchain-based evidence storage layer uses Hyperledger architecture to store energy consumption and luggage carbon footprint data.

[0019] The dynamic scheduling execution layer converts decision commands into equipment control signals and dynamically corrects strategies based on twin simulation results.

[0020] As a preferred option, the digital twin engine layer includes:

[0021] Equipment-level mirroring module: Establishes a conveyor belt power calculation model. ,in, For the real-time power of the conveyor belt, The coefficient of air resistance and mechanical friction, The coefficient of friction under load, For conveyor belt speed, The total mass of the luggage on the conveyor belt;

[0022] Carbon accounting module: Calculates the carbon footprint of a single piece of luggage. ,in, Carbon footprint per piece of luggage The total number of devices to handle this luggage, For equipment average power, For equipment The processing time for this baggage, This represents the carbon intensity factor of the power grid.

[0023] As a preferred approach, the swarm intelligence decision-making layer employs a global optimization objective function:

[0024] ,in, , , Adjustable weighting coefficients , The total number of devices. For equipment Total energy consumption integral For luggage The length of stay It is a function of standard deviation. For equipment The workload.

[0025] As a preferred option, the improved carbon-sensitive ant colony algorithm includes:

[0026] Path selection probability calculation: ,in, For intelligent agents Select path The probability, For path pheromone concentration, As a factor of pheromone importance, Path heuristic value ( The path distance. For nodes The degree of congestion, To inspire the importance factor of information, For carbon fitness factor, The carbon sensitivity coefficient, For path carbon emissions, This is the baseline value for carbon emissions along the path. The dynamic carbon sensitivity coefficient, For nodes The set of adjacent nodes, For path pheromone concentration;

[0027] Pheromones update rules: ,in, For path pheromone concentration, It is the pheromone volatile factor and 0 < , The total number of agents. For intelligent agents In the path The increase in pheromones released from the upper body Carbon learning rate factor, The total amount of pheromones is a constant. This represents the optimal carbon emission threshold for the system.

[0028] As a preferred approach, the dynamic carbon sensitivity coefficient is adjusted through a feedback mechanism:

[0029] ,in, for Carbon sensitivity coefficient at time, To adjust the step size coefficient, for Average carbon intensity over time period For airport carbon emission targets, This represents the tolerance range for carbon emissions.

[0030] As a preferred solution, the blockchain evidence storage layer implements:

[0031] A Merkle tree is constructed to store carbon data, with its root node generated by the hash concatenation of all device energy consumption data and luggage carbon footprint data.

[0032] Using zero-knowledge proof technology, we can verify whether the total carbon emissions of luggage meet the preset limit without disclosing detailed data.

[0033] As a preferred option, the dynamic scheduling execution layer includes a policy correction module:

[0034] The deviation of the comprehensive optimization index of the real-time computing system is the absolute value of the difference between the twin predicted value and the actual value divided by the predicted value.

[0035] When the deviation exceeds the threshold, adjust the energy consumption weighting coefficient α and the initial carbon sensitivity coefficient. α is adjusted proportionally based on the sign of the energy consumption deviation. Adjustments are made proportionally based on the carbon emission deviation rate.

[0036] As a preferred solution, the swarm intelligence decision-making layer implements a collaborative optimization mechanism:

[0037] Design an excitation function based on carbon integral: ,in, For intelligent agents carbon integral, This refers to the carbon emission reduction per unit of time. This is the time decay factor;

[0038] The Byzantine fault-tolerant consensus algorithm is adopted, which requires that the decision vector of each agent obtains the consent of at least twice the number of faulty nodes plus one node among its neighboring nodes.

[0039] As a preferred approach, a robustness optimization module is embedded in the digital twin engine layer:

[0040] The goal of constructing an anti-interference scheduling model is to minimize the expected value of the scheduling policy under disturbance scenarios plus the risk coefficient multiplied by the 95% confidence level.

[0041] Generate a Pareto optimal solution set consisting of policies that satisfy the following condition: no other policy is non-inferior to this policy on all optimization objectives and strictly superior to it on at least one objective.

[0042] As a preferred approach, the improved carbon-sensitive ant colony algorithm employs a convergence guarantee mechanism:

[0043] Define the potential game model.

[0044] ,in, Let be the potential function. The set of decisions made by the agent. For equipment number, For equipment energy consumption For luggage The length of stay , Number the path nodes. For path pheromone concentration;

[0045] Prove that the cross-partial derivatives of the potential function with respect to the decisions of any two agents are non-negative;

[0046] Set convergence criteria: ,in, It is a 2-norm. for The pheromone concentration vector at time t, for The pheromone concentration vector at time t, Let be the convergence accuracy constant. This is the convergence rate factor.

[0047] As can be seen from the technical solution provided by the present invention above, the beneficial effects of the airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system provided by the present invention are:

[0048] I. Strengthen low-carbon emission reduction capabilities to support dual-carbon goals:

[0049] Precise carbon footprint management: Relying on the carbon accounting module of the digital twin engine and blockchain storage technology, the carbon footprint of a single piece of baggage can be tracked and verified throughout the entire process, ensuring the credibility of carbon data and providing a reliable basis for airport carbon quota management;

[0050] Carbon-sensitive decision-making drives emission reduction: The carbon-sensitive ant colony algorithm of the swarm intelligence decision-making layer prioritizes low-carbon paths through carbon fitness factors and dynamic adjustment mechanisms, effectively reducing system carbon emissions and directly supporting the airport's carbon neutrality goal.

[0051] Collaborative optimization of energy consumption and carbon emissions: The global optimization goal balances energy consumption and efficiency through dynamic weighting, avoiding ineffective carbon emissions caused by equipment idling or overloading, and achieving collaborative operation of low carbon and high efficiency;

[0052] II. Improve processing efficiency and reduce operating costs:

[0053] Reduce baggage delays: By combining improved ant colony algorithms with digital twin congestion simulation, route congestion can be avoided in advance, improving baggage on-time performance and reducing problems caused by delays;

[0054] Balanced equipment load: Through load balancing optimization and dynamic correction mechanisms, local equipment overload is avoided, equipment lifespan is extended, and maintenance costs are reduced;

[0055] Improve decision-making efficiency: The collaborative architecture and fault-tolerant consensus mechanism of distributed intelligent agents accelerate decision-making response speed and can adapt to the baggage handling needs during peak periods;

[0056] III. Enhance system reliability and adapt to complex operating conditions:

[0057] Dynamic correction of system deviation: The deviation calculation and parameter calibration mechanism of the dynamic scheduling execution layer can compensate for the deviation between the simulation and the physical system in real time, ensuring the accuracy of strategy execution;

[0058] Enhanced anti-interference and emergency response capabilities: Robust optimization models and redundancy switching mechanisms reduce performance loss and quickly restore processing flow to ensure continuity when equipment fails or traffic fluctuates.

