Logistics monitoring and simulation system based on digital twinning

Abstract

The invention discloses a logistics monitoring and simulation system based on digital twinning, and belongs to the technical field of logistics, and the system comprises a data collection module, a digital twinning modeling module, a logistics simulation deduction module, an operation monitoring module, an optimization decision module, a data interface module, a distributed storage module, an abnormity alarm module and a three-dimensional visualization module. By means of the digital twinning technology, the logistics network model can be rapidly established in the virtual environment, the optimal path design scheme is found through data analysis and simulation experiments, and the purpose of saving the transportation cost is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of logistics, and particularly relates to a logistics monitoring and simulation system based on digital twinning. BACKGROUND

[0002] At present, with the rapid development of economy and the gradual perfection of the logistics industry, e-commerce has been integrated into everyone's life. Thousands of logistics trucks are transporting goods in various places every day. Modern logistics involves multiple links such as warehousing, transportation, distribution, and supply chain collaboration. Traditional management methods are difficult to meet the demand of large-scale and dynamic.

[0003] Chinese patent CN202010594366.1 discloses an intelligent logistics management system and a logistics management method thereof. The intelligent logistics management system comprises an order receiving module, an order identification module, a central control module, an order monitoring module, an order processing module, an order scheduling module, an order cancellation module, an order classification module, an order collection module, an order recording module, an order feedback module, an order printing module, a goods taking and placing module, a goods confirmation module, a goods scanning module, a goods marking module, a goods transportation module, a goods delivery module, and an order completion module. The application can monitor order information in real time, has a simple order processing process, high intelligence, and orderly logistics management.

[0004] Chinese patent CN202410732051.7 discloses a smart logistics management system and a logistics management method thereof. The system comprises an identification code generation module, a logistics information acquisition module, an information tracking module, an access verification module, an abnormality detection module, an identification code repair module, and an identification code repair module. The identification code generation module obtains goods entering the logistics, creates an identification code, and pairs the identification code with the goods. The identification code is printed on the goods. The abnormality detection module detects the quality of the goods in the logistics. The identification code repair module detects the integrity of the identification code on the goods. The identification code repair module obtains the identification code of the characteristic goods and reprints the identification code on the characteristic goods. By setting the abnormality detection module, the identification code repair module, and the information tracking module, the identification code of the goods can be repaired and tracked during the entire logistics transmission process.

[0005] However, the above-mentioned patents still have the following disadvantages: 1. Lack of dynamic tracking of the transportation process. The status of goods in transit depends on manual telephone confirmation. The positioning accuracy of the vehicle is only kilometer level, and it cannot identify specific road congestion or abnormal parking.

[0006] 2. The abnormality is handled by relying on preset rules. When encountering sudden road conditions, a large amount of time is spent on manually developing a detour plan, resulting in transportation overtime.

[0007] 3. The traditional navigation is relied on to plan the transportation path, and the transportation path cannot be dynamically adjusted according to actual conditions, so that the transportation cost is high. SUMMARY

[0008] The purpose of the present application is to provide a logistics monitoring and simulation system based on digital twinning, which quickly establishes a logistics network model in a virtual environment with the help of digital twinning technology, finds the optimal path design scheme through data analysis and simulation experiments, and achieves the purpose of saving transportation cost.

[0009] To achieve the above technical purpose, the technical scheme adopted by the present application is as follows: A logistics monitoring and simulation system based on digital twinning, comprising a data acquisition module, a digital twinning modeling module, a logistics simulation deduction module, an operation monitoring module, an optimization decision module, a data interface module, a distributed storage module, an abnormal alarm module and a three-dimensional visualization module; The data acquisition module is used to collect real-time transportation vehicle position, speed and cargo state data, synchronously integrate road conditions, weather and driver behavior information, and provide real-time early warning for logistics monitoring through multi-source data fusion, supporting the logistics simulation deduction module to deduce transportation strategies; The digital twinning modeling module is used to fuse transportation vehicle positioning, cargo state and real-time road condition data, and construct a virtual transportation scene that is real-time synchronized with the physical world; The logistics simulation deduction module simulates dynamic scenarios in transportation based on digital twinning models and real-time data, predicts the impact of different paths on time efficiency, energy consumption and cargo safety, generates multiple emergency schemes, and optimizes resource allocation in combination with historical transportation data to verify the feasibility of new routes; The operation monitoring module analyzes abnormal risks according to the transportation vehicle position, speed and cargo state, road conditions, weather and driver behavior data collected by the data acquisition module, timely triggers the abnormal alarm module to warn, and connects the logistics simulation deduction module to generate emergency schemes, verifies the feasibility of temporary paths, and real-time corrects the transportation plan; The optimization decision module is used to balance cost, time efficiency and risk by comprehensively integrating the data of the digital twinning modeling module, the logistics simulation deduction module and the operation monitoring module, automatically generating multiple path schemes, verifying the feasibility of the schemes through simulation, and finding the optimal path scheme; The data interface module is used to open the interaction channel between the data acquisition module, the digital twinning modeling module, the logistics simulation deduction module and the external data source, real-time receives road condition information, synchronously cleans and converts data format, distributes to the operation monitoring module to timely trigger the abnormal alarm module to warn, and simultaneously inputs road condition information parameters to the logistics simulation deduction module to support emergency scheme deduction; The distributed storage module is used to construct a multi-node collaborative data lake to realize hierarchical storage and fast retrieval of heterogeneous data. The abnormality alarm module issues a warning signal in time by real-time interaction with the operation monitoring module and abnormality judgment combined with a preset rule base. The three-dimensional visualization module is used for constructing a global monitoring interface of virtual-real mapping and dynamically presenting the three-dimensional space-time state of the transport vehicle, the goods and the environment.

