A data processing method for logistics platform

Abstract

The present invention relates to the field of logistics management technology, and in particular to a data processing method for a logistics platform. The method comprises the following steps: obtaining cargo information data from the logistics platform; extracting cargo transportation addresses from the logistics platform cargo information data to obtain cargo transportation address data; constructing a logistics transportation tracking network based on the cargo transportation address data from the standard logistics platform cargo information data to generate a logistics transportation tracking network; analyzing the remaining space of transport vehicles on the logistics transportation tracking network to obtain remaining space data for transport vehicles; analyzing the perishability of cargo transportation routes on the cargo transportation address data to generate perishability data for cargo transportation; and adjusting the placement of cargo transit space using the perishability data for cargo transportation to generate cargo space adjustment data. The present invention improves the overall efficiency and service quality of logistics through data integration, spatial optimization, route adjustment, time series prediction, and customer feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a data processing method for a logistics platform. Background Art

[0002] Early logistics data processing relied primarily on manual record-keeping and basic database management systems. This approach was not only inefficient and error-prone, but also failed to meet the real-time, accurate logistics needs. With the rapid development of technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence, data processing on logistics platforms is gradually becoming intelligent. IoT technology enables real-time data transmission across every logistics link, significantly improving the accuracy and timeliness of data collection. Cloud computing provides powerful computing and storage capabilities, enabling logistics platforms to process massive amounts of real-time data. Furthermore, the application of artificial intelligence and machine learning has made data analysis and prediction more intelligent, supporting the optimization of logistics routes, inventory management, and demand forecasting. In recent years, with the widespread adoption of big data technologies, data processing methods on logistics platforms have placed greater emphasis on deep data mining and multidimensional analysis. However, in traditional logistics, space utilization on transport vehicles is often suboptimal, resulting in wasted space and cargo damage. Furthermore, improper selection of logistics routes or failure to adjust them in a timely manner can easily lead to inefficient transportation or delays, ultimately reducing overall logistics efficiency and service quality. Summary of the Invention

[0003] Based on this, it is necessary to provide a data processing method for a logistics platform to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a data processing method for a logistics platform is provided, the method comprising the following steps:

[0005] Step S1: Obtain cargo information data from the logistics platform; extract cargo transportation addresses from the logistics platform cargo information data to obtain cargo transportation address data; construct a logistics transportation tracking network for the standard logistics platform cargo information data based on the cargo transportation address data to generate a logistics transportation tracking network;

[0006] Step S2: Analyze the remaining space of transport vehicles on the logistics transport tracking network to obtain transport vehicle remaining space data; perform a perishable analysis on the cargo transport address data to generate perishable data; use the perishable data to adjust the placement of cargo transit space to generate cargo space position adjustment data;

[0007] Step S3: collecting road environment data from the logistics transportation tracking network to obtain freight transportation road environment data; screening the freight transportation path data for remaining transportation paths to generate freight transportation remaining path data; adjusting the freight transportation path for the remaining freight transportation path data using the freight transportation road environment data to generate transportation path adjustment data; and adjusting the transportation timing based on the transportation path adjustment data to generate freight transportation timing prediction adjustment data.

[0008] Step S4: Collect logistics satisfaction feedback data on the cargo transportation timing prediction adjustment data and the cargo spatial position adjustment data to obtain cargo logistics satisfaction feedback data; optimize the logistics transportation tracking network process based on the cargo logistics satisfaction feedback data, thereby generating a logistics transportation tracking optimization strategy to perform efficient logistics operations.

[0009] By extracting cargo information data and building a logistics and transportation tracking network, the platform can obtain comprehensive, real-time transportation information for each shipment, ensuring data consistency and traceability throughout the transportation process. This tracking network effectively reduces information silos, making the entire logistics process more transparent and efficient. By analyzing the remaining space on transport vehicles, cargo loading can be optimized, improving transportation space utilization. Fragility analysis of cargo transport routes enables early identification of vulnerable goods and adjustment of their placement, reducing the risk of damage during transportation and improving cargo safety and transportation efficiency. By collecting real-time road environment data, changes in transport routes, such as traffic conditions and weather conditions, can be dynamically monitored, allowing for timely route adjustments to avoid unnecessary delays. Route adjustments ensure timely delivery and route safety, improving overall transportation efficiency. Furthermore, based on these adjustments, transport timing can be optimized, reducing time waste and ensuring on-time delivery. By collecting satisfaction feedback on cargo transport timing adjustment data and cargo spatial location adjustment data, customers' evaluations of logistics services and evolving needs can be monitored in real time. This feedback helps optimize the transportation process, further improving service quality and customer satisfaction. The optimized logistics and transportation tracking strategy makes logistics operations more efficient, better adapts to changing customer needs, and enhances the platform's competitiveness. Therefore, through data integration, space optimization, route adjustment, time series prediction, and customer feedback, the present invention improves the overall efficiency and service quality of logistics.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain cargo information data from the logistics platform;

[0012] Step S12: preprocessing the logistics platform cargo information data to generate standard logistics platform cargo information data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0013] Step S13: extracting the cargo transportation address from the cargo information data of the standard logistics platform to obtain cargo transportation address data; filtering the cargo information data of the standard logistics platform for the logistics transportation start-end storage site based on the cargo transportation address data to obtain cargo transportation logistics start storage site data and cargo transportation logistics end storage site data;

[0014] Step S14: constructing a logistics transportation tracking network for the logistics platform cargo information data based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data to generate a logistics transportation tracking network.

[0015] The present invention can eliminate noise and abnormal data, improve data quality and consistency, and thus ensure the accuracy of subsequent analysis and processing through data cleaning, denoising, missing value filling and standardization. Extracting and structuring cargo transportation address data can help accurately locate the transportation route of goods, provide clear information support for logistics planning, and thus improve the efficiency and visualization of the logistics network. By screening the data of the starting and ending storage sites, the transportation process path of the goods can be accurately determined, which not only improves the traceability of the cargo flow process, but also provides reliable data support for subsequent transportation scheduling and optimization. Constructing a logistics transportation tracking network integrates all key logistics information (such as starting sites, end sites, transportation routes, etc.) into a complete network structure, realizing full tracking and real-time monitoring of the cargo transportation process, which helps to improve the transparency of the transportation process, identify potential problems in a timely manner and make adjustments, thereby reducing transportation delays, losses and other risks.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data, the logistics platform cargo information data is configured with a logistics process to generate cargo logistics process configuration data, wherein the cargo logistics process configuration data includes a cargo loading process, a cargo transportation process, and a cargo unloading process;

[0018] Step S142: Process data of the cargo loading process, cargo transportation process, and cargo unloading process are collected through multi-source sensors to generate cargo loading process data, cargo transportation process data, and cargo unloading process data;

[0019] Step S143: Data identification is performed on the cargo loading process data, the cargo transportation process data, and the cargo unloading process data, and a logistics tracking node is constructed based on the identified cargo loading process data, the cargo transportation process data, and the cargo unloading process data to generate a logistics tracking node;

[0020] Step S144: Perform network topology design on the logistics tracking nodes to generate logistics network topology design data; assign network attributes to the logistics tracking nodes according to the logistics network topology design data, thereby generating a logistics transportation tracking network.

[0021] By clearly setting the cargo loading, transportation, and unloading processes, the present invention can accurately define the operating specifications for each link, which helps to clarify the relationship between each process node and reduce the risk of misoperation and process obstruction. At the same time, these settings can provide a clear framework and data support for subsequent logistics scheduling, optimization, and analysis. Using multi-source sensors for data collection can ensure that each link in the logistics process (such as loading, transportation, and unloading) has real-time and accurate data records. This not only improves the accuracy and completeness of the data, but also provides the necessary information support for real-time monitoring and fault detection, making the logistics process more intelligent and automated. By identifying logistics data and building logistics tracking nodes based on these identifiers, it is helpful to track the flow status of goods in each link in real time. Tracking nodes can provide precise location and timestamp information, making the transportation status of each cargo visual, and can quickly locate the specific link when problems arise, and quickly handle and optimize them. Designing the network topology of logistics tracking nodes and assigning network attributes helps to create a more structured and efficient logistics and transportation network. Through network topology, the connection relationship and data flow between nodes can be optimized, the efficiency of information transmission can be improved, and potential bottlenecks or optimization space can be analyzed, thereby further improving the efficiency and accuracy of logistics operations.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Extracting basic cargo information from the logistics and transportation tracking network based on the cargo loading process to obtain basic cargo logistics information data, where the basic cargo information includes cargo geometry information, cargo structure information, and cargo weight information; allocating logistics transportation vehicles to the cargo information data on the logistics platform based on the basic cargo logistics information data to obtain logistics transportation vehicle allocation data;

[0024] Step S22: analyzing the remaining space of the transport vehicles on the logistics transport vehicle allocation data to obtain the remaining space data of the transport vehicles; constructing a logistics transport path based on the starting storage site data and the ending storage site data of the freight transport logistics through the freight transport process to generate freight transport path data, wherein the freight transport path data includes a starting site, a ending site, and data of several transfer sites;

[0025] Step S23: performing a path geographic analysis on the cargo transportation path data to generate cargo transportation path geographic elevation fluctuation data; quantifying a path bumpiness index on the cargo transportation path data based on the cargo transportation path geographic elevation fluctuation data to generate a transportation path bumpiness index;

[0026] Step S24: Cargo vulnerability analysis is performed on the remaining space data of the transport vehicle according to the transport path bumpiness index to generate cargo transport vulnerability data; cargo transport vulnerability data is used to adjust the cargo transfer space placement of the transfer station data to generate cargo space position adjustment data.

