Bulk commodity transportation whole-process digital management method and system based on internet of things

Abstract

The application discloses a bulk commodity transportation whole-process digital management method and system based on the Internet of Things, which comprises the following steps: collecting multi-dimensional parameters of the whole transportation process through an Internet of Things terminal, screening adaptive transportation capacity through a dynamic matching optimization model of transportation capacity resources pool, and generating a preliminary transportation scheme; deducing the transportation state change trend by using a bulk commodity transportation space-time evolution prediction algorithm, and regulating the node resource occupation situation through a cross-regional transportation node load balancing model; centrally processing whole-chain data and collaborative instructions through a logistics delivery whole-process collaborative clustering management platform, and realizing whole-process closed-loop tracking of transportation by using a digital management module. The system corresponds to the method to build functional units. Through the collaborative application of multiple models and platforms, the application solves the problems of inaccurate transportation capacity adaptation, lagging prediction, uneven node load and insufficient collaboration in the prior art, improves transportation efficiency and resource utilization, and is suitable for whole-process management of bulk commodity cross-regional transportation.

Description

Technical Field

[0001] This invention relates to the field of bulk commodity transportation management technology, and in particular to a digital management method and system for the entire process of bulk commodity transportation based on the Internet of Things. Background Technology

[0002] As core materials for industrial production and infrastructure construction, the transportation of bulk commodities involves cross-regional, long-distance, and multi-node collaboration, including key aspects such as capacity scheduling, route planning, node operations, and delivery coordination. This places extremely high demands on transportation efficiency, resource utilization, and process controllability. With the deep penetration of IoT technology in the logistics field, traditional management models relying on manual scheduling and experience-based judgment are no longer adequate for the large-scale and complex demands of bulk commodity transportation. The industry urgently needs to build a full-process digital management system to achieve precise control over transportation resources, transportation status, node load, and collaborative operations. This will drive the transformation of transportation processes from passive response to proactive prediction, and from decentralized management to collaborative linkage, thereby solving prominent problems such as data fragmentation, delayed response, and resource waste during transportation.

[0003] Existing technologies related to bulk commodity transportation management have significant shortcomings: On the one hand, there is a lack of systematic integration in the allocation of transportation capacity and the prediction of transportation status. They rely heavily on single-dimensional data or simple matching rules, failing to fully integrate the multi-dimensional correlations of cargo attributes, transportation capacity characteristics, dynamic changes in routes, and node operating status. This results in insufficient accuracy in capacity matching and delayed prediction of emergencies such as congestion and node overload during transportation, making it difficult to formulate dynamically optimized transportation solutions. On the other hand, the cross-regional transportation node load control and full-process collaboration mechanisms are imperfect. An effective load balancing management system has not been established, resulting in uneven allocation of node resources and poor workflow connections. Furthermore, the lack of a centralized collaborative management platform means that there are breakpoints in the data flow, instruction distribution, and feedback correction throughout the transportation chain. This prevents efficient collaboration in capacity scheduling, node operations, and delivery connections, leading to low overall transportation efficiency and blind spots in process control. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a digital management method and system for the entire process of bulk commodity transportation based on the Internet of Things.

[0005] The technical solution adopted in this invention is a digital management method for the entire process of bulk commodity transportation based on the Internet of Things (IoT), comprising the following steps: S1, collecting capacity supply parameters, cargo attribute parameters, transportation route characteristic parameters, node facility operation parameters, and delivery timeliness-related parameters throughout the entire bulk commodity transportation process through IoT terminals, and constructing a multi-dimensional data collection matrix; S2, calling the dynamic matching optimization model of the capacity resource pool to perform correlation analysis on the collected data, screening suitable capacity units, and generating preliminary transportation plans; S3, using a bulk commodity transportation spatiotemporal evolution prediction algorithm to dynamically extrapolate the path traffic status, node congestion probability, and cargo status change trends during the transportation process; S4, using a cross-regional transportation node load balancing model to perform real-time control of the resource occupancy of each transportation node and optimize node operation processes; S5, using a logistics delivery full-process collaborative clustering management platform to centrally process and distribute the operation data, status information, and collaborative instructions of the entire transportation chain; S6, using a digital management module for the entire bulk commodity transportation process to track and dynamically update the data flow, instruction execution, and resource allocation throughout the transportation process, forming a closed-loop management link.

[0006] Furthermore, the expression for the dynamic matching optimization model of the transportation capacity resource pool is: ,in, The optimal capacity matching coefficient. Let i be the priority weight parameter for the i-th type of goods. Let be the transportation demand parameter for the i-th type of goods. Let be the adaptation time parameter between the i-th type of goods and the j-th transport unit. Let be the available state coefficient of the j-th transport unit. The historical operation adaptation rate parameter for the j-th capacity unit. Let the constraint weight parameters be those for the k-th type of transportation resources. Let K be the remaining capacity parameter for the k-th type of transportation resource. This is the capacity allocation adjustment coefficient. Let be the satisfaction parameter of the I-th transportation constraint, n be the total number of cargo categories, m be the total number of transportation resource types, and p be the total number of transportation constraints.

[0007] Furthermore, the expression for the spatiotemporal evolution prediction algorithm for bulk commodity transportation is: ,in, Coordinates at time t The predicted transport status at the location, The spatiotemporal diffusion coefficient is... For a two-dimensional Laplace operator, The weighting coefficients for the influence of speed, for Time coordinates The transport speed parameters at the location for Time coordinates The gradient vector of the predicted value, This represents the external interference influence coefficient. for Time coordinates External interference intensity parameters at the location, Initial time coordinates The baseline value for the transportation status at the location, where t is the forecast duration. It is the integral variable.

[0008] Furthermore, the expression for the cross-regional transportation node load balancing model is as follows: ,in, This is the node load balancing coefficient. Let a be the importance weight parameter for the a-th transportation node. Let be the current load rate parameter of the a-th transportation node. Let be the load capacity limit parameter for the a-th transportation node. Let be the load adjustment coefficient for the a-th transport node. Let be the average distance parameter between the a-th transportation node and its adjacent nodes. Let q be the efficiency improvement coefficient of the a-th transportation node, and q be the total number of cross-regional transportation nodes.

[0009] Furthermore, the collaborative control expression of the logistics delivery end-to-end collaborative clustering management platform is as follows: ,in, For collaborative clustering control coefficients, Let be the weight parameter for the s-th collaborative operation step. Let be the information synchronization rate parameter for the s-th collaborative operation stage. Let be the instruction execution degree parameter for the s-th collaborative operation stage. Let be the weight parameter for the t-th cluster dimension. Let be the similarity parameter for the t-th cluster dimension. For the coordinated adjustment coefficient, Let v be the collaborative impact factor for the v-th delivery stage. For the first Parameters regarding the tightness of process connections at each delivery stage. The total number of collaborative work steps. This represents the total number of clustering dimensions. This represents the total number of delivery stages.

[0010] Furthermore, the comprehensive control expression for the digital management of the entire bulk commodity transportation process is as follows: ,in, For comprehensive control values ​​of digital management, Let z be the weight parameter for the z-th management dimension. Let z be the execution compliance rate parameter for the z-th management dimension. Let y be the weight parameter of the y-th regulation link. Let be the parameter configuration value for the y-th control link. Let x be the feedback correction coefficient for the y-th control link, and let x be the total number of management dimensions. This refers to the total number of control links.

[0011] Further, S3 includes the following sub-steps: S31, real-time collection of traffic density, road capacity, weather influencing factors, and node operation time parameters along the transportation route via IoT terminals, and classification and integration of the collected parameters according to a preset data format; S32, inputting the integrated parameters into a bulk commodity transportation spatiotemporal evolution prediction algorithm, and performing point-by-point extrapolation of the transportation status at different time nodes and spatial locations through multi-layer iterative calculations within the algorithm; S33, extracting calibration status parameters during the transportation process based on the extrapolation results, and identifying potential route congestion and node overload in advance; S34, comparing and analyzing the identification results with preset thresholds to generate preliminary transportation route adjustment suggestions, providing data support for node load balancing control.