[0010] Further limitation, the data acquisition module further comprises: The device access module is used for identifying a sensor group of the transport vehicle, the sensor group comprising a temperature and humidity sensor, a vibration sensor and a vehicle-mounted camera, establishing a device database, connecting a GPS positioning device and analyzing the longitude, latitude, speed and time stamp of the transport vehicle. The protocol adaptation module is used for unifying the heterogeneous data into Apache Avro format. The data cleaning and repairing module is used for eliminating abnormal values of the temperature and humidity sensor, generating an interpolated trajectory by using a Kalman filtering algorithm when the GPS signal is lost, denoising the original data of the vibration sensor and extracting effective vibration energy features. The space-time alignment and standardization module is used for calibrating all device clocks, ensuring that the sensor group data is consistent with the GPS time stamp, converting the temperature control data into ℃ and converting the vibration intensity into m / s². The multi-source heterogeneous data fusion module is used for fusing the real-time rainfall of a meteorological API in the transport vehicle data and correcting the temperature and humidity sensor readings, dynamically adjusting the sensor group sampling frequency according to the priority of a waybill and constructing a unified data model based on the ISO 19848 standard and mapping different source fields.

[0011] Further limitation, the logistics simulation and deduction module further comprises: The road network dynamic modeling module is used for integrating a map and real-time traffic data, generating a hierarchical road network model covering expressways, national roads and provincial roads, marking physical constraints such as slope, bridge bearing and tunnel limit and real-time accessing external data such as meteorological warning and traffic accident to generate a three-dimensional visual forbidden area. The vehicle energy consumption simulation module is used for calculating energy consumption in combination with vehicle speed, load, path distance, road condition and cumulative running time. The real-time strategy verification module is used for receiving a detour scheme recommended by the optimization module and deducing a balance point of fuel consumption increase and time loss.

[0012] The logistics simulation and deduction module in the application has the following advantages: 1. Traditional management models, based on periodic plan adjustments, struggle to cope with dynamic disturbances such as sudden road conditions and environmental changes. The logistics simulation module, relying on real-time data streams from the operation monitoring module (such as vehicle location, weather warnings, and traffic control), enables the reconstruction of transportation scenarios and multi-strategy simulations. By pre-setting constraints such as cost, timeliness, and safety, the module can quickly generate and select the globally optimal solution, transforming the passive response of traditional manual scheduling into a systematic proactive decision-making process, significantly improving the scientific rigor of the solutions.

[0013] 2. By dynamically extrapolating potential risks such as mechanical failures, path disruptions, and sudden environmental changes, it accurately predicts the risk level and loss range of different response strategies, helping managers to make optimal decisions with controllable risks in complex scenarios and avoid the accumulation of trial and error costs driven by experience.

[0014] 3. Traditional management models focus on single-point optimization (such as minimizing vehicle routes) while neglecting cross-regional and multi-entity resource coordination. The simulation module, through a distributed computing architecture, integrates the real-time status and task requirements of vehicles, warehouses, and manpower to achieve dynamic matching and intelligent scheduling of global resources. Its breakthrough lies in breaking down resource silos and coordinating the generation of combined instructions such as route planning, cargo distribution, and relay transportation. This reduces empty-running rates and redundant inventory while systematically improving the utilization rate of resources across the entire supply chain.

[0015] Further specifying, the digital twin modeling module includes, The geographic information input module receives data from the data acquisition module to obtain satellite maps and road network vector data of the transportation route (including road grade and bridge height limit); it imports the coordinates of logistics nodes (warehouses, sorting centers) and marks the range of dense areas (such as freight yards and loading and unloading areas within a radius of 500m).

[0016] The dynamic object recognition module is used to receive data from the vehicle's GPS and extract the location and speed of the transport vehicle in real time. The regional density calculation module, based on the DBSCAN clustering algorithm, calculates the number of objects per unit area with logistics nodes as the center. It generates a LOD3 level accuracy model in densely populated areas of logistics nodes, with a density of >20 objects / 100㎡ (such as loading and unloading platforms). In open areas, a LOD1 level accuracy model is used, with a density of <5 objects / 100㎡ (such as highway sections). The adaptive mesh generation and LOD grading module uses a quadtree algorithm to divide the basic mesh and converts the coordinate system to UTM partition projection; it labels the mesh level according to the region density: in dense areas, it constructs millimeter-level features for stationary or low-speed objects; when a vehicle enters a highway, it triggers the LOD1 mode. A real-time rendering optimization and data synchronization module is used to enable a subdivision shader when in the LOD3 area, and load a 4K PBR material; when in the LOD1 area, use instance rendering to batch the same model; when the vehicle accelerates from the warehouse (v=0.5 m / s) to the park road (v=3 m / s): adopt a geometric gradual change technology to linearly reduce the model face number from 20,000 to 8,000; when the speed threshold is triggered, retain the LOD3 collision body and synchronously reduce the visual model precision; and synchronize the LOD state to the logistics simulation deduction module through Kafka.