[0027] By extracting basic logistics data based on the cargo's geometric, structural, and weight information, the present invention helps accurately match cargo with appropriate transport vehicles. By optimizing vehicle allocation, vehicle resource utilization can be improved, avoiding overloading or underloading, thereby achieving more efficient logistics scheduling and cost control. Analyzing the remaining space in transport vehicles can help effectively utilize the remaining space in each vehicle, reduce empty loads or wasted space, and improve logistics efficiency. This not only helps reduce transportation costs but also allows for rational scheduling and optimized allocation of transport tasks when transporting multiple cargoes. Constructing freight transport routes and analyzing geographic elevation fluctuations can identify complex terrain and elevation changes along the transport routes, which is crucial for transport planning. Route fluctuations can affect the stability and safety of transport vehicles, especially for fragile cargo. Therefore, quantifying these changes in advance can provide data support for subsequent transport risk assessment and optimization decisions. Quantifying the bumpiness index of transport routes based on geographic elevation fluctuation data helps assess the bumpiness risk faced by cargo along different routes. This is crucial for ensuring the safe transportation of fragile cargo, especially in areas with high bumps such as highways and mountain roads. This can provide real-time decision support and prevent damage to cargo during transportation. Conducting a vulnerability analysis of cargo based on the transport route's bumpiness index and the vehicle's remaining space analysis can help identify which cargo is most vulnerable to damage during transportation. This analysis not only optimizes the cargo's spatial layout but also allows for reasonable adjustments to the cargo location at transit stations to ensure better protection for vulnerable cargo, reduce the risk of cargo damage, and improve transit efficiency.

[0028] Preferably, analyzing the remaining space of transport vehicles on the logistics transport vehicle allocation data includes:

[0029] Perform initial space analysis on the logistics transport vehicle allocation data to generate initial space data for logistics transport vehicles; perform cargo space area calculation on the cargo logistics basic information data to obtain cargo space area data;

[0030] Using the cargo space area data to perform cargo storage allocation on the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation data; performing coordinate system conversion on the cargo storage space allocation data and the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation coordinate data and the initial space coordinate data of the logistics transport vehicle;

[0031] The coordinate axis difference between the initial spatial coordinate data of the logistics transport vehicle and the coordinate data of the cargo storage space allocation is calculated to obtain the remaining space data of the transport vehicle.

[0032] By performing an initial spatial analysis of logistics transport vehicles, the present invention provides a clear understanding of the available space in each transport vehicle. This is essential for ensuring efficient scheduling of subsequent transport tasks and helps transportation management systems understand the cargo capacity of each vehicle, providing an important basis for cargo allocation and space optimization. The cargo space area is calculated based on the cargo's geometric information and structure. This process ensures that the cargo's space requirements are accurately quantified, allowing for sufficient space for each item during subsequent space allocation and reducing the risk of squeezing, deformation, or damage during transportation. Using cargo space area data for cargo storage allocation allows for the rational placement of cargo within transport vehicles, avoiding vehicle instability or inefficient transportation caused by insufficient space or uneven loading. This process helps optimize the loading process, improve vehicle space utilization, and reduce unloaded space. Through coordinate system transformation, cargo storage space allocation data and initial transport vehicle space data can be standardized into a unified coordinate system, ensuring compatibility and comparability across different data sources. This operation provides a convenient data framework for subsequent spatial analysis and optimization and ensures the accuracy of coordinate transformations. By calculating the difference between the coordinate axes, the remaining space in the transport vehicle can be determined. This calculation accurately reflects the available space remaining after the vehicle is loaded with cargo, helping to optimize the vehicle's loading arrangements and ensure that every inch of space is utilized as much as possible without exceeding the maximum load. It also provides data support for subsequent optimization tasks, such as further improving space utilization efficiency by adding more cargo or adjusting cargo position.

[0033] Preferably, step S23 includes the following steps:

[0034] Step S231: Using GIS technology, geographical coordinate matching is performed on the starting station, the end station, and the transfer station data in the freight transportation route data to generate geographical coordinate matching data of the freight transportation station; geographical elevation conversion is performed on the geographical coordinate matching data of the freight transportation station to generate geographical elevation information data of the freight transportation station;

[0035] Step S232: Calculating the slope of each section of the cargo transportation station's geographic elevation information data to obtain cargo transportation section slope data; performing height difference statistics on the cargo transportation section slope data to generate cargo transportation route elevation data;

[0036] Step S233: Drawing an elevation curve for the cargo transportation segment slope data based on the cargo transportation route elevation data to generate a cargo transportation route elevation curve; performing elevation change fluctuation analysis on the cargo transportation elevation curve to generate geographic elevation fluctuation data for the cargo transportation route;

[0037] Step S234: quantifying the path bumpiness index of the cargo transportation path data according to the transportation path bumpiness quantification formula and the geographic elevation fluctuation data of the cargo transportation path to generate a transportation path bumpiness index.

[0038] This invention utilizes GIS technology to match the geographic coordinates of the starting, ending, and transit points, ensuring accurate alignment of transport route data with the real-world geographic environment. Elevation conversion provides geographic elevation information for each station, providing essential data for subsequent route analysis. This process lays the foundation for efficient route planning and transport safety analysis. Segment-by-segment slope calculation quantifies route fluctuations, deriving slope data and performing elevation statistics. This analysis helps identify steep and flat areas along the route and assess the load and stability of transport vehicles under varying terrain conditions. Elevation statistics provide guidance for load adjustment and driving behavior optimization. Elevation curves and fluctuation analysis provide a visual representation of elevation changes along the route, providing a clear data visualization for cargo transportation. This helps identify road sections with excessive bumps or significant undulations, enabling the planning of smoother routes. This not only reduces vehicle vibration but also effectively mitigates the risk of cargo damage. Based on the geographic elevation fluctuation data of transport routes and a route bumpiness quantification formula, we can accurately quantify the transport route's bumpiness index. This quantification provides a scientific basis for assessing the safety of transport vehicles and cargo. By quantifying the bumpiness index, we can help optimize transport routes and select routes suitable for different cargo characteristics, thereby improving transportation efficiency and reducing losses.

[0039] Preferably, the transport path bump quantification formula in step S234 is specifically as follows:

[0040]

[0041] Where B is the bumpiness index of the transport path, L is the total length of the path, and h(x) is the elevation at position x on the path. Expressed as the rate of change of elevation with respect to distance, It is expressed as the curvature of the path at position x, v(x) is expressed as the speed at position x, σ(x) is expressed as the road condition influencing factor, α is expressed as the path curvature influencing coefficient, β is expressed as the slope influencing coefficient, γ is expressed as the influencing coefficient of controlling traffic flow, and δ is expressed as the influencing coefficient of the road condition influencing factor on bumps.

[0042] This paper analyzes and integrates a formula to quantify transport route bumpiness. This formula identifies elevation fluctuations and slope changes as key contributors to transport route bumpiness. In particular, when a route involves steep slopes or complex curves, vehicle ups and downs, or sudden braking and acceleration, can cause greater vibration. The second derivative of elevation (i.e., curvature) describes the extent of a route's bumpiness. Areas with dramatic elevation fluctuations, where vehicles must adapt to the constantly changing terrain, typically produce more bumpiness. Slope (i.e., the first derivative of elevation) measures the degree of inclination at each location along the route. Steeper slopes (uphill or downhill) require the vehicle to constantly accelerate or brake, exacerbating the bumpiness. For example, frequent brake application during long downhill journeys can also cause vehicle vibration, increasing transport unsteadiness. Traffic flow or vehicle speed (v(x)) is significantly correlated with bumpiness. When traffic flow is high and roads are congested, vehicles frequently need to decelerate, stop, or accelerate, resulting in more vibration. At lower speeds, the vehicle is relatively stable, with less vibration. However, if the vehicle is driving under unstable traffic conditions (for example, constantly accelerating or braking while driving slowly), the bump index will increase. Weather factors (such as rain, snow, wet roads, wind speed, etc.) have a significant impact on bumps. Extreme weather conditions (such as heavy rain, heavy snow or storms) will reduce the adhesion of the road, making the vehicle unstable and increasing vibration. Wet roads (such as after rain) will increase the slippage between the vehicle tires and the road surface, resulting in unstable movement of the wheels, thereby exacerbating the vibration. The second derivative of elevation (curvature) directly affects the road's bumpiness, especially on roads with sharp turns or large inclines and downhill slopes. Larger, more dramatic elevation fluctuations result in more vibrations for the vehicle. The weighting factor α adjusts the curvature's contribution to overall bumpiness; generally, a larger α increases the impact of road bumpiness. Slope reflects the steepness of the path at different locations. Steep uphill and downhill slopes cause the vehicle's engine and brakes to work frequently, increasing vibration. The weighting coefficient β determines the contribution of slope to bumpiness. A larger slope weighting significantly increases the impact of this term. γ·(v(x)) 2, traffic flow or the speed of the vehicle directly affects the acceleration and deceleration pattern of the vehicle. Higher traffic flow means frequent braking and acceleration, which increases the bumpy feeling. When the speed or traffic flow is low, the vehicle drives more smoothly. The weight coefficient γ controls the contribution of speed to the bumpiness. A larger γ will strengthen the influence of speed on the bumpiness index. δ·(σ(x)), the weather factor σ(x) can dynamically adjust the impact of road conditions on bumpiness. For example, rainy and snowy weather, wind speed, slippery roads, etc. will aggravate bumpiness. The weight coefficient δ controls the degree of influence of weather and road conditions to ensure that the bumpiness index of the path increases under adverse conditions. When using the conventional transport path bumpiness quantification formula in this field, the transport path bumpiness index can be obtained. By applying the transport path bumpiness quantification formula provided by the present invention, the transport path bumpiness index can be calculated more accurately. This formula comprehensively quantifies the bumpiness of the transport path by combining multiple factors such as elevation change, slope, traffic flow, weather and road conditions. The contribution of each factor to the bumpiness is adjusted by the weight coefficient, so that the model can be dynamically adjusted according to the actual transportation environment. Elevation curvature and slope primarily account for the impact of the geographical environment on vehicle vibration. Large undulations and steep slopes significantly increase bumps. Traffic flow and speed reflect the instability of vehicle driving. Higher traffic flow typically means more frequent acceleration and deceleration, which exacerbates bumps. Weather and road conditions simulate the impact of the external environment. Severe weather or slippery roads can cause vehicle instability, exacerbating bumps.

[0043] Preferably, step S3 includes the following steps:

[0044] Step S31: collecting road environment data from the logistics transportation tracking network based on the cargo transportation process to obtain cargo transportation road environment data; screening the cargo transportation path data for remaining transportation paths to generate cargo transportation remaining path data;

[0045] Step S32: using the freight transport road environment data to perform a transport path congestion analysis on the freight transport remaining path data to generate transport path congestion data; and using the transport path congestion data to perform a transport path adjustment on the freight transport remaining path data to generate transport path adjustment data.