[0059] Achieve multi-scenario adaptability: By dynamically adjusting weights and using Pareto optimal solution sets, adapt to different scenario requirements (such as peak-time priority efficiency, off-peak-time priority low carbon), and improve system adaptability;

[0060] IV. Building a trusted data foundation to support end-to-end management:

[0061] Ensuring the credibility and auditability of carbon data: Merkle trees and zero-knowledge proof technology in the blockchain evidence storage layer ensure that carbon data is tamper-proof throughout the entire process, meeting compliance audit requirements;

[0062] End-to-end traceability and liability definition: By combining RFID tags and blockchain ledgers, the entire baggage handling process can be traced, providing a basis for problem identification and reducing the cost of dispute resolution;

[0063] V. Promote technological innovation and set industry benchmarks:

[0064] Integrating swarm intelligence and digital twins: Pioneering a closed-loop mechanism of "twin pre-playback - intelligent decision-making - physical feedback" to solve the lag problem of traditional scheduling and provide a paradigm for the intelligentization of complex logistics systems;

[0065] Modular architecture facilitates expansion: The five-layer loosely coupled architecture supports independent upgrades of each module, reducing upgrade costs and facilitating rapid deployment in airports of different sizes. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the overall structure of the airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0068] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0069] like Figure 1 As shown, this embodiment of the invention provides an intelligent AI-driven digital twin low-carbon scheduling system for airport baggage flow, comprising:

[0070] The physical sensing layer consists of RFID scanners, current sensors, and laser rangefinders deployed in the baggage handling area, which collects real-time data such as conveyor belt speed, robot joint angles, chute blockage rate, and baggage three-dimensional coordinates.

[0071] The digital twin engine layer constructs a full-element twin including a physical model of the equipment, a baggage flow network model, and a carbon accounting model, and achieves synchronous mapping of the physical system through real-time data-driven processes.

[0072] The swarm intelligence decision-making layer consists of intelligent agent modules distributed across various processing units, and uses an improved carbon-sensitive ant colony algorithm to generate scheduling strategies.

[0073] The blockchain-based evidence storage layer uses Hyperledger architecture to store energy consumption and luggage carbon footprint data.

[0074] The dynamic scheduling execution layer converts decision commands into equipment control signals and dynamically corrects strategies based on twin simulation results.

[0075] In this embodiment, the physical perception layer is the core of data acquisition for the airport baggage low-carbon scheduling system based on swarm intelligence and digital twins. Like the "nerve endings" of the system, it captures key physical parameters of baggage flow and equipment operation in real time through various sensors deployed in the baggage handling area, providing accurate and comprehensive raw data support for digital twin modeling and intelligent decision-making. The following section elaborates on this layer from the overall structure to the details:

[0076] I. Overall Function Overview:

[0077] The physical perception layer is primarily responsible for comprehensive, high-precision, and real-time monitoring of the physical state of the airport baggage handling area. This includes individual baggage information (such as identification, 3D dimensions, and weight), equipment operating parameters (such as conveyor belt speed, motor current, and robot joint angles), and environmental and operational data (such as chute blockage status and area temperature). Through the collaborative work of multiple types of sensors, the dynamic changes in the physical world are transformed into quantifiable digital signals. After preprocessing, these signals are transmitted to the digital twin engine layer, establishing a real-time mapping foundation between the physical system and the virtual model. Simultaneously, it provides the swarm intelligence decision-making layer with raw data for energy consumption calculation and carbon footprint accounting, making it the primary link in realizing the "perception-modeling-decision-execution" closed loop.

[0078] II. Submodule Composition and Functions:

[0079] (a) RFID scanner unit:

[0080] Baggage Identification and Information Collection: RFID scanners deployed at baggage sorting channels, conveyor belt entrances, and key nodes read data such as the unique identifier ID, flight information, and destination code from baggage tags in real time; using ultra-high frequency RFID technology (working frequency 860-960MHz), it supports simultaneous identification of multiple tags, with an identification distance of 3-5 meters, ensuring that baggage is quickly located and tracked after entering the processing area;

[0081] Data Association and Synchronization: The baggage tags read by RFID are associated with the 3D coordinates collected by the laser rangefinder and the mass data obtained by the weight sensor to form a complete baggage file containing "ID-location-size-weight-flight information," which is then processed using a formula. (in, For luggage Comprehensive information tags, For luggage for Time Baggage The three-dimensional coordinates For luggage Three-dimensional dimensions (length) Width high), For luggage The weight, For luggage Flight information is stored in a structured manner.

[0082] Anomaly detection and alarm: When the RFID scanner fails to read a baggage tag in a certain area three times consecutively (exceeding the preset threshold)... When a missing tag is detected, an alarm is triggered, indicating that the baggage tag has fallen off or is damaged. The system then uses a linked camera to perform image-assisted recognition to ensure the traceability of the baggage.

[0083] (ii) Current sensor unit:

[0084] Equipment energy consumption parameter acquisition: Hall current sensors are connected in series in the power supply circuits of electrical equipment such as conveyor belt motors, robot drive modules, and chute control motors to monitor the operating current in real time. Combined with the rated voltage of the equipment Through formula (in, For equipment exist Instantaneous power at a given moment For equipment The rated operating voltage, for Time device The operating current, For equipment The instantaneous energy consumption is calculated using the power factor, providing basic data for the carbon accounting module;

[0085] Equipment Fault Prediction: By analyzing the current change curve, when a current fluctuation exceeding the normal range is detected... (e.g., the peak starting current of the motor exceeds 1.5 times the rated current and the duration is >) When this occurs, the system determines that the equipment is in an abnormal operating state, generates an early warning signal, and transmits it to the dynamic scheduling execution layer to prompt maintenance and inspection.

[0086] Data calibration and compensation: Considering the impact of sensor temperature drift, a temperature compensation formula is adopted. (in, The calibrated current value. This is the sensor's raw current reading. For temperature coefficient, The current ambient temperature. The standard calibration temperature (25°C) is used to ensure that the current measurement accuracy is within ±0.5%.

[0087] (III) Laser rangefinder unit:

[0088] Luggage 3D coordinate and dimension measurement: Multiple sets of laser rangefinders are deployed on both sides and above the conveyor belt (measurement accuracy). (mm, sampling frequency 100Hz) The distance data of each point on the surface of the luggage is obtained through triangulation, and the three-dimensional coordinates of the luggage are calculated through coordinate transformation. and grow ( ),Width( ),high Dimensional parameters provide a spatial basis for baggage sorting route planning;

[0089] Luggage chute blockage monitoring: Laser rangefinders are installed at the entrance and bends of the luggage chute to measure distance changes within the chute in real time; if the measured distance value is less than a preset threshold for 5 consecutive sampling periods... When the diameter of the chute reaches 1 / 3 (e.g., 1 / 3 of the chute diameter), it is determined to be a chute blockage, and a blockage rate parameter is generated. (in, For chute clogging rate, For the duration of the congestion, (To monitor the total duration of the cycle), the data is transmitted to the dynamic scheduling execution layer to trigger a dredging command;

[0090] Robot working space perception: Deploy LiDAR (scanning frequency 10Hz, angular resolution 0.5°) at the end of the robotic arm and around the working area of ​​the baggage handling robot to build a point cloud map of the robot's working space, detect obstacles in real time (including unidentified baggage and abnormally protruding parts of equipment), provide obstacle avoidance data for robot joint angle adjustment, and ensure operational safety.

[0091] (iv) Data preprocessing and fusion unit:

[0092] Noise filtering and smoothing: The raw sensor data is filtered, with Kalman filtering applied to the laser ranging data. (in, for The filtered value at time step for Predicted value at time, For Kalman gain, for The measured value at time, (for measurement matrix); moving average filtering is applied to the current sensor data. (in, for The smoothed current value at any given time. The sliding window size (take 5-10). For the first The original current value at each moment is used to remove environmental interference and measurement noise.