[0017] The digital twin modeling module in the application has the following advantages: 1. Through integration with the operation monitoring module, multi-source data such as vehicle state, cargo information, and environmental parameters (such as weather and road conditions) are synchronized in real time to construct a dynamic virtual mirror of the physical world, so that the model can accurately reflect the real-time state changes of the logistics system and provide a high-fidelity basis for subsequent analysis and decision-making.

[0018] 2. By combining the real-time model with the simulation engine, predictive deduction of scenarios such as mechanical failure and path blockage is supported, which can simulate the standard evolution process of known risks and explore the chain effect of extreme scenarios through generative algorithms, significantly improving the risk prediction ability of the system.

[0019] 3. By mapping entities such as vehicles and manpower in the logistics network into interactive virtual objects, the multi-objective collaborative optimization algorithm can be quickly verified. At the same time, the execution deviation between the physical system and the virtual model is compared in real time, which can automatically trigger model parameter calibration and strategy iteration to form a closed-loop control system and continuously improve the accuracy of decision-making.

[0020] Further limited, the optimization decision module further includes, A real-time strategy generation module is used to generate a decision tree containing path changes, resource allocation, and risk response based on historical data from simulation deduction; receive real-time road conditions pushed by the logistics simulation deduction module to generate a detour plan; A resource dynamic scheduling module dynamically matches temporary orders (such as vehicles with remaining load of 30% preferentially receiving orders) according to the real-time location and load of vehicles in transit; dispatches nearby rescue vehicles when vehicle failure occurs; and changes the priority in real time according to the needs of the consignor; A dynamic path planning module generates an optimal path by integrating toll road costs, mountainous road risk values, and gas station distribution; when the GPS detects that the vehicle deviates from the predetermined path, it is re-planned in combination with real-time weather; A transportation cost analysis module calculates transportation costs by total energy consumption, fuel prices, environmental factors, and time efficiency factors; A decision verification and feedback module is configured to send the generated scheduling scheme to the logistics simulation deduction module, compare the transportation cost of the new scheme with that of the original scheme, and select the optimal scheme.

[0021] The optimization decision module in the application has the following advantages: 1. By accessing the multi-source data stream (such as vehicle status, road condition information, order change) of the running monitoring module in real time, a dynamic decision engine is constructed, and candidate schemes can be generated and iterated for sudden events (such as path blockage, emergency order), thereby breaking through the response speed bottleneck of traditional manual decision-making and realizing the transition from passive response to active intervention.

[0022] 2. By coordinating the real-time state and constraint conditions of vehicles, manpower and other resources, a multi-objective optimization algorithm is used to simultaneously calculate core indicators such as cost, timeliness, risk and energy consumption to generate an optimal solution, thereby realizing the dual leap of resource utilization rate and service quality.

[0023] 3. By continuously analyzing the historical decision effect and actual execution deviation, the model parameters and optimization weights are automatically corrected to ensure that the decision logic is always iterated synchronously with the real business scenario.

[0024] Further limited, the three-dimensional visualization module further comprises, A high-precision three-dimensional scene construction module is configured to integrate satellite remote sensing data and laser radar point cloud to generate a three-dimensional road network model containing terrain elevation, bridges and tunnels, and service area buildings; load the refined model of a specific area as needed, including the lane distribution of toll stations and the location of gas stations; A real-time dynamic data fusion module is configured to fuse vehicle-mounted GPS trajectory, weather radar data and roadside unit traffic statistics to the three-dimensional scene to form a dynamic heat map; and apply a red contour warning to speeding vehicles; An interactive decision sand table module supports dragging and modifying virtual roadblocks (such as simulating landslide positions) to real-time deduce the feasibility of vehicle detour paths; A multi-level visualization module displays the trunk transportation flow distribution in the view, and clicking on a single vehicle model can penetrate to view the temperature and humidity curves inside the cargo box and the driver's fatigue index; A simulation process playback module is configured to load historical weather data to playback vehicle trajectory deviation; and display the actual transportation path and the simulation deduced path side by side and label key divergence points.

[0025] The advantages of the application mainly lie in the following aspects: 1. The data acquisition module realizes real-time capture and cleaning of global data such as vehicles, goods and environment, breaking through the delay and error bottleneck of traditional manual input. Combined with the high-concurrency processing capability of the distributed storage module, the high-speed access and consistency of data are ensured, providing a stable data foundation for subsequent modeling and decision-making.

[0026] 2. The digital twin modeling module constructs a real-time virtual mirror of the logistics system, accurately mapping the state transitions of the physical world. The logistics simulation and deduction module simulates the potential effects of path planning, resource allocation, and other strategies based on this mirror, while the optimization decision module generates the optimal solution. The three form a closed-loop link of "perception-deduction-decision", realizing the paradigm upgrade from static rule execution to dynamic strategy adaptation.

[0027] 3. The operation monitoring module tracks core parameters such as vehicle location and equipment status in real time, and the abnormal alarm module predicts risks and triggers emergency mechanisms. Combined with the three-dimensional visualization module's stereoscopic interactive interface, managers can penetrate the global operation situation of the logistics network and synchronize the coordination of warehouse, transportation, manpower, and other resources.