[0012] Further, S4 includes the following sub-steps: S41, obtaining the transportation status prediction results generated in S3 and the current load data of each transportation node, including the node's cargo storage volume, operating status of operating equipment, number of personnel, and amount of tasks to be processed; S42, substituting the data into the cross-regional transportation node load balancing model, and obtaining the load balancing coefficient and load optimization direction of each node through model calculation; S43, formulating a node resource allocation plan based on the load optimization direction, including specific measures for adjusting cargo transfer routes, scheduling operating equipment, and optimizing personnel shifts; S44, distributing the allocation plan to each associated node through the logistics delivery full-process collaborative clustering management platform, monitoring the implementation of the plan in real time, and collecting feedback data during the implementation process to form a dynamic closed loop for node load control.

[0013] Further, S5 includes the following sub-steps: S51, receiving operational data uploaded from each link in the entire transportation chain through a data interface, including cargo loading and unloading records, transportation status data, node operation logs, and information on the execution of collaborative instructions; S52, using the data analysis module of the logistics delivery full-process collaborative clustering management platform to clean, associate, and cluster the received data, and to explore potential relationships between the data; S53, generating operational status reports for each link based on the clustering results, clarifying the operational status of each link and existing collaborative breakpoints; S54, generating targeted collaborative optimization instructions based on the operational status reports and collaborative breakpoint analysis results, and issuing them to related operational units to achieve collaborative linkage throughout the entire transportation process.

[0014] This IoT-based digital management system for the entire bulk commodity transportation process comprises: a multi-dimensional data acquisition and preprocessing unit, which establishes a bidirectional data transmission link with IoT terminals to collect and classify various parameters throughout the transportation process, providing data input for model calculations; a dynamic matching and optimization unit for transportation resources, connected to the multi-dimensional data acquisition and preprocessing unit, which uses a dynamic matching and optimization model of the transportation resource pool to process the collected data, select suitable transportation units, and generate preliminary transportation plans; and a transportation spatiotemporal evolution prediction and analysis unit, which establishes data interaction relationships with both the multi-dimensional data acquisition and preprocessing unit and the dynamic matching and optimization unit, and uses a bulk commodity transportation spatiotemporal evolution prediction algorithm to predict and analyze the transportation process. The system includes: a transportation status simulation and prediction results output; a cross-regional node load balancing control unit that receives output data from the transportation spatiotemporal evolution prediction and analysis unit, and controls node load based on the cross-regional transportation node load balancing model to optimize node operation processes; a full-process collaborative clustering management and instruction distribution unit that is bidirectionally connected to the cross-regional node load balancing control unit and the capacity resource dynamic matching and optimization unit, centrally processing data across the entire transportation chain through the logistics delivery full-process collaborative clustering management platform to generate and distribute collaborative optimization instructions; and a digital management closed-loop tracking and updating unit that establishes data transmission channels with all units to track the data flow, instruction execution, and resource allocation throughout the entire transportation process, updating management data in real time and feeding it back to all related units, forming a complete digital management closed loop.

[0015] Beneficial Effects: This invention proposes a digital management method and system for the entire process of bulk commodity transportation based on the Internet of Things (IoT). It integrates multi-dimensional parameters such as cargo attributes and transportation capacity characteristics through a dynamic matching and optimization model of the transportation capacity resource pool, replacing single-dimensional data and simple matching rules. This achieves precise matching of transportation capacity resources with transportation demand. Combined with a bulk commodity transportation spatiotemporal evolution prediction algorithm, it dynamically extrapolates path status and node congestion, significantly improving the dynamic optimization capability of transportation plans and solving the problems of insufficient accuracy in capacity matching and delayed prediction of emergencies. Through a cross-regional transportation node load balancing model, it performs real-time control of resource occupancy at each node. Coupled with a logistics delivery full-process collaborative clustering management platform, it achieves centralized processing, instruction distribution, and feedback correction of data across the entire transportation chain, constructing an efficient node load control system and a full-process collaborative mechanism. This breaks through the limitations of uneven node resource allocation, poor operational coordination, and data flow interruptions. Simultaneously, through closed-loop tracking of the entire bulk commodity transportation digital management process, it achieves precise control and dynamic updates of the entire transportation process, promoting the transformation of the transportation process from passive response to proactive prediction and from decentralized management to collaborative linkage, significantly improving transportation efficiency and resource utilization. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall steps of the method of the present invention;

[0017] Figure 2 This is a flowchart of method step S3 of the present invention;

[0018] Figure 3 This is a flowchart of method step S4 of the present invention;

[0019] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0020] Figure 5 This is a system unit composition diagram of the present invention;

[0021] Figure 6 This is a diagram of the main interface of the system of the present invention;

[0022] Figure 7 This is a diagram of the system transportation route management interface of the present invention;

[0023] Figure 8 This is a diagram of the system transportation contract management interface of the present invention. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1As shown, the IoT-based digital management method for the entire process of bulk commodity transportation includes the following steps:

[0026] S1 collects capacity supply parameters, cargo attribute parameters, transportation route characteristic parameters, node facility operation parameters, and delivery timeliness related parameters throughout the entire bulk commodity transportation process through IoT terminals, and constructs a multi-dimensional data collection matrix.

[0027] Specifically, step S1 involves data collection via IoT terminals deployed on transport vehicles, cargo packaging, node facilities, and operating equipment. These terminals include vehicle-mounted sensors, cargo status monitoring modules, node access control devices, and data collection units for operating machinery. The data collection frequency is set to once every 30 seconds to ensure real-time data availability. The collected transportation capacity supply parameters include 12 core parameters such as the rated load of transport vehicles, current load, remaining driving range, service life, historical failure rate, current location coordinates, driving speed, and driver qualification level; cargo attribute parameters include 10 key information items such as the type, density, volume, weight, fragility level, moisture protection requirements, temperature tolerance range, transportation priority, origin, and destination of bulk commodities; transportation route characteristic parameters include 9 items such as total route length, number of road segments, design speed of each road segment, number of lanes, gradient, proportion of curved road segments, number of bridges and tunnels, and historical traffic delay data; node facility operation parameters involve 8 indicators such as node storage capacity, current inventory ratio, number of loading and unloading equipment, equipment operating power, number of operators, average loading and unloading efficiency, node throughput, and number of parking spaces; delivery timeliness related parameters include 5 information items such as latest delivery time, allowable delay time, specific coordinates of delivery location, recipient's operation time window, and secondary transshipment needs. All collected data are classified and labeled according to preset data coding rules to construct a multi-dimensional data collection matrix including five dimensions: capacity, goods, routes, nodes, and timeliness. The matrix row dimension corresponds to the data collection object number, and the column dimension corresponds to the category of each parameter, ensuring structured data storage and convenient subsequent retrieval.

[0028] S2, invoke the dynamic matching and optimization model of the transportation capacity resource pool to perform correlation analysis on the collected data, screen suitable transportation capacity units and generate a preliminary transportation plan;

[0029] Specifically, step S2 initiates the call process for the dynamic matching and optimization model of the transportation capacity resource pool. First, valid data is extracted from the multi-dimensional data collection matrix constructed in S1 through the data interface. After removing outliers, the data is standardized according to the format required by the model. The standardized data includes a core subset of 12 transportation capacity supply parameters, 10 cargo attribute parameters, and 5 delivery timeliness-related parameters. When calling the model, matching threshold parameters are set, including a load capacity adaptation threshold set to 1.05 to 1.2 times the cargo weight, a range adaptation threshold set to more than 1.3 times the total length of the transportation route, a transportation priority adaptation weight set to 0.3, a cargo attribute adaptation weight set to 0.4, and a timeliness requirement adaptation weight set to 0.3, forming a three-dimensional weight allocation system. The model performs correlation analysis on the extracted data. First, it calculates the basic fit between individual transport capacity units and cargo demand, obtaining a preliminary fit score through weighted summation. Then, it performs a secondary correction by combining parameters such as the historical operation fit rate of the transport capacity unit (statistical data on the fit completion of the transport capacity unit with similar cargo in the past 6 months, with a sample size of no less than 50 groups) and current availability status (whether it is idle, whether it has undertaken other transport tasks, and the remaining time for task completion). During the screening process, transport capacity units with a preliminary fit score of 80 or above are retained first. Then, the top 20% of transport capacity units with the comprehensive corrected score are selected as candidate transport capacity units, with no less than 3 candidate transport capacity units. If there are not enough, the screening scope is expanded to the top 30% of the scores. Based on the distribution of candidate transport capacity units, the overlap of transport routes, and the convenience of node connection, three preliminary transport plans are generated. Each plan includes core content such as transport capacity unit number, transport route planning, estimated departure time, estimated arrival time, node stopping plan, and loading and unloading operation arrangements. The plans clearly mark the fit advantages and potential risks of each candidate transport capacity, providing a basis for subsequent plan optimization.