[0093] Spatiotemporal synchronization and data alignment: Based on a unified system clock (synchronization accuracy ≤1ms), the data collected by RFID scanners, current sensors, and laser rangefinders are aligned according to timestamps to resolve time deviations caused by differences in sampling frequencies of different devices; for spatial coordinate data, a preset coordinate system transformation matrix is ​​used. (in, Global coordinate system coordinates It is a 3×4 transformation matrix. (Using the sensor's local coordinate system coordinates) Transform the local coordinates of each device to the global coordinate system to achieve spatial consistency of data;

[0094] Outlier data removal and completion: By setting data reasonableness thresholds (e.g., luggage weight range 5-50kg, conveyor belt speed 0-2m / s), outliers exceeding the range are removed; for short-term data loss (≤3 sampling periods), linear interpolation is used. (in, To complete the time Data, , for Valid data that are adjacent to each other, and Complete the data to ensure data continuity;

[0095] III. Key Technology Principles:

[0096] (I) Multi-sensor collaborative sensing principle:

[0097] The physical perception layer adopts a multi-sensor fusion architecture of "RFID + current sensor + laser rangefinder" to achieve all-round monitoring based on the principle of information complementarity. RFID technology solves the problem of baggage individual identification and information association, current sensor focuses on monitoring equipment energy consumption and operating status, and laser rangefinder provides spatial size and location information. After spatiotemporal alignment of the data from the three types of sensors, a three-dimensional data dimension of "identity-energy consumption-space" is formed. Through data fusion technology, the system's ability to perceive complex working conditions is improved, overcoming the limitations of a single sensor in scenarios such as obstruction, interference, and insufficient information dimensions.

[0098] (II) Real-time data acquisition and transmission principle:

[0099] The sensors are deployed in a distributed manner, connected to the edge computing gateway via industrial Ethernet (100Mbps transmission rate), and use a publish-subscribe model (MQTT protocol) for data transmission to ensure that the collected real-time data (such as conveyor belt speed and current value) is uploaded to the digital twin engine layer at a period of 50ms. For high-frequency data (such as laser point cloud, 10Hz), lightweight processing (such as downsampling) is performed through edge nodes to reduce the data transmission bandwidth pressure. At the same time, a timestamp and verification mechanism is used to ensure the timing accuracy of the data.

[0100] (III) Data Preprocessing Optimization Principles:

[0101] To address issues such as noise, bias, and missing data in sensor data, differentiated preprocessing strategies are adopted based on the characteristics of different data types: Kalman filtering is suitable for continuous data containing Gaussian noise, such as laser ranging, and achieves dynamic noise suppression through prediction-update iteration; moving average filtering is suitable for high-frequency fluctuating data such as current, and retains trend characteristics through smoothing; linear interpolation is used for short-term data loss scenarios, and achieves efficient data completion based on the assumption of data continuity, providing high-quality data input for subsequent digital twin modeling and carbon accounting.

[0102] IV. Module Workflow:

[0103] (a) Initialization phase:

[0104] After the physical sensing layer is activated, it performs self-tests on all sensors: the RFID scanner performs tag reading tests (identifies preset test tags), the current sensor detects zero-point drift (≤5mA is normal), and the laser rangefinder measures the reference distance (deviation from the preset value ≤2mm is normal), ensuring that the hardware devices are working properly;

[0105] Complete clock synchronization of the sensor network (based on the NTP protocol) and load calibration parameters (such as transformation matrices) for each sensor. Temperature coefficient ), data thresholds (such as congestion detection distance) (Abnormal current range), establish a communication connection with the edge gateway, and enter the standby state;

[0106] (II) Data Collection Phase:

[0107] When luggage enters the processing area and triggers the entrance photoelectric sensor, the system activates the RFID scanner and laser rangefinder in that area. The RFID reader reads the luggage tag information, the laser rangefinder simultaneously collects the three-dimensional dimensions and coordinates, and the weight sensor (integrated into the conveyor belt) obtains the luggage weight, thus forming an initial luggage file.

[0108] The current sensor continuously monitors the operating current of equipment such as the conveyor belt motor and robot, and records the current value every 50ms; the laser rangefinder periodically scans the chute and robot working area (10Hz) and updates the blockage status and spatial obstacle information in real time.

[0109] (III) Data Preprocessing and Fusion Stage:

[0110] After receiving data from each sensor, the edge gateway sorts the data by timestamp, performs Kalman filtering on the laser ranging data, and performs moving average filtering on the current data to remove outliers that exceed the reasonable range.

[0111] By transforming the local coordinates of laser ranging into global coordinates, the baggage ID of RFID is associated with the laser size and weight data to generate a structured data frame.

[0112] ,in, For timestamps, For RFID tag data, This is the data after laser ranging processing. For smoothed current data, This indicates the sensor's operating status.

[0113] (iv) Data transmission and anomaly response phase:

[0114] The preprocessed structured data is transmitted to the digital twin engine layer periodically (50ms), and an anomaly detection mechanism is triggered at the same time: if an abnormal state such as missing tag, excessive current, or blocked chute is detected, an anomaly signal is generated and transmitted to the dynamic scheduling execution layer first.

[0115] When a sensor communication interruption occurs, a local caching mechanism is activated to temporarily store the collected data on the edge node (storage capacity ≥1GB). Once communication is restored, the data is retransmitted in chronological order to ensure that no data is lost.

[0116] (v) Shutdown phase:

[0117] When the system receives a shutdown command, the physical sensing layer stops data acquisition, executes the sensor shutdown process (such as the laser rangefinder entering low power mode), uploads all cached but untransmitted data, records the sensor operating status of this run (such as running time and number of anomalies), and closes the communication connection after completing data archiving.

[0118] In this embodiment, the digital twin engine layer includes:

[0119] Equipment-level mirroring module: Establishing a conveyor belt power calculation model ,in, For the real-time power of the conveyor belt, The coefficient of air resistance and mechanical friction, The coefficient of friction under load, For conveyor belt speed, The total mass of the luggage on the conveyor belt;

[0120] Carbon accounting module: Calculates the carbon footprint of a single piece of luggage. ,in, Carbon footprint per piece of luggage The total number of devices to handle this luggage, For equipment average power, For equipment The processing time for this baggage, The carbon intensity factor of the power grid;

[0121] Furthermore, the digital twin engine layer serves as the "virtual hub" of the airport baggage low-carbon scheduling system based on swarm intelligence and digital twins. By constructing a full-element digital mirror of the physical system, it achieves real-time mapping, dynamic simulation, and optimization deduction between the physical state and the virtual model, providing a precise virtual testing ground for swarm intelligence decision-making. It is the core link connecting physical perception and intelligent decision-making. The following is a detailed explanation from the overall perspective to the specifics:

[0122] I. Overall Function Overview:

[0123] Driven by real-time data collected by the physical perception layer, the digital twin engine layer constructs a full-element twin including a physical model of equipment, a baggage flow network model, a carbon accounting model, and a robust optimization model. By dynamically updating the virtual model parameters, it achieves real-time simulation of "equipment operation - baggage flow - energy consumption and carbon emissions" in the baggage handling process, accurately reproducing the spatiotemporal dynamic characteristics of the physical system. At the same time, this layer can perform multi-scenario simulations based on the current state (such as equipment failure, baggage peak), generate predicted values ​​of optimization targets, provide simulation verification data for scheduling strategies for the swarm intelligence decision-making layer, and support the closed-loop mechanism of "physical world operation - virtual model feedback - decision strategy optimization". It is the core modeling and simulation platform for achieving low-carbon scheduling and efficient operation.

[0124] II. Submodule Composition and Functions:

[0125] (a) Device-level mirroring module:

[0126] Multi-device physical modeling: Constructing high-precision physical models for core equipment such as conveyor belts, robots, and chutes;

[0127] Conveyor belt motor power model: ,in, For the real-time power of the conveyor belt, The coefficient of air resistance and mechanical friction, The coefficient of friction under load, For conveyor belt speed, The total mass of luggage on the conveyor belt is given. This model comprehensively considers the belt speed cube term (dominated by air resistance) and the load-velocity product term (dominated by frictional resistance), which better reflects the energy consumption characteristics under high load.