[0028] 4. The data interface module integrates external systems such as suppliers and carriers through open protocols, eliminating information silos. The distributed storage module supports multi-level data permission management, ensuring the security of sensitive information. The abnormal alarm module standardizes and classifies risk events and automatically pushes them to the responsible subjects, building a transparent governance system with multi-party collaboration. BRIEF DESCRIPTION OF DRAWINGS

[0029] The present application can be further illustrated by the non-limiting examples shown in the accompanying drawings; Figure 1 is a structural schematic diagram of an embodiment of the present application; Figure 2 is a structural schematic diagram of a data acquisition module in an embodiment of the present application; Figure 3 is a structural schematic diagram of a digital twin modeling module in an embodiment of the present application; Figure 4 is a structural schematic diagram of a logistics simulation and deduction module in an embodiment of the present application; Figure 5 is a structural schematic diagram of an optimization decision module in an embodiment of the present application; Figure 6 is a structural schematic diagram of a three-dimensional visualization module in an embodiment of the present application; Figure 7 is a workflow diagram of an operation monitoring module in an embodiment of the present application; Figure 8 is a workflow diagram of an abnormal alarm module in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order for those skilled in the art to better understand the present application, the technical solutions of the present application are further described below in conjunction with the drawings and examples.

[0031] As Figures 1-8As shown, the present invention provides a logistics monitoring and simulation system based on digital twins, including a data acquisition module, a digital twin modeling module, a logistics simulation and deduction module, an operation monitoring module, an optimization decision-making module, a data interface module, a distributed storage module, an anomaly alarm module, and a three-dimensional visualization module.

[0032] The data acquisition module is used to collect real-time data on the location, speed, and cargo status of transport vehicles, and simultaneously integrates road conditions, weather, and driver behavior information. Through multi-source data fusion, it provides real-time early warning for logistics monitoring and supports the logistics simulation module in deriving transportation strategies.

[0033] This data acquisition module includes: The device access module is used to identify the sensor group of the transport vehicle, which includes a temperature and humidity sensor, a vibration sensor, and an on-board camera, and to establish a device database; it also connects to a GPS positioning device to analyze the latitude, longitude, speed, and timestamp of the transport vehicle. The protocol adaptation module is used to unify heterogeneous data into the Apache Avro format; The data cleaning and repair module is used to remove abnormal values ​​from the temperature and humidity sensors, generate interpolated trajectories using the Kalman filter algorithm for periods of GPS signal loss, denoise the raw data from the vibration sensors, and extract effective vibration energy features. The spatiotemporal alignment and standardization module is used to calibrate the clocks of all devices, ensuring that sensor group data is consistent with GPS timestamps; it also converts temperature control data to °C and vibration intensity to m / s². The multi-source heterogeneous data fusion module is used to integrate real-time rainfall data from meteorological APIs into transport vehicle data and correct temperature and humidity sensor readings; dynamically adjust the sampling frequency of sensor groups according to waybill priority; and build a unified data model based on the ISO 19848 standard to map fields from different sources.

[0034] The formula for calculating the data fusion weight of the multi-source heterogeneous data fusion module is as follows: ,in, For the first The fusion weight of each data source For the first Reliability rating of each data source For the first Timeliness rating of each data source Assigning weights to road classifications This is a real-time congestion index. For the reliability score of the j-th data source, The timeliness score for the j-th data source.

[0035] In the above data fusion weight calculation formula The calculation process is as follows: Referring to ISO 18972:2021 "Logistics Sensor Data Quality Standards", the 20% error rate is the equipment scrap threshold, assigned 0.8; the 5% error rate reaches the high precision standard, assigned 1.2.

[0036] If the monthly operation rate of the equipment is less than or equal to 80% (lower than the industry stable operation standard), it is forced to be assigned a value of 0.8; if the monthly operation rate of the equipment is greater than or equal to 95% (stable operation threshold), it is forced to be assigned a value of 1.2; In the data cleaning process, the gold data source judgment rule requires forced weight reduction processing for low reliability data sources (such as assigning a value of 0.8 when the missing rate is greater than or equal to 10%; assigning a value of 1.2 when the missing rate is less than or equal to 2%); Therefore The value range of is (0.8≤ ≤1.2).

[0037] Assuming that the data index is: historical error rate 23%, monthly operation rate of equipment 75%, data missing rate 12%, the data source reliability score affected by the error rate is Since the result is less than 0.8, it is forced to be assigned a value of 0.8. The data source reliability score affected by the monthly operation rate of the equipment is Since the result is less than 0.8, it is forced to be assigned a value of 0.8. The data source reliability score affected by the missing rate is Since the result is less than 0.8, it is forced to be assigned a value of 0.8. The final value is the most stringent value among the three, =0.8.

[0038] The calculation process is as follows: Assuming that the time delay =3 seconds, the update frequency =0.2 Hz, the transmission success rate =96%, the weights are determined by the Delphi method: the time delay weight =0.5, the update frequency weight =0.3, and the transmission success rate weight =0.2.

[0039] The original data is normalized:

[0040] Set the maximum allowed delay =10 seconds, then the time delay score .

[0041] Set the ideal frequency =1 Hz, then the update frequency score .

[0042] If the transmission success rate score is directly expressed as a percentage value, then... .

[0043] Calculated according to the weighted formula .