[0030] S3 utilizes a bulk commodity transportation spatiotemporal evolution prediction algorithm to dynamically extrapolate the path traffic status, node congestion probability, and cargo status change trends during the transportation process.

[0031] Specifically, step S3 executes the calculation process of the bulk commodity transportation spatiotemporal evolution prediction algorithm. First, it extracts nine parameters from the transportation path feature parameters collected in S1, including total path length, number of road segments, design speed of each segment, and historical traffic delay data. Combined with the route planning in the preliminary transportation plan generated in S2, the prediction time range is determined to be from the departure time to the expected arrival time. The time interval is divided into 15-minute time slices, and the total number of slices is dynamically adjusted according to the transportation duration, with a maximum of 96 slices. The initial input data for the algorithm includes the initial traffic status of road segments corresponding to each time slice, the initial congestion probability of nodes, and the initial cargo status data. The initial traffic status of road segments is determined by the average of historical data from the same period over the past 7 days. The initial congestion probability of nodes is calculated based on the ratio of node throughput to capacity during the same period over the past 30 days. The initial cargo status is determined by combining the cargo attribute parameters collected in S1 with real-time monitoring data. The algorithm uses multi-level iterative calculations to extrapolate the transportation status of each time slice, each route segment, and each transit node. During the extrapolation, dynamic factors such as traffic density changes (with a set density threshold range of 5 to 80 vehicles per kilometer), weather influence factors (including three sub-parameters: precipitation, wind, and visibility, each divided into five levels), and fluctuations in node operation time (with a fluctuation range of ±15% based on historical data) are comprehensively considered. The extrapolation output includes the traffic speed, delay time, and congestion probability for each route segment in each time slice; the queue length, operation waiting time, and load rate for each node; and state parameters such as temperature changes, humidity changes, and vibration intensity of goods during transportation. This forms a complete spatiotemporal evolution prediction report for transportation, providing data support for subsequent node load control and route optimization.

[0032] S4, based on the cross-regional transportation node load balancing model, performs real-time control of the resource occupancy of each transportation node and optimizes the node operation process.

[0033] Specifically, step S4 conducts node regulation based on the cross-regional transportation node load balancing model. First, it acquires node-related prediction data from the transportation spatiotemporal evolution prediction report output by S3 via the data transmission module. This includes parameters such as congestion probability, queue length, operation waiting time, and load rate for each node in different time slices. Simultaneously, it collects real-time operational data for each transportation node, including eight real-time indicators such as current warehouse capacity occupancy, number of operating loading and unloading equipment, number of on-duty personnel, current pending cargo volume, and the expected arrival time distribution of goods en route. During model calculation, the node load balancing target is set to control the load rate between 60% and 80%. If this range is exceeded, the regulation mechanism is activated. Model input parameters include node importance weights (set to levels 1 to 5 based on the node's hierarchy in the transportation network, with corresponding weight values ​​of 0.8, 1.0, 1.2, 1.5, and 2.0), current load rate, load capacity limit, average distance to adjacent nodes, and operational efficiency improvement coefficient. The operational efficiency improvement coefficient is calculated based on the ratio of node equipment operating power, personnel configuration, and historical average efficiency. The load balancing coefficient of each node is obtained through model calculation. A coefficient below 0.6 indicates insufficient load, while a coefficient above 1.2 indicates overload. For nodes with insufficient load, the operating power of their operating equipment is adjusted to 80% of the rated power, and personnel scheduling is optimized to reduce redundant personnel. For nodes with overload, the cargo transfer route is adjusted, and some pending cargo is diverted to adjacent nodes with lower load. At the same time, the number of loading and unloading equipment operating at the node is increased, the effective working time of the operators is extended, the node operation process is optimized, and the dwell time of cargo at the node is reduced to ensure that the load of each cross-regional transportation node is balanced.

[0034] S5 centrally processes and distributes operational data, status information, and collaborative instructions across the entire transportation chain through a collaborative clustering management platform for the entire logistics delivery process.

[0035] Specifically, step S5 relies on the logistics delivery end-to-end collaborative clustering management platform to carry out full-chain data processing and instruction distribution. The platform adopts a distributed architecture and includes six core functional modules: data receiving module, data processing module, clustering analysis module, instruction generation module, instruction distribution module, and status feedback module. The platform response time is set to no more than 2 seconds. First, the data receiving module receives data uploaded from each link of the transportation chain, including cargo loading and unloading records (including loading and unloading time, loading and unloading personnel number, equipment number, workload, etc.), transportation status data (including real-time data such as vehicle location, speed, cargo temperature, humidity, vibration intensity, etc.), node operation logs (including records such as cargo entry time, exit time, inventory changes, equipment operating status, etc.), and collaborative instruction execution status (including instruction reception time, execution start time, execution completion status, and abnormal information during execution, etc.). Data transmission uses an encryption protocol to ensure security. The data processing module cleans, deduplicates, and correlates the received data, removing invalid and duplicate data, and then categorizes and organizes it according to work steps, time sequence, and data type. The clustering analysis module divides the processed data into several collaborative clusters based on cargo type, transportation route, node affiliation, and delivery time requirements. Each cluster includes data from at least three related work steps, with a clustering accuracy set at 95% or higher. The instruction generation module generates various types of instructions based on the clustering analysis results, the prediction data from S3, and the control results from S4. These instructions include adjustments to loading and unloading operations, vehicle dispatching, node resource allocation, route change, and collaborative connection instructions. The instructions clearly specify the operation object, operation content, operation time, and operation requirements. The instruction distribution module distributes the generated instructions to corresponding work units via the IoT communication network, including transport vehicle terminals, node operation equipment, and personnel handheld terminals, ensuring a distribution success rate of 99.9%. The status feedback module receives real-time instruction execution feedback data from each work unit, forming a closed-loop instruction execution mechanism.

[0036] S6 uses a digital management module for the entire bulk commodity transportation process to track and dynamically update the data flow, instruction execution, and resource allocation throughout the transportation process, forming a closed-loop management link.

[0037] Specifically, step S6 achieves end-to-end tracking and dynamic updates through a digital management module for the entire bulk commodity transportation process. This module integrates functions such as data storage, real-time monitoring, status display, update push, and historical traceability. Data storage utilizes a cloud database, supporting efficient reading, writing, and retrieval of massive amounts of data. The module first acquires data flow information from each stage (S1 to S5), including data collection time, data transmission path, data processing results, and data relationships, establishing a data flow traceability chain. This chain records the entire lifecycle information of each data point, including its generation source, processing nodes, call records, and modification traces. Simultaneously, it tracks the entire process of instruction execution, including instruction reception status, execution progress, execution results, anomalies, and handling measures. The tracking frequency is consistent with the data collection frequency, updating every 30 seconds. Resource allocation tracking includes the scheduling trajectory of transportation resources, changes in node resource allocation, and the occupancy of path resources, displaying the real-time status and distribution of each resource through a visual interface. The dynamic update process, based on real-time collected new data, instruction execution feedback results, and resource allocation changes, promptly adjusts transportation plans, node control strategies, and collaborative instructions. The update cycle is set to once every minute to ensure a high degree of alignment between management strategies and actual operational status. Through the organic integration of data flow tracking, instruction execution tracking, resource allocation tracking, and dynamic updates, a closed-loop management chain is formed. Data is exchanged in real time and status is dynamically synchronized across all links in the chain, ensuring that every node, every operation, and every allocation in the entire transportation process is under control.

[0038] Preferably, the expression for the dynamic matching optimization model of the transportation capacity resource pool is: ,in, The optimal capacity matching coefficient. Let i be the priority weight parameter for the i-th type of goods. Let be the transportation demand parameter for the i-th type of goods. Let be the adaptation time parameter between the i-th type of goods and the j-th transport unit. Let be the available state coefficient of the j-th transport unit. The historical operation adaptation rate parameter for the j-th capacity unit. Let the constraint weight parameters be those for the k-th type of transportation resources. Let K be the remaining capacity parameter for the k-th type of transportation resource. This is the capacity allocation adjustment coefficient. Let be the satisfaction parameter of the I-th transportation constraint, n be the total number of cargo categories, m be the total number of transportation resource types, and p be the total number of transportation constraints.