[0128] Robot joint energy consumption model: ,in, The total power of the robot, For the number of joints, For the first Joint output torque, For the first Joint angular velocity, For the first Joint resistivity, For the first Joint currents accurately characterize the relationship between robot motion energy consumption and joint state;

[0129] Real-time device status mapping: Based on data such as current, speed, and joint angles from the physical sensing layer, the device model parameters are updated every 100ms, using a deviation correction algorithm. (in, These are the corrected model parameters. For the theoretical parameters of the model, The correction factor is (0.1-0.3). These are measured values ​​from the physical sensing layer. (For model predictions) to achieve dynamic alignment between the virtual model and the physical device, ensuring mirror accuracy error. ;

[0130] Equipment fault simulation: Built-in library of common fault models (such as conveyor belt slippage, robot joint jamming), and fault parameters (such as conveyor belt friction coefficient) are injected. (Simulated slippage increases by 30%), simulating equipment energy consumption and operating characteristics under fault conditions, providing fault scenario data for robust decision-making;

[0131] (II) Baggage Flow Network Modeling Unit:

[0132] Spatial network topology construction: Abstracting the baggage handling area into a "node-edge" network model (in, It is a set of nodes, including key locations such as the start / end point of the conveyor belt, the chute inlet, and the sorting machine; The edge set represents the connection relationships between devices, such as conveyor belt segments and chute channels. Each edge is associated with physical parameters (length). Maximum load capacity Energy consumption coefficient );

[0133] Baggage Flow Dynamic Simulation: Based on baggage coordinate and velocity data from the physical sensing layer, a discrete event simulation method is used to track the movement trajectory of individual pieces of baggage in the network, and the result is obtained through formulas. (in, For luggage exist Location at any given moment For the edge The device speed, For simulating step size, For the edge The direction coefficient is used to calculate the position at the next moment, and the dwell time of the luggage at each node is recorded simultaneously. ;

[0134] Congestion diffusion simulation: When a certain edge Real-time load capacity When the congestion model is triggered, it is used to... (in, Speed ​​under congested conditions The congestion attenuation coefficient (2-5) is used to simulate speed decay and calculate the diffusion time of congestion to adjacent nodes, providing predictive data for path optimization.

[0135] (III) Carbon Accounting Module:

[0136] Carbon footprint of a single piece of luggage throughout the entire process: ,in, Carbon footprint per piece of luggage The total number of devices to handle this luggage, For equipment average power, For equipment The processing time for this baggage, The power grid carbon intensity factor is updated in real time with regional power grid data.

[0137] Real-time statistics of total regional carbon emissions: Aggregate carbon footprint data by processing area (e.g., departure sorting area, transit area) to generate... ,in, For the region Total carbon emissions at all times For luggage within the area Cumulative carbon footprint For equipment The idle power provides regional-level carbon emission data for carbon-sensitive decision-making;

[0138] Carbon emission trend prediction: Based on historical carbon emission data and current baggage flow, an LSTM neural network is used to predict the carbon emission curve for the next 30 minutes. ,in, for Predicting carbon emissions at any time To predict duration, Based on current baggage flow, , For historical time steps, prediction error ;

[0139] (iv) Robustness Optimization Module:

[0140] Anti-interference scheduling model: For disturbance scenarios such as equipment failure and peak baggage traffic, a robust model is constructed with two indicators: the expected value of the optimization target and the risk value. ,in, For the set of perturbed scenarios The expected value operator on, For disruptive scenarios (such as equipment malfunctions or peak baggage traffic). For the set of all possible perturbation scenarios, In the scheduling policy vector x and the perturbation scenario The optimization objective function value is as follows. To optimize the objective function value Risk value at a 95% confidence level;

[0141] Pareto optimal solution set generation: A Pareto optimal solution set is generated by solving for the balance between low carbon and high efficiency using a multi-objective genetic algorithm. ,in, This is the Pareto optimal solution set. For scheduling policy vectors, for 3D real space, For another scheduling policy vector, For the first One objective function, This means for all , This indicates that it does not exist. An index for a specific objective function, which the decision-maker can select based on real-time needs;

[0142] (v) Data synchronization and mapping unit:

[0143] Real-time data fusion: Receives structured data frames from the physical sensing layer and aligns them using timestamps (synchronization precision). The transformation between milliseconds (ms) and spatial coordinates maps sensor data to corresponding components in the virtual model, updating model parameters (such as conveyor belt speed). This ensures that the virtual model and the physical system have consistent state deviations. ;

[0144] Model lightweighting and acceleration: Model order reduction techniques are used for highly complex models (such as robot joint dynamics). (in, For the reduced-order model, PCA is used for principal component analysis. For the original model, (For model parameters) This will increase simulation speed by 5-10 times, meeting real-time requirements (single-step simulation time) ;

[0145] Data feedback mechanism: The simulation results of the virtual model (such as predicted carbon emissions and optimized target values) are packaged into feedback data frames. (in, To predict carbon emissions, To optimize the target predicted value, (Based on the congestion risk level), the data is transmitted to the swarm intelligence decision-making layer and the dynamic scheduling execution layer;

[0146] III. Module Workflow:

[0147] (a) Initialization phase:

[0148] Load the physical layout parameters (such as equipment location, network topology) and equipment physical model coefficients (such as...) of the airport baggage handling system. , ), initial carbon strength factor Construct a basic virtual twin framework;

[0149] Receive self-test data from the physical sensing layer to verify the mapping relationship between the sensor and the model (such as the deviation between the laser ranging coordinates and the virtual node). (cm), complete model calibration;

[0150] (II) Real-time mapping stage:

[0151] Real-time data (device current, luggage coordinates, speed, etc.) from the physical sensing layer is received at 50ms intervals, and the state parameters of the virtual model (such as conveyor belt power) are updated through the data synchronization unit. Luggage location );

[0152] Run a baggage flow network simulation to track the virtual trajectory of all baggage, calculate real-time indicators such as dwell time and regional carbon emissions, and trigger model correction when the deviation from physical data exceeds 10%.

[0153] (III) Simulation and Prediction Stage:

[0154] Receive the scheduling strategy of the swarm intelligence decision-making layer (e.g., conveyor belt speed adjustment, path planning), simulate the execution effect of the strategy in a virtual twin, and output the optimized target value. Carbon emission forecasting ;

[0155] For high-priority strategies (such as emergency scheduling to deal with flight delays), activate the robust optimization module, simulate the strategy performance under 3-5 disturbance scenarios, and generate a risk assessment report;

[0156] (iv) Feedback and Update Phase:

[0157] The simulation results ( , (Risk assessment) is fed back to the collective intelligence decision-making layer to support strategy iteration; real-time carbon emission data is transmitted to the blockchain evidence storage layer for carbon footprint evidence storage;

[0158] The model coefficients are updated hourly (e.g., based on historical data optimization). , The power grid carbon intensity factor is updated daily. To ensure model adaptability;

[0159] (v) Shutdown phase:

[0160] Save the daily operating data of the virtual twin (such as energy consumption simulation curves and carbon emission statistics) and generate a model accuracy report (average deviation and maximum deviation).

[0161] Shut down the simulation engine, release computing resources, and disconnect communication with the physical perception layer and decision-making layer.