[0044] Road grade weight Reflecting the inherent traffic capacity of road infrastructure, it is determined comprehensively by road type, design standards, and historical data. The calculation process is as follows:

[0045] According to national standards (such as the "Technical Standards for Highway Engineering"), roads are divided into four categories and assigned benchmarks. Values, as shown in Table 1:

[0046] Based on actual road conditions, the benchmark The value is dynamically adjusted: Hypothesis correction factor This indicates traffic control policies (such as restricted hours for trucks). If the current time is 7:00-9:00 (trucks are prohibited during the morning rush hour). The value increases by 0.2, and the benchmark value for a main road in a certain urban area... =0.5, which falls within the restricted hours for trucks: =0.5+ =0.5 + 0.2 = 0.7, indicating that the road's traffic efficiency is only 30% of the ideal state. These should be avoided as a priority.

[0047] Real-time congestion index Reflecting the real-time level of road congestion, it is dynamically calculated based on multi-source traffic data, with a value range of [0,1], where 0 represents smooth traffic and 1 represents complete congestion. The calculation process is as follows: , in The vehicle speed ratio is calculated as the real-time average vehicle speed divided by the design speed. Traffic saturation is calculated as the current traffic volume divided by the road's maximum capacity (e.g., if the maximum capacity is 2000 vehicles / hour and the actual traffic volume is 1600 vehicles). =0.8); The duration of the accident's impact, i.e., the cumulative delay caused by the accident; The threshold for the maximum impact of the accident; This is a weighting coefficient, based on the experience of urban traffic management departments.

[0048] Suppose a traffic accident occurs on a main road in a certain city, and the data is as follows: Weighting coefficients: , = 30 minutes, designed speed 40 km / h, real-time average vehicle speed 12 km / h, traffic saturation = 0.9, the accident has caused a delay of 25 minutes, then , = 0.3, substitute into the formula: ≈ 0.25.

[0049] The digital twin modeling module is used to fuse the transportation vehicle positioning, cargo state and real-time road condition data, and construct a virtual transportation scene that is real-time synchronized with the physical world; the digital twin modeling module further includes: ‌The geographic information input module acquires satellite maps and road network vector data (including road grades, bridge height limits) of the transportation path by receiving data from the data acquisition module; imports logistics node (warehouse, sorting center) coordinates, and labels dense area ranges (such as freight yards and loading / unloading areas within a radius of 500 m); ‌The dynamic object identification module is used to receive the vehicle-mounted GPS and real-time extract the transportation vehicle position and speed; ‌The area density calculation module calculates the number of objects per unit area based on the DBSCAN clustering algorithm, with the logistics node as the center, generates a LOD3 precision model in the dense area of the logistics node, and the dense area (LOD3) > 20 / 100㎡ (such as loading / unloading platforms); in the open area, a LOD1 precision model is used; the open area (LOD1) < 5 / 100㎡ (such as highway sections); The adaptive grid division and LOD grading module divides the basic grid using the quadtree algorithm, converts the coordinate system to the UTM partition projection; according to the area density, the grid level is labeled: in the dense area, millimeter-level features are constructed for stationary or low-speed objects; when the vehicle enters the highway, the LOD1 mode is triggered; The real-time rendering optimization and data synchronization module is used to enable the subdivision shader and load the 4K PBR material in the LOD3 area; in the LOD1 area, instance rendering is used to batch process the same model; when the vehicle accelerates from the warehouse to the park road, the geometric gradient technology is used to linearly reduce the model face number from 20,000 to 8,000; when the speed threshold is triggered, the LOD3 collision body is retained, and the visual model precision is reduced synchronously; the LOD state is synchronized to the logistics simulation deduction module through Kafka.

[0050] The logistics simulation deduction module simulates the dynamic scene in the transportation based on the digital twin model and real-time data, predicts the influence of different paths on time efficiency, energy consumption and cargo safety, generates multiple sets of emergency plans, optimizes resource allocation in combination with historical transportation data, and verifies the feasibility of the new route.

[0051] The logistics simulation deduction module further includes: A road network dynamic modeling module is configured to integrate a map and real-time traffic data, generate a hierarchical road network model covering expressways, national highways, and provincial highways, mark physical constraints such as slope, bridge load-bearing capacity, and tunnel height limit, and access external data such as weather warning and traffic accidents in real time to generate a three-dimensional visual forbidden area; A vehicle energy consumption simulation module is configured to calculate energy consumption in combination with vehicle speed, load, path distance, road condition, and cumulative running time; A real-time strategy verification module is configured to receive a recommended detour scheme from the optimization module and deduce a balance point between fuel consumption increase and time loss.

[0052] The energy consumption prediction formula of the vehicle energy consumption simulation module is as follows: wherein, is a total energy consumption prediction value, is a transport vehicle speed, is a real-time load, is a path distance, is a road condition attenuation coefficient, is a cumulative running time.

[0053] The road condition attenuation coefficient in the above energy consumption prediction formula is calculated as follows: The calculation formula of is as follows: wherein, is a road grade weight, is a real-time congestion index, is an environmental disturbance factor. The calculation processes of and have been described above and will not be repeated here. The environmental disturbance factor is calculated in real time through a weather API and road condition monitoring. For example: Suppose that a road section encounters an 8-level crosswind (wind speed 18 m / s) and the road is covered with 5 cm thick snow during transportation, then the environmental disturbance factor affected by wind speed is wherein, is the wind speed and 0.15 is the baseline value in plain areas. When the environmental temperature is -10℃, the engine thermal efficiency decreases by 5%, then the environmental disturbance factor affected by the environmental temperature is wherein, is the environmental temperature. The weather disturbance is integrated. The friction coefficient of a snowy road surface is and the friction coefficient of a dry road surface is , then the environmental disturbance factor affected by snow is .