[0046] Step S33: performing transportation time series prediction on the cargo transportation path data based on the transportation path adjustment data to generate cargo transportation time series prediction data;

[0047] Step S34: performing additional transportation timing adjustments on the cargo transportation timing forecast data according to the cargo space position adjustment data to generate cargo transportation timing forecast adjustment data.

[0048] By collecting environmental data on freight transport routes, the present invention provides a comprehensive understanding of road conditions, traffic conditions, and potential risk factors during transportation. This data helps select the most suitable transport routes, thereby avoiding transport delays or safety issues caused by poor road conditions. The remaining route data after screening provides foundational data for subsequent transport route optimization, facilitating the development of more rational transport plans. Congestion analysis of transport routes based on road environmental data can proactively identify sections with traffic congestion or poor traffic flow, allowing timely adjustments to transport routes to avoid unnecessary delays during transport. This analysis significantly improves transport efficiency and reduces the time wasted by goods due to congestion. Furthermore, the adjusted route data enables dynamic optimization of transport routes, ensuring a smoother transport process. Time-series forecasting of transport routes accurately predicts transport demand and traffic conditions at different time periods during the freight transport process, providing data support for logistics scheduling. This time-series forecasting data can help transport managers conduct more accurate transport scheduling and resource allocation, improve transport efficiency, and optimize various aspects of the transport process, reducing unnecessary waiting and idle loads. Further adjustments to the timing forecasts based on cargo spatial position data can optimize transportation schedules and ensure that cargo arrives at each destination on schedule. This adjustment helps avoid timing disruptions caused by insufficient space or differences in cargo characteristics, improving overall transportation efficiency. Furthermore, accurate timing adjustments can reduce transportation delays, increase customer satisfaction, and ensure efficient and reliable transportation services.

[0049] Preferably, step S33 includes the following steps:

[0050] Step S331: extracting adjustment-related features from the transport path adjustment data to obtain transport path adjustment feature data; generating time series labels from the transport path adjustment feature data to obtain transport path adjustment time series labels;

[0051] Step S332: Divide the cargo transportation path data into a data set by adjusting the time series labels of the transportation paths to generate a model training set and a model test set; train the model training set using a long short-term memory neural network algorithm to generate a cargo transportation time series prediction pre-model;

[0052] Step S333: perform model optimization iteration on the freight transportation time series prediction pre-model according to the model test set, thereby generating a freight transportation time series prediction model; import the freight transportation route data into the freight transportation time series prediction model to perform transportation time series prediction, and generate freight transportation time series prediction data.

[0053] By extracting features from transport route adjustment data, the present invention can identify key factors involved in route adjustment, such as traffic flow and changes in road conditions. These features provide the necessary information for time series label generation, allowing changes in transport routes to be accurately mapped to time series. This process can provide accurate input data for subsequent prediction models, ensuring the accuracy and reliability of time series predictions. By dividing the data set into a training set and a test set and using a long short-term memory (LSTM) neural network for training, the characteristics of time series data can be fully utilized to capture the time dependency and trend changes in transport routes. LSTM networks are good at processing and predicting time series data, so they can effectively predict future transport route changes and help optimize transport timing and scheduling. The preliminary model is optimized and iterated based on the test set data to improve the accuracy and generalization ability of the model. The optimized transport timing prediction model can make more accurate predictions in a variety of scenarios, thereby providing accurate decision support for transport managers. This process can reduce scheduling errors caused by prediction errors and improve logistics efficiency. By inputting freight transportation route data into the optimized time series forecasting model, actual transportation time series forecasting is performed. The generated forecast data can accurately predict transportation demand, traffic conditions, and cargo arrival times within different time periods, providing dispatchers with clear action guidance. Time series forecasting can optimize transportation plans, reduce delays, ensure on-time cargo arrival, improve customer satisfaction, and reduce unnecessary resource waste.

[0054] Preferably, step S4 includes the following steps:

[0055] Step S41: collecting cargo logistics satisfaction feedback data based on the cargo unloading process data and the cargo transportation time sequence prediction adjustment data and the cargo space position adjustment data to obtain cargo logistics satisfaction feedback data;

[0056] Step S42: Tracing the cargo flow of the logistics and transportation tracking network based on the cargo logistics satisfaction feedback data to generate cargo flow traceability data; optimizing the logistics and transportation tracking network based on the cargo flow traceability data to generate a logistics and transportation tracking optimization strategy to perform efficient logistics operations.

[0057] The present invention collects satisfaction feedback on transport timing prediction data and cargo spatial position adjustment data based on cargo unloading process data, directly reflecting the customer experience during transportation. By collecting satisfaction feedback data in real time during cargo transportation, problems or bottlenecks in the transportation process, such as time delays, cargo damage, and delivery accuracy, can be identified. This provides real-world, effective data support for subsequent optimization of logistics operations and helps managers understand and improve service quality. Cargo flow traceability, based on the collected logistics satisfaction feedback data, facilitates comprehensive tracking of every link in the cargo transportation process, accurately recording and monitoring every step from origin to destination. Traceability data can reveal potential problems in the transportation process, such as improper operation, timeliness issues, or missing logistics links. Tracking these links improves transparency in the logistics process and provides data support for further process optimization. Based on cargo flow traceability data, the logistics transportation tracking network can optimize processes. This optimization not only improves logistics efficiency but also helps identify bottlenecks and inefficiencies in the transportation process, such as excessive lag time and inappropriate transportation route selection. Through optimization strategies, adjustments can be made across different transport links to improve operational efficiency, reduce transportation costs, and ultimately achieve efficient logistics operations. After generating a logistics and transportation tracking optimization strategy, it can be implemented during actual transportation operations to ensure efficient logistics operations. Through real-time feedback and data-driven decision-making, each transport task can be precisely scheduled and executed, thereby reducing resource waste, improving delivery accuracy, and optimizing the customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of the steps of a data processing method for a logistics platform;

[0059] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0060] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0064] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0065] To achieve this, please refer to Figures 1 to 3 , a data processing method for a logistics platform, the method comprising the following steps:

[0066] Step S1: Obtain cargo information data from the logistics platform; extract cargo transportation addresses from the logistics platform cargo information data to obtain cargo transportation address data; construct a logistics transportation tracking network for the standard logistics platform cargo information data based on the cargo transportation address data to generate a logistics transportation tracking network;

[0067] Step S2: Analyze the remaining space of transport vehicles on the logistics transport tracking network to obtain transport vehicle remaining space data; perform a perishable analysis on the cargo transport address data to generate perishable data; use the perishable data to adjust the placement of cargo transit space to generate cargo space position adjustment data;

[0068] Step S3: collecting road environment data from the logistics transportation tracking network to obtain freight transportation road environment data; screening the freight transportation path data for remaining transportation paths to generate freight transportation remaining path data; adjusting the freight transportation path for the remaining freight transportation path data using the freight transportation road environment data to generate transportation path adjustment data; and adjusting the transportation timing based on the transportation path adjustment data to generate freight transportation timing prediction adjustment data.

[0069] Step S4: Collect logistics satisfaction feedback data on the cargo transportation timing prediction adjustment data and the cargo spatial position adjustment data to obtain cargo logistics satisfaction feedback data; optimize the logistics transportation tracking network process based on the cargo logistics satisfaction feedback data, thereby generating a logistics transportation tracking optimization strategy to perform efficient logistics operations.

[0070] By extracting cargo information data and building a logistics and transportation tracking network, the platform can obtain comprehensive, real-time transportation information for each shipment, ensuring data consistency and traceability throughout the transportation process. This tracking network effectively reduces information silos, making the entire logistics process more transparent and efficient. By analyzing the remaining space on transport vehicles, cargo loading can be optimized, improving transportation space utilization. Fragility analysis of cargo transport routes enables early identification of vulnerable goods and adjustment of their placement, reducing the risk of damage during transportation and improving cargo safety and transportation efficiency. By collecting real-time road environment data, changes in transport routes, such as traffic conditions and weather conditions, can be dynamically monitored, allowing for timely route adjustments to avoid unnecessary delays. Route adjustments ensure timely delivery and route safety, improving overall transportation efficiency. Furthermore, based on these adjustments, transport timing can be optimized, reducing time waste and ensuring on-time delivery. By collecting satisfaction feedback on cargo transport timing adjustment data and cargo spatial location adjustment data, customers' evaluations of logistics services and evolving needs can be monitored in real time. This feedback helps optimize the transportation process, further improving service quality and customer satisfaction. The optimized logistics and transportation tracking strategy makes logistics operations more efficient, better adapts to changing customer needs, and enhances the platform's competitiveness. Therefore, through data integration, space optimization, route adjustment, time series prediction, and customer feedback, the present invention improves the overall efficiency and service quality of logistics.

[0071] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of a data processing method for a logistics platform according to the present invention. In this example, the data processing method for a logistics platform includes the following steps:

[0072] Step S1: Obtain cargo information data from the logistics platform; extract cargo transportation addresses from the logistics platform cargo information data to obtain cargo transportation address data; construct a logistics transportation tracking network for the standard logistics platform cargo information data based on the cargo transportation address data to generate a logistics transportation tracking network;

[0073] In embodiments of the present invention, cargo information data is obtained from various logistics platforms (such as transportation management systems, warehouse management systems, and distribution systems). The data includes cargo name, weight, volume, transportation status, shipping address, receiving address, delivery time, and transportation vehicle information. The acquired cargo information data from the logistics platforms is preprocessed to remove redundant information and correct data format errors. Transport-related address information, including shipping address, destination address, and transit point addresses, is extracted from the cargo information data. Natural language processing (NLP) techniques, such as regular expressions and name entity recognition (NER) models, are used to ensure the accuracy and completeness of the address data. Based on the extracted cargo transportation addresses, a transportation route is constructed using Geographic Information System (GIS) technology or other map data. The transportation route connects various nodes (such as transit warehouses and distribution centers) from the shipping location to the destination, forming a spatial network for logistics transportation. Each address or transit point is considered a node. Based on the cargo transportation route, transportation routes are connected between adjacent nodes. Based on factors such as transportation time, distance, and cost, the logistics transportation route is optimized to generate the shortest or optimal route solution. Construct a graph data structure for the logistics and transportation tracking network, where nodes in the graph represent locations and edges represent transportation routes. The network can be represented using adjacency matrices, adjacency lists, and other forms. Each node contains relevant information about the location, such as address, coordinates, and historical transportation records. Each edge contains relevant information about transportation, such as transportation tools, transportation time, and transportation status. Generate a transportation network graph using a graph processing library (such as NetworkX, Gephi, etc.). Each node in the graph represents a transportation location, and the edge represents the transportation route of the goods. Update the transportation route and status of the logistics transportation process in real time. For example, through GPS positioning and real-time updates of the transportation management system, the latest progress of cargo transportation can be dynamically monitored to ensure that the tracking network reflects the current logistics status.

