Logistics data visualization method, device, equipment and storage medium

Abstract

The present application relates to the field of data visualization, and particularly relates to a logistics data visualization method, device, equipment and storage medium. The method comprises the following steps: reading a cargo electronic tag, detecting a transportation start signal sent by a logistics tool, and generating a transportation start event; collecting and recording a multi-modal positioning signal of the transportation logistics tool based on the transportation start event; performing spatial coordinate calculation on the multi-modal positioning signal, and outputting a time sequence coordinate sequence; performing global geographic coordinate mapping based on the time sequence coordinate sequence, and constructing a trajectory visualization map; identifying the latest state information of the cargo, synchronously updating the trajectory visualization map, and obtaining a real-time updated map; calculating the expected arrival time of the cargo, performing instant correlation mapping processing on the real-time updated map, and constructing a visualized logistics data graph. The present application realizes accurate and real-time visualization processing of international logistics whole-process data, and improves the readability of logistics data.

Description

Technical Field

[0001] This invention relates to the field of data visualization, and more particularly to a method, apparatus, device, and storage medium for visualizing logistics data. Background Technology

[0002] With the rapid development of global trade and cross-border e-commerce, international logistics, as a crucial link supporting the operation of the global supply chain, is experiencing an explosive growth in data volume. The vast amounts of data involved in cargo transportation, warehousing management, customs clearance, route optimization, and real-time tracking place immense information processing pressure on logistics companies in their operational decision-making and management optimization. Traditional data analysis methods struggle to intuitively and comprehensively display the complex information in cross-border transportation processes, leading to low management efficiency, delayed decision-making responses, and potentially even logistics delays and increased costs.

[0003] Traditional international logistics management methods primarily rely on tables, text reports, and static maps for data presentation. While these methods can record basic transportation data, they typically have limited information dimensions and struggle to reflect dynamic changes, anomalies, and multi-stage collaboration during cargo transportation. Furthermore, static presentation methods lack interactivity and visual analysis capabilities, failing to meet the demands of modern logistics management for real-time monitoring, risk warnings, and route optimization. As logistics networks become increasingly complex, the shortcomings of traditional methods in providing intuitive display and decision support for cross-border transportation data are becoming increasingly apparent. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a logistics data visualization method, apparatus, equipment, and storage medium, thereby resolving at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides a logistics data visualization method, comprising the following steps: Step S1: Read the cargo electronic tag, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; Step S2: Collect and record the multimodal positioning signal of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signal and output the time-series coordinate sequence; Step S3: Perform global geographic coordinate mapping based on time-series coordinate sequences to construct a trajectory visualization map; Step S4: Identify the latest status information of the goods, and update the trajectory visualization map synchronously to obtain a real-time updated map; Step S5: Calculate the estimated arrival time of the goods, perform real-time correlation mapping on the real-time updated map, and construct a visualized logistics data map.

[0006] This specification provides a logistics data visualization device for performing the logistics data visualization method described above, comprising: The detection unit is used to read the electronic tags on the goods, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; The coordinate calculation unit is used to collect and record the multimodal positioning signals of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signals, and output a time-series coordinate sequence; The visualization unit is used to perform global geographic coordinate mapping based on time-series coordinate sequences and construct trajectory visualization maps. The update unit is used to identify the latest status information of the goods and synchronize the information update of the trajectory visualization map to obtain a real-time updated map. The delay calculation unit is used to calculate the estimated arrival time of goods, perform real-time correlation mapping on the real-time updated map, and construct a visual logistics data map.

[0007] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the logistics data visualization method described in any of the above claims.

[0008] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the logistics data visualization method described in any of the preceding claims.