[0054] The operation monitoring module analyzes abnormal risks based on the data collected by the data acquisition module, including the location, speed, cargo status, road conditions, weather, and driver behavior data of the transport vehicles. It then triggers an alarm module to issue a warning in a timely manner and links with the logistics simulation module to generate emergency plans, verify the feasibility of temporary routes, and revise the transportation plan in real time.

[0055] The operation steps of this monitoring module are as follows: S101. Receive data and obtain real-time information from the data acquisition module, including vehicle location, speed, cargo temperature and humidity, road conditions (such as congestion, construction), weather (such as rain, snow, strong wind), and driver operation (such as continuous driving time). S102. Organize data and filter out erroneous data: for example, exclude abnormal coordinates with excessive GPS positioning deviation; supplement missing values. If vehicle speed data is lost for a certain period of time, calculate a reasonable value based on the speed at the time points before and after the loss. S103. Identify potential risks and, based on the processed data, determine whether any anomalies exist: S104. Activate emergency response. When a high risk is detected, trigger an alarm and transmit the risk type and severity level to the abnormal alarm module. At the same time, send a request to the logistics simulation module to generate a detour route. S105. Verify the feasibility of the emergency plan and quickly verify the temporary plan provided by the logistics simulation module: calculate whether the detour route will cause the delivery time to exceed the limit, and confirm that the new route has no conflict risk (such as heavy trucks cannot pass through bridges with a height limit of 3 meters). S106. Adjust the transportation plan and update it in real time after verification.

[0056] The optimization decision-making module integrates data from the digital twin modeling module, logistics simulation and extrapolation module, and operation monitoring module to balance cost, timeliness and risk, automatically generate multiple route options, verify the feasibility of the options through simulation, and find the optimal route option.

[0057] This optimization decision-making module includes: The real-time strategy generation module is used to generate decision trees that include route changes, resource allocation, and risk response based on historical data from simulations; it also receives real-time traffic information from the logistics simulation module and generates detour plans. The resource dynamic scheduling module dynamically matches temporary orders based on the real-time location and load of vehicles en route (e.g., vehicles with 30% remaining load are given priority); dispatches nearby rescue vehicles when a vehicle breaks down; and changes priorities in real time according to the shipper's needs. The dynamic route planning module generates the optimal route by comprehensively considering the cost of toll roads, the risk value of mountain road sections, and the distribution of gas stations; when the GPS detects that the vehicle has deviated from the predetermined route, it replans the route based on real-time weather conditions. The transportation cost analysis module calculates transportation costs based on total energy consumption, fuel prices, environmental factors, and timeliness factors. The decision verification and feedback module is used to send the generated scheduling plan to the logistics simulation and deduction module, compare the transportation costs of the new plan with the original plan, and select the optimal plan.

[0058] The transportation cost calculation formula in the above transportation cost analysis module is as follows: ,in This is the predicted total energy consumption value. For fuel prices, As environmental disturbance factors, This is the time-loss coefficient. To simulate and predict delay time.

[0059] Among them, the total energy consumption forecast value and environmental disturbance factors The calculation process has been explained previously and will not be repeated here. Time-loss coefficient The calculation needs to be combined with the cargo priority and the risk of transportation delay. For example, suppose a refrigerated truck is transporting fresh goods (total freight cost of 10,000 yuan). A multi-vehicle rear-end collision is detected on a certain section of the road, and the congestion is expected to last for 1.5 hours. On another section of the road, there is heavy fog and the speed limit is reduced to 40 km / h, resulting in a delay of 1 hour.

[0060] Goods type priority: Fresh goods are prioritized according to industry standards. =200 yuan / hour; Contractual penalty percentage: The contract stipulates that a penalty of 0.5% of the freight fee will be imposed for each hour of delay, i.e., 10000 × 0.5% = 50 yuan / hour; Initial time loss coefficient: .

[0061] Fresh produce needs to be maintained at -18℃, which increases the risk of downtime by 30% and fuel consumption by 15% in low-temperature environments. .

[0062] The data interface module is used to connect the data acquisition module, digital twin modeling module, logistics simulation and simulation module and external data sources. It receives road condition information in real time, cleans and converts the data format synchronously, distributes it to the operation monitoring module to trigger the abnormal alarm module in a timely manner, and inputs road condition information parameters into the logistics simulation and simulation module to support emergency plan simulation.

[0063] The distributed storage module is used to build a multi-node collaborative data lake to achieve hierarchical storage and fast retrieval of heterogeneous data.

[0064] The anomaly alarm module interacts with the operation monitoring module in real time and uses a preset rule base to determine anomalies and issue early warning signals in a timely manner.

[0065] The working steps of this abnormal alarm module are as follows: S201, Data Access: Receives structured data packets preprocessed by the operation monitoring module, including dynamic data of transport vehicles, environmental characteristic data, driver status, and cargo safety parameters. S202. Warning type determination: When the lateral acceleration is greater than 0.4g, a vehicle sideslip warning is triggered; when the slope of the decrease in eye movement frequency is greater than 15% / minute, a fatigue driving warning is triggered; when the road surface temperature is less than or equal to 0℃ and the humidity is greater than or equal to 80%, a road icing warning is triggered; when the temperature fluctuation in the passenger compartment exceeds ±2℃ / 15 minutes, a temperature control warning is triggered. S203. Warning signal classification: When the hazard is lethal, the warning level is red level 1; when the hazard is severe, the warning level is orange level 2; when the hazard is moderate, the warning level is yellow level 3. S204. Warning signal distribution: Red Level 1 alarm: directly activates the ESP electronic stability system to force speed reduction; Orange Level 2 alarm: project warning icons through the HUD; for the dispatch center, push alarm details and handling suggestions through a dedicated protocol; for the driver, choose between a buzzer alarm or voice broadcast.