[0074] Step S2: Analyze the remaining space of transport vehicles on the logistics transport tracking network to obtain transport vehicle remaining space data; perform a perishable analysis on the cargo transport address data to generate perishable data; use the perishable data to adjust the placement of cargo transit space to generate cargo space position adjustment data;

[0075] In this embodiment of the present invention, basic information about each transport vehicle is obtained from the logistics platform and transportation management system, including vehicle type, load capacity, remaining space, transport route, and estimated time of arrival. This data should include detailed information for each transport mission. Remaining space is calculated based on each vehicle's maximum load capacity and the weight of the currently loaded cargo. This information can come from the vehicle's sensors or the logistics management system. Combined with cargo volume information (such as the size and shape of each item), the remaining space for each vehicle is calculated using an optimization algorithm (such as a greedy algorithm or a knapsack optimization algorithm). As cargo loading and unloading progresses during transportation, the remaining space for each vehicle is updated in real time, generating remaining space data for each transport vehicle. This data includes: the remaining space (volume or weight) for each vehicle; the current loading status of each vehicle; and the loading efficiency (fullness) of each vehicle. The risks and vulnerability of cargo during transportation are assessed based on factors such as cargo type, size, material, road conditions, and weather conditions along the transport route. Vulnerability is assessed based on the material and packaging method of the cargo. For example, fragile, perishable, and easily deformable items should be specifically marked. Based on route data from the logistics and transportation tracking network, the system analyzes road conditions along the transport route (e.g., sharp bends, slopes, and passage through extreme climate zones). GIS data, traffic flow, climate forecasts, and other information are used to assess the vulnerability of each transport segment. Factors such as temperature, humidity, vibration, and external shock can negatively impact certain goods, particularly perishable, fragile, or chemical goods. Based on this analysis, cargo transport vulnerability data is generated, including: vulnerability scores for each transport route (high, medium, or low); cargo vulnerability classification (e.g., fragile, perishable, or valuable); and risk points along each route (e.g., hazardous sections, areas with inclement weather). Based on cargo vulnerability data and vehicle space analysis, the system optimizes transport routes and transit methods. The primary goal is to minimize the time vulnerable goods are exposed to hazardous routes or harsh environments, ensuring transport safety. Through space planning and cargo type matching, vulnerable goods are prioritized in the safest locations on transport vehicles or in transit warehouses. Factors to be considered include temperature and humidity control, vibration protection, shock-absorbing design, and avoiding exposure to high or low temperatures. During transportation, the placement of goods is dynamically adjusted based on real-time data (such as road conditions, weather, vehicle space changes, etc.). For example, if a certain section of the journey is marked as "highly vulnerable", the location of vulnerable goods can be adjusted in advance to avoid exposure to high-risk sections of the road. Use backpack algorithms or multi-objective optimization algorithms to ensure maximum efficiency in cargo distribution within limited transportation space, while giving priority to the protection of vulnerable goods. Through real-time monitoring during transportation, ensure that vulnerable goods are always placed in the best location. Based on the above placement adjustment plan and algorithm results, generate spatial position adjustment data for the goods.This data contains the precise location and placement of each item, ensuring that fragile goods are effectively protected.

[0076] Step S3: collecting road environment data from the logistics transportation tracking network to obtain freight transportation road environment data; screening the freight transportation path data for remaining transportation paths to generate freight transportation remaining path data; adjusting the freight transportation path for the remaining freight transportation path data using the freight transportation road environment data to generate transportation path adjustment data; and adjusting the transportation timing based on the transportation path adjustment data to generate freight transportation timing prediction adjustment data.

[0077] In this embodiment of the present invention, road environment data is acquired from sensors, road monitoring systems (such as traffic cameras and road surface monitoring sensors), GPS devices, and meteorological data sources. Intelligent sensors are deployed at key road locations to collect real-time data on road conditions, temperature and humidity, and road damage. This data is integrated with a traffic management platform or weather forecast system to obtain real-time information on traffic flow, weather, accidents, and more through an API. GPS and mobile devices on logistics transport vehicles are used to collect environmental data along the route and provide real-time feedback to the logistics management platform. The collected road environment data is integrated, formatted, and stored in a database to facilitate subsequent analysis and decision-making. Based on the logistics transport tracking network, detailed information on each transport route is extracted, including the starting point, end point, route nodes, transport time, and route distance. Routes that pass through clear roads are prioritized, avoiding areas with traffic congestion or frequent accidents. Road sections susceptible to severe weather, such as mountain roads, bridges, and highways, are avoided, as are areas particularly affected by storms, heavy snow, and icy conditions. Areas with poor road conditions and a high risk of traffic accidents (such as damaged sections and construction sites) are screened out. Combined with road environment data, existing routes are screened, eliminating routes unsuitable due to poor environmental or road conditions, resulting in feasible "remaining transport routes." This involves performing a weighted assessment of factors such as road conditions, traffic flow, and weather impacts on each route. Based on the assessment results, the remaining safe and efficient transport routes are selected. The selected remaining route data is saved as a route network, containing information such as route number, start and end points, route length, estimated route time, and road condition score. Transport routes are dynamically adjusted based on real-time road environment changes (such as congestion, accidents, and weather). For example, if a route is delayed due to inclement weather or a traffic accident, alternative remaining routes can be selected. By analyzing traffic flow and bottleneck areas along the route, transport routes are dynamically adjusted to avoid peak traffic or accident-prone areas. Transport routes are adjusted based on time-varying traffic flow and weather changes. For example, during the morning rush hour, busy urban areas can be avoided; at night, routes with low traffic flow can be selected. Using intelligent optimization algorithms (such as genetic algorithms or simulated annealing algorithms), transport routes are optimized through multiple iterations to achieve the goal of minimizing transport time, minimizing disruptions, or minimizing transport costs. Based on the route adjustment plan, detailed information about each route adjustment is recorded, including the route selection before and after the adjustment, estimated transport time, and the reason for the adjustment. Combined with road environment data and transport route adjustment data, factors influencing the timing of cargo transportation are analyzed, including: changes in traffic flow during different time periods during transportation, especially during peak hours in the morning and evening, and holidays. Weather factors (such as heavy rain and snow) affect transportation time, requiring adjustments to the estimated time. If road closures or traffic accidents occur during transportation, the transportation schedule must be readjusted.Combining traffic flow, weather, and historical transportation data, we use regression analysis, time series forecasting, or machine learning models (such as LSTM networks) to predict transportation timing. Based on the adjusted route data, we calculate the estimated transportation time for each route and compare it with other routes to select the optimal timing. Based on the route adjustment results and incorporating real-time traffic and weather changes, we adjust the transportation timing of the goods and generate transportation timing forecast adjustment data.

[0078] Step S4: Collect logistics satisfaction feedback data on the cargo transportation timing prediction adjustment data and the cargo spatial position adjustment data to obtain cargo logistics satisfaction feedback data; optimize the logistics transportation tracking network process based on the cargo logistics satisfaction feedback data, thereby generating a logistics transportation tracking optimization strategy to perform efficient logistics operations.

[0079] In this embodiment of the present invention, customer satisfaction information regarding logistics services is collected through channels such as questionnaires, customer support platforms, and social media comments. Feedback from frontline employees, such as transport drivers and warehouse personnel, is collected to capture their opinions on transportation issues, obstacles, vehicle problems, and road conditions. Combined with transportation timing adjustment data, spatial location adjustment data, and real-time monitoring data from the transportation management system, logistics-related operational data is collected, such as whether transportation is carried out on schedule; whether any delays or damage to goods occur during transportation; and whether goods arrive at the designated location as required after spatial adjustments. Feedback data from various sources is uniformly formatted and standardized to form a structured dataset for subsequent analysis. This feedback data can be stored in a database or data warehouse and updated in real time. By analyzing the collected logistics satisfaction feedback data, major transportation issues can be identified. If customers frequently report delays in transportation, the deviation between the timing adjustment data and the actual arrival time can be analyzed to identify the cause (e.g., inadequate route optimization, deteriorating road conditions, weather factors, etc.). If feedback indicates a high incidence of damaged or lost goods, it is necessary to examine whether the spatial location adjustments are reasonable and whether the safety of the transportation route is adequately guaranteed. Problems such as slow customer service responses and lack of transparency reflect system or process flaws. Data mining techniques, such as cluster analysis and association rule analysis, can be used to extract underlying patterns and patterns from large amounts of feedback data. These patterns can reveal weaknesses in specific stages or links within logistics operations. For example, if the majority of negative feedback focuses on certain transportation routes or weather conditions, this suggests that these routes or conditions require further optimization. If the majority of customer feedback concerns specific time periods or types of cargo, this suggests that these time periods or cargo types require special handling. Based on customer feedback on transportation timeliness and real-time traffic conditions, the routing algorithm can be further optimized. More dynamic data (such as real-time traffic flow and weather changes) is needed to ensure flexible and timely routing. Based on feedback on damaged or lost cargo, the spatial placement strategy for cargo can be adjusted. The order and priority of cargo placement need to be further optimized, and vulnerable cargo needs to be protected. Based on the results of feedback data analysis, a specific logistics and transportation tracking optimization strategy can be designed. This strategy primarily involves optimizing the routing algorithm based on feedback data, focusing on addressing timeliness issues. Incorporating more dynamic data (such as real-time traffic conditions and weather) allows for the development of dynamic adjustment strategies. Based on cargo damage data, we implement refined management of cargo space placement, ensuring that fragile and valuable goods receive priority protection and reducing unnecessary space waste. We also enhance customer satisfaction through process improvements, including improving customer service response times, enhancing transportation information transparency, and promptly handling customer complaints. After generating a logistics and transportation tracking optimization strategy, we integrate it into the logistics management system and monitor its effectiveness in real time.KPIs (key performance indicators) can be set to track optimization results, such as transportation punctuality, customer satisfaction, cargo damage rate, etc.