[0009] The beneficial effects of this invention are as follows: By reading the electronic tags of goods and linking them with the transportation start signal of the logistics vehicle, a transportation start event is generated only when the transportation activity actually occurs, avoiding data deviations caused by premature manual entry or delayed confirmation, and ensuring the authenticity of logistics data from the source. The transportation start event encapsulates the goods identifier, transportation vehicle identifier, and start time into standardized event data, providing a unified trigger point for subsequent positioning, status updates, and trajectory display, which is conducive to data collaboration between systems. A clear start event provides a unified time starting point for trajectory generation, transportation time statistics, and estimated arrival time calculation, improving the data alignment capabilities of international logistics across systems and regions. By collecting multimodal positioning signals (such as satellite positioning, base station positioning, inertial positioning, etc.), positioning continuity can be maintained in complex international scenarios such as ports, urban high-rise buildings, and transoceanic transportation, reducing data loss caused by the failure of a single positioning method. The calculated spatial coordinates are output in chronological order as a time-series coordinate sequence, transforming the positioning data from scattered points into an analyzable and displayable trajectory structure, laying the foundation for subsequent map mapping and visualization. By mapping global geographic coordinates, positioning data from different countries and regions are uniformly projected into the same geographic coordinate system, resolving the inconsistency of coordinate systems in international logistics. The trajectory visualization map presents complex time-series coordinate data as paths, nodes, and dynamic locations, significantly reducing the cost of understanding logistics status and improving the perception efficiency for managers and customers. By continuously identifying the latest status of goods (such as en route, stopped, clearing customs, transshipment, etc.) and updating it synchronously to the map, the information fragmentation problem of "track in motion, status lagging behind" can be avoided. Real-time map updates give each status change a clear spatial and temporal orientation, facilitating anomaly location, node confirmation, and responsibility traceability. Synchronized updates of status information and trajectory maps provide a unified data view for order systems, warehousing systems, and customer service systems, reducing the cost of duplicate confirmations and manual communication. Calculating the estimated arrival time based on the current trajectory and status upgrades the logistics system from "passive display" to "proactive prediction," providing forward-looking information for scheduling optimization and customer notification. Associating the estimated arrival time with map location forms a time-space integrated visualized logistics data map, improving the completeness and understandability of data expression. Visualized logistics data charts intuitively display the location, status, and expected arrival of goods, effectively reducing uncertainties in international logistics and enhancing customer trust and overall business operational efficiency. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a logistics data visualization method according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0011] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0012] This application provides a logistics data visualization method, apparatus, device, and storage medium. The execution entities of the method, apparatus, device, and storage medium include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0013] Please see Figures 1 to 3 This invention provides a logistics data visualization method, comprising the following steps: Step S1: Read the cargo electronic tag, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; Step S2: Collect and record the multimodal positioning signal of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signal and output the time-series coordinate sequence; Step S3: Perform global geographic coordinate mapping based on time-series coordinate sequences to construct a trajectory visualization map; Step S4: Identify the latest status information of the goods, and update the trajectory visualization map synchronously to obtain a real-time updated map; Step S5: Calculate the estimated arrival time of the goods, perform real-time correlation mapping on the real-time updated map, and construct a visualized logistics data map.

[0014] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a logistics data visualization method according to the present invention. In this example, the steps of the logistics data visualization method include: Step S1: Read the cargo electronic tag, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; In this embodiment, a high-frequency RFID scanning device reads the electronic tags on the goods to obtain the unique identifier, type, origin, and destination information. The RFID scanning device operates at a frequency of 860–960 MHz, has a reading distance of 3–8 meters, and a scanning rate of over 20 scans per second, ensuring rapid identification of batches of goods. During the reading process, the device uses an antenna array to achieve multi-angle coverage, avoiding data loss due to stacked or obstructed goods. Simultaneously, the device monitors transportation start signals emitted by the logistics vehicles carrying the goods, such as vehicle ignition signals, railway locomotive start signals, ship departure signals, or air cargo takeoff confirmation signals. The start signal detection frequency can be set to once per second, with timestamp accuracy down to the millisecond level. Upon detecting a start signal, the cargo digital information, logistics vehicle identifier, and transportation start timestamp are correlated to generate a transportation start event. This event is stored in a structured manner, including cargo ID, vehicle ID, transportation start time, origin coordinates, and transportation plan route information.

[0015] Step S2: Collect and record the multimodal positioning signal of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signal and output the time-series coordinate sequence; In this embodiment, after the transportation initiation event is triggered, continuous multimodal positioning signals are acquired from the transport vehicle, including satellite navigation signals such as GPS, BeiDou, GLONASS, and Galileo, as well as LTE / 5G base station signals and IMU inertial measurement unit acceleration and angular velocity information. The sampling frequency can be set to 1–5 frames / second, with a target trajectory point accuracy of 3–5 meters horizontal error and 5–10 meters vertical error. The acquired signals are first subjected to adaptive filtering processing, using Kalman filtering combined with a weighted moving average algorithm to eliminate multipath interference, instantaneous jumps, and noise interference. The filtered signals are used for three-dimensional spatial coordinate calculation, outputting longitude, latitude, and altitude information for each frame, and simultaneously generating a time-series coordinate sequence in chronological order. During the coordinate calculation process, IMU integration can be used to correct for short-term signal loss and satellite obstruction, ensuring trajectory continuity.

[0016] Step S3: Perform global geographic coordinate mapping based on time-series coordinate sequences to construct a trajectory visualization map; In this embodiment, after acquiring the time-series coordinate sequence, each 3D coordinate point is mapped to the Global Geographic Reference Frame (WGS-84) to ensure consistent trajectory display globally. The target accuracy for trajectory points is 3–5 meters of horizontal error, and projection correction is performed using an Earth ellipsoid model. Subsequently, the mapped trajectory points are rendered on a 3D map, including terrain elevation, road network, rivers, lakes, and ocean background. The trajectory can be displayed using a dynamic path; the path color, thickness, and animation speed can be adjusted according to the type of goods and transportation speed. For example, a bright color is displayed when the speed of vehicles on highways exceeds 60 km / h, while a dark color is used for speeds below high speed or when stationary. The trajectory rendering smoothing process uses cubic spline curve fitting, with a smoothing radius of 500 meters and a time window of 1–5 seconds to eliminate instantaneous jumps and improve continuity.