[0162] In this embodiment, the swarm intelligence decision-making layer adopts a global optimization objective function:

[0163] ,in, , , Adjustable weighting coefficients , The total number of devices. For equipment Total energy consumption integral For luggage The length of stay It is a function of standard deviation. For equipment The workload;

[0164] The improved carbon-sensitive ant colony algorithm includes:

[0165] Path selection probability calculation: ,in, For intelligent agents Select path The probability, For path pheromone concentration, As a factor of pheromone importance, Path heuristic value ( The path distance. For nodes The degree of congestion, To inspire the importance factor of information, For carbon fitness factor, The carbon sensitivity coefficient, For path carbon emissions, This is the baseline value for carbon emissions along the path. The dynamic carbon sensitivity coefficient, For nodes The set of adjacent nodes, For path pheromone concentration;

[0166] Pheromones update rules:

[0167] ,in, For path pheromone concentration, It is the pheromone volatile factor and 0 < , The total number of agents. For intelligent agents In the path The increase in pheromones released from the upper body Carbon learning rate factor, The total amount of pheromones is a constant. The optimal carbon emission threshold for the system;

[0168] The dynamic carbon sensitivity coefficient is adjusted through a feedback mechanism:

[0169] ,in, for Carbon sensitivity coefficient at time, To adjust the step size coefficient, for Average carbon intensity over time period For airport carbon emission targets, This refers to the tolerance range for carbon emissions.

[0170] The improved carbon-sensitive ant colony algorithm employs a convergence guarantee mechanism:

[0171] Define the potential game model.

[0172] ,in, Let be the potential function. The set of decisions made by the agent. For equipment number, For equipment energy consumption For luggage The length of stay , Number the path nodes. For path pheromone concentration;

[0173] Prove that the cross-partial derivatives of the potential function with respect to the decisions of any two agents are non-negative;

[0174] Set convergence criteria: ,in, It is a 2-norm. for The pheromone concentration vector at time t, for The pheromone concentration vector at time t, Let be the convergence accuracy constant. This is the convergence rate factor;

[0175] Furthermore, the swarm intelligence decision-making layer is the "intelligent brain" of the airport baggage low-carbon scheduling system based on swarm intelligence and digital twins. It consists of intelligent agent modules distributed across various processing units, and achieves global optimization decisions through an improved carbon-sensitive ant colony algorithm. This minimizes carbon emissions while ensuring baggage handling efficiency, and serves as a key decision-making hub connecting digital twin simulation and dynamic execution. The following is a detailed explanation from the overall structure to the specifics:

[0176] I. Overall Function Overview:

[0177] The swarm intelligence decision-making layer takes real-time simulation data (such as equipment energy consumption, baggage location, and carbon emission prediction) output from the digital twin engine layer as input. Through the collaborative computing of distributed intelligent agents, it constructs a "local perception-global optimization" decision-making mechanism. The core task of this layer is to generate the optimal scheduling strategy, including conveyor belt speed adjustment, baggage path planning, and robot task allocation, under the premise of meeting the timeliness of baggage transfer (such as maximum dwell time constraints), through an improved carbon-sensitive ant colony algorithm. This achieves a multi-objective balance between total energy consumption, carbon emissions, and processing efficiency. At the same time, it adapts to the deviation correction of digital twin simulation through a dynamic feedback mechanism to ensure the robustness and adaptability of the decision-making strategy in the physical system. It is the core algorithm carrier for the system to achieve the dual objectives of "low carbon and high efficiency".

[0178] II. Submodule Composition and Functions:

[0179] (a) Distributed Intelligent Agent Module:

[0180] Agent Deployment and Division of Labor: Agents are deployed in a distributed manner according to baggage handling areas (such as sorting areas, transit areas, and chute groups). Each agent is responsible for equipment scheduling and baggage route decision-making within its jurisdiction, forming an architecture of "regional autonomy + global collaboration". Agents have the ability to receive data (simulation data from digital twins), perform local computation (route evaluation), and exchange information (share decisions with neighboring agents). Millisecond-level information synchronization is achieved through lightweight communication protocols (such as MQTT-SN).

[0181] Local decision-making and global coordination: A single intelligent agent generates a local scheduling scheme based on the real-time status of its jurisdiction (such as chute blockage rate and equipment load), and uses a formula... (in, For intelligent agents Local solutions, This is regional status data. For regional carbon emission data, The calculation is performed for the regional dwell time; at the same time, the decision vectors of surrounding agents are obtained through neighborhood communication and aggregated into a global strategy using a consensus mechanism to avoid the trap of local optima.

[0182] (II) Carbon-Sensitive Ant Colony Algorithm Unit:

[0183] Path selection probability calculation: The agent adopts an improved path selection probability formula, introducing a carbon fitness factor on the basis of the traditional ant colony algorithm, and prioritizing the selection of low-carbon and efficient paths: ,in, For intelligent agents Select path The probability, For path pheromone concentration, As a factor of pheromone importance, Path heuristic value ( The path distance. For nodes (congestion level) To inspire the importance factor of information, Carbon fitness factor ( The carbon sensitivity coefficient, For path carbon emissions, (Based on path carbon emissions) The dynamic carbon sensitivity coefficient, For nodes The set of adjacent nodes, For path pheromone concentration;

[0184] Pheromones Update Rule: To enhance pheromone accumulation in low-carbon pathways, an update formula incorporating carbon emission optimization is adopted:

[0185] ,in, For path pheromone concentration, Pheromones volatile factor (0 < , The total number of agents. For intelligent agents In the path The increase in pheromones released from the upper body Carbon learning rate factor, The total amount of pheromones is a constant. (The optimal carbon emission threshold for the system); when the path carbon emission... near At that time, the increase in pheromone levels significantly increases, guiding more agents to choose this path;

[0186] Dynamic carbon sensitivity coefficient adjustment: Real-time adjustment via feedback mechanism The formula is: (in, for Carbon sensitivity coefficient at time, To adjust the step size coefficient, for Average carbon intensity over time period For airport carbon emission targets, (This refers to the tolerance range for carbon emissions); when actual carbon emissions exceed the target value... Increase sensitivity to enhance the algorithm's preference for low-carbon pathways; conversely, decrease sensitivity to balance efficiency requirements.

[0187] (III) Global Optimization Target Unit:

[0188] Construction of multi-objective optimization function: A weighted summation global objective function is constructed, with total energy consumption, maximum dwell time, and load imbalance as optimization objectives.

[0189] ,in, , , Adjustable weighting coefficients , The total number of devices. For equipment Total energy consumption integral For luggage The length of stay It is a function of standard deviation. For equipment Workload); by dynamically adjusting weights (e.g., increasing during peak flight times). Off-peak hours increase Adapt to different scenarios;

[0190] Constraint handling: The decision-making process must meet hard constraints, including: baggage transfer deadline. Maximum load of equipment ), carbon emission limit ( The penalty function method is used to incorporate constraints into the objective function, and penalty values ​​are applied to solutions that violate the constraints to ensure the feasibility of the strategy.

[0191] (iv) Collaborative Optimization Mechanism Unit:

[0192] Carbon integral incentive function: To promote collaborative low-carbon decision-making among agents, an incentive mechanism based on carbon emission reductions is designed.

[0193] ,in, For intelligent agents carbon integral, This refers to the carbon emission reduction per unit of time. The time decay factor emphasizes the high incentive of immediate emission reduction; the integral value is linked to the decision-making authority of the agent, and the agent with a higher integral has a greater weight in the global consensus, which stimulates the motivation for collaborative emission reduction.

[0194] Byzantine Fault-Tolerant Consensus: To avoid the impact of local failures on global decisions, the Byzantine Fault-Tolerant (BFT) consensus algorithm is adopted, with the following formula: ,in, For intelligent agents The decision vector, Let be the set of decision vectors of the neighborhood agents. (The upper limit for the number of faulty nodes); when it exceeds When all agents reach a consensus, the decision takes effect, ensuring that a reliable policy can still be generated even if some agents fail.