[0080] Preferably, step S1 includes the following steps:

[0081] Step S11: Obtain cargo information data from the logistics platform;

[0082] Step S12: preprocessing the logistics platform cargo information data to generate standard logistics platform cargo information data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0083] Step S13: extracting the cargo transportation address from the cargo information data of the standard logistics platform to obtain cargo transportation address data; filtering the cargo information data of the standard logistics platform for the logistics transportation start-end storage site based on the cargo transportation address data to obtain cargo transportation logistics start storage site data and cargo transportation logistics end storage site data;

[0084] Step S14: constructing a logistics transportation tracking network for the logistics platform cargo information data based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data to generate a logistics transportation tracking network.

[0085] In an embodiment of the present invention, information including cargo name, cargo weight, transportation route, transportation method, origin, and destination is obtained from the logistics platform system. This information typically comes from the logistics platform's database or sensor data acquisition system and can be obtained through various methods such as APIs and file import. The acquired logistics platform cargo information data is cleaned to remove useless data and noise, fill in missing values, and standardize the data format. Incorrect or inconsistent records, such as invalid shipping addresses or duplicate cargo information, are removed. Measurement errors or random noise in the data are removed using filtering algorithms or outlier detection methods. Missing information is filled using appropriate filling methods (e.g., mean filling, interpolation, machine learning-based prediction methods, etc.). The data is normalized or standardized to ensure that different types of data have a unified metric to facilitate subsequent analysis. The specific address data of the shipping origin and destination are extracted from the standard logistics platform cargo information data. Based on the extracted shipping addresses, the starting and ending storage sites for the cargo transportation are further screened. The screening process can be based on specific criteria, such as distance, regional scope, and accessibility. Data from the origin and destination storage sites is integrated, along with information such as transportation routes, modes, and times, to generate a logistics network. This can be represented using a graph structure from graph theory, where nodes represent storage sites and edges represent the transportation routes of goods. Based on each stop during transportation, a transportation route is constructed, and the transportation status (e.g., shipped, in transit, arrived, etc.) is recorded. Sensors, GPS, RFID, and other devices are used to track the real-time transportation status of goods, dynamically updating the tracking network.

[0086] Preferably, step S14 includes the following steps:

[0087] Step S141: Based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data, the logistics platform cargo information data is configured with a logistics process to generate cargo logistics process configuration data, wherein the cargo logistics process configuration data includes a cargo loading process, a cargo transportation process, and a cargo unloading process;

[0088] Step S142: Process data of the cargo loading process, cargo transportation process, and cargo unloading process are collected through multi-source sensors to generate cargo loading process data, cargo transportation process data, and cargo unloading process data;

[0089] Step S143: Data identification is performed on the cargo loading process data, the cargo transportation process data, and the cargo unloading process data, and a logistics tracking node is constructed based on the identified cargo loading process data, the cargo transportation process data, and the cargo unloading process data to generate a logistics tracking node;

[0090] Step S144: Perform network topology design on the logistics tracking nodes to generate logistics network topology design data; assign network attributes to the logistics tracking nodes according to the logistics network topology design data, thereby generating a logistics transportation tracking network.

[0091] In this embodiment of the present invention, the entire cargo logistics and transportation process, including loading, transportation, and unloading, is defined based on the extracted data of the starting and ending storage sites. The loading process from a warehouse or distribution center is defined, recording information such as the loading sequence, equipment, and personnel. This typically includes steps such as cargo loading, inspection, and acceptance. The processes that cargo undergoes during transportation are defined, including information such as the transportation route, transportation method (e.g., truck, ship, rail), transit stations, and transportation time. The unloading process upon arrival at the final storage site is defined, covering steps such as the unloading sequence, unloading equipment, and unloading inspection. The generated cargo logistics process definition data provides a detailed description of each logistics link, laying the foundation for subsequent process collection and network construction. Multi-source sensors (e.g., RFID, GPS, temperature and humidity sensors, cameras, etc.) are deployed in each process link to collect data from each link in real time. Data from the loading phase is collected, including loading time, cargo type, loading location, and loading personnel. Data from the transportation phase is collected, including information such as the transportation route, GPS location of the transport vehicle or transportation tool, transportation time, and vehicle condition. Data from the unloading process is collected, including unloading time, location, personnel, and method. This data collected by these sensors allows for real-time updates on the progress of each logistics process, ensuring dynamic monitoring of the entire cargo transportation process. The collected data for cargo loading, transportation, and unloading processes is labeled. This labeling can be based on information such as process step, timestamp, geographic location, and device ID. Logistics tracking nodes are constructed based on this labeled data. Each process (loading, transportation, and unloading) can be considered a node, and the connections between nodes represent the transfer of cargo from one process step to another. A loading node indicates the loading status of cargo at the starting storage station. A transportation node indicates a stop or key location during transportation. An unloading node indicates the completion of unloading at the final storage station. Each node's data identifier includes node type (loading, transportation, or unloading), timestamp, geographic location, and relevant personnel and equipment information. The topology of the logistics tracking network is designed based on these constructed logistics tracking nodes and their associated data. Using graph theory, each logistics tracking node can be considered a node in a graph, and the transportation routes between nodes can be considered edges. Network topology design involves determining the relative positions of each node, the connectivity of transportation routes, and the priority of transportation routes to ensure the efficiency and scalability of the logistics transportation network. Based on the logistics network topology design data, network attributes are assigned, such as the status of each node (loading, transportation, unloading), the transportation timeliness of each edge, transportation costs, and node reliability. The purpose of assigning network attributes is to quantify the various characteristics of nodes and edges to provide support for subsequent logistics tracking and optimization.The assigned data will form a logistics transportation tracking network, which not only includes the transportation status of the goods, but also reflects the specific attributes of each logistics link, such as transportation time, cost, transportation tools and other information.

[0092] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0093] Step S21: Extracting basic cargo information from the logistics and transportation tracking network based on the cargo loading process to obtain basic cargo logistics information data, where the basic cargo information includes cargo geometry information, cargo structure information, and cargo weight information; allocating logistics transportation vehicles to the cargo information data on the logistics platform based on the basic cargo logistics information data to obtain logistics transportation vehicle allocation data;

[0094] Step S22: analyzing the remaining space of the transport vehicles on the logistics transport vehicle allocation data to obtain the remaining space data of the transport vehicles; constructing a logistics transport path based on the starting storage site data and the ending storage site data of the freight transport logistics through the freight transport process to generate freight transport path data, wherein the freight transport path data includes a starting site, a ending site, and data of several transfer sites;

[0095] Step S23: performing a path geographic analysis on the cargo transportation path data to generate cargo transportation path geographic elevation fluctuation data; quantifying a path bumpiness index on the cargo transportation path data based on the cargo transportation path geographic elevation fluctuation data to generate a transportation path bumpiness index;

[0096] Step S24: Cargo vulnerability analysis is performed on the remaining space data of the transport vehicle according to the transport path bumpiness index to generate cargo transport vulnerability data; cargo transport vulnerability data is used to adjust the cargo transfer space placement of the transfer station data to generate cargo space position adjustment data.

[0097] In an embodiment of the present invention, during the cargo loading process, the system extracts cargo geometry, structure, and weight information from the logistics platform. Sensors or 3D scanning technology are used to extract cargo dimensions (length, width, and height) to determine the volume occupied by the cargo. Structural feature data is extracted based on the cargo type, packaging, and sensitivity (e.g., fragility or compressibility). The total weight of the cargo is obtained from sensor data or a weighing system to provide the cargo's load capacity. The system allocates transport vehicles based on the cargo's geometry, structure, and weight information. For example, high-capacity trucks may be prioritized for heavy or bulky cargo; for fragile or cargo requiring special requirements, appropriately protected transport vehicles may be selected. Logistics transport vehicle allocation data is generated, including information such as the assigned vehicle type, load capacity, and vehicle number. A space remaining analysis is performed on the assigned transport vehicles to assess the amount of available space remaining in each vehicle. Based on the vehicle's geometry and loaded cargo information, the remaining space in the vehicle is calculated, generating transport vehicle space remaining data. This data includes the vehicle's remaining space volume, load capacity, and loading priority. Based on the cargo transportation process, combined with data from the origin and destination storage sites, a cargo transportation route is constructed. Route construction includes selecting the origin and destination sites, as well as data on transfer sites. Route selection should consider factors such as geographic location, transportation vehicle accessibility, and traffic conditions. The resulting cargo transportation route data includes a origin, a destination, and several transfer sites. Data for each site includes information such as its geographic location, site capacity, and facility type. A geo-elevation analysis is performed on the cargo transportation route to analyze geo-elevation fluctuations along the route. Elevation data for each route point is obtained using a digital elevation model (DEM). Elevation changes along the route are calculated, generating geo-elevation fluctuation data for the cargo transportation route. This data records elevation changes at different points along the route and assesses the impact of elevation fluctuations on the transportation process. Based on the geo-elevation fluctuation data, a route bumpiness index is calculated to quantify the degree of route bumpiness. This bumpiness index takes into account factors such as terrain undulation, road quality, and the adaptability of transportation vehicles. This metric helps assess the amount of bumpiness encountered during transportation. The transport route bumpiness index analyzes the risk of damage to goods during transportation and generates cargo transport vulnerability data. By comparing the characteristics of fragile goods (such as fragility and deformation) with the transport route bumpiness index, cargo safety is assessed. Fragile goods are graded and labeled to ensure special protective measures are taken during transportation. Based on the cargo transport vulnerability data and the route bumpiness index, cargo storage locations at transit stations are adjusted to optimize storage methods.For example, for fragile items, you can choose to place them in the center of the vehicle to reduce the impact force they receive; for heavy goods, you can place them at the bottom to ensure that the weight is evenly distributed and generate cargo space position adjustment data. This data provides the specific location arrangement of the goods during transportation, ensuring the safety and transportation efficiency of the goods.