[0017] Step S4: Identify the latest status information of the goods, and update the trajectory visualization map synchronously to obtain a real-time updated map; In this embodiment, cargo status scanning information at transit nodes and logistics facilities is collected in real time on the trajectory visualization map, including RFID or barcode scanning, temperature and humidity sensor information, and loading and unloading status. The scanning frequency can be set to update every 5–15 minutes, and the temperature and humidity thresholds are set to 0–40°C and 30–80% relative humidity. Loading and unloading status is identified through logistics operation records. By comparing the unique cargo identifier with the transportation plan data, it is assessed whether the cargo has been loaded and unloaded, whether there are any abnormalities, or whether it is delayed. The latest status information is updated synchronously with the trajectory visualization map. For example, a status icon, color coding, or flashing animation is added to the current location of the cargo. Delayed nodes can be marked in red, and high-speed moving nodes are marked in green. The real-time updated map can display the cargo trajectory, transportation status, and delay situation, and supports continuous monitoring of cross-time zone and cross-border transportation.

[0018] Step S5: Calculate the estimated arrival time of the goods, perform real-time correlation mapping on the real-time updated map, and construct a visualized logistics data map.

[0019] In this embodiment, the remaining route length is calculated on a real-time updated map based on the latest coordinates of the goods and the preset transportation route. The estimated transportation time is then calculated by combining the type of transportation and average speed (e.g., 60–80 km / h for highway vehicles, 50–70 km / h for rail freight, and 15–20 carriages for sea freight). Subsequently, the estimated arrival time is corrected based on transit node delays, weather conditions, and congestion. For example, heavy rain or operational congestion at ports can increase delays by 0.5–2 hours, while peak congestion at railway nodes can increase delays by 30–90 minutes. The corrected arrival time is dynamically linked to the goods' trajectory, and the transportation progress and estimated arrival time are displayed on a visualized map using color coding, animated trajectories, and time labels. This visualized logistics data map intuitively displays the current location, status, transportation progress, and predicted arrival time of the goods, providing real-time and intuitive data support for cross-border logistics monitoring, scheduling optimization, and transportation plan analysis, enabling dynamic monitoring and visualized management of the entire transportation process.

[0020] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Digital information of goods is extracted by reading electronic tags on goods using high-frequency RFID scanning equipment; Identify transportation and logistics tools based on transportation logs; perform type analysis on the transportation and logistics tools to obtain logistics type identifiers; The system detects when logistics tools send a transportation start signal and records the timestamp of the start of cargo transportation. Collect data on cargo digital information, logistics type identifiers, and cargo transportation start timestamps to generate transportation initiation events.

[0021] In this embodiment, during the initial phase of international logistics transportation, high-frequency RFID scanning equipment is used to electronically identify and read the goods to obtain their digital information. RFID tags are pre-attached or embedded in the goods packaging, pallets, or container units. The tags conform to the ISO / IEC 18000-6C standard and typically operate at a frequency of 860–960 MHz. When goods pass through loading, unloading, or distribution stages, the RFID scanning equipment activates the tags via radio frequency induction, reading the digital information stored within them, including the unique identifier of the goods, goods type, origin, destination, and transit time requirements. To ensure the reliability of batch readings within warehouses or logistics parks, the high-frequency RFID scanning equipment uses an antenna array configuration, enabling a single scan distance of 3–8 meters and a polling frequency of over 20 reads per second to ensure rapid collection of large volumes of goods. Information recorded in the transportation logs, including waybill numbers, modes of transport, loading operation records, and shipping or transportation plan information, is used to identify the actual logistics vehicles used in the transportation. These vehicles may include road transport vehicles, railway freight cars, sea containers, or air freighters. After identification, the types of logistics tools are analyzed and classified into logistics type identifiers, such as "land trucks," "railway freight," "sea container shipping," and "air freight." The type analysis process incorporates the physical characteristics, carrying capacity, transportation route, and operational records of the transport tools to achieve a structured description of the logistics tools.

[0022] Detection is performed using transport initiation signals emitted by the vehicle, which can include vehicle ignition, railway locomotive start-up, ship departure signals, or air cargo takeoff confirmation signals. Detection methods can be based on sensors, onboard terminal signals, or shipping operation logs, with the time of the first valid initiation state serving as the cargo transport start timestamp. The timestamp is recorded using a unified time base, with accuracy down to the second or millisecond level, ensuring consistency of time stamps in cross-border or cross-time zone transport scenarios. By recording the transport start timestamp, the exact point in time when the cargo enters the transport state can be precisely located. The cargo's unique identifier, logistics type identifier, and transport start timestamp are stored together according to a unified data structure, generating transport event objects that can be directly used for visualization and analysis. Each transport initiation event includes cargo identity, carrier logistics vehicle type, and precise departure time, and can be displayed as an event node in the data visualization platform for trajectory display, transport progress monitoring, and transport efficiency analysis. During data aggregation, duplicate or abnormal records can be filtered to ensure the integrity and accuracy of the event data.

[0023] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Based on the aforementioned transportation initiation event, multimodal positioning signals of the transportation logistics vehicle are collected and recorded; adaptive filtering processing is performed on the multimodal positioning signals to obtain filtered signals; The positioning time difference of multiple signal sources is calculated based on the filtered signal; the positioning time difference is then timestamped to obtain a synchronized positioning signal. Calculate the base station positioning jump error of the synchronous positioning signal and extract the position error of multiple positioning sources; Based on the position error, a coordinated correction is performed to generate a precise positioning signal; Spatial coordinates are calculated from the precise positioning signal to generate a real-time coordinate sequence; Calculate the timestamp of each frame of the precise positioning signal; time-mark the real-time coordinate sequence according to the timestamp, and output the time-series coordinate sequence.