[0039] Specifically, the dynamic matching optimization model for the transportation capacity resource pool is based on multi-dimensional data collection throughout the entire transportation process, achieving precise matching by integrating the correlation parameters between cargo and transportation capacity. During implementation, cargo priority weights are divided into five gradients based on the urgency and value level of cargo transportation, with corresponding values ​​ranging from 0.6 to 1.0; the transportation demand parameter is calculated comprehensively based on cargo weight, volume, and the number of transportation batches; the adaptation time parameter is based on the average preparation time of different cargo and transportation capacity combinations statistically analyzed from historical transportation data, with values ​​accurate to the hour; the transportation capacity availability status coefficient is set according to the current operating status of the transportation unit, with a value of 1.0 for idle status, 0.1 to 0.9 for executing status calculated based on the proportion of remaining task time, and 0 for unavailable status; the historical operation adaptation rate parameter is based on statistics of the transportation unit's completion of similar cargo transportation within the past 12 months. The ratio of successful trips to total trips is rounded to two decimal places. Transportation resource constraint weights are set based on resource scarcity, with core capacity resources weighted at 0.8 to 1.2 and ordinary resources at 0.3 to 0.7. Remaining capacity parameters are calculated by subtracting the occupied capacity from the rated carrying capacity of the transport unit. The capacity allocation adjustment coefficient is dynamically adjusted based on the supply and demand relationship in the transportation market, with a value of 1.0 when supply and demand are balanced, 1.2 to 1.5 when demand exceeds supply, and 0.7 to 0.9 when demand exceeds supply. The constraint satisfaction parameter applies to each constraint, such as transportation route restrictions and node connection requirements; a value of 1.0 is assigned for compliance, 0.5 for partial compliance, and 0 for non-compliance. During implementation, the specific values ​​of various parameters are first entered. Through model calculation, multi-dimensional influencing factors are integrated to output the optimal capacity matching coefficient. The coefficient ranges from 0 to 1.0. The higher the coefficient, the stronger the adaptability between capacity and demand. Based on this, the capacity unit with the best adaptability is selected, which solves the problems of single parameters and insufficient accuracy in traditional matching and provides a scientific quantitative basis for the generation of preliminary transportation plans.

[0040] Preferably, the expression for the bulk commodity transportation spatiotemporal evolution prediction algorithm is: ,in, Coordinates at time t The predicted transport status at the location, The spatiotemporal diffusion coefficient is... For a two-dimensional Laplace operator, The weighting coefficients for the influence of speed, for Time coordinates The transport speed parameters at the location for Time coordinates The gradient vector of the predicted value, This represents the external interference influence coefficient. for Time coordinates External interference intensity parameters at the location, Initial time coordinates The baseline value for the transportation status at the location, where t is the forecast duration. It is the integral variable.

[0041] Specifically, the bulk commodity transportation spatiotemporal evolution prediction algorithm dynamically extrapolates the transportation status and achieves accurate prediction through the integrated calculation of multi-dimensional spatiotemporal parameters. During implementation, the spatiotemporal diffusion coefficient is set according to the geographical characteristics of the transportation route, with values ​​ranging from 0.8 to 1.0 for plains, 1.2 to 1.5 for mountainous areas, and 1.0 to 1.2 for hilly areas. The speed influence weighting coefficient is determined based on the ratio of the road section's design speed to the historical average traffic speed, with a value range between 0.7 and 1.3. The transportation speed parameter is collected in real time through IoT terminals and updated every 30 seconds, with the average value within 5 minutes used as the calculation input. The external interference influence coefficient is set for different interference types, with values ​​ranging from 0.3 to 0.8 for weather interference, 0.8 to 1.2 for traffic control interference, and 1.2 to 1.8 for sudden accident interference. The external interference intensity parameter is classified into levels according to the range and duration of the interference's impact on transportation, with levels 1 to 5 corresponding to values ​​of 0.2, 0.4, 0.6, 0.8, and 1.0, respectively. The initial transportation state baseline value is assigned by comprehensively considering the initial road conditions at the origin, the initial state of the goods, and the initial state of the transport capacity. The algorithm is implemented starting from the departure time and dividing the operation cycle according to a preset time interval. Within each cycle, the parameter data of the current time period is integrated, and the transportation status of subsequent time periods and spatial locations is deduced through multi-level iterative operations. The output prediction values ​​include key indicators such as traffic speed, congestion probability, and cargo status. The time accuracy of the prediction results is controlled within 15 minutes, and the spatial accuracy covers the road segment level, providing advance prediction basis for transportation route adjustment and node operation planning, and improving the controllability of the transportation process.

[0042] Preferably, the expression for the cross-regional transportation node load balancing model is: ,in, This is the node load balancing coefficient. Let a be the importance weight parameter for the a-th transportation node. Let be the current load rate parameter of the a-th transportation node. Let be the load capacity limit parameter for the a-th transportation node. Let be the load adjustment coefficient for the a-th transport node. Let be the average distance parameter between the a-th transportation node and its adjacent nodes. Let q be the efficiency improvement coefficient of the a-th transportation node, and q be the total number of cross-regional transportation nodes.

[0043] Specifically, the cross-regional transportation node load balancing model uses node operation data and forecast data as inputs to achieve dynamic control of node load through parameter calculations. During implementation, node importance weights are categorized according to the node's functional positioning in the transportation network: core hub nodes have a value of 2.0, regional center nodes have a value of 1.5, ordinary transfer nodes have a value of 1.2, branch nodes have a value of 1.0, and terminal nodes have a value of 0.8. The current load rate parameter is calculated as the ratio of the node's current resource usage to its total capacity, rounded to two decimal places. The load capacity limit parameter is determined comprehensively based on the node's storage capacity, the rated handling capacity of loading and unloading equipment, and the maximum operational capacity of personnel. The load adjustment coefficient is set based on the aging level and technological advancement of the node's equipment: 0.9 to 1.0 for newly built nodes, 0.7 to 0.9 for nodes operating for 3 to 5 years, and 0.5 to 0.7 for nodes operating for more than 5 years. The average distance parameter to adjacent nodes is calculated by averaging the straight-line distances from geographical coordinates. The operational efficiency improvement coefficient is determined based on the efficiency improvement ratio resulting from recent equipment upgrades and process optimizations, ranging from 1.0 to 1.8. When implementing the model, real-time operating data of each node and prediction data output by S3 are collected first. The parameters are then substituted to complete the calculation. The output load balancing coefficient ranges from 0 to 2.0, with 0.8 to 1.2 being the ideal balance range. A value below 0.8 indicates insufficient load, while a value above 1.2 indicates overload. Based on this, a targeted resource allocation plan is formulated. Through measures such as cargo diversion, equipment scheduling, and personnel optimization, dynamic load balancing of each node is achieved, avoiding node overload, congestion, or resource idleness and waste.

[0044] Preferably, the collaborative control expression of the logistics delivery end-to-end collaborative clustering management platform is: ,in, For collaborative clustering control coefficients, Let be the weight parameter for the s-th collaborative operation step. Let be the information synchronization rate parameter for the s-th collaborative operation stage. Let be the instruction execution degree parameter for the s-th collaborative operation stage. Let be the weight parameter for the t-th cluster dimension. Let be the similarity parameter for the t-th cluster dimension. For the coordinated adjustment coefficient, Let v be the collaborative impact factor for the v-th delivery stage. For the first Parameters regarding the tightness of process connections at each delivery stage. The total number of collaborative work steps. This represents the total number of clustering dimensions. This represents the total number of delivery stages.