[0098] Preferably, analyzing the remaining space of transport vehicles on the logistics transport vehicle allocation data includes:

[0099] Perform initial space analysis on the logistics transport vehicle allocation data to generate initial space data for logistics transport vehicles; perform cargo space area calculation on the cargo logistics basic information data to obtain cargo space area data;

[0100] Using the cargo space area data to perform cargo storage allocation on the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation data; performing coordinate system conversion on the cargo storage space allocation data and the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation coordinate data and the initial space coordinate data of the logistics transport vehicle;

[0101] The coordinate axis difference between the initial spatial coordinate data of the logistics transport vehicle and the coordinate data of the cargo storage space allocation is calculated to obtain the remaining space data of the transport vehicle.

[0102] In this embodiment of the present invention, basic spatial parameters for each transport vehicle are extracted based on logistics transport vehicle allocation data, including compartment dimensions (length, width, height), maximum load capacity, and volume. Vehicle spatial data includes the vehicle compartment volume and the available space in each compartment (excluding unusable areas such as compartment doors, obstacles, and fixed equipment). Initial spatial data for logistics transport vehicles is generated. This data represents compartment boundary information using three-dimensional coordinates and provides detailed differentiation based on vehicle types (e.g., container trucks, flatbed trucks, and refrigerated trucks). Based on basic cargo information, particularly its geometric information (length, width, and height), the space occupied by each piece of cargo is calculated. Spatial area calculation method: For regularly shaped cargo, the volume can be directly calculated using the length × width × height formula. For irregularly shaped cargo, the bounding box volume is calculated using three-dimensional scanning or other algorithms. Cargo spatial area data includes information such as the volume, surface area, and center of gravity of each piece of cargo, ensuring accurate calculation of cargo space requirements. Combining this cargo spatial area data with the initial spatial data of logistics transport vehicles allows for optimal storage and allocation of cargo. The specific process includes: evaluating the volume of each piece of cargo and the available space of the transport vehicle, and performing reasonable loading and space allocation. Prioritize arranging heavier or fragile cargo in appropriate locations on the transport vehicle (such as the center of the vehicle to avoid excessive bumps). After the cargo storage space allocation data is generated, it will include the position of each piece of cargo in the transport vehicle, the space allocation strategy, and the loading order of each cargo. Perform coordinate system conversion on the cargo storage space allocation data and the initial spatial data of the logistics transport vehicle. This step ensures that the space coordination between the cargo and the vehicle is accurate. The initial spatial coordinate data of the logistics transport vehicle is usually based on the position and direction of the vehicle's compartment, and defines the coordinates of each spatial position in the compartment in the vehicle coordinate system. Define the position of the cargo relative to the transport vehicle coordinate system, such as the docking position of the cargo and the compartment, the center point of the cargo, etc. The purpose of the coordinate system conversion is to ensure that all spatial calculations are performed in the same unified coordinate system to reduce errors caused by inconsistent coordinates. The conversion method includes: the storage location of each cargo is calculated relative to the coordinate system of a specific point in the car (such as the lower left corner of the car), and the car coordinate system is converted to the global coordinate system for further data analysis or cross-regional scheduling. The coordinate axis difference calculation is performed on the initial spatial coordinate data of the logistics transport vehicle and the cargo storage space allocation coordinate data. The difference between each cargo after loading and the free space in the car is calculated to obtain the remaining space. The result of the coordinate axis difference calculation will show the remaining space in each space area in each transport vehicle, as well as the specific storage location of each cargo. The remaining space data obtained by the coordinate axis difference calculation can be further used to generate the remaining space data for each transport vehicle. The remaining space data will include: the remaining volume of each vehicle, the distribution of available space, the location of unused space, etc.In addition, the "space utilization" of each vehicle can be further calculated based on the different types, load and volume restrictions of transport vehicles.

[0103] Preferably, step S23 includes the following steps:

[0104] Step S231: Using GIS technology, geographical coordinate matching is performed on the starting station, the end station, and the transfer station data in the freight transportation route data to generate geographical coordinate matching data of the freight transportation station; geographical elevation conversion is performed on the geographical coordinate matching data of the freight transportation station to generate geographical elevation information data of the freight transportation station;

[0105] Step S232: Calculating the slope of each section of the cargo transportation station's geographic elevation information data to obtain cargo transportation section slope data; performing height difference statistics on the cargo transportation section slope data to generate cargo transportation route elevation data;

[0106] Step S233: Drawing an elevation curve for the cargo transportation segment slope data based on the cargo transportation route elevation data to generate a cargo transportation route elevation curve; performing elevation change fluctuation analysis on the cargo transportation elevation curve to generate geographic elevation fluctuation data for the cargo transportation route;

[0107] Step S234: quantifying the path bumpiness index of the cargo transportation path data according to the transportation path bumpiness quantification formula and the geographic elevation fluctuation data of the cargo transportation path to generate a transportation path bumpiness index.

[0108] In an embodiment of the present invention, by utilizing GIS technology (geographic information system), the geographic coordinates of each site data (starting site, ending site, transfer site, etc.) in the cargo transportation path data are matched. The matching process mainly includes: matching the longitude and latitude information of each site with the actual geographic coordinate system to ensure that the location data of all sites are accurate. Use a geographic information data source (such as Amap, Google Maps or other geographic databases) to accurately locate the longitude and latitude of the site, and generate cargo transportation site geographic coordinate matching data, which contains the accurate longitude and latitude, relative position and other information of each site. Based on the cargo transportation site geographic coordinate matching data, GIS technology is further used to convert the geographic elevation of each site: elevation information can usually be obtained through a digital elevation model (DEM) or other elevation data source. The coordinates of each site are converted to an elevation, the actual altitude data of the site location is obtained, and the cargo transportation site geographic elevation information data is generated, which contains the elevation data of each transportation site. Based on the cargo transportation site geographic elevation information data, the slope of the cargo transportation path is calculated. Slope can be defined as the ratio of the height difference between two adjacent stations to the distance. It is typically calculated using the following formula: Sslope = Δh ÷ d, where Δh is the elevation difference between two adjacent stations, and d is the horizontal distance between them. The transport route is divided into segments (e.g., from the starting station to the transfer station, and from the transfer station to the final station). Slope is calculated for each segment to generate segmented slope data for freight transport. This data includes the slope value for each segment (increasing, decreasing, flat, etc.). By analyzing this segmented slope data, the rise and fall of each route segment is statistically analyzed. Specifically, this involves calculating the elevation difference for each route segment and analyzing the overall rise and fall trends during freight transport. Based on the slope of each route segment, the cumulative elevation change during transport is calculated and summarized for the entire route. This data is then used to generate freight transport route elevation data, recording the total elevation change for each segment and the associated trends. Based on this elevation data, an elevation curve graph of the freight transport route is plotted. This graph displays the geographic elevation trends along the entire route. The X-axis represents the geographical location of the route (such as the stations on the route), and the Y-axis represents the corresponding geographical elevation (altitude). The volatility of the curve shows the smoothness and undulations of the elevation changes in the transportation route, generating a cargo transportation route elevation curve. By graphically presenting the elevation fluctuations of the route, it helps to analyze the elevation changes during the transportation process. A detailed analysis of the cargo transportation elevation curve is mainly conducted to judge the bumpiness of the route through the fluctuations of the curve: analyzing the degree of undulation of the route, that is, the amplitude of the elevation changes between each station. Based on the amplitude, frequency and other characteristics of the elevation fluctuations, the volatility of the route is quantitatively evaluated to generate the geographic elevation fluctuation data of the cargo transportation route. This data describes the elevation fluctuations on the route, covering peaks, troughs and flat sections.Based on the geographic elevation fluctuation data of the transport route, a specific transport route bumpiness quantification formula is used to quantify the degree of route bumpiness. The calculation of the bumpiness index typically includes factors such as the amplitude and frequency of elevation fluctuations to generate a transport route bumpiness index, which can be used to assess the impact of vibration, bumps, and instability during transportation.

[0109] Preferably, the transport path bump quantification formula in step S234 is as follows:

[0110]

[0111] Where B is the bumpiness index of the transport path, L is the total length of the path, and h(x) is the elevation at position x on the path. Expressed as the rate of change of elevation with respect to distance, It is expressed as the curvature of the path at position x, v(x) is expressed as the speed at position x, σ(x) is expressed as the road condition influencing factor, α is expressed as the path curvature influencing coefficient, β is expressed as the slope influencing coefficient, γ is expressed as the influencing coefficient of controlling traffic flow, and δ is expressed as the influencing coefficient of the road condition influencing factor on bumps.