[0024] In this embodiment, a multimodal positioning device installed on the logistics vehicle continuously collects its spatial position data. The multimodal positioning signals include global navigation satellite signals (GPS, BeiDou, GLONASS, Galileo), ground base station signals (such as LTE and 5G base station positioning signals), and acceleration and angular velocity information provided by the inertial measurement unit (IMU). The data acquisition process is performed at set time intervals, such as 1–5 frames per second, to ensure the continuity and accuracy of the positioning data. After obtaining the raw positioning signal, it is first subjected to adaptive filtering to eliminate the impact of multipath effects, transient interference, and noise on positioning accuracy. The adaptive filtering method combines Kalman filtering and a weighted moving average algorithm. By dynamically adjusting the filtering weights, high-noise sections are smoothed first, while low-noise sections retain the spatial details of the original signal, thereby outputting a stable filtered signal. Data frames from satellite navigation, base station positioning, and inertial sensors are compared according to their acquisition time, and the time offset difference of each signal source at the same location point is calculated. Through time difference analysis, the impact of signal reception delay, processing delay, and transmission delay on the positioning results can be identified. Subsequently, the time stamps of each signal source are synchronized and mapped to a single standard time base, such as UTC time, to form a synchronization positioning signal. During the timestamp synchronization process, linear interpolation or Kalman filtering time compensation methods can be used to reasonably correct a small number of missing frames or abnormally delayed data.

[0025] Spatial variation analysis is performed on continuous frame data. When the positioning coordinates abruptly change beyond a preset threshold (e.g., more than 50 meters) compared to the previous frame, it is marked as a base station jump error. Simultaneously, satellite navigation signals and inertial measurement data are compared to calculate the deviation of each positioning source relative to the multimodal fusion reference. By analyzing the consistency and abrupt change magnitude of different signal sources in spatial location, the real-time position errors of multiple positioning sources are extracted, providing quantitative parameters for subsequent collaborative correction. This error extraction process can incorporate parameters such as signal strength, satellite visibility, and IMU measurement accuracy to ensure that the error assessment results reflect the actual positioning deviation.

[0026] Using a weighted fusion algorithm, the spatial coordinates of satellite signals, base station signals, and inertial measurement signals are combined according to their respective accuracy weights, while suppressing jump errors and noise signals. The weights can be dynamically adjusted based on the number of available satellites, signal reception strength, IMU data stability, and base station coverage quality. The precise positioning signal generated after collaborative correction processing significantly outperforms single signal sources in terms of spatial continuity, accuracy stability, and error control, providing reliable location information in complex transportation environments. Three-dimensional coordinates, including longitude, latitude, and altitude, are calculated for each frame of the positioning signal using a unified Earth reference ellipsoid model (such as WGS-84), ensuring a unified reference for all coordinates globally. During the calculation process, IMU acceleration integration and trajectory extrapolation methods can be combined to compensate for short-term signal gaps, ensuring sequence continuity. A timestamp is calculated for each frame of the precise positioning signal, using a unified time base, such as a UTC second-level timestamp. The timestamp is bound to the corresponding spatial coordinates to form a complete time-series coordinate sequence. This time-series coordinate sequence not only contains the continuous position of the goods in three-dimensional space, but also accurately records the time corresponding to the position of each frame, achieving a one-to-one correspondence between space and time. By marking and processing the time-series coordinate sequence, dynamic trajectory display, speed analysis, and transportation process monitoring can be performed on a data visualization platform.

[0027] In this embodiment, step S3 includes the following steps: Global geographic coordinate mapping is performed based on time-series coordinate sequences to generate the first dynamic spatial trajectory; Perform continuity correction and trajectory smoothing on the dynamic spatial trajectory, and output a second dynamic spatial trajectory; Real-time location identification is performed on the second dynamic spatial trajectory, and the geographic information of the location is extracted; the geographic information of the location includes geographic type, country, road traffic network, administrative division boundary and terrain elevation information; Render the map based on the geographical type of the location and generate rendering parameters; Based on the rendering parameters, the spatial scene of the second dynamic space trajectory is visualized to construct a trajectory visualization map.

[0028] In this embodiment, latitude, longitude, and altitude information in the WGS-84 reference ellipsoidal coordinate system are mapped to a global map coordinate framework recognizable by a Geographic Information System (GIS), giving each location point a clear geospatial meaning. During the mapping process, coordinate transformation rules across 180° longitude regions, polar regions, and different national geographic data need to be considered to ensure trajectory continuity and global consistency. The generated first dynamic spatial trajectory is a set of geographic locations arranged in chronological order, reflecting the dynamic movement of the transport vehicle and goods during global transportation. Potential jumps, signal gaps, or local jitter in the trajectory are identified and corrected using multi-point weighted smoothing and curve fitting methods. For example, low-pass filtering, Kalman filtering, or cubic spline curve interpolation can be used to smooth abnormal jumps while ensuring the spatial direction and movement trend of the trajectory are consistent with the actual transportation path. Continuity correction also includes compensating for short-term missing coordinate points by generating intermediate trajectory points through interpolation. The smoothed second dynamic spatial trajectory is not only spatially continuous but also more consistent with the movement characteristics of actual logistics transportation, providing a high-quality trajectory data foundation for subsequent geographic information recognition and visualization rendering. During processing, smoothing parameters can be set, such as a curve smoothing radius of 500 meters and a time window of 1 to 5 seconds, to balance trajectory details and smoothing effect.