[0066] The 3D visualization module is used to build a global monitoring interface that maps virtual and real data, dynamically presenting the 3D spatiotemporal status of transport vehicles, goods, and the environment.

[0067] This 3D visualization module includes: The high-precision 3D scene construction module integrates satellite remote sensing data and lidar point clouds to generate a 3D road network model that includes terrain elevation, bridges and tunnels, and service area buildings; it can also load detailed models of specific areas as needed, including toll station lane distribution and gas station locations. The real-time dynamic data fusion module is used to integrate vehicle GPS trajectory, weather radar data, and roadside unit traffic statistics into a three-dimensional scene to form a dynamic heat map; and to apply a red outline warning to speeding vehicles. The interactive decision-making sandbox module supports dragging and dropping to modify virtual roadblocks (such as simulating the location of a landslide) and simulates the feasibility of vehicle detour routes in real time. The multi-level visualization module displays the distribution of trunk transportation traffic in the view, and clicking on a single vehicle model allows you to see through to view the temperature and humidity curves inside the cargo box and the driver fatigue index. The simulation process replay module is used to load historical weather data and replay vehicle trajectory deviations; it displays the actual transportation route and the simulated route side by side, and marks key divergence points.

[0068] The working principle of this invention is as follows: The data acquisition module acquires real-time data such as vehicle location and cargo status through GPS, sensors, and other devices. After standardization by the data interface module, the data is stored in the distributed storage module. The digital twin modeling module calls the stored data to construct a virtual transportation scenario, generating dynamic digital twins of vehicles and road networks. The operation monitoring module analyzes the real-time data stream and triggers a tiered warning from the anomaly alarm module when an anomaly is detected. Simultaneously, it sends an emergency simulation request to the logistics simulation module. The logistics simulation module simulates detour routes and resource allocation plans based on the twin model, submits the feasibility results to the optimization decision module, and generates the final execution instructions based on constraints such as cost and timeliness, updating the digital twin model. The 3D visualization module synchronously renders the entire process dynamically, intuitively displaying vehicle trajectories, risk points, and the effectiveness of emergency plans. All modules achieve cross-platform data interoperability through the data interface module, forming a closed loop of "perception-analysis-decision-execution".

[0069] The above provides a detailed description of a logistics monitoring and simulation system based on digital twins provided by this invention. The specific embodiments are described only to aid in understanding the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the scope of protection of the claims of this invention.

Claims

1. A logistics monitoring and simulation system based on digital twinning, characterized in that: The data acquisition module, the digital twin modeling module, the logistics simulation deduction module, the operation monitoring module, the optimization decision module, the data interface module, the distributed storage module, the abnormal alarm module, and the three-dimensional visualization module are included. The data acquisition module is used for collecting real-time transportation vehicle position, speed and cargo state data, synchronously integrating road conditions, weather and driver behavior information, and providing real-time early warning for logistics monitoring through multi-source data fusion to support the logistics simulation deduction module in deducing transportation strategies. The digital twin modeling module is used for fusing transportation vehicle positioning, cargo state and real-time road condition data to build a virtual transportation scene that is real-time synchronized with the physical world. The logistics simulation deduction module simulates dynamic scenes in transportation based on the digital twin model and real-time data, predicts the influence of different paths on time efficiency, energy consumption and cargo safety, generates multiple sets of emergency plans, optimizes resource allocation in combination with historical transportation data, and verifies the feasibility of new routes. The operation monitoring module analyzes abnormal risks based on the transportation vehicle position, speed and cargo state, road condition, weather and driver behavior data collected by the data acquisition module, timely triggers the abnormal alarm module to issue a warning, and connects the logistics simulation deduction module to generate an emergency plan, verify the feasibility of a temporary path, and correct the transportation plan in real time. The optimization decision module is used for balancing cost, time efficiency and risk by comprehensively integrating the data of the digital twin modeling module, the logistics simulation deduction module and the operation monitoring module, automatically generating multiple path plans, verifying the feasibility of the plans through simulation, and finding the optimal path plan. The data interface module is used for opening an interactive channel between the data acquisition module, the digital twin modeling module, the logistics simulation deduction module and external data sources, receiving real-time road condition information, synchronously cleaning and converting data formats, distributing the data to the operation monitoring module to timely trigger the abnormal alarm module to issue a warning, and inputting road condition information parameters to the logistics simulation deduction module to support emergency plan deduction. The distributed storage module is used for building a multi-node collaborative data lake to realize hierarchical storage and rapid retrieval of heterogeneous data. The abnormal alarm module issues a warning signal in real time through interaction with the operation monitoring module and abnormal judgment based on a preset rule library. The three-dimensional visualization module is used for building a virtual-real mapping global monitoring interface to dynamically present the three-dimensional space-time state of transportation vehicles, cargo and environment.