[0045] Specifically, the collaborative control implementation of the logistics delivery end-to-end collaborative clustering management platform achieves efficient linkage between various links by integrating full-chain operation data and collaborative parameters. During implementation, the weights of collaborative operation links are set according to the degree of impact of each link on the overall delivery. Key links such as loading and unloading, node transfer, and final delivery are assigned values ​​of 0.8 to 1.0, while auxiliary links such as data transmission and information verification are assigned values ​​of 0.3 to 0.7. The information synchronization rate parameter is calculated by averaging the information upload timeliness rate and data integrity rate of the link, with a value range between 0 and 1.0. The instruction execution rate parameter is a statistical measure of the proportion of instructions received and completed on time and as required. The clustering dimension weights are set according to the clustering objectives, with a value of 0.4 for cargo type clustering, 0.3 for transportation route clustering, and 0.3 for delivery timeliness clustering. The similarity parameter is calculated by comparing the overlap and correlation of data from different operation links. The collaboration adjustment coefficient is set according to the requirements of the overall process collaboration tightness, with a value range between 0.8 and 1.2. The collaboration impact factor is determined based on the frequency of connection and dependence between the delivery link and other links. The process connection tightness parameter is comprehensively evaluated by the time spent switching between links and the information transmission delay time. When the platform is implemented, it first receives all the data uploaded by each link. After processing, it divides the data into collaborative clusters according to the clustering dimension. The collaborative clustering control coefficient is output through model calculation. The higher the coefficient, the better the collaborative effect. Based on this, collaborative optimization instructions are generated. The instructions cover the adjustment of operation sequence, optimization of resource allocation, and standardization of information transmission, ensuring smooth connection of each link in the entire transportation chain and breaking down the barriers of data fragmentation and inefficient collaboration.

[0046] Preferably, the comprehensive control expression for the digital management of the entire bulk commodity transportation process is as follows: ,in, For comprehensive control values ​​of digital management, Let z be the weight parameter for the z-th management dimension. Let z be the execution compliance rate parameter for the z-th management dimension. Let y be the weight parameter of the y-th regulation link. Let be the parameter configuration value for the y-th control link. Let x be the feedback correction coefficient for the y-th control link, and let x be the total number of management dimensions. This refers to the total number of control links.

[0047] Specifically, the comprehensive control and implementation of digital management across the entire bulk commodity transportation process achieves dynamic optimization of the entire process by integrating parameters from management dimensions and control links. During implementation, the weights of management dimensions are set according to the priority of management objectives: core dimensions such as safety management, efficiency management, and cost management have values ​​of 0.8 to 1.0, while auxiliary dimensions such as compliance management and data management have values ​​of 0.5 to 0.8. The execution compliance rate parameter is calculated by taking the weighted average of the ratio of the actual completed values ​​of each assessment indicator in that management dimension to the target value. The weights of control links are set according to the scope of influence of control measures: global control has a value of 1.0 to 1.2, and local control has a value of 0.6 to 1.0. Parameter configuration values ​​are dynamically set based on factors such as actual transportation needs, market environment, and policy requirements, including specific parameter standards for various aspects such as capacity allocation, route planning, and node operations. The feedback correction coefficient is determined based on the feedback of the effects of the control measures after implementation: 1.0 for achieving the target effect, 0.1 to 0.9 for failing to meet the target (calculated proportionally to the deviation), and 1.1 to 1.5 for exceeding expectations. During implementation, a complete list of management dimensions and control links is first compiled, real-time and historical data of various parameters are collected, and the data are substituted into the model to complete the calculation. The output digital management comprehensive control value ranges from 0.5 to 1.5. Based on this, the current management status is judged. A value below 0.8 indicates that there are shortcomings in management, while a value above 1.2 indicates that the management effect is excellent. By dynamically adjusting the parameter configuration and control measures, the continuous optimization of the entire transportation process management is achieved, ensuring that the management objectives are highly consistent with the actual operating status.

[0048] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, real-time collection of traffic density, road capacity, weather influencing factors, and node operation time parameters along the transportation route via IoT terminals, and classification and integration of the collected parameters according to a preset data format; S32, inputting the integrated parameters into the bulk commodity transportation spatiotemporal evolution prediction algorithm, and performing point-by-point extrapolation of the transportation status at different time nodes and different spatial locations through multi-layer iterative calculations within the algorithm; S33, extracting calibration status parameters during the transportation process based on the extrapolation results, and identifying potential route congestion and node overload in advance; S34, comparing and analyzing the identification results with preset thresholds to generate preliminary transportation route adjustment suggestions, providing data support for node load balancing control.

[0049] Specifically, step S3 achieves accurate prediction of the spatiotemporal evolution of transportation through four sub-steps. S31 relies on IoT terminals deployed in transport vehicles, road monitoring equipment, weather stations, and node operation areas to collect parameters in real time, such as traffic density, road capacity, weather influencing factors, and node operation time. Traffic density is calculated based on the number of vehicles per kilometer, ranging from 0 to 120. Road capacity is calculated based on the maximum number of vehicles passing through a road segment per unit time, ranging from 50 to 200. Weather influencing factors include three core indicators: precipitation (0-5 level), wind force (0-12 level), and visibility (50-5000 meters). Node operation time records the actual duration of cargo loading / unloading and transfer, ranging from 5 to 180 minutes. The collection frequency is set to once every 20 seconds. After collection, the data is integrated according to classification rules based on cargo type, route segment, node number, and timestamp to ensure data structure. S32 inputs the integrated parameters into the bulk commodity transportation spatiotemporal evolution prediction algorithm according to a preset format. The algorithm performs multi-layer iterative calculations on different time nodes... The transportation status at different spatial locations is simulated point by point, with 30 iterations. Each iteration optimizes the parameter weights based on the previous result, adjusting the weights by 0.01 to 0.05 to ensure simulation accuracy. S33 extracts key status parameters from the simulation results, such as road segment speed (0-120 km / h), congestion probability (0%-100%), and node load rate (0%-100%). By setting threshold ranges, possible abnormal situations such as path congestion and node overload are screened out. The congestion probability threshold is set at 70%, and the node load rate threshold is set at 85%. S34 compares and analyzes the identified abnormal situations with the preset thresholds to generate preliminary adjustment plans, including route adjustment directions, node avoidance suggestions, and travel time optimization. The plans clearly define the implementation priorities of each suggestion from level 1 to 5 and the data support basis, providing accurate and comprehensive data references for subsequent node load balancing control. The entire process forms a complete logical chain of data collection, algorithm simulation, anomaly identification, and plan generation, ensuring the timeliness and practicality of the prediction results.

[0050] Preferred, such as Figure 3 As shown, S4 includes the following sub-steps: S41, obtaining the transportation status prediction results generated in S3 and the current load data of each transportation node, including the node's cargo storage volume, operating status of operating equipment, number of personnel, and amount of tasks to be processed; S42, substituting the data into the cross-regional transportation node load balancing model, and obtaining the load balancing coefficient and load optimization direction of each node through model calculation; S43, formulating a node resource allocation plan based on the load optimization direction, including specific measures for adjusting cargo transfer routes, scheduling operating equipment, and optimizing personnel shifts; S44, distributing the allocation plan to each associated node through the logistics delivery full-process collaborative clustering management platform, monitoring the implementation of the plan in real time, and collecting feedback data during the implementation process to form a dynamic closed loop for node load control.

[0051] Specifically, step S4 achieves cross-regional node load balancing control through four sub-steps. S41 first obtains the transportation status prediction results generated in step S3, and simultaneously collects the current load data of each transportation node. This includes information such as the node's cargo storage volume (calculated as the ratio of actual occupied storage space to total capacity, ranging from 0% to 100%), the operating status of operating equipment (calculated as the percentage of normally operating equipment, ranging from 0% to 100%), the number of personnel on duty and qualified for operation (ranging from 5 to 200), and the number of pending tasks (calculated as a comprehensive quantification of cargo batches and processing time, ranging from 1 to 50 batches). Data collection covers all cross-regional transportation nodes, from 5 to 30, without omissions. S42 substitutes the collected data and prediction results into the cross-regional transportation node load balancing model. The model uses multi-parameter calculations to obtain the load balancing coefficient for each node (ranging from 0 to 2.0) and the load optimization direction. The load balancing coefficient is calculated based on the actual load and ideal load of the node. Based on a load deviation of 60% to 80%, the optimization direction is clearly defined, determining whether nodes require increased resource investment or task diversion. S43 develops targeted node resource allocation plans based on the load optimization direction, including adjusting cargo transfer routes, calculating transfer distances of 10 to 200 kilometers and cost coefficients of 0.8 to 1.5, prioritizing the allocation of idle equipment with a response time not exceeding 30 minutes, and optimizing personnel scheduling by dynamically adjusting shifts based on workload, with each shift lasting 4 to 8 hours. S44 distributes the allocation plan to all related nodes through a logistics delivery end-to-end collaborative clustering management platform, monitoring the plan's execution in real time and collecting feedback data every 15 minutes, including cargo transfer progress, equipment operating status, and personnel work efficiency. Based on the feedback data, the allocation plan is dynamically adjusted, forming a dynamic closed loop for node load control, ensuring that the load of each cross-regional transportation node remains within a reasonable range, improving node operational efficiency and the smoothness of the transportation chain.