[0029] The trajectory coordinates are compared with a global geographic information database to extract local geographic information, including geographic type (land, ocean, river, lake, etc.), country, road network information, administrative boundaries, and topographic elevation information. Road network information includes road grade, road direction, and traffic flow attributes. Administrative boundaries include provincial, state, county, or city-level boundary coordinates. Topographic elevation information includes spatial features such as average elevation, ridges, and valleys. Through real-time identification, each trajectory point can be associated with specific geographic attributes, giving the transportation trajectory semantic information in geographic space. Rendering styles are assigned to land, ocean, river, and elevation terrain separately; for example, land uses light green, ocean uses blue, and rivers and lakes use dark blue. Elevation changes are displayed through contour lines or gradients. Road networks reflect road grade and capacity through line thickness and color. Administrative boundaries are identified by outlines indicating different administrative areas. Rendering parameters also include transparency, lighting effects, and layer stacking order to ensure the trajectory is clearly displayed on the 3D map. During the rendering parameter generation process, the spatial matching relationship between trajectory points and map features is combined to ensure that the display style of each trajectory point is consistent with the geographical type of the location, thereby providing users with an intuitive spatial perception and environmental background on the visualization platform.

[0030] The trajectory points are drawn as dynamic paths on the rendered map in chronological order. The path color, thickness, and animation speed can be dynamically adjusted according to transportation status and time information. For example, the path is displayed in a light color during high-speed transportation and in a dark color during low-speed or stationary transportation. Trajectory points can be highlighted with halos or animated indicators to emphasize the direction of movement. The visualized map supports multi-layer overlay, dynamically combining road networks, administrative divisions, and terrain elevations with the trajectory to achieve a global visualization effect. Through spatial scene visualization processing, the constructed trajectory visualization map not only displays the movement trajectory of goods and carriers but also reflects the geographical environment and road network conditions of the transportation route.

[0031] In this embodiment, step S4 includes the following steps: Identifying global logistics facility geographic information based on transport logs; Based on global logistics facility geographic information, transit nodes are detected on the trajectory visualization map. When the time sequence coordinates are within the geofence of the transit node, the speed of the transportation logistics vehicle is calculated based on the trajectory visualization map. Based on the speed, positional change stagnation analysis is performed to mark the goods as entering a transit and lingering state; Calculate the dwell time of the transport logistics vehicle, analyze the reasons for the dwell time, and generate real-time dwell time information; Identify cargo status scan information at transit nodes; perform status assessment on the cargo status scan information to generate the latest status information; The trajectory visualization map is updated synchronously based on real-time stationary information and the latest status information to obtain a real-time updated map.

[0032] In this embodiment, information such as transit stations, ports, airports, railway hubs, and warehousing facilities recorded in the transportation logs is parsed, and combined with logistics facility registration information and a geographic information database, each facility is associated with its precise geographic coordinates. Through GIS database retrieval, the longitude, latitude, altitude, and facility attributes (facility type, capacity, operational status) of the logistics facilities are extracted, generating a structured geographic information dataset. During data extraction, digital boundaries can be delineated for road connections, import / export channels, and loading / unloading areas surrounding the facilities, forming complete geofencing data. For each frame of trajectory coordinates, it is determined whether it falls within the geofencing range of the transit node using a point-polygon spatial detection algorithm. When a trajectory point is within the geofencing, it is marked as entering the transit node state, and the movement speed of the transport logistics vehicle is calculated. Speed ​​calculation is obtained by dividing the spatial distance between coordinate points in two or more consecutive frames by the time interval; for example, the instantaneous speed can be calculated for trajectory points sampled per second, and the unit is converted to kilometers per hour. Speed ​​calculation can be used to identify stationary, slow-moving, or normal transportation states.

[0033] The speed sequence is compared with a preset stagnation threshold; for example, a speed below 1 km / h for more than 5 minutes is considered stagnant. Through temporal continuity analysis, when trajectory points continuously meet the stagnation condition, the cargo is marked as entering a transit stagnation state. Simultaneously, stagnant trajectory segments are matched with transit node geofences to ensure that stagnation occurs within a legitimate transit facility area, avoiding misjudgments. The stagnation start time is recorded as the timestamp when the speed first falls below the stagnation threshold, and the stagnation end time is the timestamp when the speed returns to normal or the trajectory leaves the transit node geofence; the difference between the two is the stagnation duration. Subsequently, cause analysis is performed based on the stagnation time and transit node attributes, such as unloading, loading, customs inspection, traffic congestion, or facility operation delays. The analysis method combines transportation log information, node operation records, and historical stagnation patterns. The generated real-time stagnation information includes transit node identifier, cargo identifier, stagnation time, stagnation cause, and spatial location.