2. The logistics monitoring and simulation system based on digital twinning according to claim 1, characterized in that: The data acquisition module further includes a device access module, a GPS positioning device, a protocol adaptation module, a data cleaning and repair module, and a data storage module. The device access module is used for identifying a sensor group of a transportation vehicle, establishing a device database, and connecting a GPS positioning device to analyze the latitude, longitude, speed and time stamp of the transportation vehicle. The protocol adaptation module is used for unifying heterogeneous data into Apache Avro format. The data cleaning and repair module is used for removing abnormal values of a temperature and humidity sensor, generating an interpolated trajectory using a Kalman filter algorithm during a GPS signal loss period, denoising original data of a vibration sensor, and extracting effective vibration energy features. ​ The space-time alignment and standardization module is used for calibrating all device clocks and ensuring that sensor group data is consistent with GPS timestamps; and converting temperature control data into °C and converting vibration intensity into m / s². The multi-source heterogeneous data fusion module is used for fusing real-time rainfall of a meteorological API in transport vehicle data, correcting temperature and humidity sensor readings, dynamically adjusting sensor group sampling frequency according to transport order priority, and constructing a unified data model based on the ISO 19848 standard to map different source fields.

3. The logistics monitoring and simulation system based on digital twinning according to claim 2, characterized in that: The logistics simulation deduction module further includes The road network dynamic modeling module is used for integrating maps and real-time traffic data, generating a hierarchical road network model covering expressways, national roads and provincial roads, marking physical constraints such as slope, bridge load-bearing capacity and tunnel height limit, and real-time accessing meteorological warning and traffic accident external data to generate three-dimensional visual forbidden areas; The vehicle energy consumption simulation module calculates energy consumption in combination with vehicle speed, load, path distance, road conditions and cumulative running time; The real-time strategy verification module is used for receiving a detour scheme recommended by the optimization module and deducing a balance point between increased fuel consumption and time loss.

4. The logistics monitoring and simulation system based on digital twinning according to claim 3, characterized in that: The digital twin modeling module further includes The geographic information input module acquires satellite maps and road network vector data of a transport path by receiving data of the data acquisition module, and imports logistics node coordinates to mark dense area ranges; The dynamic object identification module is used for receiving a vehicle-mounted GPS to extract a transport vehicle position and speed in real time; The area density calculation module calculates the number of objects per unit area based on a DBSCAN clustering algorithm, generates a LOD3-level precision model in a dense logistics node area, and uses an LOD1-level precision model in an open area; The adaptive grid division and LOD grading module divides basic grids using a quadtree algorithm, converts coordinates into UTM partition projection, and marks grid levels according to area density: in a dense area, millimeter-level features are constructed for stationary or low-speed objects; when a vehicle enters an expressway, an LOD1 mode is triggered; The real-time rendering optimization and data synchronization module is used for enabling a subdivision shader and loading 4K PBR materials in an LOD3 area; using instance rendering and batch processing the same model in an LOD1 area; when a vehicle accelerates from a warehouse to a park road, using a geometric gradient technology to linearly reduce model patches from 20,000 to 8,000; when a speed threshold is triggered, retaining an LOD3 collision body and synchronously reducing visual model precision; The LOD state is synchronized to the logistics simulation deduction module through Kafka.

5. The logistics monitoring and simulation system based on digital twinning according to claim 4, characterized in that: The optimization decision module further includes The real-time strategy generation module is used for generating a decision tree including path change, resource allocation and risk response based on historical data of simulation deduction; The real-time strategy generation module is used for generating a decision tree including path change, resource allocation and risk response based on historical data of simulation deduction; The resource dynamic scheduling module dynamically matches temporary orders according to real-time positions and loads of in-transit vehicles, schedules a nearby rescue vehicle when a vehicle fault occurs, and changes priority in real time according to the needs of consignors. The dynamic path planning module generates an optimal path by integrating the toll road cost, the risk value of the mountainous road section, and the distribution of gas stations; when the GPS detects that the vehicle deviates from the predetermined path, the path is re-planned in combination with the real-time weather; The transportation cost analysis module calculates the transportation cost by total energy consumption, fuel price, environmental factors, and time efficiency factors; The decision verification and feedback module is used to send the generated scheduling scheme to the logistics simulation deduction module, compare the transportation costs of the new scheme and the original scheme, and select the optimal scheme.

6. The logistics monitoring and simulation system based on digital twinning according to claim 5, characterized in that, The three-dimensional visualization module further includes, The high-precision three-dimensional scene construction module is used to integrate satellite remote sensing data and laser radar point clouds to generate a three-dimensional road network model containing terrain elevation, bridges, tunnels, and service area buildings; a specific area is loaded with a refined model as needed, including the lane distribution of toll stations and the location of gas stations; The real-time dynamic data fusion module is used to fuse the vehicle-mounted GPS track, weather radar data, and roadside unit flow statistics to the three-dimensional scene to form a dynamic heat map; A red contour warning is applied to the overspeed vehicle; The interactive decision sand table module supports dragging and modifying virtual roadblocks and real-time deduction of the feasibility of the vehicle detour path; The multi-level visualization module displays the trunk transportation flow distribution in the view, and clicking on a single vehicle model can penetrate to view the temperature and humidity curve of the cargo box and the driver fatigue index; The simulation process playback module is used to load weather historical data, play back the vehicle driving track deviation, display the actual transportation path and the simulation deduction path side by side, and label the key divergence points.

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