[0195] III. Key Technology Principles:

[0196] (I) The principle of collaborative decision-making by swarm intelligence:

[0197] Based on a hybrid architecture of "distributed perception-centralized optimization", each agent generates preliminary decisions through local perception data, and then achieves global collaboration through pheromone sharing (ant colony algorithm) and consensus mechanism. Pheromones, as an indirect communication medium, transform individual experience into collective knowledge, avoiding the delay caused by a large amount of data interaction. The carbon fitness factor and dynamic sensitivity coefficient endow the group with the ability to adapt to carbon emission targets, enabling the decision to dynamically switch from "efficiency first" to "low carbon-efficiency balance".

[0198] (II) Adaptive Optimization Principle of Carbon-Sensitive Algorithm:

[0199] The carbon-sensitive ant colony algorithm achieves adaptation through a dual feedback mechanism: firstly, pheromone updates and carbon emission thresholds. First, strengthen coordination and guidance on low-carbon pathways; second, carbon sensitivity coefficient. The system dynamically adjusts its carbon emissions according to the actual carbon emissions, forming a closed loop of "carbon emission deviation → coefficient adjustment → path preference change → carbon emission optimization". Compared with the traditional ant colony algorithm, its core innovation lies in deeply integrating environmental goals (carbon emissions) into the decision-making model, rather than simply optimizing time or distance.

[0200] (III) The principle of multi-objective trade-offs:

[0201] The global optimization objective is achieved through weight coefficients. , , Achieving a dynamic trade-off among multiple objectives: when Increased (e.g., during off-peak hours), the system prioritizes reducing energy consumption and carbon emissions; when Increase priority (e.g., during peak flight times) to ensure timely baggage transfer; This ensures balanced equipment load and avoids energy consumption spikes caused by localized overload; through digital twin simulation data feedback, weighting coefficients can be calibrated in real time (e.g., the degree of load imbalance in a certain area). When the threshold is exceeded, increase This enhances decision-making flexibility.

[0202] IV. Module Workflow:

[0203] (a) Initialization phase:

[0204] Load agent deployment parameters (jurisdiction range, list of neighboring agents), algorithm initial parameters (pheromone concentration) Weight , , Carbon emission threshold ), to complete the communication connection of the intelligent agent network;

[0205] Receive the initial simulation data (initial state of the device, idle energy consumption) from the digital twin engine layer and initialize the baseline value of the global optimization objective function;

[0206] (II) Decision Generation Stage:

[0207] The agent receives real-time data (baggage location, equipment power, carbon emission data) from the digital twin at 100ms intervals and calculates the carbon fitness factor for each path. With heuristics ;

[0208] Based on the path selection probability formula Generate a set of candidate paths, evaluate the comprehensive cost of each path in conjunction with the global optimization objective function, and select the locally optimal path;

[0209] (III) Global Coordination Phase:

[0210] Intelligent agents release pheromones It receives pheromone data from neighboring agents and updates the global pheromone matrix according to the update rules. ;

[0211] The BFT consensus mechanism is used to vote on the locally optimal paths of each agent, and more than [a certain number of] of them pass through the poll. Paths agreed upon are incorporated into the global scheduling strategy, and carbon integrals are calculated simultaneously. And update the agent's permissions;

[0212] (iv) Dynamic optimization stage:

[0213] Receive feedback data from the dynamic scheduling execution layer (deviation between actual carbon emissions, residence time, and simulation values), and then... The formula adjusts the carbon sensitivity coefficient and corrects it through a weighting calibration mechanism. , , ;

[0214] If digital twin simulation shows that carbon emissions from a certain path exceed the threshold... Then, through the pheromone penalty mechanism (reducing the path) Reduce the number of agents selected to achieve dynamic optimization;

[0215] (v) Strategy Output Phase:

[0216] The global scheduling strategy is converted into device control commands (such as conveyor belt speed v and robot joint angle adjustment values), packaged and sent to the dynamic scheduling execution layer.

[0217] Record the optimization target values ​​(total energy consumption, maximum residence time, and total carbon emissions) of this decision and upload them to the blockchain evidence storage layer for traceability and evaluation.

[0218] In this embodiment, the blockchain evidence storage layer is implemented as follows:

[0219] A Merkle tree is constructed to store carbon data, with its root node generated by the hash concatenation of all device energy consumption data and luggage carbon footprint data.

[0220] Using zero-knowledge proof technology, we can verify whether the total carbon emissions of luggage meet the preset limit without disclosing detailed data.

[0221] Furthermore, the blockchain evidence storage layer serves as a "trusted ledger" for the airport baggage low-carbon scheduling system based on swarm intelligence and digital twins. Built upon the Hyperledger architecture, it constructs a distributed, trusted storage system specifically responsible for the end-to-end evidence storage, verification, and traceability of equipment energy consumption and baggage carbon footprint data. This provides underlying technical support for the credibility of carbon accounting results and the auditability of system decisions. The following provides a detailed explanation from the overall structure to the specifics:

[0222] I. Overall Function Overview:

[0223] The blockchain evidence storage layer, based on distributed ledger technology, receives scheduling strategy data from the collective intelligence decision-making layer, actual operational data (such as equipment energy consumption and total carbon emissions) from the dynamic scheduling execution layer, and carbon accounting results from the digital twin engine layer. It uses cryptographic algorithms to achieve immutable storage and traceable verification of data. The core tasks of this layer include: constructing a Merkle tree structure for carbon data to ensure integrity, using zero-knowledge proofs to verify carbon emission compliance, and automatically executing data evidence storage rules through smart contracts. It provides a trusted data foundation for airport carbon management and supports full-process traceability and third-party auditing of carbon footprint. It is a key trust infrastructure for the system to achieve a closed loop of "low-carbon scheduling - trusted data - compliance verification".

[0224] II. Submodule Composition and Functions:

[0225] (a) Distributed ledger unit:

[0226] Ledger Structure and Data Partitioning: Employing a multi-channel architecture using Hyperledger Fabric, channels are divided according to data type: Device Energy Consumption Channel (stores current and power data), Carbon Footprint Channel (stores individual luggage items). Regional total carbon emissions ), scheduling strategy channels (store optimization goals and path selection data for collective intelligent decision-making); each channel corresponds to an independent ledger, and node permissions are restricted through access control lists (ACLs) (e.g., airport operators have full access to all channels, while auditors can only access the carbon footprint channel);

[0227] Block generation and consensus mechanism: Kafka sorting service is used to achieve ordered block generation. Data is uploaded to the blockchain through an "endorsement-sorting-commit" process. Intelligent agent nodes act as endorsement nodes to verify the legality of the data (such as the deviation between energy consumption data and digital twin simulation values). Recognize the formula as ,in, for Blocks generated at any time For transaction data, For endorsement verification functions, The sorting function ensures the consistency and immutability of the block data;

[0228] (ii) Merkle tree storage unit:

[0229] Merkle tree construction for carbon data: Aggregate device energy consumption and carbon footprint data by time slice (e.g., 5 minutes) to construct a Merkle tree for data integrity verification. The formula is as follows:

[0230] ,in, For carbon data Merkel tree, The root hash value. The SHA-256 hash function is used. For equipment Energy consumption data, For luggage carbon footprint, This is a string concatenation operation; the leaf nodes of the tree are individual data hashes, and the non-leaf nodes are concatenated hashes of their child node hashes. The root hash is stored in the blockchain block header to ensure that any data tampering can be detected by comparing the root hash.