[0052] Preferred, such as Figure 4 As shown, S5 includes the following sub-steps: S51, receiving operational data uploaded from each link in the entire transportation chain through a data interface, including cargo loading and unloading records, transportation status data, node operation logs, and information on the execution of collaborative instructions; S52, using the data analysis module of the logistics delivery full-process collaborative clustering management platform to clean, associate, and cluster the received data, and to explore potential relationships between the data; S53, generating operational status reports for each link based on the clustering results, clarifying the operational status of each link and existing collaborative breakpoints; S54, generating targeted collaborative optimization instructions based on the operational status reports and collaborative breakpoint analysis results, and issuing them to related operational units to achieve collaborative linkage throughout the entire transportation process.

[0053] Specifically, step S5 achieves full-process collaborative clustering management through four sub-steps. S51 receives operational data uploaded from each link in the entire transportation chain via a data interface, including cargo loading and unloading records, transit status data, node operation logs, and collaborative instruction execution information. The data reception frequency is set to once every 10 seconds, covering 10 to 15 core links such as loading and unloading at the origin, intermediate transfer, node operations, and final delivery, ensuring the comprehensiveness and real-time nature of data collection. S52 utilizes the data analysis module of the logistics delivery full-process collaborative clustering management platform to clean, correlate, and cluster the received data. The cleaning process removes abnormal and duplicate data, with a retention rate of no less than 98%. Correlation processing establishes data relationships based on cargo number, transportation order number, and node number. Clustering processing is performed according to cargo type, transportation route, and delivery time. The task is to divide the data into 8 to 12 clusters and explore potential relationships between the data. S53 generates a work status report for each stage based on the clustering results. The report includes core indicators such as the completion progress of each stage (0% to 100%), data synchronization rate (0% to 100%), and instruction execution rate (0% to 100%), clearly identifying the operational status of each stage and existing collaborative breakpoints, with a breakpoint identification accuracy of no less than 95%. S54 generates targeted collaborative optimization instructions based on the work status report and collaborative breakpoint analysis results. These instructions include adjustments to work sequence, information transmission standards, and resource allocation optimization, with a response time of no more than 5 seconds when issued to related work units. The execution status is updated every 30 minutes to track the execution effect of the instructions, achieving collaborative linkage throughout the entire transportation process, breaking down data fragmentation and process disconnect, and improving the overall collaborative efficiency.

[0054] The dynamic matching and optimization model for transportation capacity resource pools is a technical model that integrates multi-dimensional parameters throughout the entire process of bulk commodity transportation to achieve precise matching between transportation capacity and transportation demand. It is an algorithmic model that outputs the optimal matching result through multi-dimensional weighted calculations by quantifying related parameters such as cargo attributes, transportation capacity characteristics, and constraints. In terms of implementation, 12 transportation capacity supply parameters, 10 cargo attribute parameters, and 5 delivery timeliness-related parameters are first collected. Parameter weights are set according to an allocation system of cargo priority weight (0.6 to 1.0), transportation capacity adaptation weight (0.3 to 0.4), and timeliness requirement weight (0.3). The cargo load adaptation threshold is set to 1.05 to 1.2 times the cargo weight, and the range adaptation threshold is more than 1.3 times the total length of the transportation route. The adaptation rate is calculated based on no less than 50 sets of historical operation data from the past 6 months. The model integrates the parameters to generate an adaptation score, filters out transportation capacity units with scores above 80, and selects the top 20% as candidate transportation capacity, ultimately generating 3 preliminary transportation plans. This model replaces the traditional single-dimensional matching mode, improves the accuracy of capacity matching, avoids resource waste and demand mismatch. Through quantitative parameters and dynamic calculations, it shifts capacity scheduling from experience-based judgment to data-driven, making the advantages of candidate capacity matching clear, improving the feasibility of transportation solutions, laying the foundation for the efficient operation of the entire bulk commodity transportation process, and helping to improve the efficiency of transportation resource utilization by more than 30%.

[0055] The bulk commodity transportation spatiotemporal evolution prediction algorithm is a technical algorithm that dynamically extrapolates the trend of transportation status changes based on spatiotemporal parameters. It integrates data such as path characteristics, environmental factors, and node status, and predicts key indicators of the entire transportation process through multi-level iterative calculations. In terms of implementation, it collects nine transportation path characteristic parameters, sets the departure to arrival time as the prediction time range, divides the time into 15-minute time slices, and limits the number of slices to a maximum of 96. The input data includes the initial traffic status of the road segment (based on the average of historical data for the same period in the past 7 days), the initial congestion probability of nodes (the ratio of throughput to capacity for the same period in the past 30 days), and the initial status of the goods. It comprehensively considers the traffic density range of 5 to 80 vehicles per kilometer, five levels of weather influence factors, and a fluctuation range of ±15% in operation time. After 30 iterations (with each weight adjustment ranging from 0.01 to 0.05), it extrapolates parameters such as road segment traffic speed, congestion probability, and node load rate under each time slice. This algorithm identifies potential risks such as route congestion and node overload in advance, providing a scientific basis for adjusting transportation plans. It transforms transportation status prediction from a passive response to an active prevention and control, with prediction time accuracy controlled within 15 minutes and spatial accuracy covering the road segment level. This enables transportation scheduling to be forward-looking, reduces delays and losses caused by emergencies, and improves the controllability of the entire transportation process.

[0056] The cross-regional transportation node load balancing model is a management model that regulates the resource occupancy status of each transportation node to achieve efficient node operation. It is an algorithmic model that quantifies the load balancing coefficient and formulates control strategies by analyzing node operation data and prediction results. In terms of implementation, it collects eight real-time indicators, including node storage capacity occupancy rate, number of operating equipment, and personnel configuration. Node importance weights are set according to a standard of 2.0 for core hub nodes, 1.5 for regional center nodes, 1.2 for ordinary transfer nodes, 1.0 for branch nodes, and 0.8 for terminal nodes. The target load balancing value is controlled within the ideal range of 60% to 80%. Based on parameters such as the node's current load rate, carrying capacity limit, average distance to adjacent nodes, and operational efficiency improvement coefficient of 1.0 to 1.8, the model calculates and outputs a load balancing coefficient of 0 to 2.0. A coefficient below 0.6 reduces redundant resources, while a coefficient above 1.2 initiates control measures such as cargo diversion and equipment scheduling. This model optimizes node resource allocation, avoiding overload and congestion at some nodes and idle resources at others. It constructs a dynamically balanced node operation system, improving the operational efficiency of cross-regional transportation nodes by more than 25%, reducing the dwell time of goods at nodes, ensuring smooth connection of the transportation chain, and providing technical support for the continuity and stability of cross-regional transportation of bulk commodities.

[0057] The logistics delivery end-to-end collaborative clustering management platform is a carrier for realizing centralized data processing, instruction distribution, and collaborative linkage across the entire transportation chain. It is defined as a distributed architecture platform integrating data reception, processing, clustering, instruction generation, and feedback functions. In terms of implementation, the platform deploys six core functional modules with a response time of no more than 2 seconds. It receives operational data from 10 to 15 core links in the entire transportation chain every 10 seconds. After data cleaning (retention rate no less than 98%), association processing (establishing associations based on cargo number and order number), and clustering processing (dividing into 8 to 12 clusters according to cargo type, route, and timeliness), it generates status reports including indicators such as operation completion progress, data synchronization rate, and instruction execution rate. The breakpoint identification accuracy is no less than 95%. Based on the reports, collaborative optimization instructions are generated and distributed to operational units via encrypted protocols, achieving a distribution success rate of 99.9%. The execution status is updated every 30 minutes. This platform breaks down data fragmentation and collaboration gaps, enabling information synchronization and efficient execution of instructions across the entire transportation chain. It constructs a seamless collaborative linkage mechanism, ensuring smooth connections between loading and unloading operations, node transfers, and final delivery. The instruction response time is controlled within 5 seconds, solving the problems of inefficient collaboration and information lag in traditional management. This promotes the transformation of the entire bulk commodity transportation process from decentralized management to centralized and collaborative management, significantly improving overall transportation efficiency.