[0112] This paper analyzes and integrates a formula to quantify transport route bumpiness. This formula identifies elevation fluctuations and slope changes as key contributors to transport route bumpiness. In particular, when a route involves steep slopes or complex curves, vehicle ups and downs, or sudden braking and acceleration, can cause greater vibration. The second derivative of elevation (i.e., curvature) describes the extent of a route's bumpiness. Areas with dramatic elevation fluctuations, where vehicles must adapt to the constantly changing terrain, typically produce more bumpiness. Slope (i.e., the first derivative of elevation) measures the degree of inclination at each location along the route. Steeper slopes (uphill or downhill) require the vehicle to constantly accelerate or brake, exacerbating the bumpiness. For example, frequent brake application during long downhill journeys can also cause vehicle vibration, increasing transport unsteadiness. Traffic flow or vehicle speed (v(x)) is significantly correlated with bumpiness. When traffic flow is high and roads are congested, vehicles frequently need to decelerate, stop, or accelerate, resulting in more vibration. At lower speeds, the vehicle is relatively stable, with less vibration. However, if the vehicle is driving under unstable traffic conditions (for example, constantly accelerating or braking while driving slowly), the bump index will increase. Weather factors (such as rain, snow, wet roads, wind speed, etc.) have a significant impact on bumps. Extreme weather conditions (such as heavy rain, heavy snow or storms) will reduce the adhesion of the road, making the vehicle unstable and increasing vibration. Wet roads (such as after rain) will increase the slippage between the vehicle tires and the road surface, resulting in unstable movement of the wheels, thereby exacerbating the vibration. The second derivative of elevation (curvature) directly affects the road's bumpiness, especially on roads with sharp turns or large inclines and downhill slopes. Larger, more dramatic elevation fluctuations result in more vibrations for the vehicle. The weighting factor α adjusts the curvature's contribution to overall bumpiness; generally, a larger α increases the impact of road bumpiness. Slope reflects the steepness of the path at different locations. Steep uphill and downhill slopes cause the vehicle's engine and brakes to work frequently, increasing vibration. The weighting coefficient β determines the contribution of slope to bumpiness. A larger slope weighting significantly increases the impact of this term. γ·(v(x)) 2 , traffic flow or the speed of the vehicle directly affects the acceleration and deceleration pattern of the vehicle. Higher traffic flow means frequent braking and acceleration, which increases the bumpy feeling. When the speed or traffic flow is low, the vehicle drives more smoothly. The weight coefficient γ controls the contribution of speed to the bumpiness. A larger γ will strengthen the influence of speed on the bumpiness index. δ·(σ(x)), the weather factor σ(x) can dynamically adjust the impact of road conditions on bumpiness. For example, rainy and snowy weather, wind speed, slippery roads, etc. will aggravate bumpiness. The weight coefficient δ controls the degree of influence of weather and road conditions to ensure that the bumpiness index of the path increases under adverse conditions. When using the conventional transport path bumpiness quantification formula in this field, the transport path bumpiness index can be obtained. By applying the transport path bumpiness quantification formula provided by the present invention, the transport path bumpiness index can be calculated more accurately. This formula comprehensively quantifies the bumpiness of the transport path by combining multiple factors such as elevation change, slope, traffic flow, weather and road conditions. The contribution of each factor to the bumpiness is adjusted by the weight coefficient, so that the model can be dynamically adjusted according to the actual transportation environment. Elevation curvature and slope primarily account for the impact of the geographical environment on vehicle vibration. Large undulations and steep slopes significantly increase bumps. Traffic flow and speed reflect the instability of vehicle driving. Higher traffic flow typically means more frequent acceleration and deceleration, which exacerbates bumps. Weather and road conditions simulate the impact of the external environment. Severe weather or slippery roads can cause vehicle instability, exacerbating bumps.

[0113] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0114] Step S31: collecting road environment data from the logistics transportation tracking network based on the cargo transportation process to obtain cargo transportation road environment data; screening the cargo transportation path data for remaining transportation paths to generate cargo transportation remaining path data;

[0115] Step S32: using the freight transport road environment data to perform a transport path congestion analysis on the freight transport remaining path data to generate transport path congestion data; and using the transport path congestion data to perform a transport path adjustment on the freight transport remaining path data to generate transport path adjustment data.

[0116] Step S33: performing transportation time series prediction on the cargo transportation path data based on the transportation path adjustment data to generate cargo transportation time series prediction data;

[0117] Step S34: performing additional transportation timing adjustments on the cargo transportation timing forecast data according to the cargo space position adjustment data to generate cargo transportation timing forecast adjustment data.

[0118] In this embodiment of the present invention, based on freight transport route data, various sensors (such as vehicle GPS, road surveillance cameras, and traffic flow sensors) collect data on the road environment along which freight is transported. This generates freight transport route environment data. This data describes the real-time conditions of the roads involved throughout the entire transport process, facilitating further analysis of the feasibility of the transport route. Based on the freight transport route data, the remaining available portions of the transport route are analyzed. This process includes: excluding road sections that are congested, closed, or impassable based on the road environment data. Paths with good road conditions and moderate traffic flow are retained, while those significantly impacted by weather, accidents, and other factors are removed. This generates remaining freight transport route data, which describes the available routes during the transport process and ensures that freight does not encounter unforeseen road obstacles during transport. Using the freight transport route environment data, congestion analysis is performed on the remaining freight transport route data. This step primarily assesses congestion on each transport route based on real-time traffic information. Traffic monitoring data (such as traffic cameras and road sensors) is used to determine the congestion status of each route. Combined with historical data, sections prone to congestion within a specific time period are identified. Real-time monitoring of factors such as weather, accidents, and temporary road closures provides early warnings about route availability and generates transport route congestion data, including information such as the degree of congestion on each route, the expected congestion duration, and the affected areas. Based on this transport route congestion data, route adjustments are made to the remaining cargo transport routes. This process includes adjusting transport routes based on real-time traffic conditions and selecting alternative routes to avoid congested areas. Peak traffic periods are analyzed to recommend transport operations during periods of lower traffic flow. Based on real-time traffic monitoring information, route selection is adjusted during transport, generating transport route adjustment data containing the adjusted route selection plan to ensure that cargo avoids congested or obstructed areas and minimize transport delays. Based on this route adjustment data, time series forecasting is performed during cargo transport. This step uses time series analysis and forecasting models (such as regression models, time series analysis, and machine learning algorithms) to predict transport times. Specifically, the adjusted transport routes are compared with historical time series data to analyze transport times under different routes. Time series forecasting takes into account factors such as real-time traffic flow, weather conditions, and road infrastructure. Using machine learning or deep learning algorithms to train a prediction model, the system predicts transportation timing based on adjusted transportation route data, generating freight transportation timing forecast data. This data predicts the specific timing of freight transportation, including estimated arrival time and route travel time. Further adjustments are made to the freight transportation timing forecast data based on freight spatial position adjustment data (i.e., changes in freight position and storage adjustments during transportation). This includes analyzing the impact of changes in freight spatial position during transportation, such as transshipment and warehousing, on timing. Based on the freight spatial adjustment data, the transportation timing is further optimized based on the transportation timing forecast.For example, if a shipment is delayed or its spatial location changes, the estimated arrival time will be adjusted in real time. Calibration is performed using transport route adjustment data and road environment data to ensure the accuracy of the transport timing forecast. This data generates the cargo transport timing forecast adjustment data, which updates the final transport timing forecast and ensures that the timing is optimized synchronously with the cargo spatial location adjustment.

[0119] Preferably, step S33 includes the following steps:

[0120] Step S331: extracting adjustment-related features from the transport path adjustment data to obtain transport path adjustment feature data; generating time series labels from the transport path adjustment feature data to obtain transport path adjustment time series labels;

[0121] Step S332: Divide the cargo transportation path data into a data set by adjusting the time series labels of the transportation paths to generate a model training set and a model test set; train the model training set using a long short-term memory neural network algorithm to generate a cargo transportation time series prediction pre-model;

[0122] Step S333: perform model optimization iteration on the freight transportation time series prediction pre-model according to the model test set, thereby generating a freight transportation time series prediction model; import the freight transportation route data into the freight transportation time series prediction model to perform transportation time series prediction, and generate freight transportation time series prediction data.

[0123] In an embodiment of the present invention, features related to time series prediction are extracted based on transportation route adjustment data (for example, the influence of factors such as route changes, traffic congestion, and weather). These features include: starting point, end point, transfer stations along the way, route length, traffic flow, etc. Such as weather conditions (sunny, rainy, etc.), road conditions (smooth, congested, under construction, etc.). Such as the timeliness of the transportation route (peak period, non-peak period), etc. These data characterize the various factors that affect the transportation timing during the transportation route adjustment process. The extracted transportation route adjustment feature data is used to generate time series labels. Each label represents the corresponding timeliness information, such as the estimated arrival time, route travel time, etc. These labels will be used as target variables for supervised learning in model training. For each transportation route, based on historical transportation data, traffic flow, etc., corresponding time series labels (such as the transit time of each route segment, the estimated arrival time, etc.) are generated. These labels can include specific time intervals or more accurate duration predictions to ensure that the model can learn effective time series relationships. Based on the generated transport route adjustment time series labels, the entire dataset is divided into two parts: a model training set, used for model training, which should include historical route adjustment features and corresponding time series labels. A model test set, used for model validation and optimization, should include historical route adjustment features not used in training and actual time series labels. The training and test sets should be appropriately distributed over the time series to avoid data leakage, and the test set should be independent of the training set. Model training is performed using a long short-term memory (LSTM) neural network. LSTM is a neural network architecture well-suited for processing time series data, capable of capturing long-term dependencies within time series. Transport route adjustment feature data and corresponding time series labels (target variables) are input. The output is a freight transportation time series forecasting model that predicts the timeliness of transport routes or estimated arrival times based on given route adjustment features. The LSTM model training process includes data preprocessing, model architecture selection, parameter optimization, and loss function definition (such as mean squared error (MSE)). Model performance metrics (such as prediction accuracy and mean squared error (MSE)) are calculated by comparing the predicted results of the test set with the true values. The LSTM model's weights are adjusted using a backpropagation algorithm, and model accuracy is further optimized through hyperparameter optimization (such as the learning rate, number of hidden layers, and batch size). Multiple rounds of training and tuning are performed to improve prediction accuracy. Cross-validation and other methods can be used to verify the model's generalization capabilities. After multiple rounds of optimization and iteration, a highly accurate freight transportation time series prediction model is ultimately generated. This model can adjust the input transportation route data and predict transportation time series (e.g., estimated time of arrival, route travel time, etc.).The cargo transportation path data (i.e., adjusted path data and environmental data, etc.) is imported into the cargo transportation time series prediction model for prediction to obtain cargo transportation time series prediction data, which includes information such as the expected transportation time series and estimated arrival time of each transportation path.

[0124] Preferably, step S4 includes the following steps:

[0125] Step S41: collecting cargo logistics satisfaction feedback data based on the cargo unloading process data and the cargo transportation time sequence prediction adjustment data and the cargo space position adjustment data to obtain cargo logistics satisfaction feedback data;

[0126] Step S42: Tracing the cargo flow of the logistics and transportation tracking network based on the cargo logistics satisfaction feedback data to generate cargo flow traceability data; optimizing the logistics and transportation tracking network based on the cargo flow traceability data to generate a logistics and transportation tracking optimization strategy to perform efficient logistics operations.