[0034] The system reads RFID or barcode scan data, including the cargo's unique identifier, loading / unloading status, transportation anomaly indicators, and temperature and humidity sensor information. The scanned information is compared with the original cargo transportation information to assess whether the cargo status meets expectations, such as whether loading / unloading is complete, whether damage has occurred, and whether temperature and humidity levels are within acceptable limits. The status assessment process analyzes data using rule-based comparison and anomaly detection methods to generate the latest cargo status information, including status category (in transit, loaded / unloaded, anomaly), status timestamp, and status description. The system maps the dwell time, reason for dwell time, and cargo status information to corresponding trajectory points and presents this information graphically on a map, such as through color coding, icons, or animations to display the cargo's current and latest status. Real-time information synchronization updates include refreshing the color, size, and labeling of transit node trajectory points to ensure the visualized map reflects the latest logistics status.

[0035] In this embodiment, the specific steps of step S5 are as follows: The remaining path length is calculated based on the real-time updated map to generate the estimated arrival time of the goods; Identify multiple transit nodes based on a preset transportation route; Extract local weather information and congestion status of multiple transit nodes; Based on the local weather information and node congestion status, dynamic delay prediction is performed to generate dynamic delay duration; The estimated arrival time is updated and corrected based on the dynamic delay duration to obtain the arrival time; The arrival time is then mapped to the real-time updated map to create a visualized logistics data map.

[0036] In this embodiment, the temporal coordinates of the current location of the goods are spatially matched with a preset transportation route, and continuous path segment information from the current location to the destination is extracted using GIS analysis tools. The path calculation considers not only straight-line distance but also the applicable route type for the transportation vehicle, such as road vehicles along road networks, rail freight along tracks, and sea vessels along waterways. Each path segment is weighted according to road grade, tortuosity, speed limit, and transportation vehicle type to generate a practically feasible remaining distance. Subsequently, the remaining path length is combined with the average speed of the transportation vehicle to calculate the preliminary estimated arrival time. The average speed can be set based on the transportation type, such as 60–80 km / h for road freight vehicles, 50–70 km / h for rail freight cars, and 15–20 carriages for sea container ships. The transportation route is spatially matched with a global logistics facility database, and nodes on the route that intersect with logistics facility fences are marked as transit nodes, including ports, railway hubs, air cargo terminals, and cross-border warehousing facilities. The geographical extent of each transit node is modeled using GIS data, including precise coordinates, radius, or polygon boundaries to ensure accurate spatial positioning of the nodes. During the identification process, transportation plans and cargo loading / unloading plans were combined to exclude nodes on the route that were not actually used, ensuring a high degree of match between the set of transit nodes and the actual transportation routes. The identified nodes provide basic spatial data for subsequent weather and congestion information collection, dynamic delay analysis, and transit event labeling in visualization maps, while also providing node-level event triggering basis for cargo transportation status management.

[0037] Real-time weather information, including rainfall, wind speed, visibility, temperature, and extreme weather warnings, is obtained from the meteorological data interface for the node's location. This data is then standardized for quantifiable application. Simultaneously, operational monitoring data from logistics facilities or traffic flow monitoring systems are used to obtain node congestion status, such as port container yard capacity occupancy, the number of trains entering and leaving railway hubs, air cargo hangar occupancy, and road traffic flow. Weather and congestion status information are correlated with the node's spatial coordinates to form a node environmental attribute dataset that can be directly applied to delay prediction. The data collection cycle is set according to node characteristics; for example, weather information is updated hourly, and traffic flow is updated every 5–15 minutes, ensuring that the node status reflects real-time changes and providing a reliable foundation for subsequent delay calculations and dynamic visualization annotations. A node delay model is established, quantifying weather conditions, node congestion levels, and historical delay patterns into delay times. For example, heavy rain or low visibility reduces port loading and unloading efficiency, potentially increasing delays by 0.5–2 hours per hour; peak congestion at railway hubs can cause delays of 30–90 minutes per freight train; and in shipping, severe weather at sea reduces ship speeds and increases berthing waiting times. The delay at each transit node is cumulatively calculated, and combined with the transportation time between nodes along the route, the dynamic delay duration of the entire transportation route is summed to obtain the total potential time delay for cargo transportation. Delay prediction uses a rolling time window update, recalculating every 5–15 minutes to ensure that the prediction results reflect environmental changes and achieve dynamic delay time updates on the visualized map.

[0038] The remaining time along the route is accumulated and added to the delays at each transfer node, taking into account buffer time between nodes, the acceleration capacity of transport vehicles, and possible short-distance adjustments. For example, if goods pass through three transfer nodes with a total delay of 2 hours, and the original estimated arrival time was 4 PM, the revised estimated arrival time is 6 PM. This correction process reflects the dynamic environmental impacts encountered during transportation, such as weather changes, traffic congestion, and operational delays, thus providing a more dynamic prediction closer to the actual arrival time. The current location of the goods' trajectory is linked to arrival time information, and the progress of goods transportation and the estimated arrival time are displayed through color coding, icon markings, or dynamic animations. For example, gradient path colors can be used to represent transportation progress, bouncing or highlighted icons can be used to mark the estimated dwell time at transfer nodes, and arrival time labels can be displayed on the map. The visualized map can overlay the remaining route, node delays, weather information, and the current location of the goods to achieve a dynamic display of transportation status. Users can view the changes in the movement of goods and the estimated arrival time through a time slider or animation playback, achieving full-process visual monitoring.