[0058] like Figure 5As shown, a digital management system for the entire process of bulk commodity transportation based on the Internet of Things (IoT) is applied to this system. The system includes: a multi-dimensional data acquisition and preprocessing unit, which establishes a bidirectional data transmission link with IoT terminals to collect and classify various parameters throughout the bulk commodity transportation process, providing data input for model calculations; a dynamic matching and optimization unit for transportation capacity resources, connected to the multi-dimensional data acquisition and preprocessing unit, which calls the dynamic matching and optimization model of the transportation capacity resource pool to calculate the collected data, select suitable transportation capacity units, and generate preliminary transportation plans; and a transportation spatiotemporal evolution prediction and analysis unit, which establishes data interaction relationships with both the multi-dimensional data acquisition and preprocessing unit and the dynamic matching and optimization unit, and predicts the spatiotemporal evolution of bulk commodity transportation. The algorithm extrapolates transportation status and outputs prediction results; the cross-regional node load balancing control unit receives output data from the transportation spatiotemporal evolution prediction and analysis unit, and controls node load in conjunction with the cross-regional transportation node load balancing model to optimize node operation processes; the full-process collaborative clustering management and instruction distribution unit is bidirectionally connected to the cross-regional node load balancing control unit and the dynamic matching and optimization unit of transportation resources, and centrally processes the data of the entire transportation chain through the logistics delivery full-process collaborative clustering management platform to generate and distribute collaborative optimization instructions; the digital management closed-loop tracking and updating unit establishes data transmission channels with all units, tracks the data flow, instruction execution and resource allocation of the entire transportation process, updates management data in real time and feeds it back to each related unit, forming a complete digital management closed loop.

[0059] like Figure 6As shown, the main interface of the IoT-based digital management system for the entire process of bulk commodity transportation centrally presents the core data and status indicators of the entire transportation management chain, perfectly echoing the core logic of multi-dimensional data collection, digital closed-loop management, and comprehensive control in this invention. The interface covers key modules such as total number of contracts, total number of plans, various waybill statuses (new, in transit, awaiting unloading, awaiting loading, etc.), waybill trends, financial settlement, and vehicle and driver management. All data originates from multi-dimensional parameters collected by IoT terminals in S1, including capacity supply (number of drivers, total number of vehicles, number of fleets, and vehicle status distribution), cargo attributes (total cargo, classified statistics by weight / full truckload), node facility operation (data related to node operations such as pending settlement and pending verification), and delivery timeliness parameters (waybill status and receipt status). After being classified and integrated by the multi-dimensional data collection and preprocessing unit, the data is presented in a structured manner. The interface displays real-time updates of data such as in-transit waybills and the percentage of idle / in-transit / maintenance vehicles, intuitively reflecting the core requirements of S6 digital management closed-loop tracking. Through the digital management closed-loop tracking and update unit, it achieves full-process monitoring of data flow, instruction execution, and resource allocation throughout the transportation process. Simultaneously, financial settlement data and driver and vehicle due date warnings correspond to the management dimensions (cost management, compliance management) and control links of the digital management comprehensive control expression in this invention, providing intuitive execution compliance rate parameters and feedback correction basis for comprehensive control. The waybill trend chart visually presents the application of the spatiotemporal evolution prediction algorithm, helping managers dynamically adjust capacity matching and node control strategies to ensure optimal management throughout the entire process.

[0060] like Figure 7As shown, the transportation route management interface is the visual implementation of the transportation spatiotemporal evolution prediction, node load balancing control, and dynamic capacity matching functions in this invention, deeply aligning with the management logic of dynamic optimization throughout the entire process. The interface focuses on the planning, adjustment, and monitoring of transportation routes. Its core data support originates from transportation path characteristic parameters collected by IoT terminals in S3, such as traffic density, road capacity, weather influencing factors, and node operation time. This data, after being categorized and integrated, is input into the bulk commodity transportation spatiotemporal evolution prediction algorithm. Through multi-layer iterative calculations, the algorithm deduces the transportation status at different times and spaces, ultimately presenting it on the interface in the form of route recommendations and congestion warnings. The interface's route adjustment function directly echoes the route adjustment suggestions in S34, providing operable route optimization solutions for congestion and node overload risks identified by the algorithm, providing data support for cross-regional node load balancing control. Simultaneously, route planning needs to be linked with the dynamic matching results of capacity resources in S2. The interface will combine parameters such as the range and load capacity of the adapted capacity units to ensure a high degree of compatibility between route design and capacity characteristics, avoiding efficiency losses due to route-capacity mismatch. In addition, route management incorporates S4's node load balancing logic. The load status of the nodes along the route is fed back to the interface in real time, which helps managers optimize node stopping plans, adjust cargo transfer routes, and achieve collaborative optimization of route and node resources. This provides visualization support for the entire process of transportation plan generation and dynamic adjustment.

[0061] like Figure 8As shown, the transportation contract management interface is the core data entry point for end-to-end collaborative clustering management and dynamic capacity matching, fully supporting the basic functions of cargo attribute collection, capacity adaptation, and collaborative instruction generation in this invention. The interface displays detailed information such as contract number, name, shipper, cargo information (name, category, unit, billing method, total quantity, shipped quantity, etc.), and contract status. Among these, cargo category, quantity, weight calculation method, and total contract quantity are the core cargo attribute parameters required to be collected in S1. This structured data is synchronized to the multi-dimensional data collection and preprocessing unit through a data interface, providing core input for subsequent capacity matching. During the dynamic capacity resource matching process in S2, information such as total cargo quantity, load requirements, and transportation timeliness in the interface is incorporated into the dynamic matching optimization model of the capacity resource pool, serving as a key basis for selecting suitable capacity units. The planned dispatch quantity and shipped quantity in the contract provide real-time feedback on the execution progress of the matching scheme. Simultaneously, the interface data is synchronized to the logistics delivery end-to-end collaborative clustering management platform. This platform performs correlation and clustering processing on contract data, transportation capacity, and node data to identify collaborative breakpoints in cargo transportation and contract execution. For example, by using the difference between the shipped volume and the remaining cargo volume, it identifies connection problems in loading and unloading operations and node transfers, thereby generating targeted collaborative optimization instructions. Furthermore, the real-time updates to contract status and cargo remaining volume on the interface support S6's closed-loop tracking function, ensuring dynamic feedback between contract execution progress and resource allocation and instruction execution throughout the transportation process. This makes contract management a core hub connecting cargo, transportation capacity, and nodes, achieving end-to-end data collaboration and efficient linkage.

[0062] A digital management method and system for the entire process of bulk commodity transportation based on the Internet of Things (IoT) collects multi-dimensional parameters of the entire transportation process through IoT terminals. It leverages a dynamic matching and optimization model with a capacity resource pool to deeply integrate and analyze multi-dimensional information such as cargo attributes, capacity characteristics, and transportation demand. This replaces the traditional single-dimensional data matching and simple rule-based scheduling model, achieving efficient adaptation of capacity resources to transportation demand. Simultaneously, a bulk commodity transportation spatiotemporal evolution prediction algorithm dynamically extrapolates route traffic status, node congestion trends, and changes in cargo status, identifying potential risks in advance and providing a scientific basis for adjusting transportation plans. This fundamentally changes the traditional management approach of passively responding to emergencies, enabling dynamic optimization of transportation plans and significantly improving capacity utilization efficiency and the feasibility of transportation schedules.