[0127] In this embodiment of the present invention, key logistics activity information, such as unloading time, unloading location, unloading personnel efficiency, and any delays during transportation, is extracted from cargo unloading process data. The cargo transportation time series forecast data and cargo spatial position adjustment data generated in step S33 are combined with unloading process data to comprehensively collect feedback on logistics satisfaction. This feedback collection aims to understand factors affecting customer or transportation efficiency at each stage. For example, whether the cargo arrives at the unloading point on time, whether there are delays or early arrivals. Spatial adjustment data is analyzed to determine whether unloading occurs according to the planned spatial location, and whether there are suboptimal spatial layouts. This includes customer satisfaction during the unloading process, the work attitude and professionalism of logistics personnel, and other factors. Ultimately, through analysis of these factors, cargo logistics satisfaction feedback data is generated, which will serve as an important basis for subsequent process optimization. Based on this cargo logistics satisfaction feedback data, each stage of the entire logistics and transportation process is traced. The purpose of this traceability is to analyze problems and their root causes at each stage in order to improve transportation operations. Through the logistics tracking network, the cargo transportation route is traced back to determine any delays or anomalies during transportation. Backtrack each time point to analyze deviations between transportation schedules and forecasts, understanding the time distribution of each link. Check whether cargo spatial relocations are aligned with plan, identifying any inappropriate space utilization and its impact on the transportation process. Compare customer satisfaction and unloading process feedback data with each link in the transportation process to understand factors contributing to declining satisfaction. Through logistics process traceability, obtain cargo flow traceability data containing detailed transportation process information, potential issues, and customer feedback. Based on this cargo flow traceability data, optimize the logistics and transportation tracking network to improve transportation efficiency and accuracy. By analyzing transportation routes and schedules, reassess cargo transportation routes, reduce unnecessary detours or congestion, and select faster routes. Based on cargo spatial relocation data and customer feedback, optimize the spatial layout of the unloading process to reduce wasted space and time. Combine transportation schedules and logistics feedback data to optimize unloading and distribution processes, reduce waiting times and processing time, and improve efficiency. Rationally dispatch personnel based on logistics and transportation needs and employee productivity to improve overall logistics efficiency. Through these optimization measures, a logistics transportation tracking optimization strategy is generated to provide guidance for executing more efficient logistics operations.

[0128] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0129] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method for a logistics platform, characterized in that: The following steps are involved: Step S1: Obtain cargo information data from the logistics platform; extract cargo transportation addresses from the logistics platform cargo information data to obtain cargo transportation address data; construct a logistics transportation tracking network for the standard logistics platform cargo information data based on the cargo transportation address data to generate a logistics transportation tracking network; Step S2: analyzing the remaining space of transport vehicles on the logistics transport tracking network to obtain data on the remaining space of transport vehicles; performing a perishable analysis of the cargo transport address data to generate perishable data on the cargo transport; and adjusting the placement of cargo transit space using the perishable data to generate cargo space position adjustment data. Step S2 includes the following steps: Step S21: Extracting basic cargo information from the logistics and transportation tracking network based on the cargo loading process to obtain basic cargo logistics information data, where the basic cargo information includes cargo geometry information, cargo structure information, and cargo weight information; allocating logistics transportation vehicles to the cargo information data on the logistics platform based on the basic cargo logistics information data to obtain logistics transportation vehicle allocation data; Step S22: analyzing the remaining space of the transport vehicles on the logistics transport vehicle allocation data to obtain the remaining space data of the transport vehicles; constructing a logistics transport path based on the starting storage site data and the ending storage site data of the freight transport logistics through the freight transport process to generate freight transport path data, wherein the freight transport path data includes a starting site, a ending site, and data of several transfer sites; Step S23: performing a path geographic analysis on the cargo transportation path data to generate cargo transportation path geographic elevation fluctuation data; quantifying a path bumpiness index on the cargo transportation path data based on the cargo transportation path geographic elevation fluctuation data to generate a transportation path bumpiness index; Step S24: Cargo damage analysis is performed on the remaining space data of the transport vehicle according to the transport path bumpiness index to generate cargo transport damage data; cargo transport damage data is used to adjust the transfer station data to place cargo transfer space to generate cargo space position adjustment data Step S3: collecting road environment data from the logistics transportation tracking network to obtain freight transportation road environment data; screening the freight transportation path data for remaining transportation paths to generate freight transportation remaining path data; adjusting the freight transportation path for the remaining freight transportation path data using the freight transportation road environment data to generate transportation path adjustment data; and adjusting the transportation timing based on the transportation path adjustment data to generate freight transportation timing prediction adjustment data. Step S4: Collect logistics satisfaction feedback data on the cargo transportation timing prediction adjustment data and the cargo spatial position adjustment data to obtain cargo logistics satisfaction feedback data; optimize the logistics transportation tracking network process based on the cargo logistics satisfaction feedback data, thereby generating a logistics transportation tracking optimization strategy to perform efficient logistics operations.

2. The data processing method for a logistics platform according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain cargo information data from the logistics platform; Step S12: preprocessing the logistics platform cargo information data to generate standard logistics platform cargo information data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: extracting the cargo transportation address from the cargo information data of the standard logistics platform to obtain cargo transportation address data; filtering the cargo information data of the standard logistics platform for the logistics transportation start-end storage site based on the cargo transportation address data to obtain cargo transportation logistics start storage site data and cargo transportation logistics end storage site data; Step S14: constructing a logistics transportation tracking network for the logistics platform cargo information data based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data to generate a logistics transportation tracking network.

3. The data processing method for a logistics platform according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Based on the cargo transportation logistics starting storage site data and the cargo transportation logistics terminal storage site data, the logistics platform cargo information data is configured with a logistics process to generate cargo logistics process configuration data, wherein the cargo logistics process configuration data includes a cargo loading process, a cargo transportation process, and a cargo unloading process; Step S142: Process data of the cargo loading process, cargo transportation process, and cargo unloading process are collected through multi-source sensors to generate cargo loading process data, cargo transportation process data, and cargo unloading process data; Step S143: Data identification is performed on the cargo loading process data, the cargo transportation process data, and the cargo unloading process data, and a logistics tracking node is constructed based on the identified cargo loading process data, the cargo transportation process data, and the cargo unloading process data to generate a logistics tracking node; Step S144: Perform network topology design on the logistics tracking nodes to generate logistics network topology design data; assign network attributes to the logistics tracking nodes according to the logistics network topology design data, thereby generating a logistics transportation tracking network.

4. The data processing method for a logistics platform according to claim 1, characterized in that: Analysis of remaining space in transport vehicles for logistics transport vehicle allocation data includes: Perform initial space analysis on the logistics transport vehicle allocation data to generate initial space data for logistics transport vehicles; perform cargo space area calculation on the cargo logistics basic information data to obtain cargo space area data; Using the cargo space area data to perform cargo storage allocation on the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation data; performing coordinate system conversion on the cargo storage space allocation data and the initial space data of the logistics transport vehicle, thereby generating cargo storage space allocation coordinate data and the initial space coordinate data of the logistics transport vehicle; The coordinate axis difference between the initial spatial coordinate data of the logistics transport vehicle and the coordinate data of the cargo storage space allocation is calculated to obtain the remaining space data of the transport vehicle.

5. The data processing method for a logistics platform according to claim 1, characterized in that: Step S23 includes the following steps: Step S231: Using GIS technology, geographical coordinate matching is performed on the starting station, the end station, and the transfer station data in the freight transportation route data to generate geographical coordinate matching data of the freight transportation station; geographical elevation conversion is performed on the geographical coordinate matching data of the freight transportation station to generate geographical elevation information data of the freight transportation station; Step S232: Calculating the slope of each section of the cargo transportation station's geographic elevation information data to obtain cargo transportation section slope data; performing height difference statistics on the cargo transportation section slope data to generate cargo transportation route elevation data; Step S233: Drawing an elevation curve for the cargo transportation segment slope data based on the cargo transportation route elevation data to generate a cargo transportation route elevation curve; performing elevation change fluctuation analysis on the cargo transportation elevation curve to generate geographic elevation fluctuation data for the cargo transportation route; Step S234: quantifying the path bumpiness index of the cargo transportation path data according to the transportation path bumpiness quantification formula and the geographic elevation fluctuation data of the cargo transportation path to generate a transportation path bumpiness index.

6. The data processing method for a logistics platform according to claim 5, characterized in that: The transport path bump quantification formula in step S234 is as follows: Where, Expressed as the transport path bumpiness index, Expressed as the total path length, Represented as a position on the path The elevation of Expressed as the rate of change of elevation with respect to distance, Represented as a path at location The curvature at Indicated as at position The speed at Expressed as the road condition impact factor, Expressed as the path curvature influence coefficient, Expressed as the slope influence coefficient, Expressed as the influence coefficient of controlling traffic flow, Expressed as the influence coefficient of road condition factors on bumps.

7. The data processing method for a logistics platform according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: collecting road environment data from the logistics transportation tracking network based on the cargo transportation process to obtain cargo transportation road environment data; screening the cargo transportation path data for remaining transportation paths to generate cargo transportation remaining path data; Step S32: using the freight transport road environment data to perform a transport path congestion analysis on the freight transport remaining path data to generate transport path congestion data; and using the transport path congestion data to perform a transport path adjustment on the freight transport remaining path data to generate transport path adjustment data; Step S33: performing transportation time series prediction on the cargo transportation path data based on the transportation path adjustment data to generate cargo transportation time series prediction data; Step S34: performing additional transportation timing adjustments on the cargo transportation timing forecast data according to the cargo space position adjustment data to generate cargo transportation timing forecast adjustment data.

8. The data processing method for a logistics platform according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: extracting adjustment-related features from the transport path adjustment data to obtain transport path adjustment feature data; generating time series labels from the transport path adjustment feature data to obtain transport path adjustment time series labels; Step S332: Divide the cargo transportation path data into a data set by adjusting the time series labels of the transportation paths to generate a model training set and a model test set; train the model training set using a long short-term memory neural network algorithm to generate a cargo transportation time series prediction pre-model; Step S333: perform model optimization iteration on the freight transportation time series prediction pre-model according to the model test set, thereby generating a freight transportation time series prediction model; import the freight transportation route data into the freight transportation time series prediction model to perform transportation time series prediction, and generate freight transportation time series prediction data.

9. The data processing method for a logistics platform according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: collecting cargo logistics satisfaction feedback data based on the cargo unloading process data and the cargo transportation time sequence prediction adjustment data and the cargo space position adjustment data to obtain cargo logistics satisfaction feedback data; Step S42: Tracing the cargo flow of the logistics and transportation tracking network based on the cargo logistics satisfaction feedback data to generate cargo flow traceability data; optimizing the logistics and transportation tracking network based on the cargo flow traceability data to generate a logistics and transportation tracking optimization strategy to perform efficient logistics operations.

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