[0039] In this embodiment, the specific steps for calculating the remaining path length based on the real-time updated map and generating the estimated arrival time of the goods are as follows: The latest coordinates of the goods are calculated based on the real-time updated map; Identify cargo destinations and preset transportation routes based on transportation logs; Based on the latest coordinates of the cargo, the transportation progress to the destination of the cargo is calculated to obtain real-time transportation progress information. The remaining path length of the preset transportation route is calculated based on real-time transportation progress information, and the remaining path information is extracted. The estimated arrival time of the goods is calculated based on the remaining route information.

[0040] In this embodiment, the latest time frame coordinates of the cargo trajectory in the trajectory visualization map are used to extract the latest spatial point from the last collected time-series coordinates. These coordinates include longitude, latitude, and necessary altitude information, and can be converted using map projection methods (such as WGS-84 or UTM projection) to ensure consistent representation within the global geographic information framework. Simultaneously, to guarantee coordinate accuracy, the original trajectory data can be smoothed, for example, by using Kalman filtering or moving weighted averaging to eliminate instantaneous jumps and measurement errors, resulting in a stable latest location point. The cargo destination information recorded in the transportation log is analyzed, including geographic coordinates, facility type, and node attributes. Simultaneously, a preset transportation route is extracted by combining transportation plan data. The transportation route can be formed by connecting multiple segments from the starting point to the destination along highways, railways, air routes, or sea routes. Each route segment includes the starting point, ending point, and route length information. During the identification process, GIS spatial matching is used to align the transportation route with the geographic facility database, and the path nodes are serialized to ensure the spatial continuity and correct order of the transportation route. The extracted transportation route is used as a reference trajectory and compared with the latest coordinates to provide a basis for calculating transportation progress and remaining route length, while also providing data support for displaying the complete transportation route on a visual map.

[0041] The latest coordinates of the goods are compared spatially with the coordinates of the origin and destination to calculate the percentage of the actual route length completed relative to the total preset route length. Transportation progress can be expressed by the formula: Progress Percentage = (Distance Traveled ÷ Total Route Length) × 100%. The calculation also considers route tortuosity, vehicle type, and route classification; for example, the accuracy of the transportation progress estimate can be adjusted by weighting highway sections and urban roads. Real-time transportation progress information dynamically reflects the current transportation status of goods, identifies whether transportation is proceeding as planned, and identifies abnormal delays or early arrivals. It also provides real-time reference for calculating the remaining route, enabling a clear display of transportation progress on the transportation visualization map.

[0042] The remaining route segments and their spatial coordinates are determined by comparing the completed route length with the preset total route length. The remaining route information includes the starting and ending coordinates of each segment, the route length, road class or route type, and the locations of potential transfer nodes and key facilities. Different weights can be assigned based on the type of cargo transport during route length calculation; for example, the average speed of land vehicles on urban roads is lower than on highways, and shipping vessels need to consider channel curvature and port berthing time. The remaining route information can be directly used for visualization on maps, marking the future movement trajectory of cargo, and serving as the basis for calculating the estimated arrival time. After extracting the remaining route information, the estimated arrival time is calculated by dividing the route length by the average speed of the transport vehicle (e.g., 60–80 km / h for highway freight vehicles, 50–70 km / h for railway freight cars, and 15–20 carriages for ocean container ships). The current timestamp is then added to the transport time to obtain the preliminary estimated arrival time. During the calculation, the estimated arrival time can be adjusted by incorporating the estimated dwell time, congestion conditions, and historical delay data of each transfer node in the transport route. The final result provides a dynamic time forecast for goods from their current location to their destination, which can be displayed in conjunction with a real-time updated map.

[0043] In this embodiment, a logistics data visualization device is provided for executing the logistics data visualization method described above, including: The detection unit is used to read the electronic tags on the goods, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; The coordinate calculation unit is used to collect and record the multimodal positioning signals of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signals, and output a time-series coordinate sequence; The visualization unit is used to perform global geographic coordinate mapping based on time-series coordinate sequences and construct trajectory visualization maps. The update unit is used to identify the latest status information of the goods and synchronize the information update of the trajectory visualization map to obtain a real-time updated map. The delay calculation unit is used to calculate the estimated arrival time of goods, perform real-time correlation mapping on the real-time updated map, and construct a visual logistics data map.

[0044] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the logistics data visualization method described in any of the above claims.

[0045] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the logistics data visualization method described in any of the preceding claims.

[0046] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0047] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for visualizing logistics data, characterized in that, Includes the following steps: Step S1: Read the cargo electronic tag, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; Step S2: Collect and record the multimodal positioning signal of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signal and output the time-series coordinate sequence; Step S3: Perform global geographic coordinate mapping based on time-series coordinate sequences to construct a trajectory visualization map; Step S4: Identify the latest status information of the goods, and update the trajectory visualization map synchronously to obtain a real-time updated map; Step S5: Calculate the estimated arrival time of the goods, perform real-time correlation mapping on the real-time updated map, and construct a visualized logistics data map.