[0063] This method and system effectively overcome the limitations of existing technologies, such as uneven allocation of node resources, poor operational coordination, and data flow interruptions. Through a cross-regional transportation node load balancing model, it performs real-time analysis and control of data such as resource occupancy and operational efficiency of each node, optimizing node resource allocation and operational processes to avoid overloaded nodes and idle resources in others. Combined with a collaborative clustering management platform for the entire logistics delivery process, it achieves centralized processing and efficient distribution of operational data, status information, and collaborative instructions across the entire transportation chain, constructing a seamless collaborative linkage mechanism. In conjunction with closed-loop tracking of the entire digital management process for bulk commodity transportation, it provides full-process control and dynamic updates for data flow, instruction execution, and resource allocation, promoting the transformation of transportation management from decentralized and fragmented to centralized and collaborative, significantly improving the operational efficiency of cross-regional transportation nodes and the smoothness of the entire process, further strengthening the systematic nature and controllability of transportation management.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital management method for the entire process of bulk commodity transportation based on the Internet of Things, characterized in that: Includes the following steps: S1. Collect parameters related to transportation capacity supply, cargo attributes, transportation route characteristics, node facility operation, and delivery timeliness throughout the entire bulk commodity transportation process via IoT terminals to construct a multi-dimensional data collection matrix. S2. Utilize a dynamic matching and optimization model from the transportation capacity resource pool to perform correlation analysis on the collected data, select suitable transportation capacity units, and generate preliminary transportation plans. S3. Employ a bulk commodity transportation spatiotemporal evolution prediction algorithm to dynamically predict the path traffic status, node congestion probability, and cargo status change trends during the transportation process. S4. Based on a cross-regional transportation node load balancing model, perform real-time control of resource occupancy at each transportation node to optimize node operation processes. S5. Centrally process and distribute operational data, status information, and collaborative instructions across the entire transportation chain through a collaborative clustering management platform for the entire logistics delivery process. S6. Through a digital management module for the entire bulk commodity transportation process, track and dynamically update the data flow, instruction execution, and resource allocation throughout the transportation process, forming a closed-loop management link.

2. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, The expression for the dynamic matching optimization model of the transportation capacity resource pool is: ,in, The optimal capacity matching coefficient. Let i be the priority weight parameter for the i-th type of goods. Let be the transportation demand parameter for the i-th type of goods. Let be the adaptation time parameter between the i-th type of goods and the j-th transport unit. Let be the available state coefficient of the j-th transport unit. The historical operation adaptation rate parameter for the j-th capacity unit. Let the constraint weight parameters be those for the k-th type of transportation resources. Let K be the remaining capacity parameter for the k-th type of transportation resource. This is the capacity allocation adjustment coefficient. Let be the satisfaction parameter of the I-th transportation constraint, n be the total number of cargo categories, m be the total number of transportation resource types, and p be the total number of transportation constraints.

3. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, The expression for the spatiotemporal evolution prediction algorithm for bulk commodity transportation is: ,in, Coordinates at time t The predicted transport status at the location, The spatiotemporal diffusion coefficient is... For a two-dimensional Laplace operator, The weighting coefficients for the influence of speed, for Time coordinates The transport speed parameters at the location for Time coordinates The gradient vector of the predicted value, This represents the external interference influence coefficient. for Time coordinates External interference intensity parameters at the location, Initial time coordinates The baseline value for the transportation status at the location, where t is the forecast duration. It is the integral variable.

4. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, The expression for the cross-regional transportation node load balancing model is: ,in, This is the node load balancing coefficient. Let a be the importance weight parameter for the a-th transportation node. Let be the current load rate parameter of the a-th transportation node. Let be the load capacity limit parameter for the a-th transportation node. Let be the load adjustment coefficient for the a-th transport node. Let be the average distance parameter between the a-th transportation node and its adjacent nodes. Let q be the efficiency improvement coefficient of the a-th transportation node, and q be the total number of cross-regional transportation nodes.

5. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, The collaborative control expression of the logistics delivery end-to-end collaborative clustering management platform is: ,in, For collaborative clustering control coefficients, Let be the weight parameter for the s-th collaborative operation step. Let be the information synchronization rate parameter for the s-th collaborative operation stage. Let be the instruction execution degree parameter for the s-th collaborative operation stage. Let be the weight parameter for the t-th cluster dimension. Let be the similarity parameter for the t-th cluster dimension. For the coordinated adjustment coefficient, Let v be the collaborative impact factor for the v-th delivery stage. For the first Parameters regarding the tightness of process connections at each delivery stage. The total number of collaborative work steps. This represents the total number of clustering dimensions. This represents the total number of delivery stages.

6. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, The comprehensive control expression for the digital management of the entire bulk commodity transportation process is as follows: ,in, For comprehensive control values ​​of digital management, Let z be the weight parameter for the z-th management dimension. Let z be the execution compliance rate parameter for the z-th management dimension. Let y be the weight parameter of the y-th regulation link. Let be the parameter configuration value for the y-th control link. Let x be the feedback correction coefficient for the y-th control link, and let x be the total number of management dimensions. This refers to the total number of control links.

7. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, S3 includes the following steps: S31, real-time collection of traffic density, road capacity, weather influencing factors, and node operation time parameters along the transportation route via IoT terminals, and classification and integration of the collected parameters according to a preset data format; S32, inputting the integrated parameters into a bulk commodity transportation spatiotemporal evolution prediction algorithm, and performing point-by-point extrapolation of the transportation status at different time nodes and spatial locations through multi-layer iterative calculations within the algorithm; S33, extracting calibration status parameters during the transportation process based on the extrapolation results, and identifying potential route congestion and node overload in advance; S34, comparing and analyzing the identification results with preset thresholds to generate preliminary transportation route adjustment suggestions, providing data support for node load balancing control.

8. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, S4 includes the following sub-steps: S41, obtaining the transportation status prediction results generated in S3 and the current load data of each transportation node, including the node's cargo storage volume, operating status of operating equipment, number of personnel, and amount of tasks to be processed; S42, substituting the data into the cross-regional transportation node load balancing model, and obtaining the load balancing coefficient and load optimization direction of each node through model calculation; S43, formulating a node resource allocation plan based on the load optimization direction, including specific measures for adjusting cargo transfer routes, scheduling operating equipment, and optimizing personnel shifts; S44, distributing the allocation plan to each associated node through the logistics delivery full-process collaborative clustering management platform, monitoring the implementation of the plan in real time, and collecting feedback data during the implementation process to form a dynamic closed loop for node load control.

9. The method for full-process digital management of bulk commodity transportation based on the Internet of Things as described in claim 1, characterized in that, S5 includes the following steps: S51, receiving operational data uploaded from each link in the entire transportation chain through a data interface, including cargo loading and unloading records, transportation status data, node operation logs, and information on the execution of collaborative instructions; S52, using the data analysis module of the logistics delivery full-process collaborative clustering management platform to clean, associate, and cluster the received data, and to uncover potential relationships between the data; S53, generating operational status reports for each link based on the clustering results, clarifying the operational status of each link and existing collaborative breakpoints; S54, generating targeted collaborative optimization instructions based on the operational status reports and collaborative breakpoint analysis results, and issuing them to related operational units to achieve collaborative linkage throughout the entire transportation process.

10. A digital management system for the entire process of bulk commodity transportation based on the Internet of Things, characterized in that: This system is applied to the IoT-based digital management method for the entire process of bulk commodity transportation as described in claim 1, comprising: a multi-dimensional data acquisition and preprocessing unit, which establishes a bidirectional data transmission link with the IoT terminal to collect various parameters in the entire bulk commodity transportation process and classify and integrate them to provide data input for model calculation; a dynamic matching and optimization unit for transportation capacity resources, which is connected to the multi-dimensional data acquisition and preprocessing unit, calls the dynamic matching and optimization model of the transportation capacity resource pool to calculate the collected data, selects suitable transportation capacity units, and generates a preliminary transportation plan; and a transportation spatiotemporal evolution prediction and analysis unit, which establishes data interaction relationships with the multi-dimensional data acquisition and preprocessing unit and the dynamic matching and optimization unit for transportation capacity resources, and uses a bulk commodity transportation spatiotemporal evolution prediction algorithm to predict the transportation status. The system includes: a cross-regional node load balancing control unit, which receives output data from the transportation spatiotemporal evolution prediction and analysis unit, and controls node load based on the cross-regional transportation node load balancing model to optimize node operation processes; a full-process collaborative clustering management and instruction distribution unit, which is bidirectionally connected to the cross-regional node load balancing control unit and the dynamic matching and optimization unit for transportation resources, and centrally processes data across the entire transportation chain through the logistics delivery full-process collaborative clustering management platform to generate and distribute collaborative optimization instructions; and a digital management closed-loop tracking and updating unit, which establishes data transmission channels with all units to track the data flow, instruction execution, and resource allocation throughout the entire transportation process, update management data in real time, and feed it back to all related units to form a complete digital management closed loop.

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