[0231] Data retrieval and verification: When querying the carbon footprint of a piece of luggage, the system returns... Its path proof in the Merkle tree (a hash chain from the leaf node to the root node), verified by the formula Verify (in, The verification of data authenticity (using the hash set of the path proof) has a time complexity of O(n log n). ( (Total data volume) to meet real-time query requirements;

[0232] (III) Zero-knowledge proof unit:

[0233] Carbon emission compliance verification: To prove that total carbon emissions have not exceeded the limit without disclosing detailed data, zero-knowledge proof (ZK-SNARK) technology is used. The verification formula is as follows: ,in, For the total carbon footprint of all luggage, The threshold for total carbon emissions at airports. The data represents the set of equipment energy consumption data, where | denotes a conditional proof based on the energy consumption data; the proof process incorporates carbon emission calculation logic through circuit design. This is transformed into a constraint system that generates brief proofs that the prover (system) can submit to the verifier (regulatory body), allowing the verifier to confirm total carbon emissions compliance without having to view the original data;

[0234] Privacy protection and data anonymization: Sensitive data (such as the baggage carbon footprint of a specific flight) is anonymized through hash mapping. (in, To anonymize baggage tags, Personal information is removed from the random salt value, retaining only carbon emission-related attributes, thus supporting data statistical analysis while complying with privacy protection regulations (such as GDPR);

[0235] (iv) Smart Contract Unit:

[0236] Automatic execution of evidence storage rules: Deploy carbon data evidence storage contracts, defining the format and frequency of data upload to the blockchain: device energy consumption data is uploaded to the blockchain every 30 seconds, the carbon footprint of a single piece of luggage is uploaded to the blockchain within 10 seconds after processing, and the total regional carbon emissions are uploaded to the blockchain every hour on the hour; the contract is triggered by conditions. (in, For the timed on-chain interval, Automatically execute the evidence storage process for baggage handling completion events to avoid data loss due to human intervention;

[0237] Carbon Credit Settlement and Auditing: Carbon Credit Based on Swarm Intelligence Decision-Making Layer Deploy a liquidation contract to automatically redeem rewards (such as increased decision-making authority) when the agent's carbon credits accumulate to a threshold; simultaneously deploy an audit contract to allow authorized nodes (such as third-party auditing agencies) to call... This function retrieves Merkle trees and compliance certificates for carbon data over a specified time period, enabling automated auditing.

[0238] III. Module Workflow:

[0239] (a) Initialization phase:

[0240] Deploy a Hyperledger Fabric network, including 3 endorsement nodes (corresponding to different processing areas), 1 sorting node (Kafka cluster), and multiple peer nodes (airport operator, auditor, and regulator), and configure channel and access control policies;

[0241] Deploy smart contracts (evidence storage contract, liquidation contract, audit contract), initialize the root hash of the Merkle tree (empty tree state), and set the total carbon emission threshold. Interval with data on-chain ;

[0242] (II) Data Storage Stage:

[0243] Real-time reception of device energy consumption data from the physical sensing layer Carbon footprint data with digital twin engine layer The format and rationality are verified by the endorsement nodes (e.g. , Deviation from simulation value ;

[0244] Aggregate data by time slice and calculate hash value. Update the Merkle tree and calculate the new root hash. Data and root hash are automatically packaged into a transaction by triggering conditions through smart contracts. ;

[0245] Transactions are sorted to generate blocks After being sent to all peer nodes for verification, it is written into the ledger to complete the evidence storage.

[0246] (III) Verification and Query Phase:

[0247] When regulatory agencies need to verify total carbon emissions compliance, the system invokes zero-knowledge proof units to generate [the necessary documentation]. Verifiers confirm the validity of the proof through a verification algorithm without needing to view the carbon footprint data of the specific luggage.

[0248] When querying the carbon footprint of a single piece of luggage, the system returns... The corresponding Merkle tree path proof and block information allow users to verify data integrity by checking whether the path hash matches the root hash.

[0249] Automatic execution every hour via smart contract The function writes the newly generated Merkle root hash to the latest block, ensuring that the ledger is synchronized with real-time carbon data;

[0250] Third-party auditing firms can obtain carbon data Merkle trees, zero-knowledge proofs, and block metadata for a specified time period by authorizing the use of audit contracts, complete offline audits, and generate compliance reports.

[0251] (v) Archiving stage:

[0252] When baggage exceeds the traceability period (e.g., 30 days after the flight ends), historical data is migrated from the active ledger to archived storage (e.g., IPFS distributed file system) via smart contracts. Only Merkle root hash and index information are retained in the ledger, freeing up storage space while ensuring the traceability of archived data.

[0253] In this embodiment, the dynamic scheduling execution layer includes a policy correction module:

[0254] The deviation of the comprehensive optimization index of the real-time computing system is the absolute value of the difference between the twin predicted value and the actual value divided by the predicted value.

[0255] When the deviation exceeds the threshold, adjust the energy consumption weighting coefficient α and the initial carbon sensitivity coefficient. α is adjusted proportionally based on the sign of the energy consumption deviation. Adjustments should be made proportionally based on the carbon emission deviation rate;

[0256] Furthermore, the dynamic scheduling execution layer is the "execution center" of the airport baggage low-carbon scheduling system based on swarm intelligence and digital twins. It is responsible for converting the optimization strategies generated by the swarm intelligence decision-making layer into equipment control signals, and correcting the deviations between the physical system and the digital twin simulation through a real-time feedback mechanism. This ensures the accurate implementation of the scheduling strategy under complex operating conditions and is a key execution link connecting intelligent decision-making and physical operations. The following is a detailed explanation from the overall perspective to the specifics:

[0257] I. Overall Function Overview:

[0258] The dynamic scheduling execution layer takes the scheduling strategies (such as conveyor belt speed adjustment values, robot path instructions, and chute allocation schemes) output by the swarm intelligence decision-making layer as input. It forms a closed loop of "decision-execution-correction" through instruction conversion, equipment control, and real-time feedback. Its core task is to transform abstract optimization goals (such as minimizing total energy consumption and carbon emission compliance) into specific equipment action parameters; to monitor the strategy execution effect through real-time data from the physical perception layer and calculate the deviation from the digital twin simulation results; to trigger a parameter recalibration mechanism when the deviation exceeds the limit, dynamically adjusting the control signals to ensure that the actual operating state conforms to the optimization goal; at the same time, this layer has emergency response capabilities, quickly executing redundancy switching in the event of sudden equipment failure to ensure the continuity of the baggage handling process. It is the final executor of the system from "virtual decision-making" to "physical implementation".

[0259] II. Submodule Composition and Functions:

[0260] (a) Command conversion and device control unit:

[0261] Strategy parsing and signal generation: Receive the scheduling strategy from the swarm intelligence decision-making layer and parse it into control parameters executable by the device; for example:

[0262] Conveyor belt speed command: The optimized belt speed value output by the decision-making layer. Converted into motor control signal Where PWM is the duty cycle of the pulse width modulation signal. For speed-to-voltage conversion coefficient, The zero-point compensation value is used to adjust the motor speed via a frequency converter driver;

[0263] Robot path instructions: The path points planned by the decision-making layer... Convert to joint angle sequence ,in, For the first The joint angles corresponding to each path point The inverse kinematics solution function is used to control the robotic arm to move along the planned path.