2. The logistics data visualization method according to claim 1, characterized in that, The specific steps of step S1 are as follows: Digital information of goods is extracted by reading electronic tags on goods using high-frequency RFID scanning equipment; Identify transportation and logistics tools based on transportation logs; perform type analysis on the transportation and logistics tools to obtain logistics type identifiers; The system detects when logistics tools send a transportation start signal and records the timestamp of the start of cargo transportation. Collect data on cargo digital information, logistics type identifiers, and cargo transportation start timestamps to generate transportation initiation events.

3. The logistics data visualization method according to claim 1, characterized in that, The specific steps of step S2 are as follows: Based on the aforementioned transportation initiation event, multimodal positioning signals of the transportation logistics vehicle are collected and recorded; adaptive filtering processing is performed on the multimodal positioning signals to obtain filtered signals; The positioning time difference of multiple signal sources is calculated based on the filtered signal; the positioning time difference is then timestamped to obtain a synchronized positioning signal. Calculate the base station positioning jump error of the synchronous positioning signal and extract the position error of multiple positioning sources; Based on the position error, a coordinated correction is performed to generate a precise positioning signal; Spatial coordinates are calculated from the precise positioning signal to generate a real-time coordinate sequence; Calculate the timestamp of each frame of the precise positioning signal; time-mark the real-time coordinate sequence according to the timestamp, and output the time-series coordinate sequence.

4. The logistics data visualization method according to claim 1, characterized in that, Step S3 is as follows: Global geographic coordinate mapping is performed based on time-series coordinate sequences to generate the first dynamic spatial trajectory; Perform continuity correction and trajectory smoothing on the dynamic spatial trajectory, and output a second dynamic spatial trajectory; Real-time location identification is performed on the second dynamic spatial trajectory, and the geographic information of the location is extracted; the geographic information of the location includes geographic type, country, road traffic network, administrative division boundary and terrain elevation information; Render the map based on the geographical type of the location and generate rendering parameters; Based on the rendering parameters, the spatial scene of the second dynamic space trajectory is visualized to construct a trajectory visualization map.

5. The logistics data visualization method according to claim 1, characterized in that, The specific steps of step S4 are as follows: Identifying global logistics facility geographic information based on transport logs; Based on global logistics facility geographic information, transit nodes are detected on the trajectory visualization map. When the time sequence coordinates are within the geofence of the transit node, the speed of the transportation logistics vehicle is calculated based on the trajectory visualization map. Based on the speed, positional change stagnation analysis is performed to mark the goods as entering a transit and lingering state; Calculate the dwell time of the transport logistics vehicle, analyze the reasons for the dwell time, and generate real-time dwell time information; Identify cargo status scan information at transit nodes; perform status assessment on the cargo status scan information to generate the latest status information; The trajectory visualization map is updated synchronously based on real-time stationary information and the latest status information to obtain a real-time updated map.

6. The logistics data visualization method according to claim 1, characterized in that, The specific steps of step S5 are as follows: The remaining path length is calculated based on the real-time updated map to generate the estimated arrival time of the goods; Identify multiple transit nodes based on a preset transportation route; Extract local weather information and congestion status of multiple transit nodes; Based on the local weather information and node congestion status, dynamic delay prediction is performed to generate dynamic delay duration; The estimated arrival time is updated and corrected based on the dynamic delay duration to obtain the arrival time; The arrival time is then mapped to the real-time updated map to create a visualized logistics data map.

7. The logistics data visualization method according to claim 1, characterized in that, The specific steps for calculating the remaining path length based on the real-time updated map and generating the estimated arrival time of the goods are as follows: The latest coordinates of the goods are calculated based on the real-time updated map; Identify cargo destinations and preset transportation routes based on transportation logs; Based on the latest coordinates of the cargo, the transportation progress to the destination of the cargo is calculated to obtain real-time transportation progress information. The remaining path length of the preset transportation route is calculated based on real-time transportation progress information, and the remaining path information is extracted. The estimated arrival time of the goods is calculated based on the remaining route information.

8. A logistics data visualization device, characterized in that, For performing the logistics data visualization method as described in claim 1, including: The detection unit is used to read the electronic tags on the goods, detect the transportation start signal issued by the logistics vehicle, and generate a transportation start event; The coordinate calculation unit is used to collect and record the multimodal positioning signals of the transportation logistics vehicle based on the transportation initiation event; perform spatial coordinate calculation on the multimodal positioning signals, and output a time-series coordinate sequence; The visualization unit is used to perform global geographic coordinate mapping based on time-series coordinate sequences and construct trajectory visualization maps. The update unit is used to identify the latest status information of the goods and synchronize the information update of the trajectory visualization map to obtain a real-time updated map. The delay calculation unit is used to calculate the estimated arrival time of goods, perform real-time correlation mapping on the real-time updated map, and construct a visual logistics data map.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the logistics data visualization method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the logistics data visualization method according to any one of claims 1 to 7.

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