[0264] Multi-device collaborative control: For cross-regional baggage handling processes (such as from the sorting area to the transfer area), the timing of multiple devices' actions is coordinated through a time synchronization protocol (such as IEEE 1588) to ensure seamless baggage handover; for example, when baggage is about to reach the end of the conveyor belt, an opening command is sent to the target chute 500ms in advance, using a formula... (in, For the instruction trigger time, Estimated arrival time for luggage, To avoid handover delays (due to equipment response latency);

[0265] Control signal safety verification: Perform safety threshold verification on the generated control signals to ensure that the equipment operation is within a safe range (e.g., the conveyor belt speed does not exceed the mechanical limit). m / s, robot joint angle does not exceed the limit ); using formula (in, The original signal output by the decision-making level. As a safety threshold, A limiting function prevents equipment overload and ensures safe operation.

[0266] (II) Real-time feedback and deviation calculation unit:

[0267] Execution performance monitoring: Real-time collection of equipment operating parameters and baggage status data through the physical sensing layer, including: actual conveyor belt speed. Real-time motor current Actual stay time of luggage Actual carbon emissions etc., as quantitative indicators of strategy execution effectiveness;

[0268] Simulation-Actual Deviation Calculation: Compare the monitoring data with the simulation predictions of the digital twin engine layer to calculate the deviation index.

[0269] Optimize target deviation: (in, The optimization target values ​​for digital twin simulation are (total energy consumption + maximum dwell time + load imbalance). The actual optimization target value (total energy consumption + maximum dwell time + load imbalance);

[0270] Deviation of individual indicators: such as energy consumption deviation Carbon emission deviation rate This provides a detailed basis for strategy adjustments;

[0271] Deviation warning mechanism: When ( When the deviation threshold (usually set to 15%) or a single deviation exceeds the limit, an early warning signal is triggered, marked as "strategy execution abnormality", and the deviation data is packaged and sent to the strategy correction module.

[0272] (III) Strategy Correction Module:

[0273] Dynamic parameter calibration: Based on the deviation calculation results, the control parameters and decision weights are automatically adjusted. The formula is as follows:

[0274] ,in, The energy consumption deviation adjustment factor is 0.05-0.2. The sign function (increases when there is a positive deviation) (reduced when there is a negative deviation). Here is the initial carbon sensitivity coefficient for the carbon-sensitive ant colony algorithm. The carbon emission deviation rate is used to make the decision-making strategy more closely match the characteristics of the physical system by calibrating the weights and algorithm parameters.

[0275] Control signal correction: Addressing deviations between the actual equipment status and commands (e.g., actual conveyor belt speed). The proportional-integral-derivative (PID) control algorithm is used to correct the output signal in real time.

[0276] ,in,

[0277] For speed deviation, , , The parameters for the PID controller are set to ensure that the actual operating parameters of the equipment converge to the target decision value.

[0278] Strategy Iterative Optimization: When the deviation is within 3 consecutive sampling periods (100ms each) When all limits are exceeded, the deviation data is fed back to the swarm intelligence decision-making layer, triggering the strategy regeneration process. Based on the updated physical parameters (such as the actual energy consumption coefficient of the equipment), a more suitable scheduling strategy is generated to achieve continuous optimization through "feedback-iteration".

[0279] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-driven digital twin low-carbon scheduling system for airport baggage flow, characterized by: include: The physical sensing layer consists of RFID scanners, current sensors, and laser rangefinders deployed in the baggage handling area, which collects real-time data such as conveyor belt speed, robot joint angles, chute blockage rate, and baggage three-dimensional coordinates. The digital twin engine layer constructs a full-element twin including a physical model of the equipment, a baggage flow network model, and a carbon accounting model. It achieves synchronous mapping of the physical system through real-time data-driven processes. The digital twin engine layer includes: Equipment-level mirroring module: Establishing a conveyor belt power calculation model ,in, For the real-time power of the conveyor belt, The coefficient of air resistance and mechanical friction, The coefficient of friction under load, For conveyor belt speed, The total mass of the luggage on the conveyor belt; Carbon accounting module: Calculates the carbon footprint of a single piece of luggage. ,in, Carbon footprint per piece of luggage The total number of devices to handle this luggage, For equipment average power, For equipment The processing time for this baggage, The carbon intensity factor of the power grid; The swarm intelligence decision-making layer consists of intelligent agent modules distributed across various processing units, and uses an improved carbon-sensitive ant colony algorithm to generate scheduling strategies. The blockchain-based evidence storage layer uses Hyperledger architecture to store energy consumption and luggage carbon footprint data. The dynamic scheduling execution layer converts decision commands into equipment control signals and dynamically corrects strategies based on twin simulation results.

2. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 1, characterized in that: The swarm intelligence decision-making layer adopts a global optimization objective function: ,in, , , These are adjustable weighting coefficients, and , The total number of devices. For equipment Total energy consumption integral For luggage The length of stay It is a function of standard deviation. For equipment The workload.

3. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 2, characterized in that: The improved carbon-sensitive ant colony algorithm includes: Path selection probability calculation: ,in, For intelligent agents Select path The probability, For path pheromone concentration, As a factor of pheromone importance, For path heuristic values, The path distance. For nodes The degree of congestion, To inspire the importance factor of information, For carbon fitness factor, The carbon sensitivity coefficient, For path carbon emissions, This is the baseline value for carbon emissions along the path. The dynamic carbon sensitivity coefficient, For nodes The set of adjacent nodes, For path pheromone concentration; Pheromones update rules: ,in, For path pheromone concentration, It is the pheromone volatile factor and 0 < , The total number of agents. For intelligent agents In the path The increase in pheromones released from the upper body Carbon learning rate factor, The total amount of pheromones is a constant. This represents the optimal carbon emission threshold for the system.

4. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 3, characterized in that: The dynamic carbon sensitivity coefficient is adjusted through a feedback mechanism: ,in, for Carbon sensitivity coefficient at time, To adjust the step size coefficient, for Average carbon intensity over time period For airport carbon emission targets, This represents the tolerance range for carbon emissions.

5. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 1, characterized in that: The blockchain evidence storage layer implements: A Merkle tree is constructed to store carbon data, with its root node generated by the hash concatenation of all device energy consumption data and luggage carbon footprint data. Using zero-knowledge proof technology, we can verify whether the total carbon emissions of luggage meet the preset limit without disclosing detailed data.

6. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 1, characterized in that: The swarm intelligence decision-making layer implements a collaborative optimization mechanism: Design an excitation function based on carbon integral: ,in, For intelligent agents carbon integral, This refers to the carbon emission reduction per unit of time. This is the time decay factor; The Byzantine fault-tolerant consensus algorithm is adopted, which requires that the decision vector of each agent obtains the consent of at least twice the number of faulty nodes plus one node among its neighboring nodes.

7. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 1, characterized in that: The digital twin engine layer embeds a robust optimization module: The goal of constructing an anti-interference scheduling model is to minimize the expected value of the scheduling policy under disturbance scenarios plus the risk coefficient multiplied by the 95% confidence level. Generate a Pareto optimal solution set consisting of policies that satisfy the following condition: no other policy is non-inferior to this policy on all optimization objectives and strictly superior to it on at least one objective.

8. The airport baggage flow group intelligent AI-driven digital twin low-carbon scheduling system according to claim 3, characterized in that: The improved carbon-sensitive ant colony algorithm employs a convergence guarantee mechanism: Define the potential game model. ,in, Let be the potential function. The set of decisions made by the agent. For equipment energy consumption For luggage The length of stay , Number the path nodes. For path pheromone concentration; Prove that the cross-partial derivatives of the potential function with respect to the decisions of any two agents are non-negative; Set convergence criteria: ,in, It is a 2-norm. for The pheromone concentration vector at time t, for The pheromone concentration vector at time t, Let be the convergence accuracy constant. This is the convergence rate factor.

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